Search Results (3,211)

Micro-photosynthetic power cells (μPSCs), also known as biophotovoltaics (BPVs), represent sustainable and self-regenerating solutions for harvesting electricity from photosynthetic microorganisms. However, their practical deployment has been constrained by low voltage, low current output, and scaling inefficiencies. In this work, we address these limitations through a dual-optimization strategy: (i) systematic quantification of how electrode surface area influences key performance metrics, and (ii) based on our previous work we highlighted the novel hybrid modular array architectures that combine series and parallel connections of μPSCs. Three single μPSCs with electrode areas of 4.84, 19.36, and 100 cm² were fabricated and compared, revealing that while open-circuit voltage remains largely area-independent (850–910 mV), both short-circuit current and maximum power scale with electrode size. Building on these insights, two hybrid array configurations fabricated from six 4.84 cm² μPSCs achieved power outputs of 869.2 μW and 926.4 μW, equivalent to ~82–87% of the output of a large 100 cm² device, while requiring only ~29% electrode area and ~70% less reagent volume. Importantly, these arrays delivered voltages up to 2.4 V, significantly higher than a single large device, enabling easier integration with IoT platforms and ultra-low-power electronics. A meta-analysis of over 40 reported BPV/μPSC systems with different electrode surface areas further validated our findings, showing a consistent inverse relationship between electrode area and power density. Collectively, this study introduces a scalable, resource-efficient strategy for enhancing μPSC performance, providing a novel design paradigm that advances the state of the art in sustainable bioenergy and opens pathways for practical deployment in distributed, low-power and IoT applications. Full article

When internal faults occur in a microgrid, the switching between grid-connected and islanded modes can lead to extended tripping times for traditional inverse-time overcurrent (ITOC) protection and failure in coordination between protection levels. To address these issues, this paper proposes an improved inverse-time overcurrent protection scheme based on a composite parameter protection factor. This scheme utilizes the phase relationship between the positive-sequence voltage fault component at the bus and the positive-sequence current fault component in the feeder after a fault occurrence, combined with the severity of bus voltage sags, to construct a composite parameter protection factor. This factor incorporates a phase-difference acceleration factor and a voltage-sag acceleration factor, aiming to shorten the operation time of the inverse-time overcurrent protection. Furthermore, leveraging the proportional relationship between the composite parameter protection factor and the fault location, the coordination between different protection levels is optimized. Simulations were conducted using PSCAD/EMTDC. The simulation results verify the effectiveness of the proposed improved scheme under various fault scenarios. Full article

Background: Glioma remains an intractable and highly aggressive brain tumor, mainly due to the daunting obstacle presented by the blood–brain barrier (BBB). To overcome this challenge and enhance therapeutic efficacy, a dual-drug delivery system was engineered. This system co-encapsulated curcumin, a nutraceutical with multitargeted anticancer potential, with atorvastatin calcium, a repurposed anticancer agent, within lipidic nanocapsules (LNCs). Methods: LNCs were prepared via the phase inversion temperature method and optimized using a Box–Behnken design. The optimized LNCs were subsequently functionalized with folic acid (FA) to enable active targeting. FA-LNCs were characterized using XPS, TEM, in vitro release, and MTT cytotoxicity assays. Atorvastatin and curcumin were radiolabeled separately with iodine-131 to evaluate the in vivo pharmacokinetics in a glioma-bearing mouse model. Results: The optimized LNCs and FA-LNCs displayed a mean particle size of 97.98 ± 2.27 nm and 181.60 ± 2.83 nm, a polydispersity index of 0.32 ± 0.07 and 0.40 ± 0.02, and a zeta potential of −15.85 ± 1.35 mV and −11.90 ± 2.80, respectively. XPS and FTIR analyses verified FA conjugation. Both LNCs and FA-LNCs enhanced the in vitro cytotoxicity compared to free drugs; however, the most pronounced effect of FA functionalization was observed in vivo. Most significantly, FA-LNCs achieved markedly greater glioma accumulation than non-functionalized LNCs, with AUC values 2.0-fold higher for atorvastatin and 2.6-fold higher for curcumin. When compared to the free drug solutions, this efficiency was even more pronounced, with atorvastatin and curcumin showing enhancements of 8.2 and 12.4 times, respectively. Conclusions: FA-LNCs markedly improved glioma targeting efficiency and reduced systemic clearance, which underscores the therapeutic potential of integrating nutraceuticals with repurposed agents to achieve effective glioma therapy. Full article

China has witnessed extensive construction of underground powerhouses for pumped storage power stations. With the continuous increase in unit capacity, vibration problems have become particularly pronounced. Intense vibrations may not only disrupt the normal operation of hydropower units but also compromise the overall structural safety of the powerhouse. Moreover, in dynamic analyses of powerhouse structures, different parameters exert varying degrees of influence on the results, making it essential to systematically examine their impacts. This study focuses on a large-scale underground powerhouse, establishing a three-dimensional finite element model of Unit #1 to investigate its dynamic characteristics and parametric sensitivity. Through modal and harmonic response analyses, the effects of key parameters—including the zone of surrounding rock, elastic modulus of surrounding rock, dynamic elastic modulus of concrete, and damping ratio—were systematically evaluated. Results indicate that an expanded surrounding rock zone reduces natural frequency and increases dynamic displacement, with a zone twice the span length offering an optimal balance between accuracy and computational efficiency. Increasing the elastic modulus of the surrounding rock raises the natural frequency and slightly reduces displacement, while having a limited impact on dynamic stress. The dynamic elastic modulus of concrete shows a square-root relationship with natural frequency and an inverse correlation with dynamic displacement. The damping ratio has negligible influence on natural frequency, dynamic displacement, and dynamic stress. These findings provide a theoretical basis and practical guidance for parameter selection in the dynamic analysis of underground powerhouse structures, enhancing the reliability of numerical simulations. Full article

Hyperspectral remote sensing provides a powerful tool for crop nutrient monitoring and precision fertilization, yet its application is hindered by high-dimensional redundancy and inter-band collinearity. This study aimed to improve maize nitrogen estimation by constructing three types of two-dimensional full-band spectral indices—Difference Index (DI), Simple Ratio Index (SRI), and Normalized Difference Index (NDI)—combined with spectral preprocessing methods (raw spectra (RAW), first-order derivative (FD), and second-order derivative (SD)). To optimize feature selection, three strategies were evaluated: Grey Relational Analysis (GRA), Pearson Correlation Coefficient (PCC), and Variable Importance in Projection (VIP). These indices were then integrated into machine learning models, including Backpropagation Neural Network (BP), Random Forest (RF), and Support Vector Regression (SVR). Results revealed that spectral index optimization substantially enhanced model performance. NDI consistently demonstrated robustness, achieving the highest grey relational degree (0.9077) under second-derivative preprocessing and improving BP model predictions. PCC-selected features showed superior adaptability in the RF model, yielding the highest test accuracy under raw spectral input (R² = 0.769, RMSE = 0.0018). VIP proved most effective for SVR, with the optimal SD–VIP–SVR combination attaining the best predictive performance (test R² = 0.7593, RMSE = 0.0024). Compared with full-spectrum input, spectral index optimization effectively reduced collinearity and overfitting, improving both reliability and generalization. Spectral index optimization significantly improved inversion accuracy. Among the tested pipelines, RAW-PCC-RF demonstrated robust stability across datasets, while SD-VIP-SVR achieved the highest overall validation accuracy (R² = 0.7593, RMSE = 0.0024). These results highlight the complementary roles of stability and accuracy in defining the optimal pipeline for maize nitrogen inversion. This study highlights the pivotal role of spectral index optimization in hyperspectral inversion of maize nitrogen content. The proposed framework provides a reliable methodological basis for non-destructive nitrogen monitoring, with broad implications for precision agriculture and sustainable nutrient management. Full article

Urban green spaces (UGS) are crucial for mitigating rising urban land surface temperatures (LST). Rapid urbanization presents unresolved questions regarding (a) seasonal variations in the spatial co-distribution of UGS and LST, (b) the temporal and spatial changes in UGS cooling, and (c) the dominant factors driving cooling effects during different periods. This study focuses on Beijing’s Fifth Ring Road area, utilizing nearly 40 years of Landsat remote sensing imagery and land cover data. We propose a novel nine-square grid spatial analysis approach that integrates LST retrieval, profile line analysis, and the XGBoost algorithm to investigate the long-term spatiotemporal evolution of UGS cooling capacity and its driving mechanisms. The results demonstrate three key findings: (1) Strong seasonal divergence in UGS-LST correlation: A significant negative correlation dominates during summer months (June–August), whereas winter (December–February) exhibits marked weakening of this relationship, with localized positive correlations indicating thermal inversion effects. (2) Dynamic evolution of cooling capacity under urbanization: Urban expansion has reconfigured UGS spatial patterns, with a cooling capacity of UGS showing an “enhancement–decline–enhancement” trend over time. Analysis through machine learning on the significance of landscape metrics revealed that scale-related metrics play a dominant role in the early stage of urbanization, while the focus shifts to quality-related metrics in the later phase. (3) Optimal cooling efficiency threshold: Maximum per-unit-area cooling intensity occurs at 10–20% UGS coverage, yielding an average LST reduction of approximately 1 °C relative to non-vegetated surfaces. This study elucidates the spatiotemporal evolution of UGS cooling effects during urbanization, establishing a robust scientific foundation for optimizing green space configuration and enhancing urban climate resilience. Full article

Background/Objectives: Crohn’s disease (CD) is a chronic immune-mediated disorder with heterogeneous response to biologic therapies. Ustekinumab (UST), an anti-IL-12/23 monoclonal antibody, is effective in CD, but predictive biomarkers of treatment response remain lacking. This study aimed to investigate cytokine dynamics during UST induction and to evaluate their association with clinical and biochemical outcomes in an observational cohort of CD patients. Methods: We prospectively recruited 31 adult patients with moderate-to-severe active CD initiating UST therapy at a tertiary referral center. Peripheral blood and stool samples were collected at baseline and weeks 4, 8, and 16. UST trough concentrations, C-reactive protein (CRP), fecal calprotectin (FC), hemoglobin, albumin, and 13 serum cytokines (including IL-1β, IL-6, IL-8, IL-10, IL-12p70, IL-13, IL-17, IL-23, TNF-α, and OSM) were analyzed. Response was defined as a ≥70% reduction in FC at week 16, or, alternatively, CRP < 5 mg/L or a Harvey–Bradshaw Index < 3. Results: Eighteen patients (58%) achieved response at week 16. Responders showed significant reductions in FC, CRP, and disease activity, while non-responders exhibited limited biochemical improvement. Overall, UST induction was associated with a global decrease in proinflammatory cytokines, particularly TNF-α and IL-1β. Responders displayed distinct cytokine patterns, with higher IL-13 levels at week 8 and lower IL-8 concentrations at week 16 compared with non-responders. UST trough levels tended to be higher in responders, and inverse correlations were observed between drug concentrations and several cytokines, including IL-6, IL-8, IL-13, and IL-23. Conclusions: UST induction leads to measurable immunological changes in CD, with differential cytokine dynamics distinguishing responders from non-responders. These findings support the potential of cytokine signatures, in combination with therapeutic drug monitoring, as pharmacodynamic biomarkers to optimize personalized treatment strategies in CD. Full article

Objective: To investigate whether prolonged Sanda combat experience improves cognitive–motor control via neuro-cardiac coupling. Methods: Nineteen national-level Sanda athletes and nineteen matched controls completed a color-word Stroop task while concurrent EEG and ECG were recorded. The conflict adaptation effect (CAE), which refers to the ability to adjust cognitive control in response to conflicting stimuli, was compared between groups, along with P600 and LSP amplitudes and heart rate variability (RMSSD, HF); mediation analysis examined vagal recovery as a pathway. Results: Athletes responded faster and showed a larger CAE than controls (p < 0.001). ERP analyses revealed larger CAE-related P600 and LSP amplitudes in athletes (p < 0.05), with LSP amplitude inversely correlating with behavioral CAE (p < 0.05). Post-task vagal rebound (ΔRMSSD and ΔHF) was significantly greater in athletes (p < 0.05), and ΔRMSSD positively correlated with CAE (p < 0.05). Mediation analysis confirmed that vagal recovery partially mediated the association between Sanda experience and improved cognitive–motor control (p < 0.05). Conclusions: Sanda training enhances cognitive–motor control by accelerating parasympathetic recovery and optimizing neural conflict processing, providing evidence for an integrated exercise–cognition–autonomic nervous system coupling model. Full article

Integrated optical filters are fundamental and indispensable components of silicon photonics, which enhance the data throughput of high-demand communication networks. Grating-assisted filters have been widely used due to the merits they offer: flat top, low crosstalk, and no FSR. In this paper, we report an inverse-designed narrow-band silicon Bragg grating filter that unites lateral-misalignment apodization with cooperative particle swarm optimization (CPSO). The initial coupling-coefficient profile of the filter is first yielded by a layer-peeling algorithm (LPA). Subsequently, the final structure is designed by CPSO to approach the desired spectral response. The filter is fabricated on a 220 nm silicon-on-insulator platform. The measured results exhibit 3.39 nm bandwidth, 19.34 dB side lobe suppression ratio (SLSR), and 1.75 dB insertion loss. The proposed design method effectively solves the problem of excessively high side lobes in uniform gratings and LPA-designed gratings when designing narrow-bandwidth filters. Full article

Gantry cranes play a key role in modern industrial logistics. However, the traditional dynamic model based on the assumption of cable rigidity faces difficulty in accurately describing the complex swing characteristics of flexible cables, resulting in low load positioning accuracy and limited operation efficiency. To address this problem, this paper proposes a cable modeling method that considers the flexible deformation and nonlinear dynamic characteristics of the cable. Based on the theory of continuum mechanics, a flexible cable dynamic model that can accurately describe the flexible deformation and distributed mass characteristics of the cable is established. In order to solve the transportation time optimization and full-state constraint problems, a velocity trajectory optimization algorithm based on a discretization framework is proposed. Through inverse kinematics analysis and numerical integration technology, a reverse angle enumeration reasoning (RAER) method is proposed to suppress the swing of the load. Under the same constraints of distance, velocity, acceleration, cable swing angle, and residual swing angle, RAER requires a longer transportation time but achieves smaller peak swing and residual swing, making it the only algorithm that satisfies full-state constraints. Under the energy criterion, the proposed algorithm also requires the least amount of energy. Comprehensive comparisons through simulations and experiments show that the predicted swing angles of the flexible cable are highly consistent with the experimental results. Full article

The accurate monitoring of crop water status is critical for optimizing irrigation strategies in winter wheat. Compared with satellite remote sensing, unmanned aerial vehicle (UAV) technology offers superior spatial resolution, temporal flexibility, and controllable data acquisition, making it an ideal choice for the small-scale monitoring of crop water status. During 2023–2025, field experiments were conducted to predict crop water status using UAV images in the North China Plain (NCP). Thirteen vegetation indices were calculated and their correlations with observed crop water content (CWC) and equivalent water thickness (EWT) were analyzed. Four machine learning (ML) models, namely, random forest (RF), decision tree (DT), LightGBM, and CatBoost, were evaluated for their inversion accuracy with regard to CWC and EWT in the 2024–2025 growing season of winter wheat. The results show that the ratio vegetation index (RVI, NIR/R) exhibited the strongest correlation with CWC (R = 0.97) during critical growth stages. Among the ML models, CatBoost demonstrated superior performance, achieving R² values of 0.992 (CWC) and 0.962 (EWT) in training datasets, with corresponding RMSE values of 0.012% and 0.1907 g cm⁻², respectively. The model maintained robust performance in testing (R² = 0.893 for CWC, and R² = 0.961 for EWT), outperforming conventional approaches like RF and DT. High-resolution (5 cm) inversion maps successfully identified spatial variability in crop water status across experimental plots. The CatBoost-RVI framework proved particularly effective during the booting and flowering stages, providing reliable references for precision irrigation management in the NCP. Full article

Leaf water content (LWC) and chlorophyll content (CHL) are pivotal physiological indicators for assessing citrus growth and stress responses. However, conventional measurement techniques—such as fresh-to-dry weight ratio and spectrophotometry—are destructive, time-consuming, and limited in spatial and temporal resolution, making them unsuitable for large-scale monitoring. To achieve efficient large-scale monitoring, this study proposes a synergistic inversion framework integrating UAV multispectral remote sensing with intelligent optimization algorithms. Field experiments during the 2024 growing season (April–October) in western Hubei collected 263 ground measurements paired with multispectral images. Sensitive spectral bands and vegetation indices for LWC and CHL were identified through Pearson correlation analysis. Five modeling approaches—Partial Least Squares Regression (PLS); Extreme Learning Machine (ELM); and ELM optimized by Particle Swarm Optimization (PSO-ELM), Artificial Hummingbird Algorithm (AHA-ELM), and Grey Wolf Optimizer (GWO-ELM)—were evaluated. Results demonstrated that (1) VI-based models outperformed raw spectral band models; (2) the PSO-ELM synergistic inversion model using sensitive VIs achieved optimal accuracy (validation R²: 0.790 for LWC, 0.672 for CHL), surpassing PLS by 15.16% (LWC) and 53.78% (CHL), and standard ELM by 20.80% (LWC) and 25.84% (CHL), respectively; and (3) AHA-ELM and GWO-ELM also showed significant enhancements. This research provides a robust technical foundation for precision management of citrus orchards in drought-prone regions. Full article

Understanding the thermal environment of outdoor public spaces is critical for climate-responsive architectural design, evidence-based urban science, and data-driven smart city planning. Thermal comfort shapes both individual decision-making and collective behavioural patterns, offering valuable insights for designing spaces that support year-round vitality. This study investigates the relationship between thermal conditions and crowd behaviour in severe cold regions by combining behavioural mapping with on-site environmental measurements. Results show that in high-temperature conditions, spatial distribution is primarily influenced by sunlight and shade, whereas at low temperatures, sunlight has minimal effect on space use. Attendance, duration of stay, and activity intensity follow quadratic relationships with the Universal Thermal Climate Index (UTCI), with optimal values at 29 °C, 26 °C, and 27 °C, respectively. Walking speed is inversely correlated with UTCI, with the fastest speeds observed under cold discomfort, reflecting rapid departure from space. Sitting behaviour peaks at 21 °C UTCI and declines to nearly zero when UTCI is below 10 °C. A comparative analysis between Harbin and other regions reveals significant deviations from temperate zone patterns and greater similarity to subtropical behavioural responses. A key contribution of this study is the introduction of the spatial usage rate model and the foot vote method, two novel, observation-based tools that allow for the objective estimation of thermal comfort without relying solely on subjective surveys. These methods offer architects, planners, and smart city practitioners a powerful evidence-based framework to evaluate and optimise outdoor thermal performance, ultimately enhancing usability, adaptability, and public engagement in cold-climate cities. Full article

Background and Objectives: To investigate whether myeloperoxidase (MPO)-antineutrophil cytoplasmic antibody (ANCA) titres at diagnosis are associated with the risk of end-stage kidney disease (ESKD) progression in patients with microscopic polyangiitis (MPA) treated with rituximab. Materials and Methods: This retrospective cohort study included 34 patients with MPA who received rituximab. Clinical data, including MPO-ANCA titres at diagnosis and ESKD progression during follow-up, were assessed. Receiver operating characteristic (ROC) curve analysis was performed to assess whether MPO-ANCA titres could predict ESKD progression. The optimal cut-off value of MPO-ANCA titres was determined where the sum of sensitivity and specificity was at a maximum. Based on this cut-off value, patients were categorised into two groups, and the relative risk (RR) of ESKD progression was estimated. Results: During a median follow-up of 39.5 months, seven patients (20.6%) progressed to ESKD. ROC curve analysis showed a significant inverse association between MPO-ANCA titres and ESKD progression (AUC 0.254, 95% confidence interval [CI] 0.046, 0.462 p = 0.048). The optimal cut-off of MPO-ANCA titres was 81.0 IU/mL, which yielded a sensitivity and specificity of 70.4% and 85.7%, respectively. The RR of ESKD progression was significantly higher in those with MPO-ANCA titres ≤ 81.0 IU/mL than in those with MPO-ANCA titres > 81.0 IU/mL (42.9% vs. 5.0%, RR 14.250, 95% CI 1.469, 138.271). Conclusions: Lower MPO-ANCA titres at diagnosis may be associated with a higher risk of ESKD progression in rituximab-treated MPA patients. These findings suggest that MPO-ANCA titres may be useful in guiding therapeutic decisions for MPA. Full article

Water scarcity in semi-arid regions necessitates accurate soil water modeling to optimize irrigation management. This study compares three HYDRUS-1D parameterization approaches—based on the drying-branch soil water retention curve (SWRC), wetting-branch SWRC (using Shani’s drip method), and inverse modeling—to simulating soil water content at 15 cm and 45 cm depths under center-pivot irrigation in a semi-arid region. Field experiments in three maize fields provided daily soil water, soil hydraulic, and meteorological data. Inverse modeling achieved the highest accuracy (NRMSE: 2.29–7.40%; RMSE: 0.006–0.023 cm³ cm⁻³), particularly at 15 cm depth, by calibrating van Genuchten parameters against observed water content. The wetting-branch approach outperformed the drying branch at the same depth, capturing irrigation-induced wetting processes more effectively. Statistical validation confirmed the robustness of inverse modeling in reproducing temporal patterns, while wetting-branch data improved deep-layer accuracy. The results demonstrate that inverse modeling is a reliable approach for soil water simulation and irrigation management, whereas the wetting-branch parameterization offers a practical, field-adaptable alternative. This study provides one of the first side-by-side evaluations of these three modeling approaches under real-world semi-arid conditions. Full article