Anaerobic pools in the vegetable pickling industry are typical confined spaces where hydrogen sulfide (H2S) readily accumulates, leading to frequent fatal poisoning. Traditional single-point detection methods fail to capture the three-dimensional stratified distribution of H2S, and existing machine learning prediction models mainly focus on municipal sewage systems, lacking systematic research tailored to the unique structural, operational, and environmental characteristics of anaerobic pools in the pickling industry. To address this critical gap, this study proposes an interpretable multi-feature fusion prediction method that integrates structural, geometric, environmental, sewage state, and detection position features. Systematic on-site detection was conducted on 46 anaerobic pools from 38 enterprises across three major pickling regions in China, resulting in a high-quality multi-feature dataset comprising 99 sample groups covering diverse production scales and operational scenarios. After standardized feature encoding and normalization to eliminate dimensional differences, the prediction performance of seven classical machine learning algorithms was systematically compared. The results indicate that the XGBoost model achieved the best overall performance, with a coefficient of determination (R2) of 0.94. Its mean absolute error was reduced by 32% to 66% compared with the other six models. Feature importance analysis revealed that the pool closure status and sewage agitation state were the two dominant factors affecting H2S accumulation, followed by the internal temperature, wind speed, and humidity. This study makes two key contributions: (1) it establishes a transferable five-dimensional feature representation system for gas distribution prediction in confined spaces, overcoming the limitation of traditional models that rely solely on single-category environmental parameters; (2) it enables accurate three-dimensional prediction of H2S concentrations in unmeasured areas using only a few detection points, providing a low-cost and high-efficiency technical solution for pre-operation risk assessment and daily safety management. The proposed method can effectively support the optimization of the monitoring point layout and the formulation of targeted prevention measures, contributing to the reduction of H2S poisoning accidents in the vegetable pickling industry.
Green Buildings (GB) have been introduced in the construction sector to combat the greenhouse effect. Hence, this study aims to investigate and evaluate the barriers to implementing GB in Malaysia and develop a framework for evaluating those barriers. A review of the literature on systematically selected research papers was conducted to identify obstacles that may impede GB implementation. To ensure that these barriers are relevant to the situation in Malaysia, an online questionnaire survey was conducted among Malaysian construction specialists working in the industry. The results showed that 58.5% of all respondents had previous experience using GB in a project, indicating that most of those surveyed were familiar with its use. High upfront costs and additional expenses associated with GB elements were identified as the most significant barriers to GB execution. Therefore, a conceptual framework was developed for the course of action for resolving the problem.
Occupational noise exposure is a widespread hazard across multiple industries. However, the association between occupational noise exposure and cardiovascular health outcomes remains inconclusive. This study aimed to compare cardiovascular function between workers exposed and unexposed to occupational noise and to compare lipid profiles between these groups. An ex-post-facto (comparative cross-sectional) study was conducted in a denim garment manufacturing facility in Ismailia, Egypt. Two equal groups of workers exposed to occupational noise (≥85 dB[A] for ≥3 years, n=213) and unexposed workers (<85 dB[A], n=213) were recruited using systematic sampling, but three unexposed workers dropped out. Data collection included structured interviews, clinical examinations, 12-lead electrocardiography (ECG), blood pressure measurements, and laboratory lipid profile analyses. Noise exposure was assessed using both environmental and personal integrated sound level meters. Multiple logistic regression analysis was performed to identify potential predictors of ECG abnormalities. The mean ages of the noise-exposed and unexposed groups were 34.5±9.8 and 33.3±7.7 years, respectively, and females accounted for 67.6% and 66.2% of the groups, respectively. Noise-exposed workers had significantly higher pulse pressure, systolic blood pressure, low-density lipoprotein cholesterol (LDL-C), total cholesterol, triglyceride levels, and prevalence of dyslipidemia (59.2% vs. 48.6%, p<0.05). ECG abnormalities were more prevalent in the noise-exposed group than in the unexposed group (30% vs. 8%, p<0.001), with P mitrale and right bundle branch block being the most frequent findings. Duration of noise exposure, personal noise level, age, and systolic blood pressure were independent predictors of ECG abnormalities. Body mass index (BMI) and duration of noise exposure were significant predictors of dyslipidemia. Chronic occupational noise exposure was associated with elevated blood pressure, dyslipidemia, and ECG abnormalities. Longer duration and higher intensity of noise exposure were also associated with increased cardiovascular risk indicators. These findings support workplace noise-control measures, periodic cardiovascular screening, and improved use of personal protective equipment in similar garment manufacturing settings.
Ctenopharyngodon idella (grass carp) is the dominant species in freshwater aquaculture, but its farming industry is severely threatened by grass carp haemorrhagic disease (GCHD) caused by grass carp reovirus (GCRV). Autophagy plays a crucial role in viral infection, and ATG13 is a core factor for autophagy initiation. However, the functional mechanism of grass carp ATG13 (CiATG13) during GCRV-I/II infection remains unclear. In this study, the CiATG13 gene was cloned and characterized by bioinformatics analysis. The results showed that CiATG13 sequence is highly conserved in evolution, sharing the highest homology and closest evolutionary relationship with Chanodichthys erythropterus. The expression and function of CiATG13 were investigated using RT-qPCR, Western blotting, fluorescence microscopy, and CRISPR-Cas13d knockdown techniques at both cellular and individual levels. The key findings are summarized below: tissue distribution analysis revealed that CiATG13 is widely expressed in various tissues of healthy grass carp, with the highest expression in the liver, brain, and heart, and it responds actively to stimulation by pathogen-associated molecular patterns (PAMPs), such as poly (I:C) and lipopolysaccharide (LPS). GCRV-I/II infection induces the expression of CiATG13. Overexpression of CiATG13 significantly promotes GCRV-I replication, whereas knockdown of CiATG13 inhibits GCRV-I replication. Further mechanistic studies indicated that CiATG13 can induce autophagy and upregulate the expression of heat shock protein 70 (CiHSP70) through this pathway. CiHSP70 promotes GCRV-I replication, and quercetin (Qu) can block its pro-viral effect on GCRV-I/II replication by inhibiting CiHSP70. Moreover, treatment with the autophagy inhibitors chloroquine (CQ) and Spautin-1 suppressed GCRV-I replication and the associated cytopathic effect (CPE), accompanied by reduced CiHSP70 expression, overexpression of CiATG13 partially rescued these inhibitory effects.. This study reveals the molecular mechanism that CiATG13 mediates autophagy to regulate CiHSP70 expression and promote GCRV-I/II replication, enriches the understanding of the interaction between fish viruses and autophagic molecules, and provides the potential strategy targeting CiATG13 for the prevention and control of GCHD.
To examine associations between social learning styles, career preferences and academic achievement among pharmacy students. This cross-sectional study included 458 Turkish pharmacy students. Social learning styles were assessed using the Grasha-Riechmann Student Learning Styles Scale, which measures independent, dependent, participant, avoidant, collaborative, and competitive dimensions. Academic achievement and career preferences were analyzed using non-parametric tests with bootstrap confidence intervals for effect sizes. Avoidant learning tendencies showed a medium-strength negative association with academic achievement, while participant style demonstrated a positive small-to-moderate association. Students aspiring to academic careers exhibited higher independent and participant scores alongside markedly lower avoidant tendencies compared with peers pursuing community, hospital and public sector, or industry roles. Gender differences in learning styles were present but small in magnitude. Social-interaction learning styles are meaningfully related to academic performance and career orientation in pharmacy education. Avoidant tendencies may serve as academic risk markers, while participant and independent orientations align with academic career aspirations. These findings suggest value for incorporating learning-style-informed approaches into student support and career guidance.
The current situation in the automotive, aerospace, marine, and rail industry involves addressing component wear and tear. The composite materials are very appropriate for the problem's replacement because they attain great strength, high hardness, strong wear resistance, exceptional corrosion resistance, and high impact toughness. The present investigation of C355 aluminium alloy hybrid composites (C355AHCs) shows they are utilized highly suitably for the various component preparations of the automotive, aerospace, marine, and rail industries. The C355 aluminium alloy addition with graphene (0, 2 and 4 wt.%) and boron carbide (0, 3 and 6 wt.%) hybrid nanocomposites are prepared by utilizing the selective laser melting (SLM) additive process. According to ASTM G99 rules, the pin-on-disc device was utilized to perform the wear experiment test. The optical microscope (OM) and Field Emission Scanning Electron Microscope (FESEM) to examine the characterization and worn surface analysis of the C355AHCs. The wear response was assessed via experiments carried out through pin-on-disc testing tribometer, considering applied load, sliding velocity, sliding distance, and sliding time, which were modelled by means of a Box-Behnken design approach. The novel aspect of this research work is the combination of SLM-processed C355/Gr/B4C hybrid nanocomposites with ANFIS-based tribological prediction, comparative modeling using RSM, and multi-objective optimization. From the experimental findings, it was found that the wear rate falls between 0.274 g/min and 0.475 g/min, whereby the applied load emerged as the most significant factor affecting the wear behaviour. The significance of the fitted regression equation via RSM and ANOVA analysis showed a level of significance (P-value < 0.05), albeit with moderate prediction (R2 = 0.576). As a means of overcoming this problem, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed, and the results were quite accurate and smooth. ANFIS showed better accuracy and smoothness than regression modelling in terms of prediction. Wear studies of worn surfaces showed that there was a shift from adhesive and abrasive wear in the base alloy to mild abrasive wear in the hybrid nanocomposites owing to the synergy of the graphene-lubricating effect and the B4Creinforcement effect. Multi-criteria optimization of wear, frictional force, and coefficient of friction through desirability analysis resulted in optimum conditions. This paper concludes that ANFIS is highly dependable in modelling tribological properties and that the produced SLM C355/Gr/B4C hybrid nanocomposites have promising application in automobile brakes and other engineering fields.
Porcine epidemic diarrhoea virus (PEDV) is a highly contagious enteric coronavirus causing acute watery diarrhoea and high mortality in neonatal piglets, threatening the global swine industry. In recent years, GIIc subtype PEDV has spread rapidly across China via natural recombination and antigenic drift, undermining conventional vaccine efficacy. Here, we isolated and characterized a novel GIIc PEDV strain CHjx2025 from diarrheic piglets in Ji'an City, Jiangxi Province, China. Full-length genome sequencing and recombination analysis identified CHjx2025 as a natural intra-lineage recombinant of two GIIc strains (CH-JXJA-2017 as major parent and CH-SWM-RN-2025 as minor parent), with a recombination breakpoint at nucleotide 11,201 of ORF1a. Comparative analysis revealed 49 unique amino acid substitutions in the spike (S) protein core region relative to the classic vaccine strain CV777, including 10 in the core receptor-binding domain (COE) and 1 in the 2C10 neutralizing epitope. Structural modeling confirmed CHjx2025 retains a canonical homotrimeric type I fusion protein structure but exhibits distinct NTD and D0 domains versus CV777. In vitro, CHjx2025 showed strong replicative capacity, forming larger plaques and reaching 106.5TCID50/mL in Vero cells at 24 hpi. Notably, in vivo challenge induced vomiting and anorexia in neonatal piglets as early as 12 hpi, with 100% mortality within 60 h, severe intestinal villous atrophy, and unprecedented multinucleated syncytia in intestinal epithelial cells. These findings highlight the evolving diversity and enhanced pathogenicity of GIIc PEDV via intra-subtype recombination and epitope mutations, underscoring the need for continuous surveillance and GIIc-specific vaccine development to control PED outbreaks.
Randomized controlled trials (RCTs) are the gold standard for clinical care. Acute conditions such as traumatic brain injury, intracerebral hemorrhage, aneurysmal subarachnoid hemorrhage, and spinal cord injury are associated with high morbidity and mortality, yet guidelines are based on limited Class I evidence. This study aimed to examine the design, funding, outcomes, and reporting quality of phase III RCTs in neurosurgical critical care published since 1990. MEDLINE and Cochrane Central were searched for phase III RCTs published since January 1, 1990, with at least one arm in the U.S. and published in selected high-impact journals. Eligible studies included adult patients with common critical care neurosurgical pathologies evaluating interventions with clinically relevant outcomes. Two reviewers independently screened and extracted data with adjudication by a third. Analyses included χ² tests, ANOVA or Kruskal-Wallis tests, and linear regression. Among 586 records screened, 27 phase III RCTs (28,291 patients) met inclusion criteria. Most evaluated medical therapies; only three (11%) assessing surgical interventions. Six trials (22%) demonstrated significant benefit. The modified Rankin Scale or Glasgow Coma Scale were primary endpoints in 74% of studies. Quality-of-life measures were included in 37% but never showed significant improvement. Industry funding (33%) was not associated with positive outcomes (P = .62). Modern CONSORT fulfillment was observed in 22%, improving over time (P = .02). Phase III RCTs in neurosurgical critical care are limited, highlighting the need for greater emphasis on patient-centered outcomes and transparency in future research.Clinical trial number: Not applicable.
Routinely collected health data are increasingly used to generate real-world evidence for therapeutic decision-making. Their use, however, depends on the expectations of multiple stakeholders. Clinicians require clinically interpretable analyses, pharmaceutical stakeholders need robust evidence on effectiveness and safety, patient advocacy groups emphasize transparency, privacy, and meaningful outcome measures, and statisticians focus on bias control, reproducibility, and methodological rigor. Without explicit consideration of these perspectives, analyses risk being fragmented, misaligned with end-user needs, or lacking transparency. Aligning these perspectives early in the design of routine data analyses therefore remains a central challenge. We developed a stakeholder-inclusive conceptual framework for modeling routine health data, through expert panel discussions, an interdisciplinary workshop and targeted literature examples. The synthesis focused on four stakeholder perspectives: clinicians, pharmaceutical industry, patient advocates, and statisticians. To illustrate how stakeholder priorities can be translated into analytical strategies, we reviewed selected applications of multistate models (MSMs) in routine health data settings. The conceptual framework links stakeholder-specific priorities, methodological requirements and identifies shared needs for analyses that are clinically meaningful, transparent, reproducible, and able to represent patient pathways, intermediate events, treatment trajectories, disease progression, safety outcomes, and patient-reported measures. While the framework is intended to be applicable across various analytical approaches MSMs are used here to illustrate how these diverse requirements can be operationalized in practice. They can capture longitudinal health processes, competing events, recurrent or intermediate states, and state-specific outcomes while retaining an interpretable graphical structure, and the reviewed examples show their applicability across different research questions using routine health data. Beyond specific methodological choices, clinical research relies fundamentally on statistical expertise. The framework also highlights that the statistician's role varies with the complexity of the research question, ranging from consultation on standard analyses to adaptation or development of advanced methods. The stakeholder-inclusive framework provides methodological guidance for designing analyses of routine health data that are clinically meaningful, scientifically rigorous, and socially acceptable. By aligning the research question with the intended perspective from the beginning, it supports more robust and transparent evidence generation, with multistate models serving as a flexible tool to operationalize this integration.
To estimate recombinant zoster vaccine (RZV) use among adults with cancer since the 2021 US Advisory Committee on Immunization Practices (ACIP) recommendation to vaccinate immunocompromised adults aged ≥ 19 years and gather insights into oncologist practices, perceptions, barriers, and facilitators to RZV vaccination among adults with cancer. In this retrospective cohort study, RZV uptake (≥ 1 dose) and completion (2 doses) were assessed among RZV-naïve, immunocompromised patients aged ≥ 19 years with a solid tumor cancer or hematologic malignancy using IQVIA's open medical and prescription claims. Patients were followed from the ACIP voting date (October 20, 2021) until June 30, 2023. RZV uptake, series completion, time to completion, and dosing schedule compliance were described. Generalized estimation equation models assessed factors associated with uptake. Fifteen oncologists were interviewed regarding their RZV practices, perceptions, barriers, and facilitators, and themes were generated based on responses. Among 388,923 and 277,314 RZV-naïve patients with a solid tumor cancer or hematologic malignancy, cumulative RZV uptake increased gradually, reaching 7.6% and 8.6%, respectively. Series completion rates at 6 and 12 months among those who initiated RZV were 63.2% and 70.9% in the solid tumor cancer cohort and 64.7% and 71.6% in the hematologic malignancy cohort. The odds of RZV uptake varied by cancer type and tended to increase with age and household income. Oncologists reported that they primarily serve an advisory role in vaccination due to workflow and financial challenges and competing patient needs, opting instead to delegate this responsibility to primary care providers and pharmacists. RZV uptake among RZV-naïve immunocompromised patients with cancers has been slow and suboptimal since the 2021 ACIP recommendation, but series completion is likely once initiated.
With the release of partial driving automation systems that allow drivers to take their hands off the steering wheel (L2 hands-off), it has become increasingly important to evaluate their implications for driving safety. This study investigated how different driving modes influence driving safety considering drivers' pre-drive states. Using a static driving simulator, two groups of UK drivers participated in the experiment: 19 non-nightshift workers and 19 nightshift workers. Participants in each group completed drives under three driving modes: manual driving, L2 hands-on, and L2 hands-off. The drowsiness, attention behavior, hazard perception performance, and driving performance of the participants were measured. The results suggested that the L2 hands-off mode had adverse effects on drowsiness and attention behaviors in both groups, with detrimental trends also observed for hazard perception performance and driving performance. Moreover, given the adverse effects of the L2 hands-off mode, drivers with different pre-drive states adopted different response strategies. Non-nightshift workers performed more frequent takeovers in the L2 hands-off mode to avoid collisions, whereas nightshift workers responded similarly across all modes but exhibited the highest collision probability in the L2 hands-off mode. In summary, although L2 driving may reduce manual control demands, taking hands off steering wheel increases safety risks, particularly for nightshift workers. Future efforts should adopt strategies to mitigate the safety risks associated with L2 hands-off systems and assess drivers' pre-drive states before permitting the use of such systems.
The growing demand for Earth observation satellite services requires the development of efficient and fair planning solutions. As users seek to fulfill their observation requirements, the scheduling solution must balance efficiency and fairness across stakeholders. However, concerns over privacy or proprietary interests might discourage stakeholders from sharing their requests with a centralized system, highlighting the need for a distributed approach. This paper addresses the multi-objective optimization problem of distributed scheduling for Earth observation satellites. We propose two novel algorithms designed to achieve a balanced trade-off between the competing objectives of efficiency and fairness. Experimental evaluation reveals that each algorithm presents a unique trade-off between efficiency and fairness based on problem parameters.
Accurate and efficient coal classification is essential for optimizing energy production, improving resource utilization, and ensuring environmental compliance in industrial applications. Traditional methods that rely on visual inspection and laboratory analysis are often slow, subjective, and difficult to scale. In response to these challenges, this research proposes a custom task adaptive lightweight convolutional neural network that integrates Separable Convolutional Blocks, Inverted Residual Blocks, and Squeeze and Excitation mechanisms to achieve high classification performance with low computational overhead. The model is trained and evaluated on the large scale DsCGF dataset, which comprises over 270,000 images of coal, gangue, foreign objects, and unknown materials collected under real world production and non-production conditions from multiple mining regions. The experimental results show that the proposed model achieves classification accuracies of 93.73% on Anhui Guobei production dataset, 99.97% on the Anhui Guobei non-production dataset, 99.26% on the Inner Mongolia Erlintu production dataset, and 90.50% on the challenging Shanxi Wangjialing production dataset, while using only 1.72 million parameters and achieving an average inference time of 0.023 s per image. Compared with well-established transfer learning models such as MobileNetV2, DenseNet201, and Xception, the proposed architecture consistently demonstrates superior or comparable performance across accuracy, precision, recall, and F1 score metrics with significantly reduced computational complexity. To enhance interpretability and provide visual insights into model predictions, explainable artificial intelligence techniques including Grad-CAM, Grad-CAM++, and Score-CAM are employed to visualize class discriminative regions. The proposed framework provides a practical, interpretable, and resource efficient solution for coal classification and contributes meaningfully to the advancement of intelligent mining systems.
Electrical field-assisted thermophilic composting (eTC) is considered a promising technology for enhancing compost maturation, while membrane-covered composting is an effective strategy for reducing harmful gas emissions. However, the combined application of membrane and electric field to simultaneously enhance organic matter humification and mitigate greenhouse gas emissions during composting has rarely been explored. In this study, we constructed membrane and electric field co-assisted thermophilic composting (m-eTC) and confirmed its effect on promoting organic matter humification and greenhouse gas emissions reduction, respectively. The results showed that humic acid content in m-eTC was 1.22- and 1.15-fold higher than that in ordinary thermophilic composting (oTC) and eTC, respectively. Moreover, the global warming potential, expressed as CO2-equivalent emissions, was reduced by 11.9% and 9.8% compared with oTC and eTC. Microbial analyses revealed selective enrichment of humification-related bacteria (e.g., Nocardiopsis and Saccharomonospora) and regulation of key functional genes related to greenhouse gas emissions (e.g., pmoA and norB) in the m-eTC system. Interactive Mantel test and partial least-squares path modeling further demonstrated that m-eTC significantly enhanced the positive effects of composting properties and small-molecule organic acids on organic matter humification. In contrast, m-eTC attenuated the positive effects of composting properties and bacterial activity on greenhouse gas emissions. This study indicated that m-eTC is an effective strategy for simultaneously reducing greenhouse gas emissions and promoting organic matter humification, offering a promising pathway for efficient and sustainable organic solid waste management.
To evaluate the performance of an ARARAT radiomics-based artificial intelligence model using T2-weighted magnetic resonance imaging (MRI) for posthistological stratification of prostate cancer aggressiveness. A retrospective analysis of 112 biopsy-proven prostate cancer lesions (International Society of Urological Pathology [ISUP] <3 vs ≥ 3) from the PROSTATEx dataset was performed. Fixed 5-mm spherical region of interests (ROIs) were placed on axial T2-weighted MRI at radiologist-defined lesion sites, and 107 radiomic features were extracted using PyRadiomics. Machine-learning models were trained with 10-fold cross-validation and patient-level holdout testing. Model performance and feature importance of ARARAT were assessed using standard classification metrics and SHapley Additive exPlanations (SHAP). The cohort comprised 112 lesions, including 77 ISUP < 3 (68.8%) and 35 ISUP ≥ 3 (31.2%). ISUP 1 and 2 accounted for 32.1% and 36.6% of cases, respectively, while ISUP 3, 4, and 5 represented 17.9%, 7.1%, and 6.3%, respectively. Lesions were located in the peripheral zone (44.6%), anterior stroma (40.2%), and transition zone (15.2%), with no association between anatomical zone and ISUP category (P = 0.639). The random forest achieved the highest performance on the holdout set, with an area under the curve (AUC) of 0.84 (95% confidence interval [CI]: 0.63-0.98), average precision (AP) 0.70 (95% CI: 0.35-0.97), sensitivity of 85.7%, specificity of 78.6%, accuracy of 81.0%, and an F1-score of 0.75. The Brier score was 0.18, and calibration improved after isotonic regression, reducing the expected calibration error from 0.168 to 0.099. First-order and grey-level size zone matrix features were the dominant predictors. T2-weighted MRI-based radiomics enables accurate posthistological stratification of prostate cancer aggressiveness, supporting further external validation.
This study aims to optimize the composition of a model oat beverage to enhance the production of minor fatty acids, including odd-chain and cyclic fatty acids. The oat beverage composition was optimized in the Box-Behnken design, varying by the ratio of oat protein hydrolysate (0, 1, 2%), coconut oil (0, 1.5, 3.0%), and added sucrose (0, 2, 4%), while maintaining a constant ratio of oat β-glucan (2%) and oat protein (2%). In the first stage of the study, fermentation was conducted at 22 °C for 48 h using the Lactiplantibacillus plantarum PK 1.1 strain. The four best variants that promoted the biosynthesis of rare fatty acids were tested in a refined experiment. In this stage, the effects of low-temperature stress (15 °C) and osmotic stress induced by the addition of NaCl (up to 5%) were assessed. The prepared formulations were analysed for lactic acid bacteria (LAB) counts, titratable acidity, pH, apparent viscosity, and fatty acid composition. In the first experiment, the highest combined share of odd-chain and cyclic fatty acids (up to 0.353%) was observed in beverages prepared without coconut oil. This low concentration prompted a second experimental stage in which microbial stress was intensified by fermentation at 15 °C and by osmotic stress induced through NaCl addition (0%, 2.5%, or 5%). In this stage, the total share of these fatty acids increased to 1.51%, with cyclic fatty acids predominating. In both experiments LAB counts, titratable acidity, pH, and apparent viscosity were comparable to those of commercially available fermented plant-based beverages. We recommend further research to elucidate the metabolic and technological factors that influence the odd-chain and cyclic fatty acids ratio under suboptimal fermentation temperatures.
Sulfite photolyzed with far-UVC light is increasingly used for the dehalogenation of halogenated contaminants due to its high yields of hydrated electron (eaq-), but hydrogen radicals (•H) were always overlooked in previous studies. This work first revealed the critical roles of •H in rapid reductive degradation and dehalogenation of florfenicol (FLO) under near-neutral conditions. Results demonstrated that sulfite activated by far-UVC process achieved 95.2% degradation of FLO within 15 min and the defluorination and dechlorination efficiency were 92.4% and 100.0% within 30 min, respectively. Increasing pH value from 5.0 to 9.0 improved the FLO degradation and dehalogenation efficiency owing to the increase in quantum yield of sulfite, from 0.077 to 0.178 mol Einstein-1. The second-order rate constants of eaq- and •H with FLO were determined as 1.26 × 109 and 2.82 × 108 M-1s-1, respectively. Kinetic modeling coupled with probe experiments quantified the contributions of •H and eaq- to FLO degradation as 56.4% and 18.2% at pH 7.5, respectively. Furthermore, •H contributed 47.3% and 41.8% to FLO dechlorination and defluorination at pH 7.5, respectively. While Cl- and SO42- had minimal effect on FLO degradation, HA and bicarbonate slightly inhibited FLO degradation by quenching reactive species or/and light shielding. Products analysis revealed that •H promotes defluorination through both direct H/F exchange and indirect facilitation via accelerated dechlorination. This work offers an innovative perspective on the potential of •H for dehalogenation of halogenated pollutants under near-neutral conditions in far-UVC/sulfite process.
With the rapid expansion of civilian unmanned aerial vehicle applications, increasingly complex flight environments impose stricter requirements on flight stability, flying quality, and disturbance rejection. This study proposes a coupled lateral-longitudinal stabilisation framework for fixed-wing unmanned aerial vehicles based on a natural-selection multi-objective particle swarm optimisation strategy. A six-degree-of-freedom nonlinear flight dynamic model is developed under the rigid-body assumption and linearised around a trimmed steady-flight condition to derive lateral and longitudinal state-space models. According to flying quality theory, the Dutch roll mode in the lateral channel and the short-period and phugoid modes in the longitudinal channel are selected as performance indices. A unified closed-loop structure is constructed to address gain coupling between channels, and joint optimisation is performed using a natural-selection-enhanced multi-objective particle swarm optimisation algorithm. Actuator activity constraints and disturbance-response robustness criteria are incorporated to ensure control smoothness and engineering feasibility. MATLAB simulations show that the proposed method increases Dutch roll damping from 0.0766 to 0.4039 and improves the short-period damping ratio from 0.3868 to 0.8851 while satisfying Level-1 flying quality standards. Compared with the standard particle swarm optimisation algorithm, the proposed approach achieves faster convergence and higher constraint satisfaction. The optimised gains are directly applicable to the Pixhawk open-source flight control platform, providing practical guidance for low-cost stabilisation design in fixed-wing unmanned aerial vehicles. The novelty of the present work is fourfold: (i) lateral and longitudinal channels are tuned jointly rather than independently, so that gain-coupling between channels is explicitly resolved; (ii) a natural-selection mechanism is embedded in the MOPSO inner loop, which speeds up convergence by 22% versus standard MOPSO and 45-48% versus NSGA-II/III while raising the constraint-satisfaction rate to 98.5%; (iii) time-domain actuator-workload metrics (∫δ2 and ∫(dδ/dt)2) are integrated alongside frequency-domain modal objectives, making the Pareto solutions directly deployable on bandwidth-limited Pixhawk-class servos; and (iv) the resulting framework is cross-validated on two distinct fixed-wing platforms (a conventional 13.5 kg Aerosonde-class UAV and a 1.56 kg Zagi-class flying wing) covering an order-of-magnitude range in mass and aspect ratio, confirming portability of the design procedure.
Anthropogenic activities increasingly influence trace metal(loid)s (TMs) inputs to inland waters, yet the large-scale drivers of their distribution and ecological risks remain insufficiently understood. Here, we quantified 13 TMs in surface waters from 45 lakes across China to investigate spatial patterns, identify pollution sources, evaluate ecological risks, and assess predictive performance using machine learning models. TMs concentrations showed clear regional clustering, with southern lakes enriched in Ag, Cu, Fe, and Mn, while northern and northeastern lakes exhibited higher levels of As, Cr, and Ni. Positive matrix factorization indicated that Cu and Ni originated from combined agricultural, industrial, and natural sources; Cr was predominantly controlled by natural sources; and As reflected mixed contributions from natural, agricultural, and industrial inputs. Species sensitivity distribution (SSD)-based assessment identified Ag and Cu as the primary ecological risk contributors, with moderate risks occurring in 82.22% and 75.56% of lakes, respectively, and high risks observed in up to 24.44% of lakes. Risk quotient (RQ) analysis further revealed localized ecological risk hotspots for Cu, Fe, and Mn in northeastern China rather than widespread elevated risks. To support large-scale monitoring and prediction, machine learning models were developed. Among the tested algorithms, Random Forest achieved the highest predictive accuracy, highlighting the potential of machine learning approaches for as supportive tools for preliminary screening of lakes that may require enhanced monitoring or further pollution-source investigation. These results provide a large-scale cross-regional perspective on TMs contamination and support risk-based management of lake ecosystems.
While advances in high-throughput sequencing have facilitated the production of genome-wide data to illuminate genetic and phenotypic diversity and to inform management, conservation, and domestication of natural resources, the generation and processing of genome-wide variants obtained via high-throughput sequencing are prone to errors. Data filtering is an effective approach to improving data quality, but optimal filtering strategies have not been thoroughly investigated across species, especially for non-model aquatic invertebrates, such as bivalves, despite their high ecological and economic values. Given that bivalves have complex genomic architecture, exhibit special life history traits, and frequently experience drastic demographic changes, a comprehensive understanding of effects of variant filtering on downstream inferences is particularly needed. Using whole genome sequencing data, we create a collection of filtered data sets representing a full range of filtering for mapping quality, read depth, genotyping quality, missing data, and minor allele frequency to conduct population genetic and demographic analyses for six representative natural stocks of an ecologically and economically important marine bivalve, Zhikong scallop Chlamys farreri. While population structure evaluation is robust to various filtering choices, different filtering strategies apply to assessments of individual inbreeding, genetic diversity, and demographic trajectory. For inbreeding estimation, we suggest adopting higher filtering thresholds for minor allele frequency while keeping filtering criteria for other parameters relatively relaxing. For genetic diversity calculation, we recommend systematically testing both data sets that include and exclude invariants within a sample site and clearly reporting the sets of loci included for evaluation. For demographic history reconstruction, filtering options that maximize the number of reliable variants are more appropriate. Findings from this study not only offer a reference for parameter settings in variant filtering for natural scallop stocks but also demonstrate a practical framework for how to navigate tradeoffs between data quality and quantity for other similar bivalves, such as oyster, clam, and mussel. Overall, our study provides a promising guideline for obtaining reliable genome-wide variants to instruct management, conservation, and domestication programs for non-model aquatic invertebrates.