Inventions doi: 10.3390/inventions11040067

Authors: Mónica B. Torres Dávila Miguel Á. García Sánchez Mario Molina Almaraz Eduardo García Sánchez Luis E. Bañuelos García José C. Torres Dávila Ma. del Rosario Martínez Blanco Luis O. Solís Sánchez Gerardo Sánchez Sandoval Luis H. Mendoza Huizar

Calving in cattle is divided into two main stages: dilation and expulsion, during which timely assistance can reduce reproductive losses. This study presents a smart ear-mounted device as a proof-of-concept heart-rate monitoring platform for calving-stage assessment in dairy cows. The prototype preserves the form factor of a conventional ear tag and integrates a MAX30105 optical sensor, an Arduino Nano microcontroller, local micro-SD storage, and an autonomous power supply. Field tests were conducted in Holstein cows at Rancho El Pinar, Trancoso, Zacatecas, Mexico. Heart rate was recorded every 10 min and grouped according to physiological stages around calving. The results showed distinctive heart rate patterns, with higher values during dilation and lower values after delivery, supporting the use of ear-mounted heart rate monitoring as a non-invasive descriptive marker of stage-related physiological variation around labor. An average temperature profile from 70 h before to 50 h after calving was also incorporated as complementary descriptive evidence of peripartum physiological variation. Because heart rate is a non-specific physiological variable affected by stress, movement, ambient temperature, feeding, health status, and sensor contact, the present study does not propose HR as a stand-alone or definitive predictor of calving or dystocia. Instead, the device is presented as a proof-of-concept platform for future multi-indicator monitoring and validation studies. The proposed system is presented as a proof-of-concept invention that combines a practical wearable format with physiological monitoring and a conceptual decision-support logic that remains to be validated and integrated with additional indicators before any field implementation.

Inventions doi: 10.3390/inventions11040066

Authors: Xining Li Hao Tang Kai Liu Huichuan Tang Yi Zhang Guangning Wu

Ultra-high-voltage direct-current (UHVDC) transmission systems impose stringent requirements on the reliability of insulation materials used in converter transformer bushings. Epoxy resin systems are key insulating materials in resin-impregnated paper (RIP) capacitor bushings, and their processing characteristics, curing behavior, and electrical properties directly affect bushing performance. In this study, two epoxy insulation systems used for resin-impregnated paper (RIP) bushings, namely the imported Araldite LY1564/Aradur 3486 system and the domestic EP-2020/CA-3015 system, were systematically investigated through viscosity, curing, and electrical property tests. The results show that the viscosities of both resins decreased significantly with increasing temperature. At 60 °C, the viscosities of Resin A and Resin B were 151.6 mPa·s and 156.3 mPa·s, respectively. The mixed resin–hardener systems exhibited similar viscosity evolution and comparable pot life characteristics. DSC measurements revealed two-stage curing reactions for both materials, with first exothermic peak temperatures of 65.4 °C and 96.3 °C and second peak temperatures of 269.3 °C and 269.8 °C for Materials A and B, respectively. Electrical testing demonstrated that both materials exhibited similar temperature-dependent dielectric and resistivity behavior, with dielectric loss increasing at elevated temperatures and resistivity decreasing as temperature increased. The volume resistivity trends and dielectric characteristics of the two materials remained highly consistent throughout the investigated temperature range. The results indicate that Material B exhibits processing performance, curing characteristics, and electrical insulation properties comparable to those of Material A. Therefore, Material B demonstrates strong potential for application in UHVDC RIP bushing insulation systems and provides a promising alternative for the localization of key insulating materials.

Inventions doi: 10.3390/inventions11030065

Authors: Menna M. S. Elmasry Mona G. Gafar M. A. Elsabagh

Student dropout remains an important challenge in higher education because it affects degree completion, institutional resource efficiency, workforce preparation, and students’ long-term socioeconomic opportunities. This requires not only accurate predictions but also decision support that is both reliable and aware of uncertainty. This study posits that the amalgamation of probabilistic modeling, uncertainty quantification, and graph-based refinement can augment both predictive reliability and decision support for the early detection of dropouts. A reliability-centered predictive framework is presented, integrating Educational Competition Optimization (ECO)-based feature selection, probabilistic Support Vector Classification (SVC), isotonic regression for probability calibration, and split conformal prediction for distribution-free uncertainty quantification. In addition, a similarity-driven Graph-based Fuzzy Cellular Automata (Graph-FCA) refinement mechanism is developed, where student relationships are modeled using a k-nearest neighbor graph with radial basis function similarity. Entropy-based confidence weighting is used to control uncertainty-aware propagation. An Explainable Artificial Intelligence layer based on SHAP provides both global and local interpretability, and fairness-aware evaluation assesses consistency across demographic groups. The suggested framework maintains predictive performance while improving probabilistic reliability. The Graph-FCA refinement achieves an accuracy of 0.7503, which is close to the calibrated ECO–SVC baseline (Accuracy = 0.7537; Macro-F1 = 0.6704) and also reduces the Brier score. The conformal prediction layer achieves empirical coverage close to the desired confidence level, ensuring reliable uncertainty estimates. The ECO–SVC–Conformal–GraphFCA framework transforms traditional classification into a reliable, understandable, and uncertainty-aware early warning system, enhancing its usefulness for ethical and informed decision-making in engineering education.

Inventions doi: 10.3390/inventions11030064

Authors: Wanbin Ren Yuchen Liao Yinnan Zhang Yuan Meng Chao Zhang

Intermittent faults in electrical connectors refer to cases in which contact resistance exceeds a specified threshold for microseconds or less, causing transient power or signal interruptions. Accurate detection and quantitative recording of these events are important for connector reliability evaluation. In this work, an eight-channel monitoring system with nanosecond resolution for intermittent faults in electrical connectors is developed, enabling quantitative recording of intermittent events together with dynamic contact resistance (DCR) waveform acquisition. Two-stage programmable amplification is used for DCR measurement, while threshold comparison and FPGA-abased quadrature multiphase oversampling are combined to capture intermittent events. The system supports DCR measurement over 1 mΩ–10,000 mΩ with a maximum relative error of 0.41%, and provides 1.25 ns equivalent time resolution for intermittent event monitoring, with an expanded uncertainty of 0.28 ns–0.54 ns over 20 ns–10 μs. Vibration tests on high-speed connectors further demonstrate that the system captures real intermittent events under mechanical excitation and measures their durations with a maximum relative error of 0.66% relative to oscilloscope results.

Inventions doi: 10.3390/inventions11030063

Authors: Andrew H. Gould Alireza Fath

Global shifts in energy policy have contributed to an increase in electricity generation from renewable sources, which introduces unique issues with volatility and grid reliability. Robust grid-scale energy storage methods must fill the gap between generation and consumption. Flywheel energy storage (FES) is a mechanical technology that utilizes the stored kinetic energy of a rotating body, but is typically only suited for shorter-term frequency regulation due to significant windage losses. In this work, a novel Python 3.13-based simulation and optimization tool is presented and used to optimize geometric design parameters for efficiency, energy density, and other metrics. The simulation utilizes a 1 degree-of-freedom, multi-regime fluid friction model with a time-marching algorithm. The optimization functionality utilizes pyswarms, a particle swarm optimization package, with adjustable search parameters and cost functions to evaluate simulation results. Optimization parameters include geometric parameters of rotor radius, shaft radius, airgap width, and airgap height; material properties of mass and moment of inertia; and initial angular velocity. An optimal initial angular velocity is found for a particular geometry, lasting 30 times longer until self-discharge versus the worst values. This work can inform the design of flywheel systems to minimize windage losses and promote the technology’s utility for longer-term energy storage.

Inventions doi: 10.3390/inventions11030062

Authors: Muyao Lv Zejia Liu Chao Zhang Yujie Gao Zhihan Zhang Yihua Zhu Chao Luo Jiawei Yu

With the goal of achieving more accurate wind power predictions by accounting for meteorological influences comprising wind speed, together with wind direction and air pressure, this thesis proposes a method combining fuzzy C-means (FCM) clustering with a TCN–Transformer hybrid model. After preprocessing the data to remove outage and missing records, we apply the Pearson correlation coefficient to identify average wind speed and wind direction that are suitable to serve as input features for the model, together with the atmospheric pressure, as key input features. FCM clustering is then applied to partition the data into low- and high-wind-speed operating conditions, mitigating the accuracy loss caused by uniform modeling. A TCN–Transformer model is subsequently constructed, integrating local temporal feature extraction with global dependency modeling to perform prediction under each condition. The experimental results demonstrate that the proposed FCM–TCN–Transformer framework consistently achieves superior forecasting performance under both low-wind-speed and high-wind-speed conditions. Compared with benchmark models, including TCN, LSTM, GRU, BiGRU, and Transformer, the proposed method achieves lower prediction errors and higher prediction accuracy across different forecasting horizons. Furthermore, repeated experiments with multiple random seeds verify the robustness and stability of the proposed framework. These results indicate that FCM-based wind regime classification effectively reduces data heterogeneity, while the hybrid TCN–Transformer architecture successfully captures both local temporal patterns and long-range temporal dependencies. Therefore, the proposed framework provides an effective and reliable solution for short-term wind power forecasting and contributes to the secure integration of wind energy into modern power systems.

Inventions doi: 10.3390/inventions11030061

Authors: Yuting Zhang Xiaonan Shen

The integration of wind energy into power systems relies on forecasting technologies to address operational challenges caused by its volatility and intermittency. This paper proposes a computing architecture for ultra-short-term wind power forecasting. The methodology integrates an adaptive dual-stage signal processing technique with an optimized deep learning model. To manage the non-stationarity of meteorological variables, the Pearson and Maximal Information Coefficient (MIC) analyses are employed for feature selection. The ICEEMDAN algorithm is then used for initial decomposition, followed by sample entropy and K-Means clustering to assess component complexity. Variational Mode Decomposition (VMD) is applied only to the high-frequency component to further separate stochastic fluctuations while preserving relatively stable trend components. A Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) network is constructed to forecast the resulting multi-scale components. To reduce reliance on manual empirical tuning, the Crested Porcupine Optimizer (CPO) is used to fine-tune key network hyperparameters. Evaluations using operational wind-farm data indicate that the developed hybrid method captures the temporal dynamics of wind power and yields lower prediction errors than the tested benchmark models. This research provides a data-driven computing framework for renewable-energy forecasting and related operational analysis.

Inventions doi: 10.3390/inventions11030060

Authors: Nawal Louzi Mohammad Q. Al-Jamal Mahmoud AlJamal

This study proposes a novel simulation-driven intelligent framework for the performance and reliability assessment of renewable energy-integrated pavement systems by unifying coupled multiphysics finite element modeling, structured dataset generation, and graph-based artificial intelligence within a single computational paradigm. The proposed pavement is formulated as a seven-layer multifunctional infrastructure system comprising the asphalt surface, intermediate binder, base layer, thermoelectric energy layer, piezoelectric insert zone, subbase, and subgrade soil, thereby enabling simultaneous consideration of structural load transfer, thermal gradient-driven energy harvesting, moisture-sensitive support behavior, and reliability-oriented performance interpretation. A three-dimensional thermo-hydro-mechanical Abaqus model was developed to simulate the concurrent effects of moving wheel load, solar heat flux, rainfall infiltration, and internal moisture diffusion, and it was subsequently used to construct an AI-ready dataset containing 6000 simulation cases and 68 variables spanning geometric, material, environmental, traffic, uncertainty, structural, thermal, hydraulic, renewable-energy, and probabilistic reliability descriptors. To preserve the physical hierarchy of the layered pavement within the learning process, a Layer-Coupled Reliability Graph Operator Network (LaRGO-Net) was proposed, in which pavement layers are represented as interacting graph nodes linked through adaptive interlayer coupling and optimized through multi-task, physics-aware, and coupling-consistent learning. Experimental evaluation across nine progressive configurations demonstrated a monotonic improvement from baseline dense and graph-convolution models to the full LaRGO-Net formulation. The final model achieved the best overall performance with mean RMSE = 0.040, mean MAE = 0.028, mean R2=0.994, and reliability prediction accuracy characterized by F1 = 99.21 and AUC = 99.53. These results confirm that the proposed framework provides a highly accurate, physically interpretable, and reliability-aware surrogate for next-generation pavement systems capable of simultaneously supporting structural serviceability, renewable-energy functionality, and intelligent decision-making.

Inventions doi: 10.3390/inventions11030059

Authors: Khaled Chahine Mohamad Arnaout Marc Al Atem Abdallah El Ghaly Hassan N. Noura

Conventional machine learning models for perovskite solar cells predict photovoltaic parameters independently, disregarding the physical constraint PCE=Voc×Jsc×FF/100. This approach can yield mutually incompatible predictions for the four parameters, a failure mode that has not been hitherto quantified in the perovskite solar cell literature. This paper proposes a multi-head neural network with a shared backbone, physics-guided feature construction, and task-specific prediction heads, and validates it on 7176 SCAPS-1D simulations across 12 perovskite compositions. When benchmarked against architecturally matched single-task baselines, the multi-task model, optimized via 5-fold cross-validation, achieves R2 values of at least 0.994 for all four targets, with cross-fold standard deviations of 0.001. In particular, fill factor prediction improves from R2=0.617±0.254 (single-task) to 0.994±0.001 (multi-task), a 233-fold reduction in cross-fold standard deviation. Application of a physical consistency metric developed in this work reveals that 36.5% of single-task predictions exceed a 2 PCE-unit implausibility threshold, compared to only 0.01% for the multi-task model. The multi-task model outperforms the single-task baseline in all 20-fold target comparisons, with large effect sizes (Cohen’s d=1.33–8.93). These results confirm multi-task learning as an effective approach for achieving robust, stable, and internally consistent predictions in simulation-based photovoltaic virtual screening.

Inventions doi: 10.3390/inventions11030058

Authors: Chi-Hung Lo Yi-Lan Sun

Managing classroom tablets involves more than electrical charging; it also requires repeated retrieval and return, storage, plug alignment, custody, and queue control. This article presents the human-centered engineering design and field validation of a drawer-type tablet/laptop charging cart for kindergarten-to-grade-12 digital learning infrastructure. Its main contribution is a bilateral drawer-access architecture that converts a conventional front-door, single-queue cabinet into a two-sided parallel-handling product, with design decisions linked to observed school workflows through Lean Product and Process Development, jobs-to-be-done inquiry, opportunity scoring, competitor benchmarking, product-essence mapping, and prototype testing. Field observations at three schools identified six critical handling events; effective storage with reduced queueing was the highest-priority opportunity (importance = 8.6, satisfaction = 5.7, opportunity score = 11.5). Among four access concepts, the drawer-type concept achieved the shortest handling time (4.4 s/device), outperforming front-opening fixed-shelf (7.2 s/device), front-opening movable-rack (8.2 s/device), and top-opening (6.8 s/device) concepts. In classroom validation, average handling time decreased from 10.9 to 4.8 s/device, and throughput increased from 5.5 to 12.5 devices/min. These design-stage, descriptive results indicate that bilateral drawer access can reduce serial queueing while preserving storage, charging, and custody functions. They support prototype refinement rather than population-level causal inference.

Inventions doi: 10.3390/inventions11030057

Authors: Fenglin Zhang Die Chen Luo Zhang Chenxia Zhao Ming Zhang Xinyi Li Ting He Zimiao Lu Xiaohong He Gengpei Xia Dingyu Yang

Aqueous zinc-ion batteries (AZIBs) are promising for grid-scale storage due to their safety, low cost, and environmental benignity. However, water-dipole enrichment in the inner Helmholtz plane (IHP) of Zn anodes triggers hydrogen evolution, corrosion, and dendrites, limiting cycle life. We report a trace “ionic liquid–polar solvent coupling” strategy: adding only 0.01 M EMIMBF4 and 0.03 M DMSO to 2 M ZnSO4 electrolyte. Hydrophobic EMIM+ adsorbs on the IHP to expel interfacial water, while BF4− enters the primary solvation shell and DMSO penetrates both first and second shells of Zn2+, forming a water-deficient coordination environment. This interfacial–solvation synergy suppresses parasitic reactions and directs preferentially oriented Zn deposition exclusively along the (101) facet, enabling dense vertical plating and in situ formation of a compact, inorganic-rich SEI (ZnCO3–ZnSO3–Zn(OH)2). Consequently, Zn||Zn cells cycle stably for >5362 h at 1 mA cm−2/1 mAh cm−2; Zn||Cu cells achieve 1300 cycles with 99.8% average Coulombic efficiency; and Zn||V2O5 full cells retain 326.4 mAh g−1 after 500 cycles. This work shows that minimal additive loading can simultaneously engineer the electrode–electrolyte interface and crystallographic deposition pathway, offering a simple yet robust design for ultra-stable AZIBs.

Inventions doi: 10.3390/inventions11030055

Authors: Ryan Saini William B. Andreopoulos

Machine learning’s computational demands necessitate optimal performance and utilization across diverse hardware architectures. This research compares computing as spiking neural networks (CSNNs, or simulated neuromorphic computing) and regular CNNs on Apple Silicon M3 Pro with Metal Performance Shaders (MPS), and NVIDIA RTX 3070 GPU with CUDA. We run Convolutional Spiking Neural Networks (CSNNs) and traditional CNNs on two datasets (frame-based CIFAR-10; and sequential event-based DVS) to evaluate the suitability of neural net architectures and platforms for different data problems. For both CSNNs and traditional CNNs, Apple Silicon with MPS delivers better energy efficiency but longer processing times for training and inference. NVIDIA with CUDA offers faster computation in training and inference at higher energy costs for CNNs. For CSNNs, frame-based data (CIFAR-10) significantly degraded performance when proper temporal encoding was absent, while event-based data (DVS) proved more naturally suited to the CSNN architecture than frame-based inputs. Though CNNs still achieved higher empirical accuracy in the reported experiments. CSNNs also performed better on Apple Silicon (with MPS) for the sequential event-based data. RAM utilization patterns favored Apple Silicon (with MPS) across both data experiments. The CSNN architecture demanded higher memory resources than CNN, regardless of platform and dataset. NVIDIA (with CUDA) was less energy efficient for spiking neural networks (CSNNs) as compared to Apple Silicon (with MPS). We also compared how the number of time steps affects accuracy and energy consumption across hardware platforms, finding that higher accuracy correlates with energy costs as time steps increase; the accuracy-energy relation seems linear for frame-based data, while for event-based data the energy consumption remains stable increasing at higher time steps. Our cross-platform performance analysis of spiking and regular neural network architectures highlight the importance of matching platform-architecture combinations to a dataset and application requirements.

Inventions doi: 10.3390/inventions11030056

Authors: Dmitriy A. Serov Dmitry A. Zakharov Natalia A. Semenova Maxim E. Astashev Valery A. Kozlov Alexey S. Dorokhov Andrey Yu. Izmailov Sergey V. Gudkov

An evaluation of plant ploidy is an important task in breeding and biotechnology. Current methods of ploidy assessment (flow cytofluorometry and microscopy) are time-consuming and costly, and not applicable to real-world agricultural conditions. We developed an automated method for ploidy assessment based on fluorescence microscopy, which aims to accelerate and reduce the cost of plant ploidy analysis. The method is based on the automated selection of plant nuclei in fluorescence micrographs, followed by analysis of nuclear area, fluorescence intensity of the Hoechst DNA-binding probe, and nuclear geometry (circularity, roundness, solidity). The study was conducted on monocotyledonous and dicotyledonous plants with known genome sizes. Triticum aestivum Wt (6n, hexaploid) and Temp (4n, tetraploid) are monocotyledonous, and Capsella bursa-pastoris (4n, tetraploid) and Capsella rubella (2n, dT/iploid) are dicotyledonous. A simple fluorescent staining protocol combined with automated analysis using our ImageJ macro enables reliable separation of both monocotyledonous and dicotyledonous plants by genome size with an accuracy close (for dicots) or comparable (for monocots) to flow cytofluorometry. For ploidy separation in monocots, the most sensitive parameters are fluorescence intensity, nucleus area, and circularity. For ploidy separation in dicots, the most sensitive parameters are nucleus area, fluorescence intensity, and circularity.

Inventions doi: 10.3390/inventions11030054

Authors: Ali Basim Mahdi Zahraa A. Mousa Al-Ibraheemi Nabil Jalil Aklo Amer Alomarah

Ischemic stroke is a major cause of death and disability and thus requires specialized treatment. The present work describes the design and control-oriented simulation of a smart steerable microcatheter tip based on Nitinol superelastic alloy for thrombectomy. The proposed framework allows for predictive and safe catheter navigation by combining experimental material characterization, electromechanical modeling, and control design. Experimental validations of key material properties, such as hemocompatibility, corrosion resistance, and full superelastic behavior, were incorporated into an environment created in MATLAB/Simulink. The bending curvature of a safe blood vessel was exactly followed by means of delay-guaranteed bandwidth-limited dynamical feedforward and feedback regulation. Simulation-based results validate steering and dynamic response, as well as safe interaction with blood vessel walls. Ultimately, from the work described in this paper, we hope to present a proposal for an entire framework for relating biomaterial properties with control performance that could stimulate safer and more efficient robot-assisted procedures in combating thromboembolic diseases.

Inventions doi: 10.3390/inventions11030053

Authors: Heriberto Adamas-Pérez Pedro Javier García-Ramírez Edmundo Antonio Gutiérrez-Domínguez Guadalupe Jasmín Muñoz-Salazar Jesús Aguayo Alquicira Guillermo Ramírez-Zuñiga Jorge Salvador Valdez Martínez José Guadalupe Villanueva Patricio Susana Estefany De León Aldaco

This article presents a comprehensive analytical, numerical, and experimental study of the amplification and detection of weak signals in magnetically coupled electromagnetic systems, using an architecture consisting of three magnetically coupled coils. A rigorous mathematical model of the system is developed, which includes the formulation of the mutual inductance matrix and a state-space representation that captures the dynamic interaction between the coils. It is important to note that the electromagnetic subsystem is linear and that the stochastic resonance effect is achieved by incorporating an external nonlinear bistable element. In this configuration, a weak periodic signal below a threshold is applied to the primary coil, while a controlled source of Gaussian white noise is injected into a secondary coil. A third coil functions as a sensing element, capturing the superimposed magnetic response resulting from coupling effects. The voltage induced in the sensor coil is subsequently processed by a bistable nonlinear element implemented via a Schmitt trigger, which provides the nonlinearity and bistability necessary to enable stochastic resonance and the detection of the weak periodic signal. The conditions of the SR are analyzed in terms of noise intensity, coupling coefficients, and system parameters, highlighting the existence of an optimal noise level that maximizes the signal-to-noise ratio (SNR) at the output. A detailed simulation framework has been developed in MATLAB/Simulink, enabling a systematic exploration of the parameter space and the validation of theoretical predictions. The simulation results are further supported by experimental measurements obtained from a physical prototype, which show agreement with the proposed model. The main contribution of this work lies in demonstrating that magnetically coupled electromagnetic structures can effectively interact with nonlinear bistable elements to exploit stochastic resonance in the detection of weak signals, even when the electromagnetic domain itself remains linear. The results demonstrate that magnetic coupling is an effective mechanism for mediating constructive interactions between noise and weak signals, thereby improving the detection of the latter. These results extend the applicability of stochastic resonance to hybrid electromagnetic systems and demonstrate its relevance in practical applications. Potential applications include ultra-sensitive magnetic detection, low-power signal detection, magnetic transducers, and robust signal recovery in noisy electromagnetic environments, particularly in contexts where conventional linear amplification fails.

Inventions doi: 10.3390/inventions11030052

Authors: Nikolay Hinov

Ultra-fast electric vehicle (EV) charging systems are among the most demanding converter-dominated applications due to their high power levels, wide battery-voltage range, strict thermal constraints, and the need for adaptive charging control. Conventional design and tuning approaches often rely on fixed control policies and computationally expensive iterative optimization, which limits their ability to address nonlinear multi-objective trade-offs across the full charging envelope. This paper proposes a hybrid AI–quantum co-design framework for a SiC-based dual active bridge (DAB) converter intended for ultra-fast EV charging applications. The proposed approach combines a physical converter model, an AI surrogate-learning layer for rapid prediction of converter performance, and a quantum-assisted optimization layer for multi-objective exploration of design and control variables. To demonstrate the framework, a representative modular 350 kW ultra-fast charging case study is considered, implemented by four parallel 87.5 kW SiC-based DAB modules and including converter-level optimization and adaptive charging-policy refinement. The revised manuscript introduces a complete system schematic, an explicit DAB converter topology, a clarified methodological workflow, and a simulation-based proof-of-concept evaluation. Representative results indicate improved design-space exploration and more balanced trade-offs between efficiency, thermal stress, ripple, and dynamic response compared with a conventional baseline tuning approach. Although the study does not claim hardware-level quantum advantage, it provides a structured and practically interpretable computational framework for intelligent co-design of high-power charging converters.

Inventions doi: 10.3390/inventions11030051

Authors: Xinghui Li Ting Du Ziqi Zhong Haijun Chen Pan Pan Jinjun Feng

Most chip-scale atomic sensors in quantum precision measurement fields require MEMS alkali vapor cells with long lifetime operation, which is mainly restricted by the significant reduction in alkali metal in the vapor cells. An integrated circuit compatible fabrication process is proposed to realize high hermetic alkali metal cesium vapor cells with passivation layers to prevent the reduction in alkali metal. The fabricated vapor cells achieve leakage rates less than 1 × 10−13 Pa·m3/s, and can maintain cesium content well in a two-step high-temperature accelerated aging process of 115 °C for more than 2 years and 300 °C for 48 h. The high-temperature aged vapor cells are tested and assembled in miniaturized atomic clocks for trial use. The resonance performance tests indicate that the coherent population trapping widths of the vapor cells are less than 2 kHz, and the corresponding atomic clocks realize pretty good short-term stabilities of about 8.68 × 10−11 @ 1 s and 6.83 × 10−12 @ 1000 s. All results indicate that the vapor cells have long lifetime application potential in chip-scale atomic sensing devices.

Inventions doi: 10.3390/inventions11030050

Authors: M. A. Elsabagh Menna M. S. Elmasry Mona G. Gafar

Predicting student performance has emerged as an essential element of contemporary learning assessment, allowing educational organizations to determine problematic students and offer early intellectual assistance. Many machine learning (ML) methodologies prioritize predicted accuracy at the expense of interpretability and practical insights. This paper provides a framework for predicting student performance that is both risk aware and explainable utilizing a chaotic educational competition optimizer (ECO) in conjunction with a support vector classifier (SVC) to overcome existing challenges. The ECO serves as a metaheuristic feature selection technique for selecting the most significant features from a multivariate educational dataset consisting of 1195 students and 29 behavioral, demographic, and academic characteristics. Experimental findings demonstrate that ECO effectively condenses the feature space to 11 essential indications and improves generalization of model while maintaining classification robustness. Utilizing the chosen features, the ECO–SVC model attains a complete classification accuracy of 87.03%, with F1-scores of 0.92, 0.69, and 0.82 for high-, medium-, and low-performance student categories, respectively, surpassing other benchmark ML methods. The proposed framework incorporates explainable artificial intelligence (XAI) to improve transparency by utilizing local explanations and permutation-driven feature significance. The XAI research verifies that institutional support, learner engagement, and previous academic success are the most important contributing factors to predictive results. Notably the ECO functions as a classifier-independent feature selection mechanism; however, the support vector classifier (SVC) is adopted in this study due to its strong generalization capability and effectiveness in exploiting the optimized feature space. The findings are analyzed using a semiotic-linguistic framework, wherein certain qualities are correlated with symbolic, indexical, and temporal educational signs, converting numerical significance into substantive pedagogical insights. Furthermore, an initial academic risk profile strategy is established by utilizing SVC decision confidence and elucidating feature contributors. The consequent risk ratings accurately categorize students into low-, medium-, and high-risk categories, facilitating the detection of at-risk learners beyond mere final score assessment. The proposed risk-aware and explainable ECO–SVC framework enhances learning outcomes assessment by integrating interpretability, high accuracy, and proactive academic reasoning, rendering it suitable for real-life educational decision-support systems.

Inventions doi: 10.3390/inventions11030048

Authors: Or Yatzkan Orit Rotem-Mindali Reuven Cohen Eyal Yaniv David Burg

As global efforts to decarbonize the transportation sector intensify, integrating renewable energy sources into electric vehicle (EV) infrastructure has become a critical challenge, particularly under strong temporal mismatches between generation and demand. This study evaluates the potential of urban rooftop photovoltaic (PV) systems in Israel to support full electrification of the private vehicle fleet using a planning-oriented modeling framework that links energy supply, transport demand, and seasonal variability. Current annual fleet demand is estimated at 14 TWh, based on both internal combustion vehicle replacement and EV-specific consumption. A three-stage modeling framework is applied. First, national vehicle data are used to estimate total electricity demand. Second, rooftop PV generation potential is calculated using a monthly irradiance model, rooftop availability data, and system-level efficiency factors. Under these assumptions, residential rooftop PV could generate up to 81 TWh per year, corresponding to approximately 44 km2 of usable rooftop area. Third, temporal matching between supply and demand is evaluated, with explicit focus on intra-annual variability rather than only annual energy balance. Winter irradiance declines to approximately 45% of summer levels, while maintaining continuous charging requires approximately 38 GWh of energy storage. These results show that system feasibility is constrained by winter minimum generation rather than annual energy balance. The findings highlight that large-scale rooftop PV-based electrification is primarily limited by a temporal mismatch between generation and demand. This shifts the evaluation of PV-EV integration from a static annual energy perspective to a temporal system-design problem. This underscores the importance of integrating storage, grid flexibility, and system-level planning when evaluating the role of distributed PV in supporting electrified transport.

Inventions doi: 10.3390/inventions11030049

Authors: Nabil Elaadouli Ilyass El Myasse Abdelmounime El Magri Rachid Lajouad Mishari Metab Almalki Mahmoud A. Mossa

This paper presents an adaptive nonlinear control and state observation framework for energy management in standalone photovoltaic (PV) systems integrated with battery energy storage. A unified nonlinear dynamic model is developed to describe the interactions between the PV generator, the DC/DC buck converter, and the lithium-ion battery. Based on this model, a multi-mode control strategy is designed to ensure efficient and safe operation under varying environmental and loading conditions. The proposed scheme incorporates maximum power point tracking (MPPT) to maximize photovoltaic energy extraction, along with constant current (CC) and constant voltage (CV) charging modes to guarantee battery safety and longevity. To address uncertainties and unmeasured states, an adaptive nonlinear observer is developed for real-time estimation of the battery open-circuit voltage and state of charge. The observer design is supported by Lyapunov-based stability analysis, ensuring boundedness and convergence of the estimation error in the presence of modeling uncertainties and external disturbances. An energy management algorithm is further introduced to coordinate the transition between operating modes according to the estimated system states and battery constraints. The effectiveness and robustness of the proposed control and observation strategy are validated through detailed simulations in MATLAB/Simulink under varying solar irradiance conditions. The results demonstrate accurate maximum power tracking, reliable state estimation, and safe battery charging performance, highlighting the potential of the proposed approach for advanced autonomous PV–battery systems.

Inventions doi: 10.3390/inventions11030047

Authors: Zijian Zhang Wenzhe Hao Chao Luo Jiawei Yu Yihua Zhu Zhiyong Dai Guangqi Li

In permanent magnet synchronous generator-based wind energy conversion systems, generator-side measurements may become unreliable due to sensor faults, which can degrade system reliability. To address this issue, a redundant control framework based on load-side encoder feedback is proposed, where the load-side encoder serves as an alternative measurement source under sensor degradation. Compared with conventional generator-side sensing strategies, the proposed approach enhances fault tolerance without requiring additional hardware redundancy. An extended state observer is employed to estimate system states and lumped disturbances, enabling improved robustness. Simulation results show that the proposed method significantly improves speed tracking performance, reducing the root mean square error by approximately 45% compared with conventional PI control, while maintaining stable operation under sensor degradation conditions. The results demonstrate that the proposed strategy enhances system reliability and robustness in fault scenarios.

Inventions doi: 10.3390/inventions11030046

Authors: Zoltán Köntös

This paper presents a theoretical engineering feasibility analysis of the RK-X photocatalytic process for In Situ Resource Utilisation (ISRU) on Mars. Experimental validation under simulated Martian conditions is the essential next step before any mission deployment claim can be made. The RK-X process converts the two most abundant Martian resources, atmospheric carbon dioxide (CO2) and subsurface water ice (H2O), into formic acid (HCOOH) and oxygen (O2) through a fulvic acid-based photocatalytic cycle validated at the industrial scale in Hungary. A reference module processing 10 tonnes of CO2 per Earth year yields 10.459 tonnes of formic acid and 3.636 tonnes of oxygen, sufficient to sustain a six-person crew for approximately two Earth years with a 198% safety margin over nominal respiratory demand. The economic analysis indicates that importing equivalent oxygen from Earth costs $1.82–$3.64 million per year; equivalent energy storage (Li-ion) costs $30.5–$61 million for one-time use. Formic acid stores 15.25 MWh of energy in ambient-stable liquid form at a round-trip efficiency of 68.64% without cryogenic infrastructure. A photovoltaic array of 55.37 m2 provides the primary energy source; a kilowatt-class nuclear fission reactor constitutes the strategic opportunity for continuous, dust-storm-immune operation with free thermal co-generation. Three critical research gaps have been identified requiring laboratory validation before Mars deployment: (i) catalyst performance at the Martian CO2 partial pressure (p(CO2) < 10 mbar, T = 15 °C); (ii) water ice and dry ice extraction at an operational scale; and (iii) integrated closed-loop system demonstration. Built on Earth-proven chemistry with identified, addressable development pathways, the RK-X process theoretically resolves the problems of oxygen supply, seasonal energy storage, water management, and cryogenic infrastructure within a single closed-loop chemical cycle.

Inventions doi: 10.3390/inventions11030045

Authors: Chunwang Wang Zekai Zhang Wangyang Liu Junliang Liu

The performance of single-photon avalanche diodes (SPADs) is highly dependent on the operating temperature, while traditional SPAD models neglect the self-heating effect induced by avalanche current during long-term device operation, leading to insufficient prediction accuracy. This paper proposes an electro-thermal coupled SPAD simulation model that self-consistently integrates the transient thermal effects of the avalanche process with temperature-dependent electrical parameters, including junction capacitance, breakdown voltage, impact ionization coefficients, and Shockley–Read–Hall (SRH) recombination rates. The complete electro-thermal coupled model is constructed based on Sentaurus-TCAD thermal simulation and Virtuoso circuit simulation and implemented via the Verilog-A language. Simulation results demonstrate that after the device operates for 100 μs under repeated avalanche-quenching processes, the self-heating effect causes a 0.34 V shift in breakdown voltage, increases the device dead time by 3.34 ps, and simultaneously reduces the photon detection probability and elevates the dark count rate. This study conducts a systematic investigation into the performance degradation mechanism of SPAD devices induced by the self-heating effect, laying a theoretical foundation at the device self-heating level for subsequent research on the electrothermal interaction between quenching circuits and device bodies.

Inventions doi: 10.3390/inventions11030044

Authors: Luis Alejandro Arias Barragan Ricardo Alirio Gonzalez Luis Fernando Rico Victor Hugo Bernal Andrea Aparicio Ricardo Alfonso Gómez

This work presents the design, development, and proof-of-concept validation of a smart greenhouse for romaine lettuce (Lactuca sativa var. longifolia) that integrates Internet of Things (IoT) sensing/actuation with an image-based crop state assessment pipeline. The proposed pipeline combines a lightweight AI image classifier with fractal texture descriptors (box-counting fractal dimension) to support the non-destructive monitoring of leaf condition and growth stage. The system also implements resilience-oriented resource strategies, including rainwater harvesting, graywater reuse, and a hybrid power supply (photovoltaic + grid backup). Water and energy indicators are reported as estimated values derived from the prototype operating profile and literature-based baseline values (i.e., contextual comparisons rather than a contemporaneous controlled trial). Using an expanded dataset (n = 1500 images) and an independent held-out test subset (n = 350), the image classifier achieved 97.1% accuracy, with detailed precision/recall/F1 metrics reported in the Results. Overall, the proposed architecture and evaluation workflow provide an accessible and reproducible pathway toward sustainable, low-cost smart greenhouses in resource-constrained settings.

Inventions doi: 10.3390/inventions11030043

Authors: Umberto Lucia Mariarosa Astori Giulia Grisolia

Cross-linking is a therapy that strengthens the cornea and helps slow the progression of keratoconus. This therapeutic surgery has evolved from a single standardized protocol to a diverse array of techniques tailored to improve safety, efficacy, patient comfort, and accessibility. It represents a transformative advancement in keratoconus treatment. Its ability to biomechanically reinforce the cornea and halt disease progression has revolutionized patient care, reducing the burden of advanced keratoconus and improving long-term visual outcomes. Ongoing refinements in technique continue to enhance its efficacy, safety, and patient comfort, securing its role as a cornerstone of modern ophthalmic practice. This process involves creating new covalent bonds between corneal fibers using a photosensitising substance called riboflavin. The effectiveness of cross-linking can be assessed by introducing the severity index, which provides a quantitative measure of the therapeutic outcome. This index allows for a more objective evaluation for both prognostic and therapeutic purposes.

Inventions doi: 10.3390/inventions11020042

Authors: Yuxuan Huang Yuwei Chen Zhenguo Shao Feixiong Chen Yunting Shao Yifan Zhang Changming Chen

To address the increased operational risk in distribution network caused by the grid integration of distributed wind power, a distribution network risk assessment method that combines a Dirichlet process mixture model (DPMM) with the cumulant method (CM) is proposed, to achieve effective quantification of operational risk. Firstly, a DPMM is employed to cluster wind power output data, and adaptive kernel density estimation is introduced to construct a probabilistic model of wind power output, thereby improving local fitting accuracy. Secondly, uncertainties arising from wind generation and load are considered, and a probabilistic power flow model for the distribution network is established based on the CM and the Gram–Charlier series expansion, in order to obtain the probability distributions of state variables and branch power flows. Then, distribution entropy theory is introduced to quantify the severity of limit violations for state variables such as voltage and power, so that operational risk assessment is enabled. Finally, simulations are conducted on a modified IEEE 34-bus distribution test system, and the results demonstrate the effectiveness of the proposed method.

Inventions doi: 10.3390/inventions11020041

Authors: Yuxin Xu Yan Dong

Driven by renewable energy, the operating temperatures of alkaline water electrolyzers (AWEs) exhibit significant dynamic variations. Conventional control strategies rely on fixed startup parameters, causing dispatch plans to deviate from actual physical states, which leads to transient over-temperature or startup failures. To address this issue, this paper proposes a dual-layer optimization strategy for multi-electrolyzer systems based on temperature–power adaptation. First, a thermo-electro-hydrogen coupling model is established to quantitatively reveal the dynamic relationship among the initial temperature, startup power, and transition time. This relationship is utilized to construct a dynamic startup boundary, overcoming the limitations of traditional static constraints. Within the proposed framework, the upper layer utilizes a Mixed-Integer Linear Programming (MILP) model to formulate state-switching and baseline power allocation plans derived from short-term forecasts. Concurrently, the lower layer employs the Mongoose Optimization Algorithm (MOA) for real-time rolling optimization, enabling the system to actively perceive temperature variations and adaptively schedule power allocation. Simulations across typical seasonal scenarios validate the strategy’s superiority. In a typical spring scenario, compared to the traditional Daisy Chain and Rotation Control strategies, as well as the Equal Allocation strategy, the proposed approach reduces total startup time and energy consumption by 59.2% and 54.6%, respectively. Furthermore, it increases wind power accommodation rates by 17.7% and 14.2%, and total hydrogen production by 20.0% and 14.9%, respectively. These superior renewable energy utilization and production efficiencies are robustly maintained across typical seasonal scenarios. By actively perceiving actual temperatures for adaptive scheduling, the proposed strategy ultimately ensures synergy and reliability between the control strategy and actual operational constraints under fluctuating conditions.

Inventions doi: 10.3390/inventions11020040

Authors: Pei Liu Boge Wen

Despite significant advancements in communication systems, inherent limitations persist in providing reliable data transmission for emerging applications with massive data exchanges. Semantic communication offers promising solutions by extracting and transmitting meaningful information rather than raw bit sequences. However, it faces challenges from high mobility and dynamic channel conditions in wireless environments. In this paper, we design a ground-to-air network architecture that integrates a rotary-wing unmanned aerial vehicle (UAV) and ground terminals to maximize semantic transmission efficiency while maintaining low energy consumption. This approach leverages the high mobility of the UAV for flexible deployment and the data reduction capabilities of semantic communication. Therefore, we formulate a multi-objective optimization problem to simultaneously balance the total semantic transmission rate and the UAV propulsion energy consumption by jointly optimizing the UAV hovering position, semantic encoding lengths, and resource block (RB) allocation. The problem is complex, with mixed continuous and discrete variables, which necessitates an advanced optimization method. To address these challenges, we propose a novel greedy-enhanced adaptive multi-objective cellular genetic algorithm (GEAMOCell), which utilizes an adaptive neighborhood selection mechanism to balance exploration and exploitation, and employs a crowding-guided archive feedback mechanism to maintain population diversity. The simulation results demonstrate that the proposed GEAMOCell algorithm outperforms baseline algorithms in terms of convergence, semantic transmission rate, and energy efficiency.

Inventions doi: 10.3390/inventions11020039

Authors: Mohamed Mamdouh M. Ali Shoukry I. Shams Mahmoud Elsaadany Ghyslain Gagnon Abdelrazik Sebak

This article introduces a novel right-angle coax to Substrate Integrated Coaxial (SIC) transition, offering featured characteristics and performance in a compact size. An air-filled K-connector is used to ensure optimal transition in a compact form factor. The proposed transition covers the Ku-band up to 18 GHz, achieving a deep matching level below 20 dB. The transition is fabricated and tested in a back-to-back configuration, where it demonstrates impressive characteristics, including a matching level of −15 dB and an insertion loss of −0.22 dB/inch across the entire bandwidth for the back-to-back configuration.

Inventions doi: 10.3390/inventions11020038

Authors: Evgeny M. Konchekov Viktoria V. Gudkova Dmitriy A. Serov Nikolai N. Bogachev Dmitriy E. Burmistrov Tatiana I. Pavlik Leonid V. Kolik Ismail R. Seriev Vyacheslav P. Stepin Evgeny I. Grudiev Valentin D. Borzosekov Namik Gusein-zade Sergey V. Gudkov

One of the promising methods for generating low-temperature plasma to address oncological issues is a spark discharge initiated by a piezotransformer. This is attributed to its relative simplicity and versatility in application, both in the direct treatment of biological objects in vitro and in vivo, as well as indirectly through the production of plasma-activated solutions. The study presents the results of a comprehensive study of the effect of spark discharge initiated by a piezotransformer in argon flow on the metabolic activity and survival rate of cancer HEp-2 cells. For this purpose, adhesive cells cultured in DMEM were subjected to plasma exposure of different duration (from 10 to 120 s). The produced effect was assessed by studying the spark discharge optical emission spectra, as well as by measuring the concentrations of reactive oxygen and nitrogen species in the liquid medium. Cell viability and metabolic rate were determined by MTT test and fluorescence microscopy using propidium iodide (PI) and Hoechst dyes. The metabolic activity of cells is reduced by half after 20 s of treatment, cell viability reduced after 150 s of treatment. The concentration of hydrogen peroxide H2O2 reaches a value of ~50 μM, and the concentration of nitrite ion NO2− reaches a value of ~20 μM.

Inventions doi: 10.3390/inventions11020037

Authors: Ioana-Octavia Bucur Daniel-Eugeniu Crunțeanu Mădălin-Constantin Dombrovschi

Vertical axis wind turbines (VAWTs) are promising systems for urban wind energy applications because of their compact layout, omni-directional operation, and favorable integration potential. However, their broader deployment remains limited by poor self-starting capabilities and relatively low aerodynamic efficiency compared to horizontal axis wind turbines. In this study, a passive flow control concept for a straight-bladed VAWT is numerically investigated using a NACA0012 airfoil modified with 45° inclined perforations on the extrados. Four perforated configurations were generated and compared with the baseline profile through a two-stage computational approach. First, steady 2D computational fluid dynamics (CFD) simulations of the isolated airfoils were performed at a free stream velocity of 12 m/s over an angle of attack range of 0–180°. Subsequently, the most relevant aerodynamic trends were assessed at rotor level using transient 2D Moving Mesh simulations for a three-bladed wind turbine with tip speed ratios (TSRs) between 0.5 and 3.5. All perforated variants exhibited higher lift than the baseline airfoil, while the configuration with smaller, denser perforations distributed over the downstream two-thirds of the extrados provided the best overall aerodynamic performance. At TSR = 2.5, this geometry increased the mean moment coefficient from 0.044 to 0.0525 and the power coefficient from 0.109 to 0.131, corresponding to an increase in power output of approximately 20%. These results indicate that inclined extrados perforations constitute a promising passive strategy for improving the aerodynamic performance of small straight-bladed VAWTs, although further 3D and experimental validations are required.

Inventions doi: 10.3390/inventions11020036

Authors: Emilia Georgiana Prisăcariu Iulian Vlăducă

Photopolymer-based additive manufacturing processes such as stereolithography (SLA) offer high precision and surface quality but generate cured thermoset waste that is typically non-recyclable. In microgravity environments, conventional recycling approaches—based on gravitational settling, open solvent handling, and buoyancy-driven degassing—are ineffective, motivating the development of fully contained, gravity-independent material recovery systems for on-orbit manufacturing. This work presents a conceptual, design-stage closed-loop system architecture for recycling photopolymer resins in microgravity. The system integrates eight subassemblies enabling mechanical fragmentation, solvent-assisted dissolution, filtration, low-pressure degassing, pressurized storage, injection molding, and ultraviolet curing. A hermetically sealed dual-screw shredder produces resin fragments of 1–3 mm, suitable for dissolution. Gas removal is achieved through low-vacuum degassing at approximately 0.1–0.3 bar, with characteristic residence times of 5–10 min, ensuring stable processing prior to injection. Material transport is governed by mechanical conveyance and controlled pressure, eliminating reliance on gravity. The architecture maintains full containment of solids, liquids, and vapors throughout the process. Supported by engineering design considerations, the system establishes a microgravity-compatible pathway for closed-loop recycling of SLA materials. Experimental validation is planned in future work.

Inventions doi: 10.3390/inventions11020035

Authors: Shafiqul Alam Khan Damian Valles Marcelo M. Carvalho Wenquan Dong

Firefighters must locate victims reliably to carry out rescue operations within burning structures during urban firefighting events. Low visibility, reduced oxygen levels, weakened structural rigidity, and dense smoke make it difficult to locate victims. In addition to these challenges, victims may be unconscious and unable to report their locations to firefighters. This research work explores the Double Deep Q-Network (Double DQN), Dueling Deep Q-Network (Dueling DQN), and Dueling Double Deep Q-Network (D3QN) agents for an unmanned aerial vehicle (UAV) to navigate around a structure and locate trapped victims within it. The UAV’s position, Light Detection and Ranging (LiDAR), and infrared camera data are utilized as inputs for the Deep Q-Networks. The PER is used to store transitions and sample them according to priority for training. Python’s Pygame library is used in this research to create a simulated environment in which infrared camera and LiDAR data are simulated. The performance of the UAV agent is evaluated using cumulative maximum reward, reward distribution histogram, Temporal Difference (TD) error over time, and number of successful episodes. Among the three DQN UAV agents, the Dueling DQN and Double DQN have potential for real-world applications in firefighting.

Inventions doi: 10.3390/inventions11020034

Authors: Aleksandr Kulikov Pavel Ilyushin Anton Loskutov

Overhead power lines (OHPLs) are currently widely used to generate power from various types of traditional power plants and transmit power between electric power systems (EPSs). OHPLs are known to be susceptible to climatic, meteorological, man-made, and other factors, which leads to more frequent outages with damage of varying severity. Ensuring reliable operation of the EPS requires rapid and accurate fault location (FL) for emergency restoration operations and the subsequent restoration of the OHPL. This article presents the results of an analysis of various methods for FL of OHPLs under conditions of deviations in power quality indicators (PQI), which leads to additional FL errors in emergency mode parameters (EMP). The objective of the study is to develop a new method for FL on OHPLs with unsynchronized measurements from both ends under conditions of current and voltage deviations from a sinusoidal shape, based on the least-squares method. The developed method for FL on OHPLs is based on differential equations describing the currents and voltages in emergency conditions at both ends, taking into account distributed transverse (capacitive) conductivity. This significantly improves the accuracy of FL on OHPLs with unsynchronized measurements at both ends under conditions of fluctuating power quality parameters. The article presents calculation results for a specific OHPL, demonstrating the improved accuracy of FL based on the EMP. The developed method can be implemented in digital protection and automation devices for OHPLs, as well as in software for power system control centers.

Inventions doi: 10.3390/inventions11020033

Authors: Fatma A. Mihany Galal H. Galal-Edeen Ehab E. Hassanein Hanan Moussa

The rapid expansion of market-driven software product development has led to the increasing use of User-Generated Content (UGC), such as mobile application user reviews, as a valuable source of requirements. However, unlike the traditional requirements engineering (RE) process, data-driven RE introduces several challenges, particularly in requirements elicitation and prioritization. Traditional requirements prioritization techniques typically rely on stakeholders’ involvement; however, in data-driven and market-driven development contexts, explicit stakeholders are often absent. Thus, we propose a DAta-driven Requirements Prioritization (DARP) framework that integrates Natural Language Processing (NLP), topic modeling, and Large Language Models (LLMs) to automate requirements prioritization in a data-driven development context. The proposed framework utilizes BERTopic to identify latent topics in user reviews and incorporates Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to group semantically related requirements. The proposed framework introduces a robust and automated prioritization applied to mobile app reviews. The scope of the proposed framework is user-perspective prioritization. Our objective is to detect insights from app reviews to reflect the voice of the customer. The results indicate that leveraging NLP and topic modeling techniques provides an effective data-driven approach to requirements prioritization.

Inventions doi: 10.3390/inventions11020032

Authors: Zhiliang Wang Zhuo Li Keqiao Zeng Wenfu Wei Zefeng Yang Huan Zhang

Under severe ice and snow weather, ice-covered pantograph–catenary arcs affect the safe operation of high-speed trains. This study investigates the impact of ice-covered arc electrical characteristics, plasma parameters, and material ablation mechanisms. By constructing a comprehensive pantograph–catenary icing experimental platform, arc voltage, current signals, high-speed dynamic images, and emission spectra were synchronously collected under different icing thicknesses ranging from 0 to 15 mm. Research indicates that ice coverture causes frequent “extinction–reignition” phenomena during the arc initiation stage due to the latent heat absorbed by melting ice, significantly reducing the initial stability of arc combustion. Spectral analysis confirms that the arc excitation temperature and energy density are positively correlated with the concentration of hydrogen ions produced by water vapor ionization, reaching a peak under the 5 mm icing condition. Experimental results show that the average energy density of ice-covered arcs is approximately double that of the non-iced condition, causing the ablation pits on the carbon strip to exhibit characteristics of greater depth and wider copper deposition zones. This study reveals the unique mechanisms and damage characteristics of icing pantograph–catenary arcs, providing an important basis for the safe design and maintenance of pantograph–catenary systems in high-cold railway environments.

Inventions doi: 10.3390/inventions11020031

Authors: Nizamuddin Maitlo Mahmood Hussain Shah Abdullah Maitlo Ghulam Mustafa Kaleem Arshid Nooruddin Noonari

Resource allocation in sixth-generation (6G) networks must meet throughput, latency, and reliability targets while network conditions keep changing. At the same time, the telemetry needed to train good models is distributed across many devices and edge nodes, so sending it to a central server can violate privacy or data-sharing constraints. Federated learning (FL) helps, but two practical concerns usually determine whether it works in practice: how much communication is needed to achieve strong performance, and whether weaker (tail) clients benefit-not only the average client. In this study, we run large-scale FL on 6G telemetry with 200 clients and quantify the communication fairness trade-off. We evaluate FedAvg and FedProx under multiple settings and benchmark them against a strong centralized model and a local-only baseline. Results are reported as mean ± 95% confidence intervals over five random seeds. We measure the accuracy, macro-F1, AUC, and AP, and we also focus on tail behavior using the worst eligible client accuracy, p10 client accuracy, and fairness gap. By plotting the accuracy/macro-F1 against cumulative communication (bytes), we show that some configurations match the average performance while transmitting far fewer data. Finally, we find that the worst client performance improves early and then stabilizes, and a sensitivity study suggests that FedProx’s μ has a limited impact in this setup. These findings offer actionable guidance for 6G operators and system designers by quantifying how participation and dropout policies translate into concrete communication budgets and tail client behavior.

Inventions doi: 10.3390/inventions11020030

Authors: Alexa-Andreea Crisan Radu Eugen Kuncser Simona-Nicoleta Danescu Vlad Stefan Buzetelu Madalina Botu Daniel-Eugeniu Crunteanu

The transition toward sustainable aviation fuels requires dedicated experimental platforms capable of evaluating alternative fuels under realistic propulsion conditions. This study presents the development and laboratory experimental validation of a modular testing installation designed for sustainable fuels derived from plastic waste pyrolysis, intended for aerospace engine applications. The proposed system is conceived as an integrated small-scale gas turbine assembly that reproduces the functional characteristics of a jet engine and enables controlled laboratory investigations of dynamic behavior, combustion stability, and performance. The installation comprises a compressor, annular combustion chamber, and turbine mounted on a common shaft, along with a fully autonomous fuel supply system equipped with electronically controlled pumping, safety devices, and thermal conditioning of the fuel mixture via an attached Stirling engine. Combustion processes are continuously evaluated using an exhaust gas analysis system to assess fuel composition and combustion quality, while a high-speed camera operating at 50,000 fps enables detailed visualization of flame stability. Operating parameters, including temperatures, pressures, rotational speed, mass flow rates, and thrust, are monitored and recorded through an integrated control and data acquisition system with real-time analysis capabilities. Experimental results demonstrate stable operation and reliable ignition using alternative fuel mixtures, confirming the suitability of the modular installation as a versatile research platform for the assessment and comparative analysis of sustainable aerospace fuels.

Inventions doi: 10.3390/inventions11020029

Authors: Herman Cristiano Jaime Adler Diniz de Souza Raphael Carlos Santos Machado Otávio de Souza Martins Gomes

Non-Intrusive Load Monitoring (NILM) systems are increasingly applied in residential and commercial environments to disaggregate energy consumption without requiring additional hardware sensors. The integration of Machine Learning (ML) techniques has enhanced the accuracy and efficiency of load identification and classification in smart meter-based systems. This study presents a systematic review and meta-analysis aimed at identifying, classifying, and quantitatively evaluating ML models applied to NILM. Searches were conducted in the IEEE Xplore and Scopus databases, restricted to peer-reviewed publications from 2017 to 2024. Thirty studies met the eligibility criteria and were included in the quantitative synthesis using a random-effects meta-analysis model (DerSimonian–Laird estimator). The primary effect measure was the F1-score. Statistical analyses were performed using R (version 4.5.0) and Python (version 3.10.0), including heterogeneity assessment and subgroup analyses according to model type. Hybrid models, such as SVDT-KNN-MLP, LE-CRNN, and RBFNN-MOGA, achieved the highest pooled F1-scores, although supported by a limited number of studies. Traditional approaches, including CNN, KNN, and Random Forest, demonstrated consistently strong performance and broader validation, whereas Boosted Trees and RNN-based models showed lower or more variable results. Substantial heterogeneity was observed across studies, highlighting the need for dataset standardization, reproducible evaluation frameworks, and further validation of emerging hybrid architectures in diverse operational scenarios. This study contributes by providing a quantitative synthesis of machine learning models applied to NILM using a structured PRISMA-based methodology and subgroup analysis by model architecture. Unlike previous narrative reviews, this work integrates scientometric analysis with meta-analytic performance aggregation, offering a consolidated and comparative evidence base for future NILM research.

Inventions doi: 10.3390/inventions11020028

Authors: Xin Ning Rui Yu Longchen Liu Jiayi Wang Jingxin Zou Hao Wang Tian Tan Huajian Peng Xin Yang

Lightning-induced breaking accidents of medium-voltage insulated conductors pose a serious threat to the safety of distribution networks, and the key cause lies in the establishment and sustained combustion of the power-frequency follow-current arc after lightning overvoltage breakdown. This paper systematically investigates the formation mechanism and critical conditions of power-frequency follow-current arcs using combined simulation and experimental approaches. Based on the streamer discharge theory, a lightning breakdown model was established and combined with the arc energy balance equation, revealing that the establishment of power-frequency follow-current arcs is essentially determined by the post-breakdown energy competition process. The simulation results show that the required anode electric field strength for lightning breakdown is not less than 3 kV/mm. When the power-frequency voltage reaches 10 kV, Joule heating of the arc continuously exceeds heat dissipation loss, enabling restrike after zero-crossing and sustaining stable burning. Experiments verified this voltage threshold and further revealed that the arc establishment rate exhibits nonlinear growth with increasing power-frequency voltage, exceeding 90% at power-frequency voltages ≥ 10 kV. The study also reveals that increased gap distance reduces the arc establishment rate, while the introduction of insulators can enhance it by approximately 20%. This study clarifies the energy criterion for power-frequency follow-current arc establishment and the influence patterns of key parameters, providing theoretical basis and engineering reference for lightning protection design and arc suppression in medium-voltage insulated lines.

Inventions doi: 10.3390/inventions11020027

Authors: Muhammad Waheed Azam Fabio Bozzoli Ghulam Qadir Choudhary Uzair Sajjad

Micro heat exchangers (MHXs) have emerged as a critical technology for advanced thermal management in the food and pharmaceutical industries due to their high surface area-to-volume ratios, compact design, and precise temperature control. This review provides a systematic and integrated analysis of MHX technology, covering their fundamental principles, classification, design methodologies, performance enhancement techniques, and industrial applications. Unlike existing reviews, the present work establishes a unified framework that links microscale heat transfer mechanisms, such as Brownian motion, surface corrugation effects, and non-dimensional parameters, with practical design choices, manufacturing routes, and the process requirements specific to food and pharmaceutical systems. The subsequent sections explore the key performance-influencing factors, including channel geometry, surface enhancement strategies, nanofluid utilization, and governing non-dimensional numbers (e.g., Nusselt, Reynolds, and Knudsen numbers), which are systematically compared across different operating regimes. Recent advances in materials and fabrication techniques, such as laser ablation, lithography, micro-milling, embossing, and additive manufacturing, are analyzed with respect to their scalability, thermal–hydraulic performance, and industrial feasibility. Furthermore, the review highlights the emerging trends in micro heat exchanger (MHX) optimization, including computational fluid dynamics (CFD)-driven design, smart monitoring systems, and energy-efficient integration within processing lines. Finally, the paper also identifies the key challenges and limitations of micro heat exchangers, including pressure drop, fouling, scaling, manufacturing complexity, and cost constraints. These are critically discussed along with future research directions aimed at improving reliability and sustainability. By consolidating the dispersed research outcomes into a coherent, design-oriented perspective, this review offers new insights and practical guidance for researchers, engineers, and industry practitioners seeking to advance the deployment of MHXs in food and pharmaceutical processing.

Inventions doi: 10.3390/inventions11020026

Authors: Caizhi Yang Huan Zhang Like Pan Yuan Yuan Qun Yu Qing Xiong Ziqian Yang Wenfu Wei

The regularity of the catenary system and the stability of pantograph–catenary interaction are crucial for ensuring continuous and stable current collection quality in high-speed trains. Given that the dropper is a key suspension component within the catenary, the state of service integrity directly determines the regularity of, and dynamics within, the pantograph–catenary system. However, under long-term alternating loads and environmental influences, the dropper inevitably suffers damage due to strand fracture. The geometric regularity of the catenary is consequently disrupted, and the current collection quality of trains can deteriorate. While substantial efforts have been devoted to the study of pantograph–catenary dynamics under ideal or intact dropper conditions, research on current collection quality when the dropper has different types of damage remains insufficiently understood. This study focuses on the practical operational situation of high-speed railways, investigating the impact of dropper damage on current collection quality. Firstly, based on the pantograph–catenary parameters of an actual line, a dynamic model capable of simulating different types of dropper damage was built. Secondly, the current contact quality under various types of damage was explored in detail by several time-domain statistical features. Finally, within the typical speed range of 250 km/h to 350 km/h, the evolution of pantograph–catenary dynamic behavior under the combined effects of operating speed and dropper damage was analyzed, providing a theoretical basis for the reliable assessment of pantograph–catenary current collection quality and the formulation of stable operation and maintenance strategies.

Inventions doi: 10.3390/inventions11020025

Authors: Mohanad Al-Ghriybah

The rapid expansion of wind energy into complex and extreme environments has renewed interest in vertical-axis wind turbines (VAWTs) due to their omnidirectional operation, compact footprint, and potential resilience under harsh operating conditions. However, the current understanding of VAWT performance remains fragmented across aerodynamic, structural, operational, and application-specific studies. This systematic review aims to synthesize and critically evaluate VAWT research with environmental stressors as the central organizing framework, addressing performance behavior, adaptation challenges, and future research pathways. Literature searches were conducted in the Web of Science Core Collection, Scopus, IEEE Xplore, ScienceDirect, and SpringerLink databases, with Google Scholar used as a supplementary source, covering publications from 2000 to January 2026. Eligible studies focused on VAWTs operating under non-standard or extreme conditions, including icing, offshore, desert, high-turbulence, and thermally severe environments. A systematic quality assessment was applied to evaluate methodological rigor and environmental characterization, and the findings were synthesized using a qualitative–quantitative hybrid approach; no formal meta-analysis was performed. The review reveals substantial advances in unsteady aerodynamics, numerical modeling, and control strategies, but also identifies persistent discrepancies between high-fidelity simulations and real-world performance due to simplified modeling assumptions and limited full-scale experimental validation. Quantitative findings indicate that high turbulence can decrease the power output of large VAWTs by 23–42%, dust and sand in arid environments can reduce torque and power by ~25%, and air temperature increases from 15 °C to 60 °C can reduce the power coefficient of VAWTs by about 38%. Emerging approaches, including artificial intelligence-assisted design, adaptive turbine architectures, and climate-aware methodologies, show promise in addressing these limitations. The findings highlight the urgent need for coordinated long-term field measurements, improved multi-physics modeling, and interdisciplinary research to enhance the reliability and scalability of VAWTs in extreme environments. This review was not registered.

Inventions doi: 10.3390/inventions11020024

Authors: José Pereira Reinaldo Souza Ana Moita

The ionic liquids are increasingly used as versatile media capable of reshaping the electrochemical environment for hydrogen production. Their wide electrochemical windows, thermal stability, and customizable solvation structures enable these liquids to tailor the electrode–electrolyte interface in such a way that the traditional alkaline and polymer-membrane systems cannot. These features allow for reductions in the hydrogen evolution overpotentials, improved catalyst stability, and effective suppression of gas crossover, positioning the ionic liquids as promising components for advanced electrolysis systems. Despite these benefits, their broader deployment remains constrained by certain challenges. The elevated viscosity and associated mass-transport limitations complicate the cell design and energy efficiency, whereas the cost and long-term stability of many ionic liquids limit their competitiveness in industrial hydrogen production. Also, the hydrolysable anions and other reactive species increase the burden, particularly in environments where moisture and anodic potential are present. As a result, the ionic liquids electrolysis has its most promising prospects in niche and hybrid configurations like the renewable integrated systems and configurations where the tailored interfacial chemistry and long operational lifetimes outweigh the investment cost and maintenance requirements. Future progress will depend on the development of greener, task-specific ionic liquids with improved stability and lower synthesis costs, alongside hybrid electrolyte designs that balance the unique interfacial benefits of ionic liquids with the practicality of aqueous systems. Advancing these materials from laboratory research to large-scale sustainable hydrogen production will require coordinated advances in the materials compatibility, device and infrastructural architecture, and techno-economic optimization.

Inventions doi: 10.3390/inventions11020023

Authors: Alejandro Abascal Mendez Ana Del Castillo Martín Olga Álvarez Pérez-Aradros Paulo Henrique Figueiredo Vaz Ana Patricia Talayero Navales Roberto Lázaro Gastón Andrés Llombart Estopiñán

This study evaluates the impact of measurement campaign duration on wind resource characterization using three MCP (Measure–Correlate–Predict) models: Total Least Squares (TLS), Multiple Linear Regression (LR), and Quantile Gradient Boosting (GB). The analysis is based on data from 30 meteorological masts (nine primary and twenty-one secondary masts) installed worldwide across different terrains, with up to twenty-seven months of concurrent wind measurements between primary and secondary masts. Fixed campaign durations of 3, 4, 5, 6, 9, and 12 months were simulated using moving intervals to quantify the effect of measurement length on mean wind speed estimation. This working framework also serves to represent conditions typical of campaigns where LIDAR systems are used to complement meteorological mast deployments, as LIDAR units generally operate for shorter periods due to frequent relocation as part of broader measurement strategies. Wind speed estimation was assessed through metrics such as Mean Absolute Error (MAE), relative uncertainty, and monthly uncertainty reduction, taking into account terrain complexity and correlation coefficient (R2) between masts. Results indicate that extending the measurement period improves the accuracy and consistency of wind speed estimates, with significant reductions in uncertainty observed after six months. Across sites, the average monthly uncertainty reduction ranges from 0.13% to 0.41% of the mean wind speed per additional month of measurements, depending on terrain complexity and inter-mast correlation. Linear models (TLS and LR) consistently show better performance in terms of error and uncertainty reduction compared to GB. Based on an extensive and diverse MCP dataset covering multiple terrains and locations, this study provides empirically derived monthly uncertainty-reduction benchmarks for campaign-length optimisation under different site conditions, contributing to more reliable wind resource assessments and, consequently, energy yield estimates.

Inventions doi: 10.3390/inventions11020022

Authors: Baidaa Mutasher Rashed Shaker Kadhim Ali

This paper suggests a novel approach based on machine learning (ML) and deep learning (DL) for medical image classification in a fast and accurate manner. The proposed method merges the strengths of the convolutional neural network (CNN) using the VGG19 model for feature extraction with an artificial neural network (ANN) classifier for medical dataset classification. The suggested model is improved by applying the slime mold algorithm (SMA) to the task of feature selection and the particle swarm optimization (PSO) approach to optimize the ANN classifier. PSO is a crucial component in neural network design to optimize the ANN setup and hyperparameters. Through adjustments to the bias and weight parameters, the PSO approach enhances the ANN method’s ability to classify medical images. The experiments were conducted on the LC25000 histopathological dataset, which comprises 25,000 histopathological images of lung and colon cancer tissue, partitioned into five classes, each with 5000 images: lung benign tissue, lung adenocarcinoma, lung squamous cell carcinoma, colon adenocarcinoma, and colon benign tissue. The results demonstrated that the suggested model (CNN-PSO-ANN) does better at illness detection than ANN alone. The proposed model is evaluated utilizing several metrics, like accuracy, RMSE, and MAE. The accuracy rate is 94.1% when ANN is utilized independently, while the percentage increases to 98.8% when PSO is employed with the ANN. Additionally, the proposed model is compared with other medical data classification systems that utilize PSO and neural networks. The proposed model (CNN-PSO-ANN) performed better than the other models. With the suggested CNN-PSO-ANN model, diseases, especially cancer, can be found and treated earlier and better.

Inventions doi: 10.3390/inventions11020021

Authors: Heran Kang Hongyang Liu Jianfei Liu Ruichen Hao Xiang Wang Wenbo Hu Jie Chen Wei Yue Haibo Li Zongxiang Lu

To address the limitations of existing annual load scenario generation methods, including insufficient ability to represent long-term trends, excessive randomness in generated scenarios, and inadequate consideration of special holiday conditions, in this paper, an annual load curve generation method is proposed that integrates Seasonal–Trend decomposition using Loess (STL) with an improved denoising diffusion probabilistic model (DDPM). In the proposed method, the STL algorithm is first applied to decompose the annual load curve into a trend component and a daily seasonal component. The trend component is used as a baseline to ensure that the generated load curves remain consistent with the actual long-term trend characteristics. On this basis, an improved diffusion-based denoising model is employed to achieve controllable generation of different types of daily load scenarios. Finally, the generated daily load scenarios are aggregated with the trend component on an hourly basis to construct annual load scenario curves that simultaneously preserve realistic trend behavior and stochastic fluctuations. A case study based on a city in China is used to evaluate the proposed method. The results demonstrate that both the generated daily load scenarios and annual load scenarios outperform existing benchmark methods across multiple quantitative evaluation metrics, thereby validating the effectiveness of the proposed load scenario generation approach.

Inventions doi: 10.3390/inventions11020020

Authors: Menna M. S. Elmasry Mona G. Gafar M. A. Elsabagh

Because of the substantial class disparity and the intricate interactions between academic, behavioral, and socioeconomic characteristics, anticipating student academic performance and dropout rates continues to be a major issue for institutions of higher learning. To improve the dependability and credibility of multiclass student outcome prediction, this study suggests a strong, multi-objective, and uncertainty-aware predictive framework that combines the Random Forest (RF) classifier with Holistic Swarm Optimization (HSO). The suggested method creates a multi-objective optimization problem that simultaneously maximizes macro F1-score, controls model complexity, and lessens inter-class performance disparity. Thereby, the model promotes fairness across student outcome categories, in contrast to traditional optimization strategies that only concentrate on predictive accuracy. Furthermore, by utilizing ensemble-based probability dispersion, the framework integrates uncertainty-aware prediction, making it possible to identify high-risk students with different degrees of confidence to assist practical academic interventions. According to the results of experiments, the suggested HSO-RF framework greatly reduces the performance gap between outcome classes while achieving the best overall predictive performance, reaching an accuracy of 77.74%, a macro F1-score of 0.69, and a weighted F1-score of 0.76. The analysis shows that academic, socioeconomic, and administrative characteristics serve as significant markers of student motivation, stability, and vulnerability in addition to computational benefits. The suggested architecture advances appropriate and trustworthy educational data mining and offers a dependable decision-support tool for early warning systems.

Inventions doi: 10.3390/inventions11010019

Authors: Luigi Assom Aron Larsson Alessandro Chiolerio

Evaluating innovation and optimising its role in the inventions is fundamental for applied research, that requires planning the use of available resources. Traditional assessment approaches often miss to capture how innovation stagnates between the ideation and prototyping phases (the Valley of Death), and to learn how innovation emerges from intermediate-steps contributed by individuals. This paper focuses on tracing innovation as an approach enabling mapping of pathways of intermediate-steps and opportunities for valorising unplanned outcomes. We adopt a qualitative case study to explore how innovation pathways can be conceptualised through technological readiness levels. The operational settings of an EU-funded project defined the boundaries of the study. A network analysis explored relationships among themes that emerged from respondents involved in the activities, following an inductive approach to derive themes from data. Findings indicate that intermediate innovation steps, including failures, are viewed as cumulative contributions to novelty. Their documentation is seen as an investment for unlocking latent value embedded in distributed knowledge. Within this scope, we outline a blockchain-based knowledge graph as a proof-of-concept for tracing cumulative contributions, identifying breakthroughs leading to technological maturity and supporting generation of hypothesis grounded on experimental trials. As a result, we suggest that paths recombining prior knowledge into novelty encode latent value that can be interpreted as a function of the network topology, and propose a conceptual framework for analysing value by means of information theory metrics applicable to innovation graphs.

Inventions doi: 10.3390/inventions11010018

Authors: Hamid Chojaa Kawtar Tifidat Aziz Derouich Mishari Metab Almalki Mahmoud A. Mossa

Doubly Fed Induction Generators (DFIGs) are widely employed in variable-speed wind turbine systems due to their high efficiency, enhanced controllability, and economic viability. This paper presents an intelligent neural-network-based control strategy aimed at maximizing wind energy extraction while ensuring accurate speed regulation of a DFIG by continuously tracking the maximum power point under fluctuating wind conditions. Two independent control schemes are developed for the decoupled regulation of active and reactive power in a grid-connected DFIG wind turbine. The first scheme is based on conventional field-oriented control using proportional integral regulators (FOC–PI), while the second employs an Artificial Neural Network Controller (ANNC). The effectiveness of both controllers is evaluated through MATLAB/Simulink 2020 Version simulations of a 1.5 MW DFIG-based wind energy conversion system and experimentally validated using a real wind profile implemented on an eZdsp TMS320F28335 digital signal processor. The proposed control approach achieves low output ripple, a steady-state error below 0.16%, total harmonic distortion of 0.38%, and a limited overshoot of 5%. The obtained results confirm the robustness and reliability of the implemented control strategies in enhancing power capture and improving overall system stability under variable wind conditions.

Inventions doi: 10.3390/inventions11010017

Authors: Jie Zhang Longgang Guo Lin Li Jian Qin Zhiqiang Zhang Zefeng Yang

In response to thermal failure risks in ultra-high voltage (UHV) bushing online monitoring devices and maintenance equipment—caused by high heat generation of electronic components and the intrinsically low thermal conductivity of conventional resin encapsulation materials—this study proposes a novel modification strategy based on flash Joule heating (FJH). Distinct from conventional interface modification methods, the proposed approach enables cross-scale, in situ microsoldering between multi-walled carbon nanotubes (MWCNTs) and carbon fibers (CFs), constructing a multiscale reinforcement network with integrated thermal transport and mechanical load transfer pathways. The transient ultra-high-temperature thermal shock generated by FJH not only effectively removes inert impurities on CF surfaces but also drives carbon structural reconstruction, enabling graphitic-level welding of MWCNTs onto the fiber surface. This micro-welded architecture fundamentally differs from traditional filler dispersion or interface coating strategies, which often suffer from the trade-off between interfacial thermal transport and mechanical bonding. By contrast, the FJH-induced carbon–carbon bonded nodes form a continuous conductive and load-bearing network at the micro–nano scale. Characterizations using scanning electron microscopy (SEM), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS) confirm successful in situ welding of MWCNTs onto CF surfaces. Meanwhile, FJH treatment effectively removes oxygen-containing functional groups and surface impurities. Analysis of carbon bonding evolution indicates that the welding efficiency reaches its maximum at 90 V. Macroscopic performance tests demonstrate that, compared with epoxy resin, the thermal conductivity of the multiscale reinforced system increases by approximately 168%, while the mechanical strength improves by 62.72%. This study provides new theoretical insights and technical pathways for the development of next-generation polymer composite materials with both high thermal conductivity and high mechanical strength.

Inventions doi: 10.3390/inventions11010016

Authors: Xiaoyan Hu Keteng Jiang Zikai Fan Borui Liao Bingjie Li Zesen Li Yi Ge Hu Li

Accurate assessment of source-load complementarity and system regulation capacity is critical for secure dispatch and planning in high-penetration renewable power systems. Addressing limitations of existing methods—which rely heavily on static metrics, struggle to capture temporal and tail dependence characteristics, and provide insufficient support for dispatch decisions—this paper proposes a multi-level integrated evaluation framework. First, from a source—load matching perspective, we develop a novel complementarity metric, integrating real-time rate of change, temporal consistency, and tail dependency. An improved adaptive noise-complete set empirical mode decomposition combined with a hybrid Copula model is employed to isolate noise and to precisely quantify dynamic dependency structures. Second, we introduce the Minkowski measure and construct a net load fluctuation domain accounting for extreme fluctuations and coupling relationships. Subsequently, combining the Analytic Hierarchy Process (AHP) with probabilistic convolution enables multi-level comparative quantification of resource capacity and fluctuation domain requirements under varying confidence levels. Simulation results demonstrate that the proposed framework not only provides a more robust assessment of source-load complementarity but also quantitatively outputs the adequacy and risk level of system regulation capacity. This delivers hierarchical, actionable decision support for dispatch planning, significantly enhancing the engineering applicability of evaluation outcomes.

Inventions doi: 10.3390/inventions11010015

Authors: Pavel V. Matrenin Irina F. Iumanova Alexandra I. Khalyasmaa

Accurate and trustworthy forecasting of wind and photovoltaic power generation is essential for the reliable operation and planning of modern power systems. Although recent machine-learning-based forecasting solutions increasingly incorporate elements of trustworthy artificial intelligence, such as explainability, uncertainty quantification, robustness, drift monitoring, and machine learning operations, these components are typically introduced in a fragmented manner and remain weakly integrated at the architectural level, which limits their applicability in real operational environments. This paper presents a systematic review of 59 peer-reviewed journal articles published between 2019 and 2025, conducted in accordance with the PRISMA 2020 guidelines. The review includes studies focused on wind and photovoltaic power forecasting that report system architectures, frameworks, or end-to-end pipelines incorporating at least one trust-related attribute. The literature search was performed using Scopus, IEEE Xplore, MDPI, and ScienceDirect. Using a narrative and architectural synthesis, the review identifies six structural gaps hindering industrial deployment: the absence of semantic data models, shallow model-centric explainability, drift monitoring without governance mechanisms, lack of automated model lifecycle management, insufficient robustness to real-world data defects, and the absence of integrated end-to-end architectures. The evidence base is limited by the heterogeneity of architectural descriptions and the predominantly qualitative nature of reported implementations. Based on these findings, a high-level reference architecture for a trustworthy AI-based forecasting system is proposed. The architecture formalizes trustworthiness as a system-level property and integrates semantic, technological, and functional trust layers within a unified data and model lifecycle, supporting reproducible, interpretable, and operationally reliable forecasting for both wind and photovoltaic power plants.

Inventions doi: 10.3390/inventions11010014

Authors: Zhizhen Du Kai Liu Zhiqiang Dai Like Fan Guangning Wu

Photovoltaic (PV) cells play an important role in the development of green energy. However, in practical photovoltaic systems, shunting-related defects and hotspot phenomena may originate not only from manufacturing imperfections, but also from mechanical stress and environmental factors during transportation, installation, and long-term field operation. Such hotspots not only reduce the power-generation efficiency and service life of PV cells but may also pose safety risks to grid-connected photovoltaic power stations. To address this problem, a squared even-order derivative (SEOD) method based on surface temperature analysis is introduced to enable the quantitative detection of thermal defects in PV cells. In this study, typical faults in PV cells, including low-resistance defects and silicon-based deep scratches, are analyzed. A simulation model is established to correlate typical faults with their equivalent volumetric heat sources, followed by experimental validation for low-resistance defects. Based on this framework, the SEOD algorithm is developed and applied to achieve high-precision localization and quantitative characterization of thermal defects in both simulation models and experimental samples.

Inventions doi: 10.3390/inventions11010013

Authors: Shan Jiang Yingzhao Chen Rilige Chaomu Zheng Liu

Large volumes of images shared on social media have made image captioning an important tool for social hotspot identification and public opinion monitoring, where accurate visual–language alignment is essential for reliable analysis. However, existing image captioning models based on BLIP-2 (Bootstrapped Language–Image Pre-training) often struggle with complex, context-rich, and socially meaningful images in real-world social media scenarios, mainly due to insufficient cross-modal interaction, redundant visual token representations, and an inadequate ability to capture multi-scale semantic cues. As a result, the generated captions tend to be incomplete or less informative. To address these limitations, this paper proposes ECMA (Enhanced Cross-Modal Attention), a lightweight module integrated into the Querying Transformer (Q-Former) of BLIP-2. ECMA enhances cross-modal interaction through bidirectional attention between visual features and query tokens, enabling more effective information exchange, while a multi-scale visual aggregation strategy is introduced to model semantic representations at different levels of abstraction. In addition, a semantic residual gating mechanism is designed to suppress redundant information while preserving task-relevant features. ECMA can be seamlessly incorporated into BLIP-2 without modifying the original architecture or fine-tuning the vision encoder or the large language model, and is fully compatible with OPT (Open Pre-trained Transformer)-based variants. Experimental results on the COCO (Common Objects in Context) benchmark demonstrate consistent performance improvements, where ECMA improves the CIDEr (Consensus-based Image Description Evaluation) score from 144.6 to 146.8 and the BLEU-4 score from 42.5 to 43.9 on the OPT-6.7B model, corresponding to relative gains of 1.52% and 3.29%, respectively, while also achieving competitive METEOR (Metric for Evaluation of Translation with Explicit Ordering) scores. Further evaluations on social media datasets show that ECMA generates more coherent, context-aware, and socially informative captions, particularly for images involving complex interactions and socially meaningful scenes.

Inventions doi: 10.3390/inventions11010012

Authors: Peilin Li Ziyan Yan Yuchen Zhou Hongyun Li Wei Gao Dazhou Li

Dual-target drug design addresses complex diseases and drug resistance, yet existing computational approaches struggle with simultaneous multi-protein optimization. This study presents SFG-Drug, a novel dual-target molecular generation model combining Monte Carlo tree search with gated recurrent unit neural networks for simultaneous MEK1 and mTOR targeting. The methodology employed DigFrag digital fragmentation on ZINC-250k dataset, integrated low-frequency masking techniques for enhanced diversity, and utilized molecular docking scores as reward functions. Comprehensive evaluation on MOSES benchmark demonstrated superior performance compared to state-of-the-art methods, achieving perfect validity (1.000), uniqueness (1.000), and novelty (1.000) scores with highest internal diversity indices (0.878 for IntDiv1, 0.860 for IntDiv2). Over 90% of generated molecules exhibited favorable binding affinity with both targets, showing optimal drug-like properties including QED values in [0.2, 0.7] range and high synthetic accessibility scores. Generated compounds demonstrated structural novelty with Tanimoto coefficients below 0.25 compared to known inhibitors while maintaining dual-target binding capability. The SFG-Drug model successfully bridges the gap between computational prediction and practical drug discovery, offering significant potential for developing new dual-target therapeutic agents and advancing AI-driven pharmaceutical research methodologies.

Inventions doi: 10.3390/inventions11010011

Authors: Sajad Amiri Shahram Taeb Sara Gharibi Setareh Dehghanfard Somayeh Sadat Mehrnia Mehrdad Oveisi Ilker Hacihaliloglu Arman Rahmim Mohammad R. Salmanpour

Gadolinium-based contrast agents (GBCAs) are vital for glioma imaging yet pose safety, cost, and accessibility issues; predicting contrast enhancement from non-contrast MRI via machine learning (ML) provides a safer, economical alternative, as enhancement indicates tumor aggressiveness and informs treatment planning. However, scanner and population variability hinder robust model selection. To overcome this, a stability-aware framework was developed to identify reproducible ML pipelines for predicting glioma contrast enhancement across multicenter cohorts. A total of 1367 glioma cases from four TCIA datasets (UCSF-PDGM, UPENN-GB, BRATS-Africa, BRATS-TCGA-LGG) were analyzed, using non-contrast T1-weighted images as input and deriving enhancement status from paired post-contrast T1-weighted images; 108 IBSI-standardized radiomics features were extracted via PyRadiomics 3.1, then systematically combined with 48 dimensionality reduction algorithms and 25 classifiers into 1200 pipelines, evaluated through rotational validation (training on three datasets, external testing on the fourth, repeated across rotations) incorporating five-fold cross-validation and a composite score penalizing instability via standard deviation. Cross-validation accuracies spanned 0.91–0.96, with external testing yielding 0.87 (UCSF-PDGM), 0.98 (UPENN-GB), and 0.95 (BRATS-Africa), averaging ~0.93; F1, precision, and recall remained stable (0.87–0.96), while ROC-AUC varied (0.50–0.82) due to cohort heterogeneity, with the MI + ETr pipeline ranking highest for balanced accuracy and stability. This framework enables reliable, generalizable prediction of contrast enhancement from non-contrast glioma MRI, minimizing GBCA dependence and offering a scalable template for reproducible ML in neuro-oncology.

Inventions doi: 10.3390/inventions11010010

Authors: Ali Benmoussa Zakaria Chalhe Benaissa Elfahime Mohammed Radouani

This research examines the feasibility of recovering and recycling condensate water, a waste byproduct generated by Atlas Copco ZR315 FF industrial air compressors utilizing oil-free rotary screw technology with integrated dryers. Given the growing severity of global water scarcity, finding alternative water sources is essential for sustainable industrial practices. This study specifically evaluates the potential of capturing and treating compressed air condensate as a viable method for water recovery. The investigation analyzes both the quantity and quality of condensate water produced by the ZR315 FF unit. It contrasts this recovery approach with traditional water production methods, such as desalination and atmospheric water generation (AWG) via dehumidification. The findings demonstrate that recovering condensate water from industrial air compressors is a cost-effective and energy-efficient substitute for conventional water production, especially in water-stressed areas like Morocco. The results show a significant opportunity to reduce industrial water usage and provide a sustainable source of process water. This research therefore supports the application of circular economy principles in industrial water management and offers practical solutions for overcoming water scarcity challenges within manufacturing environments.

Inventions doi: 10.3390/inventions11010009

Authors: Hai Yan Xinyu Sui Ning Chen Shuo Pan

Amid the increasing demand to reduce carbon emissions, replacing diesel buses with electric buses has become a key development direction in public transportation. However, a significant challenge in this transition lies in developing efficient charging strategies and accurately determining the required fleet size, as existing research often fails to adequately account for the impact of real-time traffic congestion on energy consumption. To address this gap, in this study, an optimized charging strategy is proposed, and the necessary fleet size is calculated using a deep reinforcement learning (DRL) approach, which integrates actual route characteristics and dynamic traffic congestion patterns into an electric bus operation model. Modeling is conducted based on Beijing Bus Route 400 to ensure the practical applicability of the proposed method. The results demonstrate that the proposed DRL method ensures operational completion while minimizing charging time, with the algorithm showing rapid and stable convergence. In the multi-route scenarios investigated in this study, the DRL-based charging strategy requires 40% more electric buses, with this figure decreasing to 24% when fast-charging technology is adopted. This study provides bus companies with valuable electric bus procurement and route operation references.

Inventions doi: 10.3390/inventions11010008

Authors: Ildar A. Safiullin Ivan P. Ashaev Alexey A. Korobkov Artur K. Gaysin Adel F. Nadeev

The increasing number of Base Stations (BSs) and connected devices, coupled with their mobility, poses significant challenges and makes mobility management even more pressing. Therefore, advanced handover (HO) management technologies are required to address this issue. This paper focuses on the ping-pong HO problem. To address this issue, we propose an algorithm using Reinforcement Learning (RL) based on the Double Deep Q-Network (DDQN). The novelty of our approach is to assign specialized RL agents to users based on their mobility patterns. The use of specialized RL agents simplifies the learning process. The effectiveness of the proposed algorithm is demonstrated in tests on the ns-3 platform due to its ability to replicate real-world scenarios. To compare the results of the proposed approach, the baseline handover algorithm based on Events A2 and A4 is used. The results show that the proposed approach reduces the number of HO by more than four times on average, resulting in a more stable data rate and increasing it up to two times in the best case.

Inventions doi: 10.3390/inventions11010007

Authors: Maryna Yeromina Jan Duplak Jozef Torok Darina Duplakova Monika Torokova

Additive manufacturing (AM) has emerged as a key enabling technology in contemporary dental manufacturing, driven by its capacity for customization, geometric complexity, and seamless integration with digital design workflows. This article presents a technology-oriented narrative review of additive manufacturing in dental implant production, focusing on dominant processing routes, material systems, and emerging research trends rather than a systematic or critical appraisal of the literature. An indicative descriptive analysis of publications indexed in the Web of Science and Scopus databases between 2014 and 2024 was used to contextualize the technological development of the field and identify major research directions. Emphasis was placed on metal powder bed fusion technologies, specifically Selective Laser Melting (SLM) and Direct Metal Laser Sintering (DMLS), which enable the fabrication of titanium implants with controlled porosity and enhanced osseointegration. Ceramic AM approaches, including SLA, DLP, and PBF, are discussed in relation to their potential for aesthetic dental restorations and customized prosthetic components. The publication trend overview indicates a growing interest in ceramic AM after 2020, an increasing focus on hybrid and functionally graded materials, and persistent challenges related to standardization and the availability of long-term clinical evidence. Key technological limitations—including manufacturing accuracy, material stability, validated metrology, and process reproducibility—are highlighted alongside emerging directions such as artificial intelligence-assisted workflows, nanostructured surface modifications, and concepts enabling accelerated or immediate clinical use of additively manufactured dental restorations.

Inventions doi: 10.3390/inventions11010006

Authors: Marius Cioca Adriana Lavinia Cioca

This pilot study evaluated the visionMC system, a low-cost artificial intelligence system integrating HOG-based facial recognition and voice notifications, for workflow optimization in a family medicine practice. Implemented on a Raspberry Pi 4, the system was tested over two weeks with 50 patients. It achieved 85% recognition accuracy and an average detection time of 3.4 s. Compared with baseline, patient waiting times showed a substantial reduction in waiting time and administrative workload, and the administrative workload decreased by 5–7 min per patient. A satisfaction survey (N = 35) indicated high acceptance, with all scores above 4.5/5, particularly for usefulness and waiting time reduction. These results suggest that visionMC can improve efficiency and enhance patient experience with minimal financial and technical requirements. Larger multicenter studies are warranted to confirm scalability and generalizability. visionMC demonstrates that effective AI integration in small practices is feasible with minimal resources, supporting scalable digital health transformation. Beyond biometric identification, the system’s primary contribution is streamlining practice management by instantly displaying the arriving patient and enabling rapid chart preparation. Personalized greetings enhance patient experience, while email alerts on motion events provide a secondary security benefit. These combined effects drove the observed reductions in waiting and administrative times.

Inventions doi: 10.3390/inventions11010005

Authors: Junwen Cheng Jiahao Zhu Xin Wen Haodong Yu Wei Lin Zuqiang Xin Jiuyang Yu

To mitigate the inherent high flow resistance of conventional corrugated tubes, a novel design with alternating clockwise/counterclockwise corrugated segments separated by smooth sections is proposed. A 3D numerical model was developed to systematically evaluate the thermal-hydraulic performance of the novel tube against smooth and conventional corrugated tubes, with simulations conducted at Reynolds number (Re) = 9952–35,827. Results show both corrugated configurations enhanced heat transfer significantly relative to the smooth tube: the conventional tube had the highest Nusselt number (Nu) (1.76–1.79 times that of the smooth tube), while the novel tube achieved Nu = 1.61–1.65 times that of the smooth tube. Notably, the novel tube reduced flow resistance substantially—at Re = 35,827, its friction factor (f) was only 65.2% of the conventional tube’s. Parametric studies revealed that more corrugated segments improved heat transfer but increased pressure drop: the 72-12 configuration exhibited the best heat transfer, while the 72-2 configuration reduced f by 40.7%. The novel tube showed superior overall performance (Performance Evaluation Criterion (PEC) > 1.24 for all Re), as corrugated segments generated periodic vortices to disrupt the thermal boundary layer, while smooth segments enabled flow redevelopment and pressure recovery. This study provides valuable guidance for designing high-efficiency, low-resistance heat exchange elements.

Inventions doi: 10.3390/inventions11010004

Authors: Iulian Vlăducă Emilia Georgiana Prisăcariu

The invention concerns a screw-driven shredder for solid photopolymer resin, designed for both terrestrial use and prospective deployment in microgravity environments. The system addresses the need for efficient recycling of cured photopolymer waste generated by stereolithography (SLA) 3D printing—a process not yet implemented in orbit, but envisioned as part of future closed-loop additive manufacturing systems aboard space stations or lunar habitats. The proposed device is a compact, hermetically sealed mechanical unit composed of ten subassemblies, featuring two counter-rotating screw shafts equipped with carbide milling inserts arranged helically to achieve uniform and controlled fragmentation of solid SLA residues. The shredding process is supported by a pressurized inert fluid circuit, utilizing carbon dioxide (CO2) as a cryogenic working medium to enhance cutting efficiency, reduce heat accumulation, and ensure particle evacuation under microgravity conditions. Studies indicate that CO2-assisted cooling can reduce tool-tip temperature by 10–30 °C, cutting forces by 5–15%, and electrical power consumption by 5–12% while extending tool life by up to 50%. This invention thus provides a key component for a future in situ photopolymer recycling loop in space while also offering a high-efficiency shredding solution for Earth-based photopolymer waste management in additive manufacturing.

Inventions doi: 10.3390/inventions11010003

Authors: Juan Arquero-Gallego Carlos Gilarranz-Casado Vicente Garcia-Alcántara José Álvarez

Water resources are fundamental for human development in every possible sense; from natural development, since they are the main biological factor necessary for the development of life, to economic development, since they are essential for a large number of productive systems, especially in the primary and secondary sectors. This makes them a resource which, although at first glance may seem unlimited, is critical since their scarcity and unavailability compromise the whole of human development, greatly limiting productive and economic activity and, ultimately, social welfare. The current development of IoT technology, on the other hand, provides tools to face this problem in a technical way, allowing the adoption of distributed and automated solutions that, together with the knowledge provided by disciplines such as agricultural and alimentary engineering, make viable the development of a system that allows us to monitor and control water distribution networks (WDNs). Next, the situations that involve the mentioned problem will be detailed and different aspects will be proposed in which the implementation of the presented system is intended to have a direct impact.

Inventions doi: 10.3390/inventions11010002

Authors: Honghao Liu Guoliang Ma Kaixing Zhang

Current traditional tea processing production lines suffer from issues such as fragmented data and low levels of intelligence. This paper proposes a three-dimensional visualization system for tea processing production lines based on digital twins. Firstly, the system’s overall framework and functional architecture were established. Secondly, multi-source heterogeneous data from the production line was collected and managed through a driver architecture, enabling the construction and mapping of the digital twin information model. Thirdly, referencing the actual environment of a green tea processing line, scene-specific lighting models and rendering techniques were employed to recreate a virtual green tea processing environment. During this process, lighting optimization enhanced the realism of the system’s scenes. Finally, employing data-driven methodologies, the system dynamically simulates the operational states of various production line equipment and the morphological changes in tea leaves. This achieves comprehensive three-dimensional visualization and all-round monitoring of the tea processing production line. Experimental validation confirms the feasibility of this visualized 3D system, injecting fresh impetus into advancing intelligent tea production.

Inventions doi: 10.3390/inventions11010001

Authors: Leonid Agureev Svetlana Savushkina Artem Ashmarin

Ni–ySiC system (where y = 0.001, 0.005, and 0.015 wt.%) composite materials with enhanced mechanical properties have been fabricated and comprehensively investigated. The composites were synthesized using a combined technology involving preliminary mechanical activation of powder components in a planetary mill followed by consolidation via spark plasma sintering (SPS) at 850 °C. The microstructure and phase composition were studied by scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD). The physico-mechanical properties were evaluated by density measurements (hydrostatic weighing), three-point bending tests (25 °C and 400 °C), and Young’s modulus measurement using an ultrasonic method (25–750 °C). It was found that the introduction of ultralow amounts of SiC nanoparticles (0.001 wt.%) leads to an extreme increase in flexural strength: by 115% at 20 °C (up to 1130 MPa) and by 86% at 400 °C (up to 976 MPa) compared to pure nickel. Microstructural analysis revealed the formation of an ultrafine-grained structure (0.15–0.4 µm) with the presence of pyrolytic carbon and probable nickel silicide interlayers at the grain boundaries. Thermodynamic and kinetic modeling, including the calculation of chemical potentials and diffusion coefficients, confirmed the possibility of reactions at the Ni/SiC interface with the formation of nickel silicides (Ni2Si, NiSi) and free carbon. The scientific novelty of the work lies in establishing a synergistic strengthening mechanism combining the Hall–Petch, Orowan (dispersion), and solid solution strengthening effects, and in demonstrating the property extremum at an ultralow content of the dispersed phase (0.001 wt.%), explained from the standpoint of quantum-chemical analysis of phase stability. The obtained results are of practical importance for the development of high-strength and thermally stable nickel composites, promising for application in aerospace engineering.

Inventions doi: 10.3390/inventions10060111

Authors: Fabrizio Greco Francesco Fabbrocino Lorenzo Leonetti Arturo Pascuzzo Girolamo Sgambitterra

Simulating damage phenomena in masonry structures remains a significant challenge because of the intricate and heterogeneous nature of this material. An accurate evaluation of fracture behavior is essential for assessing the bearing capacity of these structures, thereby mitigating dramatic failures. This paper proposes an innovative adaptive concurrent multiscale model for evaluating the bearing capacity of in-plane masonry structures under in-plane loadings. Developed within a Finite Element (FE) set, the proposed model employs a domain decomposition scheme to solve a combination of fine- and coarse-scale sub-models concurrently. In regions requiring less detail, the masonry is represented by homogeneous linear elastic macro-elements. The material properties for these macro-elements are derived through a first-order computational homogenization strategy. Conversely, in areas with higher resolution needs, the masonry is modeled by accurately depicting individual brick units and mortar joints. To capture strain localization effectively in these finer regions, a Phase Field Cohesive Zone Model (PF-CZM) formulation is employed as the fracture model. The adaptive nature derives from the fact that at the beginning of the analysis, the model is entirely composed of coarse regions. As nonlinear phenomena develop, these regions are progressively deactivated and replaced by finer regions. An activation criterion identifies damage-prone regions of the domain, thereby triggering the transition from macro to micro scales. The proposed model’s validity was assessed through multiscale numerical simulations applied to a targeted case study, with the results compared to those from a direct numerical simulation. The results confirm the effectiveness and accuracy of this innovative approach for analyzing masonry failure.

Inventions doi: 10.3390/inventions10060110

Authors: Adrián Alarcón Becerra Vinícius Albernaz Lacerda Roberto Rocca Ana Patricia Talayero Navales Andrés Llombart Estopiñán

The regulation of voltage in transmission networks is becoming increasingly complex due to the dynamic behavior of modern power systems and the growing penetration of renewable generation. This study presents a comparative analysis of three artificial intelligence approaches—Deep Q-Learning (DQL), Genetic Algorithms (GAs), and Particle Swarm Optimization (PSO)—for training agents capable of performing autonomous voltage control. A unified neural architecture was implemented and tested on the IEEE 30-bus system, where the agent was tasked with adjusting reactive power set points and transformer tap positions to maintain voltages within secure operating limits under a range of load conditions and contingencies. The experiments were carried out using the GridCal simulation environment, and performance was assessed through multiple indicators, including convergence rate, action efficiency, and cumulative reward. Quantitative results demonstrate that PSO achieved 3% higher cumulative rewards compared to GA and 5% higher than DQL, while requiring 8% fewer actions to stabilize the system. GA showed intermediate performance with 6% faster initial convergence than DQL but 4% more variable results than PSO. DQL demonstrated consistent learning progression throughout training, though it required approximately 12% more episodes to achieve similar performance levels. The quasi-dynamic validation confirmed PSO’s advantages over conventional AVR-based strategies, achieving voltage stabilization approximately 15% faster. These findings underscore the potential of neuroevolutionary algorithms as competitive alternatives for advanced voltage regulation in smart grids and point to promising research avenues such as topology optimization, hybrid metaheuristics, and federated learning for scalable deployment in distributed power systems.

Inventions doi: 10.3390/inventions10060109

Authors: Karim Choubani Mohamed Ben Rabha

Global freshwater scarcity continues to escalate due to pollution, climate change, and population growth, making innovative sustainable desalination technologies increasingly vital. Solar stills offer a simple and eco-friendly method for freshwater production by utilizing renewable energy, yet their low productivity remains a major limitation. This study experimentally evaluates and quantifies several established enhancement techniques under real climatic conditions to improve evaporation and condensation efficiency. The integration of porous materials, such as black rocks, significantly improves thermal energy storage and management by retaining absorbed heat during the daytime and releasing it gradually, resulting in an average 30% increase in daily distillate production (SD = 6 mL). Additionally, forced convection using small fans enhances humid air removal and evaporation rates, increasing the average yield by approximately 11.4% (SD = 2 mL). Optical concentration through lenses intensifies solar irradiation on the evaporation surface, achieving the highest performance with an average 50% improvement in water output (SD = 5 mL). The incorporation of Phase Change Materials (PCM) is further proposed to extend thermal stability during off-sunshine hours, with materials selected based on a melting point range of 38–45 °C. To minimize nocturnal heat loss, future designs may integrate radiative cooling materials for passive night-time condensation support, by applying a radiative cooling coating to the condenser plate to enhance passive heat rejection to the sky. Overall, the validated combined use of renewable energy-driven desalination, thermal storage media, and advanced strategies presents a practical pathway toward high-efficiency solar stills suitable for sustainable buildings and decentralized water supply systems in arid regions.

Inventions doi: 10.3390/inventions10060108

Authors: Jesús Gerardo Ávila-Sánchez Manuel de Jesús López-Martínez Valeria Maeda-Gutiérrez Francisco E. López-Monteagudo Celina L. Castañeda-Miranda Manuel Rivera-Escobedo Sven Verlienden Genaro M. Soto-Zarazua Carlos A. Olvera-Olvera

The Cutting Development Chamber (CDC) design is presented as an innovative solution to crucial human challenges, such as food and plant medicinal production. Unlike conventional propagation chambers, the CDC is a much more comprehensive research tool, specifically designed to optimize plant reproduction from cuttings. It maintains precise control over humidity, temperature, and lighting, which are essential parameters for plant development, thus maximizing the success rate, even in difficult-to-propagate species. Its modular design is one of its main strengths, allowing users to adapt the chamber to their specific needs, whether for research studies or for larger-scale propagation. The most distinctive feature of this chamber is its ability to collect detailed, labeled data, such as images of plant growth and environmental parameters that can be used in artificial intelligence tasks, which differentiate it from chambers that are solely used for propagation. A study that validated and calibrated the chamber design using cuttings of various species demonstrated its effectiveness through descriptive statistics, confirming that CDC is a powerful tool for research and optimization of plant growth. In validation experiments (Aloysia citrodora and Stevia rebaudiana), the system generated 6579 labeled images and 67,919 environmental records, providing a robust dataset that confirmed stable control of temperature and humidity while documenting cutting development.

Inventions doi: 10.3390/inventions10060107

Authors: Umberto Maria Matera Matteo Faccenda Yolanda Pérez Darina Francesca Picchi Lorenzo Rossi Sergio Larreina Patricia Horcajada

The increasing significance of intellectual property (IP) in recent decades highlights its crucial role in driving innovation and shaping competitive strategies. While many studies have attempted to evaluate the technological level of specific sectors or companies, few offer a standardized and scalable approach for cross-domain comparison. This study proposes a patent-based framework to comparatively evaluate technological maturity across different fields using a concise set of intellectual property (IP) indicators. The selected metrics, renewal trends, family size, grant rate, and citation patterns, capture legal, economic, and technological dimensions of innovation without requiring field-specific calibration. We apply this approach to two representative nanomedical technologies, Metal–Organic Frameworks (MOFs) and inorganic nanoparticles (iNPs), within the domain of cancer therapy. Our analysis highlights distinct trajectories: MOFs show increasing patent activity and sustained short-term citation growth, consistent with an emerging field; iNPs exhibit signs of stabilization and declining citation intensity, suggesting greater maturity. These findings demonstrate the utility of standardized IP indicators for mapping innovation dynamics across domains. The proposed framework offers a replicable tool for strategic technology assessment, with potential applications in research prioritization, technology forecasting, and early-stage investment analysis.

Inventions doi: 10.3390/inventions10060106

Authors: Muhammad Qamar Khan Muhammad Abbas Haider Alvi Hafiza Hifza Nawaz Muhammad Umar

In the era of Industry 5.0, the digital revolution stands as the paramount tool for achieving efficiency and elevating the standards of quality and quantity. This study delves deeply into the invaluable applications of digital twins within real production settings, highlighting their transformative potential across a multitude of industries. Focusing particularly on textiles, machinery, and electronics manufacturing, the authors illustrate how digital twins enhance productivity, anticipate challenges, bolster the food supply chain, refine healthcare services, and propel sustainability initiatives within each sector. Through concrete examples, we demonstrate how digital twins can markedly decrease waste, energy consumption, and production downtime, all while elevating product quality and enabling virtualization. By virtually simulating physical systems, numerous operational issues can be mitigated, underscoring the pivotal role of digital twins in fostering hyper-personalization, sustainability, and resilience the foundational tenets of Industry 5.0. Nevertheless, this evaluation acknowledges the inherent challenges associated with the widespread adoption of digital twins, including concerns regarding data infrastructure, cybersecurity, and workforce adaptation. By presenting a balanced assessment of both the advantages and disadvantages, this review aims to guide future research and development endeavors, paving the way for the successful integration of this revolutionary technology as we journey toward Industry 5.0.

Inventions doi: 10.3390/inventions10060105

Authors: Cornel Constantin Pavel Valentin Apostol Horatiu Pop Tudor Prisecaru Claudia Ionita Adrian Chiriac

The paper identifies and describes the possibilities for heat and mechanical energy recovery within refrigeration systems using CO2 as a working fluid, employed in commercial and industrial applications. The heat and mechanical energy recovery methods that can be utilized for beneficial purposes are taken into consideration. These methods could increase the energy efficiency of the refrigeration system or the building in which it operates. This paper summarizes various configurations and recovery methods and critically compares and evaluates them (COP improvements, exergy performance, and system integration complexity) based on the data available in the literature. As a result, the internal heat exchangers can be used as a superheater, in which case the COP can increase to 35%. If the internal heat exchanger is used as a subcooler, it could lead to a COP increase of 17% compared to a CO2 refrigeration system without subcooling for an evaporating temperature of −10 °C and the temperature of the gas cooler outlet of 30 °C. The heat and mechanical energy recovery possibilities are presented using the available scientific literature.

Inventions doi: 10.3390/inventions10060104

Authors: Maytham M. Abid Marc Marín-Genescà

The growing demand for renewable energy in space-constrained environments highlights the need for compact, high-efficiency wind energy systems. Conventional bare wind turbine (BWT) arrays suffer from severe wake interactions and performance degradation when operated in tandem or closely spaced configurations. To address these limitations, this study investigates the aerodynamic performance and near-wake dynamics of a novel multi-ducted wind turbine (MDWT) system that integrates passive flow-control technique (PFCT) into an innovative fixed-duct design. The objective is to evaluate how tandem ducted arrangements with this integrated mechanism influence wake recovery, vortex dynamics, and power generation compared with multi-bare wind turbine (MBWT) system. A numerical approach is employed using the Unsteady Reynolds-Averaged Navier–Stokes (URANS) formulation with the k–ω SST turbulence model, validated against experimental data. The analysis focuses on two identical, fixed-orientation ducts arranged in tandem without lateral offset, tested under three spacing configurations. The results reveal that the ducted system accelerates the near-wake flow and displaces velocity-deficit regions downward due to the passive flow-control sheets, producing stronger inflow fluctuations and enhanced turbulence mixing. These effects improve wake recovery and mitigate energy losses behind the first turbine. Quantitatively, the MDWT array achieves total power outputs 1.99, 1.90, and 1.81 times greater than those of the MBWT array for Configurations No. 1, No. 2, and No. 3, respectively. In particular, the second duct in Configuration No. 1 demonstrates a 3.46-fold increase in power coefficient compared with its bare counterpart. These substantial gains arise because the upstream duct–PFCT assembly generates a favorable pressure gradient that entrains ambient air into the wake, while coherent tip vortices and redirected shear flows enhance mixing and channel high-momentum fluid toward the downstream rotor plane. The consistent performance across spacings further confirms that duct-induced flow acceleration and organized vortex structures dominate over natural wake recovery effects, maintaining efficient energy transfer between turbines. The study concludes that closely spaced MDWT systems provide a compact and modular solution for maximizing energy extraction in constrained environments.

Inventions doi: 10.3390/inventions10060103

Authors: Marius Cioca Adriana-Lavinia Cioca

AI-based educational chatbots can expand access to learning, but many remain limited to text-only interfaces and fixed infrastructures, while purely generative responses raise concerns of reliability and consistency. In this context, we present ROboMC, a portable and multimodal system that combines a validated knowledge base with generative responses (OpenAI) and voice–text interaction, designed to enable both text and voice interaction, ensuring reliability and flexibility in diverse educational scenarios. The system, developed in Django, integrates two response pipelines: local search using normalized keywords and fuzzy matching in the LocalQuestion database, and fallback to the generative model GPT-3.5-Turbo (OpenAI, San Francisco, CA, USA) with a prompt adapted exclusively for Romanian and an explicit disclaimer. All interactions are logged in AutomaticQuestion for later analysis, supported by a semantic encoder (SentenceTransformer—paraphrase-multilingual-MiniLM-L12-v2’, Hugging Face Inc., New York, NY, USA) that ensures search tolerance to variations in phrasing. Voice interaction is managed through gTTS (Google LLC, Mountain View, CA, USA) with integrated audio playback, while portability is achieved through deployment on a Raspberry Pi 4B (Raspberry Pi Foundation, Cambridge, UK) with microphone, speaker, and battery power. Voice input is enabled through a cloud-based speech-to-text component (Google Web Speech API accessed via the Python SpeechRecognition library, (Anthony Zhang, open-source project, USA) using the Google Web Speech API (Google LLC, Mountain View, CA, USA; language = “ro-RO”)), allowing users to interact by speaking. Preliminary tests showed average latencies of 120–180 ms for validated responses on laptop and 250–350 ms on Raspberry Pi, respectively, 2.5–3.5 s on laptop and 4–6 s on Raspberry Pi for generative responses, timings considered acceptable for real educational scenarios. A small-scale usability study (N ≈ 35) indicated good acceptability (SUS ~80/100), with participants valuing the balance between validated and generative responses, the voice integration, and the hardware portability. Although system validation was carried out in the eHealth context, its architecture allows extension to any educational field: depending on the content introduced into the validated database, ROboMC can be adapted to medicine, engineering, social sciences, or other disciplines, relying on ChatGPT only when no clear match is found in the local base, making it a scalable and interdisciplinary solution.

Inventions doi: 10.3390/inventions10060102

Authors: Juan-Carlos Gonzalez-Islas Ernesto Bolaños-Rodriguez Omar-Arturo Dominguez-Ramirez Aldo Márquez-Grajales Víctor-Hugo Guadarrama-Atrizco Elba-Mariana Pedraza-Amador

Patenting is essential for protecting intellectual property, fostering technological innovation, and maintaining competitive advantages in the global market. In Mexico, strategic planning in science, technology, and innovation requires reliable forecasting tools. This study evaluates computational models for predicting applied and granted patents between 1990 and 2024, including statistical (ARIMA), machine learning (Regression Trees, Random Forests, and Support Vector Machines), and deep learning (Long Short-Term Memory, LSTM) approaches. The workflow involves historical data acquisition, exploratory analysis, decomposition, model selection, forecasting, and evaluation using the Root Mean Square Error (RMSE), the determination coefficient (R2), and the Mean Absolute Percentage Error (MAPE) as performance metrics. To ensure generalization and robustness in the training stage, we use the cross-validation rolling origin. On the test stage, LSTM achieves the highest accuracy (RMSE = 106.91, R2=0.97, and MAPE = 0.63 for applied patents; RMSE = 283.20, R2=0.93, and MAPE = 2.65 for granted patents). However, cross-validation shows that ARIMA provides more stable performance across multiple scenarios, highlighting a trade-off between short-term accuracy and long-term reliability. These results demonstrate the potential of machine learning and deep learning as forecasting tools for industrial property management.

Inventions doi: 10.3390/inventions10060101

Authors: Pornthep Phanbua Sujitra Arwatchananukul Georgi Hristov Punnarumol Temdee

Research shows that individuals with heart failure are 60% more likely to develop dementia because of their shared metabolic risk factors. Developing a classification model to differentiate between these two conditions effectively is crucial for improving diagnostic accuracy, guiding clinical decision-making, and supporting timely interventions in older adults. This study proposes a novel method for dementia classification, distinguishing it from its common comorbidity, heart failure, using blood testing and personal data. A dataset comprising 11,124 imbalanced electronic health records of older adults from hospitals in Chiang Rai, Thailand, was utilized. Conditional tabular generative adversarial networks (CTGANs) were employed to generate synthetic data while preserving key statistical relationships, diversity, and distributions of the original dataset. Two groups of ensemble models were analyzed: the boosting group—extreme gradient boosting, light gradient boosting machine—and the bagging group—random forest and extra trees. Performance metrics, including accuracy, precision, recall, F1-score, and area under the receiver-operating characteristic curve were evaluated. Compared with the synthetic minority oversampling technique, CTGAN-based synthetic data generation significantly enhanced the performance of ensemble learning models in classifying dementia and heart failure.

Inventions doi: 10.3390/inventions10060100

Authors: Jaime Sánchez Gallego

This paper develops a theoretical framework and a numerical implementation for real-time estimation of the gross mass of heavy vehicles using only on-board signals: tire inflation pressure from the TPMS and radial deformation inferred from a monocular chassis camera. Each wheel is modeled as a single-degree-of-freedom radial oscillator with pressure-dependent stiffness kr(P) and damping cr(P). The contact patch geometry follows a compressed-arc approximation that maps radial deformation δ to contact length L(δ) and area S(δ). Two independent force surrogates are constructed—Fk=kr(P)δ and Fq=q(P)S(δ), where q(P) denotes the mean contact pressure—and fused by an adaptive Kalman filter operating at 30 Hz to recover per-wheel loads and total mass. Tuning the fusion weight λ yields a relative mass estimation error below 5% across 0.001≤δ≤0.20 m, and the maximum observed error is 4.99%. Numerical experiments using fixed-step RK4 and embedded RK45 methods confirm the accuracy and real-time feasibility on commodity hardware (runtime <33 ms per step). Uncertainty analysis based on Latin hypercube sampling, the PRCC, and Sobol indices shows robustness to parameter perturbations (±5% inflation, ±10% stiffness, ±15% damping, ±1° camera pitch, ±2 kPa TPMS bias). Observability analysis supports identifiability under the tested regimes. The estimator delivers wheel and axle loads for on-board alerts, telematics, V2X pre-screening for road user charging and weigh-in-motion technology, and friction-aware control.

Inventions doi: 10.3390/inventions10060099

Authors: Prasun Sanki Sindhura Gupta Srinivasa Rao Gampa Amarendra Alluri Mahesh Babu Basam Debapriya Das

Presently, numerous non-conventional power resources have been applied in power system networks. However, these resources are very effective in islanded microgrid (IMG) scenarios for addressing numerous operational challenges. Additionally, it is observed that the power output of most of these resources is environment-dependent and intermittent in nature. This intermittency causes a power imbalance between the overall generated power and the load demand, which results in an undesired frequency oscillation. In order to address this unwanted frequency fluctuation, this research work proposes power–frequency synchronisation considering an islanded microgrid scenario under numerous non-conventional power resources. The major contribution of this work includes implementing a suitable and optimised control scheme that effectively controls diverse power system disturbances and various uncertainties. A Fick’s law optimisation-based proportional–integral–derivative–acceleration controller (PIDA) is implemented under this proposed power scenario. Additionally, an extensive performance assessment is conducted considering different simulation test cases in order to verify the performance of the proposed control topology. Further, the effectiveness of the suggested power network is tested on a 33-bus radial distribution network. Finally, simulation results are shown to show the effectiveness of the proposed control scheme for the efficient operation of the microgrid in achieving the desired performance under the diverse operating conditions.

Inventions doi: 10.3390/inventions10060098

Authors: Xiangbo Kong Kenshi Saho Akari Takebayashi

This study presents MDSCNet, a compact radar image-based deep learning model for multi-action classification in elderly healthcare scenarios. Motivated by the need for real-time deployment on resource-constrained devices, MDSCNet employs a streamlined architecture with a small number of lightweight expansion–depthwise–projection blocks, removing complex attention and squeeze-and-excitation modules to minimize computational overhead. The model is evaluated on a millimeter-wave radar dataset covering five healthcare-related actions: lying, sitting, standing, bed-exit, and falling, performed by 15 participants on an actual electric nursing bed. The experimental results demonstrate that MDSCNet achieves accuracy comparable to state-of-the-art CNN-based methods while maintaining an extremely compact model size of only 0.29 MB, showing its suitability for practical elderly care applications where both accuracy and efficiency are critical.

Inventions doi: 10.3390/inventions10060097

Authors: Jesus GomezRomero-Borquez Carolina Del-Valle-Soto José A. Del-Puerto-Flores Juan-Carlos López-Pimentel Francisco R. Castillo-Soria Roilhi F. Ibarra-Hernández Leonardo Betancur Agudelo

This study investigates the impact of audio feedback on cognitive performance during VR puzzle games using EEG analysis. Thirty participants played three different VR puzzle games under two conditions (with and without audio) while their brain activity was recorded. To analyze concentration levels and neural engagement patterns, we employed spectral analysis combined with a preprocessing algorithm and an optimized Deep Neural Network (DNN) model. The proposed processing stage integrates feature normalization, automatic labeling based on Principal Component Analysis (PCA), and Gamma band feature extraction, transforming concentration detection into a supervised classification problem. Experimental validation was conducted under the two gaming conditions in order to evaluate the impact of multisensory stimulation on model performance. The results show that the proposed approach significantly outperforms traditional machine learning classifiers (SVM, LR) and baseline deep learning models (DNN, DGCNN), achieving a 97% accuracy in the audio scenario and 83% without audio. These findings confirm that auditory stimulation reinforces neural coherence and improves the discriminability of EEG patterns, while the proposed method maintains a robust performance under less stimulating conditions.

Inventions doi: 10.3390/inventions10060096

Authors: Omar Shalash Ahmed Métwalli Mohammed Sallam Esraa Khatab

Deception detection is considered a concern for all individuals in their everyday lives, as it greatly affects human interactions. While multiple automatic lie detection systems exist, their accuracy still needs to be improved. Additionally, the lack of adequate and realistic datasets hinders the development of reliable systems. This paper presents a new multimodal dataset with physiological data (heart rate, galvanic skin response, and body temperature), in addition to demographic data (age, weight, and height). The presented dataset was collected from 49 unique subjects. Moreover, this paper presents a polygraph-based lie detection system utilizing multimodal sensor fusion. Different machine learning algorithms are used and evaluated. Random Forest has achieved an accuracy of 97%, outperforming Logistic Regression (58%), Support Vector Machine (58% with perfect recall of 1.00), and k-Nearest Neighbor (83%). The model shows excellent precision and recall (0.97 each), making it effective for applications such as criminal investigations. With a computation time of 0.06 s, Random Forest has proven to be efficient for real-time use. Additionally, a robust k-fold cross-validation procedure was conducted, combined with Grid Search and Particle Swarm Optimization (PSO) for hyperparameter tuning, which substantially reduced the gap between training and validation accuracies from several percentage points to under 1%, underscoring the model’s enhanced generalization and reliability in real-world scenarios.

Inventions doi: 10.3390/inventions10060095

Authors: Alina Fazylova Kuanysh Alipbayev Alisher Aden Fariza Oraz Teodor Iliev Ivaylo Stoyanov

This paper reviews and analyzes three types of vertical-axis wind rotors: the classic Savonius, spiral Savonius, and Darrieus designs. Using numerical modeling methods, including computational fluid dynamics (CFD), their aerodynamic characteristics, power output, and efficiency under different operating conditions are examined. Key parameters such as lift, drag, torque, and power coefficient are compared to identify the strengths and weaknesses of each rotor. Results highlight that the Darrieus rotor demonstrates the highest efficiency at higher wind speeds due to lift-based operation, while the spiral Savonius offers improved stability, smoother torque characteristics, and adaptability in turbulent or low-wind environments. The classic Savonius, though less efficient, remains simple, cost-effective, and suitable for small-scale urban applications where reliability is prioritized over high performance. In addition, the study outlines the importance of blade geometry, tip speed ratio, and advanced materials in enhancing rotor durability and efficiency. The integration of modern optimization approaches, such as CFD-based design improvements and machine learning techniques, is emphasized as a promising pathway for developing more reliable and sustainable vertical-axis wind turbines. Although the primary analysis relies on numerical simulations, the observed performance trends are consistent with findings reported in experimental studies, indicating that the results are practically meaningful for design screening, technology selection, and siting decisions. Unlike prior studies that analyze Savonius and Darrieus rotors in isolation or under heterogeneous setups, this work (i) establishes a harmonized, fully specified CFD configuration (common domain, BCs, turbulence/near-wall treatment, time-stepping) enabling like-for-like comparison; (ii) couples the transient aerodynamic loads p(θ,t) into a dynamic FEA + fatigue pipeline (rainflow + Miner with mean-stress correction), going beyond static loading proxies; (iii) quantifies a prototype-stage materials choice rationale (aluminum) with a validated migration path to orthotropic composites; and (iv) reports reproducible wake/torque metrics that are cross-checked against mature models (DMST/actuator-cylinder), providing design-ready envelopes for small/medium VAWTs. Overall, the work provides recommendations for selecting rotor types under different wind conditions and operational scenarios to maximize energy conversion performance and long-term reliability.

Inventions doi: 10.3390/inventions10060094

Authors: Bertrand Michael L. Diola Adrian A. Borja Paolo Rommel P. Sanchez Marynold V. Purificacion Ralph Kristoffer B. Gallegos

Deoxyribonucleic acid (DNA)-based biosensors are rapid, cost-effective, and portable devices for monitoring crop pathogens. However, their on-field operations rely on a laboratory-bound heating block, which controls temperature during sample preparation. This study aimed to develop a field-deployable heating block to assist in the DNA hybridization protocol of DNA-based biosensors. It should maintain 95 °C, 55 °C, and 20 °C for 5, 10, and 5 min, respectively. It had aluminum bars, positive thermal coefficient ceramic heaters, a Peltier thermoelectric module, and DS18B20 thermistors, serving twelve 0.2 mL polymerase chain reaction (PCR) tubes. An Arduino microcontroller employing a proportional–integral–derivative (PID) algorithm with a solid-state relay was utilized. Machine performance for distilled water-filled PCR tubes showed a maximum 10 °C thermal variation. The machine maintained (96.00±0.97) °C, (55.15±2.17) °C, and (17.75±0.71) °C with root mean square errors (RMSEs) of 1.40 °C, 2.18 °C, and 2.36 °C, respectively. The average thermal rates were (0.16±0.11) °C/s, (0.29±0.11) °C/s, and (0.14±0.07) °C/s from ambient to 95 °C, 95 °C to 55 °C, and 55 °C to 20 °C, respectively. Overall, the low standard deviations and RMSEs demonstrate thermostable results and robust temperature control.

Inventions doi: 10.3390/inventions10060093

Authors: Yousef Salah Omar Shalash Esraa Khatab Mostafa Hamad Sherif Imam

Reliable water access in remote and desert-like regions remains a challenge, particularly in areas with limited infrastructure. Solar-powered submersible pumps offer a promising solution; however, optimizing their performance under variable environmental conditions remains a challenging task. This research presents an Artificial Intelligence (AI)-driven digital twin framework for modeling and optimizing the performance of a solar-powered submersible pump system. The proposed system has three core components: (1) an AI model for predicting the inverter motor’s output frequency based on the current generated by the solar panels, (2) a predictive model for estimating the pump’s generated power based on the inverter motor’s output, and (3) a mathematical formulation for determining the volume of water lifted based on the system’s operational parameters. Moreover, a dataset comprising 6 months of environmental and system performance data was collected and utilized to train and evaluate multiple predictive models. Unlike previous works, this research integrates real-world data with a multi-phase AI modeling pipeline for real-time water output estimation. Performance assessments indicate that the Random Forest (RF) model outperformed alternative approaches, achieving the lowest error rates with a Mean Absolute Error (MAE) of 1.00 Hz for output frequency prediction and 1.39 kW for pump output power prediction. The framework successfully estimated annual water delivery of 166,132.77 m3, with peak monthly output of 18,276.96 m3 in July and minimum of 9784.20 m3 in January demonstrating practical applicability for agricultural water management planning in arid regions.

Inventions doi: 10.3390/inventions10050092

Authors: Daniel Sanchez-Garcia Anuar Giménez-El-Amrani Armando Gonzalez-Muñoz Andres Sanz-Garcia

The demand for organ transplantation continues to rise worldwide, intensifying the gap between supply and demand and driving research in tissue engineering (TE). Bioprinting, particularly light-based vat photopolymerization (VP) methods such as digital light processing (DLP), has emerged as a promising strategy to fabricate complex, cell-compatible tissue constructs with high precision. In this study, we developed an open-source, bottom-up DLP bioprinter designed to provide a cost-effective and modular alternative to commercial systems. The device was built from commercially available components and custom-fabricated parts, with tolerance allocation and deviation analyses applied to ensure structural reliability. Mechanical and optical subsystems were modeled and validated, and the control architecture was implemented on the Arduino platform with a custom Python-based graphical interface. The system achieved a theoretical Z-axis resolution of 1 μm and a vertical travel range of 50 mm, with accuracy and repeatability comparable to research-grade bioprinters. Initial printing trials using polyethylene glycol diacrylate (PEGDA) hydrogels demonstrated high-fidelity microfluidic constructs with adequate dimensional precision. Collectively, these results validate the functionality of the proposed system and highlight its potential as a flexible, precise, and cost-effective platform that is also easy to customize to advance the democratization of biofabrication in TE.

Inventions doi: 10.3390/inventions10050091

Authors: Mubarak Alanazi Yassir A. Alamri

Photovoltaic (PV) fault detection faces significant challenges in distinguishing subtle defects from complex backgrounds while maintaining reliability across diverse environmental conditions. Traditional approaches struggle with scalability and accuracy limitations, particularly when detecting electrical damage, physical defects, and environmental soiling in thermal imagery. This paper presents SolarFaultAttentionNet, a novel dual-attention deep learning framework that integrates channel-wise and spatial attention mechanisms within a multi-path CNN architecture for enhanced PV fault classification. The approach combines comprehensive data augmentation strategies with targeted attention modules to improve feature discrimination across six fault categories: Electrical-Damage, Physical-Damage, Snow-Covered, Dusty, Bird-Drop, and Clean. Experimental validation on a dataset of 885 images demonstrates that SolarFaultAttentionNet achieves 99.14% classification accuracy, outperforming state-of-the-art models by 5.14%. The framework exhibits perfect detection for dust accumulation (100% across all metrics) and robust electrical damage detection (99.12% F1 score) while maintaining an optimal sensitivity (98.24%) and specificity (99.91%) balance. The computational efficiency (0.0160 s inference time) and systematic performance improvements establish SolarFaultAttentionNet as a practical solution for automated PV monitoring systems, enabling reliable fault detection critical for maximizing energy production and minimizing maintenance costs in large-scale solar installations.

Inventions doi: 10.3390/inventions10050090

Authors: Chatchapat Chaiaiad Pawantree Borthai Jatuporn Thongsri

This research details the development and evaluation of a Modern Ultrasonic Cleaning Tank (MUCT) designed to enhance cleaning efficiency in jewelry manufacturing, particularly for silver jewelry, replacing the traditional method, which was less efficient and had higher operating costs. The MUCT offers capabilities of single- or dual-frequency ultrasonic operation (28 kHz and 40 kHz) and adjustable transducer positioning. An advanced method involving computer simulations, utilizing harmonic response analysis and transient dynamic analysis, was employed to determine the acoustic pressure inside the MUCT, thereby indicating the cavitation intensity required to achieve high cleaning efficiency. Simulation results confirm that this design can distribute acoustic pressure throughout the MUCT, as intended. A prototype MUCT was assembled, and its operation was validated through foil corrosion tests, ultrasonic power concentration (UPC) measurements, and jewelry cleaning tests. The results revealed that the MUCT’s center provided the maximum UPC of 28 W/L and an acoustic pressure of 30.43 MPa, effectively operating at single and dual frequencies, and achieving superior dirt removal. The highest cleaning efficiency of 100% was achieved using dual frequency with a 97% water and 3% dishwashing liquid mixture at 60 °C, exceeding the 23.52% obtained with water at 27 °C without ultrasonic treatment. The MUCT, successfully integrated into the manufacturing process, offers customizable features to meet various cleaning needs, providing flexibility, improved performance, and cost savings.

Inventions doi: 10.3390/inventions10050088

Authors: Tarun Varshney Naresh Patnana Vinay Pratap Singh

This paper investigates frequency regulation of an airport microgrid (AIM) through the application of an integral absolute error (IAE)-assisted control approach. The islanded AIM is initially captured using a linearized transfer function model to accurately reflect its dynamic characteristics. This model is then simplified using a first-order plus dead time (FOPDT) approximation derived via a reaction-curve-based method, which balances between model simplicity and accuracy. Two different proportional–integral–derivative (PID) controllers are designed to meet distinct objectives: one focuses on set-point tracking (SPT) to maintain the target frequency levels, while the other addresses load disturbance rejection (LDR) to reduce the effects of load fluctuations. A thorough comparison of these controllers demonstrates that the SPT-mode PID controller outperforms the LDR-mode controller by providing an improved transient response and notably lower error measures. The results underscore the effectiveness of combining IAE-based control with reaction curve modeling to tune PID controllers for islanded AIM systems, contributing to enhanced and reliable frequency regulation for microgrid operations.

Inventions doi: 10.3390/inventions10050089

Authors: Emilia Georgiana Prisăcariu Iulian Vlăducă

High-precision angular positioning mechanisms are essential across diverse scientific and industrial applications, from optical instrumentation to automated mechanical systems. Conventional bronze–steel gear reduction units, while reliable, are often heavy, costly, and unsuitable for chemically aggressive or vacuum environments, limiting their use in advanced research setups. This work introduces a novel 1:360 gear reduction system manufactured by resin-based additive manufacturing, designed to overcome these limitations. The compact worm–gear assembly translates a single crank rotation into a precise one-degree indicator displacement, enabling fine and repeatable angular control. A primary application is the alignment of parabolic mirrors in schlieren systems, where accurate tilt adjustment is critical to correct optical alignment; however, the design is broadly adaptable to other precision positioning tasks in laboratory and industrial contexts. Compared with conventional assemblies, the resin-based reducer offers reduced weight, chemical and vacuum compatibility, and lower production cost. Its three-stage reduction design further enhances load-bearing capacity, achieving approximately double the theoretical torque transfer of equivalent commercial systems. These features establish the device as a robust, scalable, and automation-ready solution for high-accuracy angular adjustment, contributing both to specialized optical research and general-purpose precision engineering.

Inventions doi: 10.3390/inventions10050087

Authors: Elwin Nesan Selvanesan Poh Kiat Ng Kia Wai Liew Jian Ai Yeow Chai Hua Tay Peng Lean Chong Yu Jin Ng

The development of a multifunctional invention requires several refinements for optimizing each function. This study presents a Theory of Inventive Problem Solving (TRIZ)-based conceptual framework for enhancing an innovative multifunctional assistive technology device that integrates the functionalities of a rollator walker, wheelchair, Pilates chair, and stepladder. The limitations of the multifunctional rollator walker were identified from the user feedback of a foundational work and were then addressed by identifying the engineering and physical contradictions and problem modeling using Su-field analysis. Through TRIZ Inventive Principles, the proposed design eliminates common trade-offs between portability, stability, and usability. The conceptual enhancement incorporates features such as deployable steps, the utilization of high strength–to–weight ratio material, foldability, a passive mechanical brake-locking system, retractable armrests, the incorporation of spring-assist hinges, and the use of large tires with vibration-dampening hubs. This study contributes a novel, user-focused, and space-saving mobility solution that aligns with the evolving demands of assistive technology, laying the groundwork for future iterations involving smart control, power assist, and modular enhancements.

Inventions doi: 10.3390/inventions10050086

Authors: Natalia P. Boroznina Sergey V. Boroznin Irina V. Zaporotskova Pavel A. Zaporotskov Dmitry F. Sergeev Govindhasamy Murugadoss Nachimuthu Venkatesh Shaik Gouse Peera

This study presents a theoretical investigation of platinum-modified single-wall carbon nanotubes (SWCNTs) of types (6.0) and (6.6) for their potential application as gas sensor materials. Quantum chemical calculations using density functional theory (DFT) were performed to evaluate the interaction mechanisms with carbon monoxide (CO) and carbon dioxide (CO2) molecules. The results revealed that pristine SWCNTs exhibit weak and unstable interactions with CO and CO2, indicating limited sensing capabilities. However, the modification with platinum atoms significantly enhanced their adsorption properties. The most energetically favorable configuration was found when the platinum atom was located at the center of a C–C bond on the SWCNT surface, ensuring the stability of the metal-functionalized system. The Pt-modified SWCNTs exhibited stable sorption interactions with CO and CO2, characterized by weak van der Waals forces, enabling the reusability of the sensor without contamination. Additionally, the adsorption of these gas molecules induced changes in the band gap of the nanocomposite system, indicating a variation in conductivity upon gas exposure. The distinct band gap changes for the CO and CO2 adsorption suggest the selectivity of the sensor towards each gas. Overall, the results demonstrate that platinum modification effectively enhances the sensing performance of SWCNTs, paving the way for the development of highly sensitive and selective nanosensors for CO and CO2 detection based on changes in electronic properties upon gas adsorption.

Inventions doi: 10.3390/inventions10050085

Authors: José P. P. G. Lopes de Almeida Vinícius G. Machado

This article addresses the multifaceted challenges inherent in the development of the novel REEFS (Renewable Electric Energy From Sea) wave energy converter (WEC). Building on the submerged pressure differential principle, it frames similar WECs before focusing on REEFS that combines renewable energy generation with coastal protection, functioning as an artificial reef. The review follows chronological criteria, encompassing experimental proof-of-concept, small-scale laboratory modeling, simplified and advanced computational fluid dynamics (CFD) simulations, and the design of a forthcoming real-sea model deployment. Key milestones include the validation of a passive variable porosity system, demonstration of wave-to-wire energy conversion, and quantification of wave attenuation for coastal defense. Additionally, the study introduces a second patent-protected REEFS configuration, isolating internal components from seawater via an elastic enveloping membrane. Challenges related to scaling, numerical modeling, and funding are thoroughly examined. The results highlight the importance of the proof-of-concept as the keystone of the development process, underscore the relevance of mixed laboratory-computational approaches and emphasize the need for a balanced equilibrium between intellectual property safeguard and scientific publishing. The REEFS development trajectory offers interesting insights for researchers and developers navigating the complex innovation seas of emerging wave energy technologies.

Inventions doi: 10.3390/inventions10050084

Authors: Xu Zhang Jihong Yang Ruijie Zhao Ziquan Qin Zhuojun Xie

The evolution of the winemaking industry towards intelligent and digitalized systems is crucial for precision winemaking and ensuring product safety. In this context, the Internet of Things (IoT) provides a key strategy for real-time monitoring and data management throughout the winemaking process. However, comprehensive multi-parameter IoT-based monitoring and time-series prediction of physicochemical parameters during storage are currently lacking, limiting the ability to assess storage conditions and provide early warning of quality deterioration. To address these gaps, a multi-parameter IoT monitoring system was designed and developed to track conductivity, dissolved oxygen, and temperature in real time. Data were transmitted via a 4th-generation (4G) mobile communication module to the TLINK cloud platform for storage and visualization. An 80-day storage experiment confirmed the system’s reliability for long-term monitoring, and analysis of parameter trends demonstrated its effectiveness in assessing storage conditions and wine quality evolution. Furthermore, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN) models, and Autoregressive Integrated Moving Average (ARIMA) were implemented to predict physicochemical parameter trends. The TCN model achieved the highest predictive performance, with coefficients of determination (R2) of 0.955, 0.968, and 0.971 for conductivity, dissolved oxygen, and temperature, respectively, while LSTM and GRU showed comparable results. These results demonstrate that integrating IoT-based multi-parameter monitoring with deep learning time-series prediction enables real-time detection of abnormal storage and quality deterioration, providing a novel and practical framework for early warning throughout the wine storage process.

Inventions doi: 10.3390/inventions10050083

Authors: Peichen Cai Yutong Chai Susan Tighe Meng Wang Shunde Yin

To elucidate the fatigue damage evolution of solar road panels under long-term loading and enhance their structural durability, this study develops a particle-based discrete element model and simulates fatigue responses under different structural configurations and loading rates. A strength degradation index was established by introducing peak stress and terminal stress, enabling quantitative evaluation of strength deterioration. Combined with fracture evolution, the dominant mesoscopic damage mechanisms were revealed. The results indicate that structural configuration strongly influences fatigue performance, with square panels showing the best resistance due to geometric symmetry and stable boundary constraints. Loading rate regulates damage evolution: lower rates promote structural coordination but may delay cumulative failure, while higher rates suppress overall deformation yet increase localized fracture risk. Based on these findings, a nonlinear predictive model of the strength degradation rate was constructed (R2 = 0.935), offering reliable support for structural life prediction and design optimization. Finally, fatigue-resistant design strategies are proposed, including optimal structural configuration, controlled loading rates, bonding enhancement, and integration of online monitoring—providing both theoretical and technical guidance for high-performance, long-lifespan solar road systems.

Inventions doi: 10.3390/inventions10050082

Authors: Sergey A. Shumeyko Dmitriy E. Burmistrov Denis V. Yanykin Ilya V. Baimler Alexandr V. Simakin Maxim E. Astashev Mikhail V. Dubinin Roman Y. Pishchalnikov Ruslan M. Sarimov Valeriy A. Kozlov Alexey S. Dorokhov Andrey Yu. Izmailov

Using the method of laser ablation in liquid, submicron-sized particles of zero-valent amorphous selenium (Se SMPs) were created. A number of composite polymer materials were manufactured based on polyurea and Se SMPs at concentrations ranging 0.1–2.5 wt.%. The manufactured materials showed no significant surface or internal defects at either the macro or micro level. It was found that the Se SMPs were not uniformly distributed inside the polymer, but formed ordered areas with slightly higher and lower concentrations of the particles. It was demonstrated that the manufactured materials did not generate a significant amount of active oxygen species, which could damage biological objects such as protein molecules and DNA, while also exhibiting pronounced bacteriostatic properties without significantly affecting the growth and reproduction of mammalian cells. Materials containing 0.25 and 1% Se SMPs, when added to soil, improved the morphometric parameters of radish plants (Raphanus sativus var. sativus). These polymer composite materials based on polyurea with the addition of Se SMPs are promising functional materials for agriculture due to their antibacterial activity.

Inventions doi: 10.3390/inventions10050081

Authors: Ruey-Shin Juang Yi-Ru Li Chun-Chieh Fu Chien-Te Hsieh

Graphene quantum dots (GQDs) have emerged as promising nanomaterials due to their unique optical properties, high biocompatibility, and tunable surface functionalities. In this work, GQDs were synthesized via a one-pot hydrothermal method and further functionalized using polyethylene glycol (PEG) of various molecular weights and sodium hydroxide to tailor their photoluminescence (PL) behavior and enhance their applicability in thin-film illumination and biological staining. PEG-modified GQDs exhibited a pronounced red-shift and intensified fluorescence response due to aggregation-induced emission, with GQD-PEG (molecular weight: 300,000) achieving up to eight-fold enhancement in PL intensity compared to pristine GQDs. The influence of solvent environments on PL behavior was studied, revealing solvent-dependent shifts and emission intensities. Transmission electron microscopy confirmed the formation of core–shell GQD clusters, while Raman spectroscopy suggested improved structural ordering upon modification. The prepared GQD thin films demonstrated robust fluorescence stability under prolonged water immersion, indicating strong adhesion to glass substrates. Furthermore, the modified GQDs effectively labeled E. coli, Gram-positive, and Gram-negative bacteria, with GQD-PEG and GQD-NaOH displaying red and green emissions, respectively, at optimal concentrations. This study highlights the potential of surface-functionalized GQDs as versatile materials for optoelectronic devices and fluorescence-based bioimaging.

Inventions doi: 10.3390/inventions10050080

Authors: Qinglei Zhang Lei Luo Xiaoping Wang Dehai Zhang Haibo Li Zongxiang Lu Ying Qiao

With the implementation of China’s “dual carbon” strategy, the installed capacity of new energy has grown rapidly. Wind power and photovoltaic power have accounted for more than 40%, but the integration of power electronic apparatus into the grid has resulted in the manifestation of a system with “low inertia and weak damping”, which can easily lead to transient overvoltage problems at transmitters when high-voltage direct-current (HVDC) latching faults occur. Although a variety of dynamic reactive power optimization strategies have been proposed in the existing research, most of them are aimed at single equipment, and multi-reactive power source collaborative control schemes are lacking. In this paper, we innovatively establish a transient voltage analysis model for a new energy transmitter, derive the expression of overvoltage amplitude, and propose a method for the construction of a multi-reactive source collaborative optimization model, which can effectively suppress transient overvoltage through capacity and initial output configuration. We provide a new idea for the safe operation of a significant percentage of new energy grids. The case analysis shows that the co-optimization method outlined in this paper is an effective solution to suppress the transient overvoltage triggered by AC faults and has wide application value.

Inventions doi: 10.3390/inventions10050079

Authors: Preetham Goli Srinivasa Rao Gampa Amarendra Alluri Balaji Gutta Kiran Jasthi Debapriya Das

This paper presents a novel methodology for the optimal sizing of solar photovoltaic (PV) systems in distribution networks by determining the monthly optimum tilt and azimuth angles to maximize solar energy capture. Using one year of solar irradiation data, the Grey Wolf Optimizer (GWO) is employed to optimize the tilt and azimuth angles with the objective of maximizing monthly solar insolation. Unlike existing approaches that assume fixed azimuth angles, the proposed method calculates both tilt and azimuth angles for each month, allowing for a more precise alignment with solar trajectories. The optimized orientation parameters are subsequently utilized to determine the optimal number and placement of PV panels, as well as the optimal location and sizing of shunt capacitor (SC) banks, for the IEEE 69-bus distribution system. This optimization is performed under peak load conditions using the GWO, with the objectives of minimizing active power losses, enhancing voltage profile stability, and maximizing PV system penetration. The long-term impact of this approach is assessed through a 20-year energy and economic savings analysis, demonstrating substantial improvements in energy efficiency and cost-effectiveness.