Search Results (4,252)

Management 5.0 represents a new paradigm in business strategy and leadership that integrates sustainability, advanced digital technologies, and human-centered decision-making. The article explores the application of machine learning technologies for forecasting demand for eco-friendly vehicles as a key tool for enhancing manufacturers’ competitiveness. This research supports key UN Sustainable Development Goals (SDGs), including SDG 7 (Clean Energy), SDG 9 (Innovation and Infrastructure), SDG 11 (Sustainable Cities), and SDG 12 (Responsible Consumption). Based on an analysis of the European market from 2019 to 2023 and forecasting through 2027, a comprehensive approach was developed using ARIMA, Prophet, and Random Forest models. Empirical findings indicate that implementing predictive analytics can reduce inventory costs by 18–25% and optimize working capital by 15–20%. Model performance varied by market type: Random Forest excelled in smaller markets, while Prophet delivered strong results in trend-stable environments. The results confirm that accurate demand forecasting, supported by machine learning technologies, creates significant competitive advantages in the era of management 5.0 through production process optimization and improved market positioning. Full article

The smart grids of the future present innovative opportunities for data exchange and real-time operations management. In this context, it is crucial to integrate technological advancements with innovative planning algorithms, particularly those based on artificial intelligence (AI). AI methods offer powerful tools for planning electrical systems, including electrical distribution networks. This study presents a methodology based on reinforcement learning (RL) for evaluating optimal power flow with respect to various cost functions. Additionally, it addresses the control of dynamic constraints, such as voltage fluctuations at network nodes. A key insight is the use of historical real-world data to train the model, enabling its application in real-time scenarios. The algorithms were validated through simulations conducted on the IEEE 118-bus system, which included five case studies. Real datasets were used for both training and testing to enhance the algorithm’s practical relevance. The developed tool is versatile and applicable to power networks of varying sizes and load characteristics. Furthermore, the potential of RL for real-time applications was assessed, demonstrating its adaptability to online grid operations. This research represents a significant advancement in leveraging machine learning to improve the efficiency and stability of modern electrical grids. Full article

Road infrastructure is a critical component of modern society, with its maintenance directly influencing traffic safety and logistical efficiency. In this context, automated crack detection technology plays a vital role in reducing maintenance costs and enhancing operational efficiency. However, previous studies are limited by the fact that they provide only bounding box or segmentation mask annotations for a restricted number of crack classes and use a relatively small size of datasets. To address these limitations and advance deep learning-based crack segmentation, this study introduces a novel crack segmentation dataset that reflects real-world road conditions. The proposed dataset includes various types of cracks and defects—such as slippage, rutting, and construction-related cracks—and provides polygon-based segmentation masks captured from an egocentric, vehicle-mounted perspective. Using this dataset, we evaluated the performance of semantic and instance segmentation models. Notably, SegFormer achieved the highest Pixel Accuracy (PA) and mean Intersection over Union (mIoU) for semantic segmentation, while YOLOv7 exhibited outstanding detection performance for alligator crack class, recording an ${AP}_{50}$ of 87.2% and $AP$ of 57.5%. In contrast, all models struggled with the reflection crack type, indicating the inherent segmentation challenges. Overall, this study provides a practical and robust foundation for future research in automated road crack segmentation. Additional resources including the dataset and annotation details can be found at our GitHub repository. Full article

Wearable fall-detection systems have received significant research attention during the last years. Fall detection in wearable devices presents key challenges, particularly in balancing high precision with low power consumption—both of which are essential for the continuous monitoring of older adults and individuals with reduced mobility. This study introduces a hybrid system that integrates a threshold-based model for preliminary detection with a deep learning-based approach that combines a CNN (Convolutional Neural Network) for spatial feature extraction with a LSTM (Long Short-Term Memory) model for temporal pattern recognition, aimed at improving classification accuracy. LoRa technology enables long-range, energy-efficient communication, ensuring real-time monitoring across diverse environments. The wearable device operates in ultra-low-power mode, capturing acceleration data at 20 Hz and transmitting a 4-s window when a predefined threshold in the acceleration magnitude is exceeded. The CNN-LSTM classifier refines event identification, significantly reducing false positives. This design extends operational autonomy to 178 h of continuous monitoring. The experimental and systematic evaluation of the prototype achieved a 96.67% detection rate (sensitivity) for simulated falls and a 100% specificity in classifying conventional Activities of Daily Living as non-falls. These results establish the system as a robust and scalable solution, effectively addressing limitations in power efficiency, connectivity, and detection accuracy while enhancing user safety and quality of life. Full article

Xishuangbanna cucumber (Cucumis sativus L. var. xishuangbannanesis), as a rare and endangered cucumber germplasm resource, possesses certain irreplaceable characteristics that make it difficult to reacquire once lost. To ensure long-term preservation of this germplasm, immediate propagation and regeneration are required after successful collection. Current germplasm management relying on conventional viability testing methods often leads to seed loss. Therefore, there is an urgent need to develop a rapid and non-destructive testing technology for assessing the seed viability of Xishuangbanna cucumber. This study integrated hyperspectral imaging technology with various data preprocessing methods, feature wavelength selection algorithms, and classification models to achieve rapid and non-destructive detection of Xishuangbanna cucumber seed viability. Hyperspectral imaging was employed to acquire spectral data from the seeds. Preprocessing methods including MSC (Multivariate Scattering Correction), SNV (Standard Normal Variety), FD (First Derivative), SD (Second Derivative), and L2NN (L2 Norm Normalization) were applied to enhance spectral data quality. Feature selection algorithms such as UVE (Uninformative Variables Elimination), SPA (Successive Projections Algorithm), and CARS (Competitive Adaptive Reweighted Sampling) were utilized to identify optimal spectral bands. Combined with KNN (K-Nearest Neighbor) and LogitBoost algorithms, predictive models for seed viability were established. The results demonstrated that the L2NN-KNN model outperformed other models, achieving an accuracy of 83.33%, precision of 86.99%, and an F1-score of 0.83. This study confirms that hyperspectral imaging combined with machine learning can effectively predict the viability of Xishuangbanna cucumber seeds, providing a novel technical approach for the conservation of rare and endangered cucumber germplasm resources. The findings hold significant implications for promoting long-term preservation and sustainable utilization of this valuable genetic material. Full article

Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, this study introduces an enhanced model, YOLOv5s-CACSD, which builds upon the YOLOv5s framework. Our model integrates the channel attention (CA) mechanism, CARAFE, and Shape-IoU to improve detection accuracy while employing depthwise separable convolution to reduce model complexity. The proposed architecture was evaluated systematically on the DOTAv1.0 dataset, and our results show that YOLOv5s-CACSD achieved a 91.0% mAP@0.5, marking a 2% improvement over the original YOLOv5s. Additionally, it reduced model parameters and computational complexity by 0.9 M and 2.9 GFLOPs, respectively. These results demonstrate the enhanced detection performance and efficiency of the YOLOv5s-CACSD model, making it suitable for practical applications in land target detection for remote sensing imagery. Full article

With the rapid advancement of artificial intelligence and machine learning technologies, the demand for enhanced device computing capabilities has significantly increased. Neuromorphic computing, an emerging computational paradigm inspired by the human brain, has garnered growing attention as a promising research frontier. Inspired by the human brain’s functionality, this technology mimics the behavior of neurons and synapses to enable efficient, low-power computing. Unlike conventional digital systems, this approach offers a potentially superior alternative. This article delves into the application of nanowire materials (and devices) in neuromorphic computing simulations: First, it introduces the synthesis and preparation methods of nanowire materials. Then, it analyzes in detail the key role of nanowire devices in constructing artificial neural networks, especially their advantages in simulating the functions of neurons and synapses. Compared with traditional silicon-based material devices, it focuses on how nanowire devices can achieve higher connection density and lower energy consumption, thereby enabling new types of neuromorphic computing. Finally, it looks forward to the application potential of nanowire devices in the field of future neuromorphic computing, expecting them to become a key force in promoting the development of intelligent computing, with extensive application prospects in the fields of informatics and medicine. Full article

Background: First aid training in secondary education enhances emergency preparedness and supports public health. Despite its inclusion in many school curricula, there is no consensus on the most effective teaching methodologies. This systematic review aims to compare instructional strategies used in first aid training during Physical Education and evaluate their impact on students’ knowledge, practical skills, and confidence. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines. Six databases (SCOPUS, Web of Science, ERIC, DIALNET, MEDLINE, and PsycINFO) were searched up to December 2024. Eligible studies were quasi-experimental or observational, involved students aged 11–18, and focused on first aid instruction within Physical Education. Methodological quality was assessed using the PEDro scale. Results: Eleven studies with a total of 3069 students aged 11–18 were included. Active and technology-based methodologies outperformed traditional approaches, improving knowledge acquisition (10.2–30.5%) and practical skill development (18.6–42.3%). Long-term retention ranged from 14.2% to 45.8%, with longer interventions yielding better outcomes. Gamification, simulations, and peer learning improved CPR quality and boosted student confidence. However, most studies assessed only short-term outcomes, limiting conclusions about sustained learning. Conclusions: Active methodologies, particularly gamification, simulation, and cooperative learning, enhance knowledge retention, practical skills, and confidence in providing first aid. Although the results were consistently positive, methodological heterogeneity and limited long-term follow-up reduce their generalizability. Further high-quality, longitudinal research is needed to identify the most effective and sustainable strategies. These findings support integrating first aid training into Physical Education as a public health initiative to strengthen emergency preparedness in schools. Full article

This study investigates the integration of digital technologies in leading European museums to enhance heritage interpretation, increase visitor engagement, and contribute to sustainable tourism. As museums increasingly adapt to the digital age, they seek innovative solutions to enrich the visitor experience while promoting sustainability. This research uses a content analysis approach to examine the strategies employed by four prominent museums—the Louvre, the British Museum, the Prado Museum, and the Rijksmuseum. Key digital initiatives, including virtual tours, educational apps, and online collections, are identified as central components of their efforts to improve accessibility, facilitate interactive learning, and attract a wider global audience. Our findings highlight that these digital innovations not only provide visitors with more engaging and informative experiences but also align with sustainability objectives such as reducing carbon footprints and supporting cultural preservation. This study concludes that by leveraging smart technologies, museums are evolving into dynamic, globally connected institutions that strike a balance between conservation and visitor engagement, thereby fostering a more sustainable and inclusive approach to heritage tourism. Full article

To obtain the complete shape and pose of corn under occlusion, this study proposes a point cloud completion algorithm for completing the fragmented corn point cloud after segmentation. Considering that this work focuses on a single-class crop—corn—the proposals mainly focus on the deep learning model size and the completion of the overall shape of the corn. In this work, the 3D corn models derived from segmentation are employed to systematically output the fragmented point cloud data in batches. The Shape Coding PointAttN (SCPAN) algorithm is also proposed, which is based on PointAttN. The model’s structure is simplified to output sparse point clouds and minimize computational complexity, and a gated multilayer perceptron (MLP) containing 3D position coding is introduced to enhance the model’s spatial awareness. In addition, the prior shape encoder module is initially trained and subsequently integrated into the model to enhance its focus on shape characteristics. Compared to the original model, PointAttN, SCPAN achieves a 34.2% reduction in the number of parameters, and the inference time is reduced by 30 ms while maintaining comparable accuracy. The experimental results show that the proposed method can complete the corn point cloud more effectively, using a small model to help estimate the pose and dimensions of corn accurately. This work supports the precise phenotypic analysis of corn and similar crops, such as citrus and tomatoes, and promotes the development of smart agricultural technology. Full article

Speech Emotion Recognition (SER) technology helps computers understand human emotions in speech, which fills a critical niche in advancing human–computer interaction and mental health diagnostics. The primary objective of this study is to enhance SER accuracy and generalization through innovative deep learning models. Despite its importance in various fields like human–computer interaction and mental health diagnosis, accurately identifying emotions from speech can be challenging due to differences in speakers, accents, and background noise. The work proposes two innovative deep learning models to improve SER accuracy: a CNN-LSTM model and an Attention-Enhanced CNN-LSTM model. These models were tested on the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), collected between 2015 and 2018, which comprises 1440 audio files of male and female actors expressing eight emotions. Both models achieved impressive accuracy rates of over 96% in classifying emotions into eight categories. By comparing the CNN-LSTM and Attention-Enhanced CNN-LSTM models, this study offers comparative insights into modeling techniques, contributes to the development of more effective emotion recognition systems, and offers practical implications for real-time applications in healthcare and customer service. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by motor and non-motor dysfunctions that severely compromise patients’ quality of life. While pharmacological treatments provide symptomatic relief in the early stages, advanced PD often requires neurosurgical interventions, such as deep brain stimulation (DBS) and focused ultrasound (FUS), for effective symptom management. A significant challenge in optimizing these therapeutic strategies is the early identification and recruitment of suitable candidates for clinical trials. This review explores the role of artificial intelligence (AI) in advancing neurosurgical and neuroscience interventions for PD, highlighting the ways in which AI-driven platforms are transforming clinical trial design and patient selection. Machine learning (ML) algorithms and big data analytics enable precise patient stratification, risk assessment, and outcome prediction, accelerating the development of novel therapeutic approaches. These innovations improve trial efficiency, broaden treatment options, and enhance patient outcomes. However, integrating AI into clinical trial frameworks presents challenges such as data standardization, regulatory hurdles, and the need for extensive validation. Addressing these obstacles will require collaboration among neurosurgeons, neuroscientists, AI specialists, and regulatory bodies to establish ethical and effective guidelines for AI-driven technologies in PD neurosurgical research. This paper emphasizes the transformative potential of AI and technological innovation in shaping the future of PD neurosurgery, ultimately enhancing therapeutic efficacy and patient care. Full article

The rapid emergence of Industry 4.0 technologies has transformed manufacturing, requiring a workforce skilled in automation, data-driven decision-making, and process optimisation. While traditional education includes structured formats such as lectures and tutorials, it may not always equip graduates with the hands-on expertise demanded by modern industrial challenges. This study presents a Mixed Reality (MR)-based educational framework that promotes interactive experiences to enhance students’ engagement with and understanding of Industry 4.0 concepts, aiming to bridge the skills gap through immersive Virtual Learning Factories (VLFs). The framework was developed using a mixed-methods approach, combining qualitative feedback with quantitative benchmarking. A proof-of-concept MR application was developed and tested at the (Anonymised), simulating Industry 4.0 scenarios in an engineering education context to validate the framework. The findings indicate that MR-based learning improved students’ engagement with the academic content, leading to better knowledge retention and deeper conceptual understanding. The students also demonstrated enhanced problem-solving, process optimisation, and adaptability compared to traditional methods. The immersive nature of MR provided an interactive, context-rich environment that fostered active learning. This research highlights MR’s potential as a transformative educational tool, aligning academic training with industry needs. Future research is recommended to evaluate the framework’s scalability and long-term effectiveness. Full article

Regenerative braking systems (RBS enhance energy efficiency and range in electric vehicles (EVs) by recovering kinetic energy during braking for storage in batteries or alternative systems. This literature review examines RBS advancements from 2005 to 2024, focusing on system design, control strategies, energy storage technologies, and the impact of external and kinematic factors on recovery efficiency. Based on a systematic analysis of 89 peer-reviewed articles from Scopus, it highlights a shift from basic PID controllers to advanced predictive algorithms like Model Predictive Control (MPC) and machine learning approaches. Technologies such as brake-by-wire and in-wheel motors improve safety and stability, with the latter excelling in all-wheel-drive setups over single-axle configurations. Hybrid Energy Storage Systems (HESS), combining batteries with supercapacitors or kinetic accumulators, address power peak demands, though cost and complexity limit scalability. Challenges include high computational requirements, component reliability in harsh conditions, and lack of standardized testing. Research gaps involve long-term degradation, autonomous vehicle integration, and driver behavior effects. Future work should explore cost-effective HESS, robust predictive controls for autonomous EVs, and standardized frameworks to enhance RBS performance and support sustainable transportation. Full article

This paper offers an in-depth analysis of the role of the Internet of Things (IoT) in fire safety systems, with a particular emphasis on fire detection, localization, and evacuation. Through a comprehensive bibliometric analysis, we identify pivotal research trends and advancements in IoT-based sensors, devices, and network architectures that facilitate real-time fire management. In addition, we examine the integration of emerging technologies—such as artificial intelligence, machine learning, and quantum computing—that enhance system performance and operational efficiency. Our study further highlights critical challenges and research gaps, including issues related to dynamic system adaptability, cross-domain synergies, bio-inspired fire safety mechanisms, post-fire analysis capabilities, linguistic and cultural barriers in research, and data security and privacy concerns. Finally, we outline prospective directions for future inquiry, underscoring the need for interdisciplinary collaboration and robust cybersecurity strategies to fully harness the potential of IoT in transforming fire safety. Full article