Inventions

21 pages, 1899 KB

Open AccessArticle

Risk Assessment of Distribution Network Based on Dirichlet Process Mixture Model and the Cumulant Method

by Yuxuan Huang, Yuwei Chen, Zhenguo Shao, Feixiong Chen, Yunting Shao, Yifan Zhang and Changming Chen

Inventions 2026, 11(2), 42; https://doi.org/10.3390/inventions11020042 - 21 Apr 2026

Cited by 1 | Viewed by 444

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Recent Advances and Challenges in Emerging Power Systems: 3rd Edition)

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33 pages, 4991 KB

Open AccessArticle

Temperature–Power Adaptive Control Strategy for Multi-Electrolyzer Systems

by Yuxin Xu and Yan Dong

Inventions 2026, 11(2), 41; https://doi.org/10.3390/inventions11020041 - 21 Apr 2026

Viewed by 498

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advanced Nonlinear Control and Optimization for Renewable Energy Systems, Smart Grids and Electric Vehicles)

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22 pages, 6903 KB

Open AccessArticle

Joint Optimization of Hovering Position and Resource Allocation in UAV-Enabled Semantic Communications via Greedy-Enhanced Adaptive Cellular Genetic Algorithm

by Pei Liu and Boge Wen

Inventions 2026, 11(2), 40; https://doi.org/10.3390/inventions11020040 - 12 Apr 2026

Viewed by 850

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Revolutionizing Surveillance: Unmanned Aerial Vehicle Technology Innovations)

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11 pages, 743 KB

Open AccessArticle

Wide-Band Compact Substrate-Integrated Coaxial Line Transition

by Mohamed Mamdouh M. Ali, Shoukry I. Shams, Mahmoud Elsaadany, Ghyslain Gagnon and Abdelrazik Sebak

Inventions 2026, 11(2), 39; https://doi.org/10.3390/inventions11020039 - 9 Apr 2026

Viewed by 759

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Antenna and Microwave Components for Future Wireless Communication Systems)

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19 pages, 3196 KB

Open AccessArticle

Spark Piezotransformer-Based Discharge in Argon Flow for Stimulating Plasma-Induced Cell Death of the Human Cancer Cells HEp-2

by 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 and Sergey V. Gudkov

Inventions 2026, 11(2), 38; https://doi.org/10.3390/inventions11020038 - 8 Apr 2026

Viewed by 988

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Inventions and Innovation in Biotechnology and Materials)

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34 pages, 7536 KB

Open AccessArticle

Aerodynamic Performance Improvement of a Straight-Bladed Vertical Axis Wind Turbine Through a Modified NACA0012 Profile with Inclined Orifices

by Ioana-Octavia Bucur, Daniel-Eugeniu Crunțeanu and Mădălin-Constantin Dombrovschi

Inventions 2026, 11(2), 37; https://doi.org/10.3390/inventions11020037 - 3 Apr 2026

Cited by 1 | Viewed by 1061

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Emerging Trends and Innovations in Renewable Energy)

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19 pages, 2780 KB

Open AccessPatent Summary

Recycling Installation for Circular SLA Resin and Injection Casting in Microgravity

by Emilia Georgiana Prisăcariu and Iulian Vlăducă

Inventions 2026, 11(2), 36; https://doi.org/10.3390/inventions11020036 - 3 Apr 2026

Viewed by 790

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Inventions and Innovation in Advanced Manufacturing)

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45 pages, 7679 KB

Open AccessArticle

Conquering the Urban Firefighting Challenge: A Deep Q-Network Approach for Autonomous UAV Navigation

by Shafiqul Alam Khan, Damian Valles, Marcelo M. Carvalho and Wenquan Dong

Inventions 2026, 11(2), 35; https://doi.org/10.3390/inventions11020035 - 2 Apr 2026

Cited by 1 | Viewed by 1259

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs): Innovations and Applications)

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18 pages, 1100 KB

Open AccessArticle

Determining Fault Locations on Overhead Power Lines Under Power Quality Deviation Conditions Based on the Least Squares Method

by Aleksandr Kulikov, Pavel Ilyushin and Anton Loskutov

Inventions 2026, 11(2), 34; https://doi.org/10.3390/inventions11020034 - 31 Mar 2026

Viewed by 562

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Inventions and Innovation in Electrical Engineering/Energy/Communications)

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21 pages, 1384 KB

Open AccessArticle

Data-Driven Requirements Prioritization Framework for App Reviews

by Fatma A. Mihany, Galal H. Galal-Edeen, Ehab E. Hassanein and Hanan Moussa

Inventions 2026, 11(2), 33; https://doi.org/10.3390/inventions11020033 - 31 Mar 2026

Viewed by 1182

Abstract

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, [...] Read more.

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. Full article

(This article belongs to the Section Inventions and Innovation in Design, Modeling and Computing Methods)

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13 pages, 3674 KB

Open AccessArticle

A Study on the Impact of Ice-Covered Pantograph–Catenary Arc Characteristics and Ablation Mechanisms

by Zhiliang Wang, Zhuo Li, Keqiao Zeng, Wenfu Wei, Zefeng Yang and Huan Zhang

Inventions 2026, 11(2), 32; https://doi.org/10.3390/inventions11020032 - 25 Mar 2026

Viewed by 734

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Mechanics of Composite Materials: Strength, Deformation, and Failure Analysis)

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24 pages, 2603 KB

Open AccessArticle

Communication-Fairness Trade-Offs in Federated Learning for 6G Resource Allocation: A 200 Client Study

by Nizamuddin Maitlo, Mahmood Hussain Shah, Abdullah Maitlo, Ghulam Mustafa, Kaleem Arshid and Nooruddin Noonari

Inventions 2026, 11(2), 31; https://doi.org/10.3390/inventions11020031 - 20 Mar 2026

Cited by 1 | Viewed by 818

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Inventions and Innovation in Electrical Engineering/Energy/Communications)

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21 pages, 1759 KB

Open AccessArticle

Design of a Modular Testing Facility for Sustainable Fuels Obtained from Plastic Waste Pyrolysis for Aerospace Engines

by Alexa-Andreea Crisan, Radu Eugen Kuncser, Simona-Nicoleta Danescu, Vlad Stefan Buzetelu, Madalina Botu and Daniel-Eugeniu Crunteanu

Inventions 2026, 11(2), 30; https://doi.org/10.3390/inventions11020030 - 19 Mar 2026

Viewed by 921

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Inventions and Innovation in Energy and Thermal/Fluidic Science)

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33 pages, 2201 KB

Open AccessReview

Machine Learning Models for Non-Intrusive Load Monitoring: A Systematic Review and Meta-Analysis

by Herman Cristiano Jaime, Adler Diniz de Souza, Raphael Carlos Santos Machado and Otávio de Souza Martins Gomes

Inventions 2026, 11(2), 29; https://doi.org/10.3390/inventions11020029 - 19 Mar 2026

Cited by 1 | Viewed by 1262

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advanced Nonlinear Control and Optimization for Renewable Energy Systems, Smart Grids and Electric Vehicles)

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20 pages, 4299 KB

Open AccessArticle

Establishment Mechanism of Power-Frequency Follow-Current Arc on Medium-Voltage Insulated Conductors Under Lightning Overvoltage

by Xin Ning, Rui Yu, Longchen Liu, Jiayi Wang, Jingxin Zou, Hao Wang, Tian Tan, Huajian Peng and Xin Yang

Inventions 2026, 11(2), 28; https://doi.org/10.3390/inventions11020028 - 18 Mar 2026

Cited by 1 | Viewed by 700

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Inventions and Innovation in Electrical Engineering/Energy/Communications)

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34 pages, 4339 KB

Open AccessReview

A Review of Recent Advances in Micro Heat Exchangers in the Food and Pharmaceutical Industries

by Muhammad Waheed Azam, Fabio Bozzoli, Ghulam Qadir Choudhary and Uzair Sajjad

Inventions 2026, 11(2), 27; https://doi.org/10.3390/inventions11020027 - 16 Mar 2026

Viewed by 1660

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue New Sights in Fluid Mechanics and Transport Phenomena)

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11 pages, 8454 KB

Open AccessArticle

Impact of Damaged Dropper on Pantograph–Catenary Current Collection Quality in High-Speed Railways

by Caizhi Yang, Huan Zhang, Like Pan, Yuan Yuan, Qun Yu, Qing Xiong, Ziqian Yang and Wenfu Wei

Inventions 2026, 11(2), 26; https://doi.org/10.3390/inventions11020026 - 14 Mar 2026

Viewed by 783

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Inventions and Innovation in Biotechnology and Materials)

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44 pages, 1068 KB

Open AccessReview

Vertical-Axis Wind Turbines for Extreme Environments: A Systematic Review of Performance, Adaptation Challenges, and Future Pathways

by Mohanad Al-Ghriybah

Inventions 2026, 11(2), 25; https://doi.org/10.3390/inventions11020025 - 13 Mar 2026

Cited by 2 | Viewed by 3190

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Inventions and Innovation in Energy and Thermal/Fluidic Science)

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47 pages, 2002 KB

Open AccessReview

A Review of the Ionic Liquids for Hydrogen Production by Electrolysis

by José Pereira, Reinaldo Souza and Ana Moita

Inventions 2026, 11(2), 24; https://doi.org/10.3390/inventions11020024 - 9 Mar 2026

Viewed by 1167

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Research and Applications of Ionic Liquids)

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28 pages, 22820 KB

Open AccessArticle

A Quantitative Assessment of Uncertainty Reduction as a Function of Measurement Campaign Length Using Linear and Machine-Learning MCP Models

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

Inventions 2026, 11(2), 23; https://doi.org/10.3390/inventions11020023 - 2 Mar 2026

Viewed by 1121

Abstract

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 [...] Read more.

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 (

R^{2}

) 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. Full article

(This article belongs to the Section Inventions and Innovation in Energy and Thermal/Fluidic Science)

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26 pages, 3322 KB

Open AccessArticle

Histopathological Medical Image Classification Using ANN Optimized by PSO with CNN for Feature Extraction

by Baidaa Mutasher Rashed and Shaker Kadhim Ali

Inventions 2026, 11(2), 22; https://doi.org/10.3390/inventions11020022 - 27 Feb 2026

Viewed by 939

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Machine Learning Applications in Healthcare and Disease Prediction)

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18 pages, 2183 KB

Open AccessArticle

Annual Load Scenario Generation Using a Hybrid STL and Improved DDPM Approach

by Heran Kang, Hongyang Liu, Jianfei Liu, Ruichen Hao, Xiang Wang, Wenbo Hu, Jie Chen, Wei Yue, Haibo Li and Zongxiang Lu

Inventions 2026, 11(2), 21; https://doi.org/10.3390/inventions11020021 - 24 Feb 2026

Viewed by 655

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Inventions and Innovation in Electrical Engineering/Energy/Communications)

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27 pages, 2415 KB

Open AccessArticle

A Multi-Objective and Uncertainty-Aware Holistic Swarm Optimized Random Forest for Robust Student Performance and Dropout Prediction

by Menna M. S. Elmasry, Mona G. Gafar and M. A. Elsabagh

Inventions 2026, 11(2), 20; https://doi.org/10.3390/inventions11020020 - 24 Feb 2026

Viewed by 958

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Inventions and Innovation in Design, Modeling and Computing Methods)

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Inventions, Volume 11, Issue 2 (April 2026) – 23 articles

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