Search Results (424)

Urban lighting systems contribute significantly to energy consumption and light pollution, raising environmental and societal concerns. This paper explores the potential of Artificial Intelligence (abbreviation: AI)-driven adaptive urban lighting as a sustainable solution, framed within a multi-level perspective on socio-technical transitions. At the landscape level, increasing urbanization and global sustainability targets exert pressure for energy-efficient practices, while traditional street lighting regimes remain largely rigid and resource-intensive. At the niche level, we propose a novel adaptive lighting system integrating real-time Internet of Things (abbreviation: IoT) sensor data and machine learning algorithms to dynamically adjust illumination based on traffic, pedestrian activity, weather conditions, and ambient light. Studies demonstrate that the proposed approach can significantly reduce energy use while minimizing light pollution, without compromising safety or visibility. The results indicate that such niche innovations, supported by AI and renewable energy integration, have the potential to influence broader regime change and contribute to sustainable urban development. This research highlights the importance of combining technological innovation with socio-technical frameworks to address pressing urban environmental challenges, offering insights for policymakers, urban planners, and energy managers seeking to balance efficiency, safety, and ecological impact. Full article

Temperature prediction for partially shaded photovoltaic (PV) modules is essential for ensuring the stability and safety of PV systems. However, existing methods suffer from high computational complexity, limiting their applicability in engineering practice. Aimed at a real-time and portable algorithm that can be embedded in mobile devices for intelligent monitoring of PV stations, a simple and fast method is designed in this work for estimating the thermal behavior of PV modules under partial shading conditions. To the best of our knowledge, this is the first work in this field that achieves computational simplicity without relying on professional commercial software. The experimental results validate the accuracy of the proposed method in comparison with the multiphysics model (which is widely regarded as the benchmark in this field) while significantly improving computational efficiency. Simulations are conducted to explore the effects of shading proportions and environmental conditions. Shading proportions ranging from 6% to 90% are prone to promoting the development of hotspots under conditions that involve partial shading of an individual cell. Higher irradiance, a higher ambient temperature and a lower wind speed result in a higher temperature of the PV module. Full article

Background: Falls are a leading cause of morbidity and mortality among older adults, creating a need for technologies that can automatically detect falls and summon timely assistance. The rapid evolution of sensor technologies and artificial intelligence has led to a proliferation of fall detection systems (FDS). This systematic review synthesizes the recent literature to provide a comprehensive overview of the current technological landscape. Objective: The objective of this review is to systematically analyze and synthesize the evidence from the academic literature on fall detection technologies. The review focuses on three primary areas: the sensor modalities used for data acquisition, the computational models employed for fall classification, and the emerging trend of shifting from reactive detection to proactive fall risk prediction. Methods: A systematic search of electronic databases was conducted for studies published between 2008 and 2025. Following the PRISMA guidelines, 130 studies met the inclusion criteria and were selected for analysis. Information regarding sensor technology, algorithm type, validation methods, and key performance outcomes was extracted and thematically synthesized. Results: The analysis identified three dominant categories of sensor technologies: wearable systems (primarily Inertial Measurement Units), ambient systems (including vision-based, radar, WiFi, and LiDAR), and hybrid systems that fuse multiple data sources. Computationally, the field has shown a progression from threshold-based algorithms to classical machine learning and is now dominated by deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers. Many studies report high performance, with accuracy, sensitivity, and specificity often exceeding 95%. An important trend is the expansion of research from post-fall detection to proactive fall risk assessment and pre-impact fall prediction, which aim to prevent falls before they cause injury. Conclusions: The technological capabilities for fall detection are well-developed, with deep learning models and a variety of sensor modalities demonstrating high accuracy in controlled settings. However, a critical gap remains; our analysis reveals that 98.5% of studies rely on simulated falls, with only two studies validating against real-world, unanticipated falls in the target demographic. Future research should prioritize real-world validation, address practical implementation challenges such as energy efficiency and user acceptance, and advance the development of integrated, multi-modal systems for effective fall risk management. Full article

Blood pressure (BP) monitoring is crucial for identifying high BP (hypertension) and is an important aspect of patient care. However, traditional cuff-based methods for BP monitoring are unsuitable for continuous monitoring and can cause discomfort to patients. This survey critically examines the emerging field of cuffless BP monitoring, highlighting advances beyond traditional cuff-based methods. Technologies such as radar, optical, acoustic, and capacitive sensors offer the potential for continuous, non-invasive BP estimation, enabling applications in remote health monitoring and ambient clinical intelligence. We introduce a unifying taxonomy covering sensing modalities, physiological measurement principles, signal processing techniques, and translational challenges. Emphasis is placed on methods that eliminate subject-specific calibration, overcome motion artifacts, and satisfy international validation standards. The review also analyses Machine Learning (ML) and sensor fusion approaches that enhance predictive accuracy. Despite encouraging results, challenges remain in achieving clinically acceptable accuracy across diverse populations and real-world conditions. This work delineates the current landscape, benchmarks performance against gold standards, and identifies key future directions for scalable, explainable, and regulatory-compliant BP monitoring systems. Full article

The drying process in indirect solar dryers is strongly influenced by rapidly changing ambient conditions, resulting in highly nonlinear and dynamic system behavior. Accurate modeling is therefore essential for performance evaluation, process optimization, and reliable prediction of the drying chamber temperature, which plays a key role in ensuring efficient moisture removal while preserving the nutritional and sensory quality of dried products. In this study, a fuzzy logic–based modeling approach using the Mamdani inference system is developed to predict the drying chamber temperature over a wide range of operating conditions. Experimental measurements were carried out with solar radiation varying from 400 to 950 W/m² and ambient temperature ranging from 20 to 50 °C, covering both static and dynamic system responses. The fuzzy model employs solar radiation and ambient temperature as input variables, represented by five and three triangular membership functions, respectively, while the drying chamber temperature is defined as the output variable using five triangular membership functions (T1–T5). The Mamdani inference system consists of 15 “if–then” rules, and centroid defuzzification is applied to obtain crisp output values. Model validation across the investigated operating range demonstrates a strong agreement between predicted and experimental temperatures. For example, at a solar radiation of 700 W/m² and an ambient temperature of 46 °C, the predicted chamber temperature is 50.9 °C compared to a measured value of 51.0 °C, while at 750 W/m² and 50 °C, the predicted temperature of 52.0 °C closely matches the experimental value of 51.8 °C. Statistical evaluation yields RMSE = 0.38 °C, MAE = 0.29 °C, and R² = 0.997, demonstrating effective temperature tracking capability within the tested operating range. These results show that the Mamdani fuzzy logic approach can effectively represent the thermal behavior of an indirect solar dryer within the tested operating range. The proposed model also provides a promising basis for the future development of real-time intelligent control strategies aimed at improving energy efficiency and product quality. Full article

Accurate state estimation is a key technology for improving battery utilization and ensuring operational safety in electric vehicles. The joint estimation of the state of charge (SOC) and the state of power (SOP) over a wide temperature range is therefore essential for intelligent battery management systems. To address modeling uncertainties and estimation accuracy degradation induced by ambient temperature variations, a dual-polarization equivalent circuit thermal model incorporating temperature bias is proposed, and online parameter updating is achieved using the forgetting factor recursive least squares (FFRLS) algorithm. Furthermore, an unscented particle filter (UPF) is constructed by employing the unscented Kalman filter (UKF) as the proposal density function of the particle filter, thereby improving the estimation accuracy and convergence speed of SOC under wide temperature conditions. Based on the coupling relationship between SOC and SOP, a stepwise progressive strategy is then developed to predict the peak power state under multiple constraints, enhancing the robustness of SOP estimation. Simulation and experimental results demonstrate that the proposed method can accurately estimate SOC and SOP under complex operating conditions over a wide temperature range from −5 °C to 45 °C, exhibiting favorable convergence performance and estimation accuracy, which contributes to the safe operation and performance optimization of electric vehicle battery systems. Full article

Distributed Denial-of-Service (DDoS) attacks remain the most pervasive and operationally disruptive cyber threat and are routinely weaponized in interstate conflict (e.g., Russia–Ukraine and Stuxnet). Although attack-chain models are standard for Advanced Persistent Threat (APT) analysis, they have seldom been applied to DDoS, which is often framed as a single-step volumetric assault. However, ubiquitous intelligence and ambient connectivity increasingly enable DDoS campaigns to unfold as multi-stage operations rather than isolated floods. In parallel, large language models (LLMs) create new opportunities to strengthen traditional DDoS defenses through richer contextual understanding. Reviewing incidents from 2019 to 2024, we propose a three-phase DDoS attack chain—preparation, development, and execution—that captures contemporary tactics and their dependencies on novel hardware, network architectures, and application protocols. We classify these patterns, contrast them with conventional DDoS, survey current defenses (anycast and scrubbing, BGP Flowspec, programmable data planes, adaptive ML detection, API hardening), and outline research directions in cross-layer telemetry, adversarially robust learning, automated mitigation orchestration, and cooperative takedown. Full article

This study presents an artificial neural network (ANN)-based predictive model for evaluating the drying efficiency of a cabinet-type solar dryer used for dehydrating Plantago major leaves under natural climatic conditions. The performance of solar drying systems is strongly affected by nonlinear and time-varying factors such as solar irradiance, drying-chamber temperature, and ambient relative humidity, which limits the accuracy of conventional modeling approaches. To address this challenge, a multilayer feedforward ANN was developed using solar irradiance, chamber temperature, and relative humidity as input variables and drying efficiency as the output. Experimental data comprising 120 samples were collected during summer conditions and divided into training, validation, and testing subsets. The ANN was trained using the Levenberg–Marquardt algorithm and demonstrated strong predictive performance, achieving an overall correlation coefficient of R = 0.9556 and a low mean squared error of $1.22\times {10}^{-4}$ The results confirm that the proposed ANN model can reliably capture the nonlinear drying behavior and accurately predict drying efficiency, providing a practical tool for real-time performance evaluation and supporting the development of intelligent monitoring and control strategies for cabinet-type solar drying systems. Full article

The rapid integration of photovoltaic (PV) generation into modern power networks introduces significant operational challenges, including intermittent power production, uneven charge distribution, and reduced system reliability in multi-battery energy storage systems. Addressing these challenges requires intelligent, adaptive, and physically consistent control strategies capable of operating under uncertain environmental and load conditions. This study proposes a Physics-Informed Neural Network (PINN)-based charge allocation framework that explicitly embeds physical constraints—namely charge conservation and State-of-Charge (SoC) equalization—directly into the learning process, enabling real-time adaptive control under varying irradiance and load conditions. The proposed controller exploits real-time measurements of PV voltage, current, and irradiance to achieve optimal charge distribution while ensuring converter stability and balanced battery operation. The framework is implemented and validated in MATLAB/Simulink under Standard Test Conditions of 1000 W·m⁻² irradiance and 25 °C ambient temperature. Simulation results demonstrate stable PV voltage regulation within the 230–250 V range, an average PV power output of approximately 95 kW, and effective duty-cycle control within the range of 0.35–0.45. The system maintains balanced three-phase grid voltages and currents with stable sinusoidal waveforms, indicating high power quality during steady-state operation. Compared with conventional Proportional–Integral–Derivative (PID) and Model Predictive Control (MPC) methods, the PINN-based approach achieves faster SoC equalization, reduced transient fluctuations, and more than 6% improvement in overall system efficiency. These results confirm the strong potential of physics-informed intelligent control as a scalable and reliable solution for smart PV–battery energy systems, with direct relevance to renewable microgrids and electric vehicle charging infrastructures. Full article

This paper proposes an efficient and automated smart healthcare communication framework that integrates a two-level filtering scheme with a multi-objective Genetic Algorithm (GA) to enhance the reliability, timeliness, and energy efficiency of Internet of Medical Things (IoMT) systems. In the first stage, physiological signals collected from heterogeneous sensors (e.g., blood pressure, glucose level, ECG, patient movement, and ambient temperature) were pre-processed using an adaptive least-mean-square (LMS) filter to suppress noise and motion artifacts, thereby improving signal quality prior to analysis. In the second stage, a GA-based optimization engine selects optimal routing paths and transmission parameters by jointly considering end-to-end delay, Signal-to-Noise Ratio (SNR), energy consumption, and packet loss ratio (PLR). The two-level filtering strategy, i.e., LMS, ensures that only denoised and high-priority records are forwarded for more processing, enabling timely delivery for supporting the downstream clinical network by optimizing the communication. The proposed mechanism is evaluated via extensive simulations involving 30–100 devices and multiple generations and is benchmarked against two existing smart healthcare schemes. The results demonstrate that the integrated GA and filtering approach significantly reduces end-to-end delay by 10%, as well as communication latency and energy consumption, while improving the packet delivery ratio by approximately 15%, as well as throughput, SNR, and overall Quality of Service (QoS) by up to 98%. These findings indicate that the proposed framework provides a scalable and intelligent communication backbone for early disease detection, continuous monitoring, and timely intervention in smart healthcare environments. Full article

Core body temperature is a critical physiological indicator for assessing and diagnosing animal health status. In bovines, continuously monitoring this metric enables accurate evaluation of their physiological condition; however, traditional rectal measurements are labor-intensive and cause stress in animals. To achieve intelligent, contactless temperature monitoring in cattle, we proposed a non-invasive method based on thermal imaging combined with environmental data fusion. First, thermal infrared images of the cows’ faces were collected, and the You Only Look Once (YOLO) object detection model was used to locate the head region. Then, the YOLO segmentation network was enhanced with the Online Convolutional Re-parameterization (OREPA) and High-level Screening-feature Fusion Pyramid Network (HS-FPN) modules to perform instance segmentation of the eye socket area. Finally, environmental variables—ambient temperature, humidity, wind speed, and light intensity—were integrated to compensate for eye socket temperature, and a random forest algorithm was used to construct a predictive model of rectal temperature. The experiments were conducted using a thermal infrared image dataset comprising 33,450 frontal-view images of dairy cows with a resolution of 384 × 288 pixels, along with 1471 paired samples combining thermal and environmental data for model development. The proposed method achieved a segmentation accuracy (mean average precision, mAP_50–95) of 86.59% for the eye socket region, ensuring reliable temperature extraction. The rectal temperature prediction model demonstrated a strong correlation with the reference rectal temperature (R² = 0.852), confirming its robustness and predictive reliability for practical applications. These results demonstrate that the proposed method is practical for non-contact temperature monitoring of cattle in large-scale farms, particularly those operating under confined or semi-confined housing conditions. Full article

To address the susceptibility of traditional concrete to explosive spalling and the lack of in situ damage-monitoring methods at high temperatures, in this study, a novel self-sensing, high-temperature-resistant Engineered Cementitious Composite (ECC) was developed. The matrix contains eco-friendly glass sand reinforced with a hybrid system of polypropylene fibers (PPFs) and carbon fibers (CFs). The evolution of mechanical properties and the multimodal self-sensing characteristics of the ECC were systematically investigated following thermal treatment from 20 °C to 800 °C. The results indicate that the hybrid system exhibits a significant synergistic effect: through PFFs’ pore-forming mechanism, internal vapor pressure is effectively released to mitigate spalling, while CFs provide residual strength compensation. Mechanically, the compressive strength increased by 51.32% (0.9% CF + 1.0% PPF) at 400 °C compared to ambient temperature, attributed to high-temperature-activated secondary hydration. Regarding self-sensing, the composite containing 1.1% CF and 1.5% PPF displayed superior thermosensitivity during heating (resistivity reduction of 49.1%), indicating potential for early fire warnings. Notably, pressure sensitivity was enhanced after high-temperature exposure, with the 0.7% CF + 0.5% PPF group achieving a Fractional Change in Resistivity of 31.1% at 600 °C. Conversely, flexural sensitivity presented a “thermally induced attenuation effect” primarily attributed to high-temperature-induced interfacial weakening. This study confirms that the “pore-formation” mechanism, combined with the reconstruction of the conductive network, governs the material’s macroscopic properties, providing a theoretical basis for green, intelligent, and fire-safe infrastructure. Full article

The anticipated transition from 5G to 6G is driven not by incremental performance demands but by a widening mismatch between emerging application requirements and the capabilities of existing cellular systems. Despite rapid progress across 3GPP Releases 15–20, the current literature lacks a unified analysis that connects these standardization milestones to the concrete technical gaps that 6G must resolve. This study addresses this omission through a cross-release, application-driven review that traces how the evolution from enhanced mobile broadband to intelligent, sensing integrated networks lays the foundation for three core 6G service pillars: immersive communication (IC), everything connected (EC), and high-precision positioning. By examining use cases such as holographic telepresence, cooperative drone swarms, and large-scale Extended Reality (XR) ecosystems, this study exposes the limitations of today’s spectrum strategies, network architectures, and device capabilities and identifies the performance thresholds of Tbps-level throughput, sub-10 cm localization, sub-ms latency, and 10 M/km² device density that next-generation systems must achieve. The novelty of this review lies in its synthesis of 3GPP advancements in XR, the non-terrestrial network (NTN), RedCap, ambient Internet of Things (IoT), and consideration of sustainability into a cohesive key performance indicator (KPI) framework that links future services to the required architectural and protocol innovations, including AI-native design and sub-THz operation. Positioned against global initiatives such as Hexa-X and the Next G Alliance, this paper argues that 6G represents a fundamental redesign of wireless communication advancement in 5G, driven by intelligence, adaptability, and long-term energy efficiency to satisfy diverse uses cases and requirements. Full article

Non-communicable diseases (NCDs) impose an overwhelming burden on global health systems. Prevailing healthcare for NCDs remains largely hospital-centered, episodic, and reactive, rendering them poorly suited to address the long-term, heterogeneous, and multifactorial nature of NCDs. Rapid advances in digital technologies, artificial intelligence (AI), and precision medicine have catalyzed the development of an integrative framework for digital-intelligent precision health management, characterized by the functional integration of data, models, and decision support. It is best understood as an integrated health management framework operating across three interdependent dimensions. First, it is grounded in multidimensional health-related phenotyping, enabled by continuous digital sensing, wearable and ambient devices, and multi-omics profiling, which together allow for comprehensive, longitudinal characterization of individual health states in real-world settings. Second, it leverages intelligent risk warning and early diagnosis, whereby multimodal data are fused using advanced machine learning algorithms to generate dynamic risk prediction, detect early pathological deviations, and refine disease stratification beyond conventional static models. Third, it culminates in health management under intelligent decision-making, integrating digital twins and AI health agents to support personalized intervention planning, virtual simulation, adaptive optimization, and closed-loop management across the disease continuum. Framed in this way, digital-intelligent precision health management enables a fundamental shift from passive care towards proactive, anticipatory, and individual-centered health management. This Perspectives article synthesizes recent literature from the past three years, critically examines translational and ethical challenges, and outlines future directions for embedding this framework within population health and healthcare systems. Full article

This article presents a database focused on measuring the experimental performance of a pilot parabolic trough collector (PTC) combined with the meteorological conditions corresponding to the installation site. Water was chosen as the fluid to recirculate through the PTC circuit. The data were recorded between August and September, assuming that global radiation was adequate for use in the concentration process. The database comprises seven experimental tests, which contain variables such as time, inlet temperature, outlet temperature, ambient temperature, global radiation, diffuse radiation, wind direction, wind speed, and volumetric flow rate. Based on the data obtained from this pilot PTC system, it is possible to provide relevant information for the installation and construction of large-scale solar collectors. Furthermore, the climatic conditions considered allow key factors in the design of multiple collectors to be determined, such as the type of arrangement (series or parallel) and manufacturing materials. In addition, the data collected in this study are key to validating future theoretical models of the PTC. Finally, considering the real operating conditions of a PTC in conjunction with meteorological variables could also be useful for predicting the system’s thermal performance using artificial intelligence-based models. Full article