Search Results (2,800)

Wireless Sensor Network (WSN) technology has constrained computing resources that require efficient and low-cost cryptographic hardware to provide security services, particularly when dealing with large modular polynomial multiplication in cryptography. In this paper, a cost-efficient reconfigurable Karatsuba modular polynomial multiplier is proposed for general modulus polynomials. The modulus polynomial can be changed easily depending on the application. The proposed modular polynomial multiplier is synthesized and simulated by the AMD Vivado Design Tool. The design’s performance on ADP (Area Delay Product) has been improved compared to previous designs. It can be applied in ECC encryption to speed up the security services in WSN. Full article

Background/Objectives: Leishmania spp. are protozoan parasites transmitted by female sandflies (Phlebotomus or Lutzomyia). Clinical manifestations depend on species and host immunity. While cutaneous and visceral forms prevail, mucocutaneous involvement—particularly isolated nasal septum leishmaniasis—is rare and frequently misdiagnosed as an inflammatory, infectious, or neoplastic condition. Risk factors associated with mucocutaneous leishmaniasis include systemic or local immunodeficiency, prior renal transplantation, treatment with chronic inhaled steroids, residence in endemic areas or travel to such regions, and previous Leishmania infections. Immunosuppressed patients are at higher risk for atypical presentations and delayed diagnosis, which can result in extensive tissue destruction. Early clinical suspicion, histopathological confirmation, and prompt therapy are essential to prevent permanent mucosal damage. Therefore, a multidisciplinary approach is needed for adequate evaluation and effective treatment. Methods: A 67-year-old man with rheumatoid arthritis on methotrexate reported a two-year history of right-sided nasal obstruction and ulceration that failed to respond to antibiotics. He did not present systemic symptoms. Results: Facial CT revealed a septal deviation; the patient underwent septoplasty, and biopsy confirmed Leishmania amastigotes. Serology (rK39 immunochromatographic test) was positive. He was treated with liposomal amphotericin B at 4 mg/kg/day for five days, followed by miltefosine at 100 mg/day orally for 14 days. At an eight-week follow-up, the nasal mucosa was fully healed, obstruction was resolved, and there was no evidence of recurrence. Conclusions: Although nasal septum leishmaniasis is uncommon, it should be considered in the differential diagnosis of chronic nasal lesions, especially in immunocompromised patients or those from endemic regions. Definitive diagnosis requires biopsy with histological or molecular confirmation. Combined liposomal amphotericin B and miltefosine therapy yields high cure rates and prevents mucosal destruction. Early recognition is critical to avoid diagnostic delays and long-term sequelae. Full article

As manufacturing and logistics-oriented supply chains continue to expand in scale and complexity, and the coupling between their physical execution layers and information–decision layers deepens, the resulting high interdependence within the system significantly increases overall fragility. Driven by key technological barriers, economies of scale, and the trend toward resource centralization, supply chains have increasingly evolved into centralized structures, with critical functions such as decision-making highly concentrated in a few focal firms. While this configuration may enhance coordination under normal conditions, it also significantly increases dependency on focal nodes. Once a focal node is disrupted, the intense task, information, and risk loads it carries cannot be effectively dispersed across the network, thereby amplifying load spillovers, coordination imbalances, and information delays, and ultimately triggering large-scale cascading failures. To capture this phenomenon, this study develops a coupled network model comprising a Physical Network and an Information and Decision Risk Network. The Physical Network incorporates a tri-load coordination mechanism that distinguishes among theoretical operational load (capacity), actual production load (production output), and actual delivery load (order fulfillment), using a load sensitivity coefficient to describe the asymmetric propagation among them. The Information and Decision Risk Network is further divided into a communication subnetwork, which represents transmission efficiency and delay, and a decision risk subnetwork, which reflects the diffusion of uncertainty and risk contagion caused by information delays. A discrete-event simulation approach is employed to evaluate system resilience under various failure modes and parametric conditions. The results reveal the following: (1) under a centralized structure, poorly allocated redundancy can worsen local imbalances and amplify disruptions; (2) the failure of a focal firm is more likely to cause a full network collapse; and (3) node failures in the Communication System Network have a greater destabilizing effect than those in the Physical Network. Full article

The effect of Lonicera japonica Thunb. (LJT) extract on melanosis and quality deterioration of Pacific white shrimp (Litopenaeus vannamei) during cold storage was evaluated, using 4-hexylresorcinol (4-HR), a commercial melanosis inhibitor, as a positive control. The results showed that LJT inhibited polyphenol oxidase (PPO) activity and complexed copper ions in a dose-dependent manner (LJT < 16 mg/mL), indicating that LJT has the potential to inhibit shrimp melanization. Visual image and grayscale value analysis revealed that LJT and 4-HR effectively alleviated melanosis and delayed deterioration of shrimp quality stored at 4 °C, with LJT exhibiting a superior effect. LJT samples displayed a lower total number of colonies, total volatile base nitrogen value, and thiobarbituric reactive substance value than the others (p < 0.05), suggesting that LJT exerted the most potent inhibition on microbial growth and lipid oxidation in shrimps during cold storage. Consequently, LJT effectively retarded the melanosis and quality deterioration of shrimps. Full article

Climate change impacts on vegetation phenology, especially under extreme climate events, remain inadequately understood. Based on the Fraction of Photosynthetically Active Radiation (FPAR) from MODIS, this study extracted and investigated the end of the growing season (EOS) dynamics in semi-arid grassland of Inner Mongolia from 2003 to 2020. The relationship between the EOS and extreme climate events was examined, and the coincidence rate (CR) between these events and EOS standardized anomaly (EOS_SA) was quantified. The results showed that the EOS exhibited a significant delaying trend (1.48 days/year, p < 0.05) after 2011, with its spatial distribution patterns strongly correlated with climatic gradients. Compound dry–warm events exhibited the widest spatial extent and highest frequency among all compound extreme climate events (CECEs). The impact of extreme climate events on EOS_SA varied depending on climatic background. Extreme dry delayed EOS_SA in colder regions but advanced it in warmer regions. CECEs exerted a stronger regulatory effect on EOS_SA. Compound dry–warm events showed high CR with EOS_SA (CR > 0.4), which was higher under low temperature gradients but decreased under high gradients. The result enhances our understanding of how semi-arid grassland respond to extreme climate events, aiding the improvement of phenology models. Full article

Background/Objectives: The initiation and maintenance of breastfeeding depend on internal and external factors that can either support or hinder its success. This study aimed to examine the association between hospital-based perinatal practices and the duration of exclusive breastfeeding among Mexican mothers of infants under one year of age. Methods: An analytical cross-sectional study was conducted in Guadalajara, Mexico, using a structured questionnaire developed in Google Forms and distributed via social media managed by healthcare professionals. Elegible participants were mothers of infants aged 6 to 12 months. Data were collected between March and November 2022 and included information on infant feeding at six months, sociodemographic and obstetric characteristics, breastfeeding education, hospital-based practices, and professional support during birth. A sample size of 323 participants was estimated on a 95% confidence level, 30% expected prevalence, and 5% margin of error. Statistical analyses included chi-square tests, odds ratios, Mann–Whitney U tests, and multivariate analyses. Results: A total of 326 mothers participated. Exclusive breastfeeding lasted less than six months for 63.5% of infants, while 36.5% were exclusively breastfed from birth to six months. Bottle use in the hospital, provision of human milk substitutes during the hospital stay, and at discharge were significantly associated with shorter exclusive breastfeeding duration (p < 0.001). Predictors of not achieving six months of exclusive breastfeeding included primiparity, delayed initiation beyond the first postpartum hour, and lack of continuous rooming-in. Conclusions: Hospital-based practices significantly influence exclusive breastfeeding duration. Strengthening maternity care policies may improve adherence to recommended feeding practices. Full article

Objective: The objective of this paper is to define “poor candidates” and to conduct an analysis of preoperative selection criteria, considering factors related to the patient, tumor burden, and histopathological characteristics, in the case of patients with advanced epithelial ovarian cancer (EOC) FIGO III-IV with a low probability of optimal cytoreduction. Methodology: The authors of this narrative review conducted an analysis of articles published over a 20-year period (2005–2025), with the following selection criteria for the topics of the papers: advanced epithelial ovarian cancer (FIGOIII-IV), surgical indications in advanced ovarian cancer, poor candidates for surgery, and dependence between surgery and histopathologic and molecular type of EOC. They used using PubMed, Science Direct, and Scopus as databases. The results of the analysis were organized into three large chapters that grouped patient-related factors, tumor burden-specific factors, and histopathological criteria. Results: The authors identify a series of criteria with a high risk of unfavorable postoperative evolution, which led to delayed chemotherapy treatment and suboptimal management. These criteria are related to the patient’s field (ECOG > 3, Charlson Comorbidity Index (CCI) > 2, BMI > 25–30, hypoalbuminemia, hypokalemia), imaging or intraoperative factors predictive for residual tumor, and histopathological or genetic factors (presence of BRCA mutation favors optimal cytoreduction even in cases with high tumor burden; in the case of low-grade serous ovarian carcinoma, surgical intervention is recommended even if there are suboptimal resection criteria, accepting resection > 1 cm due to the poor response to specific chemotherapy treatment). Conclusions: Considering all these aspects, patient selection for primary debulking surgery (PDS) or NACT (neoadjuvant chemotherapy) and interval debulking surgery (IDS) should be conducted in oncological surgery centers highly specialized in gynecological neoplasms, thus ensuring an optimal therapeutic pathway for patients with EOC. Full article

Intelligent video analysis tools have advanced significantly, with numerous cameras installed in various locations to enhance security and monitor unusual events. However, the effective detection and monitoring of violent incidents often depend on manual effort and time-consuming analysis of recorded footage, which can delay timely interventions. Deep learning has emerged as a powerful approach for extracting critical features essential to identifying and classifying violent behavior, enabling the development of accurate and scalable models across diverse domains. This study presents the Int.2D-3D-CNN architecture, which integrates a two-dimensional convolutional neural network (2D-CNN) and 3D-CNNs for video-based violence recognition. Compared to traditional 2D-CNN and 3D-CNN models, the proposed Int.2D-3D-CNN model presents improved performance on the Hockey Fight, Movie, and Violent Flows datasets. The architecture captures both static and dynamic characteristics of violent scenes by integrating spatial and temporal information. Specifically, the 2D-CNN component employs lightweight MobileNetV1 and MobileNetV2 to extract spatial features from individual frames, while a simplified 3D-CNN module with a single 3D convolution layer captures motion and temporal dependencies across sequences. Evaluation results highlight the robustness of the proposed model in accurately distinguishing violent from non-violent videos under diverse conditions. The Int.2D-3D-CNN model achieved accuracies of 98%, 100%, and 98% on the Hockey Fight, Movie, and Violent Flows datasets, respectively, indicating strong potential for violence recognition applications. Full article

Cover crops (CCs) are widely recognized for their numerous benefits, including enhancing soil health, mitigating erosion, and promoting nutrient cycling, among many others. However, their outcomes vary significantly depending on site-specific biophysical conditions and agronomic management practices. This study investigates the geographic variations in cover crop outcomes across Nebraska, focusing on three critical management factors: seeding rate, termination timing, and termination-to-corn planting intervals. Using Decision Support System for Agrotechnology Transfer (DSSAT) simulations, we evaluated the effects of these practices on cover crop biomass, growth stages, and subsequent corn yield across seven sites. The results revealed that corn yield remained resilient across all sites, with no statistically significant differences (p > 0.05) across termination timings, seeding rates, or termination-to-planting intervals. A CC seeding rate analysis showed that biomass tended to increase with higher seeding densities, particularly from 200 to 250 plants m⁻², but the gains diminished beyond that, and few pairwise comparisons reached statistical significance. Termination timing had a significant effect on biomass and growth stages, with delayed termination resulting in greater biomass accumulation and advanced phenological development (e.g., Zadoks > 45), which may complicate termination efficacy. Increasing termination-to-planting intervals led to reduced biomass due to shorter growing periods, though these reductions were not associated with significant corn yield penalties. This study highlights the importance of tailoring CC management strategies to local environmental conditions and agronomic objectives. By addressing these site-specific factors, the findings offer actionable insights for farmers and land managers to optimize both ecological benefits and productivity in Nebraska’s no-till systems. Full article

Products with complex structures are structurally intricate and involve multiple professional fields and engineering construction elements, making it difficult for a single contractor to independently develop and manufacture such complex structural products. Therefore, during the research, development, and production of complex products, collaboration between manufacturers and suppliers is essential to ensure the smooth completion of projects. In this process, a complex supply chain network is often formed to achieve collaborative cooperation among all project participants. Within such a complex supply chain network, issues such as delayed delivery, poor product quality, or low resource utilization by any participant may trigger the bullwhip effect. This, in turn, can negatively impact the delivery cycle, product cost, and quality of the entire complex product, causing it to lose favorable competitive positions such as quality advantages and delivery advantages in fierce market competition. Therefore, this paper firstly explores the mechanism of complex product manufacturing and the supply network of complex product manufacturing, in order to grasp the inherent structure of complex product manufacturing with a focus on identifying symmetrical properties among supply chain nodes. Secondly, a complex product supply chain network model is constructed with the Graphical Evaluation and Review Technique (GERT), incorporating symmetry constraints to reflect balanced resource allocation and mutual dependencies among symmetrical nodes. Then, from the perspective of supply chain, we focus on identifying the shortcomings of supply chain suppliers and optimizing the management cost of the whole supply chain in order to improve the quality of complex products, delivery level, and cost saving level. This study constructs a Restricted Grey GERT (RG-GERT) network model with constrained outputs, integrates moment-generating functions and Mason’s Formula to derive transfer functions, and employs a hybrid algorithm (genetic algorithm combined with non-linear programming) to solve the multi-objective optimization problem (MOOP) for joint optimization of delivery time, quality, and cost. Empirical analysis is conducted using simulated data from Y Company’s aerospace equipment supply chain, covering interval parameters such as delivery time [5–30 days], cost [40,000–640,000 CNY], and quality [0.85–1.0], validated with industry-specific constraints. Empirical analysis using Y Company’s aerospace supply chain data shows that the model achieves a maximum customer satisfaction of 0.96, with resource utilization efficiency of inefficient suppliers improved by 15–20% (p < 0.05) after secondary optimization. Key contributions include (1) integrating symmetry analysis to simplify network modeling; (2) extending GERT with grey parameters for non-probabilistic uncertainty; (3) developing a two-stage optimization framework linking customer satisfaction and resource efficiency. Full article

The Zenith Tropospheric Delay (ZTD) is a significant atmospheric error affecting the accuracy of the Global Navigation Satellite System (GNSS). Accurate estimation of the ZTD is essential for enhancing GNSS positioning precision and plays a critical role in meteorological and climate-related applications. To address the limitations of current deep learning models in capturing long-term dependencies in ZTD sequences and overcoming computational inefficiencies, this study proposes SSMB-ZTD—an efficient deep learning model based on an improved selective State Space Model (SSM) architecture. To address the challenge of modeling long-term dependencies, we introduce a joint time and position embedding mechanism, which enhances the model’s ability to learn complex temporal patterns in ZTD data. For improving efficiency, we adopt a lightweight selective SSM structure that enables linear-time modeling and fast inference for long input sequences. To assess the effectiveness of the proposed SSMB-ZTD model, this study employs high-precision Zenith Tropospheric Delay (ZTD) products obtained from 27 IGS stations as reference data. Each model is provided with 72 h of historical ZTD inputs to forecast ZTD values at lead times of 3, 6, 12, 24, 36, and 48 h. The predictive performance of the SSMB-ZTD model is evaluated against several baseline models, including RNN, LSTM, GPT-3, Transformer, and Informer. The results show that SSMB-ZTD consistently outperforms RNN, LSTM, and GPT-3 in all prediction scenarios, with average improvements in RMSE reaching 31.2%, 37.6%, and 48.9%, respectively. In addition, compared with the Transformer and Informer models based on the attention mechanism, the SSMB-ZTD model saves 47.6% and 21.2% of the training time and 38.6% and 30.0% of the prediction time on average. At the same time, the accuracy is better than the two. The experimental results demonstrate that the proposed model achieves high prediction accuracy while maintaining computational efficiency in long-term ZTD forecasting tasks. This work provides a novel and effective solution for high-precision ZTD prediction, contributing significantly to the advancement of GNSS high-precision positioning and the utilization of GNSS-based meteorological information. Full article

The widespread adoption of Unmanned Aerial Vehicles (UAVs) in civilian domains, such as airport security and critical infrastructure protection, has introduced significant safety risks that necessitate effective countermeasures. High-Energy Laser Systems (HELSs) offer a promising defensive solution; however, when confronting large-scale malicious UAV swarms, the Dynamic Resource Target Assignment (DRTA) problem becomes critical. To address the challenges of complex combinatorial optimization problems, a method combining precise physical models with multi-agent reinforcement learning (MARL) is proposed. Firstly, an environment-dependent HELS damage model was developed. This model integrates atmospheric transmission effects and thermal effects to precisely quantify the required irradiation time to achieve the desired damage effect on a target. This forms the foundation of the HELS–UAV–DRTA model, which employs a two-stage dynamic assignment structure designed to maximize the target priority and defense benefit. An innovative MADDPG-IA (I: intrinsic reward, and A: attention mechanism) algorithm is proposed to meet the MARL challenges in the HELS–UAV–DRTA problem: an attention mechanism compresses variable-length target states into fixed-size encodings, while a Random Network Distillation (RND)-based intrinsic reward module delivers dense rewards that alleviate the extreme reward sparsity. Large-scale scenario simulations (100 independent runs per scenario) involving 50 UAVs and 5 HELS across diverse environments demonstrate the method’s superiority, achieving mean damage rates of 99.65% ± 0.32% vs. 72.64% ± 3.21% (rural), 79.37% ± 2.15% vs. 51.29% ± 4.87% (desert), and 91.25% ± 1.78% vs. 67.38% ± 3.95% (coastal). The method autonomously evolved effective strategies such as delaying decision-making to await the optimal timing and cross-region coordination. The ablation and comparison experiments further confirm MADDPG-IA’s superior convergence, stability, and exploration capabilities. This work bridges the gap between complex mathematical and physical mechanisms and real-time collaborative decision optimization. It provides an innovative theoretical and methodological basis for public-security applications. Full article

Fusarium head blight (FHB) is a major threat to wheat production that is caused by toxigenic species of the Fusarium graminearum complex. This study aimed to investigate the biochemical and molecular defense responses of Brazilian wheat genotypes (BRS 194, BRS Parrudo, and Frontana) with contrasting FHB susceptibilities following inoculation with F. graminearum (deoxynivalenol producer) and F. meridionale (nivalenol producer). Temporal patterns of antioxidant enzymes, defense-related enzymes, and gene expression (ABC-Transporter and Ca²⁺-ATPase) were analyzed from 12 to 96 h after inoculation. The ANOVA results revealed significant effects of genotypes, inoculation, and time after inoculation on most of the evaluated enzymatic activities. Frontana exhibited high basal activity for most enzymes, and after inoculation, the enzyme activity was higher than in other genotypes. BRS 194 presented delayed and fragmented activation patterns, particularly under DON-producing pathogen infection. According to the transcriptome results, inoculation with the NIV-producing pathogen upregulated both genes, reaching up to an 18-fold increase. BRS 194 showed an upregulated transcript pattern from the early hours after inoculation. Frontana showed increased transcript levels, reaching 12-fold, under DON-producing pathogen infection. These findings show that biochemical and molecular responses varied depending on genotype and the chemotype of the Fusarium isolate, highlighting the importance of early, coordinated defense activation in FHB resistance. Full article

The application of natural juices in sports nutrition is attracting growing interest due to their potential antioxidant, anti-inflammatory, and ergogenic properties. Exercise, especially when prolonged or intense, increases oxidative stress and muscle damage, leading athletes to explore dietary strategies that support recovery and enhance performance. This systematic review investigates the effectiveness of five widely studied juices—beetroot, pomegranate, cherry, watermelon, and pickle juice—in the context of athletic supplementation and recovery. A thorough search of the PubMed, Scopus, and Web of Science databases was conducted to identify studies published between 2010 and 2025. Fifty peer-reviewed articles met the inclusion criteria, examining various physiological, biochemical, and performance-related outcomes linked to juice consumption. Given the methodological diversity among studies, a qualitative synthesis was employed. The juices were compared across four key outcomes—inflammation, oxidative stress, delayed onset of muscle soreness, and exercise performance—to determine their most consistent benefits. Beetroot juice, noted for its high nitrate content, consistently enhanced oxygen efficiency and submaximal endurance, although benefits in elite or sprint athletes were less evident. Both pomegranate and cherry juices were effective in reducing muscle soreness and inflammatory markers, particularly when consumed over several days surrounding exercise. Watermelon juice, primarily through its L-citrulline content, offered antioxidant and recovery support, although performance outcomes varied. Evidence for pickle juice was limited, with no notable ergogenic effects beyond anecdotal cramp relief. Overall, natural juices can support recovery and occasionally improve performance, depending on the specific juice, dosage, and athlete characteristics. Beetroot juice stands out as the most reliable in enhancing performance, while pomegranate and cherry juices are more beneficial for recovery. Future research with standardized protocols is essential to determine optimal application across diverse athletic contexts. Full article

Background: Artocarpus heterophyllus, familiar as jackfruit, is a tropical fruit highly valued not only for its nutritional content but also for its medicinal properties, including potential antidiabetic effects. Objectives: This study aimed to evaluate the insulinotropic, β-cell proliferative and anti-hyperlipidaemic properties of the ethanol extract of unripe Artocarpus heterophyllus (EEAH) in high-fat-fed (HFF) diet-induced obese mice. Method: We evaluated acute insulin secretion and β-cell proliferation in BRIN-BD11 cells, and assessed in vitro glucose diffusion and starch digestion. In vivo, acute and chronic studies in HFF induced obese mice measured glucose tolerance, body weight, food and fluid intake, and lipid profiles. A preliminary phytochemical screening was also performed. Results: In this study, EEAH exhibited significant antidiabetic activity through multiple mechanisms. EEAH enhanced glucose-stimulated insulin secretion in BRIN-BD11 β-cells via K_ATP channel modulation and cAMP-mediated pathways, with partial dependence on extracellular calcium, and it also promoted β-cell proliferation. In vitro assays revealed its ability to inhibit starch digestion and glucose diffusion, indicating delayed carbohydrate digestion and absorption. In high-fat-fed (HFF) obese mice, the acute and chronic oral administration of EEAH improved oral glucose tolerance, reduced fasting blood glucose, decreased body weight, and normalized food and fluid intake. Lipid profile analysis showed increased HDL and reduced total cholesterol, LDL, and triglycerides, while higher doses of EEAH also enhanced gut motility. Phytochemical screening revealed the presence of bioactive compounds such as alkaloids, tannins, flavonoids, saponins, steroids, and terpenoids, which are likely responsible for these therapeutic effects. Conclusion: These findings highlight EEAH as a promising natural candidate for adjunctive therapy in managing type 2 diabetes and associated metabolic disorders and emphasize the importance of future multi-omics studies to elucidate its molecular targets and pathways. Full article