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Lead yourself to the zone and be happy: The effect of self-leadership development on flow and happiness
Background Self-leadership has been associated with several positive organizational outcomes (e.g., performance and innovation). Yet, individual subjective well-being constructs have seldom been explored in the self-leadership literature. We hypothesized that an increase in self-leadership results in increased positive affect resources, namely flow and happiness. Methods A self-leadership intervention program, interlinked with a real-world longitudinal observational effectiveness-assessment design, was used to test this hypothesis. A sample of 64 middle-managers from a for-profit organization in the fast-moving consumer goods (FMCG) sector went through one 8-week self-leadership training program, and provided 4383 daily measures of self-leadership, flow, and happiness during working hours on business days, plus 242 post-training modules assessments. Email surveys were used to assess self-leadership and dispositional flow, whereas the experiencing sampling method was used to assess situational (i.e., at-the-moment) flow and happiness. Linear mixed models and mediation analysis were applied to longitudinal data. Results As hypothesized, an increase in self-leadership was positively associated to (1) situational and dispositional experiences of flow among employees in the FMCG sector and (2) happiness in the workplace. No mediation of flow was found between self-leadership and happiness. Conclusion Results highlight the potential of self-leadership skills development and practice to shorten the distance between perceived challenges and skills in job-related tasks, as well as to make subjects feel happier in the workplace.
KG-MACNF: A nonlinear cross-modal fusion model for predicting drug-target interactions via multi-relational embedding and fine-grained structure
Drug-target interaction (DTI) prediction is essential for the development of novel drugs and the repurposing of existing ones. However, when the features of drug and target are applied to biological networks, there is a lack of capturing the relational features of drug-target interactions. And the corresponding multimodal models mainly depend on shallow fusion strategies, which results in suboptimal performance when trying to capture complex interaction relationships. Therefore, this study proposes a novel framework named KG-MACNF. This framework utilizes knowledge graph embedding (KGE) techniques to capture multi-level relational features of entities in large-scale biological networks. Simultaneously, our innovative PoolGAT network, along with CTD descriptors, is employed to extract drug structural features and protein sequence information. Finally, by employing our innovative nonlinear-driven cross-modal attention fusion network, the framework efficiently integrates these multimodal data and generates the final DTI prediction results. Experiments on two publicly available datasets, Yamanishi_08’s and BioKG, demonstrate the substantial advantages of KG-MACNF in DTI prediction. KG-MACNF demonstrates robust stability, especially under imbalanced data conditions. This study successfully overcomes the bottlenecks of prior models in utilizing modality information and feature complementarity, providing a more accurate tool for drug discovery and DTI prediction.
DXA-derived visceral adipose tissue reference values and metabolic syndrome risk threshold in an Algerian adult population
Background Visceral adipose tissue (VAT) is associated with several cardiometabolic risk factors, particularly metabolic syndrome and insulin resistance. Reference values for VAT vary across populations, genders, and ages. Data on visceral fat in the Algerian population are lacking. This study aimed to establish reference values for VAT in a general adult population. The secondary objectives were to determine cardiometabolic consequences and to propose suggested threshold values for VAT to predict metabolic syndrome. Materials and methods This cross-sectional, analytical study randomly selected participants from the electoral list of Tlemcen, Algeria. VAT was measured using dual-energy X-ray absorptiometry (DXA) General Electric Healthcare© Lunar iDXA. Results A total of 301 adults (147 men and 154 women) with a mean age of 49.3 ± 15.1 years participated. The median (25th-75th percentiles) VAT mass was 1364 g (690–2049) in men and 1060 g (585–1590) in women. Binary logistic regression analyses demonstrated that cardiometabolic risk factors, including hypertension, type 2 diabetes, dyslipidemia, metabolic syndrome, insulin resistance according to HOMA2-IR, hepatic steatosis, and sleep apnea syndrome, were significantly dependent on VAT mass. Threshold values for VAT to predict metabolic syndrome (according to International Diabetes Federation) were ≥ 1369 g in men (sensitivity: 86.2%, specificity: 74.2%, Youden’s index: 0.604) and ≥ 1082 g in women (sensitivity: 76.3%, specificity: 76.9%, Youden’s index: 0.532). Conclusion This study provides reference values for VAT in an urban Algerian adult population and highlights its importance in assessing cardiometabolic risk.
Prediction of modal parameters for thin-walled blade milling process considering material removal effect
Accurate prediction of time-varying dynamic parameters during the milling process is a prerequisite for chatter-free cutting of thin-walled parts. In this paper, a matrix iterative prediction method based on weighted parameters is proposed for the time-varying structural modes during the milling of thin-walled blade structures. The thin-walled blade finite element model is established based on the 4-node plate element, and the time-varying dynamic parameters of the workpiece during the cutting process can be obtained by modifying the thickness of the nodes through the constructed mesh element finite element model It is not necessary to re-divide the mesh elements of the thin-walled parts at each cutting position, thus improving the calculation efficiency of the dynamic parameters of the workpiece. To further improve the prediction accuracy and efficiency of the finite element model, a three-layer neural network model is constructed, which takes the calculation results of the finite element model of the plate element as training samples to obtain the neural network model. The neural network model achieves a maximum prediction error of 2.02% compared to the finite element benchmark. Meanwhile, the training time of the three-layer neural network model is about 12 seconds. When the training model is used to batch calculate the dynamic parameters of the workpiece in different cutting stages, the loading time of the model and input data is about 1.2876s, and when the number of predicted cutting stage is 360, the prediction time is only 0.0039s. Using three-layer neural network model on the premise of ensuring the calculation accuracy can greatly improve the calculation efficiency.
A nonlinear decomposition analysis of the rural-urban disparities in tobacco use among women in sub-Saharan Africa
Background Tobacco use remains a major public health challenge in sub-Saharan Africa, with significant gendered dimensions. Place of residence is an important determinant, as rural and urban contexts shape exposure, access, and consumption patterns. This study investigates rural–urban disparities in tobacco use among women in sub-Saharan Africa, with a focus on quantifying the relative contributions of socioeconomic factors. Methods We conducted a pooled cross-sectional analysis using nationally representative data from the most recent Demographic and Health Surveys (DHS) of 22 sub-Saharan African countries (2015–2022). The study sample included 350,536 women aged 15–49 years with complete data on tobacco use and relevant covariates. Tobacco use was defined as self-reported current use of cigarettes or other tobacco products. We employed a multivariate decomposition for non-linear response models to quantify the contributions of group differences in characteristics versus differences in how those characteristics affect an outcome. This technique partitions the observed rural–urban gap in tobacco use into two components: (1) endowment effects (compositional differences in characteristics such as education, household wealth, age, marital status, and employment) and (2) coefficient effects (differences in the influence of these characteristics on tobacco use between rural and urban women). Models adjusted for sampling weights and survey design effects to ensure representativeness. Results Compositional differences explained 167.48% of the rural–urban disparity in women’s tobacco use. Educational attainment and wealth index were the most significant contributors, both showing protective effects. If rural women’s education and wealth levels matched those of urban women, tobacco use prevalence would be reduced by 24.99% and 49.84%, respectively. Differences in coefficients accounted for −67.48% of the observed gap, with baseline differences in intercepts (−166.17%) driving most of this effect. These findings highlight both structural disadvantages and variations in behavioural responsiveness across residential settings. Conclusion The study demonstrates that rural–urban disparities in tobacco use among women are primarily shaped by inequalities in education and wealth. Interventions aimed at expanding educational opportunities and addressing poverty in rural communities could substantially reduce tobacco use. Additionally, tailored prevention and cessation strategies targeting women at both the lowest and highest ends of the socioeconomic spectrum are essential to mitigate disparities and advance tobacco control in sub-Saharan Africa.
Smart load balancing in cloud computing: Integrating feature selection with advanced deep learning models
The increasing dependence on cloud computing as a cornerstone of modern technological infrastructures has introduced significant challenges in resource management. Traditional load-balancing techniques often prove inadequate in addressing cloud environments’ dynamic and complex nature, resulting in suboptimal resource utilization and heightened operational costs. This paper presents a novel smart load-balancing strategy incorporating advanced techniques to mitigate these limitations. Specifically, it addresses the critical need for a more adaptive and efficient approach to workload management in cloud environments, where conventional methods fall short in handling dynamic and fluctuating workloads. To bridge this gap, the paper proposes a hybrid load-balancing methodology that integrates feature selection and deep learning models for optimizing resource allocation. The proposed Smart Load Adaptive Distribution with Reinforcement and Optimization approach, SLADRO, combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms for load prediction, a hybrid bio-inspired optimization technique—Orthogonal Arrays and Particle Swarm Optimization (OOA-PSO)—for feature selection algorithms, and Deep Reinforcement Learning (DRL) for dynamic task scheduling. Extensive simulations conducted on a real-world dataset called Google Cluster Trace dataset reveal that the SLADRO model significantly outperforms traditional load-balancing approaches, yielding notable improvements in throughput, makespan, resource utilization, and energy efficiency. This integration of advanced techniques offers a scalable and adaptive solution, providing a comprehensive framework for efficient load balancing in cloud computing environments.
How data assets influence enterprise persistent innovation: Evidence from China
This study investigates the impact of data assets on enterprise persistent innovation using panel data from Chinese A-share listed firms from 2011 to 2022. The results indicate that data assets significantly enhance both the inputs and outputs of enterprise persistent innovation, with the findings remaining robust under endogeneity tests. Mediation analysis reveals that data assets influence enterprise persistent innovation through three key channels: process innovation, business innovation, and technological innovation. The development of digital finance positively moderates this relationship across three dimensions of coverage, depth, and digitalization, indicating that digital finance amplifies the persistent innovation value of data assets. Heterogeneity analyses reveal that the persistent innovation input improves more in non-state-owned enterprises, digitally advanced firms, and non-manufacturing sectors, whereas output enhancement is most evident in large enterprises, highly digitalized firms, and organizations with strong absorptive capacity. These findings contribute to a deeper understanding of data-driven persistent innovation and provide valuable insights for policymakers developing data markets, and for firms formulating data strategies aligned with their capabilities.
Lie symmetry approach to the dynamical behavior and conservation laws of actin filament electrical models
This research explores the dynamical properties and solutions of actin filaments, which serve as electrical conduits for ion transport along their lengths. Utilizing the Lie symmetry approach, we identify symmetry reductions that simplify the governing equation by lowering its dimensionality. This process leads to the formulation of a second-order differential equation, which, upon applying a Galilean transformation, is further converted into a system of first-order differential equations. Additionally, we investigate the bifurcation structure and sensitivity of the proposed dynamical system. When subjected to an external force, the system exhibits quasi-periodic behavior, which is detected using chaos analysis tools. Sensitivity analysis is also performed on the unperturbed system under varying initial conditions. Moreover, we establish the conservation laws associated with the equation and conduct a stability analysis of the model. Employing the tanh method, we derive exact solutions and visualize them through 3D and 2D graphical representations to gain deeper insights. These findings offer new perspectives on the studied equation and significantly contribute to the understanding of nonlinear wave dynamics.
Competency of triage nurse in the emergency department: A scoping review protocol
Introduction Triage is an essential strategy to mitigate crowding and guarantee patients’ safety in emergency departments. To improve the quality of triage in emergency departments, Nurses should be equipped with the necessary competencies. Therefore, this review aims to synthesize available evidence on the competency elements required for triage nurses in emergency departments and to identify factors that influence their competency development. Methods and analysis This scoping review will be implemented following the five steps outlined by Arksey and O’Malley. We will use the PCC (population, concept, context) frameworks-Triage nurse (Population), Nursing competency (Concept), and EDs (Context)- to determine the research questions, and formulate the search terms. We will search six electronic databases including PubMed, Embase, CINAHL Plus, Web of Science, and two Chinese databases (China National Knowledge Infrastructure and Wangfang Data). Internet resources including WorldCat, and Google Books will be also searched to ensure comprehensive coverage. Studies will be selected by two independent authors based on defined eligibly criteria, and completed in August 2025. This will be followed by data extraction, and summarizing in October 2025. Then, evidence will be synthesized using descriptive statistics and thematic analysis. Five-domain Consolidated Framework for Implementation Research will be used to guide our thematic analysis of barriers and facilitators to development of competency. The results will be presented in December 2025. Findings from this scoping review will be beneficial to develop the training programs to facilitate the successful transition of nurses into effective triage nurse roles in the future. Registration The scoping review was registered in Open Science (https://osf.io/6fcr4).
Integrative network toxicology and molecular docking reveal 4-Nonylphenol’s multifaceted mechanisms in breast cancer pathogenesis
Objective This study employs integrated network toxicology and molecular docking to investigate the molecular basis underlying 4-nonylphenol (4-NP)-mediated enhancement of breast cancer susceptibility. Methods We integrated data from multiple databases, including ChEMBL, STITCH, Swiss Target Prediction, GeneCards, OMIM and TTD. Core compound-disease-associated target genes were identified through Protein-Protein Interaction (PPI) network analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were subsequently employed to elucidate the potential molecular functions and biological pathways associated with these key targets. Molecular docking using AutoDock Vina was conducted to investigate the binding interactions between the core genes and 4-NP. Furthermore, the miRDB database was utilized to identify potential microRNAs (miRNAs) that may exert regulatory control over the pivotal genes. Results Five hub breast cancer target genes associated with 4-NP exposure were screened, containing TP53, HDAC1, ESR1, CTNNB1 and MYC. GO and KEGG analyses revealed that intersecting genes mainly influenced PI3K-Akt signaling, MicroRNAs in cancer, Chemical carcinogenesis−receptor activation and MAPK signaling. Molecular docking confirmed strong binding affinities of 4-NP to these hub genes. Subsequently several high-confidence candidate regulatory miRNAs especially miR-22, -148a, -181a and −152 were identified that shed light on miRNA regulatory mechanisms by which 4-NP increases breast cancer risk. Conclusion Our study demonstrates that 4-NP exposure perturbs protein conformational of hub targets, activating cascades and dysregulating signaling pathway to potentiate breast cancer risk. Furthermore, we identify a novel miRNA-mediated regulatory axis alongside MAPK signaling as critical mechanisms underpinning 4-NP toxicity.
Optimizing international trade: Strategies for retaining or taking over transport remit in Poland’s import and export landscape—A case study of Company X
This study explores the economic implications of transport remit management in Poland’s international trade landscape, with a particular focus on the operations of a medium-sized Polish forwarding company (Company X). Employing a mixed-methods approach, the research combines quantitative analysis of government datasets, firm-level transaction data, and qualitative insights from a targeted industry survey. The case study of Company X reveals notable reluctance among Polish enterprises to assume transport remit responsibilities, particularly in import operations, due to preferences for foreign partners, limited experience with international logistics, and concerns about administrative complexity. Analysis of Incoterms® usage patterns highlights a partial recovery of forwarding services in 2021, reflecting post-pandemic adjustments in logistics strategy. While the findings cannot be generalized to the entire Polish economy, they offer a detailed, data-driven illustration of the microeconomic factors influencing transport remit decisions. The study underscores the need for transparent procedures, legal clarity, and targeted training initiatives to increase the uptake of transport remit services by domestic firms. By improving internal capabilities and reducing reliance on foreign partners, Poland could strengthen its position in global trade and enhance value creation in the logistics sector.
Validation of the German Emotional Contagion Scale and development of a mimicry brief version
The susceptibility to emotional contagion has been psychometrically addressed by the self-reported Emotional Contagion Scale. With the present research, we validated a German adaptation of this scale and developed a mimicry brief version by selecting only the four items explicitly addressing the overt subprocess of mimicry. Across three studies (N1 = 195, N2 = 442, N3 = 180), involving various external measures of empathy, general personality domains, emotion recognition, and other constructs, the total German Emotional Contagion Scale demonstrated sound convergent and discriminant validity. A bi-factor model provided acceptable fit, suggesting the factorial validity of the total scale, which is aimed to measure a general factor, representing the susceptibility to emotional contagion. Longitudinal analyses across four measurement occasions revealed high temporal stabilities for the total scale across periods of up to 1 year as well as longitudinal measurement invariance of the factor loadings and partial invariance of the intercepts and residuals. The correlation pattern of the mimicry short version was comparable to the total Emotional Contagion Scale’s correlation pattern, the unidimensional factor structure was confirmed, and it also demonstrated high temporal stabilities and longitudinal invariance. The present research underscores the relevance of susceptibility to emotional contagion and mimicry as personality constructs and provides valid measurement tools for assessing them in future research and practical contexts (e.g., assessment in the clinical or work context).
American black bear (Ursus americanus) as a potential host for Campylobacter jejuni
The Gram-negative bacterium Campylobacter jejuni is part of the commensal gut microbiota of numerous animal species and a leading cause of bacterial foodborne illness in humans. Most complete genomes of C. jejuni are from strains isolated from human clinical, poultry, and ruminant samples. Here, we characterized and compared the genomes of C. jejuni that were isolated from American black bears in three states in the southeastern United States from 2014 to 2016. Despite the limited sample size (n = 9), the isolates displayed substantial genotypic variability, including eight distinct sequence types (STs) and variable gene content encoding surface glycan structures such as capsular polysaccharides (CPS) and lipooligosaccharides (LOS). Phylogenetic analysis identified several C. jejuni host generalist strains among the isolates from bears that clustered with isolates from domestic poultry, cattle, and environmental sources. Three isolates (SKBC94, SKBC3, SKBC5) clustered with wildlife-associated strains, exhibiting mutations or deletions in loci associated with cytolethal distending toxin production and oxidative stress resistance, potentially influencing host-specific colonization. Additionally, strains SKBC3 and SKBC5 harbored distinct Entner-Doudoroff (E-D) loci, suggesting a potential evolutionary fitness advantage. This study provides the first evidence of C. jejuni colonization in American black bears, highlighting their potential role as reservoirs for diverse C. jejuni lineages from both anthropogenic and environmental sources. Further research is needed to determine the prevalence and host specificity of C. jejuni strains in black bears and their potential implications for public and wildlife health.
Do chimpanzees (Pan troglodytes) attribute preferences to virtual competitors?
Many animal species live in multi-level societies regulated by complex patterns of dominance. Avoiding competition with dominant group-mates for resources such as food and mates is an important skill for subordinate individuals in these societies, if they wish to evade harassment and aggression. Chimpanzees (Pan troglodytes) are an example of such a species. This study investigated whether chimpanzees could understand the food preferences of their competitors, and make use of this understanding to select non-contested food items. Fifteen chimpanzees were given thorough experience of the differing target preferences of two virtual competitors. In the test, subjects had to select which of the two targets to approach, based on which competitor was present. To choose correctly, they would have to integrate the competitors’ preferences from across disparate observations, and then infer, before the competitor acted, what they would do in a novel situation. We also included a control condition featuring two targets for which subjects had no information about the competitors’ potential biases. The chimpanzees rapidly learned to direct their virtual agent to collect the targets, and some responded with vocalizations and hard knocking against the screen when competitors “stole” targets from the agent the subject was guiding. However, statistical analyses showed that, both at the individual and the group level, they did not succeed in selecting the correct target item at above-chance levels. Additionally, there was no significant difference between their performance in the test and control. We identify theoretical and methodological discrepancies that could explain the contrasting results of this and other studies.
1p-Enh-regulated CYP4B1 alleviates NNK-induced heart failure and lung cancer via the STAT3 pathway
Heart failure (HF) and lung cancer (LC) often coexist, yet their shared molecular mechanisms are unclear. We analyzed transcriptome data from the NCBI Gene Expression Omnibus (GEO) database (GSE141910, GSE57338) to identify 346 HF‑related differentially expressed genes (DEGs), then combined weighted gene co-expression network analysis (WGCNA) pinpointed 70 hub candidates. Further screening of these 70 hub candidates in TCGA lung cancer cohorts via LASSO, Random Forest, and multivariate Cox regression suggested CYP4B1 as the only independent prognostic marker. Subsequent ROC analysis validated CYP4B1’s diagnostic power in both HF and LC (AUC > 0.80). Immune-cell infiltration analysis demonstrated that high CYP4B1 expression correlated with increased infiltration of M2 macrophages. Experiments revealed CYP4B1 downregulation in angiotensin II (Ang II)-induced cardiomyocytes (AC-16) and LC cells (A549 & H1703). CYP4B1 overexpression attenuated angiotensin-II–induced cardiac hypertrophy and inhibited the migration, invasion, and proliferation of LC cells. Mechanistic studies revealed that CYP4B1 suppresses the JAK-STAT3 signaling, and we identified a novel distal enhancer, 1p‑Enh, that regulates CYP4B1 expression via chromatin looping. Additionally, prolonged exposure to the tobacco carcinogen NNK suppressed 1p‑Enh activity and downregulated CYP4B1 expression. These findings demonstrate the critical role of the NNK‑induced 1p‑Enh/CYP4B1 regulatory axis in both HF and LC, suggesting that CYP4B1 may serve as a potential therapeutic target for the concurrent treatment of HF and LC.
Predictors of health care utilization in patients with post-acute sequelae of COVID-19 (PASC)
Background Research on Post-acute sequelae of COVID (PASC) has focused on the prevalence of symptoms, leaving gaps in our understanding of predictors of health care seeking. Objective To identify clinical and sociodemographic characteristics associated with PASC care seeking. Methods Retrospective cohort study of adult patients with COVID-19 diagnosis between January 1, 2021 and June 30, 2022 in a community-based comprehensive health care delivery system at 21 hospitals and medical clinics in Northern California. Primary outcome was one or more PASC care seeking encounters at least 28 days after COVID-19 diagnosis in unadjusted and multivariate analyses. Results Of 600,295 surviving COVID patients, 3,797 (0.63%) had PASC care encounters. Female sex (RR 1.29, 95% CI 1.20–1.39), non-Hispanic White race, age 40–49 years (RR 2.35, 95% CI 2.08–2.66), more severe acute COVID illness, including an ED visit (RR 4.41, 95% CI 3.92–4.96), and severe depression (RR 1.69, 95% CI 1.32–2.16) were associated with PASC care. COVID immunization (RR 0.79, 95% CI 0.72–0.85), metformin use among diabetic patients (RR 0.74, 95% CI 0.64–0.84), and diagnosis during Omicron predominance (RR 0.54, 95% CI 0.49–0.60) were associated with lower PASC care. Conclusion Higher illness severity, medical comorbidities, and infection during the Delta and pre-Delta periods were associated with PASC care seeking. COVID immunization and metformin were associated with lower PASC care seeking. These findings could be useful in understanding the patterns and burden of care seeking for a new disease entity.
Construct prediction models for low muscle mass with metabolic syndrome using machine learning
Background Metabolic syndrome (MetS) and sarcopenia are major global public health problems, and their coexistence significantly increases the risk of death. In recent years, this trend has become increasingly prominent in younger populations, posing a major public health challenge. Numerous studies have regarded reduced muscle mass as a reliable indicator for identifying pre-sarcopenia. Nevertheless, there are currently no well-developed methods for identifying low muscle mass in individuals with MetS. Methods A total of 2,467 MetS patients (aged 18–59 years) with low muscle mass assessed by dual-energy X-ray absorptiometry (DXA) were included using data from the 2011–2018 National Health and Nutrition Examination Survey (NHANES). Least Absolute Shrinkage and Selection Operator (LASSO) regression was then used to screen for important features. A total of nine Machine learning (ML) models were constructed in this study. Area under the curve (AUC), F1 Score, Recall, Precision, Accuracy, Specificity, PPV, and NPV were used to evaluate the model’s performance and explain important predictors using the Shapley Additive Explain (SHAP) values. Results The Logistic Regression (LR) model performed the best overall, with an AUC of 0.925 (95% CI: 0.9043, 0.9443), alongside strong F1-score (0.87) and specificity (0.89). Five important predictors are displayed in the summary plot of SHAP values: height, gender, waist circumference, thigh length, and alkaline phosphatase (ALP). Conclusion This study developed an interpretable ML model based on SHAP methodology to identify risk factors for low muscle mass in a young population of MetS patients. Additionally, a web-based tool was implemented to facilitate sarcopenia screening.
Migration shapes senescence in a long-lived bird
Each year, billions of animals migrate across the globe on diverse spatial and temporal scales. Migration behavior thus plays a fundamental role in the life cycle and Darwinian fitness of many organisms. While the influence of migration on early-life survival and reproduction is well documented, its effects on senescence (aging) in advanced age remain largely unexplored. Using a unique 44-y ring-resighting dataset from a long-lived, partially migratory bird species, the Greater Flamingo ( Phoenicopterus roseus ), we demonstrate that migration plays a key role in shaping age-specific trajectories of mortality and reproduction. Resident flamingos exhibit higher early-life demographic performances, with lower baseline mortality than migrants, resulting in longer adult lifespan. Residents also have a higher probability of breeding than migrants, though their breeding success is similar. However, residents seem to pay for their early-life advantages in old age, experiencing accelerated actuarial and reproductive senescence compared to migrants. Overall, our study highlights the critical impact of migration on survival and reproduction throughout life, thereby illustrating the role played by behavioral decisions in the biology of aging in long-lived vertebrates.
Shaping resilient flood control system design through net present value assessments
Designing sustainable Flood Control Systems (FCSs) requires considering both the resiliency of the system and the long-term viability of investments. In this regard, our research aimed at integrating concepts of hydrological resiliency and cost-benefit analysis to design the most effective flood control network. To do so, first, the Storm Water Management Model (SWMM) was developed for simulating flood condition. Then, this model was coupled with the Pareto Envelope-based Selection Algorithm-II (PESA-II) to identify the optimal channels’ characteristics and generate a range of non-dominated solutions that balance implementation costs, system resilience (measured by the Simple Urban Flood Resilience Index, SUFRI), and overflow. Different flood management scenarios extracted for North Al-Batinah, Oman, a region under extreme flood events, exhibited high resilience and effectively reduced system overflow with reasonable costs. This highlights the value of optimization in resolving the conflicting objectives inherent in FCS design. Finally, net present values evaluated the long-term economic viability of each management scenario. The results revealed that strategies with moderate design costs and higher SUFRI values yielded optimal financial returns and substantial flood risk reductions. Also, the selected alternative based on net present value could reduce flood volume by 77.9%. This research underscores the critical role of incorporating resilience and cost-benefit analysis into FCS design to enhance the decision-making process.
Predicting mortality dynamics in cancer patients: A machine learning approach to pre-death events
Capturing the dynamic changes in patients’ internal states as they approach death due to fatal diseases remains a major challenge in understanding individual pathologies and improving end-of-life care. However, existing methods primarily focus on specific test values or organ dysfunction markers, failing to provide a comprehensive view of the evolving internal state preceding death. To address this, we analyzed electronic health record (EHR) data from a single institution, including 8,976 cancer patients and 77 laboratory parameters, by constructing continuous mortality prediction models based on gradient-boosting decision trees and leveraging them for temporal analyses. We applied Shapley Additive exPlanations (SHAP) to assess the contribution of individual features over time and employed a SHAP-based clustering approach to classify patients into distinct subtypes based on mortality-related feature dynamics. Our analysis identified three distinct clinical patterns in patients near death, with key laboratory parameters—including albumin, C-reactive protein, blood urea nitrogen, and lactate dehydrogenase—playing a critical role. Dimensionality reduction techniques demonstrated that SHAP-based patient stratification effectively captured hidden variations in terminal disease progression, whereas traditional stratification using raw laboratory values failed to do so. These findings suggest that machine learning-driven temporal analysis can reveal clinically meaningful state transitions that conventional approaches overlook, offering new insights into the heterogeneous nature of terminal disease progression. This framework has the potential to enhance personalized risk stratification and optimize individualized end-of-life care strategies by identifying distinct patient trajectories that may inform more targeted interventions.