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Flashover prediction of polluted composite insulators based on arc time constant and velocity using CNN–LSTM
Pioglitazone protects against trimethyltin hippocampal injury by reducing pyroptosis, mitochondrial dysregulation and ER stress
A metaheuristic automated framework for quality improvement of CT imagery
Abstract Contrast-limited adaptive histogram equalization (CLAHE) is a technique often used for enhancing the quality of computed tomography (CT) images. The quality of enhanced CT slices produced by the CLAHE depends on the selection of the clip-limit (CL). The CLAHE driven with inadequate value of CL selected via trial and error may amplify noise, mask subtle structures, and hinder the overall perceptual quality of the processed CT slices. As a solution to this problem, we introduce a metaheuristic framework to facilitate automated CL selection in CLAHE, especially for CT contrast enhancement. The framework uses the whale algorithm as optimizer and a perception-based image quality evaluator (PIQE) as fitness. On 315 CT slices, the WOA-PIQE-CLAHE framework produced outputs that have lower PIQE (29.8063 ± 1.1433) and higher contrast (71.5385 ± 1.0408) compared to the low-contrast slices (PIQE = 32.0184 ± 0.9894 & Contrast = 69.3130 ± 1.0465). Increase in contrast and decrease in PIQE manifest that the framework improves tissue contrast without hindering the perceptual quality of the CT slices.
Wideband monopole MIMO antenna with pattern diversity and wide beamwidth coverage in elevation plane for C-band applications
Explainable AI for public health surveillance: investigating the persistent crisis of intentional injury mortality (suicide and homicide) in the Americas
Abstract Intentional injury mortality (IIM), comprising homicide and suicide, remains a critical public health crisis in the Americas, which not only has the highest regional homicide rates globally but is also the only region where suicide rates continue to rise. This study employs explainable artificial intelligence (XAI) to examine the structural and temporal drivers of IIM across 25 countries, based on data from the previous two decades. Two complementary models were developed: a snapshot model based on contemporaneous socioeconomic indicators and a persistence-aware model incorporating lagged effects of predictors. Analyses were conducted across both income-level categories and geographic sub-regions to uncover context-specific patterns. While both models performed at acceptable levels in distinguishing immediate and enduring effects, persistence-aware models consistently outperformed snapshot models, thereby reframing IIM as a temporally sustained phenomenon. Feature importance, interpreted through SHapley Additive exPlanations (SHAP), highlighted the varying impacts of unemployment, inflation, corruption, and economic growth across income tiers and sub-regions. The results demonstrate that a combination of short-term shocks and the long-standing effects of governance and social factors drives IIM in the Americas. These findings underscore the need for dual-horizon policy approaches that address both immediate crises and structural root causes.
Attenuation of LPS-induced inflammatory responses in J774A.1 macrophages by phenylpropanoids and ursane triterpenes from Lavandula coronopifolia Poir.
Abstract Phytochemical investigation of the non-volatile constituents of Lavandula coronopifolia Poir has led to the isolation of seven compounds ( 1 – 7 ). 2α, 3 β , 23-Trihydroxyurs-12,18-dien-28-oic acid 28- O-β - d -glucopyranoside ( 5 ) exhibited selective cytotoxic activity against A549 lung carcinoma, with EC 50 value of 11.5 µM, and showed no toxicity towards the normal HEK293T cells. The anti-inflammatory potential of 1 – 7 was assessed in lipopolysaccharide (LPS)-stimulated J774A.1 cells. Methyl rosmarinate ( 2 ), 1 β , 2 α , 3 β , 19 α , 23-pentahydroxy-urs-12-en-28-oic acid-28- O - β - d -glucopyranoside ( 3 ) and 2 α , 3 β , 23-trihydroxyurs-12, 19-dien-28-oic acid 28- O - β - d -glucopyranoside ( 6 ) effectively reduced cell migration as revealed by the scratch wound assay. They also altered cell morphology in a manner similar to dexamethasone. Furthermore, qPCR revealed that 2 , 3 and 6 significantly downregulated the expression levels of nitric oxide synthase (iNOS) and interleukin-6 (IL-6) as compared to LPS-stimulated J774A.1 cells. The results highlight the potential of 2 , 3 and 6 for anti-inflammatory therapies and 5 as a candidate for lung cancer.
Predicting the future of urban ecological resilience in China’s Yellow River Basin: a machine learning approach
Abstract Urban Ecological Resilience ( UER ) is essential for sustainable development, especially within ecologically sensitive regions such as China’s Yellow River Basin ( YRB ). Existing assessments of UER often encounter difficulties attributable to extensive regional boundaries and retrospective methodologies, thereby limiting their applicability in policymaking. To address these limitations, this study presents an innovative framework. Initially, 51 cities were classified into seven functional clusters based on ecological and industrial similarities. Subsequently, the UER for each cluster was quantified from 2010 to 2024 utilizing the Entropy Weight Method. Projections for UER from 2025 to 2027 were generated employing an XGBoost (eXtreme Gradient Boosting) model that integrates temporal features derived from historical data. The findings indicate a concerning decline in UER within the traditional heavy industry cluster, alongside fluctuating decreases in the Loess Plateau agriculture and conventional agriculture clusters. Model interpretations identify vulnerable cities and low-performing indicators, such as per capita water resources, environmental protection budgets, and industrial pollution, which are strongly correlated with these predicted declines. Conditional simulation demonstrates that targeted interventions aimed at these indicators have the potential to mitigate adverse trends. This comprehensive approach provides a quantitative, proactive tool for formulating specific strategies to enhance UER across diverse regions.
EpCAM-targeted ZIF-8 nanocarriers for enhanced doxorubicin delivery in cancer therapy: a promising approach for tumor-specific drug release
Biologically inspired optimization of construction sector eco industrial park networks using food web metrics
Abstract Industrial symbiosis (IS) and eco-industrial parks (EIPs) are increasingly promoted as practical pathways to circular economy transitions in resource-intensive sectors such as construction, where diverse waste streams, quality variability, and fragmented supply chains often constrain the number and stability of feasible exchanges. Building on biomimetic design principles, this study investigates whether construction-sector EIP networks can be optimized to better resemble selected structural patterns observed in biological food webs, and how optimization choices and participation rules influence the resulting network topology. Using a construction symbiosis database and five theoretical exchange scenarios, scenario-constrained optimization models are formulated to seek proximity to detritus-inclusive biological food-web reference values. Four objective function types (OFTs), representing alternative ways of aggregating multi-metric deviation from benchmarks, were tested in two parallel model families: one excluding connectance from the objective set and one explicitly targeting connectance to assess its conditioning role. A genetic algorithm was used to optimize the scenario-constrained network models and efficiently explore the large combinatorial solution space. Results show that structural proximity to the selected food-web benchmarks is configuration-dependent. Scenario rules and OFT choice systematically steer solutions toward distinct network morphologies, producing clear trade-offs across metrics rather than uniform improvement. Across best-performing configurations, the ratio of waste-providing to waste-receiving firms was comparatively close to benchmark levels in some cases but showed notable deviations in others, while link density and cyclicity exhibited persistent deficits, indicating that achieving dense, highly cycling structures is challenging under construction-specific feasibility constraints. Explicitly including connectance reduced the tendency of some OFTs to converge to extreme connectivity regimes and yielded more balanced metric profiles, highlighting connectance as a structuring constraint that limits extreme connectivity rather than as evidence of ecological realism. Reciprocity-oriented participation rules, particularly those requiring receiver firms to also provide exchanges, were associated with more benchmark-consistent solutions under certain OFT and connectance-included combinations, rather than uniformly dominating across all cases. For practice, the findings suggest that structurally informed bio-inspired EIP planning may benefit from treating connectance as a controlled design parameter and considering reciprocal participation policies where they are compatible with the selected objective formulation and feasibility constraints. Future research should integrate exchange quantities, cost and quality constraints, and uncertainty dynamics, and should report Pareto-efficient solution sets to support stakeholder selection and implementation.
Clinical features, outcomes, and prognostic factors of lymphocyte-depleted classical Hodgkin lymphoma: a population-based SEER analysis
Identification of serum biomarkers in acute aortic dissection using tissue-informed metabolomics methods
Repetitive transcranial magnetic stimulation is associated with improved functional recovery and time-dependent changes in apoptosis-related execution-phase markers after spinal cord injury
Research on the application of deep learning-driven urban change detection in sustainable development of hilly-area cities in western China
Abstract Urban sustainable development is a critical pathway to overcoming resource constraints in the urbanization processes and achieving synergistic high-quality economic development and ecological security in hilly areas city of western China. This study investigates the application of deep learning-driven urban change detection technology in supporting sustainable urban development, taking Nanchong, Sichuan Province—a typical hilly areas city in western China as the case study area. Three core tasks were conducted. First, region-adapted urban element datasets were constructed, including a building and road semantic segmentation dataset (BR_Data_NC) and a change detection dataset (CD_Data_NC), both tailored to the landscape characteristics of hilly urban areas. These datasets provide targeted and reliable support for training and validating deep learning models suitable for medium-resolution remote sensing imagery in hilly regions. Second, deep learning models were applied to conduct semantic segmentation of buildings and roads based on BR_Data_NC, and further performed urban change detection using CD_Data_NC. The experiments were carried out entirely on the self-constructed datasets to ensure reasonable evaluation under consistent data characteristics. Comparative experiments demonstrated that deep learning-driven change detection effectively addresses challenges in complex hilly urban environments, such as fragmented landscapes, scattered buildings, and spectrally mixed features. Third, leveraging the change detection outcomes, this study analyzed urban expansion patterns in the research area, uncovering the evolutionary characteristics and potential trends in urban spatial morphology. The findings indicate that deep learning technology offers a robust tool for dynamic urban monitoring and informed decision-making in the context of sustainable urban development in hilly areas cities of western China. This approach exhibits clear practical value for optimizing urban spatial structure, improving land-use efficiency, and supporting coordinated urban development.
Flexural behavior and crack development of reinforced geopolymer slabs with longitudinal voids: an experimental study
Abstract Geopolymer concrete, produced from the alkali activation of aluminosilicate-rich by-products such as fly ash, presents a sustainable, low-carbon substitute for standard Portland cement. Despite its acknowledged mechanical and durability advantages, researchers have not thoroughly examined the flexural performance of reinforced GC slabs particularly hollow-core designs. This paper offers a comprehensive experimental and theoretical assessment of reinforced GC solid and hollow-core slabs to fill this gap. Seven slabs were tested under four-point loading tests: two solid specimens (OPC and GC) and five hollow-core GC slabs featuring varying void sizes and shear-span-to-depth ratios (a/d). The investigation concentrated on reaction to cracking, ultimate flexural capacity, stiffness characteristics, and deflection behavior, facilitating a systematic evaluation of both material and geometric effects. The results indicate that the GC solid slab exhibited marginally superior flexural performance compared to the OPC slab, demonstrating advantageous bond properties and material uniformity. In hollow-core slabs, elevated void ratios resulted in significant decreases in cracking load, ultimate capacity, and effective stiffness, whereas alterations in a/d caused pronounced differences in strength and deflection characteristics. Reduced shear spans improved load capacity and stiffness, while extended spans led to a more pliable load–deflection response. Analytical predictions derived from traditional flexural theory closely aligned with experimental outcomes, validating the appropriateness of classical models for GC and voided slab systems. The results show that geopolymer concrete is a structurally sound and environmentally friendly alternative to regular Portland cement (OPC). Its efficacy in both solid and hollow-core configurations facilitate its wider implementation in contemporary low-carbon structural applications.