Sustainability disclosure increasingly influences investment decisions and market valuation in global capital markets. Traditional valuation frameworks primarily rely on accounting fundamentals such as earnings and book value, while the growing importance of environmental, social, and governance (ESG) information has expanded the role of non-financial indicators in strategic investment analysis. Empirical evidence regarding the value relevance of ESG disclosure in emerging markets, particularly within the Nigerian capital market, remains limited. This study investigates the relationship between ESG disclosure and firm market value using an extended Ohlson valuation framework. Annual firm-level data obtained from companies listed on the Nigerian Exchange Group were analyzed using panel regression techniques, including fixed-effects estimation and dynamic generalized method of moments (GMM) analysis. The study further examined the individual effects of ESG disclosures and evaluated the moderating role of governance quality in strengthening the value relevance of environmental and social disclosure. The results showed that ESG disclosure positively affected share prices, indicating that sustainability information contributed to investors’ valuation decisions. Governance disclosure exhibited the strongest and most consistent positive effect on market value, while social disclosure remained positively significant and environmental disclosure demonstrated a weaker but positive influence. The interaction analysis further revealed that governance quality strengthened the positive effects of environmental and social disclosure on share prices. These findings indicate that governance mechanisms improve the credibility and valuation relevance of sustainability information in emerging capital markets. This study extends the Ohlson valuation framework by integrating ESG dimensions and governance interaction effects within a data-driven market valuation model. The findings provide practical insights for corporate managers, investors, regulators, and policymakers seeking to enhance strategic sustainability reporting and improve long-term market confidence in emerging economies.
Rice is a strategic food commodity, and its supply chain involves farms, mills, distribution centres, and markets. This study presented a multi‑objective mathematical model for a rice supply chain that minimized total costs (economic objective), soil erosion caused by water used in cultivation (environmental objective), and total water consumption. A real case study from Iran including four major rice‑producing regions was analysed under three water availability scenarios. To handle uncertainty in rainfall and irrigation water, stochastic programming was applied and the multi‑objective model was solved using extended goal programming. Sensitivity analyses examined changes in water availability, objective function weights, and import limits. Results demonstrated that under water‑scarce conditions, production in arid regions (e.g., Khuzestan) was not economically or environmentally viable, leading to increased imports. The model provides a practical tool for policymakers balancing food security, cost, environmental sustainability, and water conservation.
Personnel selection represents one of the most important tasks of human resource management, while the selection of engineering personnel is of particular interest to the industrial manufacturing sector. This study dealt with the problem of selecting a quality engineer based on applications submitted in response to a job advertisement announced by a company that manufactured industrial equipment. The aim of the research is to demonstrate the robustness of a hybrid Multi-Criteria Decision-Making (MCDM) model based on the integration of the CRiteria Importance Through Intercriteria Correlation (CRITIC) method and the Square-Root based Evaluation Method (SREM). Such a model aims to reduce subjectivity in the decision-making process and serves as a supporting tool for company management in the personnel selection process. The results obtained from the case study demonstrated that the proposed model objectively and reliably identified the most suitable candidate, while taking into account different performance characteristics. The solution proved to be highly stable, since the sensitivity analysis revealed that even in the case where all criteria had equal importance, the best candidates remained at the top of the ranking list. Furthermore, this research indicated that the proposed CRITIC–SREM model could probably be a standard decision-support tool for decision-makers in personnel selection and the handling of similar problems.
Binary chronic obstructive pulmonary disease healthy control (COPD–HC) classification results in the Exasens dataset may be inflated by age and smoking-related structure. This exploratory secondary complete-case audit examined whether sensor-derived salivary dielectric response features provide internally predictive information beyond age, recorded sex/gender, and smoking status. The public Exasens dataset contained 399 records, of which 100 had complete salivary response measurements. Demographic only, salivary-response only, and combined feature sets were evaluated for binary COPD–HC and four-class label classification, using repeated stratified cross validation, limited overlap sensitivity analyses, calibration summaries, and SHapley Additive exPlanations (SHAP) based audits of model behavior. No independent external validation cohort was available. In the complete-case COPD–HC analysis, demographic only Logistic Regression achieved a balanced accuracy of 0.955 whereas the best salivary- response only model achieved 0.56. In the four-class complete-case task, combined Gradient Boosting achieved a balanced accuracy of 0.68 and a receiver operating characteristic–area under the curve (ROC–AUC) of 0.881. This represents exploratory internal separation, with performance strongest for chronic obstructive pulmonary disease (COPD) and healthy control (HC), moderate for infection-related cases, and weak for asthma. Matching and overlap weighting analyses did not support an independent COPD–HC contribution from salivary-response features, primarily because common support for age and smoking was poor. These findings should be interpreted as a secondary and hypothesis-generating dataset audit rather than validation of clinical prediction model. Transportability was uncertain under demographic shift or different salivary-response missingness structures. External validation, richer clinical metadata, and improved demographic and smoking overlap across groups are required before any diagnostic, screening, triage, risk-prediction, or clinical decision support could be considered.
The spatiotemporal spread of plant pathogens introduces substantial uncertainty into agricultural production systems, thereby complicating timely disease management and resource allocation. An information-theoretic framework was developed to quantify structural uncertainty in pathogen transmission by integrating stochastic population dynamics with system entropy. A finite agricultural field containing a homogeneous crop population was represented as a two-state system, in which individual plants existed in either a healthy or an infected state. Pathogen transmission was modeled as a birth-death process, while the probability distribution of the infection state was characterized using a binomial formulation under the assumed population structure. On this basis, system entropy was introduced as a quantitative measure of structural uncertainty. It was demonstrated that entropy followed a characteristic inverted-U-shaped trajectory, increasing during the early stages of disease propagation and reaching a maximum at an intermediate level of infection intensity, where uncertainty and system volatility were greatest. As pathogen spread approached saturation, entropy progressively decreased. This entropy maximum was shown to define a critical operational threshold at which surveillance, treatment, and resource deployment can be implemented with the greatest expected effectiveness. Beyond operational decision support, the proposed framework establishes a quantitative basis for evaluating long-term risk mitigation strategies. Reductions in entropy achieved through the deployment of disease-resistant crop varieties, improved biosecurity measures, optimized field infrastructure, or enhanced monitoring systems can be directly interpreted as reductions in structural uncertainty, thereby providing measurable indicators for minimizing disease-related economic losses, improving intervention efficiency, and strengthening the resilience of agricultural production systems and supply chains. By establishing a rigorous connection between stochastic epidemiological dynamics and information theory, the proposed framework provides a generalizable analytical foundation for uncertainty-aware agricultural management and data-driven decision-making under pathogen risk.
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