Search Results (19,415)

Investigating the optimal planting strategies for brassica vegetables under variable climatic conditions is essential for developing sustainable production systems in northern agricultural regions. However, comprehensive knowledge about how planting timing modulates growth, physiological responses, and yield parameters across different cultivars remains limited. We investigated vegetative development, root morphology, physiological efficiency, and marketable yield in six cauliflower cultivars (‘Amazing’, ‘Cheddar’, ‘Clementine’, ‘Flame Star’, ‘Snow Crown’, and ‘Vitaverde’) subjected to four planting dates (May 1, May 15, June 1, and June 15) across two growing seasons (2023–2024), followed by detailed morphological and physiological profiling. Planting date, cultivar selection, and seasonal variation significantly influenced all measured parameters (p < 0.001), with notable interaction effects observed for fresh root weight, stomatal conductance, water use efficiency, and yield components. Early planted cultivars consistently demonstrated superior performance under variable environmental conditions, maintaining higher growth rates, enhanced root development, and improved physiological efficiency, particularly ‘Flame Star’, ‘Snow Crown’, and ‘Cheddar’, compared to late-planted treatments. Recovery of optimal plant development was most pronounced at May planting dates, with early-established crops showing better maintenance of vegetative growth patterns and enhanced yield potential, including higher curd weights (585.7 g for ‘Flame Star’) and superior marketable grades. Morphological profiling revealed distinct clustering patterns, with early-planted cultivars forming separate groups characterized by elevated root biomass, enhanced physiological parameters, and superior yield characteristics. In contrast, late-planted crops showed reduced performance, indicative of environmental stress responses. We conclude that strategic early planting significantly enhances cauliflower production resilience through comprehensive optimization of growth, physiological, and yield parameters, particularly under May establishment conditions. The differential performance responses between planting dates provide insights for timing-based management strategies, while the quantitative morphological and physiological profiles offer valuable parameters for assessing crop adaptation and commercial viability potential under variable climatic scenarios in northern agricultural systems. Full article

Water scarcity has become a significant constraint to agricultural development in China. In this study, we employed bibliometric methods to systematically review the current research on crop water use efficiency (WUE) and the development trends in the North China Plain (NCP) and Northwest China (NWC). We analyzed 1569 articles (NCP = 788; NWC = 781) from the Web of Science Core Collection (1995–2025) using visualization tools such as CiteSpace and VOSviewer to investigate annual numbers of publications, leading scholars and research institutions, and then to map keyword co-occurrence and co-citation structures. Our results showed that keyword clustering exhibited high structural quality (NCP: Q = 0.7345, S = 0.8634; NWC: Q = 0.758, S = 0.8912), supporting reliable thematic interpretation. The bibliometric analysis indicates a steady growth in annual publications since 1995, with the Chinese Academy of Sciences and China Agricultural University as leading contributors. From 1995 to 2005, studies centered on irrigation, yield and field-scale WUE, emphasizing the optimization of irrigation strategies and crop productivity. During 2006–2015, the thematic focus has broadened to encompass nitrogen use efficiency, crop quality and eco-environmental performance, thereby moving toward integrated evaluation frameworks that capture ecological synergies. Since 2016, the literature now emphasizes system integration, regional adaptability, climate-response mechanisms and the ecological co-benefits of agricultural practices. Future studies are expected to incorporate indicators such as crop quality, water footprint and carbon isotope indicators to support the sustainable development of agricultural water use. This study offers insights and recommendations for developing a comprehensive crop WUE evaluation framework in China, which will support sustainable agricultural water management and the realization of national “dual carbon” targets. Full article

Extreme weather events, such as storms, pose significant challenges to the reliability and efficiency of urban road networks, making intersection design and management critical to maintaining mobility. This paper addresses the dual objectives of traffic efficiency and resilience by evaluating the performance of roundabouts, signalized, and two-way stop-controlled (TWSC) intersections under normal and storm-disrupted conditions. A mixed-method approach was adopted, combining a heuristic framework from the Highway Capacity Manual with microsimulations in AIMSUN Next. Three Polish case studies were examined; each was modeled under alternative control strategies. The findings demonstrate the superior robustness of roundabouts, which retain functionality during power outages, while signalized intersections reveal vulnerabilities when control systems fail, reverting to less efficient TWSC behavior. TWSC intersections consistently exhibited the weakest performance, particularly under high or uneven traffic demand. Despite methodological differences in delay estimation, the convergence of results through Level of Service categories strengthens the reliability of findings. Beyond technical evaluation, the study underscores the importance of resilient intersection design in climate-vulnerable regions and the value of integrating analytical and simulation-based methods. By situating intersection performance within urban resilience, this research provides actionable insights for policymakers, planners, and engineers seeking to balance efficiency with adaptability in infrastructure planning. Full article

Modern healthcare and engineering both rely on robust reliability models, where handling censored data effectively translates into longer-lasting devices, improved therapies, and safer environments for society. To address this, we develop a novel inferential framework for the ZLindley (ZL) distribution under the improved adaptive progressive Type-II censoring strategy. The proposed approach unifies the flexibility of the ZL model—capable of representing monotonically increasing hazards—with the efficiency of an adaptive censoring strategy that guarantees experiment termination within pre-specified limits. Both classical and Bayesian methodologies are investigated: Maximum likelihood and log-transformed likelihood estimators are derived alongside their asymptotic confidence intervals, while Bayesian estimation is conducted via gamma priors and Markov chain Monte Carlo methods, yielding Bayes point estimates, credible intervals, and highest posterior density regions. Extensive Monte Carlo simulations are employed to evaluate estimator performance in terms of bias, efficiency, coverage probability, and interval length across diverse censoring designs. Results demonstrate the superiority of Bayesian inference, particularly under informative priors, and highlight the robustness of HPD intervals over traditional asymptotic approaches. To emphasize practical utility, the methodology is applied to real-world reliability datasets from clinical trials on leukemia patients and hydrological measurements from River Styx floods, demonstrating the model’s ability to capture heterogeneity, over-dispersion, and increasing risk profiles. The empirical investigations reveal that the ZLindley distribution consistently provides a better fit than well-known competitors—including Lindley, Weibull, and Gamma models—when applied to real-world case studies from clinical leukemia trials and hydrological systems, highlighting its unmatched flexibility, robustness, and predictive utility for practical reliability modeling. Full article

The increasing need to optimize efficiency in port container terminals has led to the transition of operations from manual to automated or semi-automated processes. Automation involves integrating or gradually adopting digital technologies and equipment that reduce human intervention, enhance productivity, safety and sustainability. This study investigates the impact of automation on port efficiency through a comparative analysis of 20 container ports in the wider Mediterranean region, using a two-stage modeling approach. In the first stage, Data Envelopment Analysis (DEA) is applied under constant and variable returns to scale to estimate port efficiency using infrastructure, equipment, and container throughput data. The second stage employs Tobit regression to assess the effect of automated operations or systems on port efficiency, including variables such as the automation index, TEUs per employee, TEUs per ship (call) and revenue. A key contribution of this study is the development of a methodological framework for qualitatively classifying and evaluating these ports based on their level of automation, the introduction of digital technologies or equipment, and investments in new technologies. The results indicate that automation alone does not necessarily lead to higher efficiency unless it is effectively integrated into operations accompanied by adequate staff training and supported by gradual investment strategies. By contrast, cargo intensity (TEUs per call), highlights the importance of vessel size and cargo concentration in improving port performance. Full article

Rapid urbanization drives significant land use transformations, making the timely detection of newly constructed buildings a critical research focus. This study presents a novel unsupervised framework that integrates pixel-level change detection with object-level, mono-temporal building information to identify new constructions. Within this framework, we propose the Building Line Index (BLI) to capture structural characteristics from building edges. The BLI is then combined with spectral, textural, and the Morphological Building Index (MBI) to extract buildings. The fusion weight (φ) between the BLI and MBI was determined through experimental analysis to optimize performance. Experimental results on a case study in Wuhan, China, demonstrate the method’s effectiveness, achieving a pixel accuracy of 0.974, an average category accuracy of 0.836, and an Intersection over Union (IoU) of 0.515 for new buildings. Critically, at the object-level—which better reflects practical utility—the method achieved high precision of 0.942, recall of 0.881, and an F1-score of 0.91. Comparative experiments show that our approach performs favorably against existing unsupervised methods. While the single-case study design suggests the need for further validation across diverse regions, the proposed strategy offers a robust and promising unsupervised pathway for the automatic monitoring of urban expansion. Full article

Spatially explicit tools are essential for assessing biodiversity and guiding land use decisions at broad scales. This study presents a national-level approach for evaluating habitat quality as a proxy indicator for biodiversity, using Latvia as a case study. The approach integrates land use data, landscape structure, and habitat characteristics to generate habitat quality indices for agricultural and forest land. It addresses a common limitation in biodiversity planning, namely, the lack of consistent species-level data, by providing a comparative and conceptually robust way to assess how different land use types support biodiversity potential. The methodology was applied to assess current habitat quality and to simulate changes under two policy-relevant land use scenarios: the expansion of protected areas and a shift to organic farming. Results showed that expanding protected areas increased the national habitat quality index by 8.47%, while conversion to organic farming produced a smaller but still positive effect of 0.40%. Expansion of protected areas, therefore, led to a greater improvement in habitat quality compared to converting farmland to organic systems. However, both strategies offer complementary benefits for biodiversity at the landscape scale. Although national-level changes appear moderate, their spatial distribution enhances connectivity, particularly near existing protected areas, and may facilitate species movement. This approach enables national-level modelling of biodiversity outcomes under different policy measures. While it does not replace detailed species assessments, it provides a practical and scalable method for identifying conservation priorities, particularly in regions with limited biodiversity monitoring capacity. Full article

To enhance ecosystem services (ESs) benefits and promote ecological–economic–sociologic sustainability in highly urbanized regions such as the Beijing–Tianjin–Hebei (BTH) region, it is essential to assess the dynamic changes in ESs within these regions from a functional zoning perspective and to explore the interactions between ESs. This research delved into how ESs change over space and time, using land-use projections for 2035 based on Natural Development (ND), Ecological Protection (EP), Economic Construction (EC) scenarios. This study also took a close look at the interplay of these ESs across BTH and its five distinct functional zones: the Bashang Plateau Ecological Protection Zone (BS), the Northwestern Ecological Conservation Zone (ST), the Central Core Functional Zone (HX), the Southern Functional Expansion Zone (TZ), and the Eastern Coastal Development Zone (BH). We utilize the Multiple Ecosystem Service Landscape Index (MESLI) to assess the capacity to supply multiple ESs. Key results include the following: (1) Projected land-use changes for 2035 scenarios consistently show cropland and grassland declining, while forest and urbanland expand, though the magnitude of change varies by scenario. (2) Habitat quality, carbon storage, and soil conservation displayed a “high northwest–low southeast” gradient, opposite to water yield. The average MESLI value declined in all scenarios relative to 2020, with the highest value under the EP scenario. (3) Synergies prevailed between habitat quality, carbon storage, and soil conservation, while trade-offs occurred with water yield. These relationships varied spatially—for instance, habitat quality and soil conservation were weakly synergistic in the BS but showed weak trade-offs in the HX. These insights can inform management strategies in other rapidly urbanizing regions. Full article

Driver fatigue is a primary factor in traffic accidents and poses a serious threat to road safety. To address this issue, this paper proposes a multi-feature fusion fatigue detection method based on an improved YOLOv8 model. First, the method uses an enhanced YOLOv8 model to achieve high-precision face detection. Then, it crops the detected face regions. Next, the lightweight PFLD (Practical Facial Landmark Detector) model performs keypoint detection on the cropped images, extracting 68 facial feature points and calculating key indicators related to fatigue status. These indicators include the eye aspect ratio (EAR), eyelid closure percentage (PERCLOS), mouth aspect ratio (MAR), and head posture ratio (HPR). To mitigate the impact of individual differences on detection accuracy, the paper introduces a novel sliding window model that combines a dynamic threshold adjustment strategy with an exponential weighted moving average (EWMA) algorithm. Based on this framework, blink frequency (BF), yawn frequency (YF), and nod frequency (NF) are calculated to extract time-series behavioral features related to fatigue. Finally, the driver’s fatigue state is determined using a comprehensive fatigue assessment algorithm. Experimental results on the WIDER FACE and YAWDD datasets demonstrate this method’s significant advantages in improving detection accuracy and computational efficiency. By striking a better balance between real-time performance and accuracy, the proposed method shows promise for real-world driving applications. Full article

Efficient water management for irrigation is critical for sustaining plant production in arid and hyper-arid regions, where optimizing emitter type, burial depth, and irrigation scheduling can significantly enhance water-use efficiency and yield. This study evaluated the effects of continuous and intermittent subsurface irrigation using porous (PRP) and emitting (GRP) pipes at two installation depths (25 and 35 cm) on soil water distribution, potato germination, and yield under arid conditions in Saudi Arabia. Soil water content was monitored using volumetric sampling, EnviroSCAN sensors, and HYDRUS modeling, with strong agreement observed among methods (R² ≥ 0.92). Results showed that shallow emitter placement (25 cm) combined with intermittent irrigation (five pulses, WF5C) maximized soil water retention in the root zone, reducing deep percolation losses. The GRP_25cm treatment improved soil water content by up to 140.7% at 30 cm depth and achieved the highest germination (74–83%) and yields (164.5–171.7 kg). In contrast, deeper installations (35 cm) consistently underperformed. Overall, intermittent irrigation enhanced water distribution and plant performance compared with continuous flow, leading to a 40–49% yield increase. These findings highlight the importance of emitter type, placement depth, and irrigation scheduling in optimizing water-use efficiency and plant productivity. The study provides practical recommendations for sustainable irrigation strategies in arid and hyper-arid regions facing increasing water scarcity. Full article

The increasing prevalence of Autism Spectrum Disorder (ASD) is a significant health concern influenced by both genetic and environmental factors. However, limited data exist on the socio-demographic and clinical characteristics associated with ASD in our region. This cross-sectional study assessed 200 children (155 boys and 45 girls) diagnosed with ASD at Hawler Psychiatric Hospital in Erbil city between January and December 2023. The Childhood Autism Rating Scale-Second Edition (CARS-2) was used for diagnosis and severity assessment. The mean age of participants was 4.6 ± 1.8 years, with males representing 77.5% of the sample. Cesarean section was the most common mode of delivery. The average parental ages were 34.8 years for mothers and 38.5 years for fathers. The first signs of autism were noticed at a mean age of 25.7 ± 9.7 months, with the first medical consultation at 34.6 ± 15.4 months and diagnosis at 42.4 ± 15.5 months. Delayed speech was the most common reason for seeking medical help. Statistically significant associations were found between severe autism symptoms and several factors, including older child age, younger age at first assessment, delayed speech, parental consanguinity, paternal age over 40, lower paternal education, and lower socioeconomic status. These findings emphasize the critical role of early detection and the influence of both socio-demographic and clinical factors on ASD symptom severity, highlighting the need for targeted early intervention strategies to improve outcomes in affected children. Full article

Crack segmentation in images plays a pivotal role in the monitoring of structural surfaces, serving as a fundamental technique for assessing structural integrity. However, existing methods that rely solely on RGB images exhibit high sensitivity to light conditions, which significantly restricts their adaptability in complex environmental scenarios. To address this, we propose a structure-aware progressive multi-modal fusion network (SPMFNet) for RGB-thermal (RGB-T) crack segmentation. The main idea is to integrate complementary information from RGB and thermal images and incorporate structural priors (edge information) to achieve accurate segmentation. Here, to better fuse multi-layer features from different modalities, a progressive multi-modal fusion strategy is designed. In the shallow encoder layers, two gate control attention (GCA) modules are introduced to dynamically regulate the fusion process through a gating mechanism, allowing the network to adaptively integrate modality-specific structural details based on the input. In the deeper layers, two attention feature fusion (AFF) modules are employed to enhance semantic consistency by leveraging both local and global attention, thereby facilitating the effective interaction and complementarity of high-level multi-modal features. In addition, edge prior information is introduced to encourage the predicted crack regions to preserve structural integrity, which is constrained by a joint loss of edge-guided loss, multi-scale focal loss, and adaptive fusion loss. Experimental results on publicly available RGB-T crack detection datasets demonstrate that the proposed method outperforms both classical and advanced approaches, verifying the effectiveness of the progressive fusion strategy and the utilization of the structural prior. Full article

Brain tumor-related epilepsy (BTRE) is a common and debilitating symptom of central nervous system (CNS) tumors. The epileptogenic zone, defined as cortex responsible for seizure generation, is located at the peritumoral region for most tumors, and lower-grade intrinsic brain tumors have the highest seizure incidence. Surgery is often the most effective treatment for the reduction in seizures in BTRE. However, surgical decisions have historically often been made exclusively for oncologic reasons, with less emphasis on seizure control. Surgical approaches for all tumor types are reviewed, highlighting relevant risk factors. Adjunctive tools during surgery, such as intraoperative electrocorticography (ECoG), may help identify and remove surrounding brain areas which are epileptogenic. Minimally invasive surgery is also gaining traction, given its utility in treating seizures deep-seated tumors. This review explores epileptogenic brain tumors, surgery for BTRE, and emerging strategies to better achieve seizure control. Full article

This study systematically investigates the velocity characteristics and coupling mechanisms of tunnel flow fields under the interactions of natural wind, traffic wind, mechanical ventilation, and structural factors (such as transverse passages and relative positions between vehicles and fans). Using CFD simulations combined with turbulence model analyses, the flow behaviors under different coupling scenarios are explored. The results show that: (1) Under natural wind conditions, transverse passages act as key pressure boundaries, reshaping the longitudinal wind speed distribution into a segmented structure of “disturbance zones (near passages) and stable zones (mid-regions)”, with disturbances near passages showing “amplitude enhancement and range contraction” as natural wind speed increases. (2) The coupling of natural wind and traffic wind (induced by moving vehicles) generates complex turbulent structures; vehicle motion forms typical flow patterns including stagnation zones, high-speed bypass flows, and wake vortices, while natural wind modulates the wake structure through momentum exchange, affecting pollutant dispersion. (3) When natural wind, traffic wind, and mechanical ventilation are coupled, the flow field is dominated by momentum superposition and competition; adjusting fan output can regulate coupling ranges and turbulence intensity, balancing energy efficiency and safety. (4) The relative positions of vehicles and fans significantly affect flow stability: forward positioning leads to synergistic momentum superposition with high stability, while reverse positioning induces strong turbulence, compressing jet effectiveness and increasing energy dissipation. This study reveals the intrinsic laws of tunnel flow field evolution under multi-factor coupling, providing theoretical support for optimizing tunnel ventilation system design and dynamic operation strategies. Full article

Background: We investigate the influence of environmental variables and edge-interior gradients on the diversity and composition of anuran assemblages in four forest fragments in the southwestern Brazilian Amazon. Methods: A total of 590 individuals from 40 species and eight families were recorded, with Leptodactylidae being the most abundant family. Results: The Humaitá Forest Reserve (RFH) exhibited the highest species richness and diversity, while the Raimundo Irineu Serra Environmental Protection Area (APA) had the lowest. Species composition varied significantly among fragments and along the edge-interior gradient, with edges showing higher species richness. Redundancy analysis (RDA) revealed that temperature, humidity, and litter depth were the most important environmental variables structuring anuran communities. Conclusions: Edge habitats supported disturbance-tolerant species, whereas forest interiors harbored moisture-dependent specialists. These findings underscore the importance of conserving larger, less disturbed fragments and implementing management strategies that account for environmental heterogeneity. This study provides critical insights into the factors shaping anuran distribution in fragmented Amazonian landscapes, offering valuable guidance for biodiversity conservation in the region. Full article