Search Results (264)

Ventilation network feature graphs (Q-H graphs) are a key visualisation tool for mine ventilation systems, and their automated generation reduces to a combinatorial optimisation problem over independent-path permutations. Existing methods, however, exhibit three limitations: a single-dimensional evaluation criterion, inadequate nodal pressure-energy assignment, and unstable convergence in factorial-scale search spaces. This paper proposes an adaptive NSGA-II (A-NSGA-II) framework with coordinated enhancements at the evaluation, modelling, and algorithmic levels. A three-objective system that minimises split-block count, topological-spatial discrepancy, and layout fragmentation is established, together with an aggregate evaluation score (AES) for engineering decision-making; nodal pressure energies are reconstructed via the longest path on a directed acyclic graph; and topology-aware initialisation, Lagrange three-point interpolated adaptive operators, and periodic memetic local search are integrated within NSGA-II. Experiments on two mine ventilation networks (75 and 112 branches) over 30 independent trials show that A-NSGA-II consistently outperforms four benchmarks (NSGA-II, MOEA/D, SPEA2, and MOSA) in terms of split-block count, AES, and hypervolume; statistical tests confirm significant, large-effect HV advantages on the 112-branch network, while the 75-branch network shows a 56.6–71.5% reduction in HV standard deviation. Full article

Fencing is an intermittent combat sport in which performance depends on the interaction of neuromuscular qualities, aerobic support, and weapon-specific demands. However, evidence on sex-based differences in the physical capacity profiles of regional fencers remains limited. This study compared the physical capacity profiles of 27 fencers from the Liga Vallecaucana de Esgrima (13 women and 14 men; 14–31 years) in an observational, cross-sectional, comparative study. Field-based assessments included push-ups, sit-ups, squats, jump squats, pull-ups, terminal speed attained in the 20-m shuttle run test, and estimated VO2max. The analysis adopted an exploratory, estimation-oriented approach based on mean differences, 95% confidence intervals, Hedges’ g, supplementary significance testing, false discovery rate adjustment, and a directed acyclic graph to clarify causal assumptions. The most robust sex-based difference was observed in pull-up performance, with men outperforming women by 5.43 repetitions (95% CI: 3.51 to 7.45; g = 1.88), and this was the only comparison retained after FDR correction. No conclusive sex-based differences were found for push-ups, sit-ups, squats, jump squats, terminal shuttle-run speed, or estimated VO2max. Mean estimated VO2max for the overall sample was 43.48 ± 9.12 mL·kg⁻¹·min⁻¹. These findings suggest that upper-limb pulling strength may be the main distinguishing physical quality in this cohort, although its implications for individualized conditioning remain to be established. Nevertheless, the results should be interpreted as observational associations rather than causal effects because of the cross-sectional design, the small sample, the field-based measurements, the imbalance in weapon distribution, and the lack of standardized measures of training exposure. Full article

The increasing adoption of data-driven educational systems requires reliable methods to predict student readiness for future coursework and support personalised learning pathways. This study proposes a graph-enhanced ensemble framework that integrates curriculum structure and skill-gap awareness to estimate student course readiness. A global prerequisite directed acyclic graph (DAG) of university subjects was constructed to model curriculum dependencies, from which structural features including the PageRank, in-degree, out-degree, and prerequisite chain depth were derived. In parallel, a domain-informed skill cluster mapping grouped subjects into nine interpretable competency domains to enable skill-gap analysis. These curriculum-aware features were combined with academic history, behavioural engagement, and demographic indicators to produce 38 engineered features for each student–subject pair. Six models (CatBoost, XGBoost, LightGBM, FT-Transformer, MLP and TabPFN) were trained and combined using a weighted ensemble. Experiments on a real-world institutional dataset containing 20,581 students and 727,168 records achieved an AUC of 0.8908 for predicting course success. Ablation experiments demonstrate that graph-derived and skill-cluster features provide modest but statistically significant incremental value. The resulting model was integrated into a prototype personalised recommender that prioritizes curriculum-consistent learning pathways. The proposed framework provides an interpretable and curriculum-aware approach for personalised learning. While the model demonstrates strong overall performance, a moderate gender disparity in the false positive rate was observed. Results were obtained on a large longitudinal dataset from a single university, and external validation at other institutions is needed to confirm generalizability. Full article

Background and Objectives: Whether hyperglycaemia causes hypertension, hypertension worsens glycaemic control, or both conditions arise from shared metabolic drivers remains clinically consequential yet unresolved. This study applies a triangulated causal inference framework to large-scale population data to quantify the direction, magnitude, and robustness of the glucose–blood pressure relationship. The primary objective is to test for bidirectional causal effects between glycaemic status and blood pressure; secondary objectives include quantifying effect magnitudes by multiple complementary methods and assessing robustness to unmeasured confounding. Materials and Methods: We analysed 55,386 adults from the National Health and Nutrition Examination Survey (NHANES, 1999–2023). Multiple causal inference techniques were integrated: directed acyclic graph (DAG) testing, structural equation modelling (SEM) with latent constructs, propensity score matching (PSM), inverse probability weighting (IPW), doubly robust augmented IPW (AIPW), and E-value/Rosenbaum Γ sensitivity analyses, with external replication in the Framingham Heart Study data (n = 4240). Results: All of the methods used confirmed the bidirectional effects. PSM showed that hyperglycaemia increased systolic BP by 1.76 mmHg (95% CI: 0.58–2.96, p = 0.005), and hypertension increased fasting glucose by 6.55 mg/dL (95% CI: 4.61–8.58, p < 0.001), revealing a marked asymmetry favouring the BP → glucose direction. AIPW confirmed both effects (3.51 mmHg and 6.15 mg/dL, both p < 0.001). SEM identified significant bidirectional structural paths between latent glycaemic and blood-pressure constructs, with the Glycaemic → BPState path showing a negative coefficient (β = −0.15, p = 0.043), a sign reversal attributable to conditioning on the shared latent metabolic-syndrome factor. Sensitivity analyses indicated that an unmeasured confounder would need associations of RR ≥ 1.40–1.64 with both exposure and outcome to nullify these estimates, representing moderate robustness. Conclusions: The BP → glucose pathway is the dominant causal direction, suggesting that prioritisation of hypertension control may yield underappreciated benefits for glycaemic regulation. These findings support integrated cardiometabolic management strategies. Full article

Type 1 diabetes mellitus (T1DM) is a chronic, non-preventable, and incurable disease that requires lifelong insulin administration. The principal challenge is calculating the prandial insulin bolus to avoid hypoglycemia and hyperglycemia. Traditional bolus calculators are based on limited number of variables, but there are many variables that define the complex interactions among glucose levels, like carbohydrate intake, physical activity, mood, and contextual factors. While recent artificial intelligence (AI) approaches have shown promise in glucose prediction, most remain correlational and offer limited interpretability for clinical decision support. This study evaluates a causal inference-based framework for insulin bolus calculation using Directed Acyclic Graphs (DAGs) and the Greedy Relaxation of the Sparsest Permutation (GRaSP). Historical data from individuals with T1DM were analyzed, incorporating domain knowledge constraints to guide structure learning. A bootstrap-based stability analysis was conducted to evaluate the robustness of inferred relationships. Results show that integrating prior medical knowledge reduces graph complexity and improves interpretability. However, bootstrap stability reflects robustness of the learning procedure rather than causal validity. The findings suggest that the proposed framework is useful for generating plausible causal hypotheses, but not for confirming causal relationships. Further validation using conditional independence testing, equivalence class analysis, and temporal causal methods is required. However, the proposed framework focuses on generating plausible causal hypotheses rather than establishing causal validity, which requires further refutation-based validation. Full article

To examine the complex relationships among physical activity, emotion regulation, psychological stress, and mood states in college students, this study analyzed questionnaire data collected from 494 participants. Network analysis was employed to construct a global association network, compare gender differences, and characterize patterns of directed statistical dependencies via directed acyclic graph (DAG) analysis. The results showed that: (1) the network comprised 25 nodes and 94 non-zero edges, reflecting extensive conditional associations across the four domains; (2) bridge centrality analysis identified cognitive reappraisal, self-related emotions, and anger as key bridge nodes, with cognitive reappraisal exhibiting the highest bridge strength; (3) accuracy and stability analyses yielded a centrality stability coefficient (CS) of 0.749 for strength, indicating adequate network stability; (4) network comparison tests revealed no significant gender differences in overall network structure or global strength, although certain local edge weights differed; (5) DAG analysis suggested that stable directional dependencies were primarily concentrated within individual subsystems, with no marked structural differences observed between male and female groups. In conclusion, physical activity, emotion regulation, psychological stress, and mood states appear to constitute an interconnected psychological adaptation system. Cognitive reappraisal, self-related emotions, and anger likely serve as pivotal bridge nodes warranting priority in future longitudinal research and targeted interventions. Full article

The increase in delay-sensitive application tasks requires heterogeneous edge clusters to maintain low online latency and energy efficiency without relying on rigid scheduling policies. To address this, we propose HERO (Hybrid Energy-aware Ranking and Optimization), a lightweight collaborative scheduling framework. HERO utilizes a perturbation-based communication-aware multi-layer perceptron (MLP) predictor to quantify global time sensitivity and discover latent time slack in non-critical paths. A hybrid budget mechanism then converts this slack into customized DVFS decisions. These decisions are based on the inherent computational load and topological criticality to optimize energy consumption. A communication-aware hole-filling strategy dynamically recovers sporadic idle times fragmented by heterogeneous communication overhead. Extensive simulations were conducted across varying DAG depths, parallelism levels, and system utilizations. Compared to state-of-the-art algorithms (NSGA-II, SSA, TOM, and DPMC), HERO reduced the completion time by an average of 10.89% under high-density topologies, and achieved up to 4.04% energy savings across varying task depths. Full article

The joint probability distribution of observable variables from a system is constrained by the underlying causal structure. In the presence of hidden variables, untestable independencies that involve hidden variables lead to testable causally-imposed inequality constraints for observable variables, whose violation can reject the compatibility of a causal structure with data. One type of causally informative inequalities is entropic inequalities, which appear in the space of entropic terms associated with the distribution of observable variables. We derive a new type of minimum information (minInf) entropic inequalities that substantially increases causal inference power. These new entropic inequalities appear when considering the constraints that the causal structure imposes on entropic terms determined by information minimization within families of distributions that preserve sets of marginals shared with the original distribution. We introduce a new family of minInf data processing inequalities and a procedure to recursively combine different types of data processing inequalities to create tighter testable entropic inequalities. We extensively illustrate the applicability of this procedure in the instrumental causal scenario, integrating the new inequalities with standard instrumental entropic inequalities constructed with multivariate instrumental sets. We also provide additional examples with other types of entropic inequalities, such as the Information Causality and Groups-Decomposition inequalities. Full article

Efficient task allocation and coordination are critical for heterogeneous multi-agent systems operating in dynamic field environments. This paper presents a closed-loop framework that integrates Large Language Models (LLMs) with graph-based optimisation to enable end-to-end task decomposition, allocation, and adaptive execution. High-level task scripts are initially parsed by an LLM into structured execution flows, which are transformed into Directed Acyclic Graphs (DAGs) capturing action-level dependencies. A Genetic Algorithm (GA) then optimises agent-to-task assignments by minimising makespan under capability and battery constraints. To ensure robustness, the framework incorporates an LLM-driven recovery module that enables localised replanning under execution failures without interrupting unaffected agents. System-level experiments in a high-fidelity agroforestry simulation demonstrate a 37% increase ($p<0.001$) in harvesting productivity and a 19% reduction in human idle time compared to manual baselines. Under mid-execution failures, the system maintains significantly higher performance, with replanning latencies averaging 24 s. The framework scales to large fleets (up to 1000 agents) and effectively enhances human–robot collaboration through structured, dependency-aware coordination. Full article

Collaborative robots, or cobots, are designed to improve productivity and safety in industrial settings. However, effective Human–Robot Collaboration (HRC) relies heavily on the human operator’s trust in the robotic partner. This study posits that trust is significantly enhanced by the robot’s ability to adapt to unpredictable human behavior. To achieve this adaptability, we propose applying an Adversarial Reinforcement Learning (ARL) framework to the robot’s activity planning. We model the assembly process as a Markov Decision Process (MDP) on a Directed Acyclic Graph (DAG). The robot learns an assembly policy using an on-policy algorithm while a simulated human agent, trained with the same algorithm, acts as an adversary that introduces disturbances and delays. We applied the proposed approach to a simple industrial case study and evaluated it on complex assembly sequences generated synthetically. Although the ARL-trained robot did not outperform conventional assembly optimization algorithms in terms of task completion time, it guaranteed robustness against human variability. This ensured task completion within a bounded timeframe regardless of human actions. By demonstrating consistent performance and adaptability in the face of uncertainty, the robot exhibits the Ability and Benevolence components of the ABI model of trust. This fosters a more resilient and trustworthy collaborative environment. Full article

Background: While smoking is the dominant global driver of lung cancer, less than a quarter of Ethiopian patients have ever smoked, pointing to locally relevant risk factors. Evidence to guide prevention and early detection in resource-limited settings is scanty. Methods: To address this gap, we conducted a multicenter matched case–control study including 351 histopathologically confirmed primary lung cancer cases and 702 hospital-based controls matched by sex, age (±5 years), and residence. Directed acyclic graphs informed the selection of variables, and multivariable hierarchical conditional logistic regression was used to identify risk factors beyond smoking. Results: The analysis shows that lung cancer was independently associated with low education, wealth, solid-fuel use, occupational exposure, insufficient physical activity, meat-based and processed food dietary patterns, secondhand smoke (SHS), prior tuberculosis, and family history of cancer. Subgroup analysis by sex revealed consistent associations across males and females, but exposure distributions explained sex-specific patterns: smoking, occupational exposure, meat-based diets, and family history were more common among males, whereas SHS, the use of solid fuels, and processed food dietary patterns predominated in females. Conclusions: Lung cancer in Ethiopia appears to be associated with several factors in addition to smoking. Gender-sensitive public health interventions targeting these locally relevant risk factors are essential for effective prevention and early detection. Full article

Apache Spark has gained widespread adoption for large-scale data processing. However, conventional caching methods inadequately address the dual challenges of performance bottlenecks and escalating energy consumption in data-intensive workloads. This paper introduces a sustainable computing framework that integrates Directed Acyclic Graph (DAG) dependency analysis with garbage collection (GC) behavior monitoring to optimize data placement between DRAM and non-volatile memory (NVM). The proposed Intelligent Hybrid Caching Management Framework (IHCMF) dynamically predicts data access patterns and migrates cache blocks based on cost–benefit analysis, achieving a 37.5% execution time reduction over default Spark configurations in SparkBench evaluations. By improving throughput-per-watt and projecting potential benefits from NVM’s near-zero idle power and extended hardware lifespan, IHCMF provides a scalable, cost-effective caching solution for resource-constrained edge computing environments. This work demonstrates that high-performance computing can be reconciled with environmental sustainability through intelligent memory management. Full article

Background and Aims: Chronic liver diseases (CLD) represent a significant healthcare burden, mostly due to late diagnosis and numerous co-morbidities. We evaluated the effect of co-morbidities, cirrhosis, and disease etiology on hospitalization duration. Methods: Hospitalizations due to alcohol-related, viral, autoimmune, and overlapping liver disease in Belgrade, Serbia (2016–2022), were identified using pre-defined discharge codes. We investigated the hospitalization trend descriptively by plotting the relative mean change in the hospitalization length against time. Assuming the covariate relationship in the directed acyclic graph, we estimated the direct causal effect of the diagnosis type on the length of stay (LOS) by fitting pre-specified Bayesian distributional lognormal models based on domain knowledge. We conducted a post hoc analysis of the impact of cirrhosis on LOS per primary diagnosis. Results: The empirical data show a decrease in the estimated average LOS (8.25–5.51 days). For the same period, the median LOS decreased (4 days (IQR 0–12) to 1 day (IQR 1–7)). In 2021, the share of short-term hospitalizations rose to 46.94%, while the median long-term hospitalization peaked at 11.5 days (IQR 7–21). The expected LOS was the highest for the primary diagnosis of autoimmune liver disease (15.89, 95% CI [14.74, 17.2] days), followed by alcohol-related liver disease (14.22, 95% CI [13.68, 14.79] days). The largest impact of cirrhosis on LOS was observed among patients hospitalized due to viral disease (4.19, 95% CI [2.29, 6.33] days). Conclusions: The presence of co-morbidities and cirrhosis significantly affects LOS. In order to provide better treatment and reduce healthcare costs, there is the need to detect liver disease at earlier stages and better manage its associated co-morbidities. Full article

Efficient workflow scheduling in cloud–edge environments is severely bottlenecked by long-horizon dependencies and myopic resource fragmentation. This paper proposes the Decoupled Offline Sequence-based (DOS) scheduling framework to address these challenges. By decoupling expert policy learning from runtime deployment, DOS utilizes a multi-dimensional priority-aware linearization strategy to deterministically transform DAG-structured workflows into dependency-consistent sequences. Leveraging offline expert trajectories, we train UDC, a Gated CNN achieving unified sequence modeling via innovative triplet-to-unary encoding, equipped with explicit action masking to distill long-horizon spatio-temporal packing patterns. This mechanism enables rapid feed-forward inference without costly online environment interactions or policy updates. Extensive evaluations on real-world Alibaba cluster workloads demonstrate that DOS not only consistently minimizes average makespan compared to classical heuristics, but also drastically reduces resource-blocked steps under extreme concurrency versus online Actor–Critic experts. Crucially, compared to the Decision Transformer (DT) baseline, the UDC model achieves strictly scale-invariant and significantly lower inference latency, highlighting its robust scalability and practicality for large-scale continuum systems. Full article

With the increasing complexity and diverse requirements of multi-platform avionics systems, traditional static service composition approaches are gradually unable to meet the real-time, flexibility, and energy efficiency requirements of mission execution. This study proposes a service orchestration framework for complex tasks and heterogeneous environments, where task flow is modeled by directed acyclic graphs (DAG) and a formal description integrates task dependencies and resource characteristics. The architecture consists of a task parsing engine and a service orchestration engine, with the task parsing engine responsible for parsing, verifying, and generating executable task entities, and the service orchestration engine managing execution in both static and dynamic modes. Through module collaboration, this framework supports task orchestration from definition to execution. A prototype system is built and performance tests are conducted in typical operational scenarios. The experimental results show that the framework performs efficiently under complex tasks and high concurrency conditions, with high execution efficiency and good scalability. The system can effectively meet the orchestration requirements of multi-platform avionics systems. Full article