Search Results (195)

Multi-object tracking (MOT) in videos captured by Unmanned Aerial Vehicles (UAVs) is critically challenged by significant camera ego-motion, frequent occlusions, and complex object interactions. To address the limitations of conventional trackers that depend on static, rule-based association strategies, this paper introduces STC-SORT, a novel tracking framework whose core is a two-level reasoning architecture for data association. First, a Spatio-Temporal Consistency Graph Network (STC-GN) models inter-object relationships via graph attention to learn adaptive weights for fusing motion, appearance, and geometric cues. Second, these dynamic weights are integrated into a 4D association cost volume, enabling globally optimal matching across a temporal window. When integrated with an enhanced AEE-YOLO detector, STC-SORT achieves significant and statistically robust improvements on major UAV tracking benchmarks. It elevates MOTA by 13.0% on UAVDT and 6.5% on VisDrone, while boosting IDF1 by 9.7% and 9.9%, respectively. The framework also maintains real-time inference speed (75.5 FPS) and demonstrates substantial reductions in identity switches. These results validate STC-SORT as having strong potential for robust multi-object tracking in challenging UAV scenarios. Full article

An avionic weather radar antenna should be able to operate in multiple modes to cope with the change in resolution and elevation coverage as an aircraft approaches a storm cell that could expand 10 km in elevation. To solve this problem, we propose the addition of four auxiliary antenna (AuxAnt) arrays based on the phased-MIMO antenna structure to the existing avionic weather radar for future field data collection missions. Two types of signals are employed: the Type I signal transmitted by AuxAnt 1 and 2 is designed based on a non-overlapping subarray configuration, with Subarray 1 and 2 dedicated to the transmission of long and short pulses, respectively, so that the near-range blind zone is mitigated. Leveraging the waveform design and beamforming flexibility provided by the phased-MIMO antenna, pulse compressions based on frequency modulation and phase-coding are employed for wide and narrow main beams, respectively. To suppress the range sidelobes, adaptive pulse compression is used at the receiver end in substitute of the conventional matched filter. In contrast, the Type II signal transmitted by AuxAnt 3 and 4 is designed based on the contextual information so that the transmitted beampatterns have specific sidelobe levels at certain directions for interference suppression. The advantages of the proposed signaling strategy are verified with a series of ingeniously devised experiments based on real weather data. Full article

Uterine Fibroids (UFs) are the most common benign tumors in women of reproductive age, affecting ~77% of women overall and are clinically manifest in ~25% by age 50. Bromodomain and extra-terminal domain (BET) proteins play key roles in epigenetic transcriptional regulation, influencing many biological processes, such as proliferation, differentiation, and DNA damage response. Although BET dysregulation contributes to various diseases, their specific role in the pathogenesis of UFs remains largely unexplored. The present study aimed to determine the expression pattern of BET proteins in UFs and matched myometrium and further assess the impact of BET inhibitors on UF phenotype and epigenetic changes. Our studies demonstrated that the levels of Bromodomain-containing protein (BRD)2 and detection rate of BRD4 were significantly altered in UFs compared to matched myometrium, suggesting that aberrant BET protein expression may contribute to the pathogenesis of UFs. To investigate the biological effects of BET proteins, two small-molecule inhibitors, JQ1 and I-BET762, were used to assess their impact on UF cell behavior and transcriptomic profiles. Targeted inhibition of BET proteins markedly reduced UF cell viability compared with myometrial cells and induced cell cycle arrest. Unbiased transcriptomic profiling coupled with bioinformatic analysis revealed that BET inhibition altered multiple biological pathways, including G2M checkpoint, E2F targets, mitotic spindle, mTORC1 signaling, TNF-α signaling via NF-κB, and inflammatory response, as well as reprogrammed the UF cell epigenome. Notably, BET inhibition decreased the expression of several genes encoding extracellular matrix (ECM) proteins, a hallmark of UFs. Collectively, these results support that BET proteins play a pivotal role in regulating key signaling pathways and cellular processes in UFs. Targeting BET proteins may therefore represent a promising non-hormonal therapeutic strategy for UF treatment. Full article

Accurate segmentation of coastal aquaculture ponds from high-resolution remote sensing images is critical for applications such as coastal environmental monitoring, land use mapping, and infrastructure management. Semi-supervised learning (SSL) has emerged as a promising paradigm by leveraging labeled and unlabeled data to reduce annotation costs. However, existing SSL methods often suffer from pseudo-label quality degradation, manifested as boundary adhesion and intra-class inconsistencies, which significantly affect segmentation accuracy. To address these challenges, we propose Semi-BSU, a boundary-aware semi-supervised semantic segmentation framework based on the mean teacher architecture. Semi-BSU integrates two novel components: (1) a Boundary Consistency Constraint (BCC), which employs an auxiliary boundary classifier to enhance contour accuracy in pseudo labels, and (2) a Superpixel Refinement Module (SRM), which refines pseudo labels at the superpixel level to improve intra-class consistency. Comprehensive experiments conducted on GF6 and ZY1E high-resolution remote sensing imagery, covering diverse coastal environments with complex geomorphological features, demonstrate the effectiveness of our approach. With half of the training set labeled, Semi-BSU achieves an MIOU of 0.8606, F1 score of 0.8896, and Kappa coefficient of 0.8080, outperforming state-of-the-art methods including CPS, GCT, and UniMatch by 0.3–4.9% in MIOU. The method maintains a compact computational footprint with only 1.81 M parameters and 55.71 GFLOPs. Even with only 1/8 labeled data, it yields a 3.57% MIOU improvement over the supervised baseline. The results demonstrate that combining boundary-aware learning with superpixel-based refinement offers an effective and efficient strategy for high-quality pseudo-label generation and accurate mapping of coastal aquaculture ponds in remote sensing imagery. Full article

Manufacturing firms may lose profits after a servitization transition due to a mismatch between service offerings and demand, causing them to fall into the servitization paradox. The purpose of this paper is to address the reality of the mismatch between the heterogeneous needs of consumers and the levels of services provided by firms. This paper constructs a two-stage game model and proposes a servitization pricing strategy based on consumers’ willingness to pay. The results show that a premium pricing strategy yields optimal profits; a value-for-money pricing strategy is preferred only when consumers’ willingness to pay is extremely high. Further, we propose to optimize the level of demand matching by matching hidden demand. Considering the characteristics of services, this paper proposes programs based on experience and word-of-mouth marketing to achieve hidden demand matching. It was verified that based on Nash equilibria, the level of supply–demand matching and the profit of firms were improved. In practice, this research provides firms with guidance on servitization pricing strategies and offers a reference path to break the servitization paradox. Full article

Increasing the nutritional composition of food is a strategy to add added value to products in key agrochains, contributing to food security, and providing nutritional compounds available to improve selected nutritional deficiencies. An increased level of β-carotenes is an important contribution to reducing vitamin A deficiency. In Colombia, the Bioversity-CIAT alliance and Agrosavia evaluated eight cassava genotypes with the aim of identifying promising candidates for commercial release adapted to Caribbean region in Colombia. Experimental genotypes were established together with two checks, in six locations representing dry and humid Caribbean region. Agronomic evaluations, combined with culinary quality assessments and participatory evaluations of consumer preferences, enabled a comprehensive analysis of each genotype. The experimental genotypes exhibited different plant architecture, with some showing greater height and higher first branching than current varieties. However, excessive plant height in certain genotypes led to increased susceptibility to lodging, negatively affecting the quality of planting material. While only a few genotypes matched the check varieties in root yield (20 T/ha), several demonstrated significantly improved nutritional quality due to higher accumulation of total and β-carotenes (>8 µg/gr and >5 µg/gr, respectively) compared to current varieties (<2 µg/gr and 1.5 µg/gr, respectively). Cooking quality and consumer acceptance were key determinants in the final selection. Among the evaluated lines, genotype GM3426-5 stood out for its favorable agronomic performance, high provitamin A content, and excellent root and cooking quality. Nevertheless, further steps are required before its commercial release, as the product profile for cassava destined for human consumption must prioritize food quality and consumer preferences. Full article

With the increasing complexity and diversity of network threats, developing high-performance Network Intrusion Detection Systems (NIDSs) has become a critical challenge. A primary obstacle in this domain is the pervasive issue of class imbalance, where the scarcity of minority attack samples and the varying costs of misclassification severely limit the effectiveness of traditional models, often leading to a difficult trade-off between high False Positive Rates (FPRs) and low Recall. To address this challenge, this paper proposes a novel, conditionally symmetric two-stage framework, termed CSCVAE-NID (Conditionally Symmetric Two-Stage CVAE for Network Intrusion Detection). The framework operates in two synergistic stages: Firstly, a Data Augmentation Conditional Variational Autoencoder (DA-CVAE) is introduced to tackle the data imbalance problem at the data level. By conditioning on attack categories, the DA-CVAE generates high-quality and diverse synthetic samples for underrepresented classes, providing a more balanced training dataset. Secondly, the core of our framework, a Cost-Sensitive Multi-Class Classification CVAE (CSMC-CVAE), is proposed. This model innovatively reframes the classification task as a probabilistic distribution matching problem and integrates a cost-sensitive learning strategy at the algorithm level. By incorporating a predefined cost matrix into its loss function, the CSMC-CVAE is compelled to prioritize the correct classification of high-cost, minority attack classes. Comprehensive experiments conducted on the public CICIDS-2017 and UNSW-NB15 datasets demonstrate the superiority of the proposed CSCVAE-NID framework. Compared to several state-of-the-art methods, our approach achieves exceptional performance in both binary and multi-class classification tasks. Notably, the DA-CVAE module is designed to be independent and extensible, allowing the effective data that it generates to support any advanced intrusion detection methodology. Full article

Insights into motor cortex remodeling may enable the development of more effective rehabilitation strategies during the acute phase. We aim to assess the affected and unaffected motor/premotor/somatosensory cortex resting state functional connectivity (RSFC) and reactivity with continuous wave functional near-infrared spectroscopy (cw-fNIRS) in patients with ICH compared to age, sex, and comorbidity-matched subjects. We enrolled patients with acute–subacute hemispheric ICH (n = 37; two were excluded due to artifacts) and grouped them according to the side (right and left) of the stroke. Matched participants or patients with recent transient ischemic attack were enrolled as control subjects for the study (n = 44; five were excluded due to artifacts). RSFC was assessed in both affected and unaffected hemispheres by group-level seed-based (primary motor cortex, priMC) correlation analysis. FT-associated relative oxyhemoglobin (ΔHbO) changes were analyzed in affected and unaffected hemispheres with generalized linear model regression. In left hemispheric ICH, the resting state coherence between the affected priMC and the affected premotor cortex (preMC) increased (β = 0.83, 95% CI = 0.19, 1.47, p = 0.01). In contrast, in right hemispheric ICH, the coherence between the unaffected priMC and the affected preMC decreased (β = −0.6, 95% CI = −1.12, −0.09, p = 0.02). In the left hemispheric ICH, the left-hand FT was associated with increased ΔHbO over the affected preMC (β = 0.01, 95% CI = 0.003, 0.02, p = 0.01). In contrast, in right hemispheric ICH, the left-hand FT was associated with increased ΔHbO over the unaffected preMC (β = 0.02, 95% CI = 0.006, 0.04, p = 0.01). Left hemispheric preMC may be involved in motor cortex reorganization in acute ICH in either hemisphere. Further studies may be required to assess longitudinal changes in motor cortex reorganization to inform acute motor rehabilitation. Full article

Potato production in the Andean highlands demands strategies that reduce dependence on synthetic inputs without sacrificing yield. We evaluated two microbial bioinputs—Bacillus subtilis and Trichoderma viride—applied once pre-plant to seed tubers, under three organo-mineral fertilization regimes (0%, 50%, and 100% of the recommended NPK rate) in two cultivars (INIA 303-Canchán and Yungay) in field conditions in Ayacucho, Peru, using a randomized complete block, split-plot design (three replicates). Agronomic traits (plant height, root dry weight, stems per plant, tubers per plant, and plot-level yield) were analyzed with robust two-way ANOVA and multivariate methods. Combining microbial inoculation with 50% NPK sustained growth responses comparable to 100% NPK for key traits: in Yungay with T. viride, plant height at 50% NPK (≈96.15 ± 1.71 cm) was not different from 100% NPK (≈98.87 ± 1.70 cm), and root dry weight at 50% NPK (≈28.50 ± 0.28 g) matched or exceeded 100% NPK (≈16.97–22.62 g depending on cultivar–treatment). Notably, T. viride increased root biomass even without mineral fertilizer (≈27.62 ± 0.29 g in Yungay), while B. subtilis enhanced canopy vigor and stem number at full NPK (≈4.5 ± 0.29 stems). Yungay out-yielded INIA 303-Canchán overall (≈57.5 ± 2.5 kg vs. ≈42.7 ± 2.5 kg per plot). The highest yields occurred with B. subtilis + 100% NPK (≈62.88 ± 6.07 kg per plot), followed by B. subtilis + 50% NPK (≈51.7 ± 6.07 kg per plot). Plant height was the strongest correlate of yield (Spearman ρ ≈ 0.60), underscoring its value as a proxy for productivity. Overall, a single pre-plant inoculation with B. subtilis or T. viride can halve mineral fertilizer inputs while maintaining growth and sustaining high, cultivar-dependent yields in highland potato systems. Full article

This study investigates whether and how corporate commitment to environmental, social and governance (ESG) performance can mitigate involutionary competition in China’s consumer electronics manufacturing industry. By constructing a quantifiable index of involutionary competition intensity and matching it with corporation-level ESG scores, we document a statistically significant negative association between ESG performance and the degree of involutionary competition. Mechanism analysis reveals that ESG mitigates involutionary competition through two primary channels: (1) differentiation strategies that reduce price-based competition and product homogeneity, and (2) market-order regulation that curbs opportunistic behaviour and raises R&D efficiency. A modest price increase is shown to be revenue-enhancing; moreover, random-forest simulations indicate that counter-involutionary competition efforts amplify the market-share gains from cooperative R&D expenditures, accelerating post-adjustment revenue growth. This transition generates simultaneous increases in corporate profits and corporation value, breaking the previous price ceiling and establishing a sustainable development loop. The findings provide actionable insights for shifting the industry from low-level rivalry to sustainable value creation. Full article

Personnel selection represents a two-sided matching problem in which firms compete for qualified candidates by designing job-offer packages. While traditional models assume fully rational agents, real-world decision-makers often face bounded rationality due to limited information and cognitive constraints. This study develops a matching framework that incorporates bounded rationality through the Quantal Response Equilibrium, where firms and candidates act as probabilistic rather than perfect optimizers under uncertainty. Using Maximum Likelihood Estimation and organizational hiring data, we validate that both sides display bounded rational behavior and that rationality increases as the selection process advances. Building on these findings, we propose a two-stage stochastic optimization approach to determine optimal job-offer packages that balance organizational policies with candidate competencies. The optimization problem is solved using particle swarm optimization, which efficiently explores the solution space under uncertainty. Data analysis reveals that only 23.10% of low-level hiring decisions align with rational choice predictions, compared to 64.32% for high-level positions. In our case study, bounded rationality increases package costs by 26%, while modular compensation packages can reduce costs by up to 25%. These findings highlight the cost implications of bounded rationality, the advantages of flexible offers, and the systematic behavioral differences across job levels. The framework provides theoretical contributions to matching under bounded rationality and offers practical insights to help organizations refine their personnel selection strategies and attract suitable candidates more effectively. Full article

Background/Objectives: Childhood obesity rates in Jordan have reached alarming levels, with 28% of school-age children classified as overweight or obese. School-based gardening interventions show promise for promoting healthy eating behaviors, yet limited research exists in Middle Eastern contexts. This study evaluated the effectiveness of a five-month school-based vegetable gardening and nutrition education intervention on anthropometric measures, dietary intake, and knowledge, attitudes, and practices (KAP) regarding vegetable consumption among Jordanian primary school children. Methods: A quasi-experimental controlled trial was conducted with 216 students (ages 10–12 years) from two demographically matched schools in Amman, Jordan. The intervention group (n = 121) participated in weekly one-hour gardening sessions combined with nutrition education and vegetable tasting activities over five months, while the control group (n = 95) continued the standard curriculum. Outcomes measured at baseline and post-intervention included anthropometric assessments, dietary intake via 24 h recalls, and vegetable-related KAP using a validated questionnaire. Data were analyzed using paired t-tests and repeated measures ANCOVA. Results: The intervention group demonstrated significant improvements in body composition, including reductions in BMI (−1.57 kg/m²), weight (−1.88 kg), and BMI z-score (−0.37), while controls showed minimal increases. Vegetable intake showed significant time × group interaction (p-value = 0.003), with a non-significant increase in the intervention group (2.7 to 2.9 times/day) and a non-significant decrease in the controls (2.5 to 2.4 times/day). Dietary quality improved, including increased fiber intake (+2.36 g/day) and reduced saturated fat consumption (−9.24 g/day). Nutrition knowledge scores increased substantially in the intervention group (+22.31 points) compared to controls (+1.75 points; p-value ≤ 0.001). However, attitudes and practices toward vegetable consumption showed no significant changes. Conclusions: This intervention effectively improved body composition, dietary quality, and nutrition knowledge among Jordanian primary school children. These findings provide evidence for implementing culturally adapted school gardening programs as childhood obesity prevention interventions in Middle Eastern settings, though future programs should incorporate family engagement strategies to enhance behavioral sustainability. Full article

Under the dual backdrop of the Healthy China strategy and the concept of sustainable development, optimizing the spatial layout of primary healthcare facilities is important for fairly distributing healthcare resources and strengthening the resilience of the public health system in a sustainable way. This study introduces an innovative 3D spatial resilience evaluation framework, covering transmission (service accessibility), diversity (facility type matching), and stability (supply demand balance). Unlike traditional accessibility studies, the concept of “resilience” here highlights a system’s ability to adapt to sudden public health events through spatial reorganization, contrasting sharply with vulnerable systems that lack resilience. Method-wise, the study uses an improved Gaussian two-step floating catchment area method (Ga2SFCA) to measure spatial accessibility, applies a geographically weighted regression model (GWR) to analyze spatial heterogeneity factors, combines network analysis tools to assess service coverage efficiency, and uses spatial overlay analysis to identify areas with supply demand imbalances. Harbin is located in northeastern China and is the capital of Heilongjiang Province. Since Harbin is a typical central city in the northeast region, with a large population and clear regional differences, it was chosen as the case study. The case study in Harbin’s main urban area shows clear spatial differences in medical accessibility. Daoli, Nangang, and Xiangfang form a highly accessible cluster, while Songbei and Daowai show clear service gaps. The GWR model reveals that population density and facility density are key factors driving differences in service accessibility. LISA cluster analysis identifies two typical hot spots with supply demand imbalances: northern Xiangfang and southern Songbei. Finally, based on these findings, recommendations are made to increase appropriate-level medical facilities, offering useful insights for fine-tuning the spatial layout of basic healthcare facilities in similar large cities. Full article

(1) Background: Physical Education sessions subject students to various physical and physiological demands that teachers must understand to design interventions aimed at improving health and fitness. This study aimed to quantify and compare external and internal load before and after implementing two intervention programs: one based on the Game-Centered Model and another Hybrid Model that combines the Game-Centered Model with the Sport Education Model. (2) Methods: A total of 47 first-year secondary school students participated, divided into two naturally formed groups. Pre- and post-intervention assessments involved 4 vs. 4 matches monitored using WIMU Pro™ inertial measurement units and heart rate monitors to collect kinematic, neuromuscular, and physiological data. The combined use of inertial sensors and heart rate monitors enabled the objective quantification of students’ physical demands. (3) Results: No significant improvements were observed between pre- and post-tests, possibly due to the short duration of the interventions. However, the Hybrid Model generated higher levels of external load, both kinematic and neuromuscular, in the post-test. (4) Conclusions: The Hybrid Model appears more effective in increasing students’ physical engagement. Inertial sensors represent a valid and practical tool for monitoring and adjusting instructional strategies in school-based Physical Education. Full article

Quadratic programming (QP) formulations are widely used in optimal investment portfolio selection, a central problem in financial decision-making. In practice, asset allocation decisions operate at two interconnected levels: the strategic level, which allocates the budget across major asset classes, and the tactical level, which distributes the allocation within each class to individual securities or instruments. This study evaluates the Frank–Wolfe (FW) algorithm as a computationally alternative to a QP formulation implemented in CVXPY and solved using OSQP (CVXPY–OSQP solver) for tactical investment portfolio optimization. By iteratively solving a linear approximation of the convex objective function, FW offers a distinct approach to portfolio construction. A comparative analysis was conducted using a tactical portfolio model with a small number of stock assets, assessing solution similarity, computational running time, and memory usage. The results demonstrate a clear trade-off between the two methods. While FW can produce portfolio weights closely matching those of the CVXPY–OSQP solver at lower and feasible target returns, its solutions differ at higher returns near the limits of the feasible set. However, FW consistently achieved shorter execution times and lower memory consumption. This study quantifies the trade-offs between accuracy and efficiency and identifies opportunities to improve FW’s accuracy through adaptive iteration strategies under more challenging optimization conditions. Full article