Search Results (1,539)

Different coloured and intensity commercial shade nets are increasingly proposed as a low-cost tool for mitigating abiotic stress in semi-arid cropping systems, but cultivar-specific responses in grain legumes remain poorly documented. This study was conducted in an open field at the Faculty of Agriculture, Dicle University, Diyarbakır, Türkiye, under rainfed conditions to evaluate the effects of unshaded sunlight and three-color commercial shading nets in different intensities (95% shaded dark green; 40% shaded blue–green; 75% shaded black net) on the pea cultivars Araka and Vining pea. The experiment was arranged as a randomised complete block design in split plots with three replications, and plants were sampled at the slow growth (DAE 25), full blooming (DAE 45), podding (DAE 60), seed filling (DAE 75), and physiological maturity (DAE 90) stages. At DAE 75, we recorded the tallest plants under a 75% shaded black net (53.17 cm), the longest roots under sunlight (20.75 cm), the highest number of leaves per plant under 95% shaded dark green net (70.83), the largest leaf area under 75% shaded black net (471.95 cm² plant⁻¹), and the highest dry biomass under sunlight (11.46 g). At physiological maturity, the 75% shaded black net increased pod length (8.50 cm), pod weight per plant (35.10 g), number of fresh seeds per plant (27.33), and fresh seed weight (15.45 g). Complementary analyses based on a specific leaf area proxy, a harvest-index proxy, and a yield–risk matrix revealed a qualitative cultivar × shade interaction in biomass-to-seed conversion, with Vining pea performing best under a 75% shaded black net and Araka under a 95% shaded dark green net. Shade nets are therefore not a one-size-fits-all tool and should be selected in a cultivar-aware manner. Full article

Secure transmission and storage of color images remain challenging tasks due to strong inter-pixel correlations and high data volume. This work proposes a one-dimensional sine-tent-logistic-exponential map (STLEM) equipped with numerical boundary correction rules to mitigate finite-precision numerical degradation so as to enhance the unpredictability of chaos-driven cryptosystems. We benchmark STLEM against classic logistic, tent, and sine maps via Lyapunov exponents, autocorrelation, approximate entropy, permutation entropy, Lempel-Ziv complexity, and Kolmogorov–Sinai entropy. Bifurcation diagrams, the 0–1 test, and NIST statistical tests are further adopted to characterize its chaotic dynamics and randomness. Comparative results verify that STLEM achieves improved dynamical complexity and randomness performance. Built upon the proposed STLEM, this paper constructs a color-image encryption scheme that employs a 256-bit master key and two groups of chaotic parameters to produce key-related chaotic sequences. The cryptosystem integrates dynamic edge expansion, chaotic permutation, position-dependent adaptive DNA encoding, DNA-domain chained diffusion, and two successive row-column permutation phases. HMAC-SHA-256 is utilized to generate plaintext-aware initial conditions and perform ciphertext authentication prior to decryption. Experimental validations demonstrate complete plaintext recovery under valid secret inputs, while authentication rejects invalid keys and tampered ciphertexts. Ciphered images exhibit high information entropy, negligible adjacent-pixel correlations, and satisfactory number of pixel change rate (NPCR) and unified average changing intensity (UACI) metrics. Benefiting from a sufficiently large key space and $O\left(MN\log \right(MN\left)\right)$ computational complexity, the proposed scheme is resilient against brute-force attacks and well suited for secure color-image communication scenarios, rather than acting as a general-purpose replacement for standard block ciphers. Full article

Background: Patient-centered care is particularly important in assisted reproductive technology, where treatment is clinically demanding and emotionally intensive. This study documented a preliminary cultural and linguistic adaptation of a fertility-care survey for Kazakhstan and evaluated patient–staff perception gaps, item-level priorities, preliminary reliability, and exploratory associations with global care ratings. Methods: A cross-sectional survey was conducted in two assisted-reproduction clinics in Almaty. The analytic workbook contained 273 patient and 74 staff records; 173 patient response patterns were unique and 100 records matched an earlier exact pattern. Group-level patient–staff comparisons between independent respondent samples used prespecified harmonized indices, Welch tests, false-discovery-rate correction, clinic-stratified analyses, and one-pattern-per-record sensitivity analyses. Importance–experience differences were calculated within respondents with both values available. Global-rating regressions used HC3 robust inference and were treated as exploratory. Results: Staff rated cost, attitude and communication, information and education, and participation more favorably, whereas patients rated privacy, emotional support, and continuity more favorably. Accessibility and competence showed little difference. Clinic-stratified and one-pattern-per-record analyses preserved the principal contrast directions, although the Physical Comfort proxy was not robust. Several indices showed weak or negative internal consistency. Global ratings had a marked ceiling, with 79.1% at 10/10, and the HC3 joint test for the added care-index block was not significant (p = 0.080). Conclusions: This exploratory survey identified substantial, bidirectional group-level patient–staff differences and concrete item-level priorities, but the scoring and psychometric evidence are insufficient to interpret the domain indices as established measurement scales. Source-codebook verification and broader structural validation are required. Full article

Simulation studies of surplus electricity in sugarcane cogeneration almost universally assume stable nominal operation, and the sector literature qualitatively acknowledges that many surplus projects underperform; however, this discrepancy has not been systematically quantified against multiyear operational records. Using daily records from seven harvest seasons (2010–2016; 931 valid days) of a Cuban low-pressure sugar mill and the previously published simulation of its own thermal scheme (Termoazúcar STA 4.1), this study quantifies the discrepancy through the Energy Performance Gap (EPG) framework adapted from building science. The export shortfall relative to the simulated baseline ranged from 19.0% to 49.3% per harvest (38.1% aggregated over 2010–2015; 12,835 MWh unrealized) and reached 28.9% for the five-mill provincial aggregate. The gap does not arise from idle capacity—availability is 0.98–1.00, and industrial demand matches the simulated value—but from conversion, with a cane-weighted generation deficit of 5.46 kWh/t ($-12.7$%). Plant steam records proved to be accounting allocations based on fixed coefficients and cannot support correlation-based inference; what they document independently is a contiguous start-of-season regime with pressure-reducing valves in service (39 days of a single harvest), during which specific generation was 11.9 kWh/t lower at statistically identical milling rates (26.15 vs. 38.06 kWh/t; $p<0.001$). No interannual trend was detected (Mann–Kendall, $p=0.368$), although statistical power is limited, at $n=7$. A Monte Carlo characterization of the parametric uncertainty of the simulated baseline (boiler efficiency $\pm 3$%, turbine isentropic efficiency $\pm 5$%, and bagasse moisture $\pm 2$ percentage points) shows that the existence of the gap is robust—the probability of no gap is, at most, 1.1%, even under worst-case uniform perturbations—while widening the intensity-level discount interval to [0.57; 0.93]; a start-of-season depression in specific generation recurs in four of the six estimable harvests, of which the 2015 reducer regime is the most severe instance. As case-specific correction tools, operational discount factors of 0.71 (95% block-bootstrap CI [0.68; 0.75]) for export intensity and 0.62 for total seasonal energy are derived; the underlying procedure, rather than the numerical values, is proposed as transferable. Full article

Deep shale gas reservoirs are subjected to anisotropic in situ stresses that break the directional symmetry of hydraulic-fracture growth, promote propagation along the maximum horizontal principal stress, and suppress transverse spreading. This study investigates geomechanical and injection controls on fracture-network evolution in the Yi214 block of the Changning shale gas field using a field-calibrated numerical workflow. The model was calibrated against pre-design microseismic-derived metrics from Well Yi202, with relative differences of 1.8% for fracture length and 3.9% for effective fracture volume; this comparison is treated as calibration rather than independent multi-well validation. A dimensionless directionalization index, I_d = (L/L₀)/(V/V₀), is defined relative to the equal-horizontal-stress case (Δσ_h = 0) as a global proxy for the concentration of longitudinal extension relative to volumetric spreading. One-factor-at-a-time screening evaluated Young’s modulus, Poisson’s ratio, horizontal stress difference, injection rate, fluid-volume intensity, and proppant loading, while final parameter combinations were treated as well-specific engineering design selections rather than mathematical global optima. As Δσ_h increased from 0 to 15 MPa, fracture volume decreased by approximately 44% and average fracture length increased by approximately 12%, raising Id from 1.00 to 1.99; the increase in Id was driven predominantly by reduced volume retention rather than length growth alone. The final combined design cases increased simulated fracture volume by 18–27%, while the reported model-based EUR forecasts increased by 36–40%. The results provide a symmetry-based interpretation of stress-controlled fracture directionalization and a field-calibrated basis for well-specific stimulation design in deep shale reservoirs. Full article

Effective management of urban Asian water monitor (Varanus salvator (Laurenti, 1768)) populations requires precise individual identification, yet traditional physical-marking methods remain invasive and labor-intensive. This study developed a non-invasive, automated photographic re-identification (Re-ID) system using deep learning and computer vision to facilitate population monitoring in semi-urban environments. We evaluated seven deep learning configurations based on ResNet50 incorporating Squeeze-and-Excitation (SE), Convolutional Block Attention Module (CBAM), and Batch Normalization neck (BNNeck) optimizations and benchmarked them against traditional feature matching (HotSpotter) using an open-set evaluation dataset of 3311 images across 161 Side-IDs focusing on unique lateral head-scale patterns. HotSpotter demonstrated immediate field viability, achieving a Rank-1 accuracy of 99.88% and a mean Average Precision (mAP) of 90.40%. Among the deep learning architectures, the baseline ResNet50 achieved the highest Rank-1 accuracy of 75.39% and mAP of 55.97%. As a decision-support framework, the deep learning pipeline achieved over 87% Rank-5 accuracy, drastically reducing manual screening effort and cognitive load during capture–mark–recapture surveys. This non-invasive framework establishes a scalable, welfare-friendly protocol for long-term urban wildlife management and biodiversity monitoring. Full article

As web technologies shift from traditional content delivery toward high-performance computing, Web Worker-based parallelism for compute-intensive tasks such as fractal rendering has become essential for improving web application performance and interactivity. However, due to memory isolation and limited hardware access imposed by the browser sandbox, existing parallel approaches often struggle with excessive communication overhead, severe load imbalance, and straggler blocking when rendering spatially heterogeneous fractal graphics, especially under high-frequency interaction. This paper presents a task partitioning and scheduling scheme for parallel fractal rendering on Web Worker that addresses these challenges. By introducing load prediction for fractal rendering tasks, a communication-aware task partitioning scheme is achieved together with a scheduling strategy that combines interaction-aware adaptive prioritization and stale task cancellation. Experimental results show that the proposed method effectively reduces task skew and communication costs while outperforming baseline methods in term of parallel performance, load balancing, and response latency. Under high-frequency interaction, it also delivers notable improvements in system responsiveness and interaction smoothness, substantially enhancing both rendering efficiency and user experience for complex fractal graphics on the web. Full article

Quantifying groundwater depletion and aquifer–stream interactions in intensively irrigated basins remains a critical challenge for sustainable water management. This study numerically examines these issues in the San Joaquin River Basin (SJRB) using the coupled SWAT+ gwflow framework. To manage computational demands across the 28,435 km² basin, upland contributing areas were represented as inlet point sources at five gauging stations at the outlets of each of the upstream subbasins, reducing computational cost by ~45% while preserving hydrological fidelity. A fine-resolution (300 m × 300 m) model was developed for the downstream valley floor, with release from reservoirs and mountain-block runoff incorporated as a surface boundary condition. We applied the water allocation module gwflow to withdraw water from the aquifer to satisfy crop water stress based on user-defined crop water coefficient values and compared the simulated and observed spatially distributed groundwater head values. Results show a persistent decline in groundwater storage and water levels from 2006 to 2022, with extraction exceeding recharge by ~40%, resulting in an average annual storage loss of 450,175 acre-feet. Intensive pumping also caused hydraulic disconnection between groundwater and some streams, where pronounced cones of depression developed in Turlock, Merced, and western Madera subregions. These findings indicate that targeted managed aquifer recharge and reductions in groundwater extraction in the most severely depleted subregions, are essential to restore aquifer–stream connectivity and ensure long-term water security in the SJRB. Overall, integrating gwflow with SWAT+ provides critical insights into groundwater dynamics in California’s Central Valley and supports sustainable groundwater management. Full article

Different imaging modalities exhibit inherent discrepancies in intensity characteristics and information representation, resulting in pronounced heterogeneity among multimodal features. When these heterogeneous features are directly learned and fused within a unified representation space, feature coupling may arise, leading to mutual interference between global structural information and local fine-grained details. To address the limited differentiated modeling of structural and fine-grained information in existing methods, we propose Hierarchical Dual-Perspective Collaborative Modeling for Multimodal Image Fusion (HDPF). HDPF employs a Dual-Perspective Feature Aggregation Block (DFAB) to jointly exploit convolution-based local representation and Transformer-based global contextual modeling. Building upon this dual-perspective representation, HDPF further constructs two differentiated pathways dedicated to structural information and detail information, respectively, thereby providing differentiated representations of complementary multimodal information. The extracted features are subsequently reorganized and integrated by the decoder to reconstruct the final fused image. Extensive experiments are conducted on multiple infrared–visible image fusion (IVF) datasets as well as medical image fusion (MIF) tasks. On the TNO dataset, HDPF achieves VIF and MI scores of 0.80 and 3.50, respectively. On the MRI–PET fusion task, the VIF score reaches 0.61. The experimental results demonstrate that HDPF achieves competitive and relatively balanced fusion performance across the evaluated multimodal image fusion tasks. Full article

Real-time LiDAR semantic segmentation for industrial AGVs must distinguish repeated structures and weak glass returns while remaining robust to sensor-dependent intensity and tight edge computing budgets. We introduce DS-RangeNet, a lightweight range image network that processes geometry and material-sensitive intensity in separate streams. The geometry stream uses voxel-PCA descriptors, while the intensity stream uses normalized range, local intensity statistics, boundary strength, and intensity curvature. A lightweight convolutional attention block handles shallow fusion, whereas intensity–geometry cross-attention (IGCA) links deep features by estimating affinity within the guiding stream and routing values from the other stream. Centered kernel alignment (CKA) and normalized cross-covariance reveal weak similarity after separate encoding and progressively stronger alignment during fusion. On the site disjoint UBPC-9 test split, DS-RangeNet reaches 73.2% mIoU with 5.69 M parameters and 37 ms end-to-end latency on Jetson AGX Orin. A nine-fold leave-one-environment-out evaluation obtains 71.0% mIoU. The evaluation further spans SemanticPOSS and SemanticKITTI, five random seeds, 21 corruption conditions across seven families, standard cross-attention and convolution controls, a 60 min Jetson run, and cross-sensor transfer. Full article

Melatonin (N-acetyl-5-methoxytryptamine) is a regulatory molecule with potential to increase plant tolerance to water stress by modulating stomatal function, redox balance, and photosynthetic efficiency. However, its physiological effects depend on dose, species, and stress intensity, and studies on Coffea canephora under recurrent drought are scarce. This study evaluated the effects of exogenous melatonin (0, 100, 200, 300, and 400 µM) on young plants of conilon coffee genotype 02 (Clone V12) subjected to three consecutive cycles of water deficit and rehydration. The experiment was conducted in a greenhouse using a randomized block design, with evaluations of gas exchange, water potential, and biomass allocation. Melatonin improved physiological recovery after rehydration, particularly at 100 and 300 µM during the second stress cycle, when photosynthesis reached values similar to the irrigated control. The 400 µM treatment maintained root dry mass close to the control under prolonged deficit, suggesting preferential biomass allocation to the root system. Overall, melatonin effects were more pronounced during recovery than stress, indicating a possible priming action. These results highlight the potential of melatonin as a phytoprotective agent in C. canephora, although responses depend on dose and stress cycle. Full article

Ecological security pattern (ESP) planning typically emphasizes ecological structure and connectivity, but may provide limited information on long-term functional degradation occurring within ecologically important areas. To address this gap, this study develops a baseline-referenced framework for diagnosing ecological resilience degradation (ERD) by integrating ecosystem service value (ESV) dynamics, minimum cumulative resistance (MCR) modeling, and explainable machine learning (XGBoost–SHAP). Using the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) as a case study, ERD was operationally defined as long-term functional degradation within ecologically important components of the 2000 baseline network, with net ESV decline from 2000 to 2020 used as the functional-degradation signal. Two complementary ERD patterns were distinguished: source erosion and corridor interruption. The results showed that ERD exhibited a pronounced core–periphery pattern, with higher degradation intensity concentrated along the Guangzhou–Foshan–Dongguan–Shenzhen urban development corridor. Integrated ERD covered 5452.69 km², of which source erosion accounted for 4238.32 km² (77.73%) and corridor interruption for 1214.37 km² (22.27%). Source erosion represented the dominant ERD pattern in terms of spatial extent. The XGBoost model showed moderate predictive performance under spatial block cross-validation (mean validation R² = 0.638 ± 0.043), and SHAP analysis indicated that vegetation condition, proximity to water bodies, nighttime light, and elevation were among the most influential factors associated with spatial variation in ERD intensity. NDVI contributions shifted from positive to negative around 0.65, while the distance-to-water response changed most rapidly within 200–300 m. These values are interpreted as empirical transition ranges in the model response. Overall, the proposed framework links long-term ecosystem functional degradation with baseline ecological network position and provides a spatially explicit basis for identifying functionally degraded ecological areas and supporting differentiated spatial prioritization and ecological management. Full article

Background: Redo mitral valve replacement is a high-risk operation in which early mortality alone may underestimate postoperative burden. We evaluated whether concomitant tricuspid valve repair independently increases major adverse postoperative events after redo mitral valve replacement. Methods: Consecutive patients undergoing redo mitral valve replacement between 2019 and 2025 were retrospectively reviewed. Isolated redo mitral valve replacement was compared with redo mitral valve replacement plus concomitant tricuspid valve repair. The primary endpoint was major adverse postoperative events, defined as early mortality, acute kidney injury, cerebrovascular event, prolonged ventilation, reoperation, deep sternal infection, mechanical circulatory support, or permanent pacemaker implantation. Results: The final cohort included 249 patients: 134 underwent isolated redo mitral valve replacement and 115 underwent concomitant tricuspid valve repair. Patients receiving tricuspid valve repair were older, had higher pulmonary artery pressure, and had a longer interval from the previous operation. They also had longer ventilation, longer intensive care unit stay and higher rates of atrial fibrillation, atrioventricular block, and permanent pacemaker implantation. Early mortality was similar between groups. Major adverse postoperative events occurred in 122 patients. Concomitant tricuspid valve repair was not associated with major adverse postoperative events in univariable analysis. In multivariable analysis, concomitant tricuspid valve repair was not independently associated with MAPE. Increasing age, smoking, lower left ventricular ejection fraction, and longer cardiopulmonary bypass time remained independently associated with MAPE. Conclusions: Early major postoperative morbidity after redo mitral valve replacement was associated primarily with patient-related risk factors and operative complexity. Concomitant tricuspid valve repair was not independently associated with MAPE after adjustment for the available covariates. Full article

Climate change and environmental variability pose major challenges to sustainable wheat production, highlighting the need for stable and high-yielding cultivars. This study evaluated fifteen bread wheat (Triticum aestivum L.) genotypes across twelve environments during two consecutive growing seasons using a randomized complete block design with three replications. Stability and performance were assessed using Additive Main Effects and Multiplicative Interaction (AMMI), Genotype main plus Genotype × Environment (GGE) biplot, and multi-trait stability index (MTSI). AMMI combined ANOVA indicated that both grain yield per plot and falling number were significantly affected by genotype, environment and their interaction (GEI). The GGE biplot revealed that the first two principal components together explained 86.92% of the total variation in grain yield per plot and 88.27% for falling number, demonstrating the reliability of the model in interpreting GEI patterns. AMMI and GGE biplot analyses consistently identified Sakha 95 (G1), Misr 4 (G3), Sakha Line#1 (G6), Sakha Line#2 (G7), Gemmeiza Line#2 (G14), and Gemmeiza Line#3 (G15) as high-yielding and stable genotypes across environments. In contrast, Sakha Line#4 (G9), Sakha Line#6 (G11), Sakha Line#7 (G12), and Gemmeiza Line#1 (G13) showed poor adaptation and low stability. MTSI further refined selection by integrating yield and quality traits, identifying Gemmeiza Line#3, Sakha Line#3, Sakha Line#2, and Giza 171 as superior genotypes at 25% selection intensity, characterized by low MTSI values. The identification of stable, high-performing genotypes with desirable grain quality can contribute to sustainable wheat production by improving yield reliability under diverse environmental conditions and supporting more efficient cultivar selection for climate-resilient wheat production. Overall, integrating AMMI, GGE biplot, and MTSI provided a robust framework for identifying stable and high-performing wheat genotypes, supporting selection decisions in multi-environment breeding programs. Full article

Large language model (LLM) agents are becoming cognitive interfaces to data lakes, enterprise knowledge bases, vector memories, browsers, files, and software tools. This shift creates a data-governance gap: an agent may reason over large private context, yet the protected resource often lacks a verifiable record of which user intent, data object, action, destination, and semantic release were authorized. This paper proposes CapAgent, a semantic data-flow governance middleware for LLM agents in big-data cognitive-computing environments. CapAgent maps human-attested task intent into signed, attenuable, and purpose-bound capability tokens that are checked by a reference monitor before sensitive tool invocation, memory retrieval, data export, and inter-agent delegation. Its policy layer combines task templates, resource labels, destination rules, caveats, semantic release modes, and audit obligations; its runtime enforces both symbolic scope checks and semantic recoverability checks over protected facts. We present formal governance semantics, a conservative intent compiler, an explainable data-flow decision workflow, and a runnable Python middleware. A reproducible trace-replay benchmark with 600 benign and adversarial traces across five data-intensive agent scenarios reports attack success, benign success, false blocking, latency, component ablations, and audit quality. In this synthetic trace-replay evaluation, the full monitor reduces measured attack success from 100.00% under ambient execution and 12.50% under scope-only authorization to 0.00% (Wilson 95% CI [0.00, 0.95]), while retaining 75.00% benign success. In addition, we conduct a 520-trial end-to-end tool-calling benchmark with representative prompt-only, task-shield-style, CaMeL-style, scope-only, and full-CapAgent configurations; a 210-task compiler gold-standard evaluation; a 240-item semantic-release calibration set; and a 12-cell BDCC-style scalability microbenchmark. In these supplemental tests, full CapAgent obtains 0.00% ASR (95% CI [0.00, 1.06]) in the tool-calling benchmark, 87.50% exact-policy compiler match with 0.00% over-authorization, 88.89% semantic-release recall with 0.00% false-block rate, and sub-millisecond in-process authorization latency up to 100,000 resources. The results support CapAgent as an auditable governance layer for cognitive LLM agents rather than as a replacement for model-level alignment or public end-to-end agent benchmarks. Full article