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Research on incentive mechanisms for veterans’ participation in grassroots governance: An analysis based on behavioral expectations
Under the modernization of grassroots governance, veterans constitute a high-quality human resource for enhancing grassroots social governance capacity. Drawing on questionnaire survey data from 525 veterans in Guizhou Province, China, this study identifies three distinct veteran groups using latent class analysis (LCA): Utilitarian (51.3%), Mission-Oriented (23.8%), and Development-Focused (24.9%). Empirical results reveal clear heterogeneity in policy responsiveness and behavioral mechanisms across groups. First, the Utilitarian group responds most strongly to Subsidies, with participation behavior predominantly driven by economic rationality. Second, the Mission-Oriented group demonstrates honor-driven motivations, with symbolic recognition playing a central role in stimulating governance engagement. Third, the Development-Focused group prioritizes career advancement and exhibits significant responsiveness to promotion-related incentives. Based on these findings, this study constructs a mediation model of “Policy Instruments (PI)—Behavioral Expectations (BE)—Participation Behavior (PB)” and proposes an intervention pathway centered on “expectation diagnosis” and “instrument matching.” By revealing the internal mechanism through which policy instruments (PI) influence participation behavior (PB) via Behavioral expectations (BE), this research provides empirical evidence and theoretical insights for shifting incentive strategies from “uniform provision” to “precision adaptation.”.
TNF-α exacerbates postoperative plantar pain by regulating the expression of Nav1.8
Postoperative pain (POP) is one of the most common complications of surgical procedures. Using a rat plantar incision model, we investigated the interactions between tumor necrosis factor-α (TNF-α), nuclear factor-κB (NF-κB), and the voltage-gated sodium channel Nav1.8, to uncover the neural basis of POP. Our research demonstrates that, within the dorsal root ganglion (DRG), TNF-α enhances pain behaviors by driving NF-κB–dependent overexpression of Nav1.8 in neurons, further elucidating the molecular basis of POP. This study identifies the TNF-α/NF-κB/Nav1.8 axis as a critical pathway for therapeutic intervention, thereby establishing a theoretical foundation for targeting cytokine signaling to alleviate POP.
Failure of early lymphocyte recovery identifies sepsis patients with initial lymphopenia at highest risk for late mortality
Background Sepsis-induced lymphopenia is associated with adverse outcomes, yet static assessments at a single time point fail to capture the clinical significance of its dynamic evolution, and the effects of the trajectories of lymphopenia on prognosis are to be elucidated in sepsis. Methods This retrospective cohort study extracted data from the MIMIC-IV (v3.1) database. Adult septic patients with initial lymphopenia (<1.0 × 10⁹/L) were included. Group-Based Trajectory Modeling (GBTM) identified lymphocyte count trajectories. Kaplan-Meier analysis with log-rank test was used to visualize survival differences. Multivariable Cox and logistic regression assessed associations between trajectories and 7-day, 28-day, ICU, and in-hospital mortality. Subgroup analyses validated result consistency. Results A total of 1,640 patients were included. GBTM identified 4 distinct lymphocyte count trajectories. Kaplan-Meier analysis revealed that patients in Trajectory 4 (Persistent Low Level, N = 544, 33.2%), characterized by persistent lymphopenia, had the lowest cumulative 7-day and 28-day survival rates. After multivariable adjustment, Trajectory 4 was independently associated with an increased risk of 28-day mortality [adjusted HR = 1.76, 95% CI: 1.266–2.446, P = 0.001], ICU mortality (adjusted OR = 1.831, 95% CI: 1.196–2.832, P = 0.006), and in-hospital mortality (adjusted OR = 1.881, 95% CI: 1.298–2.767, P = 0.001) compared to Trajectory 1 (Sustained Recovery), and these associations remained robust across all predefined subgroups. In contrast, no significant association was observed between Trajectory 4 and 7-day mortality after full adjustment for confounders. Conclusion Lymphocyte count Trajectory 4 (Persistent Low Level) in initially lymphopenic sepsis patients is independently associated with late (28-day) but not early (7-day) mortality, as well as worse ICU and hospital outcomes. Failure of lymphopenia to recover by days 3–4 may signal a transition to the highest-risk state, suggesting a potential window for immunomodulatory strategies.
Porous superparamagnetic activated carbon from biomass: Adsorption behaviour and regeneration performance for 2,4-dichlorophenoxyacetic acid removal
This research used Tabebuia aurea leaves to develop a porous magnetic activated carbon (TA-MAC) that helps to remove 2,4-dichlorophenoxyacetic acid. BET analysis revealed a high specific surface area of 996.87 m 2 /g, while magnetic characterization confirmed the superparamagnetic nature of the adsorbent with a saturation magnetization of 3.89 emu/g, enabling easy magnetic separation after treatment. XPS analysis verified the successful adsorption of 2,4-D through changes in surface elemental composition and functional groups. The optimum adsorption conditions were achieved at pH 2 using a TA-MAC dosage of 0.5 g/L, initial 2,4-D concentration of 50 mg/L, contact time of 120 min, temperature of 303 K, and agitation speed of 150 rpm, resulting in a maximum 2,4-D removal efficiency of 79.52%. Adsorption kinetics followed the pseudo-second order model, indicating the involvement of multiple surface interactions. The maximum adsorption capacity reached 165.18 mg/g at 303 K according to the Langmuir model. Thermodynamic evaluations further supported the physisorption nature of the interaction and confirmed its exothermic character. Additionally, the adsorbent exhibited good regeneration capacity after five cycles. TA-MAC maintained high and consistent performance, achieving over 70% 2,4-D removal efficiency in all tested real water samples, demonstrating its potential for practical applications. Collectively, these findings emphasize the efficacy of the synthesized adsorbent as a practical and sustainable solution for pollution remediation.
Performance, physiology, and determinants of success in IRONMAN® 70.3: A systematic review
Background IRONMAN® 70.3 events represent a rapidly expanding endurance discipline characterized by distinct physiological demands, performance determinants, and race-specific risk profiles. Despite their global popularity, no systematic review has synthesized evidence exclusively focused on this distance. Objective To consolidate current knowledge on performance predictors, physiological responses, training characteristics, nutritional strategies, environmental influences, and medical considerations in IRONMAN® 70.3 triathletes, and to identify gaps requiring further investigation. Methods A systematic search of PubMed, Scopus, SciELO, EBSCO and Google Scholar was conducted up to 25 th November 2025. Search terms were developed according to PRISMA guidelines and included variations of ‘Ironman 70.3’, ‘half triathlon’ and ‘middle-distance triathlon’. Eligible studies reporting physiological, anthropometric, nutritional, environmental, medical, or performance-related outcomes specific to IRONMAN® 70.3. Risk of bias was assessed using the Newcastle–Ottawa Scale, the Cochrane RoB tool, and the NIH Quality Assessment Tool, according to study design. Results A total of 86 studies were included, predominantly observational in design, with sample sizes ranging from 1 to 852,721 participants, mostly trained male triathletes aged 25–39 years. Participation has increased across age groups, with pronounced growth among female and masters triathletes. Peak performance in professional male triathletes is reached at approximately age 28, and in female triathletes at age 32. Across large datasets, cycling appeared to be the strongest predictor of overall race time, accounting for the largest proportion of performance variance. Physiologically, competition was associated with transient reductions in immune function, reversible muscle damage, and shifts in hydration and electrolyte balance, while higher intracellular water and efficient fat oxidation were associated with better outcomes. Conclusions Evidence specific to IRONMAN® 70.3 is limited by small sample sizes, heterogeneous designs, male-dominated cohorts, and insufficient sex-specific analyses. Future research should distinguish recreational from elite triathletes, incorporate balanced sex representation, and apply standardized physiological and environmental monitoring to refine targeted recommendations for performance, health, and safety.
Do common dopaminergic variants modulate processing speed in cognitive aging? A longitudinal candidate gene study
Amid a global shift toward older populations, understanding the mechanisms of cognitive aging is a public health priority. Processing speed shows age-related decline and predicts dementia risk. Neuroimaging links dopaminergic system integrity to cognitive performance in aging, but the contribution of common genetic variation remains unclear. This study tested whether common dopaminergic variants influence 12-year processing speed decline, performance at age 70, and other cognitive domains, with exploratory analyses of post-mortem pathology. A total of 89 linkage disequilibrium-independent variants (derived from 957 SNPs) across nine dopamine pathway genes ( TH , DDC , DRD1-3 , SLC6A3 , COMT , DBH , PPP1R1B ) were analysed in 1,539 participants from The University of Manchester Longitudinal Study of Cognition in Normal Healthy Old Age. Across single-variant, gene-based, and unweighted pathway allele score analyses, no associations survived multiple testing correction (Bonferroni p < 5.62 × 10⁻ 4 ). For processing speed decline, the strongest nominal signals were DRD2 rs10789943 (p = 0.0066) and DBH rs2005663 (p = 0.0074), followed by DRD2 rs12805897 (p = 0.013). For performance at age 70, the leading signal was DRD2 rs11214607 (p = 0.0025). Gene-based tests were non-significant (strongest: DRD2 for slopes p = 0.063; DRD2 for intercepts p = 0.019), and the dopamine pathway allele score was unassociated with decline (β = 0.001, p = 0.969) and performance (β = 0.009, p = 0.721). Null findings extended to fluid reasoning, episodic memory, and vocabulary, and to post-mortem analyses (neuropathology n = 116; synaptic density n = 50), including SNP-marker and marker-trajectory tests. With 80% power to detect single variants explaining at least 1.19% of variance and allele score effects explaining at least 0.51% of variance, no moderate-to-large effects of common dopaminergic variation on cognitive aging trajectories were detected. Smaller effects, or mechanisms not captured by common variant analyses such as rare variants, epigenetic regulation, or gene-environment interactions, may contribute to individual differences in cognitive aging.
A hybrid deep learning model for user story effort estimation
Accurate effort estimation of user stories is a key challenge in agile software development due to both subjectivity and the complexity of natural language requirements. This paper proposes a hybrid Deep Learning (DL) model for data driven effort estimation using large scale textual data and advanced semantic modeling. One of the significant contributions is the development of a dataset of 6,956 user stories which was collected from many heterogeneous sources and then meticulously cleaned and refined to 4,079 high quality instances, by using a systematic preprocessing and expert validation. Following a Design Science Research (DSR) methodology, a hybrid model integrating a pre-trained Bidirectional Encoder Representations from Transformers (BERT) encoder with a Long Short-Term Memory (LSTM) is developed to capture both contextual semantics and sequential dependencies in user story description. The DL model is evaluated against multiple Machine Learning (ML) baselines using a robust multi-metric framework. Experimental findings show the superior performance of the proposed model with a Mean Absolute Error (MAE) = 0.6481, Root Mean Square Error (RMSE) = 1.4559 and = 0.6581, which is a huge improvement over the conventional methods. To ensure practical relevance in discrete Scrum planning, the continuous model outputs were mapped to the standard Fibonacci sequence, achieving a classification accuracy of 72%. To guarantee the consistency of performance improvements, the statistical validation is done with the Wilcoxon Signed-Rank Test to show that the improvements are significant (p < 0.05). Moreover, the model is operationalized as web based decision support system to enable real time estimation in agile software development. The results reveal the efficacy of using large scale curated datasets alongside hybrid DL model to reduce estimation bias and boost predictive quality to deliver a scalable and strong solution for intelligent agile project management. Future work will focus on integrating eXplainable AI (XAI) techniques and validating the model across real world industrial datasets to further enhance transparency and generalizability.
The epistemic advantages of representative deliberation
It is widely thought that deliberative quality improves with the number of participants: the more voices in the room, the better the collective judgment. This “wisdom of the crowds” intuition suggests that representative deliberation — in which a subset of deliberators acts on behalf of the larger group — should be epistemically inferior to full, plenary deliberation. We test this using a computational agent-based model in which deliberators exchange evidence for and against a proposition and are evaluated on how accurately their collective beliefs track an objective truth. Varying four conditions — the length of deliberation, the distribution of available evidence, problem difficulty, and agents’ memory capacity — we find that representative deliberation frequently matches or outperforms full deliberation. This advantage does not stem from any superior epistemic ability of the representatives themselves. Rather, it emerges from two structural features of the two-tier process: the selective triage of the strongest available evidence, and the periodic resetting of polarized or entrenched beliefs that a second phase of deliberation enables. Our findings suggest that representative structure can be an epistemic asset rather than a liability — not despite, but because of the constraints it imposes.
SPPIPred: Stacking-based ensemble learning model for identification of protein-protein interaction
Protein-protein interactions (PPIs) are essential for various biological functions and are crucial in drug discovery, signaling pathways, and network reconstruction. This study presents SPPIPred, an advanced machine learning-based model designed for precise PPI prediction. The SPPIPred model was constructed using five feature extraction methods: Pseudo amino acid composition (PAAC), Composition transition distribution (CTDC), Dipeptide composition (DPC), Word2Vec, and FastText. Among these, FastText emerged as the most effective for encoding protein sequences. Despite the application of feature selection techniques, the analysis revealed that the original raw feature dimensions yielded superior results compared to the selected features. The model used seven machine learning classifiers, including Decision Tree (DT), Extra Trees Classifier (ETC), CatBoost (CAT), XGBoost (XGB), LightGBM (LGBM), Random Forest (RF), and the stacking model named SPPIPred. SPPIPred demonstrated exceptional accuracy rates of 0.9989 in the H pylori dataset and 0.9991 in the S cerevisiae dataset, with Matthews correlation coefficients (MCC) of 0.9982 and 0.9979, respectively. These findings highlight the effectiveness and reliability of the SPPIPred model, offering valuable insights to researchers in the field of bioinformatics and improving applications within bioengineering and pharmaceutical development.
Difference in excess mortality during the COVID-19 pandemic depending on marital status in Japan
Differences in excess mortality during the coronavirus disease 2019 pandemic across marital statuses in Japan were investigated using data from across the country. Mortality data from the Vital Statistics records spanning 2010–2023 were utilized. Age-standardized mortality rates were computed by sex, marital status, year, and cause of death, and expected post-pandemic rates were estimated using pre-pandemic values with a quasi-Poisson regression model. Furthermore, the age-standardized percentage of the excess number of deaths relative to the expected number of deaths (hereafter, P-score) after the pandemic started was calculated by sex, marital status, and cause of death, using the expected and observed number of deaths. A declining trend in all-cause mortality rate was observed across all groups before the pandemic, regardless of sex and marital status, while mortality rates increased across all groups after the pandemic began. The largest decrease in all-cause mortality rate was observed among never-married persons before the pandemic in both men and women, while the smallest increase after the pandemic was observed among married persons. The highest age-standardized P-score for all-cause mortality was observed among never-married men and women, while that for married persons was the lowest among men. For women, the age-standardizedP-scores were closer across groups, with confidence intervals overlapping among marital-status categories. In contrast, there was a significant difference in the age-standardized P-scores between never-married and married persons among women aged <65 years. In conclusion, the percentage of excess all-cause mortality was the highest among never-married persons during the pandemic period in Japan, particularly in men, and it is important to continue to monitor the trend in the future.
Reliability, construct validity, and usefulness of an assessment device for agility and motor-cognitive reactive stepping performance in community-dwelling older adults
Age-related cognitive and motor decline impairs daily functioning and increases fall risk. Although both domains interact in everyday activities, they are often assessed separately, limiting ecological validity. This study examined reliability and construct validity of an assessment device for agility and motor–cognitive reactive stepping performance in older adults. In a test–retest design, 72 older adults (48 women; 73.7 ± 7.6 years) completed agility tests (Random Star Run and a dual task with multiple object tracking) and motor-cognitive tasks on the SKILLCOURT across four sessions: familiarisation, test day 1 and day 2 (7 days apart), and test day 3 (3 months later). Agility tasks required reactive changes of direction in response to visual cues, while motor-cognitive stepping tasks assessed simple and choice reaction time, task switching, 2-back, and Stroop word-color interference. Intersession Reliability was evaluated using intraclass correlation coefficients (ICC) and coefficients of variation (CV), learning effects with linear mixed models, and construct validity via age-adjusted regression against established motor (Timed-up-and-Go; TUG; single-/dual task) and seated PC-based cognitive measures of reaction speed and executive functions corresponding to the SKILLCORT tasks. Significant learning effects were observed mainly between test days 1 and 2. Relative reliability was moderate to excellent for SKILLCOURT agility, simple reaction time, and Stroop word-color performance (ICC = 0.70–0.94), but lower for choice reaction time, task switching, and the 2-back task (ICC = 0.49–0.66). Absolute reliability was generally good (CV = 4.6–7.2%), except for the SKILLCOURT task switching and 2-back (~12%) and, in particular, the dual task agility test (32%). Random Star Run agility was predicted by TUG single-task performance, PC-based Stroop interference control, and simple reaction time (R 2 = 0.77), while dual task agility was predicted by TUG dual task and PC-based 2-back performance (R 2 = 0.37). All motor-cognitive outcomes were significantly associated with corresponding PC-based cognitive measures (R 2 = 0.20–0.41). The SKILLCOURT demonstrated good reliability for the Random Star Run agility test and most motor-cognitive measures, although adequate familiarisation appears necessary to minimise learning effects. Construct validity was confirmed for agility measures, while reactive stepping tasks appeared to involve greater motor demands and task interference, which may weaken correlations with comparable PC-based cognitive tests.
Inhibitory effect of 17β-estradiol on the THIK-1 channel
A two-pore domain K + (K2P) channel, THIK-1, plays important roles in microglia and macrophage. THIK-1 is known to be activated by arachidonic acid and G protein-coupled receptors and inhibited by anesthetics. Steroids, such as cholesterol, estradiol and progesterone, are known to modulate several K + channels and they might be potential modulators of THIK-1. We examined the effects of steroids on THIK-1 and found that estradiol inhibits mouse THIK-1 by approximately 40% (IC 50 = 4.9 ± 1.5 μM). Docking simulations of THIK-1 with estradiol indicated possible docking sites, which were further assessed by introducing an alanine mutation into a residue at or near these locations. The F142A, V269A, and Y273A mutations reduced the inhibitory effect of estradiol. These residues are situated within the upper cavity above the Y gate in THIK-1 (pond), suggesting that the pond conformation is essential for estradiol-mediated inhibition. Conversely, the F145A and F276A mutations, located outside this region, were inhibited by 10 nM estradiol and enhanced inhibition by estradiol, estrone, estriol, and progesterone, likely due to conformational changes that facilitate steroid inhibition. The mouse T237S mutation, which corresponds to the reported human THIK-1 variant, produced effects similar to those seen with the F145A and F276A mutations, but to a lesser degree. In summary, estradiol-mediated THIK-1 inhibition depends on residues located in the pond, which may have physiological or pathological significance for THIK-1 variants which are inhibited by low concentration of estradiol.
Prognostic impact of shock at ICU admission in acute respiratory failure
Background Acute respiratory failure (ARF) is a leading cause of intensive care unit (ICU) admission and is frequently complicated by shock. However, the prognostic significance of shock at ICU admission across the heterogeneous ARF population remains incompletely defined. We aimed to evaluate the association between shock at ICU admission and mortality in patients with ARF. Methods This retrospective cohort study included 3,497 adult patients with ARF admitted to a tertiary medical center ICU in South Korea between January 2019 and December 2023. Shock was defined as the need for vasopressor or inotropic support at ICU admission. The primary outcome was hospital mortality, and the secondary outcome was ICU mortality. Multivariable Cox proportional hazards models were used to estimate adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs). Results Among 3,497 patients, 652 (18.6%) had shock at ICU admission. Patients with shock had significantly higher ICU mortality (46.4% vs. 17.1%) and hospital mortality (56.4% vs. 26.2%) compared with those without shock. After adjustment for potential confounders, shock remained independently associated with increased ICU mortality (aHR 1.84; 95% CI 1.56–2.17) and hospital mortality (aHR 1.58; 95% CI 1.37–1.82). This association was consistent across subgroups stratified by illness severity, respiratory support modality, and neutropenia status. Conclusion Shock at ICU admission was independently associated with increased mortality in patients with ARF, with consistent findings across clinically relevant subgroups. The presence of shock at ICU admission may serve as a readily identifiable marker for risk stratification in this population.
Therapeutic efficacy of artemether-lumefantrine plus single low dose primaquine for the treatment of uncomplicated Plasmodium falciparum malaria in a high transmission setting, Western Ethiopia
Background The development and spread of drug-resistant parasites continue to threaten progress toward malaria elimination. Therapeutic efficacy and molecular resistance marker studies are needed to guide national control programs. In African settings, evidence of partial resistance to artemisinin-based combination therapies (ACTs) associated with Pfkelch13 mutations is accumulating, and World Health Organization (WHO) recommends regular monitoring of first line antimalarial drugs for early detection of resistant parasites. In this study, we evaluated the efficacy of artemether-lumefantrine (AL) combined with a single low dose of primaquine (PQ) for treating uncomplicated Plasmodium falciparum malaria in a co-endemic area where P. falciparum predominates. Methods and findings One hundred twenty-three patients with P. falciparum mono-infection were enrolled between November 2020 to March 2021 and treated with artemether-lumefantrine (AL) plus a single low dose of primaquine (PQ) as per the national malaria treatment guideline and followed up for 28 days. Ethical approval was obtained from the AHRI/ALERT ethics committee (Po/23/19), and the study was registered at Pan-African clinical trials registry (PACTR) with unique identification number of PACTR202509595696440. Pfmsp2 capillary electrophoresis (CE) genotyping was used to differentiate recrudescence from new infections. More than half (56.1%) of the participants had high parasitemia (>10,000 parasites/μL) at enrollment. On day 3, 16.9% (20/118) remained parasitemic, and of the 10 individuals with detectable gametocytes at enrollment, only 3.4% remained gametocytemic on day 3, and 100% parasite clearance was observed on day 7, respectively. Multiplicity of infection was 3.8 at enrollment and 1.7 at the time of recurrence. The adequate clinical and parasitological responses at 28-day (ACPR) of per protocol analysis (PPA) was 73.7% for PCR-uncorrected and 91.3% for PCR-corrected, respectively and while, the intention-to-treat analysis (ITA), the Kaplan–Meier estimated treatment success at day 28 was 93.2% (95% CI: 88.5–98.2) after PCR correction, compared with 78.3% (95% CI: 71.0–86.4) in the PCR-uncorrected analysis. In our study assessment, no cases of severe malaria or serious adverse events occurred. Conclusions The efficacy observed in this study, although remaining above the WHO policy change threshold after PCR correction, may indicate a potential decline in AL’s effectiveness in this high transmission setting. However, because antimalarial drug concentrations were not measured and evening doses were not fully directly observed, reduced drug exposure or imperfect adherence cannot be excluded as possible contributors to the observed treatment outcomes Therefore, we suggest regular therapeutic efficacy monitoring and further investigation using advanced molecular techniques, such as next-generation sequencing (NGS), to enable early detection of resistance-associated parasite variants that may compromise treatment efficacy.
Exploring administrative staff’s acceptance of generative AI in Chinese vocational colleges: A UTAUT-guided thematic study
As Generative Artificial Intelligence (GenAI) technologies reshape institutional processes, higher vocational colleges are increasingly exploring their potential in administrative management. Guided by the Unified Theory of Acceptance and Use of Technology (UTAUT), this qualitative study examines how administrative personnel perceive and accept GenAI in vocational college administration. In-depth, semi-structured interviews were conducted with 16 administrative staff from three vocational colleges in Henan Province, China. Data were analyzed using ATLAS.ti-assisted thematic analysis through a hybrid deductive–inductive coding strategy. A deductive seed codebook was developed based on the four UTAUT constructs, while inductive coding captured emergent themes. To enhance analytical rigor, two coders independently analyzed five transcripts, achieving an inter-coder agreement of Cohen’s κ = 0.84. Thematic saturation was reached after the 13th interview, with no new themes emerging from the subsequent sessions. The findings largely corroborate the relevance of the four core UTAUT constructs, performance expectancy, effort expectancy, social influence, and facilitating conditions, in explaining GenAI acceptance among administrative staff. Participants recognized GenAI’s potential to improve operational efficiency, decision-making, service responsiveness, and workflow standardization. However, they also expressed concerns regarding technical complexity, resources limitations, policy ambiguity, data privacy, accountability, and job security. These findings suggest several GenAI-specific contextual factors, including governance readiness, perceived trustworthiness and controllability, institutional digital maturity, and ethical safeguards. The study provides an exploratory, context-specific application of UTAUT to vocational college administration and offers practical guidance for GenAI training, governance, and responsible implementation.
Organisational scale and fraud volatility: An exploratory agent-based simulation of occupational fraud dynamics
Fraud is commonly studied as an individual-level phenomenon. Less attention has been given to how fraud behaves at the level of organisational systems. Empirical research on the relationship between organisational size and fraud has produced mixed findings, partly because detected-incident datasets cannot reveal the underlying generative mechanisms of fraud. This study addresses that gap by examining whether fraud scales systematically with organisational size, and whether social influence and network connectivity moderate that relationship. This study is an exploratory, conceptual agent-based simulation not empirically calibrated to real-world data. An agent-based model was developed in which employees interact within simulated organisations under varying structural and behavioural conditions. Simulations were run across nine organisational sizes (25–6,400 agents), ten levels of susceptibility to social influence (q_avg = 0 to 0.9), and five levels of network connectivity scaling (αk = 0–1), yielding 9,000 simulations in total. Log-log regression was used to estimate scaling exponents for three outcomes: mean fraud, peak fraud, and fraud volatility. Both average and peak fraud levels scale approximately linearly with organisational size. However, fraud volatility scales superlinearly (β = 1.393), meaning that a doubling of organisational size is associated with approximately 2.6 times greater variability in the level of fraud. Susceptibly to social influence and network connectivity significantly moderate the volatility of fraud activity but do not significantly affect mean fraud levels. Theoretically, these findings establish organisational scale as a structural mechanism shaping fraud dynamics and introduce fraud volatility as a distinct dimension of risk that scales disproportionately with size — one that average incidence measures fail to capture. Practically, proportional fraud control resourcing is adequate for managing average fraud levels, but managing volatility requires a different approach. Because fraud volatility is largely invisible in real organisations, the findings support reorienting fraud risk identification away from individual-centred frameworks toward affordance mapping — the systematic analysis of structural conditions that make fraud possible — as a more complete basis for directing counter-fraud effort than case-led detection alone.
Exploration of neuropeptides to identify potential target for regulating feeding behavior and development in Eurygaster integriceps
Neuropeptides regulate diverse physiological processes in insects, including feeding, reproduction, and development, and have therefore emerged as promising species-specific targets for next-generation pest control strategies. In this study, we focused on 13 feeding-related neuropeptides previously identified from our whole-body RNA-seq dataset of Eurygaster integriceps . Using a combination of bioinformatics and expression analyses, we characterized precursor structures, predicted mature peptides, and quantified expression across developmental stages, between sexes, and in key feeding-associated organs of head and gut. Several neuropeptides, including Ast-A , Ast-B , Ast-C , AKH , SIF , ITP , Burs , sNPF , NPF , and Crz showed higher expression in males than in females, suggesting sexually dimorphic regulation of feeding and metabolism. Moreover, most neuropeptides were expressed at higher levels in the head compared to the gut, which is consistent with central neuro-regulatory functions. Furthermore the expression pattern of neuropeptides of life cycle developmental stages revealed peak expression in early developmental stages, along with stage-specific variation indicating coordinated regulation of growth, feeding behavior, and digestive physiology. Taken together, these findings provide the first detailed molecular and expression atlas of feeding-related neuropeptides in E. integriceps and offer foundational insights for developing future neuropeptide-based, species-specific, and environmentally safe biocontrol strategies.
Data driven multiscale modelling of paroxysmal brain transitions using DC-coupled electrophysiological data
We introduce a novel parameter estimation framework for a slow-fast neuronal model using DC-coupled electrophysiological data recorded from the WAG-Rij rat model of generalised seizures. In this animal model, fluctuations in extracellular potassium concentrations are hypothesised to drive infra-slow oscillations ( I S O ) that precede spike-wave discharges. We construct a biophysically motivated slow-fast dynamical system in which seizures are triggered by fluctuations in extracellular potassium concentrations to model the in vivo observations. Specifically, we interpret I S O s dynamics (mathematically) as the integral transform (or low-pass filter) of extracellular potassium concentrations, facilitating real time tracking of physiological states. Model parameters are estimated from empirical data, using an expectation-maximisation approach that optimises a regularised likelihood function, while biological states are inferred through the unscented Kalman filter. The inferred model allows tracking changes in latent proxy of extracellular potassium concentrations from DC-coupled electrophysiological recordings (exhibiting paroxysmal transitions) under the assumption that in our preclinical model extracellular potassium dynamics contribute to seizure generation. We validate the consistency of inferred hidden biological states across longer datasets containing multiple seizure events that were not utilised during parameter estimation. The results demonstrate that I S O s provide sufficient information to infer latent ionic dynamics and support the conceptualisation of seizure onset as bifurcation-driven transitions modulated by the ionic changes.
Experimental and numerical investigation of the mechanical performance of natural–synthetic hybrid composite laminates
Fiber-reinforced polymer composites have attracted considerable attention in engineering applications owing to their high strength-to-weight ratio, corrosion resistance, and tailorable mechanical properties. Hybridization provides an effective approach for combining the advantageous characteristics of different reinforcement materials to achieve improved overall laminate performance. In the present study, three six-ply hybrid composite laminates, namely J2/C2/J2, J2/G2/J2, and C2/G2/C2, with a [0°/90°] stacking sequence and a relatively high fiber content obtained through controlled hand lay-up fabrication, were fabricated using the hand lay-up technique. Test specimens were prepared according to the relevant ASTM standards and subjected to tensile, compressive, and flexural testing. In addition, finite element simulations were performed using ANSYS Mechanical APDL 2020 R1 to validate the experimental observations. The experimental results demonstrated that natural–synthetic fiber hybridization significantly enhanced the mechanical performance of the investigated laminates. The incorporation of carbon fiber into jute-based laminates resulted in an average strength improvement of approximately 88%, whereas glass-fiber incorporation produced an improvement of approximately 55%. Among the investigated configurations, the C2/G2/C2 laminate exhibited the highest tensile strength (approximately 140 MPa), compressive strength (approximately 39 MPa), and flexural strength (approximately 109 MPa). The numerical predictions showed good agreement with the experimental measurements, with maximum deviations below 1.2%. The results confirm the effectiveness of hybridization in enhancing the mechanical response of composite laminates and identify the C2/G2/C2 configuration as the most promising among the investigated laminate systems. These findings demonstrate the influence of natural–synthetic fiber hybridization on laminate mechanical performance and provide useful information for the design of lightweight composite structural components. Further studies involving pressure-vessel fabrication and internal-pressure testing are required before pressure-vessel applications can be established.
A novel dynamic Optuna hybrid Harris Hawks Optimization approach for classification of CAD
Coronary Artery Disease (CAD) is a leading cause of mortality worldwide and is primarily associated with atherosclerotic plaque formation, resulting in coronary artery stenosis. The accurate prediction of CAD using deep learning models is often constrained by the limitations of conventional optimization techniques, including premature convergence and limited adaptability of the models. To address these challenges, this study proposes a Dynamic Optuna Hybrid Harris Hawks Optimization (Dynamic Optuna H-HHO) framework to enhance the performance of deep learning–based CAD prediction models. The proposed approach integrates dynamic parameter adjustment, adaptive escape energy mechanisms, and Optuna-based hyperparameter tuning. The framework was applied to optimize several deep-learning classifiers, including ResNet-50, VGG-16, InceptionV3, and MobileNet, using a coronary artery stenosis dataset. The performance was evaluated through a comparative analysis with models optimized using the conventional Hybrid Harris Hawks Optimization (H-HHO) algorithm. The experimental results indicate that the proposed Dynamic Optuna H-HHO framework consistently improves the predictive accuracy across all evaluated models. InceptionV3 achieved the highest accuracy of 97.9%, followed by MobileNet with 97.6%, compared with the maximum accuracy of 82.46% obtained using traditional HHO-based optimization. By combining adaptive optimization strategies with automated hyperparameter tuning, the proposed framework provides a robust and scalable solution for improving the accuracy of coronary artery disease prediction.