Search Results (16,273)

Video frame interpolation in ultra-high-definition extreme motion scenes remains highly challenging due to large displacements, nonlinear motion, and occlusions that disrupt spatio-temporal symmetry. To address this issue, this study proposes a frame interpolation method for extreme motion scenes based on dual alignment and region-adaptive interaction from the perspectives of cross-frame localization and adaptive reconstruction. Specifically, we design a two-stage motion information alignment strategy that obtains two types of motion information via optical flow estimation and offset estimation, and it progressively guides reference pixels for accurate long-range cross-frame localization, mitigating structural misalignment caused by limited receptive fields while simultaneously alleviating spatiotemporal asymmetry caused by inconsistent inter-frame motion speed and direction. Based on this, we introduce a region-adaptive interaction module that automatically adapts motion representations for different regions through cross-frame interaction and leverages distinct attention pathways to accurately capture both the global context and local high-frequency motion details. This achieves a dynamic feature fusion tailored to regional characteristics, significantly enhancing the model’s ability to perceive the overall structure and texture details in extreme motion scenarios. In addition, the introduction of a motion compensation module explicitly captures pixel motion relationships by constructing a global correlation matrix that compensates for the positioning errors of the dual alignment module in extreme motion or occlusion areas. The experimental results demonstrate that the proposed method achieves excellent overall performance in ultra-high-definition extreme motion scenes, with a PSNR improvement of 0.05 dB over state-of-the-art methods. In multi-frame interpolation tasks, it achieves an average PSNR gain of 0.31 dB, demonstrating strong cross-scene interpolation capability. Full article

As digital images proliferate across open networks, securing them against unauthorized access has become imperative. However, many recent image encryption algorithms are limited by weak chaotic dynamics and inadequate cryptographic design. To overcome these, we propose a new 2D coupling-enhanced cubic hyperchaotic map with exponential parameters (2D-CCHM-EP). By incorporating exponential terms and strengthening interdependence among state variables, the 2D-CCHM-EP exhibits strict local expansiveness, effectively suppresses periodic windows, and achieves robust hyperchaotic behavior, validated both theoretically and numerically. It outperforms several recent chaotic maps in key metrics, yielding significantly higher Lyapunov exponents and Kolmogorov–Sinai entropy, and passes all NIST SP 800-22 randomness tests. Leveraging the 2D-CCHM-EP, we further develop a hierarchical significance-aware multi-image encryption algorithm (MIEA-CPHS). The core of MIEA-CPHS is a hierarchical significance-aware encryption strategy that decomposes input images into high-, medium-, and low-significance layers, which undergo three, two, and one round of vector-level adaptive encryption operations. An SHA-384-based hash of the fused data dynamically generates a 48-bit adaptive control parameter, enhancing plaintext sensitivity and enabling integrity verification. Comprehensive security analyses confirm the exceptional performance of MIEA-CPHS: near-zero inter-pixel correlation (<0.0016), near-ideal Shannon entropy (>7.999), and superior plaintext sensitivity (NPCR $\approx 99.61\%$, UACI $\approx 33.46\%$). Remarkably, the hierarchical design and vectorized operations achieve an average encryption throughput of 87.6152 Mbps, striking an outstanding balance between high security and computational efficiency. This makes MIEA-CPHS highly suitable for modern high-throughput applications such as secure cloud storage and real-time media transmission. Full article

Background: The efficiency of vermicomposting in reducing antibiotic resistance genes (ARGs) in dairy manure may be compromised by co-pollutants like arsenic (As) and microplastics. Specifically, plasmids serving as carriers and vectors of ARGs were largely distributed in this process. However, the impact of As and microplastics on plasmids carrying ARGs during vermicomposting is largely unknown. Methods: This study utilized a controlled experimental design and applied plasmid metagenomics to investigate the individual and combined effects of As and polyethylene terephthalate (PET) microplastics on plasmid-mediated ARG dynamics during vermicomposting. Results: We found that vermicomposting alone mainly enriched non-mobilizable plasmids, while PET microplastics selectively promoted conjugative and mobilizable plasmids, whereas As significantly increased all plasmid types. Moreover, both PET or As alone and combined exposure (PET and As) increased total ARG abundance, with their combination inducing synergistic ARG enrichment despite unchanged total plasmid abundance. Furthermore, co-occurrence network analysis combined with ARGs/plasmid ratio assessments demonstrated that As influences ARGs through co-selective pressure by enriching ARGs co-localized with As resistance genes (e.g., the ars operon) on plasmids while simultaneously promoting horizontal gene transfer (HGT) via activation of oxidative stress and SOS response pathways. In contrast, PET primarily facilitates ARG dissemination through a “metabolism-resistance” coupling strategy by enriching colonizing bacteria with PET-degrading capacity. Their co-exposure formed As-enrichment hotspots on PET microplastic surfaces, functioning as a “super-mixer” that selectively screened for superbugs carrying potent resistance mechanisms (e.g., bla_OXA-50 and mdtB/mdtE). Conclusions: This study provides the first plasmidome-level evidence of synergistic ARG propagation by As and PET microplastics during vermicomposting, highlighting mobile genetic elements’ critical role in co-pollutant risk assessments. Full article

Large-scale dredging in the middle Huai River has induced complex geomorphic responses that compromise the long-term stability of river regulation infrastructure. To evaluate these impacts, a one-dimensional numerical model was employed, calibrated and validated using field measurements and physical modeling, to simulate 30-year channel evolution under both baseline and dredged scenarios. Results indicate that dredging reversed the reach-scale sediment budget from net erosion (69.80 × 10⁴ m³) to net deposition (87.67 × 10⁴ m³), while eliciting highly heterogeneous local responses. In the Liufangdi Reach, dredging produced a tripartite pattern: depositional amplification in the south branch of the Upper-Liufangdi Reach, an erosion-to-deposition transition in the Erdaohe Reach, and intensified erosion in the north branch of the Lower-Liufangdi Reach. The main channel accounted for over 84% of net volumetric changes, driving the observed morphological adjustments, while dredging promoted synchronization between main channel and floodplain evolution and established stable flow redistribution within branching channels. These findings indicate the importance of implementing spatially differentiated dredging strategies informed by sediment availability, offering critical guidance for reconciling flood control objectives with long-term morphological stability in engineered river systems. Full article

The rapid global shift toward transportation electrification has positioned e-mobility as a key part of low-carbon transition strategies. Saudi Arabia, as a major energy producer undergoing economic diversification under Vision 2030, has recently increased its policy efforts for electric mobility. This study performs a qualitative document analysis of 52 national policies, strategies, and institutional publications issued between 2010 and 2025, creating a longitudinal dataset of 1240 coded references. Using a PRISMA-aligned screening process and NVivo-based thematic coding, the analysis highlights main policy trends, institutional priorities, and implementation challenges influencing the Kingdom’s e-mobility transition. Results show a clear shift from early technology-neutral sustainability rhetoric to a more explicit policy framework focusing on industrial localization, charging infrastructure growth, renewable energy integration, and regulatory development after 2020. Despite these advances, gaps remain in governance coordination, market readiness, charging accessibility, and user adoption incentives. The paper provides a systematically mapped view of Saudi Arabia’s e-mobility policy landscape and places it within global transition trends. The findings offer practical insights for policymakers aiming to strengthen implementation, accelerate adoption, and align transport electrification with national decarbonization goals. Full article

Vespid wasps (Hymenoptera: Vespidae) represent ecologically and economically significant insect resources, possessing extremely high nutritional and medicinal value. In response to escalating market demand and declining wild populations, artificial indoor rearing of vespid wasps has emerged as a sustainable strategy. This approach not only helps conserve and utilize this valuable resource, supporting traditional medicine and local cuisine, but also contributes to invasive species control in affected regions. The large-scale rearing of wasps must integrate their biological characteristics with advanced rearing technologies to achieve rational rearing practices, while continuously optimizing management models. This article systematically reviews the biological characteristics of wasps, traditional rearing methods, and the current status of wasp rearing. It focuses on the methods and key technologies for the outdoor rearing of Vespa mandarinia and year-round indoor rearing. Additionally, it discusses the advantages of artificial indoor rearing, identifies critical technical challenges, and provides a summary and outlook on the future development trends, aiming to provide theoretical support for the large-scale and industrialized development of indoor wasp rearing. Full article

Accurate and reliable short-term wind speed forecasting plays a crucial role in efficient operation and integration of wind energy generation. This research study introduces an innovative deep hybrid model that combines Convolutional Neural Networks (CNN) with Double Bidirectional Long Short-Term Memory (DBiLSTM) networks to enhance wind speed forecasting accuracy in Australia. Thirteen years of hourly wind speed data were collected from two wind-rich potential sites in Tasmania, Australia. The CNN component effectively captures local temporal patterns, while the DBiLSTM layers model long-range dependencies in both forward and backward directions. The proposed CNNDBiLSTM model was compared against three traditional benchmark models: Multiple Linear Regression (MLR), Support Vector Regression (SVR), and Categorical Boosting (CatBoost). The proposed framework can effectively support wind farm planning, operational reliability, and grid integration strategies within the renewable energy sector. A comprehensive evaluation framework across both Australian study sites (Flinders Island Airport, Scottsdale) showed that the CNNDBiLSTM consistently outperformed the baseline models. It achieved the highest correlation coefficients (r = 0.987–0.988), the lowest error rates (RMSE = 0.392–0.402, MAE = 0.294–0.310), and superior scores across multiple efficiency metrics (ENS, WI, LM). The CNNDBiLSTM demonstrated strong adaptability across coastal and inland environments, showing potential for real-world use in renewable-energy resource forecasting. The wind speed analysis and forecasting show Flinders with higher and consistent wind speed as a more viable option for large-scale wind energy generation than Scottsdale in Tasmania. Full article

Background: The mental health of medical students is a global concern, with high rates of depression, anxiety, burnout, and suicidal ideation. Despite growing awareness, mental health services in higher education remain inconsistent and underfunded in many countries. Poland represents a particularly vulnerable case, with a rapidly expanding medical education sector, limited public investment in student mental health. This study presents the third edition of a nationwide assessment evaluating psychological support services for medical students across all accredited Polish medical universities in 2025. Methods: An 11-item questionnaire was administered to all 33 Polish universities offering medical degree programmes. The questionnaire assessed service availability, delivery modalities, language accessibility, funding, communication strategies, crisis response, and targeted support for vulnerable student groups. Data collection took place between January and March 2025, with a response rate of 79% (N = 26). Results: Out of 26 institutions, 25 reported offering some form of psychological support for medical students. Services were free of charge at most universities and typically managed by internal university units. Hybrid delivery models were predominant (84%), while only 48% imposed no limit on the number of consultations. Multilingual support was available at 84% of institutions, and 80% provided supplementary mental health initiatives, such as workshops and mindfulness sessions. However, only 36% offered targeted services for at-risk groups, and just one institution published data on service utilization. Significant disparities were observed in funding, scope, accessibility, and evaluation practices. While some institutions demonstrated innovative and inclusive approaches, many lacked integration, transparency, and sustainability. Conclusions: Despite measurable progress since 2019, psychological support for Polish medical students remains fragmented and reactive. While many academic institutions have taken meaningful steps to support student mental health, lasting and equitable change might require systemic coordination and long-term investment. Without it, even the most dedicated local efforts risk being insufficient- ultimately undermining not only students’ well-being, but also the quality, safety, and sustainability of the future healthcare workforce. Full article

This study investigates inflow turbulence strategies for large-eddy simulations (LES) of urban boundary layers under time-varying atmospheric conditions. A combined approach integrating a digital-filter-based synthetic turbulence generator (STG) with the cell perturbation method (CPM) is proposed to reduce turbulence adjustment distance and improve vertical mixing. Using the PALM model, 24 h simulations were conducted over a real urban domain in Seoul, capturing diurnal transitions in stability and wind direction. Six experiments were compared: two reference runs with extended upstream fetch, and four analysis runs without fetch, applying different inflow strategies (NOT, STG, CPM, and CPM + STG). Results indicate that CPM + STG mitigates abrupt structural transitions and sustains turbulence kinetic energy (TKE) more consistently than STG alone, while requiring lower computational cost than extended-fetch configurations. Under unstable daytime conditions, CPM + STG enhanced vertical mixing and preserved local boundary-layer height closer to background values, whereas nighttime performance was dominated by building-induced shear regardless of inflow strategy. These findings suggest that the combined CPM + STG approach achieves a balance between physical realism and computational efficiency, demonstrating its potential as a robust inflow strategy for time-varying urban LES within limited domain sizes. Full article

Introduction: The incidence of NSCLC increases with age, with a median of approximately 70 years at diagnosis. Historically, treatment strategies for locally advanced cancers have been developed predominantly in younger populations, often excluding elderly patients who may present with multiple comorbidities, severely impaired lung function, or decreased performance status, leading to a lack of age-relevant clinical data. Therefore, we performed a subanalysis of real-world data from the ALLSTAR study to investigate the impact of durvalumab and the radiation regimen (sequential versus concurrent) on clinical outcome in elderly patients with unresectable stage III NSCLC. Methods: We included a total of 171 patients in this subanalysis. All patients were diagnosed with unresectable stage III NSCLC. Patients were divided into two age groups, ≥70 (41%) and <70 years (59%). All of them received curative chemoradiotherapy with (66%) or without (34%) durvalumab. Results: Patients were followed up for a median time of 25.1 months (range: 3.3–52.1). In the elderly group, patients who did not receive durvalumab consolidation had a median PFS of 17 months (95%-CI: 12.4—not reached) and a higher risk of progression (HR = 2.2; 95%-CI: 1–4.6) than those treated with durvalumab, which had a median PFS of 37 months (95%-CI: 24.5—not reached). This difference was statistically significant (log rank p = 0.026). Moreover, the Cox model yielded a hazard ratio suggesting a higher risk of locoregional (HR = 3.8; 95%-CI: 1.28–11.48; log rank p-value =0.01) as well as local recurrence (HR = 5.5: 95%-CI: 1.67–18.1: p-value =0.002) in patients who received sequential chemoradiotherapy compared to those with concomitant chemoradiotherapy in the same age group. In an exploratory analysis based on a Mann–Whitney U test, we did not find significant difference in toxicity between the two age groups. Conclusions: Durvalumab was associated with prolonged progression-free survival, while concomitant radiotherapy showed a trend towards improvement in locoregional and local control in patients aged ≥70. There was no significant difference in treatment toxicity found in the exploratory Mann–Whitney U analysis between the two age groups. Full article

Background/Objectives: Breast density, as defined by the Breast Imaging Reporting and Data System (BI-RADS), reduces mammographic sensitivity and is a risk factor for breast cancer. However, its association with cancer risk and stage at diagnosis remains debated, with limited large-scale evidence. Methods: We conducted a nationwide retrospective cohort study of 952,755 Korean women who underwent screening mammography between 2013 and 2014, with breast cancer diagnoses identified over a 5-year follow-up period. Breast density was categorized by BI-RADS criteria (A–D). Breast cancer risk was evaluated using logistic regression, with odds ratios representing relative odds of developing breast cancer during the 5-year interval. Stage at diagnosis was classified as localized versus regional/distant disease according to national cancer registry records. Results: During 5 years of follow-up, 11,286 women (1.2%) were diagnosed with breast cancer. Higher breast density was significantly associated with increased breast cancer risk: multivariable-adjusted odds ratios (ORs) were 1.174 (95% CI, 1.093–1.260), 1.268 (95% CI, 1.186–1.356), and 1.287 (95% CI, 1.196–1.385) for BI-RADS B–D, respectively, compared with BI-RADS A (all p < 0.001). However, the risk of advanced stage (regional/distant vs. localized) disease at diagnosis did not significantly differ across breast density categories except for a modest association in BI-RADS B (OR 1.16, 95% CI, 1.01–1.33; p = 0.035). Conclusions: Higher breast density was independently associated with increased breast cancer risk but not with advanced-stage disease at diagnosis. These findings underscore the importance of individualized screening strategies for women with dense breasts. Full article

Understanding how climate policies shape local emissions, population dynamics, and consumption patterns is essential for achieving carbon peaking and neutrality goals. As the climate change governance regime evolves, it is inevitable that, in addition to the central government, county-level regulatory actors will be involved in decision-making. This study utilizes a quasi-natural experiment to analyze county-level panel data from 2007 to 2017 as research objects. The nationwide low-carbon pilot policies established in 2010 and 2012 serve as the primary focus of this study. We employ a staggered Difference-in-Differences model to empirically analyze the impact of these pilot programs on carbon emission reductions. The results show that the policy significantly reduces carbon emissions by 30.52% on average, with pronounced spatial heterogeneity across central, suburban, and remote counties. Population redistribution contributes to emission reductions but raises equity concerns in remote counties. Meanwhile, residents remain in a high-carbon consumption phase, revealing the limitations of production-focused policies. These findings highlight the importance of integrating demand-side measures and spatially differentiated strategies into China’s climate governance framework. Full article

Background: Ewing sarcoma (ES) is a highly aggressive malignant tumor that predominantly affects children and young adults. Despite advances in multimodal therapy, relapse and refractory disease remain the leading causes of treatment failure. High-dose chemotherapy followed by autologous stem cell transplantation (HDCT-ASCT) has been proposed as a consolidation strategy for high-risk or relapsed ES; however, its clinical value remains controversial. Methods: We retrospectively analyzed 46 consecutive patients with locally advanced or metastatic ES who underwent HDCT-ASCT after at least one prior systemic therapy line. Clinical, pathological, and transplant-related variables were evaluated for associations with overall survival (OS), post-transplant OS (OS-2), and progression-free survival (PFS). Survival was estimated using the Kaplan–Meier method, and prognostic factors were assessed by Cox proportional hazards modeling. Results: The median age at diagnosis was 23 years (range: 14–55). Median OS from diagnosis was 42 months, while post-transplant OS-2 and PFS were 8 and 5 months, respectively. Younger patients (≤23 years) had significantly longer OS (50 vs. 34 months; p = 0.027). Liver metastasis predicted inferior OS (HR = 5.411; p = 0.006), whereas lung metastasis was associated with shorter OS-2 (HR = 2.672; p = 0.025) and PFS (HR = 6.037; p = 0.016). Treatment-related mortality was low (2.1%), though hematologic toxicity was universal. Overall, HDCT-ASCT provided transient disease control, with modest benefit confined to younger, chemosensitive, and medically fit patients. Conclusions: In this real-world cohort, HDCT-ASCT was feasible and safe but offered limited survival advantage in heavily pretreated Ewing sarcoma. Prognosis was primarily influenced by age and metastatic distribution, particularly hepatic and pulmonary involvement. These findings support a risk-adapted, biology-driven approach reserving HDCT-ASCT for selected patients and highlight the need for post-transplant maintenance strategies integrating targeted or immunotherapeutic modalities. Full article

The intelligent fault detection of power plant equipment in industrial settings often grapples with challenges such as insufficient real-time performance and interference from complex backgrounds. To address these issues, this paper proposes an image recognition and classification model based on the VMamba architecture. At the core of our feature extraction module, we have improved and optimized the two-dimensional state space (SS2D) algorithm to replace the traditional convolution operation. Rooted in State-Space Models (SSMs), the SS2D module possesses a global receptive field by design, enabling it to effectively capture long-range dependencies and establish comprehensive contextual relationships between local and global features. Crucially, unlike the self-attention mechanism in Vision Transformers (ViT) that suffers from quadratic computational complexity, VMamba achieves this global modeling with linear complexity, significantly enhancing computational efficiency. Furthermore, we employ an enhanced PAN-FPN multi-scale feature fusion strategy integrated with the Squeeze-and-Excitation (SE) attention mechanism. This combination optimizes the spatial distribution of feature representations through channel-wise attention weighting, facilitating the effective integration of cross-level spatial features and the suppression of background noise. This study thus presents a solution for industrial equipment fault diagnosis that achieves a superior balance between high accuracy and low latency. Full article

The Indonesian coastline holds significant potential for aquaculture but is increasingly vulnerable to climate change impacts such as land subsidence, salinization, and floodings. Ensuring stable income for local communities is essential, especially during extreme events like King Tides, which cause extensive floodings. This study assessed the productivity and economic returns of an agaroid seaweed monoculture compared to co-cultivation with Giant tiger prawn, Milkfish, and Barramundi during a King Tide. The experiment was conducted in conventional ponds with seaweed monoculture or combined with one of the three other commodities. The experiment ran from May until October in 2022 and was performed in triplicate. Floodings equalized water parameters. The results demonstrated that all systems provided stable income, with co-cultivation increasing profitability. Average revenues per hectare were USD 777 (seaweed monoculture), USD 832 (with shrimp), USD 1622 (with Milkfish), and USD 2014 (with Barramundi). Agar content was significantly higher in the seaweed monoculture, and gel strength was found to be significantly higher in the seaweeds co-cultivated with shrimp and Milkfish. Total agar production did not differ between the treatments. These findings suggest that integrated aquaculture systems can enhance income resilience while supporting food security in climate-impacted coastal zones. The approach offers a promising strategy for combining livelihood stability with adaptive coastal management and reduced environmental impact but needs to be tailored to local conditions. Full article