Search Results (748)

This study explains how integrating with foreign suppliers fortifies a buying firm’s supply-chain resilience, captured here as heightened market responsiveness. Drawing on information-processing theory, we argue that supplier integration equips buyers with richer, faster information flows that enable timely adaptation to market shocks. Extending value-congruence theory, we posit that this resilience dividend depends on simultaneous cultural alignment at two levels—national and organizational. Survey data from 174 manufacturing firms engaged in international buyer–supplier relationships across East Asia, North America, Latin America and Europe were analyzed via hierarchical regression. Results confirm that foreign supplier integration has a positive main effect on market responsiveness. Crucially, a significant three-way interaction (integration × national-culture congruence × organizational-culture congruence) reveals that the responsiveness—and thus resilience—payoff materializes only when both cultural layers are highly congruent; congruence at just one layer is insufficient. By demonstrating the contingent, multilevel nature of resilience benefits, this research advances the global supply-chain literature in three ways: (1) it unites information-processing and value-congruence perspectives to clarify when integration generates adaptive capability; (2) it positions dual-level cultural fit as a prerequisite for resilient performance; and (3) it offers region-spanning evidence that guides managers in designing culturally attuned integration strategies to withstand market turbulence. Full article

Remote sensing objects often exhibit significant scale variations, high aspect ratios, and diverse orientations. The anisotropic spatial distribution of such objects’ features leads to the conflict between feature representation and boundary regression caused by the coupling of different attribute parameters: previous detection methods based on square-kernel convolution lack the overall perception of large-scale or slender objects due to the limited receptive field; if the receptive field is simply expanded, although more context information can be captured to help object perception, a large amount of background noise will be introduced, resulting in inaccurate feature extraction of remote sensing objects. Additionally, the extracted features face issues of feature conflict and discontinuous loss during parameter regression. Existing methods often neglect the holistic optimization of these aspects. To address these challenges, this paper proposes SODE-Net as a systematic solution. Specifically, we first design a multi-scale fusion and spatially orthogonal convolution (MSSO) module in the backbone network. Its multiple shapes of receptive fields can naturally capture the long-range dependence of the object without introducing too much background noise, thereby extracting more accurate target features. Secondly, we design a multi-level decoupled detection head, which decouples target classification, bounding-box position regression and bounding-box angle regression into three subtasks, effectively avoiding the coupling problem in parameter regression. At the same time, the phase-continuous encoding module is used in the angle regression branch, which converts the periodic angle value into a continuous cosine value, thus ensuring the stability of the loss value. Extensive experiments demonstrate that, compared to existing detection networks, our method achieves superior performance on four widely used remote sensing object datasets: DOTAv1.0, HRSC2016, UCAS-AOD, and DIOR-R. Full article

Patterns of family caregiving of older adults have been identified based on aspects such as care-related tasks and intensity and are associated with caregiver well-being. A gap remains, however, in understanding how individual-, relational-, and cultural-level factors concurrently inform caregiving groups within multicultural families. In this study, we identified caregiving patterns among Mexican American individuals aiding older adults by drawing from a variety of care recipient and caregiver characteristics. We also assessed relationships between established subgroups and perceived caregiver stress. Using data from the 2016 Hispanic Established Populations for the Epidemiological Study of the Elderly (Caregiver supplement, Wave 9, N = 460), we performed latent class analysis to determine caregiving subgroups from 8 indicator variables representing patient needs, family characteristics, and caregiver health and support. Findings identified four caregiving subgroups that varied based on older adults’ care needs and caregivers’ family status, nativity, and health. Results from multivariable linear regression indicated that caregivers from the Moderate Burden/Non-cohabitating group perceived significantly less stress than those in the Elevated Burden & Health Risk group. In conclusion, we demonstrated how multi-level factors shape caregiving patterns, which can inform support efforts for multicultural families. Full article

Poor rural households still face vulnerability of the sustainable livelihood capacity caused by multiple risk disturbances even after they are lifted out of poverty, and become vulnerable poverty-eradicated households. However, quantifying the spatiotemporal heterogeneity of the impact of rural household livelihood vulnerability on resilience from a multi-risk perspective remains a challenge. This study integrates the theoretical connotations of livelihood vulnerability and resilience to develop a systematic analysis framework of sustainable livelihood-vulnerability-resilience for rural households from the perspective of multi-risk disturbance, and reveals the dynamic interaction process and mechanism of the three. On this basis, the VEP model for forward-looking and multi-risk perspectives, which embeds multiple risk factors as feature vectors, and the cloud-based fuzzy integrated evaluation method are employed to measure rural households’ livelihood vulnerability and resilience, respectively. Subsequently, based on clarifying the correlation between the two, we use the quantile regression method and factor contribution model to reveal the spatiotemporal impact mechanism of multi-level and multi-risk dominated vulnerability of rural households on resilience. These methods collectively enable us to quantify the spatiotemporal heterogeneity of vulnerability and resilience impacts from a risk perspective, taking a step forward and broadening the analytical perspective in the field of sustainable livelihoods research. The case study in Fugong County of China shows that, both rural households’ livelihood vulnerability and resilience exhibit spatiotemporal heterogeneity, and the negative correlation between the two gradually increases over time; as the level of livelihood vulnerability increases, the internal main contributing factors of livelihood resilience and their degree of contribution change accordingly; as the types of risks that dominate vulnerability change, the impact of vulnerability on the overall livelihood resilience and its internal dimensions also varies, where the change in resilience is greatest when the vulnerability is dominated by social risks, while the least change occurred when vulnerability is dominated by labor and income risks. This study provides a feasible methodological reference and a technical foundation for decision-making aimed at guiding rural households out of poverty sustainably and achieving sustainable livelihood. It can effectively enhance the predictive and post-event coping capacity of vulnerable rural households when subjected to multi-risk disturbances. Full article

To explore the mechanisms by which water, fertilizers, and dissolved oxygen affect the physiological growth and yield quality of custard apple, this study aims to optimize water–fertilizer–oxygen coupling regulation schemes for custard apple in dry hot valley regions through a multi-level fuzzy evaluation method, thereby addressing issues such as soil compaction and reduced aeration caused by long-term water and fertilizer drip irrigation. The experiment was conducted on custard apple in a dry, hot valley area, employing orthogonal and quadratic regression-orthogonal designs. Three factors were set at multiple levels: irrigation amount (60–100% ETc), fertilization rate (1500–1900 kg·ha⁻¹), and dissolved oxygen concentration (6–10 mg·L⁻¹). Custard apple development, production, and attributes were assessed. The two-year trial from 2023 to 2024 demonstrated that the new shoots, leaf area, and net photosynthetic rate of plants treated with W3F2O1 (100% ETc, 1700 kg·ha⁻¹ fertilization rate, and high oxygen 6 mg·L⁻¹) and W3F3O2 (100% ETC, 1500 kg·ha⁻¹ fertilization rate, and high oxygen 8 mg·L⁻¹) were significantly superior to those of W1F1O1 (60% ETc, 1900 kg·ha⁻¹ fertilization rate, and high oxygen 6 mg·L⁻¹), with a single-plant yield of 10.31 kg, and increases in diameter and length of 31.6% and 27.6%, respectively (p < 0.05); quality indicators were also optimal under W3F3O2 (100% ETC, 1500 kg·ha⁻¹ fertilization rate, and high oxygen 8 mg·L⁻¹) treatment, with soluble sugar and vitamin C levels increasing by 17.3% and 29.9%, respectively, compared to the control. Using a multi-level fuzzy evaluation to comprehensively evaluate the water–fertilizer–oxygen coupling, the comprehensive productivity of custard apples was significantly improved by optimizing the root zone microenvironment. It is recommended that dry hot valleys adopt an optimized range of 82.5–100% ETc irrigation, 1650–1847.86 kg·ha⁻¹ fertilization, and 7.4–9.25 mg·L⁻¹ dissolved oxygen, providing a theoretical basis for precise irrigation and sustainable cultivation of tropical fruit trees. Full article

With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding demand-driven operational planning for charging stations and degrading the user experience. This study leverages three pre-trained language models to perform sentiment classification and multi-level topic identification on 168,129 user reviews from Beijing, facilitating a comprehensive understanding of user feedback. The experimental results reveal significant task-model specialization: RoBERTa-WWM excels in sentiment analysis (accuracy = 0.917) and fine-grained topic identification (Micro-F1 = 0.844), making it ideal for deep semantic extraction. Conversely, ELECTRA, after sufficient training, demonstrates a strong aptitude for coarse-grained topic summarization, highlighting its strength in high-level semantic generalization. Notably, the models offer capabilities beyond simple classification, including autonomous label normalization and the extraction of valuable information from comments with low information density. Furthermore, integrating textual and spatial analyses revealed striking patterns. We identified an urban–rural emotional gap—suburban users are more satisfied despite fewer facilities—and used geographically weighted regression (GWR) to quantify the spatial differences in the factors affecting user satisfaction in Beijing’s districts. We identified three types of areas requiring differentiated strategies, as follows: the northwestern region is highly sensitive to equipment quality, the central urban area has a complex relationship between supporting facilities and satisfaction, and the emerging adoption area is more sensitive to accessibility and price factors. These findings offer a data-driven framework for charging infrastructure planning, enabling operators to base decisions on real-world user feedback and tailor solutions to specific local contexts. Full article

Despite the increased research on violence toward teachers and public policies aimed at protecting teachers from violence, knowledge of the factors contributing to teachers’ sense of safety at school remains limited. Drawing from socioecological theory, we examined the contributions of both teachers’, parents’, students’, and schools’ characteristics to teachers’ sense of feeling unsafe in school. Specifically, we examined teachers’ individual and work characteristics (sex, age, years of experience, and working in the regular classroom or not), their perceptions of school violence, and their relationships with students and their peers. At the school level, we examined the school size, poverty level, and school-level reports of parents’, students’, and teachers’ perception of the school climate and school violence. The sample consisted of 9625 teachers (73% female), 126,301 students, and 56,196 parents from 2116 schools with a low socioeconomic status in Chile. Descriptive statistics showed that most teachers do not feel afraid (72.9%) nor thought that their job was dangerous (74.6%). A hierarchical multivariate regression analysis and multilevel analyses showed that teachers with higher perceptions of feeling unsafe were females or reported being “other sex”, had fewer years of experience, worked mainly in the classroom, perceived a higher level of school violence, and had worse perceptions of peer relationships and teacher–student relationships. These teachers were mostly in schools with higher poverty levels, larger enrollment, and higher student-reported and parent-reported school violence compared to the rest of the sample of low-SES Chilean schools. We discuss the implications of these findings for preventive school interventions and programs regarding school violence and teacher turnover. Full article

Background/Objectives: Recent data shows increasing diabetes prevalence among African Americans. Youth with a family history of diabetes are at high risk for diabetes. This study explores the multilevel risk factors associated with a family history of diabetes among African American youth in public housing. Methods: This study used a cross-sectional, quantitative, and community-based participatory research (CBPR) approach. The research team, comprising community stakeholders and academic researchers, employed respondent-driven sampling (RDS) for data collection (survey) and used univariate and bivariate analyses to examine variable relationships. A sequential logistic regression highlighted factors influencing the likelihood of having a family history of diabetes. Results: The final sample (n = 190, mean age 18.5 years, 58% female) included 35% of youth with a family history of diabetes. Forty-six percent reported medium to severe household hardships. Results suggest that reporting a family history of diabetes is correlated with maternal substance use (tau-b = 0.27 **) and alcohol problems (tau-b = 0.16 ***), paternal substance use (tau-b = 0.17 *), and eating fewer fruits (tau-b = 0.17 *). With an odds ratio (OR) of 1.70 [0.68, 4.13] and attributable fraction among the exposed at 41.3%, the final model (3) was not significant [χ² = 11.19(8)]. Thus, we fail to reject the null hypothesis that the model fits the data well. Fewer vegetable consumption (OR = 15.08, p < 0.001), higher soda consumption (OR = 0.06, p < 0.001), severe household hardships (OR = 5.82, p < 0.01), and maternal substance use problems (OR = 6.81, p < 0.05) predicted a higher likelihood of a history of diabetes. Conclusions: Our study calls attention to the need to reevaluate interventions for hardships and substance use in diabetes management, particularly in poor neighborhoods and among minority families. Full article

Detection of defects on steel surface is crucial for industrial quality control. To address the issues of structural complexity, high parameter volume, and poor real-time performance in current detection models, this study proposes a lightweight model based on an improved YOLOv11. The model first reconstructs the backbone network by introducing a Reversible Connected Multi-Column Network (RevCol) to effectively preserve multi-level feature information. Second, the lightweight FasterNet is embedded into the C3k2 module, utilizing Partial Convolution (PConv) to reduce computational overhead. Additionally, a Group Convolution-driven EfficientDetect head is designed to maintain high-performance feature extraction while minimizing consumption of computational resources. Finally, a novel WISEPIoU loss function is developed by integrating WISE-IoU and POWERFUL-IoU to accelerate the model convergence and optimize the accuracy of bounding box regression. The experiments on the NEU-DET dataset demonstrate that the improved model achieves a parameter reduction of 39.1% from the baseline and computational complexity of 49.2% reduction in comparison with the baseline, with an mAP@0.5 of 0.758 and real-time performance of 91 FPS. On the DeepPCB dataset, the model exhibits reduction of parameters and computations by 39.1% and 49.2%, respectively, with mAP@0.5 = 0.985 and real-time performance of 64 FPS. The study validates that the proposed lightweight framework effectively balances accuracy and efficiency, and proves to be a practical solution for real-time defect detection in resource-constrained environments. Full article

As China’s urbanization deepens, the spatial structure of residential areas and land use patterns has undergone profound transformations, with the differentiation of housing prices emerging as a key indicator of urban spatial dynamics and socioeconomic stratification. This study examines the spatial and temporal evolution of residential housing prices in Qingdao’s main urban area over a 20-year period, using data from three representative years (2003, 2013, and 2023) to capture key stages of change. It employs Local Indicators of Spatial Association (LISA) spatial and temporal path and leap analyses, as well as Geographically and Temporally Weighted Regression (GTWR) modeling. The results show that Qingdao’s housing price patterns exhibit distinct spatiotemporal heterogeneity, characterized by multi-level transitions, leapfrog dynamics and strong spatial dependence. The urban center and coastal zones demonstrate positive synergistic growth, while some inland and peripheral areas show negative spatial coupling. Evident is the spatial restructuring from a monocentric to a polycentric pattern, driven by shifts in industrial layout, policy incentives, and transportation infrastructure. Key driving factors, such as community attributes, locational conditions, and amenity support, show differentiated impacts across regions and over time. Business agglomeration and educational resources are primary positive drivers in central districts, whereas natural environments and commercial density play a more complex role in peripheral areas. These findings provide empirical evidence to inform our understanding of housing market dynamics and offer insights into urban planning and the design of equitable policies in transitional urban systems. Full article

Astrocytic tumors are a heterogeneous group of glial neoplasms characterized by marked differences in biological behavior and patient prognosis. Transforming growth factor-beta (TGF-β) signaling plays a pivotal role in astrocytoma pathogenesis; however, the extent and mechanisms of its epigenetic regulation remain poorly understood. This study aimed to investigate how promoter methylation and microRNA-mediated mechanisms regulate key genes within the TGF-β signaling pathway across various astrocytoma grades. Tumor tissue samples from 65 patients with WHO grade II–IV astrocytomas were analyzed using Affymetrix gene expression and microRNA microarrays. Promoter methylation of TGF-β signaling genes was assessed using methylation-specific polymerase chain reaction (MSP). Gene expression was validated by reverse transcription quantitative polymerase chain reaction (RT-qPCR), and protein levels were quantified using enzyme-linked immunosorbent assay (ELISA). MicroRNA targets were predicted using bioinformatic tools, and survival analyses were conducted using Kaplan–Meier and Cox regression models. Six genes—SMAD1, SMAD3, SKIL, BMP2, SMAD4, and MAPK1—showed significant upregulation in high-grade tumors (fold change > 5.0, p < 0.05), supported by RT-qPCR and protein-level data. Promoter hypomethylation and reduced expression of regulatory microRNAs (e.g., hsa-miR-145-5p targeting SMAD3) were more common in higher-grade tumors. Protein–protein interaction analysis indicated strong functional interconnectivity among the overexpressed genes. High protein levels of SMAD1, SMAD3, and SKIL were significantly associated with shorter overall survival (p < 0.001). This multi-level analysis reveals that astrocytic tumor progression involves epigenetic derepression and microRNA-mediated dysregulation of TGF-β signaling. Elevated expression of SMAD1, SMAD3, and SKIL emerged as strong prognostic indicators, underscoring their potential as biomarkers and therapeutic targets in astrocytic tumors. Full article

Accurate fatigue life prediction of aircraft landing gear is crucial for ensuring flight safety and preventing catastrophic structural failures. However, traditional empirical methods face significant limitations in capturing complex multiaxial loading conditions, while machine learning approaches suffer from lack of interpretability in critical safety applications. To address the dual challenges of prediction accuracy and model interpretability, a multi-agent reinforced symbolic regression (MA-RSR) framework is proposed by integrating multi-agent reinforcement learning with symbolic regression (SR) techniques. Specifically, MA-RSR employs a collaborative mechanism that decomposes complex mathematical expressions into parallel components constructed by independent agents, effectively addressing the search space explosion problem in traditional SR. The system incorporates Transformer-based architecture to enhance symbolic selection capabilities, while an intelligent masking mechanism ensures mathematical rationality through multi-level constraints. To demonstrate effectiveness of the proposed method, validation is conducted using SAE4340 steel multiaxial fatigue data and landing gear finite element simulation. The MA-RSR framework successfully discovers two mathematical expressions achieving R² of 0.96. Compared to traditional empirical formulas, MA-RSR achieves prediction accuracy improvements exceeding 50% while providing complete interpretability that machine learning methods lack. Furthermore, the multi-agent collaborative mechanism significantly enhances search efficiency through parallel expression construction compared to existing symbolic regression approaches. Full article

Background/Objectives: Anterior cervical discectomy and fusion (ACDF) is a common procedure for treating cervical spondylotic myelopathy. Limited research exists on the predictors of subsidence following ACDF. Subsidence can compromise surgical outcomes, alter alignment, and predispose patients to further complications, making it essential to prevent and understand it. This study aims to identify key risk factors for clinically significant subsidence and evaluate its impact on cervical alignment parameters in a large, diverse patient population. Methods: We conducted a retrospective review of patients who underwent ACDF between 2013 and 2022 at a single institution. Subsidence was calculated as the mean change in anterior and posterior disc height, with clinically significant subsidence being defined as three millimeters or more. Univariate analysis was followed by regression modeling to identify subsidence predictors and analyze patterns. Subgroup analyses stratified patients by implant type, number of levels fused, and cage material. Results: A total of 96 patients with 141 levels of ACDF met the inclusion criteria. Patients with significant subsidence were younger on average (52.44 vs. 55.94 years; p = 0.074). Those with less postoperative lordosis were more likely to experience significant subsidence (79.5% vs. 90.2%; p = 0.088). Patients with significant subsidence were more likely to have standalone implants (38.5% vs. 16.7%; p < 0.01), taller cages (6.62 mm vs. 6.18 mm; p < 0.05), and greater loss of segmental lordosis (7.33 degrees vs. 3.31 degrees; p < 0.01). Multivariate analysis confirmed that standalone implants were a significant independent predictor of subsidence (OR 2.679; p < 0.05), and greater subsidence was positively associated with loss of segmental lordosis (OR 1.089; p < 0.01). Subgroup analysis revealed that multi-level procedures had a higher incidence of subsidence (35.7% vs. 28.1%; p = 0.156), and PEEK cages demonstrated similar subsidence rates compared to titanium constructs (28.1% vs. 29.4%; p = 0.897). Conclusions: Standalone implants are the strongest independent predictor of significant subsidence, and those that experience subsidence also show greater loss of segmental lordosis, although not overall lordosis. These findings have implications for surgical planning, particularly in patients with borderline bone quality or requiring multi-level fusions. The results support the use of plated constructs in high-risk patients and emphasize the importance of individualized surgical planning based on patient-specific factors. Further research is needed to explore these findings and determine how they can be applied to improve ACDF outcomes. Full article

Background: China is confronted with the dual challenges of deeply interwoven population aging and the digitalization process. The digital integration and mental health issues of the elderly group are becoming increasingly prominent. Objectives: The present study aimed to analyze the pathways through which individual, family, and social factors influence Internet use in the elderly through a multi-level analysis framework, to examine the association between Internet use and mental health with a view to providing empirical evidence for digital technology-based mental health intervention programs for the elderly, and to promote the scientific practice of the goal of healthy aging. Methods: Based on the data of the 2021 China General Social Survey (CGSS) and provincial Internet development indicators, a mixed cross-sectional dataset was constructed. Logistic hierarchical regression and OLS regression methods were adopted to systematically investigate the multi-level factors associated with Internet use among the elderly group and its association with mental health. Results: The results indicate that individual resources (younger age, higher education level, and good health status) and family technical support (family members’ Internet access) are strongly associated with Internet usage among the elderly, while regional Internet penetration rate appears to operate indirectly through micro-mechanisms. Analysis of the association with mental health showed that Internet use was related to a lower score of depressive tendency (p < 0.05), and this association remained robust after controlling for variables at the individual, family, and social levels. Conclusions: The research results provide empirical evidence for the health promotion policies for the elderly, advocating the construction of a collaborative intervention framework of “individual ability improvement–intergenerational family support–social adaptation for the elderly” to bridge the digital divide and promote the digital integration of the elderly population in China. Full article

Remote sensing targets typically exhibit characteristics of gradual scale changes and diverse orientations. Most existing remote sensing detectors adapt to these differences by adding multi-level structures for feature fusion. However, this approach leads to incomplete coverage of the overall target by the extracted local features, resulting in the loss of critical directional information and an increase in computational complexity which affect the detector’s performance. To address this issue, this paper proposes a Rotation Target Detection Network based on Multi-kernel Interaction and Hierarchical Expansion (MIHE-Net) as a systematic solution. Specifically, we first refine scale modeling through the Multi-kernel Context Interaction (MCI) module and Hierarchical Expansion Attention (HEA) mechanism, achieving sufficient extraction of local features and global information for targets of different scales. Additionally, the Midpoint Offset Loss Function is employed to mitigate the impact of gradual scale changes on target direction perception, enabling precise regression for targets across various scales. We conducted comparative experiments on three commonly used remote sensing target datasets (DOTA, HRSC2016, and UCAS-AOD), with mean average precision (mAP) as the core evaluation metric. The mAP values of the method in this paper on the three datasets reached 81.72%, 92.43%, and 91.86% respectively, which were 0.65%, 1.93%, and 1.87% higher than those of the optimal method, significantly outperforming existing one-stage and two-stage detectors. Through multi-scale feature interaction and direction-aware optimization, MIHE-Net effectively addresses the challenges posed by scale gradation and direction diversity in remote sensing target detection, providing an efficient and feasible solution for high-precision remote sensing target detection. Full article