Search Results (15,543)

Silver selenide (Ag₂Se) is a promising n-type thermoelectric material for near-room-temperature energy harvesting due to its high electrical conductivity and low lattice thermal conductivity. In this study, Ag₂Se-based composites were synthesized using a cold sintering process (CSP), enabling densification at low temperature under applied pressure. Reduced graphene oxide (RGO) was incorporated into the Ag₂Se matrix in small amounts (0.25–1.0 wt.%) to enhance thermoelectric performance. Structural analysis confirmed phase-pure β-Ag₂Se, while SEM and TEM revealed homogeneous RGO dispersion and strong interfacial adhesion. RGO addition led to a reduced carrier concentration due to carrier trapping by oxygen-bearing functional groups, resulting in decreased electrical conductivity. However, the absolute Seebeck coefficient increased with RGO content, maintaining a balanced power factor. Simultaneously, RGO suppressed thermal conductivity to below 0.75 W m⁻¹ K⁻¹ at room temperature. The optimal composition, 0.75 wt.% RGO, exhibited the highest average zT of 0.98 across the temperature range from room temperature to 383 K. These results demonstrate that combining the CSP with RGO incorporation offers a scalable and cost-effective strategy for enhancing the thermoelectric performance of Ag₂Se-based materials. Full article

Continuous monitoring of electrical parameters is essential for understanding energy consumption, assessing power quality, and analyzing load behavior. This paper presents a dataset comprising measurements of three-phase voltages and currents, active and reactive power (per phase and total), power factor, and system frequency. The data was collected between April and December 2024 in the low-voltage system of a university laboratory, using high-accuracy power analyzers installed at the point of common coupling. Measurements were recorded every 10 min, generating 79 files with 432 records each, for a total of approximately 34,128 entries. To ensure data quality, the values were validated, erroneous entries removed, and consistency verified using power triangle relationships. The curated dataset is provided in tabular (CSV) format, with each record including a timestamp, three-phase voltages, three-phase currents, active and reactive power (per phase and total), power factor (per phase and global), and system frequency. This dataset offers a comprehensive characterization of electrical behavior in a university laboratory over a nine-month period. It is openly available for reuse and can support research in power system analysis, renewable energy integration, demand forecasting, energy efficiency, and the development of machine learning models for smart energy applications. Full article

Utilizing a dataset of 190 risk factors spanning over three decades, we apply a swarm-based classification model to estimate factor velocity and analyze its implications for asset pricing. Our results show that slower-moving factors generate higher abnormal returns than their faster-moving counterparts, underscoring the critical role of price adjustment speed in market dynamics. Furthermore, our results suggest that trading frictions impede the rapid assimilation of information, contributing to the observed return patterns. This research offers new insights into return predictability and demonstrates the potential of swarm intelligence as a powerful tool for financial modeling. Full article

The ocean, as one of the largest thermal energy storage bodies on earth, has great potential as a thermal-electric energy reserve. Application of the relatively fixed-temperature ocean as the heat sink, and using concentrated solar energy as the heat source, one may construct a mobile power station on the ocean’s surface. However, a traditional solar-based heat source requires a large footprint to concentrate the light beam, resulting in bulky parabolic dishes, which are impractical under ocean engineering scenarios. For buoy-sized applications, the small form factor of the energy collector can only achieve limited temperature differential, and its energy quality is deemed to be unusable by traditional spring-loaded free piston Stirling engines. Facing these challenges, a low-temperature differential free piston Stirling engine is presented. The engine features a large displacer piston (ϕ136, 5 mm thick) made of corrugated board, and an aluminum power piston (ϕ10). Permanent magnets embedded in both pistons couple them through magnetic attraction rather than a mechanical spring. This magnetic “spring” delivers an inverse-exponential force–distance relation: weak attraction at large separations minimizes damping, while strong attraction at small separations efficiently transfers kinetic energy from the displacer to the power piston. Engine dynamics are captured by a lumped-parameter model implemented in Simulink, with key magnetic parameters extracted from finite-element analysis. Initial results have shown that the laboratory prototype can operate continuously across heater-to-cooler temperature differences of 58–84 K, sustaining flywheel speeds of 258–324 RPM. Full article

Analyzing accident reports to absorb past experiences is crucial for construction site safety. Current methods of processing textual accident reports are time-consuming and labor-intensive. This research applied the LDA topic model to analyze construction accident reports, successfully identifying five main types of accidents: Falls from Height (23.5%), Struck-by and Contact Injuries (22.4%), Slips, Trips, and Falls (21.8%), Hot Work & Vehicle Hazards (18.1%), and Lifting and Machinery Accidents (14.2%). By mining the rich contextual details within unstructured textual descriptions, this research revealed that environmental factors constituted the most prevalent category of contributing causes, followed by human factors. Further analysis traced the root causes to deficiencies in management systems, particularly poor task planning and inadequate training. The LDA model demonstrated superior effectiveness in extracting interpretable topics directly mappable to engineering knowledge and uncovering these latent factors from large-scale, decade-spanning textual data at low computational cost. The findings offer transformative perspectives for improving construction site safety by prioritizing environmental control and management system enhancement. The main theoretical contributions of this research are threefold. First, it demonstrates the efficacy of LDA topic modeling as a powerful tool for extracting interpretable and actionable knowledge from large-scale, unstructured textual safety data, aligning with the growing interest in data-driven safety management in the construction sector. Second, it provides large-scale, empirical evidence that challenges the traditional dogma of “human factor dominance” by systematically quantifying the critical role of environmental and managerial root causes. Third, it presents a transparent, data-driven protocol for transitioning from topic identification to causal analysis, moving from assertion to evidence. Future work should focus on integrating multi-dimensional data for comprehensive accident analysis. Full article

The magnitude of core loss is a crucial factor affecting the efficiency of power converters. Due to the complex mechanism of core loss, diverse influencing factors, and the strong coupling characteristics between materials and operating conditions, traditional core loss prediction models struggle to achieve high-precision prediction of core loss. Based on the Artificial Neural Network (ANN), this paper investigates core loss under high-frequency sinusoidal excitation. The core loss training data is processed using a logarithmic transformation method, and an ANN core loss prediction model is established with temperature, frequency, and magnetic flux density as features. The results show that, compared with non-logarithmic processing, logarithmic transformation of the data can effectively improve the prediction accuracy (PA) of the ANN model. Within the ±10% error range, the maximum PA of the ANN prediction model reaches 98.48%, and the minimum Mean Absolute Percentage Error (MAPE) can be as low as 2.58%. In addition, a comparison with the Steinmetz Equation (SE) and K-nearest neighbor (KNN) prediction models reveals that, for four materials, within the ±10% error range of the true core loss values, the minimum PA of the ANN model is 93.33% with an average of 95.38%; the minimum PA of the KNN model is 43.94% with an average of 62.07%; and the minimum PA of the SE model is 14.91% with an average of 19.83%. Furthermore, the MAPE of the ANN model is within 5%. Full article

A star tracker lens works in the environment with the temperatures ranging from −40 °C to 80 °C (a range of 120 °C), which makes athermalization a crucial step in the design. Traditional approaches could spend quite an amount of iterative process in between the optimization for nominal condition and athermalization. It is highly desired that the optimization can start with a thermally robust layout to improve the design efficiency. This study takes the star tracker lens module with seven elements as the base for investigating the possible layout variation on dioptric power distribution and thermo-optic coefficient $dn/dT$ of the material, which are the two major factors of the layout interacting with each other to influence the thermal stability of the overall lens module. All the possible layouts are optimized firstly for the nominal condition at T = 20 °C, and only those meeting the optical performance specifications are selected for thermal performance evaluation. A merit function based on a thin lens model which represents the focal plane drift over a temperature range of 120 °C is then used as the criteria for ranking the layout variations passing the first stage. The layouts at top ranking exhibiting low focal plane drift become potential candidates as the final solution. The proposed methodology provides an efficient approach for designing thermally resilient star tracker optics, especially addressing the harsh thermal conditions encountered in Low Earth Orbit missions. Full article

Fused silica optical components with antireflection (AR) coatings are key components in high-power laser systems. However, their reliability is severely challenged by multi-wavelength irradiation and the presence of unavoidable matrix surface defects. To investigate the coupling effects of electric field modulation between multi-wavelength irradiation, AR coating layers, and defects in AR-coated fused silica, this paper uses the finite-difference time-domain (FDTD) method to simulate the nanoscale electric field intensity distribution in fused silica coated with a double-layer AR coating at three different design wavelengths using multi-wavelength lasers. The effects of electric field coupling between the coating layers and defects are analyzed for three representative scratch geometries. The results show that when the incident wavelength matches the AR design wavelength, the interface field is effectively suppressed, resulting in a smoother field distribution and localized hot spots. Conversely, mismatched wavelengths induce severe field distortion, producing multiple hot spots and lateral interference fringes. Wide, shallow scratches are particularly sensitive to wavelength mismatch, with a 532 nm AR coating exhibiting a global maximum enhancement factor of 1.63442 for 355 nm incident light. These findings highlight the coupling effects of scratch geometry, AR coating dispersion, and laser wavelength on electric field modulation. This research provides valuable insights for optimizing antireflection coatings and improving defect tolerance in multi-wavelength laser applications, helping to improve the reliability of high-power laser systems. Full article

This study explores the development of a water-based hybrid thermoelectric (TE) material composed of Sb₂Te₃ nanoparticles (NPs) and polyethylene oxide (PEO). Sb₂Te₃ NPs were synthesized via the microwave-assisted colloidal route, where X-ray diffraction confirmed the purity and quality of the Sb₂Te₃ NPs. Key properties, including the Seebeck coefficient (S), electrical conductivity (σ), power factor (PF), and long-term stability, were studied. X-ray photoelectron spectroscopy (XPS) analysis revealed that exposure to water and oxygen leads to NP oxidation, which can be partially mitigated by hydrochloric acid (HCl) treatment, though this does not halt ongoing oxidation. Scanning electron microscopy (SEM) images displayed a percolation network of NPs within the PEO matrix. While the initial σ was high, a decline occurred over eight weeks, resulting in similar conductivity among all samples. The effect of surface treatments, such as 1,6-hexanedithiol (HDT), was demonstrated to enhance long-term stability. The results highlight both the challenges and potential of Sb₂Te₃/PEO hybrids for TE applications, especially regarding oxidation and durability, and underscore the need for improved synthesis and processing techniques to optimize their performance. This study provides valuable insights for the design of next-generation hybrid TE materials and emphasizes the importance of surface chemistry control in polymer–inorganic nanocomposites. Full article

We investigate the off-shell generalized parton distributions (GPDs) and transverse momentum dependent parton distributions (TMDs) of kaons within the framework of the Nambu–Jona-Lasinio model, employing proper time regularization. Compared to the pion case, the off-shell effects in kaons are of similar magnitude, modifying the GPDs by about 10–$25\%$, which is notable. The absence of crossing symmetry leads to odd powers in the x-moments of the off-shell GPDs, giving rise to new off-shell form factors. We analyze the relations among these kaon off-shell form factors by analogy with electromagnetic form factors. Our results extend the off-shell GPDs from pions to kaons and simultaneously address the associated off-shell form factors. We also compare the off-shell and on-shell gravitational form factors of the kaon. In addition, the off-shell kaon TMD shows a stronger dependence on the momentum fraction x than its on-shell counterpart. Full article

In this study, structural optimization and trap effect analysis of a 4H-SiC–based p–i–n betavoltaic (BV) cell were performed using Silvaco ATLAS TCAD (version 5.30.0.R) simulations combined with an electron-beam (e-beam) irradiation model. First, the optimum device structure was derived by varying the thickness of the intrinsic layer (i-layer), the thickness of the p-layer, and the doping concentration of the i-layer. Under 17 keV e-beam irradiation, the electron–hole pairs generated in the i-layer were effectively separated and transported by the internal electric field, thereby contributing to the short-circuit current density (J_SC), open-circuit voltage (V_OC), and maximum output power density (P_{out_max}). Subsequently, to investigate the effects of traps, donor- and acceptor-like traps were introduced either individually or simultaneously, and their densities were varied to evaluate the changes in device performance. The simulation results revealed that traps degraded the performance through charge capture and recombination, with acceptor-like traps exhibiting the most pronounced impact. In particular, acceptor-like traps in the i-layer significantly reduced V_OC from 2.47 V to 2.07 V and P_{out_max} from 3.08 μW/cm² to 2.28 μW/cm², demonstrating that the i-layer is the most sensitive region to performance degradation. These findings indicate that effective control of trap states within the i-layer is a critical factor for realizing high-efficiency and high-reliability SiC-based betavoltaic cells. Full article

The primary objective of this study is to develop a machine learning-based predictive model using corrosion rate data for magnesium alloys compiled from the literature. Corrosion rates measured under different deformation rates and heat treatment parameters were analyzed using artificial intelligence algorithms. Variables such as chemical composition, heat treatment temperature and time, deformation state, pH, test method, and test duration were used as inputs in the dataset. Various regression algorithms were compared with the PyCaret AutoML library, and the models with the highest accuracy scores were analyzed with Gradient Extra Trees and AdaBoost regression methods. The findings of this study demonstrate that modelling corrosion behaviour by integrating chemical composition with experimental conditions and processing parameters substantially enhances predictive accuracy. The regression models, developed using the PyCaret library, achieved high accuracy scores, producing corrosion rate predictions that are remarkably consistent with experimental values reported in the literature. Detailed tables and figures confirm that the most influential factors governing corrosion were successfully identified, providing valuable insights into the underlying mechanisms. These results highlight the potential of AI-assisted decision systems as powerful tools for material selection and experimental design, and, when supported by larger databases, for predicting the corrosion life of magnesium alloys and guiding the development of new alloys. Full article

Plant viral infections continue to pose a significant and ongoing threat to global food security, especially in the context of climatic instability and intensive agricultural practices. The CRISPR/Cas system has emerged as a powerful tool for developing virus-resistant crops by enabling precise modifications to viral genomes or plant susceptibility factors. Nonetheless, the efficacy and dependability of CRISPR-based antiviral approaches are limited by challenges in guide RNA design, off-target effects, insufficiently annotated datasets, and the intricate biological dynamics of plant–virus interactions. This paper summarizes the latest advancements in the incorporation of artificial intelligence (AI) methodologies, including machine learning and deep learning algorithms, into the CRISPR design and optimization framework. It examines how convolutional and recurrent neural networks, transformer architectures, and generative models like AlphaFold2, RoseTTAFold, and ESMFold can be used to predict protein structures, score sgRNAs, and model host–virus interactions. AI-enhanced methods have been proven to improve target specificity, Cas protein performance, and in silico validation. This paper aims to establish a foundation for next-generation genome editing strategies against plant viruses and promote the adoption of AI-powered CRISPR technologies in sustainable agriculture. Full article

Introduction: Cigarette smoking is unquestionably associated with an increase in morbidity and mortality worldwide, exerting significant adverse effects on respiratory health. The impact of tobacco persists in the epigenome long after smoking cessation. Furthermore, the offspring of smokers may also be affected by the detrimental effects of smoking. Material and methods: The modifications made to the body, such as DNA methylation, histone modification, and regulation by non-coding RNAs, do not change the DNA sequence but can influence gene expression. In respiratory disease, multigenerational effects have been reported in humans, with an increased risk of asthma or COPD and decreased lung function in offspring, despite them not being exposed to smoke. Prenatal nicotine exposure leads to pulmonary pathology that persists across three consecutive generations, supported by animal studies conducted by Rehan et al. Significant advances in high-throughput genomic and epigenomic technologies have enabled the discovery of molecular phenotypes. These either reflect or are influenced by them. Due to the hidden environmental effects and the rise of artificial intelligence (AI) in biomedical research, new predictive models are emerging that not only explain complex data but also enable earlier detection and prevention of smoking-related diseases. In this narrative review, we synthesise the latest research on how smoking affects gene regulation and chromatin structure, emphasising how tobacco can increase vulnerability to multiple diseases. Discussion: For many years, it was widely believed that diseases are solely inherited through genetics. However, recent research in epigenetics has led to a significant realisation: environmental factors play a crucial role in an individual’s life. External influences leave a mark on DNA that can influence future health and offer insights into potential illnesses. In this context, it is possible that in the future, doctors might treat people not as a whole but as individual beings, with personalised medication, tests, and other approaches. Conclusions: The accumulated evidence suggests that exposure to various environmental factors is associated with multigenerational changes in gene expression patterns, which may contribute to increased disease risk. The application of artificial intelligence in this domain is currently a crucial tool for researching potential future health issues in individuals, and it holds a powerful prospect that could transform current medical and scientific practice. Full article

As a multi-ethnic border region of China, Xinjiang hosts revolutionary cultural tourism resources (RCTRs) that embody historical memory and the spirit of frontier reclamation, while also playing a strategic role in strengthening national identity and maintaining regional stability. Yet, their spatial distribution is highly uneven due to geographical, historical, and socio-economic constraints. This study analyzes 135 representative sites using a dual framework of spatial pattern analysis and driving mechanism quantification. Nearest neighbor index, imbalance index, Lorenz curve, geographic concentration index, kernel density estimation, and hotspot analysis results reveal a clustered “multi-core–peripheral attenuation” pattern with pronounced regional disparities. GIS-based overlay analysis identifies natural thresholds of moderate elevation (834–2865 m) and gentle slopes (0–8.65°), while socio-economic factors such as transportation corridors and population density amplify clustering effects. Geographic Detector results confirm road network density (q = 0.85, p < 0.01) and historical site density (q = 0.79, p < 0.01) as dominant drivers, with interactions between natural and social factors enhancing explanatory power above 0.90. These findings highlight the coupled influence of topographic suitability and socio-economic accessibility. Policy recommendations include optimizing road network layouts, adopting tiered heritage protection, and fostering cross-regional cooperation. The study provides scientific evidence for balanced development and sustainable conservation of RCTRs, contributing to the achievement of sustainable development goals (SDGs) related to cultural heritage, regional equity, and inclusive growth Full article