Search Results (56)

Background/Objectives: Autism spectrum disorder (ASD) exhibits remarkable genetic heterogeneity involving hundreds of risk genes; however, the mechanism by which these genes organize within biological networks to contribute to disease pathogenesis remains incompletely understood. This study aims to elucidate these organizational principles and identify critical network bottlenecks using a novel integrative computational framework. Methods: We analyzed 893 SFARI genes using a three-pronged computational approach: (1) a Machine Learning Dynamic Perturbation Propagation algorithm; (2) a hypergraph construction method explicitly modeling multi-gene complexes by integrating protein–protein interactions, co-expression modules, and curated pathways; and (3) Hypergraph Neural Network embeddings for gene clustering. Validation was performed using hub-independent features to address potential circularity, followed by a druggability assessment to prioritize therapeutic targets. Results: The hypergraph construction captured 3847 multi-way relationships, representing a 45% increase in biological relationships compared to pairwise networks. The perturbation algorithm achieved a 51% higher correlation with TADA genetic evidence than random walk methods. Analysis revealed a hierarchical organization where 179 hub genes exhibited a 3.22-fold increase in degree centrality and a 4.71-fold increase in perturbation scores relative to non-hub genes. Hypergraph Neural Network clustering identified five distinct gene clusters, including a “super-hub” cluster of 10 genes enriched in synaptic signaling (4.2-fold) and chromatin remodeling (3.9-fold). Validation confirmed that 8 of these 10 genes co-cluster even without topological information. Finally, we identified high-priority therapeutic targets, including ARID1A, POLR2A, and CACNB1. Conclusions: These findings establish hierarchical network organization principles in ASD, demonstrating that hub genes maintain substantially elevated perturbation states. The identification of critical network bottlenecks and pharmacologically tractable targets provides a foundation for understanding autism pathogenesis and developing precision medicine approaches. Full article

Visual SLAM systems face significant performance degradation under dynamic lighting conditions, where traditional feature extraction methods suffer from reduced keypoint detection and unstable matching. This paper presents LDFE-SLAM, a novel visual SLAM framework that addresses illumination challenges through a Light-Aware Deep Front-End (LDFE) architecture. Our key insight is that low-light degradation in SLAM is fundamentally a geometric feature distribution problem rather than merely a visibility issue. The proposed system integrates three synergistic components: (1) an illumination-adaptive enhancement module based on EnlightenGAN with geometric consistency loss that restores gradient structures for downstream feature extraction, (2) SuperPoint-based deep feature detection that provides illumination-invariant keypoints, and (3) LightGlue attention-based matching that filters enhancement-induced noise while maintaining geometric consistency. Through systematic evaluation of five method configurations (M1–M5), we demonstrate that enhancement, deep features, and learned matching must be co-designed rather than independently optimized. Experiments on EuRoC and TUM sequences under synthetic illumination degradation show that LDFE-SLAM maintains stable localization accuracy (∼1.2 m ATE) across all brightness levels, while baseline methods degrade significantly (up to 3.7 m). Our method operates normally down to severe lighting conditions (30% ambient brightness and 20–50 lux—equivalent to underground parking or night-time streetlight illumination), representing a 4–6× lower illumination threshold compared to ORB-SLAM3 (200–300 lux minimum). Under severe (25% brightness) conditions, our method achieves a 62% tracking success rate, compared to 12% for ORB-SLAM3, with keypoint detection remaining above the critical 100-point threshold, even under extreme degradation. 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

The conversion of CO₂ into valuable chemicals such as formic acid offers a promising approach to reducing CO₂ emissions. This study presents a techno-economic assessment of two continuous catalytic processes for formic acid production via carbon dioxide (CO₂) hydrogenation. The processes differ in the type of nitrogenous base used, operating under either homogeneous or heterogeneous catalytic conditions. Process simulations and techno-economic evaluations were performed in SuperPro Designer^TM for a medium-scale facility with an annual CO₂ processing capacity of around 14 kMT. The homogeneous catalysis pathway demonstrated superior plant performance, producing 13.03 kMT of formic acid per year at 99.78% purity. In contrast, the heterogeneous pathway required higher capital investment and exhibited lower overall efficiency. The techno-economic analysis confirmed the economic viability of the homogeneous process, with a production cost of $1.18/kg and favorable investment indicators, whereas the heterogeneous route proved economically unattractive under the evaluated assumptions. Sensitivity analysis identified the selling price of formic acid as the most critical profitability parameter, with the homogeneous process remaining robust across varying conditions. Overall, homogeneous catalytic CO₂ hydrogenation demonstrates a technically efficient and economically promising process for the chemical transformation of CO₂, contributing to carbon management. Full article

Recycling multilayer films (MLFs) presents significant challenges to achieving circularity. Mechanical recycling, solvolysis (chemical recycling), and dissolution (physical recycling) have been introduced in the past with their strengths and weaknesses. This study uses a series of advanced, pilot-scale processes to improve the quality of recyclates. These include Near Infrared/Digital Watermarking (NIR/DW), super-critical CO₂ decontamination, dissolution, and innovative mechanical recycling techniques (METEOR and multi-nano layering, MNL). Findings from TRL 5–8 pilots show that recycling different MLF compositions with two routes (dissolution-based and METEOR/MNL-based) can improve the overall quality but this comes with a trade-off. Using 10% recycled content from PET/PE and metalized PP films in 2050 could even increase greenhouse gas (GHG) emissions by 21% and 85%, respectively, compared to landfill incineration. However, PE/PA and PE/EVOH films showed GHG reductions of 0.5% and 4%, respectively. Raising recycled content from 0% to 50% can cut GHG emissions by 36%. These results challenge the current 10% recycled content target, advocating for a more ambitious goal of exceeding 25% by 2050 to enhance sustainability. Full article

Balancing the development of agricultural cold chain logistics with ecological conservation remains a critical challenge for green cold chain logistics in China’s three northeastern provinces. This study evaluates the efficiency of green cold chain logistics to promote synergy between logistics development and ecological sustainability. Using CiteSpace for keyword co-occurrence analysis and literature extraction, an evaluation index system comprising eight input and output indicators was constructed. The super-efficiency Slacks-Based Measure (SBM) model and the Malmquist–Luenberger (ML) productivity index were employed to assess efficiency from static and dynamic perspectives, respectively. Kernel density estimation was used to examine spatial distribution patterns, and the Dagum Gini coefficient was applied to decompose regional disparities. The results indicate that (1) overall efficiency remains relatively low, with ML index changes primarily driven by technological progress; (2) substantial regional differences exist among the three provinces in terms of distribution location, shape, and degree of polarization; and (3) inter-regional disparities are the main source of variation. A Tobit model further identified the key influencing factors, indicating that the level of economic development, growth of the tertiary industry, and informatization are the main drivers. These findings provide valuable insights for optimizing regional green cold chain logistics and promoting sustainable agricultural development. Full article

Cadmium and arsenic co-contamination found in mining actions indicates major effluence in adjacent farmland soils, disturbing the plant physiology and soil’s microbial community. Phosphorus (P) plays a vital role in reducing soil contamination from Cd and As bioavailability and uptake by plants. However, the right P sources for remediation approaches are critical and still require further research in Cd- and As-contaminated soil. This study aimed to explore the effects of different phosphorus fertilizer sources on Lolium perenne growth and its physiological and rhizosphere microbial diversity under combined contamination with Cd and As. Pot experiments were performed with seven treatments including SSP (single super phosphate), DAP (diammonium phosphate), MAP (monoammonium phosphate), CaP (calcium phosphate), HighCaP (high calcium phosphate), RP (rock phosphate), and no phosphorus fertilizer application (CK) with five replications in the RCB design. The SSP treatment showed the greatest plant height (15.7 cm), hay yield (3567.6 kg·ha⁻¹), and enhanced antioxidant defense activities. It also achieved the highest phosphorus accumulation rate (0.63 g·kg⁻¹) with reduced Cd and As uptake. In addition, SSP promoted higher non-protein sulfhydryl (NPT) and phytochelatin synthetase (PCs) contents along with γ-glutamylcysteine synthetase (γ-ECS) activity, and enriched the rhizosphere microbial community, where the Sphingomonas abundance was 7.08% higher than for other treatments. Therefore, this result indicates that SSP can improve the yield and physiology in L. perenne, as well as soil the rhizosphere microbial community structure, while reducing Cd and As accumulation in plants under Cd and As stress. Full article

Carbon dioxide capture, utilization, and storage for enhanced oil recovery (CCUS-EOR) represents an effective strategy for reducing CO₂ emissions while improving oil recovery efficiency. However, harsh environmental conditions during the process can induce a supercritical state in captured CO₂, which may undermine the structural integrity of tubular components through corrosion. This study systematically investigated the corrosion behaviors of two tubing steels (P110 and Super 13Cr) in 20 MPa supercritical CO₂ containing trace H₂S/O₂ impurities at 60–120 °C using weight loss tests and surface analysis. The results demonstrate that in water-unsaturated supercritical CO₂ with ≤500 ppmv H₂S, both steels exhibited low general corrosion rates (P110: 0.03 mm/y; S13Cr: 0.01 mm/y), with incomplete surface films partially covering grinding traces. However, S13Cr suffered pitting corrosion at >500 ppmv H₂S. Oxygen introduction triggered severe general/localized corrosion characterized by cracked, non-protective surface films. Reducing O₂ to 500 ppm yielded thin, continuous protective films, eliminating pitting. Temperature critically influenced S13Cr corrosion: decreasing from 120 °C to 60 °C increased the corrosion rates from 0.0031 mm/y to 0.08 mm/y due to enhanced water precipitation and impurity gas dissolution. These findings establish impurity thresholds to ensure acceptable corrosion performance. Full article

Understanding the carbon emission efficiency of apple production (APCEE) is critical for promoting green and low-carbon agricultural development. However, the spatiotemporal dynamics and driving factors of APCEE in China remain inadequately explored. This study employs life cycle assessment, super-efficiency slacks-based measures, and a panel Tobit model to evaluate the carbon footprint, APCEE, and its determinants in China’s two major production regions from 2003 to 2022. The results reveal that: (1) Producing one ton of apples in China results in 0.842 t CO₂e emissions. Land carbon intensity and total carbon emissions peaked in 2010 (28.69 t CO₂e/ha) and 2014 (6.52 × 10⁷ t CO₂e), respectively, exhibiting inverted U-shaped trends. Carbon emissions from various production areas show significant differences, with higher pressure on carbon emission reduction in the Loess Plateau region, especially in Gansu Province. (2) The APCEE in China exhibits a W-shaped trend (mean: 0.645), with overall low efficiency loss. The Bohai Bay region outperforms the Loess Plateau and national averages. (3) The structure of the apple industry, degree of agricultural mechanization, and green innovation positively influence APCEE, while the structure of apple cultivation, education level, and agricultural subsidies negatively impact it. Notably, green innovation and agricultural subsidies display lagged effects. Moreover, the drivers of APCEE differ significantly between the two major production regions. These findings provide actionable pathways for the green and low-carbon transformation of China’s apple industry, emphasizing the importance of spatially tailored green policies and technology-driven decarbonization strategies. Full article

A number of studies have been carried out to analyze the theoretical performance of super critical carbon dioxide (S-CO₂) systems, but monitoring its actual performance when gross errors appear in measurements can be quite challenging. This paper proposes a robust data reconciliation framework to cope with the gross errors happening in S-CO₂ systems. A schematic S-CO₂ recompression cycle was constructed with different types of measurement sensors, and various estimators with tuned parameters were evaluated to compare their performance. A hybrid strategy with two optimization solvers was designed to ensure the convergence of the solution. Results demonstrated the effectiveness of the proposed robust data reconciliation framework, where the mean relative error (MRE) of all measurements can be reduced from 1.02% to 0.39%, and the MRE of the gross errors can even be reduced from 4.79% down to only 1.11%. Statistics indicated that the Welsch estimator offered the best overall performance, while the Cauchy estimator proved to be more stable. The methods and conclusions provided in this paper can inspire subsequent research on data processing and the operation optimization of real S-CO₂ systems. Full article

This study investigates the application of large language models (LLMs) in sentiment analysis of financial news and their use in developing effective investment strategies. We conducted sentiment analysis on news articles related to the top 30 companies listed on Nasdaq using both discriminative models such as BERT and FinBERT, and generative models including Llama 3.1, Mistral, and Gemma 2. To enhance the robustness of the analysis, advanced prompting techniques—such as Chain of Thought (CoT), Super In-Context Learning (SuperICL), and Bootstrapping—were applied to generative LLMs. The results demonstrate that long strategies generally yield superior portfolio performance compared to short and long–short strategies. Notably, generative LLMs outperformed discriminative models in this context. We also found that the application of SuperICL to generative LLMs led to significant performance improvements, with further enhancements noted when both SuperICL and Bootstrapping were applied together. These findings highlight the profitability and stability of the proposed approach. Additionally, this study examines the explainability of LLMs by identifying critical data considerations and potential risks associated with their use. The research highlights the potential of integrating LLMs into financial strategy development to provide a data-driven foundation for informed decision-making in financial markets. Full article

Chronic hepatitis B (HBV), alongside hepatitis D virus (HDV) super-/co-infection and chronic hepatitis C (HCV), are major contributors to cirrhosis, end-stage liver disease, hepatocellular carcinoma (HCC), and liver-related mortality. Despite significant progress in antiviral treatments and HBV vaccination, viral hepatitis remains a global health burden. Vulnerable populations, such as those experiencing homelessness, migrants, and economically disadvantaged groups, are disproportionately impacted by these infections, often facing barriers to care and exclusion from traditional health systems. This leads to undiagnosed cases and ongoing transmission, undermining global efforts to eliminate HBV and HCV by 2030, as outlined by the World Health Organization (WHO). Recent studies highlight the importance of tailored interventions to address health inequalities. For instance, on-site community-based screening initiatives targeting marginalized groups have shown promise, achieving higher linkage to care rates without monetary incentives. These approaches not only enhance diagnosis but also facilitate integration into healthcare systems, addressing both public health and social disparities. This review underscores the need for targeted strategies to promote the early detection and management of HBV and HCV in underserved populations. Such efforts are critical to advancing the WHO’s elimination goals, improving health outcomes, and addressing the broader social determinants of health. Full article

Water is critical for the sustenance of life and pH is an important parameter in monitoring its quality. Solid-state pH sensors provide a worthy alternative to glass-based electrodes due to many advantages such as low cost, longer shelf life, simpler manufacturing, easier operation, miniaturization, and integration into electronic systems. Cobalt oxides are relatively cheaper and more abundantly available than ruthenium oxide. This work aims to reduce the environmental impact of screen-printed pH sensors by mixing Co₃O₄ and RuO₂ in five molar proportions (30%, 40%, 50%, 60%, and 70%) and investigating the influence of oxide proportions on the pH-sensing properties of the resulting composition using potentiometric characterization, scanning electron microscopy, X-ray diffraction, surface profilometry, and electron dispersive spectroscopy. Although all the developed compositions showed super- or near-Nernstian sensitivity with good linearity, the sensors based on 50 mol% Co₃O₄-50 mol% RuO₂ were the best due to superior sensitivity, selectivity, and stability. Fabricated sensors were applied in real-life environmental, municipal, and commercial water samples, including those from various depths in the Baltic Sea, and were found to be accurate in comparison to a glass electrode. Full article

Double perovskite materials have emerged as key players in the realm of advanced materials due to their unique structural and functional properties. This research mainly focuses on the synthesis and comprehensive characterization of Ba₂CoWO₆ double perovskite nanopowders utilizing a high-temperature conventional solid-state reaction technique. The successful formation of Ba₂CoWO₆ powders was confirmed through detailed analysis employing advanced characterization techniques. Rietveld refinement of X-ray diffraction (XRD) and Raman data established that Ba₂CoWO₆ crystallizes in a cubic crystal structure with the space group Fm-3m, indicative of a highly ordered perovskite lattice. The typical crystallite size, approximately 65 nm, highlights the nanocrystalline nature of the material. Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) discovered a distinctive morphology characterized by spherical shaped particles, suggesting a complex particle formation process influenced by synthesis conditions. To probe the electronic structure, X-ray Photoelectron Spectroscopy (XPS) identified cobalt and tungsten valence states, critical for understanding dielectric properties associated with localized charge carriers. The semiconducting character of the synthesized Ba₂CoWO₆ nanocrystalline material was confirmed through UV-Visible analysis, which revealed an energy bandgap value of 3.3 eV, which aligns well with the theoretical predictions, indicating the accuracy and reliability of the experimental results. The photoluminescence spectrum exhibited two distinct emissions in the blue-green region. These emissions were attributed to the transitions ³P₀→³H₄, ³P₀→³H₅, and ³P₀→³H₆, primarily resulting from the contributions of Ba²⁺ ions. The dielectric characteristics of the compound were analyzed across a different range of frequencies, spanning from 1 kHz to 1 MHz. Magnetic characterization using Vibrating Sample Magnetometry (VSM) revealed antiferromagnetic behavior of Ba₂CoWO₆ ceramics at room temperature, attributed to super-exchange interactions between Co³⁺ and W⁵⁺ ions mediated by oxygen ions in the perovskite lattice. Additionally, first-principles calculations based on the Generalized Gradient Approximation (GGA+U) with a modified Becke–Johnson (mBJ) potential were employed to gain a deeper understanding of the structural and electronic properties of the materials. This approach involved systematically varying the Hubbard U parameter to optimize the description of electron correlation effects. These results deliver an extensive understanding of the structural, optical, morphological, electronic, and magnetic properties of Ba₂CoWO₆ ceramics, underscoring their potential for electronic and magnetic device applications. Full article

In this paper, we investigate traffic bottlenecks, a primary cause of congestion that significantly impacts the overall efficiency of traffic networks. To address this challenge, a multi-segment variable speed limit control strategy is proposed to mitigate moving bottlenecks, particularly those on super-long-span bridges. First, an extended macro-traffic flow model, built upon the classic MetaNet framework, is proposed as a state-space model to capture the critical characteristics of long segments, which is a key contribution of this paper. Next, a fast prediction model is developed to forecast traffic flow states in lane-drop bottlenecks with restricted passing capacity over long road segments. Then, a controller leveraging the state-compensation flow model is designed to regulate the future evolution of bottleneck density. Finally, the multi-segment variable speed predictive control (MVSPC) strategy is validated on a simulation platform integrating PYTHON and SUMO, and its performance is compared with both traditional and advanced methods. The results demonstrate that under varying traffic flow levels, particularly in high-demand scenarios, the strategy achieves significant improvements in efficiency, safety, and environmental metrics. These include a 62.44% reduction in waiting time, a 95.32% decrease in potential collisions, and reductions in emissions: 26.4% in CO₂, 14.11% in CO, 26.53% in NO, and 32.90% in NOX. The proposed strategy is particularly effective for long segments, such as super-long-span bridges. Full article