Groundwater in the Eastern Salt Range of Pakistan is the primary source of drinking water for local communities but is increasingly threatened by combined geogenic enrichment and anthropogenic activities, particularly mining and agriculture. In mineralized semi-arid regions, distinguishing overlapping contamination sources and associated risks remains a key challenge. This study evaluates the spatial distribution of heavy metal(loid)s (HMs), identifies dominant contamination sources, and assesses ecological and human health risks in groundwater from the Chakwal District. A total of sixty groundwater samples were analyzed for Co, Cr, Cu, Mn, Ni, Zn, Pb, Fe, Cd, and As. Results indicate substantial spatial variability, with contamination hotspots concentrated near major coal mining areas. Chromium exhibited elevated concentrations (mean = 113 µg/L), exceeding WHO guideline values at several locations, while Cu and Zn occurred at elevated levels relative to background conditions. Ecological risk assessment revealed extremely high risk in 42 This visual summary illustrates the sources, transport pathways, and impacts of heavy metal(loid) (HM) contamination in groundwater of the Chakwal District, Eastern Salt Range, Pakistan. It depicts how anthropogenic activities, including coal mining, cement industry operations, agricultural inputs (fertilizers), livestock, and domestic wastewater discharge, release contaminants at the surface. These HMs infiltrate through soils and fractured lithologies via percolation processes, reaching aquifers and surface water bodies, ultimately resulting in contaminated groundwater used for domestic consumption. Spatial distribution maps highlight contamination hotspots across the study area. Multivariate statistical analyses (PCA, HCA, and PMF) are applied to identify and differentiate geogenic, industrial, agricultural, and mixed pollution sources. The framework further integrates ecological risk assessment using the Potential Ecological Risk Index (PERI) and human health risk evaluation through Hazard Index and Total Carcinogenic Risk, identifying zones of elevated vulnerability. The figure emphasizes the combined influence of lithology and human activities on groundwater contamination and supports targeted monitoring and management in mining-affected regions. Groundwater metal(loid) contamination shows pronounced spatial hotspots. Chromium exceeds WHO limits; Cd and As drive extreme ecological risk. Multivariate models resolve industrial, agricultural, and geogenic sources. Non-carcinogenic and carcinogenic risks exceed accepted safety thresholds. Integrated geospatial-statistical approach supports risk-based groundwater management.
PurposeBy integrating social cognitive theory and the job demands-resources model, this study investigates how employees experience artificial intelligence (AI) in the workplace.Design/methodology/approachTwo experimental survey studies were conducted with employees in the United Arab Emirates. Study 1 (N = 520) tested the effects of perceived usefulness on self-efficacy, competence, and decision control. Study 2 (N = 480) extended the model to employee well-being, testing mediation via decision control and moderation by algorithmic accountability. Measurement validity was established through confirmatory factor analysis, and hypotheses were tested using MANCOVA, regression, and PROCESS mediation/moderation models.FindingsPerceived AI usefulness enhanced self-efficacy, competence, and decision control. Both self-efficacy and competence mediated the usefulness-control relationship, with self-efficacy exerting the stronger effect. Algorithmic accountability expanded the positive impact of control on well-being.Originality/valueThe study offers a novel theoretical integration and experimental evidence showing how workplace AI meaningfully shapes employees' capability, control, and well-being.
Abstract Why do foreign investors exit Central and Eastern Europe (CEE) despite formal alignment with EU institutional standards? We argue that divestment reflects not cyclical volatility but strategic responses to institutional fault lines, the non-linear interaction between de jure legal frameworks and de facto enforcement credibility. Analysing panel data (1990–2024) for 20 CEE economies through a hybrid econometric–machine learning framework (ARDL bounds testing, Dumitrescu–Hurlin causality, Random Forest with SHAP diagnostics), we find that: (1) rule-based governance (corruption control) Granger-predicts divestment (Z = 3.897, p = 0.001), whereas formal rule-of-law indicators show ambiguous effects; (2) macroeconomic instability robustly elevates exit risk (β = 0.277, p = 0.001); and (3) trade openness and human capital amplify divestment only when enforcement credibility is weak. Economic scale dominates predictive power (72.0%) but reflects historical FDI exposure, not causal drivers. Critically, machine learning reveals that divestment tipping points emerge from combinations of high inflation, low corruption control, and high openness, patterns invisible to linear models. These findings reframe capital flight as a rational response to institutional dissonance rather than market failure. For policymakers, the implication is clear: EU cohesion policy should shift from formal harmonisation towards performance-based governance that prioritises verifiable anti-corruption enforcement and macroeconomic credibility.
Scientists are becoming more vocal about the importance of effective and dependable energy storage systems. These systems play a crucial role in managing our energy needs and ensuring a sustainable future. Enhancing their efficiency will be key to addressing current energy challenges. In most industrial and engineering practices like heat exchangers, geothermal systems, and semiconductor fabrication latent heat storage and phase-change mechanisms are indispensable as melting heat is the key to thermal management, energy optimization, and thus the current study explores the behavior of a two-dimensional Maxwell nanofluid under radiative heat transfer over a variable-thickness deformable surface, subjected to the physical effects of a magnetic field and stagnation-point flow. Besides, the investigation would incorporate melting heat effects under nonlinear stratification to reflect enhanced and realistic heat transfer behaviors. For better thermal and reactive performances, this study will be investigating the mechanics of heat generation-absorption in parallel with the effects of chemical reactions. Additionally, an intensive study of thermophoretic diffusion and Brownian dynamics will be conducted. The governing equations for velocity, temperature, and nanoparticle concentration were reduced to dimensionless nonlinear ODEs under boundary layer assumptions by applying appropriate transformations. The NDSolve method solves the equations numerically. The analysis presents the effects of the main parameters affecting flow, temperature, and concentration on the drag on the surface and the rates of heat and mass transfer. The results reveal that the applied magnetic field diminishes fluid velocity, while the velocity ratio and the melting parameters enhance it. Thermal stratification and melting effects lower the fluid temperature, while thermophoresis and Brownian motion raise it. Likewise, increase in Lewis number and chemical reaction parameters diminishes nanoparticle concentration. The results exhibit extremely good agreement with existing literature.
