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.

Inventions doi: 10.3390/inventions10050078

Authors: Mohamadou Hamadama Mouctar Mohamed G Hassan Nuno Bimbo Syed Zaheer Abbas Ihab Shigidi

The rising level of atmospheric carbon dioxide (CO2) is a major driver of climate change, highlighting the need to develop carbon capture and storage (CCS) technologies quickly. This paper offers a comparative review of three main groups of porous adsorbent materials—zeolites, metal–organic frameworks (MOFs), and activated carbons—for their roles in CO2 capture and long-term storage. By examining their structural features, adsorption capacities, moisture stability, and economic viability, the strengths and weaknesses of each material are assessed. Additionally, five different methods for delivering these materials into depleted oil and gas reservoirs are discussed: direct suspension injection, polymer-assisted transport, foam-assisted delivery, encapsulation with controlled release, and preformed particle gels. The potential of hybrid systems, such as MOF–carbon composites and polymer-functionalized materials, is also examined for improved selectivity and durability in underground environments. This research aims to connect materials science with subsurface engineering, helping guide the selection and use of adsorbent materials in real-world CCS applications. The findings support the optimization of CCS deployment and contribute to broader climate change efforts and the goal of achieving net-zero emissions. Key findings include CO2 adsorption capacities of 3.5–8.0 mmol/g and surface areas up to 7000 m2/g, with MOFs demonstrating the highest uptake and activated carbons offering cost-effective performance.

Inventions doi: 10.3390/inventions10050077

Authors: Syed Taha Khursheed Moein Imani Foumani Yunhua Luo Guo-zhen Zhu

This study focuses on developing and implementing crystal plasticity finite element modeling (CPFEM) codes on the ANSYS platform. The code incorporates a plasticity constitutive law that describes the behaviors of basal, prismatic, and pyramidal slips in magnesium, and is validated against plane-strain compression experiments and simulations using established codes on the ABAQUS CAE platform. The validated CPFEM code is applied to simulate the dislocation-induced nanoindentation response of pure magnesium across different crystallographic orientations, allowing visualization of strain distributions associated with different slips. Consistent with experimental observations, basal slip is identified as the primary active slip, whereas prismatic and pyramidal slips show varying activities with respect to the direction of the indentation. Novelty arises from an ANSYS–native CPFEM implementation that is cross-validated against published ABAQUS simulations and an experiment under a single, consistent constitutive set. This framework enables orientation-resolved mapping of slip system activity and subsurface strain fields under spherical nanoindentation, providing analysis capability seldom available in prior ANSYS–based studies.

Inventions doi: 10.3390/inventions10050076

Authors: Jonghwan Baek Seolha Kim Chang-Hee Lee Myeongsu Jeong Jin-Ho Suh Jaeyoul Lee

This study presents the development and validation of an automated oyster classification system designed to classify oysters by size and place them into trays for freezing. Addressing limitations in conventional manual processing, the proposed system integrates a vision-based recognition algorithm and a delta robot (parallel robot) equipped with a soft gripper. The vision system identifies oyster size and optimal grasp points using image moment calculations, enhancing the accuracy of classification for irregularly shaped oysters. Experimental tests demonstrated classification and grasping success rates of 99%. A process simulation based on real industrial conditions revealed that seven units of the automated system are required to match the daily output of 7 tons achieved by 60 workers. When compared with a theoretical 100% success rate, the system showed a marginal production loss of 715 oysters and 15 trays. These results confirm the potential of the proposed system to improve consistency, reduce labor dependency, and increase productivity in oyster processing. Future work will focus on gripper design optimization and parameter tuning to further improve system stability and efficiency.

Inventions doi: 10.3390/inventions10050075

Authors: Pavel O. Savelev Andrei I. Shumeiko Victor D. Telekh

The development of dynamic missions of small satellites requires the development of efficient, compact, and reliable propulsion systems (PSs). This paper investigates a propellant storage and supply system (PSSS), utilizing alternative solid-state propellants in the form of wire. To establish the background to the suggested solutions implemented in the proposed system, two types of comparative analysis were performed. The first one compared different types of propellant management system designs while the second juxtaposes a variety of propellants. It is shown that the solid-state systems for small satellite operations are advantageous, while the selection of propellants should be focused on safe operations and operational requirements. The principle of operation and structural design of the proposed wire-based solid-state propellant management system are discussed, including the assessment of its engineering realization. The strategies to mitigate the potential problems with the system’s operations such as propellant unwanted deposition and corrosive effects are suggested. An example of using the proposed system is provided, which considers a deep space dynamic mission case. The proposed PSSS architecture is dedicated to increasing the energy efficiency, resilience to environmental factors, and suitability for small satellite platforms, including that of the CubeSat format.

Inventions doi: 10.3390/inventions10050074

Authors: Wenyan Zhang Kai Zhang Ti Li Wenhua Deng

China frequently experiences natural disasters, making emergency language services a key link in information transmission, cross-lingual communication, and resource coordination during disaster relief. Traditional contingency plans rely on manual experience, which results in low efficiency, limited coverage, and insufficient dynamic adaptability. Large language models (LLMs), with their advantages in semantic understanding, multilingual adaptation, and scalability, provide new technical approaches for emergency language services. Our study establishes the country’s first generative evaluation index system for emergency language service contingency plans, covering eight major dimensions. Through an evaluation of 11 mainstream large language models, including Deepseek, we find that these models perform excellently in precise service stratification and resource network stereoscopic coordination but show significant shortcomings in legal/regulatory frameworks and mechanisms for dynamic evolution. It is recommended to construct a more comprehensive emergency language service system by means of targeted data augmentation, multi-model collaboration, and human–machine integration so as to improve cross-linguistic communication efficiency in emergencies and reduce secondary risks caused by information transmission barriers.

Inventions doi: 10.3390/inventions10050073

Authors: Yuji Kobayashi Nobuyoshi Takamitsu Rikuto Suga Kotaro Miyake Yogo Takada

In recent years, the deterioration of social infrastructure such as bridges has become a serious issue in many countries around the world. To maintain the functionality of aging bridges over the long term, it is necessary to conduct regular inspections, detect damage at an early stage, and perform timely repairs. However, inspections require significant cost and time, and ensuring the safety of inspectors remains a major challenge. As a result, inspection using robots has attracted increasing attention. This study focuses on a wire-driven bridge inspection robot designed to inspect the underside of bridge girders. To use this robot, wires must be installed in the space beneath the girders. However, it is difficult to install wires over areas such as rivers. To address this problem, we developed a robotic arm capable of throwing a spear attached to a string. In order to throw the spear accurately to the target location, a three-dimensional dynamic model of the spear in flight was constructed, considering the tension of the string. Using this model, we accurately estimated the required throwing conditions and confirmed that the robotic arm could successfully throw the spear to the target location.

Inventions doi: 10.3390/inventions10040072

Authors: Juan J. Vallejo Tejero María Martínez Gómez Francisco J. Muñoz Gutiérrez Alejandro Rodríguez Gómez

This study presents a comprehensive numerical simulation and thermal performance analysis of a novel modular floating solar still system, featuring integrated heat-pipe vacuum tube collectors, designed for seawater desalination. This innovative system—subject of International Patent Application WO 2023/062261 A1—not only aims to enhance efficiency and scalability beyond traditional solar stills, but also addresses the significant environmental challenge of concentrated brine discharge inherent in conventional desalination methods. The study evolved from an initial theoretical model to a rigorous dynamic thermal model, validated using real hourly meteorological data from Málaga, Andalusia, Spain. This modelling approach was developed to quantify heat transfer mechanisms and accurately predict system performance. The refined hourly simulation forecasts an annual freshwater production of approximately 174 L per unit. Notably, a preliminary economic assessment estimates the Cost of Produced Water per Litre (CPL) at 0.7509 EUR/litre, establishing a valuable baseline for future optimisation. These findings underscore the critical importance of dynamic hourly simulations for realistic performance prediction and validate the technical and preliminary economic feasibility of this novel approach. The system’s projected output, modular floating design, and significant environmental advantages position it as a promising and sustainable solution for freshwater production, particularly in coastal regions and sensitive marine ecosystems. This work provides a solid foundation for future experimental validation, cost optimisation, and scalable implementation of renewable energy-driven desalination.

Inventions doi: 10.3390/inventions10040071

Authors: Tomas Kačinskas Saulius Baskutis

This study introduces a MATrix LABoratory (MATLAB, version R2024b, update 1 (24.2.0.2740171))-based automated system for the detection and measurement of indication areas in coated surfaces, enhancing the accuracy and efficiency of quality control processes in metal, polymeric and thermoplastic coatings. The developed code identifies various indication characteristics in the image and provides numerical results, assesses the size and quantity of indications and evaluates conformity to ISO standards. A comprehensive testing method, involving non-destructive penetrant testing (PT) and radiographic testing (RT), allowed for an in-depth analysis of surface and internal porosity across different coating methods, including aluminum-, copper-, polytetrafluoroethylene (PTFE)- and polyether ether ketone (PEEK)-based materials. Initial findings had a major impact on indicating a non-homogeneous surface of obtained coatings, manufactured using different technologies and materials. Whereas researchers using non-destructive testing (NDT) methods typically rely on visual inspection and manual counting, the system under study automates this process. Each sample image is loaded into MATLAB and analyzed using the Image Processing Tool, Computer Vision Toolbox, Statistics and Machine Learning Toolbox. The custom code performs essential tasks such as image conversion, filtering, boundary detection, layering operations and calculations. These processes are integral to rendering images with developed indications according to NDT method requirements, providing a detailed visual and numerical representation of the analysis. RT also validated the observations made through surface indication detection, revealing either the absence of hidden defects or, conversely, internal porosity correlating with surface conditions. Matrix and graphical representations were used to facilitate the comparison of test results, highlighting more advanced methods and materials as the superior choice for achieving optimal mechanical and structural integrity. This research contributes to addressing challenges in surface quality assurance, advancing digital transformation in inspection processes and exploring more advanced alternatives to traditional coating technologies and materials.

Inventions doi: 10.3390/inventions10040070

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

To improve the mechanical stability and service durability of solar road structures, this study systematically investigates the mechanical response characteristics of photovoltaic panels with different geometric shapes—including triangles, rectangles, squares, regular pentagons, and regular hexagons—under consistent boundary and loading conditions using the discrete element method (DEM). All panels have a uniform thickness of 10 cm and equivalent surface areas to ensure shape comparability. Side lengths vary among the shapes: square panels with sides of 0.707 m, 1.0 m, and 1.5 m; triangle 1.155 m; rectangle (aspect ratio 1:2) 0.707 m; pentagon 1.175 m; and hexagon 0.577 m. Results show that panel geometry significantly influences stress distribution and deformation behavior. Although triangular panels exhibit higher ultimate bearing capacity and failure energy, they suffer from severe stress concentration and low stiffness. Regular hexagonal panels, due to their geometric symmetry, enable more uniform stress and displacement distributions, offering better stability and crack resistance. Size effect analysis reveals that larger panels improve load-bearing and energy dissipation capacity but exacerbate edge stress concentration and reduce overall stiffness, leading to more pronounced “thinning” deformation and premature failure. Failure mode analysis further indicates that shape governs crack initiation and path, while size determines crack propagation rate and failure extent—revealing a coupled shape–size mechanical mechanism. Regarding assembly, honeycomb arrangements demonstrate superior mechanical performance due to higher compactness and better load-sharing characteristics. The study ultimately recommends the use of small-sized regular hexagonal units and optimized splicing structures to balance strength, stiffness, and durability. These findings provide theoretical guidance and parameter references for the structural design of solar roads.

Inventions doi: 10.3390/inventions10040069

Authors: Jorge Julian Moreno Rubio Edison Ferney Angarita Malaver Jairo Alonso Mesa Lara

This paper presents the design, implementation, and characterization of a broadband power amplifier (PA) with a reconfigurable architecture, capable of efficient operation across a wide frequency range of 0.2–3.6 GHz. Leveraging Gallium Nitride (GaN) devices, the PA achieves high efficiency and power, essential for broadband and high-frequency applications. By swapping the roles of the main and peak amplifiers, the PA achieves Doherty behavior at two related frequencies, 1.4 and 2.8 GHz, where the first is exactly half of the second, while maintaining consistent efficiency and output power across the remaining band in non-Doherty modes. Characterization results confirm the reliability and versatility of the proposed design, showcasing its ability to deliver robust performance across both Doherty and non-Doherty operational ranges. This combination of GaN technology and innovative reconfigurability makes the PA highly suitable for broadband applications requiring high efficiency, flexibility, and wideband coverage. Moreover, the simplicity of the proposed design makes it not only practical for implementation but also highly competitive among state-of-the-art solutions.

Inventions doi: 10.3390/inventions10040068

Authors: Vitalii Susanin Igor Kabashkin

The classical V-Model has served as the foundational framework for aerospace systems engineering, but its scope terminates upon aircraft certification, creating a significant gap in addressing reliability degradation during operational service. This study introduces the W-model framework—a comprehensive lifecycle management approach that extends the V-Model to systematically integrate reliability-centered component modifications with established aerospace development practices. The W-model incorporates a structured six-phase reliability-centered modification methodology that transforms operational data into certified design improvements through systematic reliability monitoring, candidate selection, design reviews, development, and certification processes. A detailed case study on the aviation pneumatic bypass valve demonstrates the methodology. Application of the W-model resulted in a 36% improvement in the mean time between failures and a significant reduction in unscheduled removals. The W-model represents a paradigm shift from reactive maintenance strategies to proactive, data-driven reliability enhancement, providing a systematic approach that maintains the rigor and traceability required for commercial aviation while enabling continuous reliability growth throughout the complete aircraft lifecycle.

Inventions doi: 10.3390/inventions10040067

Authors: Lim Shing Wang Muhammad Haarith Firdaous Pg Emeroylariffion Abas

Maintaining well integrity is essential in the oil and gas industry to prevent environmental hazards, operational risks, and economic losses. Cement bond log (CBL) tools are essential in evaluating cement bonding and ensuring wellbore stability. This study presents a patent landscape review of CBL technologies, based on 3473 patent documents from the Lens.org database. After eliminating duplicates and irrelevant entries, 167 granted patents were selected for in-depth analysis. These were categorized by technology type (wave, electrical, radiation, neutron, and other tools) and by material focus (formation, casing, cement, and borehole fluid). The findings reveal a dominant focus on formation evaluation (59.9%) and a growing reliance on wave-based (22.2%) and other advanced tools (25.1%), indicating a shift toward high-precision diagnostics. Geographically, 75% of granted patents were filed through the U.S. Patent and Trademark Office, and 97.6% were held by companies, underscoring the dominance of corporate innovation and the minimal presence of academia and individuals. The review also identifies notable patents that reflect significant technical innovations and discusses their role in advancing diagnostic capabilities. These insights emphasize the need for broader collaboration and targeted research to advance well integrity technologies in line with industry goals for operational performance and safety.

Inventions doi: 10.3390/inventions10040066

Authors: Emilia Georgiana Prisăcariu Iulian Vlăducă Oana Maria Dumitrescu Sergiu Strătilă Raluca Andreea Roșu

Originally introduced in the 1960s by DISA Elektronik as a calibration tunnel for hot-wire anemometers, the Type 55D41 has now been reengineered into a versatile and modern aerodynamic test platform. While retaining key legacy components, such as the converging nozzle and the 55D42 power unit, the upgraded system features a redesigned modular test section with optical-grade quartz windows. This enhancement enables compatibility with advanced flow diagnostics and visualization methods, including PTV, DIC, and schlieren imaging. The modernized facility maintains the precision and flow stability that made the original design widely respected, while expanding its functionality to meet the demands of contemporary experimental research. Its architecture supports the aerodynamic characterization of micro-scale static pressure probes used in aerospace, propulsion, and micro gas turbine applications. Special attention is given to assessing the influence of probe tip geometry (e.g., conical, ogive), port positioning, and stem interference on measurement accuracy.

Inventions doi: 10.3390/inventions10040065

Authors: Abdurrahman Yilmaz Hasan Kivrak

Path planning algorithms for mobile robots and autonomous systems have advanced considerably, yet challenges remain in navigating complex environments while satisfying non-holonomic constraints and achieving precise target orientation. Phase portraits are traditionally used to analyse dynamical systems via equilibrium points and system trajectories, and can be a powerful framework for addressing these challenges. In this work, we propose a novel orientation-aware path planning algorithm that uses phase portrait dynamics by treating both obstacles and target poses as equilibrium points within the environment. Unlike conventional approaches, our method explicitly incorporates non-holonomic constraints and target orientation requirements, resulting in smooth, feasible trajectories with high final pose accuracy. Simulation results across 28 diverse scenarios show that our method achieves zero final orientation error with path lengths comparable to Hybrid A*, and planning times reduced by 52% on the indoor map and 84% on the playpen map relative to Hybrid A*. These results highlight the potential of phase portrait-based planning as an effective and efficient method for real-time autonomous navigation.

Inventions doi: 10.3390/inventions10040064

Authors: Jinfei Wu Xinghua Shan Shuo Zhao

Optimizing passenger train line planning is a complex task that involves balancing operational costs and passenger service quality. This study investigates the adjustment and optimization of train line plans to better align with passenger demand and operational constraints, while minimizing systematic costs. These costs include train operation expenses (e.g., line usage fees and station service fees), passenger travel costs, and hidden costs such as imbalances in station stops. Line usage fees refer to charges for using railway tracks, whereas station service fees cover services provided at train stations. The optimization process employs a Simulated Annealing Algorithm to adjust train compositions, capacity configurations, and stop patterns to better match passenger demand. The results indicate a 13.89% reduction in the objective function value, reflecting improved overall efficiency. Notably, most costs are reduced, including train operating costs and passenger travel costs. However, ticketing service fees—which are calculated as a percentage of passenger fare revenue—increased slightly due to additional backtracking in passenger travel paths, which raised the total fare collected. Overall, the optimization improves the operational performance of the train network, enhancing both efficiency and service quality.

Inventions doi: 10.3390/inventions10040063

Authors: Georgiana-Alexandra Moroşanu Florin-Ioan Moroșanu Florin Susac Virgil-Gabriel Teodor Viorel Păunoiu Nicuşor Baroiu

The paper presents the CNC manufacturing technology of the ”Double fixing fork” part as a module with educational purpose, being designed as a training support for students and other parties, facilitating the practical learning of CNC processing technology. Its technological manufacturing process involved a careful analysis of the geometry, material, tolerances, as well as functional requirements to ensure precision and reliability in operation. The material from which the part was made is a polymer material (PEHD 1000) selected both for its mechanical characteristics and for its compatibility with processing technologies. The results demonstrated high precision and adaptability, reduced execution times and the possibility of achieving complex geometries in a relatively short time. The developed module supports skill development in CNC programming and operation and is suitable for replication in other academic environments. Programming allowed for more precise control of the cutting tool trajectory and processing parameters. The paper represents an important contribution to the training of future specialists, paying special attention to the growing interdisciplinarity in manufacturing technology and the development of technical skills necessary for future engineers in the numerically controlled machinery sector.

Inventions doi: 10.3390/inventions10040062

Authors: Spyridon D. Mourtas Shuai Li Xinwei Cao Bolin Liao Vasilios N. Katsikis

The weights and structure determination (WASD) neuronet (or neural network) is a single-hidden-layer feedforward neuronet that exhibits an excellent approximation ability, despite its simple structure. Thanks to its strong generalization, fast speed, and ease of implementation, the WASD neuronet has been the subject of many modifications, including metaheuristics, and applications in a wide range of scientific fields. As it has garnered significant attention in the last decade, the aim of this study is to provide an extensive overview of the WASD framework. Furthermore, the WASD has been effectively used in numerous real-time learning tasks like regression, multiclass classification, and binary classification due to its exceptional performance. In addition, we present WASD’s applications in social science, business, engineering, economics, and medicine. We aim to report these developments and provide some avenues for further research.

Inventions doi: 10.3390/inventions10040061

Authors: Tahar Hassaine Daouadji Fazilay Abbès Tayeb Bensatallah Boussad Abbès

This study introduces a new method for modeling the nonlinear behavior of adhesively bonded composite steel–concrete T-beam systems. The model characterizes the interfacial behavior between the steel beam and the concrete slab using a strain compatibility approach within the framework of linear elasticity. It captures the nonlinear distribution of shear stresses over the entire depth of the composite section, making it applicable to various material combinations. The approach accounts for both continuous and discontinuous bonding conditions at the bonded steel–concrete interface. The analysis focuses on the top flange of the steel section, using a T-beam configuration commonly employed in bridge construction. This configuration stabilizes slab sliding, making the composite beam rigid, strong, and resistant to deformation. The numerical results demonstrate the advantages of the proposed solution over existing steel beam models and highlight key characteristics at the steel–concrete interface. The theoretical predictions are validated through comparison with existing analytical and experimental results, as well as finite element models, confirming the model’s accuracy and offering a deeper understanding of critical design parameters. The comparison shows excellent agreement between analytical predictions and finite element simulations, with discrepancies ranging from 1.7% to 4%. This research contributes to a better understanding of the mechanical behavior at the interface and supports the design of hybrid steel–concrete structures.

Inventions doi: 10.3390/inventions10040060

Authors: Pedro Moltó-Balado Josep-Lluis Clua-Espuny Silvia Reverté-Villarroya Victor Alonso-Barberán Maria Teresa Balado-Albiol Andrea Simeó-Monzó Jorge Canela-Royo Alba del Barrio-González

Background/Objectives: Atrial fibrillation (AF) is a prevalent arrhythmia associated with a high risk of major adverse cardiovascular events (MACEs). This study aimed to compare the predictive ability of an ML model and the CHA2DS2-VASc score in predicting MACEs in AF patients using machine learning (ML) techniques. Methods: A cohort of 40,297 individuals aged 65–95 from the Terres de l’Ebre region (Catalonia, Spain) and diagnosed with AF between 2015 and 2016 was analyzed. ML algorithms, particularly AdaBoost, were used to predict MACEs, and the performance of the models was evaluated through metrics such as recall, area under the ROC curve (AUC), and accuracy. Results: The AdaBoost model outperformed CHA2DS2-VASc, achieving an accuracy of 99.99%, precision of 0.9994, recall of 1, and an AUC of 99.99%, compared to CHA2DS2-VASc’s AUC of 81.71%. A statistically significant difference was found using DeLong’s test (p = 0.0034) between the models, indicating the superior performance of the AdaBoost model in predicting MACEs. Conclusions: The AdaBoost model provides significantly better prediction of MACE in AF patients than the CHA2DS2-VASc score, demonstrating the potential of ML algorithms for personalized risk assessment and early detection in clinical settings. Further validation and computational resources are necessary to enable broader implementation.

Inventions doi: 10.3390/inventions10040059

Authors: Lars Skaug Mehrdad Nojoumian

Accurate geospatial data are essential for intelligent transportation systems and automated reporting applications, as location precision directly impacts safety analysis and decision-making. GPS devices are now routinely employed by law enforcement officers when filing vehicle crash reports, yet our investigation reveals that significant data quality issues persist. The high apparent precision of GPS coordinates belies their actual accuracy as we find that approximately 20% of crash sites need correction—results consistent with existing research. To address this challenge, we present a novel credibility scoring and correction algorithm that leverages a state-of-the-art multimodal large language model (LLM) capable of integrated visual and textual reasoning. Our framework synthesizes information from structured coordinates, crash diagrams, and narrative text, employing advanced artificial intelligence techniques for comprehensive geospatial validation. In addition to the LLM, our system incorporates open geospatial data from Overture Maps, an emerging collaborative mapping initiative, to enhance the spatial accuracy and robustness of the validation process. This solution was developed as part of research leading to a patent for autonomous vehicle routing systems that require high-precision crash location data. Applied to a dataset of 5000 crash reports, our approach systematically identifies records with location discrepancies requiring correction. By uniting the latest developments in multimodal AI and open geospatial data, our solution establishes a foundation for intelligent data validation in electronic reporting systems, with broad implications for automated infrastructure management and autonomous vehicle applications.

Inventions doi: 10.3390/inventions10040058

Authors: Gabriel Miguel Castro Martins Murillo Ferreira dos Santos Mathaus Ferreira da Silva Juliano Emir Nunes Masson Vinícius Barbosa Schettino Iuri Wladimir Molina William Rodrigues Silva

This paper presents a robust hardware system designed for future detection of faults in wind turbines by analyzing audible noise signals. Predictive maintenance strategies have increasingly relied on acoustic monitoring as a non-invasive method for identifying anomalies that may indicate component wear, misalignment, or impending mechanical failures. The proposed device captures and processes sound signals in real-time using strategically positioned microphones, ensuring high-fidelity data acquisition without interfering with turbine operation. Signal processing techniques are applied to extract relevant acoustic features, facilitating future identification of abnormal sound patterns that may indicate mechanical issues. The system’s effectiveness was validated through rigorous field tests, demonstrating its capability to enhance the reliability and efficiency of wind turbine maintenance. Experimental results showed an average transmission latency of 131.8 milliseconds, validating the system’s applicability for near real-time audible noise monitoring in wind turbines operating under limited connectivity conditions.

Inventions doi: 10.3390/inventions10040057

Authors: Howard Jun Hao Oh Kia Wai Liew Poh Kiat Ng Boon Kian Lim Chai Hua Tay Chee Lin Khoh

The objective of this study is to design, develop, and analyze a tri-wheeled trolley integrated with a motor that incorporates adjustable and foldable features. The purpose of a trolley is to allow users to easily transport items from one place to another. However, problems arise when transporting objects across challenging surfaces, such as up a flight of stairs, using a conventional cart. This innovation uses multiple engineering skills to determine and develop the best possible design for a stair-climbing trolley. A tri-wheel mechanism is integrated into its motorized design, meticulously engineered for adjustability, ensuring compatibility with a wide range of staircase dimensions. The designed trolley was constructed considering elements and processes such as a literature review, conceptual design, concept screening, concept scoring, 3D modelling, engineering design calculations, and simulations. The trolley was tested, and the measured pulling force data were compared with the theoretical calculations. A graph of the pulling force vs. load was plotted, in which both datasets showed similar increasing trends; hence, the designed trolley worked as expected. The development of this stair-climbing trolley can benefit people living in rural areas or low-cost buildings that are not equipped with elevators and can reduce injuries among the elderly. The designed stair-climbing trolley will not only minimize the user’s physical effort but also enhance safety. On top of that, the adjustable and foldable features of the stair-climbing trolley would benefit users living in areas with limited space.