The present study demonstrates the effect of direct solar drying (DSD) and hot air drying (HAD) on the quality attributes of Fuji apple slices. DSD samples took a longer time (150–180 min) to dry and simultaneously reached higher equilibrium moisture content at the end of rehydration than HAD samples. DSD samples have higher rehydration ability, dry matter holding capacity, and water absorption capacity than HAD samples. Among several empirical models, the Weibull model is the best fit with higher R² (0.9977), lower root mean square (0.0029), and chi-square error (0.0031) for describing the rehydration kinetics. Rehydrated HAD samples showed better color characteristics than DSD in terms of overall color change, chroma, and hue angle values. Whereas the hardness and chewiness of rehydrated DSD samples were better than HAD samples because of higher dry matter holding capacity in DSD. Apart from color retention, the DSD samples showed better rehydration capacity and a good texture upon rehydration than HAD slices.

Islamabad’s 2019 ban on single-use plastic shopping bags aimed to reduce plastic waste, but compliance is limited. This study evaluates the effectiveness of the ban as well as other factors in curtailing plastic bag use in Islamabad. Regression modeling within a rational choice framework analyzed survey data from 406 retailers across 18 selected urban and rural markets. We found that the subjective belief that a fine was unlikely (β = −16.10; t = −3.90; p < 0.001), likely (β = −24.99; t = −4.95; p < 0.001), or very likely (β = −43.84; t = −4.07; p < 0.001) for selling bags versus very unlikely was significantly associated with lower usage. Additionally, older retailer age (β = −0.25; p < 0.001) and more education (β = −0.77; p < 0.01) were associated with lower plastic bag usage. Business registration (β = −3.94; p < 0.10) and trade membership (β = −4.04; p < 0.05) also decreased use. Rural location (zone II: β = 13.28; p < 0.001) and plastic bags stock availability (β = 16.75; p < 0.001) increased use. Awareness, viewing bags as “Good”, unlikely fines and lack of substitutes lowered use. Results provide insights to inform more effective policies for reducing plastic waste.

This paper examines the effect of governance in Sub-Saharan African (SSA) countries. Specifically, this study investigates (i) the interacting impact of government efficiency, regulatory quality, and the rule of law alongside other socioeconomic variables to determine foreign capital inflow (FCI) based on each economic SSA bloc; and (ii) the characteristic drivers of FCI, impacting economic growth in the SSA countries. Descriptive statistics, static models, least square dummy variables (LSDVs) and the dynamic system general method of moment (GMM) were employed as the study’s estimating techniques. Based on the result of the LSDV, food security and the rule of law significantly impact FCI in the sub-economic blocs in the region. Only six countries across the four economic blocs responded to food security and the rule of law in the model. The dynamic system-GMM provided evidence of five socioeconomic variables and three governance variables contributing to FCI. The findings revealed (i) regulatory quality and the rule of law are governance variables that significantly impacted FCI; and (ii) food security failed to significantly impact FCI in the SSA region. However, inflation, life expectancy, the human capital index, exchange rate and gross domestic product (GDP) growth impacted FCI significantly. In the aggregate, inflation, regulatory quality, exchange rate and the human capital index exhibited positive relationships, while other variables such as life expectancy, government effectiveness and the rule of law appeared significant but inversely impacted FCI in the SSA region. The key policy implication recommendation from this study is that a good legal framework could moderate the flow of foreign capital in favour of growth as it creates a strong foundation for sustainable economic development in the region.

Heat stress amplified by climate change causes excessive reductions in labor capacity, work injuries, and socio-economic losses. Yet studies of corresponding impact assessments and adaptation developments are insufficient and incapable of effectively dealing with uncertain information. This gap is caused by the inability to resolve complex channels involving climate change, labor relations, and labor productivity. In this paper, an optimization-based productivity restoration modeling framework is developed to bridge the gap and support decision-makers in making informed adaptation plans. The framework integrates a multiple-climate-model ensemble, an empirical relationship between heat stress and labor capacity, and an inexact system costs model to investigate underlying uncertainties associated with climate and management systems. Optimal and reliable decision alternatives can be obtained by communicating uncertain information into the optimization processes and resolving multiple channels. Results show that the increased heat stress will lead to a potential reduction in labor productivity in China. By solving the objective function of the framework, total system costs to restore the reduction are estimated to be up to 248,700 million dollars under a Representative Concentration Pathway of 2.6 (RCP2.6) and 697,073 million dollars under RCP8.5 for standard employment, while less costs found for non-standard employment. However, non-standard employment tends to restore productivity reduction with the minimum system cost by implementing active measures rather than passive measures due to the low labor costs resulting from ambiguities among employment statuses. The situation could result in more heat-related work injuries because employers in non-standard employment can avoid the obligation of providing a safe working environment. Urgent actions are needed to uphold labor productivity with climate change, especially to ensure that employers from non-standard employment fulfill their statutory obligations.

Finding the right technique to optimize a complex problem is not an easy task. There are hundreds of methods, especially in the field of metaheuristics suitable for solving NP-hard problems. Most metaheuristic research is characterized by developing a new algorithm for a task, modifying or improving an existing technique. The overall rate of reuse of metaheuristics is small. Many problems in the field of logistics are complex and NP-hard, so metaheuristics can adequately solve them. The purpose of this paper is to promote more frequent reuse of algorithms in the field of logistics. For this, a framework is presented, where tasks are analyzed and categorized in a new way in terms of variables or based on the type of task. A lot of emphasis is placed on whether the nature of a task is discrete or continuous. Metaheuristics are also analyzed from a new approach: the focus of the study is that, based on literature, an algorithm has already effectively solved mostly discrete or continuous problems. An algorithm is not modified and adapted to a problem, but methods that provide a possible good solution for a task type are collected. A kind of reverse optimization is presented, which can help the reuse and industrial application of metaheuristics. The paper also contributes to providing proof of the difficulties in the applicability of metaheuristics. The revealed research difficulties can help improve the quality of the field and, by initiating many additional research questions, it can improve the real application of metaheuristic algorithms to specific problems. The paper helps with decision support in logistics in the selection of applied optimization methods. We tested the effectiveness of the selection method on a specific task, and it was proven that the functional structure can help the decision when choosing the appropriate algorithm.

Based on digital technology, the digital economy has typical characteristics of high efficiency, greenness, intelligence, innovation, strong penetration and so on, which can promote the sporting goods manufacturing industry (SGMI) to realize the goal of green development. This study selects panel data from 30 provinces in China over the period of 2011 to 2022. And the green total factor productivity of the sporting goods manufacturing industry (SGTFP) is used to reflect the green development of SGMI. The level of digital economy development (DIG) and the SGTFP are measured by using the entropy method and the Super-SBM model with undesirable outputs. Based on the method of coupling coordination degree model, the coordinated development degree of DIG and SGTFP is analyzed first. Then, by making use of the fixed effect model, intermediary effect model and spatial Durbin model, the influence of DIG on the green development of SGMI and its mechanism are empirically studied. The results show that DIG, SGTFP and the degree of their coupling and coordination are generally on the rise. The benchmark regression results show that the coefficient of DIG on SGTFP is 0.213; that is, the digital economy can significantly promote the improvement of green development in SGMI. According to the analysis of the spatial Durbin model, the impact of the digital economy on SGTFP has a certain spatial spillover, that is, the development of digital economy in the region will have a certain promoting effect on the green development of SGMI in the surrounding region. The intermediary effect model analyzes the influence mechanism and finds that the digital economy mainly boosts SGTFP through green innovation technology and energy consumption structure.