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Ice flood disaster risk assessment of the Inner Mongolia reach in the Yellow River based on unascertained measure theory-variable fuzzy sets
Ice flood disasters represent the most severe natural hazards affecting the Yellow River during winter-spring periods, posing significant threats to residents’ safety and impeding regional socioeconomic development in the basin. Despite their critical implications, systematic risk assessment methodologies tailored for Yellow River ice floods remain underdeveloped. Current approaches inadequately address the inherent uncertainty and fuzziness characteristics of ice flood disaster systems, thereby compromising assessment accuracy. To bridge this gap, this study develops an integrated risk assessment model combining Unascertained Measure Theory (UMT) and Variable Fuzzy Set Theory (VFST). The hybrid model was implemented in three typical reaches of the Yellow River’s Inner Mongolia section: Tokto County, Jungar Banner, and Qingshuihe County. Risk evaluation results classified Tokto County and Qingshuihe County as “medium-risk” areas, while Jungar Banner was identified as a “high-risk” zone. Validation analyses confirmed strong consistency between model outputs and historical disaster patterns, demonstrating the framework’s reliability. This research establishes a novel methodological foundation for ice flood risk quantification and provides actionable insights for disaster mitigation strategies in cold-region river systems. The proposed approach offers technical support for enhancing flood management decision-making processes in the Yellow River Basin.
Reinforcement learning from human feedback improves automatic speech recognition models for ancient Thai language
Equity, diversity, and inclusion in post-secondary student mental health and well-being research: A scoping review protocol
Objective The objective of this scoping review is to systematically identify and map the extent, range, and nature of research that explicitly articulates Equity, Diversity, and Inclusion (EDI) within post-secondary student (PSS) mental health and well-being research, and to describe how EDI is conceptualized, operationalized, and implemented across study designs and research processes. Introduction Research addressing PSS mental health and well-being has grown substantially; concurrently, EDI has become a prominent institutional and research priority within post-secondary systems. Despite this convergence, no evidence synthesis has systematically examined how EDI is explicitly integrated into PSS mental health and well-being research. Inclusion criteria This review will include research studies of any design that explicitly articulate EDI within PSS mental health and well-being research. Eligible studies must focus on students enrolled in accredited post-secondary institutions and examine mental health or well-being within post-secondary education contexts. Methods The review will be conducted in accordance with JBI methodology for scoping reviews and guided by the Population–Concept–Context framework. A three-step search strategy will be implemented. Electronic database searches will be conducted in MEDLINE, PsycINFO, CINAHL, EMBASE, ERIC, and Scopus or Web of Science, supplemented by targeted searches of eligible grey literature and reference list screening. Records will undergo title and abstract screening by two independent reviewers, followed by full-text screening. Data will be extracted using a structured, pilot-tested tool designed to capture how EDI is defined, framed, and integrated across stages of the research process. Extracted data will be synthesized using descriptive summary statistics, basic qualitative content analysis, and tabular and graphical mapping to address the review questions. The protocol was developed through sustained engagement with PSS with lived experience of mental health and well-being, including students from equity-deserving groups.
Thoughtless use of generative artificial intelligence and college students’ self-directed learning: a multi-group SEM analysis of gender differences
School absenteeism among children and adolescents aged 6–19 years with sickle cell disease in Uganda: A comparative cross-sectional study
Introduction Sickle cell disease (SCD) contributes substantially to school absenteeism due to recurrent pain, infections, and frequent hospital visits that interrupt learning and long-term academic progress. In Uganda, where the SCD burden is high, the extent and drivers of absenteeism are not well documented, limiting the development of supportive school health strategies. This study assessed the prevalence and associated factors of absenteeism among children and adolescents with SCD to inform education and health policy. Methods A comparative cross-sectional study was conducted at Mulago National Referral Hospital’s Sickle Cell Clinic in Kampala, Uganda, from 18 July to 03 November 2024. Children aged 6–19 years with SCD and their siblings or peers were consecutively enrolled. Chronic absenteeism: missing ≥10% of expected school days in the previous term (~89 days), was the primary outcome. Sociodemographic, school-related, and clinical data were collected through structured interviews and medical record review. Chi-square and Wilcoxon rank-sum tests were used to compare participant characteristics, while logistic regression identified factors associated with chronic absenteeism, generating odds ratios (ORs) and 95% confidence intervals (CIs). Results A total of 358 participants were enrolled (179 with SCD and 179 peers). Children with SCD missed a median of 5 school days per term (interquartile range [IQR]: 2–10), compared with 0 days (IQR: 0–3) among peers (p < 0.001). Chronic absenteeism affected 33% of children with SCD versus 6% of peers (OR 7.3, 95% CI: 3.7–14.4). In multivariable analysis, class repetition (adjusted OR [aOR] 2.5, 95% CI: 1.1–5.9) and hospital admissions (aOR 3.6, 95% CI: 1.9–6.8) were significantly associated with absenteeism. Conclusions Learners with SCD experienced significantly high rates of absenteeism compared to their peers, with one-third chronically absent. Frequent hospital admissions and prior class repetition were strongly associated, highlighting the need for integrated health-education strategies to enhance attendance and academic outcomes.
National and subnational analysis of self-reported diabetes mellitus and health inequalities in Europe
Abstract Diabetes mellitus (DM) has been a major public health concern with increased epidemiologic and economic burden in Europe. We aimed to estimate self-reported national and subnational DM prevalence and inequalities in individuals aged 50 years and over in 13 European countries between 2010 and 2019. Microdata were extracted from waves 4 (2010/2011), 5 (2013), 6 (2015), 7 (2017/2018) and 8 (2019) of the Survey of Health, Ageing and Retirement in Europe (SHARE) database. The analysis was conducted at both national and subnational levels, comprising 13 European countries. Health inequalities were assessed using the gini coefficient (GC) and concentration index (CI). Socioeconomic risk factors were included in the inequality analysis (urbanization, income, and educational level) in addition to multilevel regression model. The prevalence of DM decreased from 11% in wave 4 to 10.6% in wave 8, with the lowest recorded in Switzerland at 5.9% in wave 5 and the highest observed in Czechia at 16.7% in wave 8. DM prevalence varied widely subnationally, ranging from 2.1% in wave 8 Zentralschweiz (Switzerland) to 28.5% in wave 8 in Střední Čechy in Czechia. The GC demonstrated an increase from 21.9% (95% CI 17.0, 24.5) in wave 4 to 22.5% (95% CI 19.4,25.2) in wave 8, with Switzerland exhibiting the highest coefficient in wave 8 [30.1% (95% CI 7.9, 41.7)]. The CIs for income and educational level at national level were consistently low and mostly negative. The education and income CI ranged from [− 0.0182 (95% CI – 0.0242, − 0.0122) to − 0.0010 (95% CI − 0.0021, − 0.0001)] and [− 0.0278 (95% CI − 0.0411, −0.0166) to − 0.0018 (95% CI – 0.0032, – 0.0005)] across waves 4 and 8, respectively. The CI for urbanization displayed both negative and positive values, but most were not statistically significant ( p > 0.05). Inequalities exist in the self-reported prevalence of DM both nationally and subnationally, with elevated rates observed among individuals with lower educational and income levels. Addressing these significant health inequalities calls for the development of prevention programs and policies by the European countries and subnational regions, specifically tailored to prioritize and support vulnerable groups.
Minimal mutational requirements for conversion of a telomere resolvase into a Cre-like site-specific recombinase
Hairpin telomere resolvases comprise a family of enzymes that produce the hairpin (hp) telomeres of bacteria and their phages that possess linear chromosomes and plasmids terminated by covalently closed hp telomeres. The hp telomeres overcome the dual issues of end replication and protection faced by all organisms with linear genomes. The hp telomeres are produced from replicated intermediates in which the hp telomeres have been converted into replicated telomere ( rTel ) junctions possessing inverted repeat symmetry. The telomere resolvases process the rTel junctions in a reaction with mechanistic similarities to that promoted by type IB topoisomerases and tyrosine recombinases. The telomere resolvase of both Borrelia burgdorferi (ResT) and Agrobacterium tumefaciens (TelA) have been shown to be able to promote, under certain conditions, a Cre-like recombination between rTel junctions to produce the Holliday junction (HJ) intermediate typical of recombination reactions promoted by tyrosine recombinases. For TelA mutation of the enzyme was required to unmask this normally cryptic activity. A complex combination of autoinhibition domain deletion and point mutation of TelA had even been shown to completely switch the activity of TelA from a telomere resolvase to a recombinase. We report here that mutation of a pair of aspartic acid residues in TelA is sufficient to accomplish this switch in activity.
Shear behavior and mechanical model of novel truss-type steel-reinforced recycled concrete short beam
Abstract This study presents an experimental and theoretical investigation on the shear behavior of a novel truss-type steel-reinforced recycled concrete short beam (TSRSB). Five TSRSB specimens with varying recycled coarse aggregate (RCA) replacement ratios ( r = 0%, 50%, 100%) and shear span-to-depth ratios ( λ = 0.76, 1.14, 1.52) were tested under concentrated loading to analyze their failure modes, load-displacement responses, and strain developments. The results indicate that reducing the shear span-to-depth ratio significantly enhances the shear capacity and initial stiffness; for instance, the ultimate load of the specimen with λ = 0.76 was 70.1% higher than that with λ = 1.52. In contrast, the RCA replacement ratio had a negligible impact on the ultimate load, with a maximum reduction of only 3.2%. A sophisticated finite element model was developed and validated against the experimental results, demonstrating high accuracy with a mean ratio of simulated-to-experimental ultimate load of 1.07. Based on the identified “truss-arch” mechanical model, a practical formula for predicting the shear capacity of TSRSB is proposed. This formula incorporates the influence of the RCA replacement ratio via a reduction factor ( α = 20/(20 + r )) and superposes the contributions of the recycled concrete, stirrups, vertical and diagonal web members of the truss, and the steel flanges. Within the tested parameter ranges ( r = 0 ~ 100%, λ = 0.52 ~ 1.14), the proposed formula shows good agreement with the experimental data, yielding a mean calculated-to-experimental shear capacity ratio of 1.00 with a coefficient of variation of 0.01. Therefore, the formula can provide a preliminary reference for the shear capacity calculation of specimens falling within these parameter ranges.
Epidemiology and seasonal patterns of hospitalized dengue cases in Muscat Governorate, Oman (2022–2023): A retrospective study
This study describes the epidemiological, clinical, and seasonal characteristics of hospitalized dengue fever (DF) cases in Muscat Governorate, Oman (2022–2023) and identifies factors associated with wet-season dengue occurrence. A retrospective analytical study was conducted using national dengue surveillance data of 345 laboratory-confirmed DF patients admitted to Sultan Qaboos University Hospital (SQUH) and the Royal Hospital (RH). Sociodemographic, clinical, and laboratory data were analyzed using descriptive statistics and logistic regression to identify factors independently associated with wet-season dengue cases. The results showed that of 345 patients, 54.8% were male, and 94.5% were Omani nationals. Most admissions occurred at RH (56.8%) and during the dry season (58.3%). Adults aged 41–60 years comprised the largest age group (44.6%). A distinct seasonal pattern was observed, with peaks in April-May of both years. Seeb Wilayat accounted for 69.3% of cases, followed by Bawshar (26.1%) (p = 0.027). Clinically, 84.6% had dengue with warning signs, while 15.4% developed severe dengue. Locally acquired infections represented 98.6% of cases. Hypertension (31.9%) and diabetes mellitus (25.2%) were the most common comorbidities. PCR positivity and hospital admission were more frequent during the wet season; however, these patterns are likely to reflect differences in testing and referral practices rather than increased dengue transmission. Overall, 96.2% of patients recovered, and 3.8% (n = 13) died. Multivariable analysis indicated that wet-season admissions were more likely to occur at SQUH (aOR = 1.72; 95% CI = 1.06–2.82; p = 0.030) and among PCR-confirmed cases (aOR = 1.38; 95% CI = 1.02–1.87; p = 0.040); these associations likely reflect hospital referral and diagnostic practices rather than increased transmission risk. In conclusion, hospitalized dengue cases in Muscat Governorate exhibited clear seasonal and geographic clustering, while associations with PCR positivity and hospital of admission reflect health-system and diagnostic patterns rather than increased dengue incidence. The predominance of locally acquired infections indicates ongoing endemic transmission. Strengthening vector surveillance, particularly in Seeb and Bawshar, and enhancing hospital preparedness prior to seasonal peaks remain important public health strategies, supported by existing literature rather than this study alone.
Development of refined surface layers and correlated microstructure–damping behavior in friction stir processed AA2024 alloy
Upstream migration capacity of Acrossocheilus fasciatus: Behavioral strategies in response to hydraulic conditions and implications for low-head weir design
Current fishway design guidelines are mostly derived from large anadromous fish species, with scarce species-specific data for small-bodied stream cyprinids such as Acrossocheilus fasciatus . This study investigated the upstream passage capacity of the cyprinid fish A. fasciatus over low-head weirs in relation to body length through controlled flume experiments. We quantified the passage success rate (PSR), behavioral strategies, and key kinematic metrics of upstream movement. Results showed that, under the specific experimental conditions, passage performance and strategy usage pattern were significantly associated with body length. Trial groups with larger mean body length showed a higher proportion of pure leaping strategy (PLS), mean body length per trial group exhibited an extremely strong positive linear relationship with mean leap distance ( R ² = 0.96, p < 0.001). Conversely, groups with smaller mean body length showed a higher proportion of Leaping-Swimming Strategy (LSS) usage. The association between body length and leap height was weaker. Under the specific hydraulic and structural conditions tested in this study, we observed the following trends: a weir drop of 25–30 cm allowed partial passage of A. fasciatus ; the fixed 45° sloped crest profile used in this experiment was associated with prevalent adoption of the LSS by small-bodied individuals (≤13 cm); a downstream pool depth >30 cm and an inter-weir pool length >50 cm were sufficient to accommodate the observed leaping kinematics. These findings provide observational data from a single laboratory configuration for understanding the passage-related bio-behavioral characteristics of small-bodied mountain stream fishes such as A. fasciatus . All values reported are preliminary observations under controlled conditions, and their potential relevance to engineering design requires validation through multi-condition comparative experiments and field monitoring.
CircRNA-cZFR is upregulated in bladder cancer and promotes tumorigenesis via UBE2V1 regulation
The reinforcing effect of anti-caries treatments on acidic challenge resistance of irradiated enamel
Aim To investigate the effect of applying the following anticaries treatments on the surface microhardness (SMH) of irradiated enamel after pH-cycling: Nanohydroxyapatite (nano-HA), resin infiltration (RI), combination of nano-HA and RI, or curcumin-loaded mesoporous nano-silica (Cur/MSN). Methodology In this in vitro experimental study, 30 sound molars were mesiodistally sectioned into two halves, yielding 60 enamel samples, and randomly divided into six groups (n = 10). Group 1 was kept as non-irradiated. The samples from the other five groups were subjected to fractionated X-ray radiation for 6 weeks, with a total dose of 60 Gy, delivered in 2 Gy fractions 5 days per week. After irradiation, each group was processed as follows: 2) no treatment; 3) treated with 15% nano-HA; 4) treated with RI; 5) treated with a combination of 15% nano-HA and RI; 6) treated with Cur/MSN (3 mg/ml). The initial SMH (SMH 0 ) was measured after irradiation and the final (SMH 1 ) after anticaries treatments and pH cycling. pH cycling consisted of a 3-hour immersion in a demineralizing solution, followed by 21 hours in a remineralization solution at 37 °C for 5 days. The surface micromorphology of irradiated groups after pH-cycling was assessed. Results Mean [±SD] SMH 0 of irradiated enamel was significantly lower than that of the non-irradiated one (330.7 ± 23.6 vs 262.9 ± 28.8; P < 0.001). Irradiated enamels treated with all anti-caries treatments showed a significantly higher SMH 1 compared to those of no-treatment (192.2 ± 28.7; all P < 0.01). SMH 1 of Ir/nano-HA (302.2 ± 16.4) or Ir/nano-HA + RI (305.4 ± 22.1) groups were higher than those of Ir/RI (245.5 ± 35.1; P < 0.001) and not different from Ir/Cur/MSN group (285.9 ± 16.4; P = 0.638 and 0.435 respectively). The increase in SMH 1 compared to SMH 0 (ΔSMH) was significant only in Ir/nano-HA (39.0; P = 0.003) and Ir/nano-HA + RI groups (42.9; P = 0.001). Conclusion Treatment with nano-HA and a combination of nano-HA with RI demonstrated significant potential to improve the resistance of the irradiated enamel surface to acidic challenges.
Data fusion of EnMAP and sentinel-2 for high-resolution soil fertility assessment in wheat cultivation of central Khuzestan plain
A grasp point generation algorithm for waste handling based on a generative reasoning network
In the process of urban kitchen waste sorting, robots often encounter issues such as slipping and empty grabs when attempting to grasp dirty waste objects like plastic bottles and glass bottles. This paper proposes a garbage grasping framework based on the Channel Exchange Generative Residual Inference Network (CE-GR-NET), which synthesizes optimal grasping trajectories through the fusion of hierarchical visual features. The object detection network identifies and locates recyclable bottles among solid waste, while CE-GR-NET uses RGB and depth images to generate grasping points for plastic recyclable bottles. Experimental results show that, on the Cornell Grasping Dataset, the proposed method achieves good inference performance and fast inference speed in single-object solid waste scenarios, ultimately generating grasping boxes for plastic recyclable bottles in RGB images. On the self-constructed multi-source urban kitchen waste images, the proposed method generates grasping boxes for targets and regresses the corresponding object categories simultaneously, achieving an image-based model accuracy of 96.03%, an object-based model accuracy of 94.40%, and a grasping object classification accuracy of 97.87%.
A study on the use of a mechanical shaker for harvesting orange trees
Abstract In this study, a locally fabricated limb-shaking mechanical harvester prototype was developed and field tested to increase the efficiency of orange harvesting and reduce reliance on labor-intensive manual practices. The prototype was evaluated at a fixed vibration amplitude of 50 mm under different tractor PTO speeds (200–450 rpm; 3.33–7.5 Hz) and shaking durations (15–45 s). Fruit detachment efficiency increased significantly with increasing PTO speed and shaking time, whereas shorter shaking durations improved field productivity by allowing more trees to be harvested per hour. Intermediate operating conditions achieved detachment efficiencies comparable to those of the maximum treatment while requiring lower specific energy input. In particular, operation at 350 rpm for 30 s provided an optimal balance between fruit removal efficiency (81.4%), specific energy consumption, and harvesting throughput. These findings demonstrate that moderate PTO speeds and shaking durations can improve the energy efficiency of mechanical orange harvesting while maintaining high detachment performance, supporting more sustainable and cost-effective citrus production. Statistical analysis confirmed that both PTO speed and shaking duration significantly affected fruit detachment efficiency and harvesting productivity.
The running performance characteristics of U16 and U18 inter-county Ladies Gaelic Football players: An analysis across halves, playing positions, and age grades
This study examined the running performance demands of U16 and U18 inter-county Ladies Gaelic Football match play across halves, playing positions, and age grades using 10 Hz portable GPS technology (Catapult One, Melbourne, Australia). A total of 113 inter-county players (U16: height 166 ± 5.5 cm, body mass 59.5 ± 6.8 kg; U18: height 168 ± 5.1 cm, body mass 63.0 ± 8.2 kg) participated in the study. Data from competitive championship matches were collected over two seasons from nine U16 and twelve U18 games, resulting in 76 (U16) and 107 (U18) player profiles, respectively. A mixed repeated measures multivariate analysis of variance (MANOVA) evaluated the effects of age grade, positional line (full-back, half-back, midfield, half-forward, full-forward), and half of play (1st vs. 2nd) on variables including: Total Distance (TD), High-Speed Running (HSR), Max Speed, Relative Distance (m·min -1 ), Distance in Speed Zones 4 and 5, Accelerations (≥ 3 m·s ⁻ 2 ), and Decelerations (≤ −3 m·s ⁻ 2 ). Multivariate analysis for within- and between-subjects factors showed significant main effects for halves of play ( p < 0.001, η p 2 = 0.431), team ( p < 0.001, η p 2 = 0.300), and playing position ( p < 0.001, η p 2 = 0.183) on the combined dependent variables. Results demonstrated a consistent decline in running performance across halves, regardless of position or age. The ‘middle eight’ positions demonstrated the highest match-play demands and a distinct running profile compared to ‘inside’ lines, while U18 players showed significantly higher total and high-speed running distances than U16 players. This research is the first to examine the running performance profiles of U16 and U18 inter-county LGF players and may aid coaching by providing reference data for talent identification, individual player monitoring, and guiding developmental progression within the player pathway.
Optimization the synergy of magnetic whole cell technology and microfluidic reactor for the production of epoxidized fatty acids from vegetable oil refinery by-product
A hybrid BiLSTM–transformer–GCN architecture with API fusion for adaptive transportation resource analytics
Intelligent Transportation Systems (ITS) generate large volumes of heterogeneous spatiotemporal data from connected vehicles, road infrastructure, and external contextual services. Effectively discovering, classifying, and managing these transportation resources in real time remains challenging due to dynamic traffic conditions and nonlinear dependencies. This paper proposes a novel hybrid deep learning framework that integrates Bidirectional Long Short-Term Memory (BiLSTM), Transformer encoders, and Graph Convolutional Networks (GCN) with API-based contextual feature fusion for adaptive resource discovery and classification. A multi-objective learning strategy jointly optimizes supervised classification and unsupervised anomaly discovery through reconstruction-based latent feature refinement. Experiments conducted on the Barcelona Accident dataset and the Metro Interstate Traffic Volume dataset demonstrate the superiority of the proposed method. On the combined dataset, the framework achieves an accuracy of 0.882, F1-score of 0.864, and AUC of 0.911, outperforming traditional baselines such as Random Forest (accuracy 0.839) and deep CNN–LSTM (accuracy 0.857). For anomaly discovery, it obtains an AUROC of 0.836 and AUPRC of 0.784 on the Barcelona dataset, and AUROC of 0.819 on the Metro dataset, while achieving the lowest reconstruction error of 0.198. The ablation study shows performance degradation when API fusion (−4.1% accuracy) or GCN modeling (−2.8% accuracy) is removed, confirming their contribution. Inference latency analysis further demonstrates practical deployability with a competitive accuracy–latency trade-off.