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Improving the Thermal Efficiency of Gasket Plate Heat Exchangers Used in Vegetable Oil Processing
Review of Printed Log-Periodic Dipole Array Antenna Design for EMC Applications

Journal Description

Inventions

Inventions is an international, scientific, peer-reviewed, open access journal published bimonthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), Inspec, Ei Compendex and other databases.
Journal Rank: JCR - Q2 (Engineering, Multidisciplinary) / CiteScore - Q1 (General Engineering)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 21.8 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 1.9 (2024); 5-Year Impact Factor: 2.3 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2411-5134

Latest Articles

11 pages, 596 KiB

Open AccessArticle

Prediction of Major Adverse Cardiovascular Events in Atrial Fibrillation: A Comparison Between Machine Learning Techniques and CHA₂DS₂-VASc Score

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

Inventions 2025, 10(4), 60; https://doi.org/10.3390/inventions10040060 - 22 Jul 2025

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 [...] Read more.

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. Full article

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

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21 pages, 730 KiB

Open AccessArticle

A Multimodal Artificial Intelligence Framework for Intelligent Geospatial Data Validation and Correction

by Lars Skaug and Mehrdad Nojoumian

Inventions 2025, 10(4), 59; https://doi.org/10.3390/inventions10040059 - 22 Jul 2025

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 [...] Read more.

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. Full article

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

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22 pages, 15962 KiB

Open AccessArticle

Audible Noise-Based Hardware System for Acoustic Monitoring in Wind Turbines

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

Inventions 2025, 10(4), 58; https://doi.org/10.3390/inventions10040058 - 17 Jul 2025

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, [...] Read more.

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. Full article

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

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33 pages, 6169 KiB

Open AccessArticle

An Innovative Solution for Stair Climbing: A Conceptual Design and Analysis of a Tri-Wheeled Trolley with Motorized, Adjustable, and Foldable Features

by Howard Jun Hao Oh, Kia Wai Liew, Poh Kiat Ng, Boon Kian Lim, Chai Hua Tay and Chee Lin Khoh

Inventions 2025, 10(4), 57; https://doi.org/10.3390/inventions10040057 - 16 Jul 2025

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, [...] Read more.

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. Full article

(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications, 2nd Volume)

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20 pages, 3414 KiB

Open AccessArticle

Improvement in the Interception Vulnerability Level of Encryption Mechanism in GSM

by Fawad Ahmad, Reshail Khan and Armel Asongu Nkembi

Inventions 2025, 10(4), 56; https://doi.org/10.3390/inventions10040056 - 14 Jul 2025

Data security is of the utmost importance in the domain of real-time environmental monitoring systems, particularly when employing advanced context-aware intelligent visual analytics. This paper addresses a significant deficiency in the Global System for Mobile Communications (GSM), a widely employed wireless communication system [...] Read more.

Data security is of the utmost importance in the domain of real-time environmental monitoring systems, particularly when employing advanced context-aware intelligent visual analytics. This paper addresses a significant deficiency in the Global System for Mobile Communications (GSM), a widely employed wireless communication system for environmental monitoring. The A5/1 encryption technique, which is extensively employed, ensures the security of user data by utilizing a 64-bit session key that is divided into three linear feedback shift registers (LFSRs). Despite the shown efficacy, the development of a probabilistic model for assessing the vulnerability of breaking or intercepting the session key (Kc) has not yet been achieved. In order to bridge this existing knowledge gap, this study proposes a probabilistic model that aims to evaluate the security of encrypted data within the framework of the Global System for Mobile Communications (GSM). The proposed model implements alterations to the current GSM encryption process by the augmentation of the quantity of Linear Feedback Shift Registers (LFSRs), consequently resulting in an improved level of security. The methodology entails increasing the number of registers while preserving the session key’s length, ensuring that the key length specified by GSM standards remains unaltered. This is especially important for environmental monitoring systems that depend on real-time data analysis and decision-making. In order to elucidate the notion, this analysis considers three distinct scenarios: encryption utilizing a set of five, seven, and nine registers. The majority function is employed to determine the registers that will undergo perturbation, hence increasing the complexity of the bit arrangement and enhancing the security against prospective attackers. This paper provides actual evidence using simulations to illustrate that an increase in the number of registers leads to a decrease in the vulnerability of data interception, hence boosting data security in GSM communication. Simulation results demonstrate that our method substantially reduces the risk of data interception, thereby improving the integrity of context-aware intelligent visual analytics in real-time environmental monitoring systems. Full article

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

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11 pages, 1132 KiB

Open AccessArticle

Custom-Tailored Radiology Research via Retrieval-Augmented Generation: A Secure Institutionally Deployed Large Language Model System

by Michael Welsh, Julian Lopez-Rippe, Dana Alkhulaifat, Vahid Khalkhali, Xinmeng Wang, Mario Sinti-Ycochea and Susan Sotardi

Inventions 2025, 10(4), 55; https://doi.org/10.3390/inventions10040055 - 8 Jul 2025

Large language models (LLMs) show promise in enhancing medical research through domain-specific question answering. However, their clinical application is limited by hallucination risk, limited domain specialization, and privacy concerns. Public LLMs like GPT-4-Consensus pose challenges for use with institutional data, due to the [...] Read more.

Large language models (LLMs) show promise in enhancing medical research through domain-specific question answering. However, their clinical application is limited by hallucination risk, limited domain specialization, and privacy concerns. Public LLMs like GPT-4-Consensus pose challenges for use with institutional data, due to the inability to ensure patient data protection. In this work, we present a secure, custom-designed retrieval-augmented generation (RAG) LLM system deployed entirely within our institution and tailored for radiology research. Radiology researchers at our institution evaluated the system against GPT-4-Consensus through a blinded survey assessing factual accuracy (FA), citation relevance (CR), and perceived performance (PP) using 5-point Likert scales. Our system achieved mean ± SD scores of 4.15 ± 0.99 for FA, 3.70 ± 1.17 for CR, and 3.55 ± 1.39 for PP. In comparison, GPT-4-Consensus obtained 4.25 ± 0.72, 3.85 ± 1.23, and 3.90 ± 1.12 for the same metrics, respectively. No statistically significant differences were observed (p = 0.97, 0.65, 0.42), and 50% of participants preferred our system’s output. These results validate that secure, local RAG-based LLMs can match state-of-the-art performance while preserving privacy and adaptability, offering a scalable tool for medical research environments. Full article

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

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18 pages, 484 KiB

Open AccessArticle

Short-Term Forecasting of Total Aggregate Demand in Uncontrolled Residential Charging with Electric Vehicles Using Artificial Neural Networks

by Giovanni Panegossi Formaggio, Mauro de Souza Tonelli-Neto, Danieli Biagi Vilela and Anna Diva Plasencia Lotufo

Inventions 2025, 10(4), 54; https://doi.org/10.3390/inventions10040054 - 8 Jul 2025

Electric vehicles are gaining attention and being adopted by new users every day. Their widespread use creates a new scenario and challenge for the energy system due to the high energy storage demands they generate. Forecasting these loads using artificial neural networks has [...] Read more.

Electric vehicles are gaining attention and being adopted by new users every day. Their widespread use creates a new scenario and challenge for the energy system due to the high energy storage demands they generate. Forecasting these loads using artificial neural networks has proven to be an efficient way of solving time series problems. This study employs a multilayer perceptron network with backpropagation training and Bayesian regularisation to enhance generalisation and minimise overfitting errors. The research aggregates real consumption data from 200 households and 348 electric vehicles. The developed method was validated using MAPE, which resulted in errors below 6%. Short-term forecasts were made across the four seasons, predicting the total aggregate demand of households and vehicles for the next 24 h. The methodology produced significant and relevant results for this problem using hybrid training, a few-neuron architecture, deep learning, fast convergence, and low computational cost, with potential for real-world application. The results support the electrical power system by optimising these loads, reducing costs and energy generation, and preparing a new scenario for EV penetration rates. Full article

(This article belongs to the Special Issue Revolutionizing Mobility: Unleashing the Power of Software-Defined Networking for Electric Vehicle Communication)

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14 pages, 2407 KiB

Open AccessPatent Summary

Automated Calibration Mechanism for Color Filter Integration in Quantitative Schlieren Systems with Rectangular Light Sources

by Emilia Georgiana Prisăcariu and Iulian Vlăducă

Inventions 2025, 10(4), 53; https://doi.org/10.3390/inventions10040053 - 4 Jul 2025

This paper introduces an automated calibration system for color filters used in quantitative schlieren imaging, developed in response to prior findings highlighting the need for automation to reduce calibration time, minimize human error, and improve data accuracy and repeatability. Drawing from the authors’ [...] Read more.

This paper introduces an automated calibration system for color filters used in quantitative schlieren imaging, developed in response to prior findings highlighting the need for automation to reduce calibration time, minimize human error, and improve data accuracy and repeatability. Drawing from the authors’ experimental experience and practical application, the system demonstrates a significant enhancement in calibration efficiency—reducing the process from 2–5 h manually to just 15–30 min, representing time savings of up to 90%. Positioning accuracy improves from ±50–100 μm in manual setups to ±1–10 μm through precision-controlled automation, substantially lowering variability and increasing the reliability of pixel calibration curves. While calibration accuracy remains dependent on flow characteristics and post-processing capabilities, the system’s use of larger color filters—validated analytically and experimentally—further increases contrast sensitivity by 10–20%, enhancing the extraction of physical parameters such as velocity, temperature, and pressure fields. The setup features a modular, scalable architecture with a user-friendly interface, making it adaptable to diverse experimental environments and suitable for users at varying levels of expertise. Its iterative optimization and high-throughput capabilities position this system as a robust, flexible solution for advancing schlieren imaging techniques and enabling next-generation optical diagnostics in fluid dynamics research. Full article

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

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9 pages, 1016 KiB

Open AccessArticle

TinyML-Based Swine Vocalization Pattern Recognition for Enhancing Animal Welfare in Embedded Systems

by Tung Chiun Wen, Caroline Ferreira Freire, Luana Maria Benicio, Giselle Borges de Moura, Magno do Nascimento Amorim and Késia Oliveira da Silva-Miranda

Inventions 2025, 10(4), 52; https://doi.org/10.3390/inventions10040052 - 4 Jul 2025

Cited by 1

The automatic recognition of animal vocalizations is a valuable tool for monitoring pigs’ behavior, health, and welfare. This study investigates the feasibility of implementing a convolutional neural network (CNN) model for classifying pig vocalizations using tiny machine learning (TinyML) on a low-cost, resource-constrained [...] Read more.

The automatic recognition of animal vocalizations is a valuable tool for monitoring pigs’ behavior, health, and welfare. This study investigates the feasibility of implementing a convolutional neural network (CNN) model for classifying pig vocalizations using tiny machine learning (TinyML) on a low-cost, resource-constrained embedded system. The dataset was collected in 2011 at the University of Illinois at Urbana-Champaign on an experimental pig farm. In this experiment, 24 piglets were housed in environmentally controlled rooms and exposed to gradual thermal variations. Vocalizations were recorded using directional microphones, processed to reduce background noise, and categorized into “agonistic” and “social” behaviors using a CNN model developed on the Edge Impulse platform. Despite hardware limitations, the proposed approach achieved an accuracy of over 90%, demonstrating the potential of TinyML for real-time behavioral monitoring. These findings underscore the practical benefits of integrating TinyML into swine production systems, enabling early detection of issues that may impact animal welfare, reducing reliance on manual observations, and enhancing overall herd management. Full article

(This article belongs to the Special Issue Inventions and Innovation in Smart Sensing Technologies for Agriculture)

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28 pages, 1210 KiB

Open AccessArticle

A Multi-Ray Channel Modelling Approach to Enhance UAV Communications in Networked Airspace

by Fawad Ahmad, Muhammad Yasir Masood Mirza, Iftikhar Hussain and Kaleem Arshid

Inventions 2025, 10(4), 51; https://doi.org/10.3390/inventions10040051 - 1 Jul 2025

Cited by 1

In recent years, the use of unmanned aerial vehicles (UAVs), commonly known as drones, has significantly surged across civil, military, and commercial sectors. Ensuring reliable and efficient communication between UAVs and between UAVs and base stations is challenging due to dynamic factors such [...] Read more.

In recent years, the use of unmanned aerial vehicles (UAVs), commonly known as drones, has significantly surged across civil, military, and commercial sectors. Ensuring reliable and efficient communication between UAVs and between UAVs and base stations is challenging due to dynamic factors such as altitude, mobility, environmental obstacles, and atmospheric conditions, which existing communication models fail to address fully. This paper presents a multi-ray channel model that captures the complexities of the airspace network, applicable to both ground-to-air (G2A) and air-to-air (A2A) communications to ensure reliability and efficiency within the network. The model outperforms conventional line-of-sight assumptions by integrating multiple rays to reflect the multipath transmission of UAVs. The multi-ray channel model considers UAV flights’ dynamic and 3-D nature and the conditions in which UAVs typically operate, including urban, suburban, and rural environments. A technique that calculates the received power at a target UAV within a networked airspace is also proposed, utilizing the reflective characteristics of UAV surfaces along with the multi-ray channel model. The developed multi-ray channel model further facilitates the characterization and performance evaluation of G2A and A2A communications. Additionally, this paper explores the effects of various factors, such as altitude, the number of UAVs, and the spatial separation between them on the power received by the target UAV. The simulation outcomes are validated by empirical data and existing theoretical models, providing comprehensive insight into the proposed channel modelling technique. Full article

(This article belongs to the Special Issue Revolutionizing Mobility: Unleashing the Power of Software-Defined Networking for Electric Vehicle Communication)

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19 pages, 3233 KiB

Open AccessArticle

Mathematical Modeling of the Influence of Electrical Heterogeneity on the Processes of Salt Ion Transfer in Membrane Systems with Axial Symmetry Taking into Account Electroconvection

by Ekaterina Kazakovtseva, Evgenia Kirillova, Anna Kovalenko and Mahamet Urtenov

Inventions 2025, 10(4), 50; https://doi.org/10.3390/inventions10040050 - 30 Jun 2025

This article proposes a 3D mathematical model of the influence of electrical heterogeneity of the ion exchange membrane surface on the processes of salt ion transfer in membrane systems with axial symmetry; in particular, we investigate an annular membrane disk in the form [...] Read more.

This article proposes a 3D mathematical model of the influence of electrical heterogeneity of the ion exchange membrane surface on the processes of salt ion transfer in membrane systems with axial symmetry; in particular, we investigate an annular membrane disk in the form of a coupled system of Nernst–Planck–Poisson and Navier–Stokes equations in a cylindrical coordinate system. A hybrid numerical–analytical method for solving the boundary value problem is proposed, and a comparison of the results for the annular disk model obtained by the hybrid method and the independent finite element method is carried out. The areas of applicability of each of these methods are determined. The proposed model of an annular disk takes into account electroconvection, which is understood as the movement of an electrolyte solution under the action of an external electric field on an extended region of space charge formed at the solution–membrane boundary under the action of the same electric field. The main regularities and features of the occurrence and development of electroconvection associated with the electrical heterogeneity of the surface of the membrane disk of the annular membrane disk are determined; namely, it is shown that electroconvective vortices arise at the junction of the conductivity and non-conductivity regions at a certain ratio of the potential jump and angular velocity and flow down in the radial direction to the edge of the annular membrane. At a fixed potential jump greater than the limiting one, the formed electroconvective vortices gradually decrease with an increase in the angular velocity of rotation until they disappear. Conversely, at a fixed value of the angular velocity of rotation, electroconvective vortices arise at a certain potential jump, and with its subsequent increase gradually increase in size. Full article

(This article belongs to the Section Inventions and Innovation in Applied Chemistry and Physics)

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38 pages, 3183 KiB

Open AccessArticle

Exploring a Blockchain-Empowered Framework for Enhancing the Distributed Agile Software Development Testing Life Cycle

by Muhammad Shoaib Farooq, Junaid Nasir Qureshi, Fatima Ahmed, Momina Shaheen and Sameena Naaz

Inventions 2025, 10(4), 49; https://doi.org/10.3390/inventions10040049 - 30 Jun 2025

Revolutionizing distributed agile software testing, we propose BCTestingPlus, a groundbreaking blockchain-based platform. In the traditional distributed agile software testing lifecycle, software testing has suffered from a lack of trust, traceability, and security in communication and collaboration. Furthermore, developers’ failure to complete unit testing [...] Read more.

Revolutionizing distributed agile software testing, we propose BCTestingPlus, a groundbreaking blockchain-based platform. In the traditional distributed agile software testing lifecycle, software testing has suffered from a lack of trust, traceability, and security in communication and collaboration. Furthermore, developers’ failure to complete unit testing has been a significant bottleneck, causing delays and contributing to project failures. Introducing BCTestingPlus, a transformative blockchain-based architecture engineered to overcome these challenges. This framework integrates blockchain technology to establish an inherently transparent and secure environment for software testing. BCTestingPlus operates on a private Ethereum blockchain network, offering superior control and privacy. By implementing smart contracts on this network, BCTestingPlus ensures secure payment verification and efficient acceptance testing. Crucially, it aligns development and testing teams toward shared objectives and guarantees equitable compensation for their efforts. The experimental results and findings conclusively show that this innovative approach demonstrates that BCTestingPlus significantly enhances transparency, bolsters trust, streamlines coordination, accelerates testing, and secures communication channels for all parties involved in the distributed agile software testing lifecycle. It delivers robust security for both development and testing teams, ultimately transforming the efficiency and reliability of distributed agile software testing. Full article

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

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22 pages, 1595 KiB

Open AccessReview

Machine Learning Applications for Diagnosing Parkinson’s Disease via Speech, Language, and Voice Changes: A Systematic Review

by Mohammad Amran Hossain, Enea Traini and Francesco Amenta

Inventions 2025, 10(4), 48; https://doi.org/10.3390/inventions10040048 - 27 Jun 2025

Parkinson’s disease (PD) is a progressive neurodegenerative disorder leading to movement impairment, cognitive decline, and psychiatric symptoms. Key manifestations of PD include bradykinesia (the slowness of movement), changes in voice or speech, and gait disturbances. The quantification of neurological disorders through voice analysis [...] Read more.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder leading to movement impairment, cognitive decline, and psychiatric symptoms. Key manifestations of PD include bradykinesia (the slowness of movement), changes in voice or speech, and gait disturbances. The quantification of neurological disorders through voice analysis has emerged as a rapidly expanding research domain, offering the potential for non-invasive and large-scale monitoring. This review explores existing research on the application of machine learning (ML) in speech, voice, and language processing for the diagnosis of PD. It comprehensively analyzes current methodologies, highlights key findings and their associated limitations, and proposes strategies to address existing challenges. A systematic review was conducted following PRISMA guidelines. We searched four databases: PubMed, Web of Science, Scopus, and IEEE Xplore. The primary focus was on the diagnosis, detection, or identification of PD through voice, speech, and language characteristics. We included 34 studies that used ML techniques to detect or classify PD based on vocal features. The most used approaches involved free speech and reading-speech tasks. In addition to widely used feature extraction toolkits, several studies implemented custom-built feature sets. Although nearly all studies reported high classification performance, significant limitations were identified, including challenges in comparability and incomplete integration with clinical applications. Emerging trends in this field include the collection of real-world, everyday speech data to facilitate longitudinal tracking and capture participants’ natural behaviors. Another promising direction involves the incorporation of additional modalities alongside voice analysis, which may enhance both analytical performance and clinical applicability. Further research is required to determine optimal methodologies for leveraging speech and voice changes as early biomarkers of PD, thereby enhancing early detection and informing clinical intervention strategies. Full article

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

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20 pages, 332 KiB

Open AccessArticle

Preliminary Considerations on the Co-Production of Biomethane and Ammonia from Algae and Bacteria

by Umberto Lucia and Giulia Grisolia

Inventions 2025, 10(4), 47; https://doi.org/10.3390/inventions10040047 - 26 Jun 2025

Ammonia is a critical compound for numerous industrial processes; however, the conventional methods for its production present substantial environmental challenges. Co-producing biofuels and ammonia from biomass through anaerobic digestion offers a promising alternative to address these concerns. This study presents a theoretical assessment [...] Read more.

Ammonia is a critical compound for numerous industrial processes; however, the conventional methods for its production present substantial environmental challenges. Co-producing biofuels and ammonia from biomass through anaerobic digestion offers a promising alternative to address these concerns. This study presents a theoretical assessment of the co-production of biomethane and ammonia from microalgae and cyanobacteria, utilising water from abandoned mine and quarry pit-lakes—specifically focusing on the Alessandria district as a case study. The analysis is based on the average values reported in the literature for the anaerobic digestion of selected biomass types. The results highlight Arthrospira platensis, Chlamydomonas reinhardtii, Chlorella spp., and Chlorella pyrenoidosa as the most promising species due to their superior yields of both ammonia and biomethane. This work aims to promote new opportunities for repurposing disused mining pit-lakes, contributing to the development of sustainable pathways for the integrated production of biofuels and ammonia. In this context, exploring integrated biorefinery systems within a bio-based economy represents an auspicious direction for future research, potentially enhancing the process efficiency and reducing costs. Full article

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

22 pages, 5083 KiB

Open AccessFeature PaperArticle

Intelligent Mobile-Assisted Language Learning: A Deep Learning Approach for Pronunciation Analysis and Personalized Feedback

by Fengqin Liu, Korawit Orkphol, Natthapon Pannurat, Thanat Sooknuan, Thanin Muangpool, Sanya Kuankid and Montri Phothisonothai

Inventions 2025, 10(4), 46; https://doi.org/10.3390/inventions10040046 - 24 Jun 2025

This paper introduces an innovative mobile-assisted language-learning (MALL) system that harnesses deep learning technology to analyze pronunciation patterns and deliver real-time, personalized feedback. Drawing inspiration from how the human brain processes speech through neural pathways, our system analyzes multiple speech features using spectrograms, [...] Read more.

This paper introduces an innovative mobile-assisted language-learning (MALL) system that harnesses deep learning technology to analyze pronunciation patterns and deliver real-time, personalized feedback. Drawing inspiration from how the human brain processes speech through neural pathways, our system analyzes multiple speech features using spectrograms, mel-frequency cepstral coefficients (MFCCs), and formant frequencies in a manner that mirrors the auditory cortex’s interpretation of sound. The core of our approach utilizes a convolutional neural network (CNN) to classify pronunciation patterns from user-recorded speech. To enhance the assessment accuracy and provide nuanced feedback, we integrated a fuzzy inference system (FIS) that helps learners identify and correct specific pronunciation errors. The experimental results demonstrate that our multi-feature model achieved 82.41% to 90.52% accuracies in accent classification across diverse linguistic contexts. The user testing revealed statistically significant improvements in pronunciation skills, where learners showed a 5–20% enhancement in accuracy after using the system. The proposed MALL system offers a portable, accessible solution for language learners while establishing a foundation for future research in multilingual functionality and mobile platform optimization. By combining advanced speech analysis with intuitive feedback mechanisms, this system addresses a critical challenge in language acquisition and promotes more effective self-directed learning. Full article

(This article belongs to the Special Issue Advances and Innovations in Deep Learning: Unveiling Multidisciplinary Applications and Challenges)

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40 pages, 5775 KiB

Open AccessArticle

Parametric Evaluation of Soil Nail Configurations for Sustainable Excavation Stability Using Finite Element Analysis

by Omid Bahramipour, Reza Moezzi, Farhad Mahmoudi Jalali, Reza Yeganeh Khaksar and Mohammad Gheibi

Inventions 2025, 10(4), 45; https://doi.org/10.3390/inventions10040045 - 24 Jun 2025

The advancement of sustainable infrastructure relies on innovative design and computational modeling techniques to optimize excavation stability. This study introduces a novel approach to soil nail configuration optimization using finite element analysis (FEA) with Plaxis software (V22). Various soil nail parameters—including length, angle, [...] Read more.

The advancement of sustainable infrastructure relies on innovative design and computational modeling techniques to optimize excavation stability. This study introduces a novel approach to soil nail configuration optimization using finite element analysis (FEA) with Plaxis software (V22). Various soil nail parameters—including length, angle, and spacing—were analyzed to achieve the most efficient stabilization while minimizing costs. Results indicate that a 10-degree nail inclination from the horizontal provides an optimal balance between tensile and shear forces, reducing deformation (18.12 mm at 1 m spacing) and enhancing the safety factor (1.52). Increasing nail length significantly improves stability, but with diminishing returns beyond a threshold, while nail diameter shows minimal impact. Soil type also plays a crucial role, with coarse-grained soils (friction angle 35°) demonstrating superior performance compared to fine-grained soils (friction angle 23°). This research contributes to the field of computational modeling and intelligent design by integrating advanced simulation techniques for geotechnical stability analysis, providing an innovative and data-driven framework for parametric evaluation of soil nail configurations. Full article

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

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17 pages, 5666 KiB

Open AccessArticle

Mechatronic and Robotic Systems Utilizing Pneumatic Artificial Muscles as Actuators

by Željko Šitum, Juraj Benić and Mihael Cipek

Inventions 2025, 10(4), 44; https://doi.org/10.3390/inventions10040044 - 23 Jun 2025

This article presents a series of innovative systems developed through student laboratory projects, comprising two autonomous vehicles, a quadrupedal walking robot, an active ankle-foot orthosis, a ball-on-beam balancing mechanism, a ball-on-plate system, and a manipulator arm, all actuated by pneumatic artificial muscles (PAMs). [...] Read more.

This article presents a series of innovative systems developed through student laboratory projects, comprising two autonomous vehicles, a quadrupedal walking robot, an active ankle-foot orthosis, a ball-on-beam balancing mechanism, a ball-on-plate system, and a manipulator arm, all actuated by pneumatic artificial muscles (PAMs). Due to their flexibility, low weight, and compliance, fluidic muscles demonstrate substantial potential for integration into various mechatronic systems, robotic platforms, and manipulators. Their capacity to generate smooth and adaptive motion is particularly advantageous in applications requiring natural and human-like movements, such as rehabilitation technologies and assistive devices. Despite the inherent challenges associated with nonlinear behavior in PAM-actuated control systems, their biologically inspired design remains promising for a wide range of future applications. Potential domains include industrial automation, the automotive and aerospace sectors, as well as sports equipment, medical assistive devices, entertainment systems, and animatronics. The integration of self-constructed laboratory systems powered by PAMs into control systems education provides a comprehensive pedagogical framework that merges theoretical instruction with practical implementation. This methodology enhances the skillset of future engineers by deepening their understanding of core technical principles and equipping them to address emerging challenges in engineering practice. Full article

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

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28 pages, 3797 KiB

Open AccessArticle

Evaluation of Traditional and Data-Driven Algorithms for Energy Disaggregation Under Sampling and Filtering Conditions

by Carlos Rodriguez-Navarro, Francisco Portillo, Isabel Robalo and Alfredo Alcayde

Inventions 2025, 10(3), 43; https://doi.org/10.3390/inventions10030043 - 13 Jun 2025

Cited by 1

Non-intrusive load monitoring (NILM) enables the disaggregation of appliance-level energy consumption from aggregate electrical signals, offering a scalable solution for improving efficiency. This study compared the performance of traditional NILM algorithms (Mean, CO, Hart85, FHMM) and deep neural network-based approaches (DAE, RNN, Seq2Point, [...] Read more.

Non-intrusive load monitoring (NILM) enables the disaggregation of appliance-level energy consumption from aggregate electrical signals, offering a scalable solution for improving efficiency. This study compared the performance of traditional NILM algorithms (Mean, CO, Hart85, FHMM) and deep neural network-based approaches (DAE, RNN, Seq2Point, Seq2Seq, WindowGRU) under various experimental conditions. Factors such as sampling rate, harmonic content, and the application of power filters were analyzed. A key aspect of the evaluation was the difference in testing conditions: while traditional algorithms were evaluated under multiple experimental configurations, deep learning models, due to their extremely high computational cost, were analyzed exclusively under a specific configuration consisting of a 1-s sampling rate, with harmonic content present and without applying power filters. The results confirm that no universally superior algorithm exists, and performance varies depending on the type of appliance and signal conditions. Traditional algorithms are faster and more computationally efficient, making them more suitable for scenarios with limited resources or rapid response requirements. However, significantly more computationally expensive deep learning models showed higher average accuracy (MAE, RMSE, NDE) and event detection capability (F1-SCORE) in the specific configuration in which they were evaluated. These models excel in detailed signal reconstruction and handling harmonics without requiring filtering in this configuration. The selection of the optimal NILM algorithm for real-world applications must consider a balance between desired accuracy, load types, electrical signal characteristics, and crucially, the limitations of available computational resources. Full article

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

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31 pages, 4042 KiB

Open AccessArticle

A Smart Hydration Device for Children: Leveraging TRIZ Methodology to Combat Dehydration and Enhance Cognitive Performance

by Robin Edmund Jin Hong Tan, Way Soong Lim, Chai Hua Tay, Kia Wai Liew, Jian Ai Yeow, Peng Lean Chong and Yu Jin Ng

Inventions 2025, 10(3), 42; https://doi.org/10.3390/inventions10030042 - 5 Jun 2025

Amid globalization and rising global temperatures, dehydration has emerged as a critical issue, especially for children who are more vulnerable due to their higher body surface-to-weight ratio. The issue is even more concerning given that adequate water intake is important for cognitive development, [...] Read more.

Amid globalization and rising global temperatures, dehydration has emerged as a critical issue, especially for children who are more vulnerable due to their higher body surface-to-weight ratio. The issue is even more concerning given that adequate water intake is important for cognitive development, particularly in children since brain development is critical during early years. This study addressed this challenge by, first, designing a smart hydration device based on the Theory of Inventive Problem Solving (TRIZ). Then, this study proceeded with prototyping and testing the smart hydration device to promote increased daily water intake among Malaysian children. The device demonstrated improved water consumption and increased drinking frequency among children. Additionally, the children displayed improved cognitive performance. However, this study was limited to a specific age group and the device requires adult supervision for charging. Therefore, further research is necessary to tackle these limitations. Nevertheless, this smart device represents a promising step forward in fostering better hydration habits among children. Full article

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

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24 pages, 994 KiB

Open AccessReview

Acoustic Energy Harvested Wireless Sensing for Aquaculture Monitoring

by Zhencan Yang, Longgang Ma, Ruihua Zhang, Jiawei Zhang, Feng Liu and Xinqing Xiao

Inventions 2025, 10(3), 41; https://doi.org/10.3390/inventions10030041 - 5 Jun 2025

As society develops, the aquaculture industry faces challenges such as environmental changes and water contamination. Water quality monitoring and preventive measures have become essential to prevent property losses. Traditional water quality monitoring methods rely on manual sampling and laboratory analysis, which are inefficient [...] Read more.

As society develops, the aquaculture industry faces challenges such as environmental changes and water contamination. Water quality monitoring and preventive measures have become essential to prevent property losses. Traditional water quality monitoring methods rely on manual sampling and laboratory analysis, which are inefficient and costly. Additionally, the operational lifespan of conventional water quality sensors is limited by battery capacity, making long-term and continuous monitoring difficult to ensure. This review focuses on water quality sensor systems and provides a comprehensive analysis of self-powered schemes utilizing acoustic energy harvesting technology. It comprehensively discusses the overall architecture of self-powered sensors, energy harvesting principles, piezoelectric transducer mechanisms, and wireless transmission technologies. It also covers acoustic energy enhancement devices and the types and development status of piezoelectric materials used for acoustic energy harvesting. Furthermore, the review systematically summarizes and analyses the current applications of these sensors in aquaculture monitoring and evaluates their advantages, disadvantages, and prospects. Full article

(This article belongs to the Special Issue Inventions and Innovation in Smart Sensing Technologies for Agriculture)

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