Search Results (6,117)

Global citrus exports rely heavily on temperature-controlled logistics to safeguard fruit quality and minimise postharvest losses. Temperature management remains a critical factor governing citrus quality throughout export logistics. Yet the extent to which historical shipment temperature data can meaningfully predict fruit condition at arrival has never been systematically assessed. This study presents a comprehensive review of how historical temperature records have been used to assess citrus quality within export supply chains, highlighting the lack of longitudinal temperature–quality correlations in existing research. Using PRISMA 2020 guidelines and Kitchenham’s three-phase review framework, 35 relevant peer-reviewed articles published between 2013 and 2025 were analysed. Bibliometric mapping identified dominant research concentrations in experimental cold chain studies and simulation-based approaches, with emerging themes around digital twins and virtual cold chain technologies. The review shows that current research predominantly employs controlled experimental designs and computational simulations to quantify temperature-driven deterioration, including chilling injury, decay rate, and weight loss. Although real-time temperature monitoring in commercial shipments is emerging, temperature deviations are rarely assessed alongside direct quality metrics. Although several studies have examined shipment temperatures alongside arrival-quality outcomes, these analyses are generally limited in duration, scope, or sensor resolution. Consequently, rigorous, multi-year, longitudinal datasets that pair detailed shipment temperature histories with standardised fruit-quality assessments remain largely unavailable, constraining the empirical validation of temperature–quality relationships in real export conditions. This gap significantly limits predictive capability in real-world export contexts. The review highlights the urgent need for a coordinated, long-term data infrastructure that integrates temperature and quality measurements across global citrus supply chains. Establishing such datasets, particularly in major exporting regions such as South Africa, would enable more robust modelling of temperature impacts, support the optimisation of cold chain practices, and contribute to international food loss-reduction goals. Full article

Chronic kidney disease (CKD) is a progressively worsening condition that erodes renal function over time, reduces quality of life, and can ultimately culminate in kidney failure with far-reaching systemic complications. In addition to reduced filtration, worsening kidney function disrupts mineral homeostasis and leads to CKD–mineral and bone disorder (CKD-MBD). Dysregulated calcium handling and maladaptive endocrine responses contribute to bone pathology and increase cardiovascular calcification risk; therefore, serial calcium monitoring remains clinically relevant for longitudinal CKD management. Conventional calcium measurements are typically obtained with centralized analyzers or laboratory assays (e.g., colorimetry and electrode/optical readouts). Despite high accuracy, the required instrumentation, controlled operating conditions, and pretreatment steps complicate rapid point-of-care deployment, especially when only microliter-scale biofluids are available. Accordingly, this study develops a finger-actuated microfluidic colorimetric platform capable of determining calcium ion concentrations in human biofluids, such as whole blood, serum, and urine. The platform integrates a three-dimensional PMMA/paper microchip with a compact reader that maintains stable temperature control while enabling CMOS-based optical detection. With just 6 μL of sample, a brief finger press propels the biofluid across an internal filtration layer, generating serum or cleaned urine that subsequently reacts with a pre-deposited murexide reagent. Under optimized conditions (1.6% reagent, 50 °C, 3 min), the signal follows a strong logarithmic relationship with calcium concentration (Y = 47.273 ln X + 28.890; R² = 0.9905), supporting quantification over 1–40 mg/dL and a detection limit of 0.2 mg/dL. Across 80 clinical CKD specimens spanning serum, whole blood, and urine, results aligned closely with the NM-BAPTA reference assay, with R² values exceeding 0.97. Full article

Background/Objectives: Biorelevant in vitro dissolution testing is used increasingly to predict complex mechanisms in the gastrointestinal (GI) tract that determine oral bioavailability. However, the limited use of non-compendial systems is driven by the lack of widely accepted, standardized validation frameworks. This ongoing gap continues to restrict their adoption relative to United States Pharmacopeia (USP) apparatus. While the physiological relevance and biopredictive capabilities of the tiny-TIM and TIM-1 in vitro GI models have been demonstrated in previous studies, their inter-laboratory reproducibility has not been systematically established. Therefore, this international ring study evaluates the reproducibility of in vitro simulations of GI transit and absorption of paracetamol in fasted- and fed-state conditions in tiny-TIM and TIM-1. Methods: Three laboratories used TIM-1 and five used tiny-TIM to simulate oral administration of a 500 mg paracetamol solution to a healthy adult. Paracetamol solution was selected as a well-characterized and widely available BCS I compound to minimize formulation and solubility effects and focus on system performance, enabling the generation of a generic validation dataset for the reproducibility of TIM experiments. Results: Paracetamol bioaccessibility profiles were repeatable and reproducible (all pairwise f₂ > 50). Maximum differences in total bioaccessible paracetamol were 0.9% (TIM-1) and 2.8% (tiny-TIM) within laboratories and 3.4 and 5.9% between laboratories. Inter-lab variability at individual time points remained <4.0% (fasted) and 5.2% (fed). Both TIM models produced biopredictive metrics, correctly predicting no food effect on total paracetamol bioaccessibility and capturing delayed t_max. Gastric and intestinal environments showed repeatable pH, temperature, and GI transit characteristics, with fluctuations across transit stages that mirrored reported in vivo patterns. Conclusions: These results demonstrate that TIM systems can reproducibly simulate gastrointestinal conditions across laboratories and generate consistent measurements of drug product performance, despite the complexity of the dynamic processes involved. While this evaluation involving a single BCS I drug solution should not be directly extrapolated to experiments with poorly soluble compounds or different formulations, it supports the use of TIM systems as robust in vitro models in drug product development. This study provides a standardized, inter-laboratory, baseline performance dataset to support regulatory submissions incorporating TIM data and enable more confident interpretation of TIM experiments. Full article

This study presents the experimental characterization of the volumetric and transport properties of pseudo-binary mixtures of commercial diesel and residual chicken fat methyl ester biodiesel over the temperature range of 293.15–353.15 K at 0.078 MPa. Density measurements were performed using a U-shaped vibrating-tube densimeter; kinematic viscosities were obtained using Cannon–Fenske capillary viscometers. The results show that density decreased with increasing temperature and diesel content. The excess molar volume (V^E) was negative for all mixtures; the strongest volumetric contraction took place at around x₁ ≈ 0.4–0.6. The Redlich–Kister equation and the Prigogine–Flory–Patterson (PFP) model were applied to represent excess molar volumes, with an absolute average deviation (AAD) lower than 14.92%. The thermal expansion coefficient (${\alpha }_P$) and its excess property (${\alpha }_P^E$) further confirmed the existence of non-ideal mixing driven by polar–apolar interactions. The kinematic viscosity ($\nu$) was confirmed to be temperature-dependent and increased with the amount of FAMEs; this effect can be associated with the greater polarity and structural rigidity of esters. The McAllister model also adequately reproduced the dynamic viscosity ($\eta$) with an AAD < 4.2%. Furthermore, an increase in the activation enthalpy (${\Delta H}^{\ne }$) was observed at higher FAME fractions, indicating a high energy demand is required to overcome the internal energy barrier for the initial displacement of the molecules. Full article

Arch–gravity dams feature both arch action and large concrete volume, yet targeted research on temperature control and crack prevention for this type remains insufficient. To address this, a Two-Parameter Decision Chart Method for predicting allowable placing temperature, an Analytical–Numerical Hybrid Estimation Method for estimating cooling durations, and the Comprehensive Cracking Risk Index (CCRI) for assessing lifecycle concrete safety are proposed, forming a complete design methodology. A case study on a proposed project using full-process simulation quantitatively evaluates the contribution of various measures in mitigating thermal stress across dam zones. Results show that without measures, the CCRI values for interior and surface concrete reach 68.9% and 38.1%, respectively. After implementing combined optimization measures targeting the control of maximum temperature, final temperature before grouting, and internal–external temperature difference throughout the entire process, both CCRI values are reduced to zero. Contribution analysis reveals distinct zonal effectiveness: for interior concrete, low-temperature placement with first-stage cooling contributes most (59.9%); for surface concrete, second- and third-stage cooling dominates (72.7%). Therefore, in practical engineering applications for temperature control and crack prevention in arch–gravity dams, a combination of measures centered on controlling the maximum temperature, optimizing the cooling process, and enhancing surface insulation should be adopted based on the characteristics of interior and surface zones, thereby improving cracking safety. Full article

This study develops an interpretable machine learning (ML) surrogate to predict hourly indoor air temperature and discomfort indicators for a representative Mexican social-housing prototype in San Luis Potosí (cold semi-arid, Köppen–Geiger BSk). A four-zone EnergyPlus model with constant window opening (50%) and no internal gains was used to generate a parametric dataset spanning 24 building orientations, seven roof solar absorptance levels, and two neighborhood configurations (surrounded vs. corner). Zone-specific bagged-tree regression models were trained in MATLAB using weather predictors, temporal indicators, and weather-memory features (including outdoor temperature lags and rolling averages). Orientation and roof absorptance were included as explicit design predictors, enabling the surrogate model to generalize across the full combinatorial design space rather than requiring a separate model for each configuration. Interpretability was assessed with SHAP values. Evaluated on orientation–absorptance combinations deliberately held out during training, the surrogate achieved high accuracy across zones of the house (R² = 0.98–0.99; RMSE = 0.31–0.67 °C) with stable, near-zero-centered residuals. When propagated into adaptive-comfort metrics computed directly relative to the monthly neutral temperature $T_n$, ML predictions preserved the main cold and hot discomfort degree-hour patterns across the full design space. The proposed surrogate enables rapid, physically consistent comfort-oriented screening of roof finishes and orientation choices in naturally ventilated social housing. Full article

Thin-walled laminated cooling plates integrate internal channels and pin-fin cores, producing reduced and spatially non-uniform stiffness that changes welding restraint and distortion. This study investigates stiffness-controlled plastic-strain evolution in laser butt welding of GH3230 laminated plates, with geometrically identical solid plates as reference. A coupled heat-transfer and thermo-mechanical finite element model was developed in MSC Marc using a composite Gaussian surface–volumetric moving heat source and temperature-dependent properties. The thermal solution was validated against near-weld thermal cycles and fusion geometry; mechanical predictions were evaluated by CMM distortion and residual-stress measurements. Both structures show comparable residual-stress magnitudes and spatial trends, indicating that residual stress is governed mainly by the local weld thermal gradient. In contrast, the laminated plate exhibits larger angular/bending distortion. Simulations show that, although the plastic-strain pattern is similar, the laminated plate develops higher peak plastic strain confined to a narrower band near the weld, with the transverse plastic strain dominating. Plastic strain–temperature paths reveal continued transverse plastic-strain accumulation during cooling with limited recovery, consistent with restraint redistribution induced by stiffness non-uniformity. An equivalent restraint–stiffness spring model explains this “narrower-but-stronger” plastic zone and links stiffness to yielding and residual plastic-strain magnitude, supporting distortion prediction and stiffness-informed control of welded laminated cooling plates. Full article

Conventional thermoelectric generators (TEGs) suffer from thermal resistance introduced by ceramic substrates and thermal interface materials, which limits the achievable temperature gradient across the junctions and reduces conversion efficiency. To overcome this limitation, a pin-fin integrated thermoelectric device (iTED) is proposed, in which the hot-side heat exchanger is incorporated directly into the hot-side interconnector, eliminating the ceramic and associated greases. An explicitly coupled thermal-fluid-electric finite-volume model is developed in ANSYS Fluent’s user-defined scalar (UDS) environment to quantify the simultaneous thermal-fluid-electric behavior of the iTED for inlet temperatures of 350 $\le T_{\mathrm{in}}\mathrm{K}\le$ 650, Reynolds numbers of 3000 $\le \mathrm{Re}\le$ 15,000, and load resistances ranging from 0.01 to ${10}^6$% of the internal device resistance ($R_{\mathrm{int}}$), for a fixed cold-side temperature of 300 K. The model is validated against established tube-bank correlations (2.2% agreement in pumping power) and a one-dimensional Explicit Thomson Model (1.2–6.9% agreement across all electrical system response quantities). Compared with an equivalently sized conventional TEG, the iTED achieves a 4.6-fold higher maximum power output (23.9 [W] vs. 5.2 [W] at Re = 15,000), a 2.8-fold higher thermal conversion efficiency (8.1% vs. 2.9%), and a 4.8-fold higher performance index (7.8 [-] vs. 1.6 [-] at Re = 3000), all at $T_{\mathrm{in}}$ = 650 K. A performance index analysis reveals that lower Reynolds numbers and higher inlet temperatures maximize the net power benefit, delineating the operational envelope in which the iTED produces more electrical power than is needed for fluid pumping. These findings demonstrate that device-level restructuring—specifically, the elimination of interfacial thermal resistance via integrated pin-fin heat exchangers—can yield performance improvements comparable to or exceeding those achievable through material advances alone. Full article

This study compared three internal surface treatments of zirconia copings—silane alone (control), airborne-particle abrasion followed by silane, and high-temperature hydrofluoric acid etching followed by silane—regarding initial pull-out retention strength, retention after thermocycling, failure mode assessed by scanning electron microscopy (SEM), and surface wettability. Sixty-three monolithic zirconia copings were allocated to three groups (n = 21) according to surface treatment and cemented to titanium bases with a self-adhesive resin cement. Initial pull-out tests were performed. A subset (n = 10 per group) underwent thermocycling followed by repeat testing. Failure modes were analysed by SEM, and wettability was measured using the sessile drop method. Surface roughness and crystalline phase were additionally characterized by white-light interferometry and X-ray diffraction (XRD), respectively. High-temperature acid etching produced significantly higher initial pull-out forces than airborne-particle abrasion and silane alone, with mean values 125% higher than control and 42.6% higher than airborne-particle abrasion. After thermocycling, acid-etched specimens maintained the highest retention, whereas airborne-particle abrasion showed critical loss. SEM revealed predominantly cement remnants on zirconia in the acid-etched group, indicating a stronger zirconia–cement interface. Acid etching also yielded significantly lower contact angles, reflecting improved wettability. High-temperature hydrofluoric acid etching followed by silanization provided superior and more stable retention, more favourable failure modes, and improved wettability. Full article

The present study investigates the influence of low temperatures on the starting torque, viscous friction, and power intensity of a precision cycloidal reducer TwinSpin TS 140-115-E-P19-0583. Two types of plastic greases with differing viscosities were compared in the experiment: Castrol TT-1 (low-viscosity, optimised for low-temperature) and Vigo RE-0 (higher viscosity, designated for greater loads). The measurements were taken in a climate chamber in the temperature ranging from +24 °C to −20 °C in the mode accounting for no external load. The results have shown that Castrol TT-1 maintains its beneficial rheological properties at as low as −20 °C, which is manifested in a low starting torque (~0.30 Nm) and low power intensity (~0.33 kW). On the contrary, Vigo RE-0 shows a significant increase in friction: at −20 °C, the starting torque is 1.0–1.1 Nm and the power intensity of the operation increases to consume more than 1.5 kW. The correct choice of lubricant is a critical factor for reliable cold-start behaviour under no-load, internal-loss-dominated conditions. This study provides a rare experimentally verified low-temperature assessment of starting torque, viscous friction, and power intensity in fully assembled TwinSpin precision cycloidal reducers lubricated with greases of markedly different viscosity classes, addressing an important gap in the existing literature. Full article

The present study deals with the problem of irregular temperature distribution, simultaneous under-firing and over-firing, and their resultant efficiency and quality problems in a mechanical lime vertical kiln powered by domestic waste flue gas. The numerical simulation and structure optimization were carried out based on a 150 kg/h pilot-scale kiln. This combined model was built on the ANSYS Fluent 2022 R1 platform with UDF and UDS, incorporating limestone decomposition kinetics to enable the solution of gas and solid energy equations separately, and simulation of complex transfer and reaction processes. To correct the separation of flows at one inlet, a symmetric four-direction (00, 900, 1800, 2700) air intake plan was suggested. The findings show that this design essentially transforms the internal flow field into uniform and symmetrical temperature and concentration distributions. The calcination region contained both gas and solid temperatures in the optimum range to produce active lime. Specifically, the optimized kiln achieved a temperature range of 1190–1450 K in the calcination zone, a decomposition rate of approximately 82.7% (compared to 5.3% in the original model), and an increase in effective CaO content from 81.7% to 87.7%, with validation errors below 15%. It was demonstrated that the model is reliable, since the outlet simulated values correlated well with the measured ones. The preheating, calcining, and cooling zones’ heights of the optimized kiln adhered to the design requirements. This research is innovative in its application of a multi-physics coupling model with a varying heat source in a kiln and, in turn, identifies the synergism improvement process in the flow, temperature, concentration, and reaction fields. Full article

Precise regulation of the postharvest storage environment is critical for reducing losses and maintaining potato quality. Semi-underground storage facilities are widely used in major potato-producing regions of northern China; however, pronounced spatiotemporal heterogeneity in the internal temperature field often leads to localized quality deterioration. To enable accurate sensing and proactive prediction of temperature dynamics in such facilities, this study investigated a typical semi-underground potato storage cellar in Wuchuan County, Inner Mongolia. A high-density sensor network was deployed to collect temperature data, and the spatiotemporal variation patterns of the internal temperature field were systematically analyzed. The results indicate that, at the same vertical height, spatial temperature gradually increases from the entrance toward the interior of the cellar. Both the maximum and minimum temperatures in the entrance zone are lower than those in other regions, while the highest temperatures are observed near the rear wall. Based on the collected data, hierarchical clustering was employed to partition the internal temperature field into three spatiotemporal pattern clusters with significant differences. Key representative monitoring locations were then identified using the Spearman correlation coefficient. An AdaBoost-based prediction model was subsequently developed to estimate the temperatures at other test locations within each cluster using measurements from the representative points. The results demonstrate that the proposed model maintains high prediction accuracy while substantially reducing dependence on a dense sensor network. The overall MAE ranges from 0.075 to 0.373 °C, and the sensor reduction ratio reaches 87%. This approach provides a paradigm for low-cost intelligent monitoring and offers theoretical support and decision-making guidance for the smart regulation of potato storage environments. By optimizing the monitoring of potato storage environments, this study can reduce monitoring system costs and resource consumption, providing technical support for building a sustainable potato supply chain and delivering significant economic benefits in promoting the development of a resource-conserving potato industry. Full article

Artificial neural networks (ANNs) are widely used to approximate nonlinear mappings, yet their ability to capture thermodynamic behaviour in dynamic physical systems remains insufficiently characterised. This study investigates how representational capacity influences surrogate modelling accuracy for a crank-angle-resolved internal combustion engine (ICE) simulation with a maximum dynamic state dimension of six. Two feedforward ANN configurations are evaluated: a low-capacity 5–5 architecture containing 84 trainable parameters and a high-capacity 25–25–25 architecture containing 1554 parameters (18.5× larger). Both networks approximate the nonlinear mapping from five embedded operating parameters to four peak thermodynamic outputs (maximum pressure, pressure phasing, maximum temperature, and temperature phasing). Evaluation across 53,178 operating points demonstrates that the high-capacity configuration reduces root mean squared error by factors of 30–50× relative to the low-capacity network, decreasing peak temperature error from 17.68 K to 0.36 K and peak pressure error from 0.116 MPa to 0.0025 MPa. Although both models achieve coefficients of determination exceeding 0.99, the low-capacity network exhibits heavy-tailed residual distributions and regime-dependent error amplification, whereas the high-capacity model reduces both central dispersion and extreme-case error. These results demonstrate that high correlation alone does not guarantee engineering reliability in nonlinear thermodynamic systems. Distribution-level analysis, including percentile and extreme-case characterisation, is required to evaluate engineering robustness. The findings provide a quantitative framework linking ANN capacity, nonlinear dynamic system representation, and predictive robustness. Full article

Climate change is reshaping global hydrological cycles, intensifying scarcity and heightening health risks in vulnerable regions. This study examines the health impacts of climate-driven water scarcity across the Middle East, South Asia, and Sub-Saharan Africa using data on water availability, climate variability, and health outcomes. The study uses a multi-regional mixed methods approach that brings together climate, hydrology, governance, and health data to explore how climate-driven water scarcity affects public health in South Asia, Sub-Saharan Africa, and the MENA region. It combines quantitative climate and health indicators with qualitative evaluations of water system vulnerability to compare exposure pathways and health outcomes across regions. Findings show that rising temperatures, altered rainfall, declining groundwater, and recurrent droughts undermine water security, leading to increased disease burdens through four pathways: (1) waterborne illnesses from unsafe or insufficient supplies; (2) reduced hygiene due to limited access; (3) food insecurity from crop failures; and (4) mental health stress, conflict, and displacement from water competition. Women, children, and low-income households face disproportionate impacts. Current adaptation measures are fragmented, highlighting the need for integrated water governance to build climate resilience. Recommended strategies include community-based water safety planning, digital water monitoring, and embedding health metrics in climate–water policies. This cross-regional analysis supports equitable, climate-resilient health systems and informs interventions to mitigate water scarcity under accelerating climate change. This study directly supports global policy agendas by providing evidence that advances the objectives of the Sustainable Development Goals and international frameworks on climate resilience, water security, and food and health protection. Full article

The intermittent and variable nature of solar energy poses challenges for maintaining stable thermal performance in solar heating systems. One effective approach to mitigate this limitation is to store surplus thermal energy during periods of high solar irradiance and release it when solar input is insufficient. Phase change materials (PCMs) are particularly suitable for this purpose due to their ability to absorb and release large amounts of latent heat during phase transition. The aim of this work is to develop a mathematical model of a flow-through tank containing a phase change material in the form of a spherical packed bed. Including longitudinal dispersion in the model equations allows for a more accurate description of the heat transfer process in a tank containing PCM elements. Simulation calculations based on the model were carried out to demonstrate its potential applicability to practical problems. The influence of the following parameters on the process was investigated: tank volume, water flow rate, phase change temperature, process duration, dispersion coefficient during water flow, radius of the packed-bed elements, and cyclic variations of the inlet water temperature. A significant influence of the axial dispersion coefficient in the tank containing PCM on the outlet water temperature profile was demonstrated. It was found that the internal heat transfer coefficient within the packing elements containing PCM falls within the range of 58–145 W/(m²K). Full article