Search Results (1,641)

Photovoltaic (PV) technology is now one of the main options for expanding the power of low-carbon electricity generation. However, in practical operation, PV systems still face several persistent difficulties, including the variability of solar irradiance, gradual performance degradation, fault occurrence, suboptimal control, and the growing complexity of grid-connected operation. These issues explain why artificial intelligence (AI) has become increasingly relevant in PV research, not only as a prediction tool, but also to improve monitoring, control, diagnosis, and decision-making. This review investigates the applications of AI in the major stages of the PV system lifecycle: solar resource assessment, power forecasting, fault detection, condition monitoring, system sizing, maximum power point tracking (MPPT), and grid integration. Rather than treating these applications as separate research topics, the review attempts to connect them through the common factors that determine their practical value: data quality, sensing configuration, model complexity, physical operating conditions, and deployment constraints. The reviewed studies indicate that AI-based MPPT methods can achieve tracking efficiencies close to 99%, while recent forecasting models, particularly LSTM, CNN–LSTM, and transformer-based architectures, can reduce prediction errors under changing weather conditions. At the same time, PV fault detection is moving beyond electroluminescence image classification toward more practical multimodal strategies that combine infrared thermography, RGB and drone imagery, electrical measurements, and SCADA/IoT data. Nevertheless, the progress reported in the literature should be interpreted with caution. Many proposed models are still evaluated on limited or non-standardized datasets, and their performance may decrease when they are transferred to different PV technologies, climates, fault severities, or operating conditions. Other recurring limitations include class imbalance, high computational cost, weak generalization, and the limited interpretability of deep-learning models. For this reason, hybrid neural networks, explainable AI, physics-informed learning, edge-AI, federated learning, and quantum machine learning are discussed as possible directions for making AI-based PV solutions more reliable and deployable. This review aims to critically synthesize recent advances and remaining gaps in order to support the practical integration of AI into efficient, reliable, and sustainable PV systems. Full article

Diagnosing faults across different smart homes is hard: sensor names, layouts, and daily routines differ from home to home, so a model trained in one home rarely works in another. We study an ontology-guided framework for cross-home fault diagnosis, but our main contribution is a benchmark-validity audit—a systematic check of whether the datasets used to evaluate such systems actually measure fault detection. Using the public Center for Advanced Studies in Adaptive Systems (CASAS) smart-home datasets (homes hh101–hh110) and real household power data (HomeC, UMass Smart*), we show that much of the high cross-home accuracy reported on these benchmarks is an artifact of features that re-encode the labelling rules rather than evidence of transfer: when those features are removed, the macro-averaged F1 score (macro-F1) collapses toward the level obtained with randomly permuted labels. We therefore treat these datasets as semantic-transfer and benchmark-validity studies, not fault-detection results. The framework’s distinguishing component is a counterfactual calibration layer that returns a probability for its recommended intervention; on a controlled structural causal model with known interventions, it achieves a Brier skill score of $0.369$ for intervention-success probabilities. Separately, on the simulation-derived LBNL Fan Coil Unit benchmark, a conventional gradient-boosted multiclass fault classifier achieves accuracy comparable to a random forest but about six times lower expected calibration error ($0.026$ vs. $0.159$) under a scenario-matched split. This calibration advantage does not generalize to held-out simulation scenarios, where the calibration error rises to $0.372$; we report this negative result as a limitation. We are explicit about scope: the ontology reasoner and the real-stream causal graph are only partially implemented, and the counterfactual recommendations are validated only under controlled or simulated conditions, not in deployed homes. The results are intended for researchers who build or benchmark sensor-based fault-diagnosis models, for dataset curators, and for practitioners who need calibrated rather than merely accurate outputs. All code, the proxy-label rules, and the leakage audit are released. Full article

Recently, the Internet of Things (IoT) has evolved into the Artificial Intelligence of Things (AIoT) through its combination with artificial intelligence (AI) technology and has become capable of providing intelligent services in all industrial sectors. Globally, energy consumption within buildings is continuously increasing alongside the advancement of IT and AI technologies. Since this increase is attributed to various causes—ranging from large-scale climate change to small-scale indoor environmental factors (air quality) and health factors—research aimed at reducing indoor energy consumption is actively underway. In particular, in the home environment where people spend a significant portion of their day, maintaining indoor air quality (IAQ) is critical for health, and energy conservation in heating, ventilation, and air conditioning (HVAC) systems is essential. In Korea, the number of single-person households is increasing and was expected to reach 36.1% of all households by 2024, leading people to live in increasingly smaller homes. This study aimed to verify residents using contactless facial recognition to prevent pandemics such as COVID-19 and to provide comfortable indoor air quality. Resident facial recognition was performed by identifying residents’ faces in images captured by the Pi camera using OpenCV’s Haar feature-based cascade classifier. Indoor air quality measurements were conducted in four indoor locations, measuring various environmental factors (PM_2.5, CO₂, etc.) based on environmental sensors and the IoT. Furthermore, to manage indoor air quality, AI was utilized based on the measurement data to classify the four spaces, with a success rate of 96%. Additionally, considering the indoor area of the experimental environment (97 m²), it was confirmed that operating a 70 W air purifier only when the resident is indoors can reduce power consumption by approximately 33–75% compared to running it 24 h a day. Full article

Conventional solvent-based and hot-melt road marking paints face significant challenges regarding high volatile organic compound (VOC) emissions and limited durability, necessitating the development of eco-friendly, high-performance alternatives. In this study, a waterborne acrylic road marking paint was systematically formulated and optimized using an L₁₆(4⁵) orthogonal experimental design coupled with a comprehensive weighted scoring method integrating subjective and objective (entropy) weights. Four key formulation parameters (pigment-to-binder ratio, titanium dioxide content, ground calcium carbonate content, and coalescing agent dosage) were investigated, with abrasion resistance, hiding power, luminance factor, and stain resistance as evaluation criteria. The optimized formulation was identified through range analysis of comprehensive scores and subsequently subjected to rigorous performance characterization, including retroreflectivity optimization, Taber and accelerated abrasion testing, UV-accelerated weathering, skid resistance, and VOC emissions measurement using a self-designed sealed chamber system. Benchmark comparisons against commercial waterborne and hot-melt paints demonstrated that the developed formulation achieves superior abrasion resistance, exceptional weatherability, and meaningfully lower VOC emissions. Field application on an operational highway section in Liaoning Province, China, confirmed the practical constructability and performance reliability of the optimized paint under real-world construction conditions. This research provides both theoretical guidance and practical validation for the design of sustainable, durable, and highly visible road marking materials, contributing to the advancement of environmentally responsible transportation infrastructure. Full article

The evolution of Advanced Metering Infrastructure (AMI) requires reliable, energy-efficient, and scalable communication technologies for connecting large numbers of smart meters and gateways with utility backend systems. Among 3GPP Cellular Internet of Things (CIoT) technologies, Narrowband IoT (NB-IoT) and LTE-M are promising candidates due to their extended coverage, low cost, and power efficiency. However, selecting between them remains challenging because performance depends on deployment environments, spatial distribution, and radio signal conditions. This study addresses the CIoT network selection problem in AMI networks by applying machine learning to predict the appropriate communication technology from smart meter location and Reference Signal Received Power (RSRP). Three supervised learning algorithms, namely Decision Tree, Support Vector Machine, and XGBoost, were evaluated using field measurement datasets from two AMI deployment areas. A spatial holdout strategy was applied to assess performance in unseen geographical regions. Decision Tree achieved the best performance in Area 1, with an accuracy of 0.7143 and an F1-score of 0.6154. In Area 2, XGBoost achieved the highest performance, with an accuracy of 0.9732 and an F1-score of 0.9388. The results demonstrate the feasibility of ML-based CIoT selection under spatially heterogeneous and imbalanced deployment conditions. Full article

Ocean current energy is abundant, yet its exploitation is severely constrained by the low-velocity conditions typical of most marine environments, where conventional lift-type turbines exhibit poor self-starting capability and low efficiency. This review provides the first comprehensive synthesis of research on free-stream Archimedes spiral hydrokinetic turbines (ASHTs), a class of drag-dominated rotors developed specifically for low-velocity kinetic energy harvesting. A unified classification is introduced, dividing ASHTs into single-blade long-axis (SL-ASHT) and three-blade short-axis (TS-ASHT) configurations. The energy conversion mechanisms, governed by pressure difference and hydrodynamic force synergy within helical passages, are elucidated, and the influence of critical geometric parameters is assessed. For SL-ASHTs, the analysis highlights exceptional self-starting capability (cut-in velocity: 0.1 m/s), a starting torque coefficient of 0.52, a maximum power coefficient of 0.51, and passive yaw adaptability that limits efficiency variation to below 2% over yaw angles of 0–40°. TS-ASHTs feature a compact architecture and higher rotational speed, facilitating direct generator coupling. With variable blade-angle distributions, thin airfoils, and non-uniform gap ratios, the power coefficient reaches 0.312. Performance-enhancement measures, including multi-parameter optimization, ducts, and winglets, deliver power gains of up to 35%, 122%, and 12%, respectively. This review further identifies critical barriers to engineering deployment: sediment erosion, cyclic fatigue, performance degradation under large yaw angles, and wake interactions. Future priorities include multi-objective optimization, advanced materials and flow control, full-scale sea trials, multiphysics coupling, array layout optimization, and hybrid energy system integration. By establishing a coherent classification and performance-evaluation framework, this work demonstrates that ASHTs offer strong potential as core devices for large-scale utilization of low-velocity ocean current and river hydrokinetic energy. Full article

The Cryogenic Anticoincidence (CryoAC) detector is a critical subsystem designed to reduce the particle background for the X-ray Integral Field Unit (X-IFU) instrument onboard the NewAthena space observatory, the next ESA X-ray Large mission. Advancing this technology to Technology Readiness Level 5 (TRL5) requires a unified validation spanning both cleanroom microfabrication repeatability and mK low temperature operational performance. This work presents the complete development cycle of the Demonstration Model 1.2 (DM 1.2), which is aimed at completing the TRL5 demonstration path featured by all the critical technologies operating simultaneously. First, single-process verification protocols were established for Iridium pulsed laser deposition, Reactive Ion Etching (RIE), and deep silicon trenching via the Bosch process. Second, three identical single-pixel prototypes were fabricated and subjected to mK characterization. Four-wire resistance measurement results confirmed a 2/3 production yield against strict design targets (T_C ~100 mK). Finally, functional testing at a bath temperature of 50 mK using a VTT FAB4 SQUID readout demonstrated excellent performance, including a low-energy threshold of ~0.6 keV, a pixel power dissipation of 5.15 nW, and an energy resolution ΔE_FWHM = 735 eV at 6 keV. These combined achievements successfully validate the entire manufacturing and operational baseline against all primary space mission requirements. This paper has to be considered as a review of the CryoAC technology path toward the TRL5 achievement; main findings will be reported and discussed. Details are relegated to other papers. Full article

We present the results of tests conducted with a state-of-the-art drone-based multi-channel magnetometer system (SENSYS MagDrone R4) developed for efficient high-resolution near-surface surveys. The primary aim is to advance the application of this technology in proximal sensing and to identify acquisition strategies capable of achieving data quality comparable to that of established ground-based magnetometer surveys. The system is evaluated under varying operational conditions, with particular emphasis on the influence of flight altitude, heading, velocity, and UAV platform characteristics on magnetic data quality. Signal properties are analyzed using power spectral methods to identify and quantify platform- and survey-related sources of interference. The results reveal the noise sources and amplifiers affecting UAV-based magnetic measurements and demonstrate how survey design can substantially increase the signal-to-noise ratio. Based on these findings, practical recommendations for data acquisition and processing are proposed, contributing to the development of best-practice guidelines for high-resolution archeological, explosive ordnance (EO), and other near-surface magnetic survey applications. Full article

This paper presents an experimental comparative study of four advanced maximum power point tracking (MPPT) strategies—backstepping, adaptive backstepping, sliding mode control, and vision-based MPPT—combined with three wireless communication technologies: IEEE 802.15.4, Wi-Fi, and 3G. The comparison is performed on a distributed photovoltaic (PV) platform under common power-stage conditions in harmonized irradiance scenarios, including abrupt uniform-irradiance transients and controlled partial shading patterns. Under uniform irradiance, adaptive backstepping achieved the best overall dynamic behavior, with the shortest average convergence time of 0.045 s using IEEE 802.15.4, compared with 0.052 s for Wi-Fi and 0.115 s for 3G, while all tested configurations maintained tracking efficiencies above 98.8%. Under partial shading, the ranking changed substantially: the vision-based MPPT provided the best GMPP-oriented performance, reaching the highest tracking success rate and the lowest energy loss. In particular, its lost energy increased from 0.20% with IEEE 802.15.4 to 0.45% with 3G, whereas conventional backstepping increased from 0.65% to 1.35% over the same communication range. Latency measurements showed that IEEE 802.15.4 exhibited the lowest median end-to-end delay and the smallest dispersion, Wi-Fi showed intermediate behavior, and 3G introduced the largest latency and variability. The results demonstrate that MPPT performance in distributed PV systems depends on both the control strategy and the communication architecture and that a unified experimental assessment of both layers is required to identify the optimal MPPT–communication combination for each operating scenario. Full article

This article proposes, to the best of our knowledge, the first dual-port compact dual-polarized ultrawideband wearable textile antenna covering lower UHF bands for off-body communications in Internet-of-Things-based wireless body area network (IoT-based WBAN) scenarios. The antenna covers key bands for diverse services, including the 470–510 MHz LoRa WAN, 700 MHz offline emergency communication, 900 MHz NB-IoT, and 1–1.2 GHz satellite internet bands. The antenna adopts a square-ring loaded wide slot structure and a multi-mode resonant feeding structure to achieve ultrawideband operation. Moreover, it utilizes oppositely placed advanced microstrip feeding networks to excite the horizontal and vertical polarization modes, respectively, and four narrow slots around the wide slot to extend the current path, thus enabling a compact size of 0.30 × 0.28 × 0.0035 λ_l³ (where λ_l is the largest operating wavelength). Measured −10 dB impedance bandwidths are 119.1% (0.35–1.38 GHz) for Port 1 and 115.9% (0.39–1.37 GHz) for Port 2 on the human body, with more than 19 dB port isolation over the operating band. The measured average gains are about 4.21 dBi for Port 1 and 3.54 dBi for Port 2 on the human body, respectively. Specific absorption rate analysis confirms compliance with the IEEE C95.1 limit at 0.5 W input power. Wireless transmission experiments at IoT bands further validate reliable off-body links with excellent signal-to-noise ratios for both polarizations. The antenna shall be very attractive for off-body communications in IoT-based WBAN scenarios. Full article

We present a comprehensive high-energy overview of all-heavy tetraquark production at the High-Luminosity Large Hadron Collider, combining recent advances in heavy-exotic spectroscopy with state-of-the-art fragmentation-based phenomenology. Our study builds upon the newly released TQ4Q2.0 fragmentation framework, which provides a complete leading-power description of fully heavy S-wave tetraquarks with scalar ($0^{++}$), axial-vector ($1^{+-}$), and tensor ($2^{++}$) quantum numbers. The formalism incorporates all active gluon- and heavy-quark-induced production channels within nonrelativistic QCD factorization and implements threshold-aware DGLAP evolution through the HF-NRevo scheme. Particular attention is devoted to the all-bottom sector, whose larger mass scale offers a distinctive laboratory for exploring multiquark formation mechanisms and testing the universality of fragmentation dynamics across heavy-flavor systems. We review the theoretical foundations of the TQ4Q program and discuss the uncertainty budget associated with color-composite long-distance matrix elements and perturbative multiscale variations. Building on this framework, we present precision predictions for bottom tetraquark production in association with jets at HL-LHC energies, obtained within the (sym)JETHAD environment at NLL/NLO⁺ accuracy. The resulting phenomenology highlights the discovery potential of future high-luminosity measurements and illustrates the impact of modern resummation techniques on rare-hadron observables. This work establishes the TQ4Q2.0 framework as a reliable phenomenological benchmark for collider studies of all-heavy tetraquarks and provides a unified roadmap for future investigations of multiquark dynamics at present and forthcoming hadron facilities. Full article

Higher education is undergoing a rapid transformation driven by advancements in artificial intelligence (AI), creating new opportunities to support the development of higher-order cognitive skills through carefully designed learning environments. This study aimed to examine the effectiveness of an educational program that integrates AI-powered intelligent assistants into a structured e-learning environment to support the development of digital entrepreneurship and future-oriented problem-solving skills among graduate students. A quasi-experimental design was used with 82 graduate students, randomly assigned to two groups: an experimental group (n = 41) and a control group (n = 41). Both groups studied the same educational content and completed identical learning activities; however, the experimental group participated in a structured learning framework that included AI-powered intelligent assistants, guided learning activities, and instructor facilitation, while the control group completed the same activities without AI support. The educational program comprised five modules focusing on chatbot design, intelligent platform development, digital content production, educational data analysis, and future-oriented problem-solving. Learning outcomes were assessed using a Digital Entrepreneurship Product Rating Scale and a Future-Oriented Problem-Solving Scale, which measures skills in visualization, prediction, foresight, and planning. The results revealed statistically significant differences favoring the experimental group on both outcome scales, with large effect sizes. These findings suggest that integrating AI-powered intelligent assistants within a structured learning framework may support the development of digital entrepreneurship and future-oriented problem-solving skills among graduate students. This study contributes empirical evidence to the potential educational value of integrating AI-powered intelligent assistants with pedagogically designed learning activities to foster higher-level cognitive development in higher education. Full article

The following is a detailed account of how the flexibility of a drive train affects maximum power point tracking (MPPT) for wind turbines. The study uses the National Renewable Energy Laboratory 5 MW reference turbine as a base model with Kaimal spectrum having felt waves creating turbulent winds. This study compares both rigid and flexible drive trains that undergo identical turbulence conditions (average wind speed 9.0 m/s and flight weather turbulence, 16% T.I.). The results show that the flexible unit provides an 8.95 percent area increase in mean power coefficient, (p < 0.001, Cohen’s d = 2.108) while the rigid unit produces a 4173 percent (p < 10⁻⁴⁷, Cohen’s d = 31.8) sudden rise in power during catastrophic loss of control and produce a direct torque oscillation of over 153 MN·m RMS between the two configurations. The conclusion of this research establishes that PID Controllers that are manually tuned based on measurements taken with rigid drive trains cannot be used with flexible drivetrains. Therefore, it is necessary to either re-tune the PID Controller quickly after switching between form of drive train; and/or implement advanced control strategies to effectively manage these situations in the future. This study shows that rigid-model assumptions are fundamentally inadequate for contemporary turbine control design, which directly results in decreased energy yield. The findings necessitate a paradigm shift towards control strategies that specifically take structural dynamics into consideration in order to produce vibration-resilient MPPT for next-generation turbines. Full article

Traumatic brain injury (TBI) is a leading cause of long-term neurological disability worldwide and is frequently associated with persistent impairments in consciousness, cognition, mood, and functional independence. Despite advances in acute neurocritical care, effective therapies that enhance neurological recovery remain limited. Neuromodulation has emerged as a promising strategy to augment neuroplasticity and improve recovery through both pharmacologic and non-pharmacologic approaches. This narrative review summarizes current evidence supporting pharmacologic neuromodulatory therapies, including central nervous system stimulants (methylphenidate and modafinil), dopaminergic agents (amantadine and bromocriptine), acetylcholinesterase inhibitors (donepezil and rivastigmine), and selective serotonin reuptake inhibitors (sertraline and fluoxetine). Mechanisms of action, clinical efficacy, adverse effects, and practical considerations across the acute, subacute, and chronic phases of TBI recovery are discussed. Emerging non-pharmacologic neuromodulation techniques, including repetitive transcranial magnetic stimulation, transcranial direct current stimulation, electroconvulsive therapy, vagus nerve stimulation, and deep brain stimulation, are also reviewed. Although amantadine remains the only neuromodulator supported by moderate-quality guideline recommendations for accelerating recovery in disorders of consciousness, accumulating evidence suggests that several additional pharmacologic and non-pharmacologic neuromodulation interventions may improve attention, executive function, fatigue, mood, and rehabilitation participation in carefully selected patients. However, current evidence is limited by heterogeneous study populations, small sample sizes, inconsistent outcome measures, and a paucity of long-term randomized controlled trials. Future research should prioritize adequately powered comparative studies, standardized outcome measures, biomarker-guided patient selection, and multimodal treatment strategies to optimize neurological recovery following TBI. Full article

Achieving precise microstructure control in composite dielectric layers remains a key challenge for enhancing the sensitivity and reducing the power consumption of flexible capacitive sensors. In this work, an ultrasonic needle-induced acoustic streaming strategy is proposed to regulate the spatial distribution of Fe particles within a silicone matrix, enabling controllable particle migration and aggregation in liquid silicone. Multiphysics simulations reveal that, at an excitation frequency of 75.49 kHz, Fe particles are effectively driven toward the ultrasonic focal region, forming a tunable microstructure. Experimental results confirm that this method enables precise morphological control of the composite dielectric layer. The composite with 25 wt% Fe exhibits the highest measured relative permittivity of about 3.45, enabling a capacitive sensor sensitivity of 0.423 kPa⁻¹ in the 0–1 kPa range. After acoustic-streaming optimization and integration into a four-unit capacitive array, the device achieved 0.509 kPa⁻¹ sensitivity, retained 92.04% of its response after 5000 cycles at 3 kPa, and maintained 97.8% of its initial capacitance after 24 h. The proposed approach provides an effective route to improving sensor performance through microstructure engineering while maintaining low electrical loss. This work not only advances the design of high-performance functional composites but also expands the application of acoustic streaming techniques in low-power flexible electronics. Full article