Search Results (11,080)

Background/Objectives: Pes planus is characterized by loss of medial longitudinal foot arch, resulting potentially in dysfunction in balance. Chronic ankle instability (CAI) is related to sensorimotor control deficits. Both of these two musculoskeletal disorders have a diminishing effect on joint proprioception. The present study examined the impact of flatfoot on balance in individuals with CAI. Methods: A total of 28 students (15 men, 13 women; 18–23 years, M = 20.46, SD = 1.07) were assigned to CAI with pes planus (n = 15) or CAI only (n = 13). Balance was assessed using the Y-balance test (YBT) and modified star excursion balance test (mSEBT) in three directions (anterior, posteromedial, and posterolateral), alongside the Cumberland ankle instability tool (CAIT). Group differences were analyzed with independent t tests or Mann–Whitney U tests (α = 0.05). Results: The findings of the study did not show statistically significant differences between the two groups in the balance variable [mSEBT/anterior left foot (t = 0.239, p = 0.865); mSEBT/posteromedial left foot (t = −0.048, p = 0.562); mSEBT/posterolateral left foot (t = 0.164, p = 0.258); mSEBT/anterior right foot (t = −0.433, p = 0.748); mSEBT/posteromedial right foot (t = 0.745, p = 0.606); mSEBT/posterolateral right foot (t = 0.263, p = 0.680); YBT/anterior left foot (U = 96.00, p = 0.93); YBT/posteromedial left foot (U = 94.50, p = 0.87); YBT/posterolateral left foot (U = 96.00, p = 0.93); YBT/anterior right foot (U = 95.50, p = 0.92); YBT/posterolateral right foot (U = 82.50, p = 0.45)]. However, a trend towards significance was found as patients with flatfeet had a weaker performance in balance tests [posteromedial direction of the YBT for the right foot (U = 70.00, p = 0.12)]. Conclusions: Although pes planus did not seem to affect the balance ability of individuals with CAI, future studies should confirm the relationship of pes planus and CAI with a larger group, including variables such as ankle range of motion, muscle strength, and functional activity level. A better understanding of the above relationship may lead to more precise diagnostic processes and more efficient therapies in CAI. Full article

This work presents a numerical study of the water-exit characteristics of a bioinspired unmanned aerial-underwater vehicle (UAUV) equipped with a front-mounted propeller. A robust solution framework was established on the basis of a modified Shear Stress Transport (SST) turbulence model, volume of fluid (VOF) multiphase formulation, overset grid technique, and six degrees of freedom (6-DOF) motion model; the framework was verified against a canonical water-exit case of a sphere. Inspired by the morphology and water-exit behavior of flying fish, a bioinspired three-dimensional (3D) model was designed. Using this framework, the effects of the front-mounted propeller configuration, exit velocity, and exit angle were examined; the exit process under different conditions was analyzed; and the relationship between exit drag and exit state was quantified. The results demonstrate that the proposed approach can resolve the water-exit performance of the bioinspired UAUV in detail. Folding the front-mounted propeller effectively reduces exit drag and mitigates high-pressure concentrations on the blades. When the exit velocity is ≥8 m/s and the exit angle θ ≤ 30°, the peak exit drag does not surpass 90.004 N. The peak exit drag exhibits a pronounced quadratic relationship with both exit velocity and exit angle. To ensure safe water exit, the UAUV should avoid exiting with the front-mounted propeller deployed and avoid excessively low exit velocities and overly large exit angles. The numerical investigation of exit drag provides effective bioinspired design guidelines and a feasible analysis strategy for UAUV development. In conclusion, the findings provide crucial insights for designing more efficient bioinspired UAUVs, particularly in terms of minimizing water-exit drag and optimizing the configuration of the front-mounted propeller. Full article

Frequent occurrences of pests and diseases in tomatoes severely restrict yield and quality improvements. Traditional detection methods are labor-intensive and prone to errors, while advancements in deep learning provide a promising solution for rapid and accurate identification. However, existing deep learning-based models often face high computational complexity and a large number of parameters, which hinder their deployment on resource-constrained edge devices. To overcome this limitation, we propose a novel lightweight detection model named YOLOv11n-KL based on the YOLOv11n framework. In this model, the feature extraction capability for small targets and the overall computational efficiency are enhanced through the integration of the Conv_KW and C3k2_KW modules, both of which incorporate the KernelWarehouse (KW) algorithm, and the Detect_LSCD detection head is employed to enable parameter sharing and adaptive multi-scale feature calibration. The results indicate that YOLOv11n-KL achieves superior performance in tomato pest and disease detection, balancing lightweight design and detection accuracy. The model achieves an mAP@0.5 of 92.5% with only 3.0 GFLOPs and 5.2 M parameters, reducing computational cost by 52.4% and improving mAP@0.5 by 0.9% over YOLOv11n. With its low complexity and high precision, YOLOv11n-KL is well-suited for resource-constrained applications. The proposed YOLOv11n-KL model offers an effective solution for detecting tomato pests and diseases, serving as a useful reference for agricultural automation. Full article

In the breeding scene, limited by the small number of samples and environmental interference such as illumination occlusion, sika deer behavior recognition still faces challenges such as insufficient feature representation and weak cross-scale modeling ability. To this end, this study builds a lightweight improved model SDB-YOLO based on YOLOv11n. Firstly, the FPSC module is proposed to enhance the correlation between multi-scale features through the shared convolution mechanism, so as to significantly improve the quality of feature fusion under the condition of small samples. Secondly, the Ghost feature generation and dynamic convolution strategy are introduced into the C3k2 module to construct the C3_GDConv structure, so as to strengthen the fine-grained behavior pattern modeling ability and reduce redundant calculations. In addition, the CBAM attention mechanism is added to the neck of the network to further improve the ability of key information extraction and enhance the discrimination of feature expression. Finally, the EfficientHead was used to replace the original detection head to obtain a more robust training process and higher detection accuracy in small-sample scenarios. Experimental results show that SDB-YOLO achieves 90.2% detection accuracy with only 4.3 GFLOPs of calculation, which achieves significant performance improvement compared with YOLOv11n, and verifies the effectiveness and lightweight advantages of the proposed method in small-sample special animal behavior recognition tasks. Full article

Conventional urea nitrogen (N) fertilizers are characterized by low use efficiency, resulting in substantial economic losses and environmental degradation. To address this issue, we developed a novel carbon-based stabilized coated urea by incorporating biochar, the urease inhibitor NBPT, and the nitrification inhibitor DCD through a low-energy in situ coating process. This study evaluated the effects of this fertilizer on N transformation and loss via soil column leaching and ammonia volatilization experiments, as well as its impact on choy sum (Brassica chinensis L.) yield, N use efficiency (NUE), and product quality under field conditions. Results indicated that coatings containing both NBPT and DCD (specifically, formulations with 0.5%NBPT + 1.0%DCD, and 1.0%NBPT + 1.5%DCD) significantly reduced cumulative ammonium-N leaching by 41.5–53.8% and nitrate-N leaching by 45.3–59.4% compared to conventional urea. All coated treatments suppressed ammonia volatilization by over 10%, with the highest inhibition (26.92%) observed in the treatment with 1.0%NBPT + 1.5%DCD. The synergistic coating also modulated key soil enzyme activities involved in N cycling. Field trials demonstrated that the formulations with 0.5%NBPT + 1.0%DCD and 0.5%NBPT + 1.5%DCD increased choy sum yield by 56.1% and 58.1%, respectively, while significantly improving NUE and agronomic efficiency. Moreover, these treatments enhanced vegetable quality by reducing nitrate content and increasing vitamin C and soluble sugar concentrations. In conclusion, this carbon-based stabilized coated urea, which integrates biochar with NBPT and DCD, represents a promising strategy for minimizing N losses, improving NUE, and advancing sustainable vegetable production. Full article

This study investigated how corn processing and protein degradability affect ruminal fermentation and feeding behavior in lactating Holstein cows. Twenty cows (averaging 162 ± 70 days in lactation, 666 ± 7 kg body weight, and 36.0 ± 7.8 kg/day milk yield) were assigned in a Latin square design with four 21-day periods and four treatments arranged in a 2 × 2 factorial: corn processing [ground corn (GC) vs. steam-flaked corn (SFC)] and crude protein (CP) degradability [high (HCP) vs. low (LCP)]. Ruminal samples were collected at eight time points (0, 2, 4, 6, 8, 10, 12 and 16 h) post-feeding to analyze pH, ammonia, and short-chain fatty acids, while feeding behavior was recorded visually every 5 min for 48 h. Corn processing and protein degradability interacted to influence rumen ammonia nitrogen (p = 0.057), urinary pH, (p = 0.041), nitrogen secretion and efficiency (p = 0.538), and feeding (min/kg DM; p = 0.049) and rumination times (min/kg DM, p = 0.001; min/kg NDF, p = 0.001), reflecting changes in nitrogen metabolism. Steam-flaked corn decreased the acetate/propionate ratio and enhanced propionate production, improving nitrogen retention and reducing urinary N losses, while highly degradable protein increased ruminal NH₃-N and branched-chain VFA concentrations, particularly when combined with ground corn. Additionally, steam flaking reduced feed selectivity and increased rumination efficiency, supporting more effective use of nutrients for milk N secretion and overall nitrogen utilization efficiency in dairy cows. Overall, diets varying in corn processing and protein degradability altered ruminal metabolism, nutrient utilization, feeding behavior, and diet selectivity in lactating cows, highlighting their importance in optimizing dairy cow performance. Full article

Aquaculture systems face escalating ecological risks due to the widespread use and persistence of antibiotics, which disrupt microbial-mediated nitrogen cycling and exacerbate greenhouse gas (GHG) emissions. This review synthesizes the recent research on how common antibiotics, such as sulfonamides, quinolones, tetracyclines, and macrolides, with the concentration ranging from μg/L to mg/L, alter microbial community structure, functional gene expression (e.g., amoA, nirK, and nosZ), and key nitrogen transformation processes. These disruptions inhibit nitrogen-removal efficiency by 25–55%, promote the accumulation of toxic intermediates (e.g., NH₄⁺ and NO₂⁻), and enhance emissions of potent GHGs of nitrous oxide (N₂O) and methane (CH₄). The effects are influenced by antibiotic type; concentration; environmental conditions; and interactions with co-contaminants such as heavy metals (Cu²⁺ and Pb²⁺ at 50–200 μg/L) and microplastics (0.1–10 mg/L), which can synergistically amplify ecological risks by 20–40%. The research in this field has largely focused on the toxicity of individual antibiotics, so significant gaps remain regarding combined pollution effects, long-term microbial adaptation, and molecular-scale mechanisms. This review synthesizes research on the impacts of aquaculture antibiotics on microbial nitrogen cycling and GHG emissions, identifying key mechanisms and research gaps. Its significance lies in laying a scientific foundation for integrated antibiotics pollution control strategies and bridging basic research with practical aquaculture management to advance the sustainability of aquaculture ecosystems. Full article

A cobalt-containing zeolitic imidazolate framework (ZIF-67) was carbonized by different routes to composite materials (cZIFs) composed of metallic Co, Co₃O₄, and N-doped carbonaceous phase. The effect of the carbonization procedure on the water pollutant removal properties of cZIFs was studied. Higher temperature and prolonged thermal treatment resulted in more uniform particle size distribution (as determined by nanoparticle tracking analysis, NTA) and surface charge lowering (as determined by zeta potential measurements). Surface-governed environmental applications of prepared cZIFs were tested using physical (adsorption) and electrochemical methods for dye degradation. Targeted dyes were methylene blue (MB) and methyl orange (MO), chosen as model compounds to establish the specificity of selected remediation procedures. Electrodegradation was initiated via an intermediate reactive oxygen species formed during oxygen reduction reaction (ORR) on cZIFs serving as electrocatalysts. The adsorption test showed relatively uniform adsorption sites at the surface of cZIFs, reaching a removal of over 70 mg/g for both dyes while governed by pseudo-first-order kinetics favored by higher mesoporosity. In the electro-assisted degradation process, cZIF samples demonstrated impressive efficiency, achieving almost complete degradation of MB and MO within 4.5 h. Detailed analysis of energy consumption in the degradation process enabled the calculation of the current conversion efficiency index and the amount of charge associated with O₂^•−/^•OH generation, normalized by the quantity of removed dye, for tested materials. Here, the proposed method will assist similar research studies on the removal of organic water pollutants to discriminate among electrode materials and procedures based on energy efficiency. Full article

This work reports the design and realization of a silicon-based micro photovoltaic generator (MPG) fabricated using a standard 0.18 μm complementary metal oxide semiconductor (CMOS) technology. The device harvests optical energy and converts it into electrical power through the photovoltaic effect, leveraging a network of engineered p–n junctions formed within the semiconductor. A grid-structured architecture is adopted, in which patterned p-type regions are embedded inside an n-well platform. This configuration expands the effective junction area, increases carrier-collection paths, and strengthens the internal electric field, thereby enhancing photocurrent generation. To further improve optical coupling, a specialized post-CMOS treatment is introduced. A wet etching is used to selectively remove the silicon dioxide layer that normally covers the junction regions in CMOS processes. Eliminating this dielectric layer enables direct photon penetration into the depletion region minimizes reflection-related losses, resulting in a significant improvement in device performance. Under an illumination intensity of 1000 W/m², the fabricated microgenerator delivers an open-circuit voltage of 0.49 V, a short-circuit current of 239 µA, and a maximum output power of 90 µW. The device exhibits an overall energy conversion efficiency of 12.9%, confirming the effectiveness of the grid-like junction design and the post-processing oxide removal. Full article

The integration of artificial intelligence (AI) tools, such as the chat generative pre-trained transformer (ChatGPT), into health professions education is rapidly accelerating, creating new opportunities for personalized learning and clinical preparation. These tools have demonstrated the potential to enhance learning efficiency and critical thinking. However, concerns regarding reliability, academic integrity, and potential overreliance highlight the need to better understand how healthcare students adopt and perceive these technologies in order to guide their effective and responsible integration into educational frameworks. This nationwide, cross-sectional, survey-based study was conducted between February and April 2024 among undergraduate students enrolled in medical, pharmacy, nursing, dental, and allied health programs in Saudi Arabia. An online questionnaire collected data on ChatGPT usage patterns, satisfaction, perceived benefits and risks, and attitudes toward integrating them into the curricula. Among 1044 participants, the prevalence of ChatGPT use was 69.25% (n = 723). Students primarily utilized the tool for content summarization, assignment preparation, and exam-related study. Key motivators included time efficiency and convenience, with improved learning efficiency and reduced study stress identified as major benefits. Conversely, major challenges included subscription costs and difficulties in formulating effective prompts. Furthermore, concerns regarding overreliance and academic misconduct were frequently reported. In conclusion, the adoption of generative AI tools such as ChatGPT among healthcare students in Saudi Arabia was high, driven by its perceived ability to enhance learning efficiency and personalization. To maximize its benefits and minimize risks, institutions should establish clear policies, provide faculty oversight, and integrate AI literacy into the education of health professionals. Full article

With the advancement of modern agriculture and increasing scarcity of water and fertilizer resources, determining optimal water and nitrogen (N) management strategies for intercropping systems is critical for ensuring system productivity and enhancing resource-use efficiency. This study employed field experiments to investigate the effects of different water and N treatments on grain yield, aboveground biomass, leaf area index (LAI), photosynthetic parameters, chlorophyll fluorescence characteristics, and radiation use efficiency (RUE) in a maize–soybean intercropping system. The experiment consisted of three cropping systems (maize monoculture, soybean monoculture, and maize–soybean intercropping), two irrigation regimes (rain-fed and supplemental irrigation), and three N-application rates for maize (240, 180, and 120 kgN ha⁻¹). The results demonstrated that supplementary irrigation significantly enhanced the LAI and photosynthetic capacity of both maize and soybean during critical growth stages, thereby promoting increases in grain yield and aboveground biomass. Intercropping significantly improved the productivity and photosynthetic performance of maize compared to monoculture, whereas soybean exhibited a reduction under intercropping conditions. Furthermore, irrigation regime and N rate had significant interactive effects on the photosynthetic performance of maize at the tasseling stage. In the intercropping system, a 25% reduction in the conventional application rate of N for maize maintained system productivity, whereas a 50% reduction substantially decreased maize yield and photosynthetic performance. The intercropping system achieved land equivalent ratios (LERs) ranging from 1.06 to 1.11 and RUE advantages (ΔRUE) of 1.52 to 1.64, demonstrating significant superiority in land and light resource utilization. Considering both productivity and resource-use efficiency, supplemental irrigation combined with 180 kgN ha⁻¹ N application for maize represents the optimal water and N management strategy for achieving high yield and efficiency in maize–soybean intercropping systems in the Guanzhong plain. Full article

Defect engineering has been demonstrated to be an attractive strategy to improve the catalytic performance of g−C₃N₄−based catalysts. Herein, three graphite carbon nitrides (labeled “CN”) containing a certain number of cyano groups and nitrogen vacancies are prepared successfully by calcination of the dicyandiamide−based CN in the presence of KOH, and the performances of the corresponding Pd−based catalysts are evaluated by using the formic acid (FA) dehydrogenation as a probe reaction. The characterizations of X−ray diffraction (XRD), scanning transmission electron microscopy (STEM), X−ray photoelectron spectra (XPS), hydrogen temperature−programmed desorption (H₂−TPD), and hydrogen spillover experiments indicate that the high catalytic activity of Pd/CNK−0.5 is mainly attributed to its high efficient hydrogen spillover, relatively high dispersity of Pd species, and basicity due to the introduction of a proper amount of cyano groups and nitrogen vacancies. The low initial activity of Pd/CNK−0.75 may mainly be ascribed to its low hydrogen spillover ability and the strongly chemisorbed hydrogen on Pd single atoms or small clusters. Full article

Nowadays, optimization methods are widely used to adjust controller parameters and tune their optimal values in order to enhance the efficiency and performance of dynamic systems. In this study, the parameters of a linear Proportional–Integral (PI) controller were optimized by using five different optimization algorithms, such as Artificial Tree Algorithm (ATA), Particle Swarm Optimization (PSO), Differential Evolution Algorithm (DEA), Constrained Multi-Objective State Transition Algorithm (CMOSTA), and Adaptive Fire Forest Optimization (AFFO). The optimized controllers were implemented in real time for temperature control of a Heat-flow System (HFS) under various step and time-varying reference signals. In addition, the Ziegler–Nichols (Z–N) method was also applied to the system as a benchmark to compare the temperature tracking performance of the proposed optimization methods. To further evaluate the performance of each optimization algorithm, Mean Absolute Error (MAE) values were calculated, and improvement ratios were obtained. The experimental results showed that the proposed optimization methods provided more successful reference tracking and enhanced controller performance as well. Full article

Spent mushroom substrate (SMS)-derived bio-based polyurethane coatings typically exhibit poor hydrophobicity and short nutrient release durations, limiting their ability to satisfy long-term crop requirements. This study developed improved controlled-release urea by preparing water-repellent and compact bio-polymer coatings from recyclable SMS using non-toxic siloxane and nano-SiO₂ modifiers through simple processes. The dual modification markedly reduced water absorption (from 6.60% to 4.43%) and porosity (from 6.32% to 3.92%), creating a dense coating with lotus-leaf-like nanoscale surface protrusions and fewer intermembrane pores. As a result, the nitrogen (N) release period of the dual-modified bio-polymer-polyurethane-coated urea (SBPCU) with a 7% coating thickness was extended from 23 days to 42 days. Phytotoxicity assessments confirmed the excellent biosafety of the bio-polymer coating, revealing no adverse effects on maize growth and even promotional effects at low concentrations. This approach offers a sustainable, eco-friendly, and scalable strategy for producing bio-polymer-coated urea from agricultural waste, serving as a viable alternative to petrochemical coatings while improving nutrient use efficiency and biosafety. Full article

Resource-constrained sensor nodes in Internet-of-Things (IoT) and embedded sensing applications frequently rely on low-cost microcontrollers, where even basic algorithmic choices directly impact latency, energy consumption, and memory footprint. This study evaluates six sorting algorithms—Bubble Sort, Insertion Sort, Selection Sort, Merge Sort, Quick Sort, and Heap Sort—in the restricted environment that microcontrollers provide. Three Arduino boards were used: Arduino Uno, Arduino Leonardo, and Arduino Mega 2560. Each algorithm was implemented in its unoptimized form and tested on datasets of increasing size, emulating buffered time-series sensor readings in random, ascending, and descending order. Execution time, number of write operations, and memory usage were measured. The tests show clear distinctions between the slower $O\left(n^2\right)$ algorithms and the more efficient $O(nlogn)$ algorithms. For random inputs of $n=1000$ elements, Bubble Sort required 1,958,193.75 $\mathsf{\mu }$son average, whereas Quick Sort completed it in 54,260.50 $\mathsf{\mu }$s and Heap Sort in 92,429.00 $\mathsf{\mu }$s, i.e., speedups of more than one order of magnitude compared to the quadratic baseline. These gains, however, come with very different memory footprints. Merge Sort kept the runtime below 100,000 $\mathsf{\mu }$s at $n=1000$ but required approximately 2023 bytes of additional static random-access memory (SRAM), effectively exhausting the 2 kB SRAM of the Arduino Uno. QuickSort used approximately 311 bytes of extra SRAM and failed to process larger ascending and descending datasets on the more constrained boards due to its recursive pattern and stack usage. Heap Sort offered the best overall trade-off: it successfully executed all tested sizes up to the SRAM limit of each board while using only about 12–13 bytes of additional SRAM and keeping the runtime below 100,000 $\mathsf{\mu }$s for $n=1000$. The results provide practical guidelines for selecting sorting algorithms on 8-bit AVR Arduino-class microcontrollers, which are widely used as simple sensing and prototyping nodes operating under strict RAM, program-memory, and energy constraints. Full article