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.
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.
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 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.
Sage is highlighted for its biological activity since it is composed of a plethora of bioactive compounds which can be used in various applications. The present study herein concerned the isolation of the essential oil derived from Salvia triloba L. crop residues by applying microwave-assisted extraction. The isolated essential oil was examined for its chemical compounds by performing GC-MS analysis and for its antimicrobial activity. The obtained results revealed that the major components of the derived essential oil were thujone which formed the predominant compound accounting for 25.83% followed by camphor, cis-thujone and eucalyptol that were found to be at high levels in essential oil's composition reaching 17.86%, 9.90% and 8.97%, respectively. Additionally, latter essential oil revealed significant antimicrobial activity against E. coli in a ratio of 10 mg/mL, whereas the inhibitory activity for both L. monocytogenes and S. enterica was observed in an amount of 12 mg/mL. Therefore, the presented results lead to a new pathway concerning the exploitation of latter essential oil as a natural antimicrobial agent, which could be adopted by the food industry during multiple steps of the production process, including preservation, packaging, and processing of perishable products such as meat, dairy, and fresh products, as a natural alternative to synthetic preservatives.
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.
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.
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.
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.
Soybean is a short-day crop that exhibits high sensitivity to photoperiod variation. Changes in photoperiod regulate flowering and developmental processes in soybean, ultimately influencing its yield, quality, and geographical adaptability. However, the genetic mechanisms underlying variation in soybean growth periods remain poorly understood. In this study, flowering duration (FD), maturity duration (MD), reproductive duration (RD), and the ratio of reproductive to flowering duration (RD/FD) were investigated in both a natural population and a recombinant inbred line (RIL) population. Linkage analysis and genome-wide association studies (GWAS) were employed to identify genetic loci associated with growth period-related traits. A total of 51 quantitative trait loci (QTLs) controlling growth period traits were detected in the RIL population, among which 12 QTLs were consistently identified across multiple environments, demonstrating environmental stability and pleiotropic effects on multiple growth period traits. Concurrently, 119 significant quantitative trait nucleotides (QTNs) associated with growth period traits were identified in the natural population, including six environmentally stable QTNs and four pleiotropic QTNs that influenced multiple traits. Through integrated linkage and association analyses, two major loci were mapped on chromosomes 4 and 19. Additionally, three candidate genes were identified on chromosome 4, among which Glyma.04G125500 encodes a histone-lysine N-methyltransferase protein. Haplotype analysis further confirmed that allelic variation in this gene was significantly associated with variation in growth period traits. Furthermore, kompetitive allele-specific PCR (KASP) marker validation demonstrated that Tof11 significantly modulated growth period variation under both e1e1/e2e2 and E1E1/E2E2 genetic backgrounds. Collectively, these findings provide a theoretical foundation for elucidating the genetic and molecular mechanisms underlying the regulation of soybean growth period traits.
Fine particulate matter (PM2.5; aerodynamic diameter ≤2.5 µm) is a leading contributor to the global burden of disease and is associated with substantial morbidity and mortality. However, its health effects are often evaluated within single-exposure frameworks, limiting causal inference and reducing the translational relevance of evidence for policy development. In this study, we reconceptualise PM2.5 within the exposome framework by integrating external exposures, internal biological responses, and structural determinants of health across the life course. PM2.5 comprises a complex and dynamic mixture of pollutants from ambient and household sources, including biomass combustion, traffic emissions, and industrial activities. After inhalation, PM2.5 particles can penetrate the respiratory tract, and finer fractions may enter the systemic circulation, where they induce oxidative stress, inflammation, epigenetic dysregulation, and endocrine disruption. These mechanisms contribute to a broad spectrum of adverse health outcomes, including cardiovascular, respiratory, metabolic, and neurodegenerative diseases as well as cancers. The multi-level and temporally accumulated nature of these effects highlights the limitations of reductionist exposure assessment and underscores the need for integrative, systems-based approaches. Integrating cumulative impact assessment with exposome-wide approaches that leverage satellite, clinical, land-use, urban planning, and socioeconomic data can improve the characterisation of context-specific cumulative health effects. Positioning PM2.5 as a sentinel indicator of cumulative environmental risk provides a unifying framework for exposome-informed science and supports the development of more precise, equitable, and policy-relevant interventions.
Bisphenol A (BPA) is a well-known endocrine-disrupting chemical, yet its potential migration into traditional spice matrices during processing remains a critical and overlooked food safety concern. To address this knowledge gap, this study presents the first comprehensive investigation of BPA concentrations in isot spice and assesses the associated dietary health risks. A total of 46 samples (23 homemade and 23 industrial) from Şanlıurfa, Turkey, were analyzed using a validated ELISA kit, along with pH and CIELAB color parameters. Estimated Daily Intake (EDI) and Target Hazard Quotient (THQ) were calculated for adult and pediatric populations using Monte Carlo simulations. BPA concentrations ranged from 0.40 to 361.30 μg/kg (mean: 73.88 ± 19.15 μg/kg) in homemade samples and from 0.49 to 285.39 μg/kg (mean: 67.15 ± 14.87 μg/kg) in industrial samples, with no significant differences between groups (p > 0.05). While dietary risks were within safe limits according to US EPA doses, assessment based on stricter European Food Safety Authority (EFSA) thresholds revealed that THQ values significantly exceeded 1, particularly for children, indicating potential chronic health risks. These findings highlight the prevalence of BPA in traditional spices and underscore the need for improved production and packaging practices to reduce consumer exposure.
An adaptive fractional-order model predictive control (FO-MPC) framework of DC-DC boost converters, which incorporates the Exponential Recursive Least Squares (ERLS) identification, the use of the fractional-order dynamics, and the application of the Grey Wolf Optimization (GWO) is presented in this paper. An important discovery is that the combination creates synergistic effects: ERLS convergence is improved by 47% compared to standalone implementations, since the damping of adaptation transients is of fractional-order-damping-like-density, which previous combined methods (such as Wang et al. (2020)) or (Ghamari et al. (2025)) did not provide. The ERLS algorithm allows adaptation model free and convergence in 15 samples even without the use of exact mathematical models. An optimized α = 0.85, fractional-order operator in the noise rejection case and better stability margins is observed, which is 15dB more than the traditional MPC implementations. GWO, executed offline during commissioning, achieves 25 to 30 times faster convergence than conventional metaheuristics (GA, PSO) when tuning the controller parameters. Arduino DUE (84 MHz ARM Cortex-M3) hardware validation has shown that settling time is significantly decreased to 0.42s (83% lower than the baseline), that overshoot is kept to less than 1% (95% lower than the baseline), and that steady-state error is only 20mV (87% smaller than the baseline). The controller is stable in the 30% variations in parameter and 10 times changes in load with an execution time of 85µs, which is compatible with 10 kHz control frequency. Monte Carlo simulations (n = 1000) confirm a success rate of 98.2% in combined disturbances, and statistical significance is validated using the Wilcoxon signed-rank tests (p < 0.001, Cohen's d > 2.0). The industrial use has been tested and supported with 168 h continuous operation and IEC 61000-4-3 EMI compliance test.
In this study, we report an efficient three-component domino reaction of isatin, pipecolic acid, and alkyl 2-(1-methyl-2-oxoindolin-3-ylidene) acetate that provided a simple and convenient route to synthesis of novel methyl-2,2″-dioxo-1',5',6',7',8',8a'-hexahydrodispiro[indoline-3,2'-indolizine-3',3″-indoline]-1'-carboxylate derivatives. This regio- and diastereoselective transformation presumably proceeds through a domino sequence involving azomethine ylide formation followed by a [3 + 2] cycloaddition reaction, leading to the construction of the bisspirooxindole scaffold in a one-pot operation. Preliminary biological evaluation revealed that the synthesized compounds exhibited significant cytotoxic activity against MCF-7 breast cancer cells. The docking study suggested that compound 3d can act as an estrogen receptor alpha (ER-α) modulator, and can be considered a promising lead compound.
The intrinsically photosensitive retinal ganglion cells (ipRGCs) are the third class of photoreceptors apart from rods and cones, containing the photopigment melanopsin and are characterized into different subtypes (M1-M6) that support conscious visual perception. They transmit the light information to the brain through complex circuits serving both non-image-forming and image-forming functions. Of the total population, there are four main ipRGCs, such as M2, M4, M5 and M6 that project substantially to the image-forming visual pathway and target the dorsal lateral geniculate nucleus (dLGN). Recent research has shown that they can modulate the excitatory and inhibitory balance in the primary visual cortex (V1) and thereby boost cortical computations for orientation discrimination. However, the projections of these ipRGCs to the V1 and how they integrate melanopsin signals with the conventional retinal ganglion cells is not yet clear. In this review, we summarize the morphological, physiological and behavioural characteristics of the ipRGC population that project to dLGN and contribute to image processing. Further research to understand how they can potentially enhance sensory representations in the visual cortex would offer therapeutic advantage in several eye diseases causing blindness.
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.
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.
Among high-bleeding risk (HBR) patients undergoing coronary stenting, abbreviated dual antiplatelet therapy (DAPT) reduces bleeding without ischemic risk trade-off; whether these benefits persist across the entire spectrum of bleeding risk has not been investigated. The aim of this study is to explore the value of the novel PRECISE-HBR score as a risk stratification tool to guide DAPT duration in patients at high bleeding risk. The XIENCE Short DAPT program combined 3 international single-arm studies of HBR patients treated with cobalt-chromium everolimus-eluting stents who discontinued DAPT at 1 month (XIENCE 28 USA/Global) or 3 months (XIENCE 90), if event free and treatment adherent. Bleeding risk was classified as nonhigh (PRECISE-HBR score ≤22), high (score 23-26), or very high (score ≥27). Clinical outcomes were assessed between 1 and 12 months using propensity score stratification. Among 3,364 patients, the PRECISE-HBR score was ≤22, 23-26, and ≥27 in 359 (10.7%), 744 (22.1%), and 2,261 (67.2%), respectively. Rates of BARC (Bleeding Academic Research Consortium) type 3-5 bleeding (0.3%, 2.5%, 5.6%) and death or myocardial infarction (2.9%, 4.6%, 9.8%) increased progressively across risk categories. One- versus 3-month DAPT was associated with a significant reduction in BARC type 3-5 bleeding in patients with a score ≥27 (HR: 0.59, 95% CI: 0.39-0.88) but not in those <27 (HR: 2.31, 95% CI: 0.89-5.99; P-interaction = 0.012). Ischemic risk was similar between 1- and 3-month DAPT, irrespective of the PRECISE-HBR score (P-interaction = 0.40). The PRECISE-HBR score identified patients at increased risk for both bleeding and ischemic events who seemed to derive greater benefit from 1-month DAPT after stent implantation.