Search Results (1,850)

This work provides a systematic experimental study for the electrochemical desalination of saline water using an electrospun permselective polyvinylidene difluoride (PVDF) membrane. Several nano additives were initially screened during membrane development; however, only the materials that demonstrated stable dispersion, reproducible membrane formation, and consistent electrochemical behaviour, namely graphene oxide (GO) and carbon nanotubes (CNTs) were selected for full analysis in this study. Accordingly, the study focuses on pure PVDF, PVDF/GO, and PVDF/CNTs membranes integrated with an alternating Ag/AgCl electrode system. The silver electrode is prepared by spray-coating of silver nanoparticles on high surface carbon cloth, whereas the AgCl electrode was prepared electrochemically from the Ag electrode using a three-electrode electrochemical cell. The electrochemical behaviour of various modified electrodes (bare carbon cloth, Ag/carbon cloth, Ag/nafion/carbon black/PVDF, and Ag/nafion/carbon cloth) was evaluated using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and X-Ray Diffraction (XRD). The electrode prepared using Nafion and PVDF as binders with carbon black as conductive additive exhibited the highest current response and lowest charge-transfer resistance. When coupled with this optimized electrode, the PVDF/GO membrane delivered the best desalination performance, achieving an ion removal efficiency of 68%, a salt adsorption capacity (SAC) of 775.40 mg/g, and a specific energy consumption (SEC) of 16.17 kJ/mole values superior to those reported in the literature. Full article

In this study, thermoplastic polyurethane (TPU) parts were fabricated using fused filament fabrication (FFF) by varying key process parameters, namely extruder temperature (210–230 °C), layer thickness (200–400 µm), and printing speed (30–50 mm/s). A Box–Behnken experimental design was used to systematically evaluate the combined influence of these parameters on surface roughness (R_a), dimensional deviation (DD), and ultimate tensile strength (UTS). After fabrication, all specimens were subjected to a Tetrahydrofuran (THF)-based chemical smoothing process to modify surface characteristics. Surface roughness measurements showed a substantial reduction after chemical smoothing, with values decreasing from an initial range of 13.17 ± 0.21–15.87 ± 0.23 µm to 4.01 ± 0.18–7.35 ± 0.16 µm, corresponding to an average decrease of approximately 50–72%. Dimensional deviation improved moderately, from 260–420 µm in the as-printed condition to 160–310 µm after post-processing, representing a reduction of about 20–38%. Mechanical testing revealed a consistent increase in UTS following chemical smoothing, with values improving from 30.24–40.30 ± 0.52 MPa to 33.97–47.94 ± 0.36 MPa, yielding an average increase of approximately 10–24%. Then, the experimental data were used for multi-objective optimization of the FFF process parameters, using a non-dominated sorting genetic algorithm (NSGA-II) implemented in Python 3.11, to identify best parameter combinations that provide a balanced surface quality, dimensional accuracy, and mechanical performance. Full article

Network intrusion detection has challenges that fundamentally differ from language and vision tasks typically addressed by Transformer models. In particular, network traffic features lack inherent ordering, datasets are extremely class-imbalanced (with benign traffic often exceeding 80%), and reported accuracies in the literature vary widely (57–95%) without systematic explanation. To address these challenges, we propose a controlled experimental study that isolates and quantifies the impact of tokenization strategies on Transformer-based intrusion detection systems. Specifically, we introduce and compare three tokenization approaches—feature-wise tokenization (78 tokens) based on CICIDS2017, a sample-wise single-token baseline, and an optimized sample-wise tokenization—under identical training and evaluation protocols on a highly imbalanced intrusion detection dataset. We demonstrate that tokenization choice alone accounts for an accuracy gap of 37.43 percentage points, improving performance from 57.09% to 94.52% (100 K data). Furthermore, we show that architectural mechanisms for handling class imbalance—namely Batch Normalization and capped loss weights—yield an additional 15.05% improvement, making them approximately 21× more effective than increasing the training data by 50%. We achieve a macro-average AUC of 0.98, improve minority-class recall by 7–12%, and maintain strong discrimination even for classes with as few as four samples (AUC 0.9811). These results highlight tokenization and imbalance-aware architectural design as primary drivers of performance in Transformer-based intrusion detection and contribute practical guidance for deploying such models in modern network infrastructures, including IoT and cloud environments where extreme class imbalance is inherent. This study also presents practical implementation scheme recommending sample-wise tokenization, constrained class weighting, and Batch Normalization after embedding and classification layers to improve stability and performance in highly unstable table-based IDS problems. Full article

The increasing integration of solar energy into the power grid necessitates robust fault detection and diagnosis (FDD) guidelines to ensure energy continuity and optimize the performance of grid-connected photovoltaic (GCPV) systems. This research addresses a gap in the literature by systematically evaluating machine learning (ML) algorithms for the detection and classification of AC-side faults (inverter and grid faults) in GCPV systems. We utilized three commonly employed algorithms, namely K-Nearest Neighbors (KNN), Logistic Regression (LR), and Artificial Neural Networks (ANNs), to develop fault detection models. These models were trained using a monthly electrical dataset obtained from the AYCEM-GES-GCPV power plant in Giresun, Turkiye, and their performance was rigorously evaluated using classification accuracy, Area Under the Curve (AUC), and Receiver Operating Characteristic (ROC) analyses. The results demonstrate that the algorithms are highly effective in fault detection, with AUC values consistently exceeding the critical threshold. The obtained accuracies for KNN, LR, and ANN were 0.9826, 0.782, and 0.7096, respectively. These findings emphasize the high effectiveness of ML algorithms, with KNN exhibiting the best performance, for identifying AC-side faults in GCPV installations. While the study focused on AC-side fault detection, subsequent work developed a smart card module to identify complex DC side electrical faults and built a PV array for experimental testing. Full article

Wireless power transfer (WPT) technology, leveraging the unique advantage of contactless power supply, has been recognized as a core power supply solution for mobile devices such as automated guided vehicles (AGVs) and electric vehicles (EVs). However, its transmission performance is highly susceptible to lateral offset, longitudinal misalignment, and angular deflection of the coils, resulting in a sharp decline in efficiency and unstable output. This has become a key bottleneck restricting the engineering application of the technology. This paper presents a comprehensive review focusing on the misalignment tolerance technologies for WPT systems. First, taking the LCC-S/LCC topology as an example, the influence of coil misalignment on the system output performance is analyzed, and various misalignment tolerance methods are enumerated. Subsequently, the basic principles and main research achievements of four categories of misalignment tolerance technologies, namely coupling structure optimization, compensation topology optimization, control strategies, and alignment guidance technology, are systematically summarized, with their limitations identified. Finally, the future research directions of misalignment tolerance technologies are discussed. Full article

Objective: Given the global trend of ecological protection and sustainable development, Global Geoparks have become an essential platform for resource conservation and regional growth. Changzhi City in Shanxi Province, China, is actively applying for Global Geopark status, relying on its rich geoheritage sites, cultural history, and natural landscapes. This paper presents a systematic evaluation of the city’s geodiversity and relic value, analyzes the feasibility of establishing a Global Geopark in Changzhi City, and provides scientific support for Changzhi City’s Global Geopark application. Methods: Geodiversity data were collected by region using a 1:25,000 grid for sampling. Four methods were adopted for evaluation, namely, the Shannon diversity index, Simpson diversity index, entropy weight method (EWM), and Pielou evenness index. Upon comprehensive comparison of the four approaches, the most suitable approach was selected to produce the final results. For the value evaluation of the geoheritage, a combination of the analytic hierarchy process and the entropy weight method was employed. Results: (1) According to the results of all four methods, the geodiversity of Changzhi City is higher in the eastern and western regions and lower in the central area. (2) The geoheritage sites are mainly distributed in the eastern part of the city and have relatively high relic value. (3) Changzhi City contains abundant natural reserves and cultural resources, meeting the fundamental requirements for Global Geopark construction. Specifically, 38 townships across eight counties were identified as potential geopark areas, encompassing 54 geoheritage sites, 76 provincial-level or higher cultural-relic protection sites, and 15 provincial-level or higher natural protected areas, with a total area of 4458.51 km². Conclusions: Our results suggest that the Shannon diversity index is an effective tool for evaluating geodiversity in Changzhi City. Based on the region’s geological and natural conditions, the delineated geopark area is feasible. In summary, our findings provide essential references for the protection and sustainable development of geoheritage sites, geodiversity, and geoparks and offer strong theoretical and data support for Changzhi City’s Global Geopark application. Full article

This study aimed to systematically investigate the chemical constituents and bioactivities of the traditional wild tea plant Camellia kwangsiensis Chang. An HPLC method was first established to simultaneously quantify five major components. Subsequently, extensive isolation was performed using chromatographic techniques, and the structures of isolated compounds were elucidated by spectroscopic methods. Their biological potential was evaluated through antioxidant (DPPH and ABTS⁺ radical scavenging), α-glucosidase inhibitory, and anti-inflammatory (inhibition of nitric oxide production) assays. The LC-MS/MS analyses confirmed the absence of caffeine, theophylline, and theobromine. A total of 19 phenolic compounds were first isolated and identified, including one new proanthocyanidin, namely kwangsienin A (1), and 18 known phenolic components with six proanthocyanidins (2–7), one catechin (8), six flavonol glycosides (9–14), and five simple phenols (15–19). Notably, the proanthocyanidins displayed stronger or comparable antioxidant and α-glucosidase suppressive activity than the positive controls. In conclusion, C. kwangsiensis, rich in proanthocyanidins and naturally caffeine-free, represents a promising plant resource for developing decaffeinated functional tea beverages with antioxidant and hypoglycemic potential. Full article

The rapid growth of precision agriculture has accelerated the deployment of plant protection unmanned aerial vehicles (UAVs). However, reliable data resources for vision-based intelligent supervision of operational states, such as whether a UAV is currently spraying, remain limited. Most publicly available UAV detection datasets target urban security and surveillance scenarios, where annotations emphasize object localization rather than agricultural operation state recognition, making them insufficient for farmland spraying supervision. Therefore, agricultural-oriented data resources are needed to cover diverse backgrounds and include operation state labels, thereby supporting both academic research and practical deployment. In this study, we construct and release the first multi-background dataset dedicated to agricultural UAV spraying behavior recognition. The dataset contains 9548 high-quality annotated images spanning the following six typical backgrounds: green cropland, bare farmland, orchard, woodland, mountainous terrain, and sky. For each UAV instance, we provide both a bounding box and a binary operation state label, namely spraying and flying without spraying. We further conduct systematic benchmark evaluations of mainstream object detection algorithms on this dataset. The dataset captures agriculture-specific challenges, including a high proportion of small objects, substantial scale variation, motion blur, and complex dynamic backgrounds, and can be used to assess algorithm robustness in real-world agricultural settings. Benchmark results show that YOLOv5n achieves the best overall performance, with an accuracy of 97.86% and an mAP@50 of 98.30%. This dataset provides critical data support for automated supervision of plant protection UAV spraying operations and precision agriculture monitoring platforms. Full article

To address the depletion of shallow coal resources, mining activities have progressed to greater depths, where rock masses contain numerous fractures due to complex geological conditions, making grouting reinforcement essential for ensuring stability. Using digital image correlation, this study investigated the strain evolution characteristics of grouted fractured specimens of three rock types—mudstone, coal–rock, and sandstone—under uniaxial compression. Analysis of the strain evolution process focused on two typical fracture inclinations of 0° and 60°, while examination of the peak strain characteristics covered five inclinations, namely 0°, 15°, 30°, 45°, and 60°. The findings indicate that the mechanical response varies systematically with lithology and fracture inclination. The post-peak curves differ significantly among rock types: coal–rock shows a gentle descent, mudstone exhibits a rapid strength drop but higher residual strength, and sandstone is characterized by “serrated” fluctuations. The failure mode transitions from tensile splitting at a horizontal inclination of 0° to shear failure at inclinations of 15°, 30°, 45°, and 60°. Strain nephograms corresponding to the peak stress point D reveal sharp, band-shaped zones of strain localization. The maximum principal strain exhibits a non-monotonic trend, first increasing and then decreasing with increasing inclination angle. For grouted coal–rock and sandstone, the peak values of 47.47 and 45.00 occur at α = 45°. In contrast, grouted mudstone reaches a maximum value of 26.80 at α = 30°, indicating its lower susceptibility to damage. The study systematically clarifies the strain evolution behavior of grouted fractured rock masses, providing a theoretical basis for evaluating the effectiveness of reinforcement and predicting failure mechanisms. Crucially, the findings highlight mudstone’s role as a high-integrity medium and the particular vulnerability of horizontal fractures, offering direct guidance for the targeted grouting design in stratified rock formations. Full article

Drilling resistance (DR) measurement is a promising non-destructive technique for evaluating the mechanical properties of concrete. However, the reliability and repeatability of DR measurements are still limited by an insufficient understanding of how drill rotational speed influences the recorded drilling response. In addition, a systematic investigation of the influence of rotational speed on multiple drilling response parameters simultaneously is still lacking. This study investigates the relationship between imposed rotational speed and DR parameters—namely, rotational speed reduction, drilling force, and electrical power consumption—measured during controlled drilling tests in C30 and C50 concretes. A laboratory-developed DR testing methodology with constant feed rate and synchronized RPM, force, and power measurements was applied. Five nominal drilling speeds (in the range of 1400–2200 RPM) were examined. The results show clear, speed-dependent trends across all measurements. Strong correlations between nominal and in-hole rotational speeds were observed, while drilling force exhibited a nonlinear dependence on rotational speed. This study reveals distinct drilling behavioral signatures that differentiate concrete strength classes and clarify the mechanical origin of drilling-induced RPM reduction. The findings confirm that DR parameters, when analyzed collectively rather than individually, provide valuable diagnostic information and have strong potential for application in the non-destructive evaluation of concrete structures. Full article

Modern power transmission systems are required to meet increasingly stringent demands, including a wide range of transmission ratios, compact dimensions, high precision, energy efficiency, reliability, and thermal stability under dynamic operating conditions. Among the solutions that satisfy these requirements, cycloidal reducers are particularly prominent, with their application continuously expanding in industrial robotics, computer numerical control (CNC) machines, and military and transportation systems, as well as in the satellite industry. However, as with all mechanical power transmissions, friction in the contact zones of load-carrying elements in cycloidal reducers leads to power losses and an increase in operating temperature, which in turn results in a range of adverse effects. These undesirable phenomena strongly depend on lubrication conditions, namely on the type and properties of the applied lubricant. Although manufacturers’ catalogs provide general recommendations for lubricant selection, they do not address the fundamental tribological mechanisms in the most heavily loaded contact pairs. At the same time, the available scientific literature reveals a significant lack of systematic and experimentally validated studies examining the influence of lubricant type on the energetic and thermal performance of cycloidal reducers. To address this identified research gap, this study presents an analytical and experimental investigation of the effects of different lubricant types—primarily greases and mineral oils—on the thermal stability and efficiency of cycloidal reducers. The results demonstrate that grease lubrication provides lower total power losses and a more stable thermal operating regime compared to oil lubrication, while oil film thickness analyses indicate that the most unfavorable lubrication conditions occur in the contact between the eccentric bearing rollers and the outer raceway. These findings provide valuable guidelines for engineers involved in cycloidal reducer design and lubricant selection under specific operating conditions, as well as deeper insight into the lubricant behavior mechanisms within critical contact zones. Full article

Objectives: This study presents the development of a bilingual, expert-evaluated question–answer (Q&A) dataset, named PMR-Q&A, designed for training large language models (LLMs) in the field of Physical Medicine and Rehabilitation (PMR). Methods: The dataset was created through a systematic and semi-automated framework that converts unstructured scientific texts into structured Q&A pairs. Source materials included eight core reference books, 2310 academic publications, and 323 theses covering 15 disease categories commonly encountered in PMR clinical practice. Texts were digitized using layout-aware optical character recognition (OCR), semantically segmented, and distilled through a two-pass LLM strategy employing GPT-4.1 and GPT-4.1-mini models. Results: The resulting dataset consists of 143,712 bilingual Q&A pairs, each annotated with metadata including disease category, reference source, and keywords. A representative subset of 3000 Q&A pairs was extracted for expert validation to evaluate the dataset’s reliability and representativeness. Statistical analyses showed that the validation sample accurately reflected the thematic and linguistic structure of the full dataset, with an average score of 1.90. Conclusions: The PMR-Q&A dataset is a structured and expert-evaluated resource for developing and fine-tuning domain-specific large language models, supporting research and educational efforts in the field of physical medicine and rehabilitation. Full article

Aiming at the wind-blown snow disasters plaguing tunnel portals along the China-Tajikistan Highway Phase II Project, this study optimizes the protective parameters of wind deflectors through numerical simulation to improve the disaster prevention efficiency of tunnel portals in alpine mountainous areas. Three core control parameters of wind deflectors, namely horizontal distance from the tunnel portal (L), plate inclination angle (β), and top installation height (h), were selected as the research objects. Single-factor numerical simulation scenarios were designed for each parameter, and an L9(3³) orthogonal test was further adopted to formulate 9 groups of multi-parameter combination scenarios, with the snow phase volume fraction at 35 m on the leeward side of the tunnel portal set as the core evaluation index. A computational fluid dynamics (CFD) model was established to systematically investigate the influence laws of each parameter on the wind field structure and snow drift deposition characteristics at tunnel portals and clarify the flow field response rules under different parameter configurations. Single-factor simulation results show that the wind deflector exerts distinct regulatory effects on the wind-snow flow field with different parameter settings: when L = 6 m, the disturbance zone of the wind deflector precisely covers the main wind flow development area in front of the tunnel portal, which effectively lifts the main incoming flow path, compresses the recirculation zone (length reduced from 45.8 m to 22.3 m), and reduces the settlement of snow particles, achieving the optimal comprehensive prevention effect; when β = 60°, the leeward wind speed at the tunnel portal is significantly increased to 10–12 m/s (from below 10 m/s), which effectively promotes the transport of snow particles and mitigates the accumulation risk, being the optimal inclination angle; when h = 2 m, the wind speed on both the windward and leeward sides of the tunnel portal is significantly improved, and the snow accumulation risk at the portal reaches the minimum. Orthogonal test results further quantify the influence degree of each parameter on the snow prevention effect, revealing that the horizontal distance from the tunnel portal is the most significant influencing factor. The optimal parameter combination of the wind deflector is determined as L = 6 m, β = 60°, and h = 2 m. Under this optimal combination, the snow phase volume fraction at 35 m on the leeward side of the tunnel portal is 0.0505, a 12.3% reduction compared with the non-deflector condition; the high-concentration snow accumulation zone is shifted 25 m leeward, and the high-value snow phase volume fraction area (>0.06) disappears completely, which can effectively alleviate the adverse impact of wind-blown snow disasters on the normal operation of tunnel portals. The research results reveal the regulation mechanism of wind deflector parameters on the wind-snow flow field at alpine tunnel portals and determine the optimal protective parameter combination, which can provide important theoretical reference and technical support for the prevention and control of wind-blown snow disasters at tunnel portals in similar alpine mountainous areas. Full article

The development of efficient spinel oxide catalysts for low-temperature oxidation of volatile organic compounds (VOCs) remains an important research objective. In this work, Ru was doped into a CoMn₂O₄ spinel to enhance its catalytic activity toward toluene oxidation and the underlying promotion mechanism of Ru doping was systematically investigated. The resulting Ru-CoMn₂O₄ catalyst showed remarkable performance, with T₉₀ reaching approximately 224 °C at a WHSV of 60,000 cm³ g⁻¹ h⁻¹ and nearly 100% CO₂ selectivity above 200 °C. Mechanism studies revealed that the reaction followed both Mars–van Krevelen (MvK) and Eley–Rideal (E–R) pathways. The reaction rates were strongly influenced by the oxidizing capacity of the catalyst, the abundance of highly valent surface species (namely Co³⁺, Mn⁴⁺, and Ru⁴⁺), adsorbed toluene, lattice oxygen, gaseous toluene, and adsorbed oxygen. With Ru doping, new Ru–O–Mn and Ru–O–Co chains formed in the CoMn₂O₄ spinel structure, leading to a moderate enhancement in oxidizing ability and a moderate increase in the concentration of highly valent surface species, adsorbed toluene, and lattice oxygen. Although a slight reduction in adsorbed oxygen was observed, Ru doping significantly boosted the overall toluene oxidation activity of CoMn₂O₄. In summary, Ru-CoMn₂O₄ represented a promising catalyst for the efficient oxidation of VOCs. Full article

With the continuous and rapid growth of global photovoltaic (PV) installed capacity, the fluctuation, intermittence, and randomness of its output aggravate the inertia loss of traditional power systems, which poses severe challenges to grid voltage stability, frequency regulation, and safe operation of equipment. Stability control of PV power stations has become a necessary aspect of technical support for the construction of new power systems (NPSs). In this paper, a technical analysis framework of stability control of photovoltaic power stations is systematically constructed. First, the core stability problems of photovoltaic systems are sorted out. Then, a technical review of the three control levels, namely the equipment, system, and grid, is carried out. At the same time, the application potential of emerging technologies such as data-driven and AI control, digital twin predictive control, and advanced grid-forming (GFM) inverters is described. Based on existing reviews, this paper proposes an equipment–system–grid hierarchical analysis framework and explicitly integrates emerging technologies with classical methods. This framework provides references for the selection, engineering deployment, and future research directions of stability control technologies for photovoltaic power plants, while also offering technical support for the safe and efficient operation of high-penetration renewable energy power grids. Full article