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Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

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17 pages, 9779 KB  
Article
Optimizing Speech Emotion Recognition with Machine Learning Based Advanced Audio Cue Analysis
by Nuwan Pallewela, Damminda Alahakoon, Achini Adikari, John E. Pierce and Miranda L. Rose
Technologies 2024, 12(7), 111; https://doi.org/10.3390/technologies12070111 - 11 Jul 2024
Cited by 10 | Viewed by 5917
Abstract
In today’s fast-paced and interconnected world, where human–computer interaction is an integral component of daily life, the ability to recognize and understand human emotions has emerged as a crucial facet of technological advancement. However, human emotion, a complex interplay of physiological, psychological, and [...] Read more.
In today’s fast-paced and interconnected world, where human–computer interaction is an integral component of daily life, the ability to recognize and understand human emotions has emerged as a crucial facet of technological advancement. However, human emotion, a complex interplay of physiological, psychological, and social factors, poses a formidable challenge even for other humans to comprehend accurately. With the emergence of voice assistants and other speech-based applications, it has become essential to improve audio-based emotion expression. However, there is a lack of specificity and agreement in current emotion annotation practice, as evidenced by conflicting labels in many human-annotated emotional datasets for the same speech segments. Previous studies have had to filter out these conflicts and, therefore, a large portion of the collected data has been considered unusable. In this study, we aimed to improve the accuracy of computational prediction of uncertain emotion labels by utilizing high-confidence emotion labelled speech segments from the IEMOCAP emotion dataset. We implemented an audio-based emotion recognition model using bag of audio word encoding (BoAW) to obtain a representation of audio aspects of emotion in speech with state-of-the-art recurrent neural network models. Our approach improved the state-of-the-art audio-based emotion recognition with a 61.09% accuracy rate, an improvement of 1.02% over the BiDialogueRNN model and 1.72% over the EmoCaps multi-modal emotion recognition models. In comparison to human annotation, our approach achieved similar results in identifying positive and negative emotions. Furthermore, it has proven effective in accurately recognizing the sentiment of uncertain emotion segments that were previously considered unusable in other studies. Improvements in audio emotion recognition could have implications in voice-based assistants, healthcare, and other industrial applications that benefit from automated communication. Full article
(This article belongs to the Topic Theoretical and Applied Problems in Human-Computer Intelligent Systems)
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20 pages, 4441 KB  
Article
Adsorption of HFO-1234ze(E) onto Steam-Activated Carbon Derived from Sawmill Waste Wood
by Huiyuan Bao, Md. Amirul Islam and Bidyut Baran Saha
Technologies 2024, 12(7), 104; https://doi.org/10.3390/technologies12070104 - 5 Jul 2024
Cited by 8 | Viewed by 2771
Abstract
This study utilizes waste Albizia lebbeck wood from a sawmill to prepare activated carbon adsorbents and explores their potential application in adsorption cooling systems with a novel hydrofluoroolefin (HFO) refrigerant characterized by a low global warming potential. Activated carbon was synthesized through a [...] Read more.
This study utilizes waste Albizia lebbeck wood from a sawmill to prepare activated carbon adsorbents and explores their potential application in adsorption cooling systems with a novel hydrofluoroolefin (HFO) refrigerant characterized by a low global warming potential. Activated carbon was synthesized through a simple and green steam activation method, and the optimal carbon shows a specific surface area of 946.8 m2/g and a pore volume of 0.843 cm3/g. The adsorption isotherms of HFO-1234ze(E) (Trans-1,3,3,3-tetrafluoropropene) on the activated carbon were examined at 30, 40, and 50 °C up to 400 kPa using a customized constant-volume variable-pressure system, and significant adsorption of 1.041 kg kg−1 was achieved at 30 °C and 400 kPa. The experimental data were fitted using both the Dubinin–Astakhov and Tóth models, and both models provided excellent fit results. The D–A adsorption model simulated the net adsorption capacity at possible operating temperatures. The isosteric of adsorption was determined using the Clausius–Clapeyron and modified Dubinin–Astakhov equations. In addition, the specific cooling effect and coefficient of performance were also studied. Full article
(This article belongs to the Special Issue Recent Advances in Applied Activated Carbon Research)
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21 pages, 10290 KB  
Article
Smartphone-Based Citizen Science Tool for Plant Disease and Insect Pest Detection Using Artificial Intelligence
by Panagiotis Christakakis, Garyfallia Papadopoulou, Georgios Mikos, Nikolaos Kalogiannidis, Dimosthenis Ioannidis, Dimitrios Tzovaras and Eleftheria Maria Pechlivani
Technologies 2024, 12(7), 101; https://doi.org/10.3390/technologies12070101 - 3 Jul 2024
Cited by 37 | Viewed by 15829
Abstract
In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases [...] Read more.
In recent years, the integration of smartphone technology with novel sensing technologies, Artificial Intelligence (AI), and Deep Learning (DL) algorithms has revolutionized crop pest and disease surveillance. Efficient and accurate diagnosis is crucial to mitigate substantial economic losses in agriculture caused by diseases and pests. An innovative Apple® and Android™ mobile application for citizen science has been developed, to enable real-time detection and identification of plant leaf diseases and pests, minimizing their impact on horticulture, viticulture, and olive cultivation. Leveraging DL algorithms, this application facilitates efficient data collection on crop pests and diseases, supporting crop yield protection and cost reduction in alignment with the Green Deal goal for 2030 by reducing pesticide use. The proposed citizen science tool involves all Farm to Fork stakeholders and farm citizens in minimizing damage to plant health by insect and fungal diseases. It utilizes comprehensive datasets, including images of various diseases and insects, within a robust Decision Support System (DSS) where DL models operate. The DSS connects directly with users, allowing them to upload crop pest data via the mobile application, providing data-driven support and information. The application stands out for its scalability and interoperability, enabling the continuous integration of new data to enhance its capabilities. It supports AI-based imaging analysis of quarantine pests, invasive alien species, and emerging and native pests, thereby aiding post-border surveillance programs. The mobile application, developed using a Python-based REST API, PostgreSQL, and Keycloak, has been field-tested, demonstrating its effectiveness in real-world agriculture scenarios, such as detecting Tuta absoluta (Meyrick) infestation in tomato cultivations. The outcomes of this study in T. absoluta detection serve as a showcase scenario for the proposed citizen science tool’s applicability and usability, demonstrating a 70.2% accuracy (mAP50) utilizing advanced DL models. Notably, during field testing, the model achieved detection confidence levels of up to 87%, enhancing pest management practices. Full article
(This article belongs to the Section Information and Communication Technologies)
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16 pages, 12863 KB  
Article
Multi-Objective Optimisation of the Battery Box in a Racing Car
by Chao Ma, Caiqi Xu, Mohammad Souri, Elham Hosseinzadeh and Masoud Jabbari
Technologies 2024, 12(7), 93; https://doi.org/10.3390/technologies12070093 - 25 Jun 2024
Cited by 3 | Viewed by 3532
Abstract
The optimisation of electric vehicle battery boxes while preserving their structural performance presents a formidable challenge. Many studies typically involve fewer than 10 design variables in their optimisation processes, a deviation from the reality of battery box design scenarios. The present study, for [...] Read more.
The optimisation of electric vehicle battery boxes while preserving their structural performance presents a formidable challenge. Many studies typically involve fewer than 10 design variables in their optimisation processes, a deviation from the reality of battery box design scenarios. The present study, for the first time, attempts to use sensitivity analysis to screen the design variables and achieve an efficient optimisation design with a large number of original design variables. Specifically, the sensitivity analysis method was proposed to screen a certain number of optimisation variables, reducing the computational complexity while ensuring the efficiency of the optimisation process. A combination of the Generalised Regression Neural Network (GRNN) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was employed to construct surrogate models and solve the optimisation problem. The optimisation model integrates these techniques to balance structural performance and weight reduction. The optimisation results demonstrate a significant reduction in battery box weight while maintaining structural integrity. Therefore, the proposed approach in this study provides important insights for achieving high-efficiency multi-objective optimisation of battery box structures. Full article
(This article belongs to the Collection Electrical Technologies)
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19 pages, 3968 KB  
Article
Transformer-Based Water Stress Estimation Using Leaf Wilting Computed from Leaf Images and Unsupervised Domain Adaptation for Tomato Crops
by Makoto Koike, Riku Onuma, Ryo Adachi and Hiroshi Mineno
Technologies 2024, 12(7), 94; https://doi.org/10.3390/technologies12070094 - 25 Jun 2024
Cited by 10 | Viewed by 3660
Abstract
Modern agriculture faces the dual challenge of ensuring sustainability while meeting the growing global demand for food. Smart agriculture, which uses data from the environment and plants to deliver water exactly when and how it is needed, has attracted significant attention. This approach [...] Read more.
Modern agriculture faces the dual challenge of ensuring sustainability while meeting the growing global demand for food. Smart agriculture, which uses data from the environment and plants to deliver water exactly when and how it is needed, has attracted significant attention. This approach requires precise water management and highly accurate real-time monitoring of crop water stress. Existing monitoring methods pose challenges such as the risk of plant damage, costly sensors, and the need for expert adjustments. Therefore, a low-cost, highly accurate water stress estimation model was developed that uses deep learning and commercially available sensors. The model uses the relative stem diameter as a water stress index and incorporates data from environmental sensors and an RGB camera, which are processed by the proposed daily normalization. In addition, domain adaptation in our Transformer model was implemented to enable robust learning in different areas. The accuracy of the model was evaluated using real cultivation data from tomato crops, achieving a coefficient of determination (R2) of 0.79 in water stress estimation. Furthermore, the model maintained a high level of accuracy when applied to different areas, with an R2 of 0.76, demonstrating its high adaptability under different conditions. Full article
(This article belongs to the Section Environmental Technology)
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27 pages, 13538 KB  
Article
A New LCL Filter Design Method for Single-Phase Photovoltaic Systems Connected to the Grid via Micro-Inverters
by Heriberto Adamas-Pérez, Mario Ponce-Silva, Jesús Darío Mina-Antonio, Abraham Claudio-Sánchez, Omar Rodríguez-Benítez and Oscar Miguel Rodríguez-Benítez
Technologies 2024, 12(6), 89; https://doi.org/10.3390/technologies12060089 - 12 Jun 2024
Cited by 20 | Viewed by 7627
Abstract
This paper aims to propose a new sizing approach to reduce the footprint and optimize the performance of an LCL filter implemented in photovoltaic systems using grid-connected single-phase microinverters. In particular, the analysis is carried out on a single-phase full-bridge inverter, assuming the [...] Read more.
This paper aims to propose a new sizing approach to reduce the footprint and optimize the performance of an LCL filter implemented in photovoltaic systems using grid-connected single-phase microinverters. In particular, the analysis is carried out on a single-phase full-bridge inverter, assuming the following two conditions: (1) a unit power factor at the connection point between the AC grid and the LCL filter; (2) a control circuit based on unipolar sinusoidal pulse width modulation (SPWM). In particular, the ripple and harmonics of the LCL filter input current and the current injected into the grid are analyzed. The results of the Simulink simulation and the experimental tests carried out confirm that it is possible to considerably reduce filter volume by optimizing each passive component compared with what is already available in the literature while guaranteeing excellent filtering performance. Specifically, the inductance values were reduced by almost 40% and the capacitor value by almost 100%. The main applications of this new design methodology are for use in single-phase microinverters connected to the grid and for research purposes in power electronics and optimization. Full article
(This article belongs to the Topic Advances in Solar Technologies)
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24 pages, 8552 KB  
Article
Path Planning for Autonomous Mobile Robot Using Intelligent Algorithms
by Jorge Galarza-Falfan, Enrique Efrén García-Guerrero, Oscar Adrian Aguirre-Castro, Oscar Roberto López-Bonilla, Ulises Jesús Tamayo-Pérez, José Ricardo Cárdenas-Valdez, Carlos Hernández-Mejía, Susana Borrego-Dominguez and Everardo Inzunza-Gonzalez
Technologies 2024, 12(6), 82; https://doi.org/10.3390/technologies12060082 - 3 Jun 2024
Cited by 17 | Viewed by 8742
Abstract
Machine learning technologies are being integrated into robotic systems faster to enhance their efficacy and adaptability in dynamic environments. The primary goal of this research was to propose a method to develop an Autonomous Mobile Robot (AMR) that integrates Simultaneous Localization and Mapping [...] Read more.
Machine learning technologies are being integrated into robotic systems faster to enhance their efficacy and adaptability in dynamic environments. The primary goal of this research was to propose a method to develop an Autonomous Mobile Robot (AMR) that integrates Simultaneous Localization and Mapping (SLAM), odometry, and artificial vision based on deep learning (DL). All are executed on a high-performance Jetson Nano embedded system, specifically emphasizing SLAM-based obstacle avoidance and path planning using the Adaptive Monte Carlo Localization (AMCL) algorithm. Two Convolutional Neural Networks (CNNs) were selected due to their proven effectiveness in image and pattern recognition tasks. The ResNet18 and YOLOv3 algorithms facilitate scene perception, enabling the robot to interpret its environment effectively. Both algorithms were implemented for real-time object detection, identifying and classifying objects within the robot’s environment. These algorithms were selected to evaluate their performance metrics, which are critical for real-time applications. A comparative analysis of the proposed DL models focused on enhancing vision systems for autonomous mobile robots. Several simulations and real-world trials were conducted to evaluate the performance and adaptability of these models in navigating complex environments. The proposed vision system with CNN ResNet18 achieved an average accuracy of 98.5%, a precision of 96.91%, a recall of 97%, and an F1-score of 98.5%. However, the YOLOv3 model achieved an average accuracy of 96%, a precision of 96.2%, a recall of 96%, and an F1-score of 95.99%. These results underscore the effectiveness of the proposed intelligent algorithms, robust embedded hardware, and sensors in robotic applications. This study proves that advanced DL algorithms work well in robots and could be used in many fields, such as transportation and assembly. As a consequence of the findings, intelligent systems could be implemented more widely in the operation and development of AMRs. Full article
(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)
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19 pages, 3842 KB  
Article
Intelligent Cane for Assisting the Visually Impaired
by Claudiu-Eugen Panazan and Eva-Henrietta Dulf
Technologies 2024, 12(6), 75; https://doi.org/10.3390/technologies12060075 - 27 May 2024
Cited by 27 | Viewed by 27700
Abstract
Those with visual impairments, including complete blindness or partial sight loss, constitute a significant global population. According to estimates by the World Health Organization (WHO), there are at least 2.2 billion people worldwide who have near or distance vision disorders. Addressing their needs [...] Read more.
Those with visual impairments, including complete blindness or partial sight loss, constitute a significant global population. According to estimates by the World Health Organization (WHO), there are at least 2.2 billion people worldwide who have near or distance vision disorders. Addressing their needs is crucial. Introducing a smart cane tailored for the blind can greatly improve their daily lives. This paper introduces a significant technical innovation, presenting a smart cane equipped with dual ultrasonic sensors for obstacle detection, catering to the visually impaired. The primary focus is on developing a versatile device capable of operating in diverse conditions, ensuring efficient obstacle alerts. The strategic placement of ultrasonic sensors facilitates the emission and measurement of high-frequency sound waves, calculating obstacle distances and assessing potential threats to the user. Addressing various obstacle types, two ultrasonic sensors handle overhead and ground-level barriers, ensuring precise warnings. With a detection range spanning 2 to 400 cm, the device provides timely information for user reaction. Dual alert methods, including vibrations and audio signals, offer flexibility to users, controlled through intuitive switches. Additionally, a Bluetooth-connected mobile app enhances functionality, activating audio alerts if the cane is misplaced or too distant. Cost-effective implementation enhances accessibility, supporting a broader user base. This innovative smart cane not only represents a technical achievement but also significantly improves the quality of life for visually impaired individuals, emphasizing the social impact of technology. The research underscores the importance of technological research in addressing societal challenges and highlights the need for solutions that positively impact vulnerable communities, shaping future directions in research and technological development. Full article
(This article belongs to the Section Assistive Technologies)
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10 pages, 1848 KB  
Article
Speckle Plethysmograph-Based Blood Pressure Assessment
by Floranne T. Ellington, Anh Nguyen, Mao-Hsiang Huang, Tai Le, Bernard Choi and Hung Cao
Technologies 2024, 12(5), 70; https://doi.org/10.3390/technologies12050070 - 18 May 2024
Cited by 1 | Viewed by 5433
Abstract
Continuous non-invasive blood pressure (CNBP) monitoring is of the utmost importance in detecting and managing hypertension, a leading cause of death in the United States. Extensive research has delved into pioneering methods for predicting systolic and diastolic blood pressure values by leveraging pulse [...] Read more.
Continuous non-invasive blood pressure (CNBP) monitoring is of the utmost importance in detecting and managing hypertension, a leading cause of death in the United States. Extensive research has delved into pioneering methods for predicting systolic and diastolic blood pressure values by leveraging pulse arrival time (PAT), the time difference between the proximal and distal signal peaks. The most widely employed pairing involves electrocardiography (ECG) and photoplethysmography (PPG). Possessing similar characteristics in terms of measuring blood flow changes, a recently investigated optical signal known as speckleplethysmography (SPG) showed its stability and high signal-to-noise ratio compared with PPG. Thus, SPG is a potential surrogate to pair with ECG for CNBP estimation. The present study aims to unlock the untapped potential of SPG as a signal for non-invasive blood pressure monitoring based on PAT. To ascertain SPG’s capabilities, eight subjects were enrolled in multiple recording sessions. A third-party device was employed for ECG and PPG measurements, while a commercial device served as the reference for arterial blood pressure (ABP). SPG measurements were obtained using a prototype smartphone-based system. Following the completion of three scenarios—sitting, walking, and running—the subjects’ signals and ABP were recorded to investigate the predictive capacity of systolic blood pressure. The collected data were processed and prepared for machine learning models, including support vector regression and decision tree regression. The models’ effectiveness was evaluated using root-mean-square error and mean absolute percentage error. In most instances, predictions utilizing PATSPG exhibited comparable or superior performance to PATPPG (i.e., SPG Rest ± 12.4 mmHg vs. PPG Rest ± 13.7 mmHg for RSME, and SPG 8% vs. PPG 9% for MAPE). Furthermore, incorporating an additional feature, namely the previous SBP value, resulted in reduced prediction errors for both signals in multiple model configurations (i.e., SPG Rest ± 12.4 mmHg to ±3.7 mmHg for RSME, and SPG Rest 8% to 3% for MAPE). These preliminary tests of SPG underscore the remarkable potential of this novel signal in PAT-based blood pressure predictions. Subsequent studies involving a larger cohort of test subjects and advancements in the SPG acquisition system hold promise for further improving the effectiveness of this newly explored signal in blood pressure monitoring. Full article
(This article belongs to the Topic Smart Healthcare: Technologies and Applications)
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20 pages, 1173 KB  
Article
Application and Challenges of the Technology Acceptance Model in Elderly Healthcare: Insights from ChatGPT
by Sang Dol Kim
Technologies 2024, 12(5), 68; https://doi.org/10.3390/technologies12050068 - 13 May 2024
Cited by 39 | Viewed by 17721
Abstract
The Technology Acceptance Model (TAM) plays a pivotal role in elderly healthcare, serving as a theoretical framework. This study aimed to identify TAM’s core components, practical applications, challenges arising from its applications, and propose countermeasures in elderly healthcare. This descriptive study was conducted [...] Read more.
The Technology Acceptance Model (TAM) plays a pivotal role in elderly healthcare, serving as a theoretical framework. This study aimed to identify TAM’s core components, practical applications, challenges arising from its applications, and propose countermeasures in elderly healthcare. This descriptive study was conducted by utilizing OpenAI’s ChatGPT, with an access date of 10 January 2024. The three open-ended questions administered to ChatGPT and its responses were collected and qualitatively evaluated for reliability through previous studies. The core components of TAMs were identified as perceived usefulness, perceived ease of use, attitude toward use, behavioral intention to use, subjective norms, image, and facilitating conditions. TAM’s application areas span various technologies in elderly healthcare, such as telehealth, wearable devices, mobile health apps, and more. Challenges arising from TAM applications include technological literacy barriers, digital divide concerns, privacy and security apprehensions, resistance to change, limited awareness and information, health conditions and cognitive impairment, trust and reliability concerns, a lack of tailored interventions, overcoming age stereotypes, and integration with traditional healthcare. In conclusion, customized interventions are crucial for successful tech acceptance among the elderly population. The findings of this study are expected to enhance understanding of elderly healthcare and technology adoption, with insights gained through natural language processing models like ChatGPT anticipated to provide a fresh perspective. Full article
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25 pages, 4707 KB  
Article
Digital Twin Models for Personalised and Predictive Medicine in Ophthalmology
by Miruna-Elena Iliuţă, Mihnea-Alexandru Moisescu, Simona-Iuliana Caramihai, Alexandra Cernian, Eugen Pop, Daniel-Ioan Chiş and Traian-Costin Mitulescu
Technologies 2024, 12(4), 55; https://doi.org/10.3390/technologies12040055 - 18 Apr 2024
Cited by 24 | Viewed by 7997
Abstract
This article explores the integration of Digital Twins in Systems and Predictive Medicine, focusing on eye diagnosis. By utilizing the Digital Twin models, the proposed framework can support early diagnosis and predict evolution after treatment by providing customized simulation scenarios. Furthermore, a structured [...] Read more.
This article explores the integration of Digital Twins in Systems and Predictive Medicine, focusing on eye diagnosis. By utilizing the Digital Twin models, the proposed framework can support early diagnosis and predict evolution after treatment by providing customized simulation scenarios. Furthermore, a structured architectural framework comprising five levels has been proposed, integrating Digital Twin, Systems Medicine, and Predictive Medicine for managing eye diseases. Based on demographic parameters, statistics were performed to identify potential correlations that may contribute to predispositions to glaucoma. With the aid of a dataset, a neural network was trained with the goal of identifying glaucoma. This comprehensive approach, based on statistical analysis and Machine Learning, is a promising method to enhance diagnostic accuracy and provide personalized treatment approaches. Full article
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22 pages, 3814 KB  
Article
Experimental and Numerical Analysis of a Novel Cycloid-Type Rotor versus S-Type Rotor for Vertical-Axis Wind Turbine
by José Eli Eduardo González-Durán, Juan Manuel Olivares-Ramírez, María Angélica Luján-Vega, Juan Emigdio Soto-Osornio, Juan Manuel García-Guendulain and Juvenal Rodriguez-Resendiz
Technologies 2024, 12(4), 54; https://doi.org/10.3390/technologies12040054 - 17 Apr 2024
Cited by 6 | Viewed by 4464
Abstract
The performance of a new vertical-axis wind turbine rotor based on the mathematical equation of the cycloid is analyzed and compared through simulation and experimental testing against a semicircular or S-type rotor, which is widely used. The study examines three cases: equalizing the [...] Read more.
The performance of a new vertical-axis wind turbine rotor based on the mathematical equation of the cycloid is analyzed and compared through simulation and experimental testing against a semicircular or S-type rotor, which is widely used. The study examines three cases: equalizing the diameter, chord length and the area under the curve. Computational Fluid Dynamics (CFD) was used to simulate these cases and evaluate moment, angular velocity and power. Experimental validation was carried out in a wind tunnel that was designed and optimized with the support of CFD. The rotors for all three cases were 3D printed in resin to analyze their experimental performance as a function of wind speed. The moment and Maximum Power Point (MPP) were determined in each case. The simulation results indicate that the cycloid-type rotor outperforms the semicircular or S-type rotor by 15%. Additionally, experimental evidence confirms that the cycloid-type rotor performs better in all three cases. In the MPP analysis, the cycloid-type rotor achieved an efficiency of 10.8% which was 38% better than the S-type rotor. Full article
(This article belongs to the Section Environmental Technology)
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14 pages, 6198 KB  
Article
Monitoring of Hip Joint Forces and Physical Activity after Total Hip Replacement by an Integrated Piezoelectric Element
by Franziska Geiger, Henning Bathel, Sascha Spors, Rainer Bader and Daniel Kluess
Technologies 2024, 12(4), 51; https://doi.org/10.3390/technologies12040051 - 9 Apr 2024
Cited by 6 | Viewed by 5416
Abstract
Resultant hip joint forces can currently only be recorded in situ in a laboratory setting using instrumented total hip replacements (THRs) equipped with strain gauges. However, permanent recording is important for monitoring the structural condition of the implant, for therapeutic purposes, for self-reflection, [...] Read more.
Resultant hip joint forces can currently only be recorded in situ in a laboratory setting using instrumented total hip replacements (THRs) equipped with strain gauges. However, permanent recording is important for monitoring the structural condition of the implant, for therapeutic purposes, for self-reflection, and for research into managing the predicted increasing number of THRs worldwide. Therefore, this study aims to investigate whether a recently proposed THR with an integrated piezoelectric element represents a new possibility for the permanent recording of hip joint forces and the physical activities of the patient. Hip joint forces from nine different daily activities were obtained from the OrthoLoad database and applied to a total hip stem equipped with a piezoelectric element using a uniaxial testing machine. The forces acting on the piezoelectric element were calculated from the generated voltages. The correlation between the calculated forces on the piezoelectric element and the applied forces was investigated, and the regression equations were determined. In addition, the voltage outputs were used to predict the activity with a random forest classifier. The coefficient of determination between the applied maximum forces on the implant and the calculated maximum forces on the piezoelectric element was R2 = 0.97 (p < 0.01). The maximum forces on the THR could be determined via activity-independent determinations with a deviation of 0.87 ± 7.29% and activity-dependent calculation with 0.18 ± 3.96% deviation. The activities could be correctly predicted using the classification model with 95% accuracy. Hence, piezoelectric elements integrated into a total hip stem represent a promising sensor option for the energy-autonomous detection of joint forces and physical activities. Full article
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27 pages, 22078 KB  
Article
Numerical Study of the Influence of the Structural Parameters on the Stress Dissipation of 3D Orthogonal Woven Composites under Low-Velocity Impact
by Wang Xu, Mohammed Zikry and Abdel-Fattah M. Seyam
Technologies 2024, 12(4), 49; https://doi.org/10.3390/technologies12040049 - 5 Apr 2024
Cited by 9 | Viewed by 3005
Abstract
This study investigates the effects of the number of layers, x-yarn (weft) density, and z-yarn (binder) path on the mechanical behavior of E-glass 3D orthogonal woven (3DOW) composites during low-velocity impacts. Meso-level finite element (FE) models were developed and validated for 3DOW composites [...] Read more.
This study investigates the effects of the number of layers, x-yarn (weft) density, and z-yarn (binder) path on the mechanical behavior of E-glass 3D orthogonal woven (3DOW) composites during low-velocity impacts. Meso-level finite element (FE) models were developed and validated for 3DOW composites with different yarn densities and z-yarn paths, providing analyses of stress distribution within reinforcement fibers and matrix, energy absorption, and failure time. Our findings revealed that lower x-yarn densities led to accumulations of stress concentrations. Furthermore, changing the z-yarn path, such as transitioning from plain weaves to twill or basket weaves had a noticeable impact on stress distributions. The research highlights the significance of designing more resilient 3DOW composites for impact applications by choosing appropriate parameters in weaving composite designs. Full article
(This article belongs to the Section Innovations in Materials Science and Materials Processing)
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15 pages, 9991 KB  
Article
Carbon Fiber Polymer Reinforced 3D Printed Composites for Centrifugal Pump Impeller Manufacturing
by Gabriel Mansour, Vasileios Papageorgiou and Dimitrios Tzetzis
Technologies 2024, 12(4), 48; https://doi.org/10.3390/technologies12040048 - 3 Apr 2024
Cited by 7 | Viewed by 5371
Abstract
Centrifugal pumps are used extensively in various everyday applications. The occurrence of corrosion phenomena during operation often leads to the failure of a pump’s operating components, such as the impeller. The present research study examines the utilization of composite materials for fabricating centrifugal [...] Read more.
Centrifugal pumps are used extensively in various everyday applications. The occurrence of corrosion phenomena during operation often leads to the failure of a pump’s operating components, such as the impeller. The present research study examines the utilization of composite materials for fabricating centrifugal pump components using additive manufacturing as an effort to fabricate corrosion resistant parts. To achieve the latter two nanocomposite materials, carbon fiber reinforced polyamide and carbon fiber reinforced polyphenylene sulfide were compared with two metal alloys, cast iron and brass, which are currently used in pump impeller manufacturing. The mechanical properties of the materials are extracted by performing a series of experiments, such as uniaxial tensile tests, nanoindentation and scanning electron microscope (SEM) examination of the specimen’s fracture area. Then, computational fluid dynamics (CFD) analysis is performed using various impeller designs to determine the fluid pressure exerted on the impeller’s geometry during its operation. Finally, the maximum power rating of an impeller that can be made from such composites will be determined using a static finite element model (FEM). The FEM static model is developed by integrating the data collected from the experiments with the results obtained from the CFD analysis. The current research work shows that nanocomposites can potentially be used for developing impellers with rated power of up to 9.41 kW. Full article
(This article belongs to the Section Manufacturing Technology)
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16 pages, 371 KB  
Article
Impact Localization for Haptic Input Devices Using Hybrid Laminates with Sensoric Function
by René Schmidt, Alexander Graf, Ricardo Decker, Stephan Lede, Verena Kräusel, Lothar Kroll and Wolfram Hardt
Technologies 2024, 12(4), 47; https://doi.org/10.3390/technologies12040047 - 1 Apr 2024
Viewed by 4331
Abstract
The required energy savings can be achieved in all automotive domains through weight savings and the merging of manufacturing processes in production. This fact is taken into account through functional integration in lightweight materials and manufacturing in a process close to large-scale production. [...] Read more.
The required energy savings can be achieved in all automotive domains through weight savings and the merging of manufacturing processes in production. This fact is taken into account through functional integration in lightweight materials and manufacturing in a process close to large-scale production. In previous work, separate steps of a process chain for manufacturing a center console cover utilizing a sensoric hybrid laminate have been developed and evaluated. This includes the process steps of joining, forming and inline polarization as well as connecting to an embedded system. This work continues the research process by evaluating impact localization methods to use the center console as a haptic input device. For this purpose, different deep learning methods are derived from the state of the art and analyzed for their applicability in two consecutive studies. The results show that MLPs, LSTMs, GRUs and CNNs are suitable to localize impacts on the novel laminate with high localization rates of up to 99%, and thus the usability of the developed laminate as a haptic input device has been proven. Full article
(This article belongs to the Section Innovations in Materials Science and Materials Processing)
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15 pages, 2761 KB  
Article
Enhancing Patient Care in Radiotherapy: Proof-of-Concept of a Monitoring Tool
by Guillaume Beldjoudi, Rémi Eugène, Vincent Grégoire and Ronan Tanguy
Technologies 2024, 12(4), 46; https://doi.org/10.3390/technologies12040046 - 29 Mar 2024
Viewed by 3771
Abstract
Introduction: A monitoring tool, named Oncology Data Management (ODM), was developed in radiotherapy to generate structured information based on data contained in an Oncology Information System (OIS). This study presents the proof-of-concept of the ODM tool and highlights its applications to enhance patient [...] Read more.
Introduction: A monitoring tool, named Oncology Data Management (ODM), was developed in radiotherapy to generate structured information based on data contained in an Oncology Information System (OIS). This study presents the proof-of-concept of the ODM tool and highlights its applications to enhance patient care in radiotherapy. Material & Methods: ODM is a sophisticated SQL query which extracts specific features from the Mosaiq OIS (Elekta, UK) database into an independent structured database. Data from 2016 to 2022 was extracted to enable monitoring of treatment units and evaluation of the quality of patient care. Results: A total of 25,259 treatments were extracted. Treatment machine monitoring revealed a daily 11-treatement difference between two units. ODM showed that the unit with fewer daily treatments performed more complex treatments on diverse locations. In 2019, the implementation of ODM led to the definition of quality indicators and in organizational changes that improved the quality of care. As consequences, for palliative treatments, there was an improvement in the proportion of treatments prepared within 7 calendar days between the scanner and the first treatment session (29.1% before 2020, 40.4% in 2020 and 46.4% after 2020). The study of fractionation in breast treatments exhibited decreased prescription variability after 2019, with distinct patient age categories. Bi-fractionation once a week for larynx prescriptions of 35 × 2.0 Gy achieved an overall treatment duration of 47.0 ± 3.0 calendar days in 2022. Conclusions: ODM enables data extraction from the OIS and provides quantitative tools for improving organization of a department and the quality of patient care in radiotherapy. Full article
(This article belongs to the Topic Smart Healthcare: Technologies and Applications)
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21 pages, 799 KB  
Article
An Artificial Bee Colony Algorithm for Coordinated Scheduling of Production Jobs and Flexible Maintenance in Permutation Flowshops
by Asma Ladj, Fatima Benbouzid-Si Tayeb, Alaeddine Dahamni and Mohamed Benbouzid
Technologies 2024, 12(4), 45; https://doi.org/10.3390/technologies12040045 - 25 Mar 2024
Cited by 6 | Viewed by 3480
Abstract
This research work addresses the integrated scheduling of jobs and flexible (non-systematic) maintenance interventions in permutation flowshop production systems. We propose a coordinated model in which the time intervals between successive maintenance tasks as well as their number are assumed to be non-fixed [...] Read more.
This research work addresses the integrated scheduling of jobs and flexible (non-systematic) maintenance interventions in permutation flowshop production systems. We propose a coordinated model in which the time intervals between successive maintenance tasks as well as their number are assumed to be non-fixed for each machine on the shopfloor. With such a flexible nature of maintenance activities, the resulting joint schedule is more practical and representative of real-world scenarios. Our goal is to determine the best job permutation in which flexible maintenance activities are properly incorporated. To tackle the NP-hard nature of this problem, an artificial bee colony (ABC) algorithm is developed to minimize the total production time (Makespan). Experiments are conducted utilizing well-known Taillard’s benchmarks, enriched with maintenance data, to compare the proposed algorithm performance against the variable neighbourhood search (VNS) method from the literature. Computational results demonstrate the effectiveness of the proposed algorithm in terms of both solution quality and computational times. Full article
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31 pages, 1533 KB  
Review
Applied Deep Learning-Based Crop Yield Prediction: A Systematic Analysis of Current Developments and Potential Challenges
by Khadija Meghraoui, Imane Sebari, Juergen Pilz, Kenza Ait El Kadi and Saloua Bensiali
Technologies 2024, 12(4), 43; https://doi.org/10.3390/technologies12040043 - 24 Mar 2024
Cited by 70 | Viewed by 17955
Abstract
Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers’ experiences, tend to be error-prone and lack precision across vast [...] Read more.
Agriculture is essential for global income, poverty reduction, and food security, with crop yield being a crucial measure in this field. Traditional crop yield prediction methods, reliant on subjective assessments such as farmers’ experiences, tend to be error-prone and lack precision across vast farming areas, especially in data-scarce regions. Recent advancements in data collection, notably through high-resolution sensors and the use of deep learning (DL), have significantly increased the accuracy and breadth of agricultural data, providing better support for policymakers and administrators. In our study, we conduct a systematic literature review to explore the application of DL in crop yield forecasting, underscoring its growing significance in enhancing yield predictions. Our approach enabled us to identify 92 relevant studies across four major scientific databases: the Directory of Open Access Journals (DOAJ), the Institute of Electrical and Electronics Engineers (IEEE), the Multidisciplinary Digital Publishing Institute (MDPI), and ScienceDirect. These studies, all empirical research published in the last eight years, met stringent selection criteria, including empirical validity, methodological clarity, and a minimum quality score, ensuring their rigorous research standards and relevance. Our in-depth analysis of these papers aimed to synthesize insights on the crops studied, DL models utilized, key input data types, and the specific challenges and prerequisites for accurate DL-based yield forecasting. Our findings reveal that convolutional neural networks and Long Short-Term Memory are the dominant deep learning architectures in crop yield prediction, with a focus on cereals like wheat (Triticum aestivum) and corn (Zea mays). Many studies leverage satellite imagery, but there is a growing trend towards using Unmanned Aerial Vehicles (UAVs) for data collection. Our review synthesizes global research, suggests future directions, and highlights key studies, acknowledging that results may vary across different databases and emphasizing the need for continual updates due to the evolving nature of the field. Full article
(This article belongs to the Section Information and Communication Technologies)
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27 pages, 4248 KB  
Review
Applications of 3D Reconstruction in Virtual Reality-Based Teleoperation: A Review in the Mining Industry
by Alireza Kamran-Pishhesari, Amin Moniri-Morad and Javad Sattarvand
Technologies 2024, 12(3), 40; https://doi.org/10.3390/technologies12030040 - 15 Mar 2024
Cited by 42 | Viewed by 8175
Abstract
Although multiview platforms have enhanced work efficiency in mining teleoperation systems, they also induce “cognitive tunneling” and depth-detection issues for operators. These issues inadvertently focus their attention on a restricted central view. Fully immersive virtual reality (VR) has recently attracted the attention of [...] Read more.
Although multiview platforms have enhanced work efficiency in mining teleoperation systems, they also induce “cognitive tunneling” and depth-detection issues for operators. These issues inadvertently focus their attention on a restricted central view. Fully immersive virtual reality (VR) has recently attracted the attention of specialists in the mining industry to address these issues. Nevertheless, developing VR teleoperation systems remains a formidable challenge, particularly in achieving a realistic 3D model of the environment. This study investigates the existing gap in fully immersive teleoperation systems within the mining industry, aiming to identify the most optimal methods for their development and ensure operator’s safety. To achieve this purpose, a literature search is employed to identify and extract information from the most relevant sources. The most advanced teleoperation systems are examined by focusing on their visualization types. Then, various 3D reconstruction techniques applicable to mining VR teleoperation are investigated, and their data acquisition methods, sensor technologies, and algorithms are analyzed. Ultimately, the study discusses challenges associated with 3D reconstruction techniques for mining teleoperation. The findings demonstrated that the real-time 3D reconstruction of underground mining environments primarily involves depth-based techniques. In contrast, point cloud generation techniques can mostly be employed for 3D reconstruction in open-pit mining operations. Full article
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15 pages, 3321 KB  
Article
Pioneering a Framework for Robust Telemedicine Technology Assessment (Telemechron Study)
by Sandra Morelli, Carla Daniele, Giuseppe D’Avenio, Mauro Grigioni and Daniele Giansanti
Technologies 2024, 12(3), 37; https://doi.org/10.3390/technologies12030037 - 8 Mar 2024
Cited by 2 | Viewed by 4145
Abstract
The field of technology assessment in telemedicine is garnering increasing attention due to the widespread adoption of this discipline and its complex and heterogeneous system characteristics, making its application complex. As part of a national telemedicine project, the National Center for Innovative Technologies [...] Read more.
The field of technology assessment in telemedicine is garnering increasing attention due to the widespread adoption of this discipline and its complex and heterogeneous system characteristics, making its application complex. As part of a national telemedicine project, the National Center for Innovative Technologies in Public Health at the Italian National Institute of Health played the role of promoting and utilizing technology assessment tools within partnership projects. This study aims to outline the design, development, and application of assessment methodologies within the telemedicine project proposed by the ISS team, utilizing a specific framework developed within the project. The sub-objectives include evaluating the proposed methodology’s effectiveness and feasibility, gathering feedback for improvement, and assessing its impact on various project components. The study emphasizes the multifaceted nature of action domains and underscores the crucial role of technology assessments in telemedicine, highlighting its impact across diverse realms through iterative interaction cycles with project partners. Both the impact and the acceptance of the methodology have been assessed by means of specific computer-aided web interviewing (CAWI) tools. The proposed methodology received significant acceptance, providing valuable insights for refining future frameworks. The impact assessment revealed a consistent quality improvement trend in the project’s products, evident in methodological consolidations. The overall message encourages similar initiatives in this domain, shedding light on the intricacies of technology assessment implementation. In conclusion, the study serves as a comprehensive outcome of the national telemedicine project, witnessing the success and adaptability of the technology assessment methodology and advocating for further exploration and implementation in analogous contexts. Full article
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21 pages, 4650 KB  
Article
Measurement of Light-Duty Vehicle Exhaust Emissions with Light Absorption Spectrometers
by Barouch Giechaskiel, Anastasios Melas, Jacopo Franzetti, Victor Valverde, Michaël Clairotte and Ricardo Suarez-Bertoa
Technologies 2024, 12(3), 32; https://doi.org/10.3390/technologies12030032 - 28 Feb 2024
Cited by 7 | Viewed by 5006
Abstract
Light-duty vehicle emission regulations worldwide set limits for the following gaseous pollutants: carbon monoxide (CO), nitric oxides (NOX), hydrocarbons (HCs), and/or non-methane hydrocarbons (NMHCs). Carbon dioxide (CO2) is indirectly limited by fleet CO2 or fuel consumption targets. Measurements [...] Read more.
Light-duty vehicle emission regulations worldwide set limits for the following gaseous pollutants: carbon monoxide (CO), nitric oxides (NOX), hydrocarbons (HCs), and/or non-methane hydrocarbons (NMHCs). Carbon dioxide (CO2) is indirectly limited by fleet CO2 or fuel consumption targets. Measurements are carried out at the dilution tunnel with “standard” laboratory-grade instruments following well-defined principles of operation: non-dispersive infrared (NDIR) analyzers for CO and CO2, flame ionization detectors (FIDs) for hydrocarbons, and chemiluminescence analyzers (CLAs) or non-dispersive ultraviolet detectors (NDUVs) for NOX. In the United States in 2012 and in China in 2020, with Stage 6, nitrous oxide (N2O) was also included. Brazil is phasing in NH3 in its regulation. Alternative instruments that can measure some or all these pollutants include Fourier transform infrared (FTIR)- and laser absorption spectroscopy (LAS)-based instruments. In the second category, quantum cascade laser (QCL) spectroscopy in the mid-infrared area or laser diode spectroscopy (LDS) in the near-infrared area, such as tunable diode laser absorption spectroscopy (TDLAS), are included. According to current regulations and technical specifications, NH3 is the only component that has to be measured at the tailpipe to avoid ammonia losses due to its hydrophilic properties and adsorption on the transfer lines. There are not many studies that have evaluated such instruments, in particular those for “non-regulated” worldwide pollutants. For this reason, we compared laboratory-grade “standard” analyzers with FTIR- and TDLAS-based instruments measuring NH3. One diesel and two gasoline vehicles at different ambient temperatures and with different test cycles produced emissions in a wide range. In general, the agreement among the instruments was very good (in most cases, within ±10%), confirming their suitability for the measurement of pollutants. Full article
(This article belongs to the Section Environmental Technology)
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26 pages, 6974 KB  
Review
Energy Efficiency in Additive Manufacturing: Condensed Review
by Ismail Fidan, Vivekanand Naikwadi, Suhas Alkunte, Roshan Mishra and Khalid Tantawi
Technologies 2024, 12(2), 21; https://doi.org/10.3390/technologies12020021 - 5 Feb 2024
Cited by 47 | Viewed by 11688
Abstract
Today, it is significant that the use of additive manufacturing (AM) has growing in almost every aspect of the daily life. A high number of sectors are adapting and implementing this revolutionary production technology in their domain to increase production volumes, reduce the [...] Read more.
Today, it is significant that the use of additive manufacturing (AM) has growing in almost every aspect of the daily life. A high number of sectors are adapting and implementing this revolutionary production technology in their domain to increase production volumes, reduce the cost of production, fabricate light weight and complex parts in a short period of time, and respond to the manufacturing needs of customers. It is clear that the AM technologies consume energy to complete the production tasks of each part. Therefore, it is imperative to know the impact of energy efficiency in order to economically and properly use these advancing technologies. This paper provides a holistic review of this important concept from the perspectives of process, materials science, industry, and initiatives. The goal of this research study is to collect and present the latest knowledge blocks related to the energy consumption of AM technologies from a number of recent technical resources. Overall, they are the collection of surveys, observations, experimentations, case studies, content analyses, and archival research studies. The study highlights the current trends and technologies associated with energy efficiency and their influence on the AM community. Full article
(This article belongs to the Collection Review Papers Collection for Advanced Technologies)
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40 pages, 12154 KB  
Review
A Review of Machine Learning and Deep Learning for Object Detection, Semantic Segmentation, and Human Action Recognition in Machine and Robotic Vision
by Nikoleta Manakitsa, George S. Maraslidis, Lazaros Moysis and George F. Fragulis
Technologies 2024, 12(2), 15; https://doi.org/10.3390/technologies12020015 - 23 Jan 2024
Cited by 307 | Viewed by 46482
Abstract
Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics, [...] Read more.
Machine vision, an interdisciplinary field that aims to replicate human visual perception in computers, has experienced rapid progress and significant contributions. This paper traces the origins of machine vision, from early image processing algorithms to its convergence with computer science, mathematics, and robotics, resulting in a distinct branch of artificial intelligence. The integration of machine learning techniques, particularly deep learning, has driven its growth and adoption in everyday devices. This study focuses on the objectives of computer vision systems: replicating human visual capabilities including recognition, comprehension, and interpretation. Notably, image classification, object detection, and image segmentation are crucial tasks requiring robust mathematical foundations. Despite the advancements, challenges persist, such as clarifying terminology related to artificial intelligence, machine learning, and deep learning. Precise definitions and interpretations are vital for establishing a solid research foundation. The evolution of machine vision reflects an ambitious journey to emulate human visual perception. Interdisciplinary collaboration and the integration of deep learning techniques have propelled remarkable advancements in emulating human behavior and perception. Through this research, the field of machine vision continues to shape the future of computer systems and artificial intelligence applications. Full article
(This article belongs to the Collection Review Papers Collection for Advanced Technologies)
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22 pages, 8965 KB  
Article
A Mixed Reality Design System for Interior Renovation: Inpainting with 360-Degree Live Streaming and Generative Adversarial Networks after Removal
by Yuehan Zhu, Tomohiro Fukuda and Nobuyoshi Yabuki
Technologies 2024, 12(1), 9; https://doi.org/10.3390/technologies12010009 - 11 Jan 2024
Cited by 11 | Viewed by 5613
Abstract
In contemporary society, “Indoor Generation” is becoming increasingly prevalent, and spending long periods of time indoors affects well-being. Therefore, it is essential to research biophilic indoor environments and their impact on occupants. When it comes to existing building stocks, which hold significant social, [...] Read more.
In contemporary society, “Indoor Generation” is becoming increasingly prevalent, and spending long periods of time indoors affects well-being. Therefore, it is essential to research biophilic indoor environments and their impact on occupants. When it comes to existing building stocks, which hold significant social, economic, and environmental value, renovation should be considered before new construction. Providing swift feedback in the early stages of renovation can help stakeholders achieve consensus. Additionally, understanding proposed plans can greatly enhance the design of indoor environments. This paper presents a real-time system for architectural designers and stakeholders that integrates mixed reality (MR), diminished reality (DR), and generative adversarial networks (GANs). The system enables the generation of interior renovation drawings based on user preferences and designer styles via GANs. The system’s seamless integration of MR, DR, and GANs provides a unique and innovative approach to interior renovation design. MR and DR technologies then transform these 2D drawings into immersive experiences that help stakeholders evaluate and understand renovation proposals. In addition, we assess the quality of GAN-generated images using full-reference image quality assessment (FR-IQA) methods. The evaluation results indicate that most images demonstrate moderate quality. Almost all objects in the GAN-generated images can be identified by their names and purposes without any ambiguity or confusion. This demonstrates the system’s effectiveness in producing viable renovation visualizations. This research emphasizes the system’s role in enhancing feedback efficiency during renovation design, enabling stakeholders to fully evaluate and understand proposed renovations. Full article
(This article belongs to the Section Construction Technologies)
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18 pages, 3587 KB  
Article
Extended-Window Algorithms for Model Prediction Applied to Hybrid Power Systems
by Fu-Cheng Wang and Hsiao-Tzu Huang
Technologies 2024, 12(1), 6; https://doi.org/10.3390/technologies12010006 - 5 Jan 2024
Cited by 2 | Viewed by 2972
Abstract
This paper proposes extended-window algorithms for model prediction and applies them to optimize hybrid power systems. We consider a hybrid power system comprising solar panels, batteries, a fuel cell, and a chemical hydrogen generation system. The proposed algorithms enable the periodic updating of [...] Read more.
This paper proposes extended-window algorithms for model prediction and applies them to optimize hybrid power systems. We consider a hybrid power system comprising solar panels, batteries, a fuel cell, and a chemical hydrogen generation system. The proposed algorithms enable the periodic updating of prediction models and corresponding changes in system parts and power management based on the accumulated data. We first develop a hybrid power model to evaluate system responses under different conditions. We then build prediction models using five artificial intelligence algorithms. Among them, the light gradient boosting machine and extreme gradient boosting methods achieve the highest accuracies for predicting solar radiation and load responses, respectively. Therefore, we apply these two models to forecast solar and load responses. Third, we introduce extended-window algorithms and investigate the effects of window sizes and replacement costs on system performance. The results show that the optimal window size is one week, and the system cost is 13.57% lower than the cost of the system that does not use the extended-window algorithms. The proposed method also tends to make fewer component replacements when the replacement cost increases. Finally, we design experiments to demonstrate the feasibility and effectiveness of systems using extended-window model prediction. Full article
(This article belongs to the Topic Emerging AI+X Technologies and Applications)
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16 pages, 5042 KB  
Article
Towards a Bidirectional Mexican Sign Language–Spanish Translation System: A Deep Learning Approach
by Jaime-Rodrigo González-Rodríguez, Diana-Margarita Córdova-Esparza, Juan Terven and Julio-Alejandro Romero-González
Technologies 2024, 12(1), 7; https://doi.org/10.3390/technologies12010007 - 5 Jan 2024
Cited by 23 | Viewed by 6565
Abstract
People with hearing disabilities often face communication barriers when interacting with hearing individuals. To address this issue, this paper proposes a bidirectional Sign Language Translation System that aims to bridge the communication gap. Deep learning models such as recurrent neural networks (RNN), bidirectional [...] Read more.
People with hearing disabilities often face communication barriers when interacting with hearing individuals. To address this issue, this paper proposes a bidirectional Sign Language Translation System that aims to bridge the communication gap. Deep learning models such as recurrent neural networks (RNN), bidirectional RNN (BRNN), LSTM, GRU, and Transformers are compared to find the most accurate model for sign language recognition and translation. Keypoint detection using MediaPipe is employed to track and understand sign language gestures. The system features a user-friendly graphical interface with modes for translating between Mexican Sign Language (MSL) and Spanish in both directions. Users can input signs or text and obtain corresponding translations. Performance evaluation demonstrates high accuracy, with the BRNN model achieving 98.8% accuracy. The research emphasizes the importance of hand features in sign language recognition. Future developments could focus on enhancing accessibility and expanding the system to support other sign languages. This Sign Language Translation System offers a promising solution to improve communication accessibility and foster inclusivity for individuals with hearing disabilities. Full article
(This article belongs to the Section Assistive Technologies)
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15 pages, 1887 KB  
Article
Graph Learning and Deep Neural Network Ensemble for Supporting Cognitive Decline Assessment
by Gabriel Antonesi, Alexandru Rancea, Tudor Cioara and Ionut Anghel
Technologies 2024, 12(1), 3; https://doi.org/10.3390/technologies12010003 - 24 Dec 2023
Cited by 4 | Viewed by 4770
Abstract
Cognitive decline represents a significant public health concern due to its severe implications on memory and general health. Early detection is crucial to initiate timely interventions and improve patient outcomes. However, traditional diagnosis methods often rely on personal interpretations or biases, may not [...] Read more.
Cognitive decline represents a significant public health concern due to its severe implications on memory and general health. Early detection is crucial to initiate timely interventions and improve patient outcomes. However, traditional diagnosis methods often rely on personal interpretations or biases, may not detect the early stages of cognitive decline, or involve invasive screening procedures; thus, there is a growing interest in developing non-invasive methods benefiting also from the technological advances. Wearable devices and Internet of Things sensors can monitor various aspects of daily life together with health parameters and can provide valuable data regarding people’s behavior. In this paper, we propose a technical solution that can be useful for potentially supporting cognitive decline assessment in early stages, by employing advanced machine learning techniques for detecting higher activity fragmentation based on daily activity monitoring using wearable devices. Our approach also considers data coming from wellbeing assessment questionnaires that can offer other important insights about a monitored person. We use deep neural network models to capture complex, non-linear relationships in the daily activities data and graph learning for the structural wellbeing information in the questionnaire answers. The proposed solution is evaluated in a simulated environment on a large synthetic dataset, the results showing that our approach can offer an alternative as a support for early detection of cognitive decline during patient-assessment processes. Full article
(This article belongs to the Section Assistive Technologies)
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18 pages, 3118 KB  
Article
Transformative Approach for Heart Rate Prediction from Face Videos Using Local and Global Multi-Head Self-Attention
by Smera Premkumar, J. Anitha, Daniela Danciulescu and D. Jude Hemanth
Technologies 2024, 12(1), 2; https://doi.org/10.3390/technologies12010002 - 22 Dec 2023
Cited by 2 | Viewed by 5077
Abstract
Heart rate estimation from face videos is an emerging technology that offers numerous potential applications in healthcare and human–computer interaction. However, most of the existing approaches often overlook the importance of long-range spatiotemporal dependencies, which is essential for robust measurement of heart rate [...] Read more.
Heart rate estimation from face videos is an emerging technology that offers numerous potential applications in healthcare and human–computer interaction. However, most of the existing approaches often overlook the importance of long-range spatiotemporal dependencies, which is essential for robust measurement of heart rate prediction. Additionally, they involve extensive pre-processing steps to enhance the prediction accuracy, resulting in high computational complexity. In this paper, we propose an innovative solution called LGTransPPG. This end-to-end transformer-based framework eliminates the need for pre-processing steps while achieving improved efficiency and accuracy. LGTransPPG incorporates local and global aggregation techniques to capture fine-grained facial features and contextual information. By leveraging the power of transformers, our framework can effectively model long-range dependencies and temporal dynamics, enhancing the heart rate prediction process. The proposed approach is evaluated on three publicly available datasets, demonstrating its robustness and generalizability. Furthermore, we achieved a high Pearson correlation coefficient (PCC) value of 0.88, indicating its superior efficiency and accuracy between the predicted and actual heart rate values. Full article
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14 pages, 809 KB  
Article
AI-Enabled Compressive Spectrum Classification for Wideband Radios
by Tassadaq Nawaz and Ramasamy Srinivasaga Naidu
Technologies 2023, 11(6), 182; https://doi.org/10.3390/technologies11060182 - 13 Dec 2023
Cited by 3 | Viewed by 3466
Abstract
Cognitive radio is a promising technology that emerged as a potential solution to the spectrum shortage problem by enabling opportunistic spectrum access. In many cases, cognitive radios are required to sense a wide range of frequencies to locate the spectrum white spaces; hence, [...] Read more.
Cognitive radio is a promising technology that emerged as a potential solution to the spectrum shortage problem by enabling opportunistic spectrum access. In many cases, cognitive radios are required to sense a wide range of frequencies to locate the spectrum white spaces; hence, wideband spectrum comes into play, which is also an essential step in future wireless systems to boost the throughput. Cognitive radios are intelligent devices and therefore can be opted for the development of modern jamming and anti-jamming solutions. To this end, our article introduces a novel AI-enabled energy-efficient and robust technique for wideband radio spectrum characterization. Our work considers a wideband radio spectrum made up of numerous narrowband signals, which could be normal communications or signals disrupted by a stealthy jammer. First, the receiver recovers the wideband from significantly low sub-Nyquist rate samples by exploiting compressive sensing technique to decrease the overhead caused by the high complexity analog-to-digital conversion process. Once the wideband is recovered, each available narrowband signal is given to a cyclostationary feature detector that computes the corresponding spectral correlation function and extracts the feature vectors in the form of cycle and frequency profiles. Then profiles are concatenated and given as input features set to an artificial neural network which in turn classifies each NB signal as legitimate communication with a specific modulation or disrupted by a stealthy jammer. The results show a classification accuracy of about 0.99 is achieved. Moreover, the algorithm highlights significantly high performances in comparison to recently reported spectrum classification techniques. The proposed technique can be used to design anti-jamming systems for military communication systems. Full article
(This article belongs to the Special Issue Perpetual Sensor Nodes for Sustainable Wireless Network Applications)
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15 pages, 3226 KB  
Review
Door-Opening Technologies: Search for Affordable Assistive Technology
by Javeed Shaikh-Mohammed, Yousef Alharbi and Abdulrahman Alqahtani
Technologies 2023, 11(6), 177; https://doi.org/10.3390/technologies11060177 - 11 Dec 2023
Cited by 1 | Viewed by 4926
Abstract
To the authors’ knowledge, currently, there is no review covering the different technologies applied to opening manual doors. Therefore, this review presents a summary of the various technologies available on the market as well as those under research and development for opening manual [...] Read more.
To the authors’ knowledge, currently, there is no review covering the different technologies applied to opening manual doors. Therefore, this review presents a summary of the various technologies available on the market as well as those under research and development for opening manual doors. Four subtopics—doorknob accessories, wheelchair-mounted door-opening accessories, door-opening robots, and door-opening drones—were used to group the various technologies for manually opening doors. It is evident that opening doors is a difficult process, and there are different ways to solve this problem in terms of the technology used and the cost of the end product. The search for an affordable assistive technology for opening manual doors is ongoing. This work is an attempt to provide wheelchair users and their healthcare providers with a one-stop source for door-opening technologies. At least one of these door-opening solutions could prove beneficial to the elderly and some wheelchair users for increased independence. The ideal option would depend on an individual’s needs and capabilities, and occupational therapists could assess and recommend the right solutions. Full article
(This article belongs to the Section Assistive Technologies)
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24 pages, 4277 KB  
Article
An Advanced Solution Based on Machine Learning for Remote EMDR Therapy
by Francesca Fiani, Samuele Russo and Christian Napoli
Technologies 2023, 11(6), 172; https://doi.org/10.3390/technologies11060172 - 6 Dec 2023
Cited by 29 | Viewed by 5169
Abstract
For this work, a preliminary study proposed virtual interfaces for remote psychotherapy and psychology practices. This study aimed to verify the efficacy of such approaches in obtaining results comparable to in-presence psychotherapy, when the therapist is physically present in the room. In particular, [...] Read more.
For this work, a preliminary study proposed virtual interfaces for remote psychotherapy and psychology practices. This study aimed to verify the efficacy of such approaches in obtaining results comparable to in-presence psychotherapy, when the therapist is physically present in the room. In particular, we implemented several joint machine-learning techniques for distance detection, camera calibration and eye tracking, assembled to create a full virtual environment for the execution of a psychological protocol for a self-induced mindfulness meditative state. Notably, such a protocol is also applicable for the desensitization phase of EMDR therapy. This preliminary study has proven that, compared to a simple control task, such as filling in a questionnaire, the application of the mindfulness protocol in a fully virtual setting greatly improves concentration and lowers stress for the subjects it has been tested on, therefore proving the efficacy of a remote approach when compared to an in-presence one. This opens up the possibility of deepening the study, to create a fully working interface which will be applicable in various on-field applications of psychotherapy where the presence of the therapist cannot be always guaranteed. Full article
(This article belongs to the Topic Smart Healthcare: Technologies and Applications)
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30 pages, 22313 KB  
Article
Laplace Transform-Based Modelling, Surge Energy Distribution, and Experimental Validation of a Supercapacitor Transient Suppressor
by Sadeeshvara Silva Thotabaddadurage
Technologies 2023, 11(6), 173; https://doi.org/10.3390/technologies11060173 - 6 Dec 2023
Cited by 1 | Viewed by 4386
Abstract
The discovery of the transient-surge-withstanding capability of electrochemical dual-layer capacitors (EDLCs) led to the development of a unique, commercially beneficial circuit topology known as a supercapacitor transient suppressor (STS). Despite its low component count, the new design consists of a transient-absorbing magnetic core [...] Read more.
The discovery of the transient-surge-withstanding capability of electrochemical dual-layer capacitors (EDLCs) led to the development of a unique, commercially beneficial circuit topology known as a supercapacitor transient suppressor (STS). Despite its low component count, the new design consists of a transient-absorbing magnetic core which takes the form of a coupled inductor placed between the AC-main- and load-side varistors. With an introduction to the structural features of metal oxide varistors (MOVs), gas tubes, thyristors, and EDLCs, this research presents a frequency (S)-domain analysis of an STS circuit to accurately model the surge propagation through its coupled inductor. Transient energy distribution trends among STS components are estimated in this paper, with an emphasis on peak energies absorbed and dissipated by the various inductive, capacitive, and resistive circuit elements. Moreover, this study reveals STS transient-mode test waveforms validated by a standard lightning surge simulator with supporting simulation plots based on LTSpice numerical techniques. Both experimental and simulation results are consistent, with the analytical findings showing 90% of the peak transient propagating through the primary coil, whereas only 10% is shared into the secondary coil of the coupled inductor. In addition, it is proven that the two STS MOVs dissipate over 50% of the transient energy for a standard 6 kV/3 kA combinational surge, while the magnetic core absorbs over 20% of the energy. All test procedures conducted during this research adhere to IEEE C62.41/IEC 61000-4-5 standards. Full article
(This article belongs to the Collection Electrical Technologies)
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11 pages, 881 KB  
Article
Maze Solving Mobile Robot Based on Image Processing and Graph Theory
by Luis A. Avila-Sánchez, Carlos Sánchez-López, Rocío Ochoa-Montiel, Fredy Montalvo-Galicia, Luis A. Sánchez-Gaspariano, Carlos Hernández-Mejía and Hugo G. González-Hernández
Technologies 2023, 11(6), 171; https://doi.org/10.3390/technologies11060171 - 5 Dec 2023
Cited by 6 | Viewed by 5833
Abstract
Advances in the development of collision-free path planning algorithms are the main need not only to solve mazes with robotic systems, but also for their use in modern product transportation or green logistics systems and planning merchandise deliveries inside or outside a factory. [...] Read more.
Advances in the development of collision-free path planning algorithms are the main need not only to solve mazes with robotic systems, but also for their use in modern product transportation or green logistics systems and planning merchandise deliveries inside or outside a factory. This challenge increases as the complexity of the task in its structure also increases. This paper deals with the development of a novel methodology for solving mazes with a mobile robot, using image processing techniques and graph theory. The novelty is that the mobile robot can find the shortest path from a start-point to the end-point into irregular mazes with abundant irregular obstacles, a situation that is not far from reality. Maze information is acquired from an image and depending on the size of the mobile robot, a grid of nodes with the same dimensions of the maze is built. Another contribution of this paper is that the size of the maze can be scaled from 1 m × 1 m to 66 m × 66 m, maintaining the essence of the proposed collision-free path planning methodology. Afterwards, graph theory is used to find the shortest path within the grid of reduced nodes due to the elimination of those nodes absorbed by the irregular obstacles. To avoid the mobile robot to travel through those nodes very close to obstacles and borders, resulting in a collision, each image of the obstacle and border is dilated taking into account the size of the mobile robot. The methodology was validated with two case studies with a mobile robot in different mazes. We emphasize that the maze solution is found in a single computational step, from the maze image as input until the generation of the Path vector. Experimental results show the usefulness of the proposed methodology, which can be used in applications such as intelligent traffic control, military, agriculture and so on. Full article
(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)
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21 pages, 3566 KB  
Article
Towards Robust Obstacle Avoidance for the Visually Impaired Person Using Stereo Cameras
by Bismark Kweku Asiedu Asante and Hiroki Imamura
Technologies 2023, 11(6), 168; https://doi.org/10.3390/technologies11060168 - 28 Nov 2023
Cited by 15 | Viewed by 6530
Abstract
We propose a novel obstacle avoidance strategy implemented in a wearable assistive device, which serves as an electronic travel aid (ETA), designed to enhance the safety of visually impaired persons (VIPs) during navigation to their desired destinations. This method is grounded in the [...] Read more.
We propose a novel obstacle avoidance strategy implemented in a wearable assistive device, which serves as an electronic travel aid (ETA), designed to enhance the safety of visually impaired persons (VIPs) during navigation to their desired destinations. This method is grounded in the assumption that objects in close proximity and within a short distance from VIPs pose potential obstacles and hazards. Furthermore, objects that are farther away appear smaller in the camera’s field of view. To adapt this method for accurate obstacle selection, we employ an adaptable grid generated based on the apparent size of objects. These objects are detected using a custom lightweight YOLOv5 model. The grid helps select and prioritize the most immediate and dangerous obstacle within the user’s proximity. We also incorporate an audio feedback mechanism with an innovative neural perception system to alert the user. Experimental results demonstrate that our proposed system can detect obstacles within a range of 20 m and effectively prioritize obstacles within 2 m of the user. The system achieves an accuracy rate of 95% for both obstacle detection and prioritization of critical obstacles. Moreover, the ETA device provides real-time alerts, with a response time of just 5 s, preventing collisions with nearby objects. Full article
(This article belongs to the Section Assistive Technologies)
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19 pages, 328 KB  
Review
A Survey of the Diagnosis of Peripheral Neuropathy Using Intelligent and Wearable Systems
by Muhammad Talha, Maria Kyrarini and Ehsan Ali Buriro
Technologies 2023, 11(6), 163; https://doi.org/10.3390/technologies11060163 - 17 Nov 2023
Cited by 11 | Viewed by 6674
Abstract
In recent years, the usage of wearable systems in healthcare has gained much attention, as they can be easily worn by the subject and provide a continuous source of data required for the tracking and diagnosis of multiple kinds of abnormalities or diseases [...] Read more.
In recent years, the usage of wearable systems in healthcare has gained much attention, as they can be easily worn by the subject and provide a continuous source of data required for the tracking and diagnosis of multiple kinds of abnormalities or diseases in the human body. Wearable systems can be made useful in improving a patient’s quality of life and at the same time reducing the overall cost of caring for individuals including the elderly. In this survey paper, the recent research in the development of intelligent wearable systems for the diagnosis of peripheral neuropathy is discussed. The paper provides detailed information about recent techniques based on different wearable sensors for the diagnosis of peripheral neuropathy including experimental protocols, biomarkers, and other specifications and parameters such as the type of signals and data processing methods, locations of sensors, the scales and tests used in the study, and the scope of the study. It also highlights challenges that are still present in order to make wearable devices more effective in the diagnosis of peripheral neuropathy in clinical settings. Full article
(This article belongs to the Collection Selected Papers from the PETRA Conference Series)
13 pages, 5279 KB  
Article
Immersive VR (Virtual Reality) Simulator for Vein Blood Sampling
by Jun-Seong Kim, Kun-Woo Kim, Seong-Won Yang, Joong-Wha Chung and Seong-Yong Moon
Technologies 2023, 11(6), 158; https://doi.org/10.3390/technologies11060158 - 8 Nov 2023
Cited by 7 | Viewed by 5039
Abstract
Vein blood sampling is a method of mass blood sampling that involves drawing blood from a vein for blood type discrimination, confirmation of various physiological indicators, disease diagnosis, etc.; it is the most commonly used blood sampling method. An important aspect of vein [...] Read more.
Vein blood sampling is a method of mass blood sampling that involves drawing blood from a vein for blood type discrimination, confirmation of various physiological indicators, disease diagnosis, etc.; it is the most commonly used blood sampling method. An important aspect of vein blood sampling is the search for the exact location of the vein for insertion of the syringe to draw blood. This is influenced by obesity as well as skin and blood vessel conditions in the patient and the experience of the clinical technologist, nurse, and resident who performs the blood sampling. Frequent practice is required to effectively perform blood sampling techniques. However, due to the many limitations of the practice room or laboratory, there is a problem of using only a limited environment and model for clinical practice. As a result, many medical educational institutions have situations in which only fragmentary clinical practices are performed, and it is difficult to practice many blood sampling skills, so they do not provide enough experience to understand the actual skill field. In this paper, we propose a virtual-reality-based vein blood sampling simulator that allows the practice of blood sampling techniques without limitation. The proposed vein blood sampling simulator can operate a 3D model related to vein blood sampling using an HMD controller and a haptic device in a virtual space for vein blood sampling practice by wearing an HMD (head-mounted display). Vein blood sampling can also be practiced through interaction with the patient 3D model. In addition, the effectiveness of a simulator developed for dental students was verified, and as a result of the verification, the potential of the proposed vein blood sampling simulator was confirmed. Full article
(This article belongs to the Topic Smart Healthcare: Technologies and Applications)
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27 pages, 77222 KB  
Tutorial
How Activated Carbon Can Help You—Processes, Properties and Technological Applications
by Miklas Scholz
Technologies 2023, 11(6), 153; https://doi.org/10.3390/technologies11060153 - 1 Nov 2023
Cited by 12 | Viewed by 17534
Abstract
Activated carbon has many potential applications in both the liquid and gas phases. How activated carbon can help practitioners in industry is explained. This practical teaching article introduces the first part of the special issue on Recent Advances in Applied Activated Carbon Research [...] Read more.
Activated carbon has many potential applications in both the liquid and gas phases. How activated carbon can help practitioners in industry is explained. This practical teaching article introduces the first part of the special issue on Recent Advances in Applied Activated Carbon Research by providing a handbook explaining the basic applications, technologies, processes, methods and material characteristics to readers from different backgrounds. The aim is to improve the knowledge and understanding of the subject of activated carbon for non-adsorption experts such as professionals in industry. Therefore, it is written in a comprehensible manner and dispenses with detailed explanations to complex processes and many background references. This handbook does not claim to be complete and concentrates only on the areas that are of practical relevance for most activated carbon applications. Activated carbon and its activation and reactivation are initially explained. Adsorption and relevant processes are outlined. The mechanical, chemical and adsorption properties of activated carbon are explained. The heart of the handbook outlines key application technologies. Other carbonaceous adsorbents are only introduced briefly. The content of the second part of the special issue is highlighted at the end. Full article
(This article belongs to the Special Issue Recent Advances in Applied Activated Carbon Research)
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52 pages, 10354 KB  
Review
Application of Modeling and Control Approaches of Piezoelectric Actuators: A Review
by Mithun Kanchan, Mohith Santhya, Ritesh Bhat and Nithesh Naik
Technologies 2023, 11(6), 155; https://doi.org/10.3390/technologies11060155 - 1 Nov 2023
Cited by 48 | Viewed by 10325
Abstract
Piezoelectric actuators find extensive application in delivering precision motion in the micrometer to nanometer range. The advantages of a broader range of motion, rapid response, higher stiffness, and large actuation force from piezoelectric actuators make them suitable for precision positioning applications. However, the [...] Read more.
Piezoelectric actuators find extensive application in delivering precision motion in the micrometer to nanometer range. The advantages of a broader range of motion, rapid response, higher stiffness, and large actuation force from piezoelectric actuators make them suitable for precision positioning applications. However, the inherent nonlinearity in the piezoelectric actuators under dynamic working conditions severely affects the accuracy of the generated motion. The nonlinearity in the piezoelectric actuators arises from hysteresis, creep, and vibration, which affect the performance of the piezoelectric actuator. Thus, there is a need for appropriate modeling and control approaches for piezoelectric actuators, which can model the nonlinearity phenomenon and provide adequate compensation to achieve higher motion accuracy. The present review covers different methods adopted for overcoming the nonlinearity issues in piezoelectric actuators. This review highlights the charge-based and voltage-based control methods that drive the piezoelectric actuators. The survey also includes different modeling approaches for the creep and hysteresis phenomenon of the piezoelectric actuators. In addition, the present review also highlights different control strategies and their applications in various types of piezoelectric actuators. An attempt is also made to compare the piezoelectric actuator’s different modeling and control approaches and highlight prospects. Full article
(This article belongs to the Collection Electrical Technologies)
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18 pages, 2838 KB  
Article
Neonatal Hypoxic-Ischemic Encephalopathy Grading from Multi-Channel EEG Time-Series Data Using a Fully Convolutional Neural Network
by Shuwen Yu, William P. Marnane, Geraldine B. Boylan and Gordon Lightbody
Technologies 2023, 11(6), 151; https://doi.org/10.3390/technologies11060151 - 25 Oct 2023
Cited by 9 | Viewed by 5525
Abstract
A deep learning classifier is proposed for grading hypoxic-ischemic encephalopathy (HIE) in neonates. Rather than using handcrafted features, this architecture can be fed with raw EEG. Fully convolutional layers were adopted both in the feature extraction and classification blocks, which makes this architecture [...] Read more.
A deep learning classifier is proposed for grading hypoxic-ischemic encephalopathy (HIE) in neonates. Rather than using handcrafted features, this architecture can be fed with raw EEG. Fully convolutional layers were adopted both in the feature extraction and classification blocks, which makes this architecture simpler, and deeper, but with fewer parameters. Here, two large (335 h and 338 h, respectively) multi-center neonatal continuous EEG datasets were used for training and testing. The model was trained based on weak labels and channel independence. A majority vote method was used for the post-processing of the classifier results (across time and channels) to increase the robustness of the prediction. A dimension reduction tool, UMAP, was used to visualize the model classification effect. The proposed system achieved an accuracy of 86.09% (95% confidence interval: 82.41–89.78%), an MCC of 0.7691, and an AUC of 86.23% on the large unseen test set. Two convolutional neural network architectures which utilized time-frequency distribution features were selected as the baseline as they had been developed or tested on the same datasets. A relative improvement of 23.65% in test accuracy was obtained as compared with the best baseline. In addition, if only one channel was available, the test accuracy was only reduced by 2.63–5.91% compared with making decisions based on the eight channels. Full article
(This article belongs to the Special Issue Selected Papers from ICNC-FSKD 2023 Conference)
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28 pages, 2632 KB  
Review
Green Electrospun Nanofibers for Biomedicine and Biotechnology
by Elyor Berdimurodov, Omar Dagdag, Khasan Berdimuradov, Wan Mohd Norsani Wan Nik, Ilyos Eliboev, Mansur Ashirov, Sherzod Niyozkulov, Muslum Demir, Chinmurot Yodgorov and Nizomiddin Aliev
Technologies 2023, 11(5), 150; https://doi.org/10.3390/technologies11050150 - 23 Oct 2023
Cited by 32 | Viewed by 7317
Abstract
Green electrospinning harnesses the potential of renewable biomaterials to craft biodegradable nanofiber structures, expanding their utility across a spectrum of applications. In this comprehensive review, we summarize the production, characterization and application of electrospun cellulose, collagen, gelatin and other biopolymer nanofibers in tissue [...] Read more.
Green electrospinning harnesses the potential of renewable biomaterials to craft biodegradable nanofiber structures, expanding their utility across a spectrum of applications. In this comprehensive review, we summarize the production, characterization and application of electrospun cellulose, collagen, gelatin and other biopolymer nanofibers in tissue engineering, drug delivery, biosensing, environmental remediation, agriculture and synthetic biology. These applications span diverse fields, including tissue engineering, drug delivery, biosensing, environmental remediation, agriculture, and synthetic biology. In the realm of tissue engineering, nanofibers emerge as key players, adept at mimicking the intricacies of the extracellular matrix. These fibers serve as scaffolds and vascular grafts, showcasing their potential to regenerate and repair tissues. Moreover, they facilitate controlled drug and gene delivery, ensuring sustained therapeutic levels essential for optimized wound healing and cancer treatment. Biosensing platforms, another prominent arena, leverage nanofibers by immobilizing enzymes and antibodies onto their surfaces. This enables precise glucose monitoring, pathogen detection, and immunodiagnostics. In the environmental sector, these fibers prove invaluable, purifying water through efficient adsorption and filtration, while also serving as potent air filtration agents against pollutants and pathogens. Agricultural applications see the deployment of nanofibers in controlled release fertilizers and pesticides, enhancing crop management, and extending antimicrobial food packaging coatings to prolong shelf life. In the realm of synthetic biology, these fibers play a pivotal role by encapsulating cells and facilitating bacteria-mediated prodrug activation strategies. Across this multifaceted landscape, nanofibers offer tunable topographies and surface functionalities that tightly regulate cellular behavior and molecular interactions. Importantly, their biodegradable nature aligns with sustainability goals, positioning them as promising alternatives to synthetic polymer-based technologies. As research and development continue to refine and expand the capabilities of green electrospun nanofibers, their versatility promises to advance numerous applications in the realms of biomedicine and biotechnology, contributing to a more sustainable and environmentally conscious future. Full article
(This article belongs to the Special Issue Electrospinning Technologies for Biomedical and Biotechnological Applications—Volume II)
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16 pages, 1564 KB  
Article
Exploring the Digital Atmosphere of Museums: Perspectives and Potential
by Sofia Paschou and Georgios Papaioannou
Technologies 2023, 11(5), 149; https://doi.org/10.3390/technologies11050149 - 22 Oct 2023
Cited by 5 | Viewed by 5603
Abstract
This paper contributes to the field of museum and visitor experience in terms of atmosphere by discussing the “museum digital atmosphere” or MDA, a notion that has been introduced and found across museums in Greece. Research on museum atmospherics has tended to focus [...] Read more.
This paper contributes to the field of museum and visitor experience in terms of atmosphere by discussing the “museum digital atmosphere” or MDA, a notion that has been introduced and found across museums in Greece. Research on museum atmospherics has tended to focus on physical museum spaces and exhibits. By “atmosphere”, we mean the emotional state that is a result of public response adding to the overall museum experience. The MDA is therefore studied as the specific emotional state caused by the use of digital applications and technologies. The stimulus–organism–response or SOR model is used to define the MDA, so as to confirm and reinforce the concept. To that end, a qualitative methodological approach is used; we conduct semi-structured interviews and evaluate findings via content analysis. The sample consists of 17 specialists and professionals from the field, namely museologists, museographers, museum managers, and digital application developers working in Greek museums. Ultimately, this research uses the SOR model to reveal the effect of digital tools on the digital atmosphere in Greek museums. It also enriches the SOR model with additional concepts and emotions taken from real-life situations, adding new categories of variables. This research provides the initial data and knowledge regarding the concept of the MDA, along with its importance. Full article
(This article belongs to the Special Issue Immersive Technologies and Applications on Arts, Culture and Tourism)
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18 pages, 6018 KB  
Article
Preparation and Characterization of Thermoresponsive Polymer Scaffolds Based on Poly(N-isopropylacrylamide-co-N-tert-butylacrylamide) for Cell Culture
by Gilyana K. Kazakova, Victoria S. Presniakova, Yuri M. Efremov, Svetlana L. Kotova, Anastasia A. Frolova, Sergei V. Kostjuk, Yury A. Rochev and Peter S. Timashev
Technologies 2023, 11(5), 145; https://doi.org/10.3390/technologies11050145 - 18 Oct 2023
Cited by 5 | Viewed by 3687
Abstract
In the realm of scaffold-free cell therapies, there is a questto develop organotypic three-dimensional (3D) tissue surrogates in vitro, capitalizing on the inherent ability of cells to create tissues with an efficiency and sophistication that still remains unmatched by human-made devices. In this [...] Read more.
In the realm of scaffold-free cell therapies, there is a questto develop organotypic three-dimensional (3D) tissue surrogates in vitro, capitalizing on the inherent ability of cells to create tissues with an efficiency and sophistication that still remains unmatched by human-made devices. In this study, we explored the properties of scaffolds obtained by the electrospinning of a thermosensitive copolymer, poly(N-isopropylacrylamide-co-N-tert-butylacrylamide) (P(NIPAM-co-NtBA)), intended for use in such therapies. Two copolymers with molecular weights of 123 and 137 kDa and a content of N-tert-butylacrylamide of ca. 15 mol% were utilized to generate 3D scaffolds via electrospinning. We examined the morphology, solution viscosity, porosity, and thickness of the spun matrices as well as the mechanical properties and hydrophobic–hydrophilic characteristics of the scaffolds. Particular attention was paid to studying the influence of the thermosensitive polymer’s molecular weight and dispersity on the resultant scaffolds’ properties and the role of electroforming parameters on the morphology and mechanical characteristics of the scaffolds. The cytotoxicity of the copolymers and interaction of cells with the scaffolds were also studied. Our findings provide significant insight into approaches to optimizing scaffolds for specific cell cultures, thereby offering new opportunities for scaffold-free cell therapies. Full article
(This article belongs to the Special Issue Electrospinning Technologies for Biomedical and Biotechnological Applications—Volume II)
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39 pages, 25112 KB  
Review
Recent Advances in the 3D Printing of Pure Copper Functional Structures for Thermal Management Devices
by Yue Hao Choong, Manickavasagam Krishnan and Manoj Gupta
Technologies 2023, 11(5), 141; https://doi.org/10.3390/technologies11050141 - 15 Oct 2023
Cited by 28 | Viewed by 11020
Abstract
Thermal management devices such as heat exchangers and heat pipes are integral to safe and efficient performance in multiple engineering applications, including lithium-ion batteries, electric vehicles, electronics, and renewable energy. However, the functional designs of these devices have until now been created around [...] Read more.
Thermal management devices such as heat exchangers and heat pipes are integral to safe and efficient performance in multiple engineering applications, including lithium-ion batteries, electric vehicles, electronics, and renewable energy. However, the functional designs of these devices have until now been created around conventional manufacturing constraints, and thermal performance has plateaued as a result. While 3D printing offers the design freedom to address these limitations, there has been a notable lack in high thermal conductivity materials beyond aluminium alloys. Recently, the 3D printing of pure copper to sufficiently high densities has finally taken off, due to the emergence of commercial-grade printers which are now equipped with 1 kW high-power lasers or short-wavelength lasers. Although the capabilities of these new systems appear ideal for processing pure copper as a bulk material, the performance of advanced thermal management devices are strongly dependent on topology-optimised filigree structures, which can require a very different processing window. Hence, this article presents a broad overview of the state-of-the-art in various additive manufacturing technologies used to fabricate pure copper functional filigree geometries comprising thin walls, lattice structures, and porous foams, and identifies opportunities for future developments in the 3D printing of pure copper for advanced thermal management devices. Full article
(This article belongs to the Collection Review Papers Collection for Advanced Technologies)
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13 pages, 3366 KB  
Article
Detecting Airborne Pathogens: A Computational Approach Utilizing Surface Acoustic Wave Sensors for Microorganism Detection
by Sharon P. Varughese, S. Merlin Gilbert Raj, T. Jesse Joel and Sneha Gautam
Technologies 2023, 11(5), 135; https://doi.org/10.3390/technologies11050135 - 2 Oct 2023
Cited by 6 | Viewed by 3611
Abstract
The persistent threat posed by infectious pathogens remains a formidable challenge for humanity. Rapidly spreading infectious diseases caused by airborne microorganisms have far-reaching global consequences, imposing substantial costs on society. While various detection technologies have emerged, including biochemical, immunological, and molecular approaches, these [...] Read more.
The persistent threat posed by infectious pathogens remains a formidable challenge for humanity. Rapidly spreading infectious diseases caused by airborne microorganisms have far-reaching global consequences, imposing substantial costs on society. While various detection technologies have emerged, including biochemical, immunological, and molecular approaches, these methods still exhibit significant limitations such as time-intensive procedures, instability, and the need for specialized operators. This study presents an innovative solution that harnesses the potential of surface acoustic wave (SAW) sensors for the detection of airborne microorganisms. The research involves the establishment of a sensor model within the framework of COMSOL Multiphysics, utilizing a predefined piezoelectric multi-physics interface and employing a 2D modeling approach. Chitosan, selected as the sensing film for the model, interfaces with lithium niobate (LiNbO3), the chosen piezoelectric material responsible for detecting airborne pathogens. The analysis of microbe presence centers on solid displacement and electric potential frequencies, operating within the 850–900 MHz range. Notably, the first and second resonant frequencies are identified at 856 and 859 MHz, respectively. To enhance understanding, this study proposes a novel mathematical model grounded in Stokes’ Law and mass balance equations. This model serves to analyze microbe concentration, offering a fresh perspective on quantifying the presence of airborne pathogens. Through these endeavors, this research contributes to advancing the field of airborne microorganism detection, offering a promising avenue for addressing the challenges posed by infectious diseases. Full article
(This article belongs to the Section Environmental Technology)
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42 pages, 13997 KB  
Article
Multi-Scale CNN: An Explainable AI-Integrated Unique Deep Learning Framework for Lung-Affected Disease Classification
by Ovi Sarkar, Md. Robiul Islam, Md. Khalid Syfullah, Md. Tohidul Islam, Md. Faysal Ahamed, Mominul Ahsan and Julfikar Haider
Technologies 2023, 11(5), 134; https://doi.org/10.3390/technologies11050134 - 30 Sep 2023
Cited by 49 | Viewed by 8211
Abstract
Lung-related diseases continue to be a leading cause of global mortality. Timely and precise diagnosis is crucial to save lives, but the availability of testing equipment remains a challenge, often coupled with issues of reliability. Recent research has highlighted the potential of Chest [...] Read more.
Lung-related diseases continue to be a leading cause of global mortality. Timely and precise diagnosis is crucial to save lives, but the availability of testing equipment remains a challenge, often coupled with issues of reliability. Recent research has highlighted the potential of Chest X-ray (CXR) images in identifying various lung diseases, including COVID-19, fibrosis, pneumonia, and more. In this comprehensive study, four publicly accessible datasets have been combined to create a robust dataset comprising 6650 CXR images, categorized into seven distinct disease groups. To effectively distinguish between normal and six different lung-related diseases (namely, bacterial pneumonia, COVID-19, fibrosis, lung opacity, tuberculosis, and viral pneumonia), a Deep Learning (DL) architecture called a Multi-Scale Convolutional Neural Network (MS-CNN) is introduced. The model is adapted to classify multiple numbers of lung disease classes, which is considered to be a persistent challenge in the field. While prior studies have demonstrated high accuracy in binary and limited-class scenarios, the proposed framework maintains this accuracy across a diverse range of lung conditions. The innovative model harnesses the power of combining predictions from multiple feature maps at different resolution scales, significantly enhancing disease classification accuracy. The approach aims to shorten testing duration compared to the state-of-the-art models, offering a potential solution toward expediting medical interventions for patients with lung-related diseases and integrating explainable AI (XAI) for enhancing prediction capability. The results demonstrated an impressive accuracy of 96.05%, with average values for precision, recall, F1-score, and AUC at 0.97, 0.95, 0.95, and 0.94, respectively, for the seven-class classification. The model exhibited exceptional performance across multi-class classifications, achieving accuracy rates of 100%, 99.65%, 99.21%, 98.67%, and 97.47% for two, three, four, five, and six-class scenarios, respectively. The novel approach not only surpasses many pre-existing state-of-the-art (SOTA) methodologies but also sets a new standard for the diagnosis of lung-affected diseases using multi-class CXR data. Furthermore, the integration of XAI techniques such as SHAP and Grad-CAM enhanced the transparency and interpretability of the model’s predictions. The findings hold immense promise for accelerating and improving the accuracy and confidence of diagnostic decisions in the field of lung disease identification. Full article
(This article belongs to the Special Issue Medical Imaging & Image Processing III)
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17 pages, 6492 KB  
Article
Multi-Classification of Lung Infections Using Improved Stacking Convolution Neural Network
by Usharani Bhimavarapu, Nalini Chintalapudi and Gopi Battineni
Technologies 2023, 11(5), 128; https://doi.org/10.3390/technologies11050128 - 17 Sep 2023
Cited by 9 | Viewed by 3365
Abstract
Lung disease is a respiratory disease that poses a high risk to people worldwide and includes pneumonia and COVID-19. As such, quick and precise identification of lung disease is vital in medical treatment. Early detection and diagnosis can significantly reduce the life-threatening nature [...] Read more.
Lung disease is a respiratory disease that poses a high risk to people worldwide and includes pneumonia and COVID-19. As such, quick and precise identification of lung disease is vital in medical treatment. Early detection and diagnosis can significantly reduce the life-threatening nature of lung diseases and improve the quality of life of human beings. Chest X-ray and computed tomography (CT) scan images are currently the best techniques to detect and diagnose lung infection. The increase in the chest X-ray or CT scan images at the time of training addresses the overfitting dilemma, and multi-class classification of lung diseases will deal with meaningful information and overfitting. Overfitting deteriorates the performance of the model and gives inaccurate results. This study reduces the overfitting issue and computational complexity by proposing a new enhanced kernel convolution function. Alongside an enhanced kernel convolution function, this study used convolution neural network (CNN) models to determine pneumonia and COVID-19. Each CNN model was applied to the collected dataset to extract the features and later applied these features as input to the classification models. This study shows that extracting deep features from the common layers of the CNN models increased the performance of the classification procedure. The multi-class classification improves the diagnostic performance, and the evaluation metrics improved significantly with the improved support vector machine (SVM). The best results were obtained using the improved SVM classifier fed with the features provided by CNN, and the success rate of the improved SVM was 99.8%. Full article
(This article belongs to the Special Issue Medical Imaging & Image Processing III)
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13 pages, 766 KB  
Article
Using Simple Interactive Technology to Help People with Intellectual and Visual Disabilities Exercise Functional Physical Responses: A Case Series Study
by Giulio E. Lancioni, Gloria Alberti, Chiara Filippini, Valeria Chiariello, Nirbhay N. Singh, Mark F. O’Reilly and Jeff Sigafoos
Technologies 2023, 11(5), 120; https://doi.org/10.3390/technologies11050120 - 7 Sep 2023
Cited by 6 | Viewed by 2956
Abstract
The study assessed a new interactive technology system for helping six people with intellectual and visual disabilities exercise relevant physical responses embedded within a fairly straightforward activity (i.e., placing objects in containers). Activity responses consisted of the participants taking objects from the floor [...] Read more.
The study assessed a new interactive technology system for helping six people with intellectual and visual disabilities exercise relevant physical responses embedded within a fairly straightforward activity (i.e., placing objects in containers). Activity responses consisted of the participants taking objects from the floor or a low shelf and placing those objects in a container high up in front of them (thus bending their body and legs and stretching their arms and hands). The technology involved a portable computer, a webcam, and three mini speakers whose basic functions included monitoring the participants’ responses, delivering preferred stimulation contingent on the responses and verbal encouragements/prompts for lack of responses, and assisting in data recording. The study was conducted following a non-concurrent multiple baseline design across participants. During baseline (i.e., when the system was used only for data recording), the participants’ mean frequency of responses per session varied between zero and nearly 12. During intervention (i.e., when the system was fully working), the participants’ mean frequency of responses per session increased to between about 34 and 59. Mean session duration varied between nearly 10 and over 14 min. The new system may be a valuable tool for supporting relevant physical activity engagement in people with intellectual and multiple disabilities. Full article
(This article belongs to the Section Assistive Technologies)
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22 pages, 5470 KB  
Article
An Intelligent System-Based Coffee Plant Leaf Disease Recognition Using Deep Learning Techniques on Rwandan Arabica Dataset
by Eric Hitimana, Omar Janvier Sinayobye, J. Chrisostome Ufitinema, Jane Mukamugema, Peter Rwibasira, Theoneste Murangira, Emmanuel Masabo, Lucy Cherono Chepkwony, Marie Cynthia Abijuru Kamikazi, Jeanne Aline Ukundiwabo Uwera, Simon Martin Mvuyekure, Gaurav Bajpai and Jackson Ngabonziza
Technologies 2023, 11(5), 116; https://doi.org/10.3390/technologies11050116 - 1 Sep 2023
Cited by 28 | Viewed by 7570
Abstract
Rwandan coffee holds significant importance and immense value within the realm of agriculture, serving as a vital and valuable commodity. Additionally, coffee plays a pivotal role in generating foreign exchange for numerous developing nations. However, the coffee plant is vulnerable to pests and [...] Read more.
Rwandan coffee holds significant importance and immense value within the realm of agriculture, serving as a vital and valuable commodity. Additionally, coffee plays a pivotal role in generating foreign exchange for numerous developing nations. However, the coffee plant is vulnerable to pests and diseases weakening production. Farmers in cooperation with experts use manual methods to detect diseases resulting in human errors. With the rapid improvements in deep learning methods, it is possible to detect and recognize plan diseases to support crop yield improvement. Therefore, it is an essential task to develop an efficient method for intelligently detecting, identifying, and predicting coffee leaf diseases. This study aims to build the Rwandan coffee plant dataset, with the occurrence of coffee rust, miner, and red spider mites identified to be the most popular due to their geographical situations. From the collected coffee leaves dataset of 37,939 images, the preprocessing, along with modeling used five deep learning models such as InceptionV3, ResNet50, Xception, VGG16, and DenseNet. The training, validation, and testing ratio is 80%, 10%, and 10%, respectively, with a maximum of 10 epochs. The comparative analysis of the models’ performances was investigated to select the best for future portable use. The experiment proved the DenseNet model to be the best with an accuracy of 99.57%. The efficiency of the suggested method is validated through an unbiased evaluation when compared to existing approaches with different metrics. Full article
(This article belongs to the Special Issue Image and Signal Processing)
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22 pages, 2712 KB  
Article
An Exposimetric Electromagnetic Comparison of Mobile Phone Emissions: 5G versus 4G Signals Analyses by Means of Statistics and Convolutional Neural Networks Classification
by Simona Miclaus, Delia B. Deaconescu, David Vatamanu and Andreea M. Buda
Technologies 2023, 11(5), 113; https://doi.org/10.3390/technologies11050113 - 24 Aug 2023
Cited by 10 | Viewed by 11118
Abstract
To gain a deeper understanding of the hotly contested topic of the non-thermal biological effects of microwaves, new metrics and methodologies need to be adopted. The direction proposed in the current work, which includes peak exposure analysis and not just time-averaged analysis, aligns [...] Read more.
To gain a deeper understanding of the hotly contested topic of the non-thermal biological effects of microwaves, new metrics and methodologies need to be adopted. The direction proposed in the current work, which includes peak exposure analysis and not just time-averaged analysis, aligns well with this objective. The proposed methodology is not intended to facilitate a comparison of the general characteristics between 4G and 5G mobile communication signals. Instead, its purpose is to provide a means for analyzing specific real-life exposure conditions that may vary based on multiple parameters. A differentiation based on amplitude-time features of the 4G versus 5G signals is followed, with the aim of describing the peculiarities of a user’s exposure when he runs four types of mobile applications on his mobile phone on either of the two mobile networks. To achieve the goals, we used signal and spectrum analyzers with adequate real-time analysis bandwidths and statistical descriptions provided by the amplitude probability density (APD) function, the complementary cumulative distribution function (CCDF), channel power measurements, and recorded spectrogram databases. We compared the exposimetric descriptors of emissions specific to file download, file upload, Internet video streaming, and video call usage in both 4G and 5G networks based on the specific modulation and coding schemes. The highest and lowest electric field strengths measured in the air at a 10 cm distance from the phone during emissions are indicated. The power distribution functions with the highest prevalence are highlighted and commented on. Afterwards, the capability of a convolutional neural network that belongs to the family of single-shot detectors is proven to recognize and classify the emissions with a very high degree of accuracy, enabling traceability of the dynamics of human exposure. Full article
(This article belongs to the Special Issue Intelligent Reflecting Surfaces for 5G and Beyond)
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