To investigate the effect of the location of vacuum insulation panels on the thermal insulation performance of marine reefer containers, a 20^ft mechanical refrigeration reefer container was employed in this paper, and the physical and mathematical models of three kinds of envelopes composed of vacuum insulation panels (VIP) and polyurethane foam (PU) were numerically established. The heat transfer of three types of envelopes under unsteady conditions was simulated. In order to be able to analyze theoretically, the Rasch transform is used to analyze the thermal inertia magnitude by calculating the thermal transfer response frequency and the thermal transfer response coefficient for each model, and the results are compared with the simulation results. The results implied that the insulation performance of VIP external insulation is the best. The delay times of each model obtained from the simulation results are 0.81 h, 1.45 h, 2.03 h, and 2.24 h, while the attenuation ratios are 8.93, 20.39, 20.62, and 21.78, respectively; the delay times calculated from the theoretical analysis are 0.78 h, 1.43 h, 1.99 h, and 2.20 h, respectively; and the attenuation ratios are 8.84, 20.31, 20.55, and 21.72, respectively. The carbon reduction effect of VIP external insulation is also the best. The most considerable carbon reduction is 3.65894 kg less than the traditional PU structure within 24 h. The research has a guiding significance for the research and progress of the new generation of energy-saving reefer containers and the insulation design of the envelope of refrigerated transportation equipment.

Soil salinization is a difficult challenge for agricultural productivity and environmental sustainability, particularly in arid and semi-arid coastal regions. This study investigates the spatial variability of soil electrical conductivity (EC) and its relationship with key cations and anions (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl⁻, CO₃²⁻, HCO₃⁻, SO₄²⁻) along the southeastern coast of the Caspian Sea in Iran. Using a combination of field-based soil sampling, laboratory analyses, and Landsat 8 spectral data, linear Multiple Linear Regression and Partial Least Squares Regression (MLR, PLSR) and nonlinear Artifician Neural Network and Support Vector Machine (ANN, SVM) modeling approaches were employed to estimate and map soil EC. Results identified Na⁺ and Cl⁻ as the primary contributors to salinity (r = 0.78 and r = 0.88, respectively), with NaCl salts dominating the region’s soil salinity dynamics. Secondary contributions from Potassium Chloride KCl and Magnesium Chloride MgCl₂ were also observed. Coastal landforms such as lagoon relicts and coastal plains exhibited the highest salinity levels, attributed to geomorphic processes and anthropogenic activities. Among the predictive models, the SVM algorithm outperformed others, achieving higher R² values and lower RMSE (RMSE_Test = 27.35 and RMSE_Train = 24.62, respectively), underscoring its effectiveness in capturing complex soil-environment interactions. This study highlights the utility of digital soil mapping (DSM) for assessing soil salinity and provides actionable insights for sustainable land management, particularly in mitigating salinity and enhancing agricultural practices in vulnerable coastal systems.

A fresh interest has been accorded to metal iodides due to their fascinating physicochemical properties such as high ionic conductivity, variable optical properties, and high thermal stabilities in making micro and macro devices. Breakthroughs in cathodic preparation and metallization of metal iodides revealed new opportunities for using these compounds in various fields, especially in energy conversion and materials with luminescent and sensory properties. In energy storage metal iodides are being looked at due to their potential to enhance battery performance, in optoelectronics the property of the metal iodides is available to create efficient LEDs and solar cells. Further, their application in sensing devices, especially in environmental and medical monitoring has been quite mentioned due to their response towards environmental changes such as heat or light. Nevertheless, some challenges are still in question, including material stability, scale-up opportunities, and compatibility with other technologies. This work highlights the groundbreaking potential of metal iodide-based nanomaterials, emphasizing their transformative role in innovation and their promise for future advancements.

Nickel Oxide (NiO) nanoparticles (NPs), doped with manganese (Mn) and cobalt (Co) at concentrations up to 8%, were synthesized using the composite hydroxide method (CHM). X-ray diffraction (XRD) analysis confirmed the formation of a cubic NiO structure, with no additional peaks detected, indicating successful doping. The average crystallite size was determined to range from 15 to 17.8 nm, depending on the dopant concentration. Scanning electron microscopy (SEM) images revealed mostly spherical, agglomerated particles, likely due to magnetic interactions. Fourier Transform Infrared Spectroscopy (FTIR) confirmed the incorporation of Mn and Co into the NiO lattice, consistent with the XRD results. The dielectric properties exhibited a high dielectric constant at low frequencies, which can be attributed to ion jump orientation and space charge effects. The imaginary part of the dielectric constant decreased with increasing frequency, as it became harder for electrons to align with the alternating field at higher frequencies. Both the real and imaginary dielectric constants showed behavior consistent with Koop’s theory, increasing at low frequencies and decreasing at higher frequencies. Dielectric loss was primarily attributed to dipole flipping and charge migration. AC conductivity increased with frequency, and exhibited higher conductivity at high frequencies due to small polaron hopping. These co-doped NPs show potential for applications in solid oxide fuel cells.

We present an innovative enthalpy method for determining the thermal properties of phase change materials (PCM). The enthalpy-temperature relation in the “mushy” zone is modelled by means of a fifth order Obreshkov polynomial with continuous first and second order derivatives at the zone boundaries. The partial differential equation (PDE) for the conduction of heat is rewritten so that the enthalpy variable is not explicitly present, rendering the equation nonlinear. The thermal conductivity of the PCM is assumed to be temperature dependent and is modelled by a fifth order Obreshkov polynomial as well. The method has been applied to lauric acid, a standard prototype. The latent heat and the conductivity coefficient, being the model parameters, were retrieved by fitting the measurements obtained through a simple experimental procedure. Therefore, our proposal may be profitably used for the study of materials intended for heat-storage applications.

This research introduces a novel framework integrating stochastic finite element analysis (FEA) with advanced circular statistical methods to optimize heat pump efficiency under material uncertainties. The proposed methodologies and optimization focus on balancing the mean efficiency and variability by adjusting the concentration parameter of the Von Mises distribution, which models directional variability in thermal conductivity. The study highlights the superiority of the Von Mises distribution in achieving more consistent and efficient thermal performance compared to the uniform distribution. We also conducted a sensitivity analysis of the parameters for further insights. The results show that optimal tuning of the concentration parameter can significantly reduce efficiency variability while maintaining a mean efficiency above the desired threshold. This demonstrates the importance of considering both stochastic effects and directional consistency in thermal systems, providing robust and reliable design strategies.