Search Results (370)

Sub-meter-resolution Polarimetric Synthetic Aperture Radar (PolSAR) imagery enables precise building footprint extraction but introduces complex scattering correlated with fine spatial structures. This change renders both traditional methods, which rely on simplified scattering models, and existing deep learning approaches, which sacrifice spatial detail through multi-looking, inadequate for high-precision extraction tasks. To address this, we propose an Orthogonal Dual-Resolution Network (ODRNet) for end-to-end, precise segmentation directly from single-look complex (SLC) data. Unlike complex-valued neural networks that suffer from high computational cost and optimization difficulties, our approach decomposes complex-valued data into its orthogonal real and imaginary components, which are then concurrently fed into a Dual-Resolution Branch (DRB) with Bilateral Information Fusion (BIF) to effectively balance the trade-off between semantic and spatial details. Crucially, we introduce an auxiliary Polarization Orientation Angle (POA) regression task to enforce physical consistency between the orthogonal branches. To tackle the challenge of diverse building scales, we designed a Multi-scale Aggregation Pyramid Pooling Module (MAPPM) to enhance contextual awareness and a Pixel-attention Fusion (PAF) module to adaptively fuse dual-branch features. Furthermore, we have constructed a VHR PolSAR building footprint segmentation dataset to support related research. Experimental results demonstrate that ODRNet achieves 64.3% IoU and 78.27% F1-score on our dataset, and 73.61% IoU with 84.8% F1-score on a large-scale SLC scene, confirming the method’s significant potential and effectiveness in high-precision building extraction directly from SLC. Full article

To address the path-planning challenge for unmanned aerial vehicles (UAVs) in complex environments, this study presents an improved pelican optimization algorithm enhanced with multiple strategies (MIPOA). The proposed method introduces four main improvements: (1) using chaotic mapping to spread the initial search points more evenly, thereby increasing population variety; (2) incorporating a random Lévy-flight strategy to improve the exploration of the search space; (3) integrating a differential evolution approach based on Cauchy mutation to strengthen individual diversity and overall optimization ability; and (4) adopting an adaptive disturbance factor to speed up convergence and fine-tune solutions. To evaluate MIPOA, comparative tests were carried out against classical and modern intelligent algorithms using the CEC2017 and CEC2022 benchmark sets, along with a custom UAV environmental model. Results show that MIPOA converges faster and achieves more accurate solutions than the original pelican optimization algorithm (POA). On CEC2017 in 30-, 50-, and 100-dimensional cases, MIPOA attained the best average ranks of 1.57, 2.37, and 2.90, respectively, and achieved the top results on 26, 21, and 19 test functions, outperforming both POA and other advanced algorithms. For CEC2022 (20 dimensions), MIPOA obtained the highest Friedman average rank of 1.42, demonstrating its effectiveness in complex UAV path-planning tasks. The method enables the generation of faster, shorter, safer, and collision-free flight paths for UAVs, underscoring the robustness and wide applicability of MIPOA in real-world UAV path-planning scenarios. Full article

In view of the complex flight scenarios existing in UAV path planning, it is necessary to model the UAV flight trajectory. When constructing the model, cost factors such as the minimum flight path of the UAV, obstacle avoidance, flight altitude, and trajectory smoothness are fully taken into account. To reduce the overall flight cost, a novel secretary bird optimization algorithm (NSBOA) is proposed in this paper, which effectively addresses the limitations of traditional algorithms in handling UAV path planning tasks. First of all, the Singer chaotic map is adopted to initialize the population instead of the conventional random initialization method. This improvement increases population diversity, enables the initial population to be more evenly distributed in the search space, and further accelerates the algorithm’s convergence speed in the subsequent optimization process. Second, an adaptive adjustment mechanism is integrated with the Levy flight mechanism to optimize the core logic of the algorithm, with a specific focus on improving the exploitation stage. By introducing appropriate perturbations near the current optimal solution, the algorithm is guided to jump out of local optimal traps, thereby enhancing its global optimization capability and avoiding premature convergence caused by insufficient population diversity. By comparing and analyzing NSBOA with SBOA, WOA, PSO, POA, NGO, and HHO algorithms in 12 common evaluation functions and CEC 2017 test functions, and applying NSBOA to the UAV path optimization problem, the simulation results show the effectiveness and superiority of the proposed scheme. Full article

Background/Objectives: Older individuals are more vulnerable to stress and trauma. Although pressure injuries (PIs) are recognized as a significant complication, the specific impact of frailty on PI development in older Asian trauma patients remains insufficiently explored. This study aims to investigate the factors associated with the occurrence of hospital-acquired pressure injuries (HAPU) in older patients aged ≥65 years, including frailty. Methods: This study is a retrospective secondary data analysis of 3418 older trauma patients admitted to a regional trauma center (including ICU and trauma ward) from 1 January 2020 to 31 December 2023. Patients with PIs present on admission (POA) were excluded to strictly analyze new PI occurrence. Frailty was assessed using the mFI-5. Results: The mean age of participants was 77.33 years. During hospitalization, 2.5% (n = 84) of patients developed new PIs. Multivariate logistic regression identified that higher frailty score (Odds Ratio [OR] = 1.59, 95% Confidence Interval [CI]: 1.26–2.02), lower BMI (OR = 0.93, 95% CI: 0.86–0.99), hypoalbuminemia (OR = 0.55, 95% CI: 0.36–0.84), and prolonged hospital stay (OR = 1.05, 95% CI: 1.04–1.06) were independently associated with PI occurrence. Chronological age was not a significant predictor in the multivariate model. Conclusions: Frailty, nutritional status (BMI, albumin), and prolonged hospital stay are significant factors associated with HAPU in older trauma patients. Full article

The maritime industry faces significant challenges from energy consumption and air pollution. Fuel cells, especially hydrogen types, offer a promising clean alternative with high energy density and rapid refueling, but their slow dynamic response necessitates integration with lithium batteries (energy storage) and supercapacitors (power storage). This paper investigates a hybrid vessel power system combining a fuel cell with a Hybrid Energy Storage System (HESS) to address these limitations. An improved droop control strategy with adaptive coefficients is developed to ensure balanced State of Charge (SOC) and precise current sharing, enhancing system performance. A comprehensive protection strategy prevents overcharging and over-discharging through SOC limit management and dynamic filter adjustment. Furthermore, the Parrot Optimization Algorithm (POA) optimizes HESS capacity configuration by simultaneously minimizing battery degradation, supercapacitor degradation, DC bus voltage fluctuations, and system cost under realistic operating conditions. Simulations show SOC balancing within 100 s (constant load) and 135 s (variable load), with the lithium battery peak power cut by 18% and the supercapacitor peak power increased by 18%. This strategy extends component life and boosts economic efficiency, demonstrating strong potential for fuel cell-powered vessels. Full article

This study investigates the influence of array height, irradiance, and wind speed on temperature difference and thermal gradients in photovoltaic (PV) arrays operating in hot, arid conditions. A field experiment was conducted in Mesa, Arizona (latitude 33° N), using two fixed-tilt PV module arrays installed at different elevations—one at 1 m and the other at 2 m above ground level. Each array comprised seven monocrystalline PV modules arranged in a single row with an 18° tilt angle optimized for summer performance. Data were collected between June and September 2025, and the analysis was restricted to 10:00–13:00 h to avoid shading and ensure uniform irradiance exposure on both arrays. Measurements included module backsheet temperatures at the center and edge modules, ambient temperature, plane-of-array (POA) irradiance, and wind speed. By maintaining identical orientation, tilt, and exposure conditions across all PV configurations, the influence of array height was isolated by comparing module operating temperatures between the 1-m and 2-m installations (inter-array comparison). Under the same controlled conditions, the setup also enabled an examination of how the intra-array comparison affects temperature gradients along the PV modules themselves, thereby revealing edge-center thermal non-uniformities. Results indicate that the 2 m array consistently operated 1–3 °C cooler than the 1 m array, confirming the positive impact of elevation on convective cooling. This reduction corresponds to a 0.4–0.9% improvement in module efficiency or power based on standard temperature coefficients of crystalline silicon modules. The 1 m array exhibited a mean edge–center intra-array temperature gradient of −1.54 °C, while the 2 m array showed −2.47 °C, indicating stronger edge cooling in the elevated configuration. The 1 m array displayed a broader temperature range (−7 °C to +3 °C) compared to the 2 m array (−5 °C to +2 °C), reflecting greater variability and weaker convective uniformity near ground level. The intra-array temperature gradient became more negative as irradiance increased, signifying intensified edge cooling under higher solar loading. Conversely, wind speed inversely affected ΔT, mitigating thermal gradients at higher airflow velocities. These findings highlight the importance of array height (inter-array), string length (intra-array), irradiance, and wind conditions in optimizing PV system thermal and electrical performance. Full article

Kentucky bluegrass powdery mildew, caused by the fungus Blumeria graminis f. sp. poae, is a destructive disease affecting Poa pratensis L. In this study, endophytic bacteria were isolated from the resistant Kentucky bluegrass cultivar ‘Taihang’. Employing a combination of conidia germination inhibition assays and control efficacy tests, the biocontrol endophytic bacterial strains were screened. The impact of inoculation with the powdery mildew pathogen and biocontrol endophytic bacteria on the difference in endophytic bacterial community in the leaves of Kentucky bluegrass were studied via Illumina Miseq high-throughput 16S ribosomal RNA gene sequencing technology. A total of 18 endophytic bacterial isolates were obtained from ‘Taihang’, belonging to 3 phyla: Proteobacteria (3 isolates), Actinobacteria (6 isolates), and Firmicutes (9 isolates). The conidia germination assay revealed that isolates 6213 (Bacillus sp.) and 718 (Neobacillus sp.) exhibited the strongest inhibitory against Blumeria graminis f. sp. poae, with inhibition rate exceeding 80%. Isolate 718 exhibited superior control efficacy over strain 6213. A concentration of 10⁹ colony-forming units per milliliter (CFU/mL) was the most effective in suppressing powdery mildew on Kentucky bluegrass. The abundance of Proteobacteria on Kentucky bluegrass after the application of isolate 718 may enhance the resistance of Kentucky bluegrass to powdery mildew, and the dominant endophytic bacterial communities were Burkholderiales, Burkholderiaceae and Cupriavidus, indicating that the application of isolate 718 modulated the plant’s response to powdery mildew infection. These results demonstrate that isolate 718 enhanced the resistance of Kentucky bluegrass against powdery mildew by reshaping the endophytic bacterial community within the leaves. These findings provide molecular insights into plant−pathogen−endophytic bacteria interactions and support the development of sustainable strategies, eco-friendly strategies for plant diseases management. Full article

Healthcare collaborative processes still encounter major challenges, particularly regarding the interoperability of heterogeneous information systems, the traceability of medical interventions, and the secure sharing of patient data under strict privacy regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). This paper presents a patient-centric, blockchain-based framework designed to overcome these limitations. The proposed solution integrates smart contracts and non-fungible tokens (NFTs) within the Ethereum blockchain to ensure data integrity, traceability, and privacy preservation. Furthermore, a compliance-by-design mechanism is embedded into the smart contracts to enable self-supervision of collaborative workflows without third-party intervention. A Proof-of-Authority (PoA) consensus protocol is also adopted to optimize validation efficiency and significantly reduce computational and energy costs. Full article

Seed germination and early seedling development represent critical stages for turfgrass establishment under increasingly frequent drought and salinity constraints. This study evaluated the germination performance of three cultivars of Lolium perenne L. and three cultivars of Poa pratensis L. exposed to iso-osmotic drought stress simulated with polyethylene glycol (PEG) and salt stress induced by NaCl. Germination percentage, mean germination time, germination index, seedling vigor index, and radicle and plumule elongation were quantified, and post-stress recovery tests assessed the reversibility of stress effects. Osmotic restriction imposed by PEG caused stronger inhibition of germination and seedling growth than NaCl at equivalent water potentials. L. perenne showed higher overall tolerance, maintaining faster emergence and greater seedling vigor across treatments, while P. pratensis was more sensitive but exhibited substantial germination recovery after stress removal. Cultivar-dependent variation was evident in both species, and multivariate analyses consistently differentiated tolerant and sensitive genotypes. The contrasting germination strategies, with rapid activation in L. perenne and delayed, recovery-oriented germination in P. pratensis, highlight species-specific adaptive responses to water and salt stress. These findings provide a physiological basis for selecting resilient turfgrass cultivars suited to drought- and salinity-prone environments, contributing to sustainable turfgrass establishment and management. Full article

Plant growth-promoting bacteria (PGPB) can act as biostimulants, improving the growth of plants in sustainable agriculture systems that seek to reduce synthetic agrochemical input. Bacteria present in seeds are closely associated with vertical transmission and thus represent a potential trove of biostimulants. Capsicum species are notable for producing capsaicin, a compound with antimicrobial activity that may influence microbial communities associated with pepper fruits and seeds. Using Luria–Bertani (LB) media infused with capsaicin, we isolated bacteria from bell peppers, jalapeno peppers, and habanero peppers, which we verified to have different levels of capsaicin through high-performance liquid chromatography with ultraviolet detection (HPLC-UV). Minimum inhibitory concentration (MIC) assays indicated that the capsaicin resistance of isolated bacteria did not correlate with the pungency level of the host pepper variety. Of the total isolated bacteria, four showed promise as plant growth promoters; two belong to the genera Pseudomonas, one Agrobacterium, and one Bacillus. Our isolates tested positively for potassium and phosphate solubilization, urease production, and indole-3-acetic acid (IAA) phytohormone production. Inoculation of these bacteria into surface-sterilized red clover (Trifolium pratense) and Kentucky bluegrass (Poa pratensis) showed significant improvements in germination rate, seedling root length, and seedling shoot height. These results show that the pungency of peppers does not influence the capsaicin resistance of isolated bacteria. Additionally, seedborne PGPB have the potential for plant growth improvement through various mechanisms, reducing the need for synthetic chemicals. Full article

Cost overruns remain one of the most persistent challenges in construction and infrastructure project management, often undermining efficiency, sustainability, and stakeholder trust. With the rise of digital transformation, artificial intelligence (AI) and machine learning (ML) provide new opportunities to enhance predictive decision-making and strengthen project control. This study introduces a digital project management framework that integrates the Pelican Optimization Algorithm (POA) with Light Gradient Boosting Machine (LGBM) to deliver reliable and interpretable cost overrun forecasting. The proposed POA-LightGBM model leverages metaheuristic-driven hyperparameter optimization to improve predictive performance and generalization. A comprehensive evaluation using multiple error metrics Coefficient of Determination (R²), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) demonstrates that POA-LGBM significantly outperformed baseline LGBM and alternative metaheuristic configurations, achieving an average R² of 0.9786. To support transparency in digital project environments, SHapley Additive exPlanations (SHAPs) were employed to identify dominant drivers of cost overruns, including actual project cost, energy consumption, schedule deviation, and material usage. By embedding AI-enabled predictive analytics into digital project management practices, this study contributes to advancing digital transformation in project delivery, offering actionable insights for cost control, risk management, and sustainable infrastructure development. Full article

The study investigated the effects of intracerebroventricular (ICV) administration of the endocannabinoid anandamide (AEA) on suppression of gonadotropin-releasing hormone (GnRH)/luteinizing hormone (LH) secretion during lipopolysaccharide (LPS)-induced inflammation in ewes at the follicular phase of the estrous cycle. Animals were divided into three groups: control, LPS (intravenous, IV; 400 ng/kg), and LPS + AEA (ICV; 100 µM/animal). In LPS-treated ewes, AEA increased GnRH concentration in the preoptic area (POA) and upregulated GnRH mRNA expression in the POA and anterior hypothalamus (AHA). Central administration of AEA decreased the circulating concentration of cortisol in LPS-treated ewes. Moreover, AEA lowered proinflammatory interleukin (IL)-1β and increased anti-inflammatory IL-10 protein expressions in the hypothalamus of LPS-treated ewes. However, ICV AEA did not reverse the inflammation-associated reduction in LH secretion. These findings show that acute central administration of AEA abolishes the inhibitory effect of inflammation on GnRH synthesis in the POA and even stimulates it, likely through attenuation of central inflammation, as reflected by IL-1β and IL-10 changes in the POA. Nevertheless, short-term AEA administration was insufficient to counteract the inflammation-mediated suppression of LH secretion. Further studies are needed to explore the role of endocannabinoids (ECBs) in modulating GnRH/LH secretion under inflammatory conditions, particularly with prolonged exposure. Full article

Cloud computing has transformed modern IT infrastructure by enabling scalable, on-demand access to virtualized resources. However, the rapid growth of cloud services has intensified energy consumption across data centres, increasing operational costs and carbon footprints. Traditional load-balancing methods, such as Round Robin and First-Fit, often fail to adapt dynamically to fluctuating workloads and heterogeneous resources. To address these limitations, this study introduces a Reinforcement Learning-guided hybrid optimization framework that integrates the Black Eagle Optimizer (BEO) for global exploration with the Pelican Optimization Algorithm (POA) for local refinement. A lightweight RL controller dynamically tunes algorithmic parameters in response to real-time workload and utilization metrics, ensuring adaptive and energy-aware scheduling. The proposed method was implemented in CloudSim 3.0.3 and evaluated under multiple workload scenarios (ranging from 500 to 2000 cloudlets and up to 32 VMs). Compared with state-of-the-art baselines, including PSO-ACO, MS-BWO, and BSO-PSO, the RL-enhanced hybrid BEO–POA achieved up to 30.2% lower energy consumption, 45.6% shorter average response time, 28.4% higher throughput, and 12.7% better resource utilization. These results confirm that combining metaheuristic exploration with RL-based adaptation can significantly improve the energy efficiency, responsiveness, and scalability of cloud scheduling systems, offering a promising pathway toward sustainable, performance-optimized data-centre management. Full article

With the rapid development of smart manufacturing and the low-altitude economy, drone technology—as a vital component of next-generation intelligent equipment—has garnered significant attention from researchers. Path planning, one of the core challenges in drone technology advancement, directly impacts the efficiency and safety of drone mission execution. However, most existing drone path planning algorithms suffer from issues such as requiring extensive interactive information or being prone to getting stuck in local optima. This study introduces a multi-strategy enhanced Pelican Optimization Algorithm (MIPOA) tailored for UAV path planning. To improve the quality of the initial population, a hybrid initialization approach combining low-discrepancy sequences with heuristic refinement is developed. The low-discrepancy component promotes a more uniform distribution across the search space, while the heuristic mechanism enhances the fitness of selected individuals and reduces redundant exploration. Furthermore, a subgroup mean-guided updating strategy is designed to accelerate convergence toward the global optimum. To maintain exploration ability, a random reinitialization boundary mechanism is incorporated, effectively preventing premature convergence. To validate the algorithm’s performance, MIPOA is compared with eleven benchmark metaheuristics on the CEC2017 test suite, and statistical analyses confirm its superior optimization capability. Finally, MIPOA is applied to 3D UAV path planning under four threat scenarios in a realistic environment, demonstrating robust adaptability and achieving successful mission completion. Full article

The vegetation of municipal solid waste (MSW) landfills and its ecosystem functions are often overlooked, despite their importance for enhancement and stabilization of biodiversity. The selected landfill is located in the cadastral area of Bystřice pod Hostýnem (Czech Republic). A total of 92 plant species were recorded during a two-year vegetation assessment at three sites of the MSW landfill. The species Lolium perenne, Arrhenatherum elatius, and Poa pratensis significantly dominated the restored parts of the landfill. The species Urtica dioica, Chelidonium majus, and Atriplex sagittata were dominant in the actively used parts of the landfill. Chenopodium album, Atriplex sagittata, and Amaranthus retroflexus were dominant in the composting zone. The vegetation of MSW landfills represents an ecologically important element with the ability to increase the biodiversity of the landscape. Nevertheless, there are also risks, e.g., the possibility of contamination of food chain with hazardous substances from waste. The spread of diaspores of certain species across the landscape and the spread of non-indigenous plant species can have negative ecological consequences. MSW landfills are often perceived only as technical facilities that solve the environmental problem of waste management. However, our results bring a new perspective on landfills as an environment for the biosphere. Full article