Search Results (126)

Despite notable advancements in smart home technologies, residential energy management continues to face critical challenges. These include the complex integration of intermittent renewable energy sources, issues related to data latency, interoperability, and standardization across diverse systems, the inflexibility of centralized control architectures in dynamic environments, and the difficulty of accurately modeling and influencing occupant behavior. To address these challenges, this study proposes an intelligent multi-agent system designed to accurately estimate and control energy consumption in residential buildings, with the overarching objective of optimizing energy usage while maintaining occupant comfort and satisfaction. The methodological approach employed is a hybrid framework, integrating multi-agent system architecture with system dynamics modeling and agent-based modeling. This integration enables decentralized and intelligent control while simultaneously simulating physical processes such as heat exchange, insulation performance, and energy consumption, alongside behavioral interactions and real-time adaptive responses. The system is tested under varying conditions, including changes in building insulation quality and external temperature profiles, to assess its capability for accurate control and estimation of energy use. The proposed tool offers significant added value by supporting real-time responsiveness, behavioral adaptability, and decentralized coordination. It serves as a risk-free simulation platform to test energy-saving strategies, evaluate cost-effective insulation configurations, and fine-tune thermostat settings without incurring additional cost or real-world disruption. The high fidelity and predictive accuracy of the system have important implications for policymakers, building designers, and homeowners, offering a practical foundation for informed decision making and the promotion of sustainable residential energy practices. Full article

Maize seed variety classification has become essential in agriculture, driven by advancements in non-destructive sensing and machine learning techniques. This study introduced an efficient method for maize variety identification by combining hyperspectral imaging with a framework that integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Spectral data were acquired by hyperspectral imaging technology from five maize varieties and processed using Savitzky–Golay (SG) smoothing, along with standard normal variate (SNV) preprocessing. To enhance feature selection, the competitive adaptive reweighted sampling (CARS) algorithm was applied to reduce redundant information, identifying 100 key wavelengths from an initial set of 774. This method successfully minimized data dimensionality, reduced variable collinearity, and boosted the model’s stability and computational efficiency. A CNN-LSTM model, built on the selected wavelengths, achieved an accuracy of 95.27% in maize variety classification, outperforming traditional chemometric models like partial least squares discriminant analysis, support vector machines, and extreme learning machines. These results showed that the CNN-LSTM model excelled in extracting complex spectral features and offering strong generalization and classification capabilities. Therefore, the model proposed in this study served as an effective tool for maize variety identification. Full article

Musculoskeletal (MSK) disorders remain a major global cause of disability, with diagnostic complexity arising from their heterogeneous presentation and multifactorial pathophysiology. Recent advances across imaging modalities, molecular biomarkers, artificial intelligence applications, and point-of-care technologies are fundamentally reshaping musculoskeletal diagnostics. This review offers a novel synthesis by unifying recent innovations across multiple diagnostic imaging modalities, such as CT, MRI, and ultrasound, with emerging biochemical, genetic, and digital technologies. While existing reviews typically focus on advances within a single modality or for specific MSK conditions, this paper integrates a broad spectrum of developments to highlight how use of multimodal diagnostic strategies in combination can improve disease detection, stratification, and clinical decision-making in real-world settings. Technological developments in imaging, including photon-counting detector computed tomography, quantitative magnetic resonance imaging, and four-dimensional computed tomography, have enhanced the ability to visualize structural and dynamic musculoskeletal abnormalities with greater precision. Molecular imaging and biochemical markers such as CTX-II (C-terminal cross-linked telopeptides of type II collagen) and PINP (procollagen type I N-propeptide) provide early, objective indicators of tissue degeneration and bone turnover, while genetic and epigenetic profiling can elucidate individual patterns of susceptibility. Point-of-care ultrasound and portable diagnostic devices have expanded real-time imaging and functional assessment capabilities across diverse clinical settings. Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. Wearable sensors and mobile health technologies extend continuous monitoring beyond traditional healthcare environments, generating real-world data critical for dynamic disease management. However, standardization of diagnostic protocols, rigorous validation of novel methodologies, and thoughtful integration of multimodal data remain essential for translating technological advances into improved patient outcomes. Despite these advances, several key limitations constrain widespread clinical adoption. Imaging modalities lack standardized acquisition protocols and reference values, making cross-site comparison and clinical interpretation difficult. AI-driven diagnostic tools often suffer from limited external validation and transparency (“black-box” models), impacting clinicians’ trust and hindering regulatory approval. Molecular markers like CTX-II and PINP, though promising, show variability due to diurnal fluctuations and comorbid conditions, complicating their use in routine monitoring. Integration of multimodal data, especially across imaging, omics, and wearable devices, remains technically and logistically complex, requiring robust data infrastructure and informatics expertise not yet widely available in MSK clinical practice. Furthermore, reimbursement models have not caught up with many of these innovations, limiting access in resource-constrained healthcare settings. As these fields converge, musculoskeletal diagnostics methods are poised to evolve into a more precise, personalized, and patient-centered discipline, driving meaningful improvements in musculoskeletal health worldwide. Full article

Oat is a highly nutritious cereal crop, and the moisture content of its seeds plays a vital role in cultivation management, storage preservation, and quality control. To enable efficient and non-destructive prediction of this key quality parameter, this study presents a modeling framework integrating hyperspectral imaging (HSI) technology with a dual-optimization machine learning strategy. Seven spectral preprocessing techniques—standard normal variate (SNV), multiplicative scatter correction (MSC), first derivative (FD), second derivative (SD), and combinations such as SNV + FD, SNV + SD, and SNV + MSC—were systematically evaluated. Among them, SNV combined with FD was identified as the optimal preprocessing scheme, effectively enhancing spectral feature expression. To further refine the predictive model, three feature selection methods—successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA)—were assessed. PCA exhibited superior performance in information compression and modeling stability. Subsequently, a dual-optimized neural network model, termed Bayes-ASFSSA-BP, was developed by incorporating Bayesian optimization and the Adaptive Spiral Flight Sparrow Search Algorithm (ASFSSA). Bayesian optimization was used for global tuning of network structural parameters, while ASFSSA was applied to fine-tune the initial weights and thresholds, improving convergence efficiency and predictive accuracy. The proposed Bayes-ASFSSA-BP model achieved determination coefficients (R²) of 0.982 and 0.963, and root mean square errors (RMSEs) of 0.173 and 0.188 on the training and test sets, respectively. The corresponding mean absolute error (MAE) on the test set was 0.170, indicating excellent average prediction accuracy. These results significantly outperformed benchmark models such as SSA-BP, ASFSSA-BP, and Bayes-BP. Compared to the conventional BP model, the proposed approach increased the test R² by 0.046 and reduced the RMSE by 0.157. Moreover, the model produced the narrowest 95% confidence intervals for test set performance (Rp²: [0.961, 0.971]; RMSE: [0.185, 0.193]), demonstrating outstanding robustness and generalization capability. Although the model incurred a slightly higher computational cost (480.9 s), the accuracy gain was deemed worthwhile. In conclusion, the proposed Bayes-ASFSSA-BP framework shows strong potential for accurate and stable non-destructive prediction of oat seed moisture content. This work provides a practical and efficient solution for quality assessment in agricultural products and highlights the promise of integrating Bayesian optimization with ASFSSA in modeling high-dimensional spectral data. Full article

The direct-drive turntable serves as the core actuator in high-precision macro-micro drive systems, where its positioning accuracy fundamentally determines overall system performance. Accurate error prediction and compensation technology represent a critical prerequisite for achieving continuous error compensation and predictive control in direct-drive turntables, making research on positioning error modeling, prediction, and compensation of vital importance. This study presents a dynamic continuous error compensation model for direct-drive turntables, based on an analysis of positioning error mechanisms and the implementation of a “decomposition-modeling-integration-correction” strategy, which features high flexibility, adaptability, and online prediction-correction capabilities. Our methodology comprises four key stages: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based decomposition of historical error data, development of component-specific prediction models using Tree-structured Parzen Estimator (TPE)-optimized Light Gradient Boosting Machine (LightGBM) algorithms for each Intrinsic Mode Function (IMF), integration of component predictions to generate initial values, and application of the Adaptive Prediction Correction (APC) module to produce final predictions. Validation results demonstrate substantial performance improvements, with compensated positioning error ranges reduced from [−31.83″, 41.59″] to [−15.09″, 12.07″] (test set) and from [−22.50″, 9.15″] to [−8.15″, 8.56″] (extrapolation test set), corresponding to standard deviation reductions of 71.2% and 61.6%, respectively. These findings conclusively establish the method’s effectiveness in significantly enhancing accuracy while maintaining prediction stability and operational efficiency, underscoring its considerable theoretical and practical value for error compensation in precision mechanical systems. Full article

In modern data management systems, structured query language (SQL) databases, as a mature and stable technology, have become the standard for processing structured data. These databases ensure data integrity through strongly typed schema definitions and support complex transaction management and efficient query processing capabilities. However, data sparsity—where most fields in large table sets remain unused by most queries—leads to inefficiencies in access optimization. We propose a grouping-based approach (GARMT) that partitions SQL queries into fixed-size groups and applies a modified FP-Growth algorithm (GFP-Growth) to identify frequent table access patterns. Experiments on a real-world dataset show that grouping significantly reduces runtime—by up to 40%—compared to the ungrouped baseline while preserving rule relevance. These results highlight the practical value of query grouping for efficient pattern discovery in sparse database environments. Full article

The advent of Industry 4.0 brought about digitalisation and the integration of advanced technologies into industrial processes, with wireless networks emerging as a key enabler in the interconnection of smart devices, cyber–physical systems, and data analytics platforms. With the development of Industry 5.0 and its emphasis on human–machine collaboration, Wi-Fi has positioned itself as a viable alternative for industrial wireless connectivity, supporting seamless communication between robots, automation systems, and human operators. However, its adoption in critical applications remains limited due to persistent concerns over latency, reliability, and interference in shared-spectrum environments. This study evaluates the practical performance of Wi-Fi standards from 802.11n (Wi-Fi 4) to 802.11be (Wi-Fi 7) across three representative environments: residential, laboratory, and industrial. Six configurations were tested under consistent conditions, covering various frequency bands, channel widths, and traffic types. Results prove that Wi-Fi 6/6E delivers the best overall performance, particularly in low-interference 6 GHz scenarios. Wi-Fi 5 performs well in medium-range settings but is more sensitive to congestion, while Wi-Fi 4 consistently underperforms. Early Wi-Fi 7 hardware does not yet surpass Wi-Fi 6/6E consistently, reflecting its ongoing development. Despite these variations, the progression observed across generations clearly demonstrates incremental gains in throughput stability and latency control. While these improvements already provide tangible benefits for many industrial communication scenarios, the most significant leap in industrial applicability is expected to come from the effective implementation of high-efficiency mechanisms. These include OFDMA, TWT, scheduled uplink access, and enhanced QoS features. These capabilities, already embedded in the Wi-Fi 6 and 7 standards, represent the necessary foundation to move beyond conventional best-effort connectivity and toward supporting critical, latency-sensitive industrial applications. Full article

Three-dimensional architectural greening (TAG) enables the integration of ecological, economic, and social advantages via the effective use of multidimensional space in a variety of forms, making it a significant method for enhancing spatial quality in densely populated cities. TAG technology has expanded the scope and capabilities of urban greening. It has the ability to provide green space, improve urban ecology and aesthetics, and alleviate the conflict between limited land resources and rising demand for greening throughout the urbanization process. Currently, there is a lack of a systematic assessment approach that focuses on the public’s visual perception of TAG. It is critical to focus on advances in visual perception approaches and create a “people-oriented perception driven” evaluation system that serves as a scientific foundation for urban three-dimensional greening initiatives. First, this study created a database of 300 TAG cases and selected classic cases using screening, classification, and sampling. Second, three experiments were set up for the study, including the use of the semantic differential (SD) method, and scenic beauty estimation (SBE) for subjective evaluation, and the eye-tracking experiment for objective evaluation. Finally, this study compared subjective and objective evaluations and demonstrated that both two approaches had a certain amount of accuracy. It also investigated the relationship between spatial features and public visual perceptions using methods such as factor and correlation analysis. The three effective methods for evaluating the quality of TAG based on visual perception that are presented in this study—two subjective and one objective—use standardized images, are quick and simple to use, and make up for the drawbacks of conventional strategies like indirectness, inefficiency, and time-consuming data collection. They also form a solid foundation for the real-world application of categorization prediction. In addition to being adaptable to a wide range of application settings, these two assessment paths—subjective evaluation and objective evaluation—can be integrated to complement one another and provide scientific references for future TAG designs and spatial decision making. Full article

Urban drone applications require efficient path planning to ensure safe and optimal navigation through complex environments. Drawing inspiration from the collective intelligence of animal groups and electoral processes in human societies, this study integrates hierarchical structures and group interaction behaviors into the standard Particle Swarm Optimization algorithm. Specifically, competitive and supportive behaviors are mathematically modeled to enhance particle learning strategies and improve global search capabilities in the mid-optimization phase. To mitigate the risk of convergence to local optima in later stages, a mutation mechanism is introduced to enhance population diversity and overall accuracy. To address the challenges of urban drone path planning, this paper proposes an innovative method that combines a path segmentation and prioritized update algorithm with a cubic B-spline curve algorithm. This method enhances both path optimality and smoothness, ensuring safe and efficient navigation in complex urban settings. Comparative simulations demonstrate the effectiveness of the proposed approach, yielding smoother trajectories and improved real-time performance. Additionally, the method significantly reduces energy consumption and operation time. Overall, this research advances drone path planning technology and broadens its applicability in diverse urban environments. Full article

PolyJet™ 3D printing is an additive manufacturing (AM) technology from Stratasys^TM. It has been used for applications such as tissue mimicking, printing anatomical models, and surgical planning. The materials available from Stratasys^TM have the inherent capabilities of producing a number of PolyJet™ materials with a range of physical properties that can be utilized for representing realistic tissue behavior mechanically. The preset materials available in the PolyJet™ printing software version 1.92.17.44384 GrabCAD^TM Print allow the user to manufacture materials similar to biological tissue, but the combinations of possibilities are limited and might not represent the broad spectrum of all tissue types. The purpose of this study was to determine the combination of PolyJet™ materials that most accurately mimicked a particular biological tissue mechanically. A detailed Design of Experiment (DOE) methodology was used to determine the combination of material mixtures and printing parameters and to analyze their mechanical properties that best matched the biological tissue properties available in the literature of approximately 50 different tissue types. Uniaxial tensile testing was performed according to the ASTM standard D638-14 of samples printed from Stratasys J850 digital anatomy printer to their determined stress–strain properties. The obtained values were subsequently validated by comparing them with the corresponding mechanical properties of biological tissues available in the literature. The resulting model, developed using the DOE approach, successfully produced artificial tissue analogs that span a wide range of mechanical characteristics, from tough, load-bearing tissues to soft, compliant tissues. The validation confirmed the effectiveness of the model in replicating the diverse mechanical behavior of various human tissues. Overall, this paper provides a detailed methodology of how materials and settings were chosen in GrabCAD^TM Print software and Digital Anatomy Creator^TM (DAC) to achieve an accurate artificial tissue material. Full article

This review article explores the transformative impact of next-generation technologies on patient care and rehabilitation. The advent of next-generation tools has revolutionized the fields of patient care and rehabilitation, providing modern solutions to improve scientific outcomes and affected person studies. Powered through improvements in artificial intelligence, robotics, and smart devices, these improvements are reshaping healthcare with the aid of improving therapeutic approaches and personalizing treatments. In the world of rehabilitation, robotic devices and assistive technology are supplying essential help for people with mobility impairments, promoting more independence and healing. Additionally, wearable technology and real-time tracking systems permit continuous fitness information monitoring, taking into consideration early analysis and extra effective, tailored interventions. In clinical settings, these modern-day innovations have automated diagnostics, enabled remote patient-monitoring, and brought virtual rehabilitation systems that expand the reach of clinical experts. This comprehensive review delves into the evolution, cutting-edge programs, and destiny potential of that equipment by examining their capability to deliver progressed care even while addressing growing needs for efficient healthcare solutions. Furthermore, this review explores the challenges related to their adoption, including ethical considerations, accessibility barriers, and the need for refined regulatory standards to ensure their safe and widespread use. Full article

Bacterial biofilms play an important role in the pathogenesis of infectious diseases but are also very relevant in other fields such as the food industry. This fact has led to an increased focus on the early identification of these structures as prophylaxes to prevent biofilm-related contaminations or infections. One of the objectives of the present study was to assess the effectiveness of NIR (Near Infrared) spectroscopy in the detection and differentiation of biofilms from different bacterial species, namely Staphylococcus epidermidis, Staphylococcus aureus, Enterococcus faecium, Salmonella Typhymurium, Escherichia coli, Listeria monocytogenes, and Lactiplantibacillus plantarum. Additionally, we aimed to examine the capability of this technology to specifically identify S. aureus biofilms on glass surfaces commonly used as storage containers and processing equipment. We developed a detailed methodology for data acquisition and processing that takes into consideration the biochemical composition of these biofilms. To improve the quality of the spectral data, SNV (Standard Normal Variate) and Savitzky–Golay filters were applied, which correct systematic variations and eliminate random noise, followed by an exploratory analysis that revealed significant spectral differences in the NIR range. Then, we performed principal component analysis (PCA) to reduce data dimensionality and, subsequently, a Random Forest discriminant statistical analysis was used to classify biofilms accurately and reliably. The samples were organized into two groups, a control set and a test set, for the purpose of performing a comparative analysis. Model validation yielded an accuracy of 80.00% in the first analysis (detection and differentiation of biofilm) and 93.75% in the second (identification of biofilm on glass surfaces), thus demonstrating the efficacy of the proposed method. These results demonstrate that this technique is effective and reliable, indicating great potential for its application in the field of biofilm detection. Full article

Optical coherence tomography (OCT) has been extensively utilized in cardiovascular diagnostics due to its high resolution, rapid imaging capabilities; however, its adaptation for cerebrovascular applications remains constrained by the narrow, tortuous anatomical structure of cerebral vessels. To address these limitations, this study introduces a cerebrovascular-specific OCT (bOCT) catheter, an advanced adaptation of the cardiovascular OCT (cOCT) catheter, with significant structural modifications for improved access to brain blood vessels. The bOCT catheter incorporates a braided wire within a braided tube, strategically reinforcing axial strength. The distal shaft was reconfigured as a single-lumen structure, facilitating unified movement of the rotating fiber optic core and guidewire, thereby reducing guidewire bending and augmenting force transmission stability. Additionally, the anterior protrusion was removed and replaced with a dual-lumen configuration, significantly enhancing lesion accessibility. The bOCT catheter’s performance was validated in a 3D physical model and an animal model, demonstrating pronounced enhancements in flexibility, pushability, and navigability. Notably, the pushability through curved flow paths significantly improved, enhancing access to cerebral blood vessels. Therefore, this innovation promises to revolutionize cerebrovascular diagnostics with high-resolution imaging suited to the complex brain vasculature, setting a new standard in intravascular imaging technology. Full article

This study adopted a socio-technical approach to optimizing key factors for implementing the ETHICS (Effective Technical and Human Implementation of Computer-based Systems) framework in long-term care. Accurate record-keeping by nurse aides is essential, and deploying suitable information technology solutions can greatly improve operational efficiency. To achieve a comprehensive understanding of system requirements and information needs, the researchers combined the Fuzzy Delphi method, FAHP (Fuzzy Analytic Hierarchy Process), and TISM (Total Interpretive Structural Modeling), addressing both human and technical dimensions. The findings highlighted that the efficient allocation of human resources, a consultative and participative work environment, and adequate time to deliver high-quality care are crucial for enhancing record-keeping practices and overall operational efficiency. This improvement will ultimately lead to a higher care quality, cost savings, and better resource utilization. Additionally, adapting to changes in technology, regulations, economic conditions, demographics, industry standards, and organizational practices remains critical. By promoting a balanced integration of technical capabilities with human factors, this approach supports the effective design of socio-technical systems in long-term care settings. Full article

Time-Sensitive Networking (TSN) in avionics is being considered as a standard capable of providing more real-time and safety-critical capabilities than AFDX, the current standard in avionics communications. A TSN profile is therefore being developed for the aerospace domain by IEEE. Hence, this research outlines the deployment of a testbed aimed at validating TSN in avionics to ensure that this technology meets the stringent timing and latency requirements of avionics applications. To this end, a daisy chain or ring topology has been set up for the validation of the testbed, as this topology provides redundancy of paths without the need to add additional devices. This work presents latency and jitter measurements under different priority configurations and different channel saturation conditions, showing no packet loss and demonstrating the reliability of TSN for time-critical applications. A comparison with Avionics Full Duplex Ethernet (AFDX) simulations was also made, highlighting the weaknesses of AFDX. By providing a comprehensive platform for testing and validation, the testbed will contribute to the advancement of TSN technology in aerospace applications, ultimately improving the safety and efficiency of aircraft operations. Full article