Abstract Ensuring proper work–rest cycles is a promising strategy for relieving fatigue in construction workers. However, inadequate recovery is common among construction workers due to inappropriate rest arrangements, increasing the risk of excessive fatigue. How to effectively develop rest plans for construction workers is challenging because individual differences in recovery rates can reduce the effectiveness of standardized rest schedules, while fully personalized rest arrangements are rarely feasible on construction sites. This study aims to predict the recovery patterns of construction workers after working to exhaustion, providing a basis for recovery pattern–based management that considers individual differences while remaining practically feasible. Field studies were conducted at five construction sites in mainland China, involving 199 construction workers, between March and July 2024. Personal and physiological variables of the workers, along with environmental parameters on construction sites, were collected. Three fundamental recovery patterns—rapid, moderate, and slow recovery—were identified through time-series clustering analysis. Construction workers with rapid recovery patterns can achieve approximately 95% recovery after a 40-min rest, while those with moderate and slow recovery patterns achieve approximately 73% and 49% recovery, respectively. To determine the recovery pattern of each worker, a machine-learning-based prediction model was developed that comprehensively considered the personal characteristics of workers and the environmental conditions to which they were exposed. The developed model demonstrated good performance, achieving an accuracy of 87% on the external test dataset. This study proposes grouping construction workers by their predicted recovery patterns to implement tailored rest schedules. This approach ensures adequate recovery by balancing individual physiological differences with on-site practical constraints, thereby providing actionable decision support for rest planning.
Third-party food delivery services (TPFDs) have reshaped how consumers experience restaurant meals. Although a substantial body of research on TPFDs exists, measurement of the Off-Premise Dining Experience (OPDineX) remains fragmented across levels of abstraction in variables measured (high-order vs attribute-based), discrete journey stages, and measures from other contexts. Addressing this gap, this study develops and validates the OPDineX scale as a unified, journey-based measure capturing customers' holistic experience with salient attributes across pre-meal, in-meal, and post-meal phases. The study followed a four-stage scale development process, and qualitative item generation and refinement were combined with four quantitative studies conducted in Australia and Singapore. The resulting OPDineX scale comprises eight dimensions and demonstrates strong reliability, convergent, discriminant, and predictive validity, as well as measurement invariance across contexts. By operationalising customer experience at the attribute level while preserving a coherent journey structure, OPDineX advances the measurement of customer experience and provides a robust diagnostic tool for TPFD research and managerial decision-making.
This experimental study focused on rock weathering to understand the geochemical processes involved in the host rock matrix and the water at different time intervals. In this study, five different tanks were designed in the laboratory, consisting of montmorillonite clay (T1), coarse peridotite and clay (T2), finer peridotite and clay (T3), peridotite (T4) and only lake water as a control (T5). The T1-T4 tanks were allowed to react with natural lake water in a laboratory setup for eight weeks. During the experiment, weekly measurements were taken for physical parameters, major ions, and trace metals in water. The results show that increasing pH and HCO3- initiates the rock weathering by acquiring CO2 directly from the air at the early stage of the experiment. Subsequently, precipitation of carbonate minerals, resulting from the removal of Ca2+ and Mg2+ from the liquid in the later stages of the experiment. Principal component analysis also confirms the early dissolution of minerals as a carbon source, followed by enhanced weathering leads to the precipitation of carbonate minerals. Based on solid chemistry, experiments with clay (T1) and peridotite (T4) enhance mineral precipitation, whereas a mixture of rocks (T2 and T3) supports mineral dissolution. T1 exhibits a high adsorption capacity for Fe and Ni, attributed to both ion exchange and surface complexation. In these experiments, carbonates would nucleate preferentially on or within clay-rich aggregates, where Ca2+, Mg2+ and HCO3- are concentrated at the grain-water interface via cation exchange and surface complexation, thereby enhancing early crystal growth.
Coal gasification fine slag (CGFS) contains considerable residual carbon, but its separation is challenging due to fine particle size and complex phase associations. This study compares conventional flotation and enhanced gravity separation for CGFS, integrating response surface methodology and machine learning to predict combustible recovery and analyze parameter effects. Results show that flotation achieved only similar to 54% combustible recovery even at high reagent dosages, while enhanced gravity separation reached up to 96.8% under optimized conditions (20 Hz frequency, 0.020 MPa pressure, 30 g/L solid concentration). Fuerstenau curves confirm higher separation efficiency (1.587 vs. 1.288) for gravity separation. The machine learning model achieved superior prediction accuracy (R-2 = 0.95) compared to RSM (R-2 = 0.85), revealing parameter influence order: frequency > pressure > concentration. This study demonstrates that enhanced gravity separation offers a reagent-free, highly effective alternative for CGFS upgrading, while data-driven modeling provides significant advantages over conventional regression methods for complex separation processes.
In-situ hydrogen (H2) generation accompanying carbon dioxide (CO2) mineralization in basalt offers a promising dual pathway for renewable energy generation and geological carbon storage. Currently, the interactive effects of thermodynamics, hydrochemistry, and reservoir parameters on CO2 sequestration and H2 generation mechanisms remain unclear. Therefore, a two-dimensional multiphysics reactive transport model, combined with an L25(56) orthogonal design, is developed to assess the sensitivity of six key factors (porosity, horizontal permeability, permeability anisotropy ratio, temperature, pressure, and pH) on the coupled CO2 mineralization-H2 production process. Simulations reveal that the injected CO2 induces the dissolution and redox reactions of Fe-rich minerals (e.g., olivine), generating substantial in-situ H2 while converting CO2 into stable carbonate minerals (e.g., magnesite and siderite). The spatial distribution of these reactions is controlled by the acidic fluid migration. Sensitivity analysis indicates that reservoir temperature is the primary controlling factor for both CO2 mineralization efficiency and total H2 production, with statistical significance far exceeding other factors. pH serves as a critical secondary factor for H2 output, where acidic environments significantly enhance H2 generation. Reservoir pressure determines the H2 phase transition behavior by regulating the solubility threshold. Furthermore, porosity exhibits distinct control effects: it correlates negatively with CO2 mineralization, while adequate porosity is a prerequisite for the formation of free-phase H2 gas caps. This study demonstrates that the optimal geological conditions for CO2 mineralization and H2 production do not fully overlap, providing a differentiated scientific basis for engineering site selection to balance carbon reduction and energy production.
