Green Closed-Loop Supply Chain Network Design During the Coronavirus (COVID-19) Pandemic: a Case Study in the Iranian Automotive Industry

A novel multi-objective robust possibilistic flexible programming to design a sustainable apparel closed-loop supply chain network

Designing a sustainable Closed-Loop Supply Chain (CLSC) network is imperative for the apparel industry, given its escalating adverse effects on economic, environmental, and social dimensions. In this study, a novel tri-objective location-allocation optimization model is specifically developed for designing a sustainable apparel CLSC, incorporating the industry's unique facilities. The aim of the model is to simultaneously minimize the costs and negative environmental impacts while maximizing social benefits under demands and returns uncertainty. A notable research contribution lies in addressing the unique challenges of treating different types of returns, including commercial, End Of Use (EOU) and End Of Life (EOL) returns due to their uncertain quality and quantity. Additionally, the model optimizes the environmental performance levels of production facilities, a novel aspect in the apparel CLSC research. Moreover, the flexibility of constraints related to the demand fulfilment is considered. To cope with such flexibility and uncertainties, a new hybrid robust possibilistic flexible programming model is developed, by extending the previous methodologies. A core innovation of this solution approach lies in the pioneering utilization of hexagonal fuzzy numbers for uncertain epistemic parameters, making a significant advancement in the field of CLSC. Comparative analysis with the similar studies demonstrates the superiority of the proposed model, incorporating hexagonal fuzzy numbers over the method using triangular fuzzy numbers. Furthermore, AUGMECON method using lexicographic optimization is applied to handle the multi-objective model. The application of the proposed model is shown focusing on Southwestern Ontario in Canada. The results reveal that commercial and EOU returns have a more detrimental impact on economic, environmental, and social sustainability aspects compared to EOL returns.

A systematic literature review of logistics services outsourcing

Logistics is critical in every company's supply chain (SC), and outsourcing helps businesses concentrate on their core competencies. Third-party logistics (3 PL) or logistics service providers (LSPs) assist businesses in cutting costs while improving performance, sustainability, and revenue. Logistics evaluation and LSPs choice are complicated and critical components of value delivery. This study aims to review logistics outsourcing literature to understand the trends, prospects, factors, and strategies used in logistics companies' outsourcing choices. This work examines the literature on LSPs selection published between 2010 and 2023. This paper uses VOSviewer (version 1.6.19) to visualize the relationships. Pricing, timely shipment, service quality, reliability, agility, technology, and consumer feedback are the most commonly utilized, whereas societal and environmental factors are seldom used. The study comprises journal publications, the year, selection criteria, and assessment methodologies. Numerous scholars have discovered and employed many critical selection criteria. Many investigators have also embraced multi-criteria decision-making (MCDM) methodologies, and their fuzzy form is widely used. In conclusion, recommendations for theorists and managers, limits, and future directions for research are offered.

What shapes food waste behaviors? New insights from a comprehensive action determination model

The sharp increase in food waste poses a serious threat to food security and environmental sustainability. However, most existing studies have investigated the determinants of food waste behaviors in a separate behavioral process without considering the potential impacts of different factors in an integrated process. To narrow this gap, a comprehensive action determination model (CADM), which integrates network embeddedness and incentive measures, was constructed to explore the impact of various determinants in different processes on food waste behaviors, using data collected from 913 residents in eastern China via an online survey. The empirical results showed that environmental concern was the largest positive factor in predicting personal norms (β = 0.80, p < 0.001), followed by network embeddedness. With the habitual process considered, residents with ingrained waste habits were more likely to waste food (β = 0.38, p < 0.001). Moreover, the normative process alleviates behavioral decisions via intentions to reduce food waste. This study confirmed the differences in the situational process and suggested that menu tips increase food waste behaviors, while incentive measures reinforce the influence of intentions on behavior. We therefore address the insufficient ingredients on the effects of different processes on behavior and provide a new perspective for formulating behavioral intervention policies.

Technical innovation, renewable energy consumption, and CO2 emissions in the USA: a cross-quantile approach

This study investigates whether technological innovation and the consumption of renewable energy tend to reduce the emissions of CO2 in the USA by analyzing datasets from January 2010 to May 2022. The main contribution to this study is that we applied a cross-quantile approach, which possesses several strengths compared to other methods used for directional predictability. The empirical results of this research can be concluded as three points: (1) both the consumption of renewable energy and technological innovation significantly and negatively impacted the emissions of CO2 in the short run (i.e., 1 month) across high quantiles, which gradually diminished over time (i.e., 3 months, 12 months, and 24 months), implying that technological innovation and the consumption of renewable energy possess a short-lived effect on CO2 emissions, respectively; (2) this relationship remains significant for causal links spanning 1 and 3 months and 1 and 2 years when the consumption of renewable energy and technological innovation are treated as control variables respectively; (3) a recursive cross-quantilogram was constructed to support further our findings, which showed that the consumption of renewable energy and technological innovation tend to negatively impact the emissions of CO2 across all quantiles. These results imply that an increase in the consumption of renewable energy and technological innovation can curb CO2 emissions in the USA; these effects tend to be more lasting when technological innovation and the consumption of renewable energy are combined. Therefore, future policies focused on curbing the emissions of CO2 should pay attention to the combined effect, which is the promotion of technological innovation and the exploitation of renewable energy sources in the USA.

Multivariate and scale-dependent controls of deep soil carbon after afforestation in a typical loess-covered region

Afforestation is beneficial to improving soil carbon pools. However, due to the lack of deep databases, the variations in soil carbon and the combined effects of multiple factors after afforestation have yet to be adequately explored in >1 m deep soils, especially in areas with deep-rooted plants and thick vadose zones. This study examined the multivariate controls of soil organic carbon (SOC) and inorganic carbon (SIC) in 0-18 m deep under farmland, grassland, willow, and poplar in loess deposits. The novelty of this study is that the factors concurrently affecting deep soil carbon were investigated by multiwavelet coherence and structural equation models. On average, the SOC density (53.1 ± 5.0 kg m-2) was only 12% of SIC density (425.4 ± 13.8 kg m-2), with depth-dependent variations under different land use types. In the soil profiles, the variations in SOC were more obvious in the 0-6 m layer, while SIC variations were mainly observed in the 6-12 m layer. Compared with farmland (SOC: 17.0 kg m-2; SIC: 122.9 kg m-2), the plantation of deciduous poplar (SOC: 28.5 kg m-2; SIC: 144.2 kg m-2) increased the SOC and SIC density within the 0-6 m layer (p < 0.05), but grassland and evergreen willow impacted SOC and SIC density insignificantly. The wavelet coherence analysis showed that, at the large scale (>4 m), SOC and SIC intensities were affected by total nitrogen-magnetic susceptibility and magnetic susceptibility-water content, respectively. The structural equation model further identified that SOC density was directly controlled by total nitrogen (path coefficient = 0.64) and indirectly affected by magnetic susceptibility (path coefficient = 0.36). Further, SOC stimulated the SIC deposition by improving water conservation and electrical conductivity. This study provides new insights into afforestation-induced deep carbon cycles, which have crucial implications for forest management and enhancing ecosystem sustainability in arid regions.

Green process innovation, green product innovation, leverage, and corporate financial performance; evidence from system GMM

Natural resource usage has produced various environmental challenges. Green process innovation has been considered a viable option that can help both industry and society. This study investigates the impact of green process innovation and green product innovation on corporate financial performance. We based our findings on a sample of 280 listed non-financial firms operating in South Asia. Information was gathered from firms' annual and CSR reports from 2012 to 2022. This study's data was analyzed using a two-step dynamic panel system GMM, correlation analysis, multicollinearity diagnostic tests, and descriptive statistics. Corporate financial performance is measured with ROA, ROE and Tobin's Q. Overall findings of the study show that green innovation has a significant positive impact on all measures of financial performance. Investing in the innovation of green products and green process can assist businesses in avoiding environmental concerns and regulatory penalties, while also assisting them in establishing new market prospects and achieving new levels of success with their green products. In addition, developing products that are friendly to the environment is tightly connected to expanding green competencies, promoting a company's green image, and improving the company's financial performance. Particularly useful for policymakers in developing countries, the study's findings can be used to introduce paradigm-shifting legislation and penalties that speed up business adoption of green process innovation.

Predicting Covid-19 pandemic waves with biologically and behaviorally informed universal differential equations

During the COVID-19 pandemic, it became clear that pandemic waves and population responses were locked in a mutual feedback loop in a classic example of a coupled behavior-disease system. We demonstrate for the first time that universal differential equation (UDE) models are able to extract this interplay from data. We develop a UDE model for COVID-19 and test its ability to make predictions of second pandemic waves. We find that UDEs are capable of learning coupled behavior-disease dynamics and predicting second waves in a variety of populations, provided they are supplied with learning biases describing simple assumptions about disease transmission and population response. Though not yet suitable for deployment as a policy-guiding tool, our results demonstrate potential benefits, drawbacks, and useful techniques when applying universal differential equations to coupled systems.

Replacement of failed items in a two commodity retrial queueing-inventory system with multi-component demand and vacation interruption

This study investigates a crucial aspect of inventory management, which is the process of replacing failed items. In dynamic commercial environments, it is essential to efficiently and strategically replace failed items to maintain operational efficiency and ensure profitability. We consider a two-commodity retrial queueing-inventory system with vacation interruption. Upon purchasing the first commodity, the second commodity is provided as a complimentary item. In contrast, no item is given as a complimentary for the purchase of the second item. Only the first commodity is stored in a dedicated pooled storage for replacement when it fails. The ( s , Q ) policy governs replenishing the first commodity while the second is replenished through instantaneous ordering. The model considers the multi-component demand rate for customer arrivals. Server vacations are initiated during customer absence in waiting hall or when the first commodity is unavailable. We formulate a level-dependent quasi-birth-and-death process, and its steady-state probability vector is computed using Neuts and Rao's truncation method. The stability condition for the system is derived, and various system performance measures, including expected total cost, number of replaceable items, and customers in the waiting hall and orbit, are established. The comparative analysis between the system with replacement is done with the regular model without replacement, which revealed the efficiency of replacement. The analysis of multi-component demand towards homogeneous arrival highlights the impact of multi-component demand on boosting customer arrival. Also, parametric sensitivity analysis has been conducted numerically over total cost, mean number of failed items for replacement, and mean number of customers in the waiting hall and orbit.

Effects of microfinance and small loans centre on poverty reduction in Wa West District, Ghana

Poverty, a pervasive and consequential global issue, has garnered significant attention due to its wide-reaching prevalence and profound implications. Various strategies, including microfinance, have been implemented to tackle this pressing concern. One such strategy is the Microfinance and Small Loans Centre (MASLOC) in Ghana, which aims to reduce poverty. However, despite its potential, MASLOC's role in poverty reduction has yet to be thoroughly examined, leading to inconclusive findings, and necessitating this study. A single case study design addressed this research gap and provided valuable insights. A comprehensive dataset was compiled through interviews and observations involving 40 MASLOC beneficiaries and officials. Thematic analysis was utilized to dissect the collected data, revealing noteworthy patterns and trends. The study's outcomes shed light on MASLOC's effectiveness in mitigating poverty. Specifically, it was found that MASLOC played a pivotal role in poverty reduction by bolstering income levels, amplifying consumption patterns, facilitating access to fundamental necessities, and enabling the accumulation of valuable assets. Nevertheless, the analysis also highlighted specific challenges. Issues related to loan repayment and staffing emerged as constraints within the MASLOC framework. In essence, the study established that MASLOC contributes to the overarching goal of poverty reduction. The findings are helpful because fostering a positive attitude towards loan repayment is crucial, and this endeavour should be complemented by the strategic recruitment of competent staff members who can effectively navigate the intricacies of the scheme to ensure the sustainability of MASLOC. What sets this study apart is its innovative exploration of the impact of MASLOC on poverty-a primarily overlooked facet. By delving into this uncharted territory, the study enriches the ongoing discourse surrounding government microfinance schemes' influence on its beneficiaries. This research contributes not only to the academic realm but also to the practical realm, as it offers actionable insights for policymakers in poverty reduction.

Hybrid deep learning model based smart IOT based monitoring system for Covid-19

Recently, COVID-19 becomes a hot topic and explicitly made people follow social distancing and quarantine practices all over the world. Meanwhile, it is arduous to visit medical professionals intermittently by the patients for fear of spreading the disease. This IoT-based healthcare monitoring system is utilized by many professionals, can be accessed remotely, and provides treatment accordingly. In context with this, we designed an IoT-based healthcare monitoring system that sophisticatedly measures and monitors the parameters of patients such as oxygen level, blood pressure, temperature, and heart rate. This system can be widely used in rural areas that are linked to the nearest city hospitals to monitor the patients. The collected data from the monitoring system are stored in the cloud-based data storage and for the classification our approach proposes an innovative Recurrent Convolutional Neural Network (RCNN) based Puzzle optimization algorithm (PO). Based on the outcome further treatments are made with the assistance of physicians. Experimental analyses are made and analyzed the performance with state-of-art works. The availability of more data storage capacity in the cloud can make physicians access the previous data effortlessly.

A paradigmatic approach to the molecular descriptor computation for some antiviral drugs

In theoretical chemistry, topological indices are commonly employed to model the physico-chemical properties of chemical compounds. Mathematicians frequently use Zagreb indices to calculate a chemical compound's strain energy, melting point, boiling temperature, distortion, and stability. The current global pandemic caused by the new SARS-CoV-2, also known as COVID-19, is a significant public health concern. Various therapy modalities are advised. The issue has become worse since there hasn't been enough counseling. Researchers are looking at compounds that might be used as SARS and MERS therapies based on earlier studies. In several quantitative structure-property-activity relationships (QSPR and QSAR) studies, a variety of physiochemical properties are successfully represented by topological indices, a sort of molecular descriptor that just specifies numerical values connected to a substance's molecular structure. This study investigates several irregularity-based topological indices for various antiviral medicines, depending on the degree of irregularity. In order to evaluate the effectiveness of the generated topological indices, a QSPR was also carried out using the indicated pharmaceuticals, the various topological indices, and the various physiochemical features of these antiviral medicines. The acquired results show a substantial association between the topological indices being studied by the curve-fitting approach and the physiochemical properties of possible antiviral medicines.

Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model

Introduction

Breast cancer stands as the second most deadly form of cancer among women worldwide. Early diagnosis and treatment can significantly mitigate mortality rates.

Purpose

The study aims to classify breast ultrasound images into benign and malignant tumors. This approach involves segmenting the breast's region of interest (ROI) employing an optimized UNet architecture and classifying the ROIs through an optimized shallow CNN model utilizing an ablation study.

Method

Several image processing techniques are utilized to improve image quality by removing text, artifacts, and speckle noise, and statistical analysis is done to check the enhanced image quality is satisfactory. With the processed dataset, the segmentation of breast tumor ROI is carried out, optimizing the UNet model through an ablation study where the architectural configuration and hyperparameters are altered. After obtaining the tumor ROIs from the fine-tuned UNet model (RKO-UNet), an optimized CNN model is employed to classify the tumor into benign and malignant classes. To enhance the CNN model's performance, an ablation study is conducted, coupled with the integration of an attention unit. The model's performance is further assessed by classifying breast cancer with mammogram images.

Result

The proposed classification model (RKONet-13) results in an accuracy of 98.41 %. The performance of the proposed model is further compared with five transfer learning models for both pre-segmented and post-segmented datasets. K-fold cross-validation is done to assess the proposed RKONet-13 model's performance stability. Furthermore, the performance of the proposed model is compared with previous literature, where the proposed model outperforms existing methods, demonstrating its effectiveness in breast cancer diagnosis. Lastly, the model demonstrates its robustness for breast cancer classification, delivering an exceptional performance of 96.21 % on a mammogram dataset.

Conclusion

The efficacy of this study relies on image pre-processing, segmentation with hybrid attention UNet, and classification with fine-tuned robust CNN model. This comprehensive approach aims to determine an effective technique for detecting breast cancer within ultrasound images.

Hybrid Whale Archimedes Optimization-based MLPNN model for soil nutrient classification and pH prediction

Soil fertility and environmental factors play an important role in improving productivity and cropland quality in the agricultural sector. A new prediction and classification model for the potential of soil nutrients and hydrogen (pH) levels is proposed. The proposed model, Hybrid Whale Archimedes Optimization-based Multilayer Perceptron Neural Network (HWAO-MLPNN), is utilized for soil features classification of pH levels, and the soils are collected from the villages such as phosphorous (P), organic carbon (OC), boron (B), and potassium (K). The village-wise soil fertility prediction and classification model aims to improve soil health, reduce harmful fertilizer usage, enhance environmental quality, and achieve more profits. The proposed model combines the Multilayer Perceptron Neural Network (MLPNN) model and the Hybrid Whale Archimedes Optimization (HWAO) algorithm to enhance the classification performance on the validation data. The Marathwada dataset is selected to validate the soil nutrient prediction and classification model, and various measuring units such as cross-validation accuracy, Area Under Curve (AUC), accuracy, Mean Squared Error (MSE), G-mean, precision, specificity, and sensitivity are used for evaluation. The comparative study of this paper shows that the proposed HWAO-MLPNN achieved more classification accuracy of 98.1%, cross-validation accuracy of 98.3% for pH classification, and cross-validation accuracy of 97.9% for soil nutrient classification. The proposed model can be utilized to accurately classify soil nutrients and pH levels, which can have a significant impact on improving soil health, reducing harmful fertilizer usage, enhancing environmental quality, and ultimately increasing profitability in the agricultural sector.

Antifragile, sustainable, and agile supply chain network design by considering resiliency, robustness, risk, and environmental requirements

This research suggests an Antifragile, Sustainable and Agile Supply Chain Network Design (ASASCND) as a new network design that integrates these concepts considering resiliency, robustness, risk, and environmental requirements. The cost function combines a novel method with robust stochastic optimization and Entropic Value at Risk (EVaR). This model combines expected value, maximum and EVaR of cost as an objective function. This research adds antifragility by the effect of learning on variable parameters, sustainability by considering the environmental and social issues, resiliency and agility by flexible capacity, and multi-resource and demand satisfaction constraints to the model. The case study is in the automotive industry. This model compares the main problem by considering antifragility without thinking about antifragility. The ASASCND cost is - 0.3% less than without considering antifragility. In addition, when the conservatism coefficient grows, the cost function increase. In addition, the antifragility coefficient and the confidence level affect positively, and the agility coefficient negatively affects the cost function. Expanding the model scale changes the cost function and time computation because the antifragility coefficient changes variable cost. Finally, managerial insights and practical implications are explained.

A scenario-based sustainable dual-channel closed-loop supply chain design with pickup and delivery considering social conditions in a natural disaster under uncertainty: a real-life case study

In the context of the COVID-19 pandemic, utilizing the potential of online markets to sell products has become increasingly important for creating competitive advantages and ensuring the growth and survival of businesses. The pandemic has disrupted traditional business practices, and with social distancing measures in place, consumers have turned to online channels to meet their needs. As a result, businesses that have adapted quickly to online markets have been able to maintain their customer base and revenue streams. Thus, considering the potential of online markets is of utmost importance in the current pandemic situation. In this regard, the present research aims to provide a practical framework for creating a green and sustainable closed-loop supply chain network (SCLSCN), including the integration of online markets, to assist managers in making decisions that support economic, environmental, and social goals. Accordingly, a multi-objective mixed integer linear programming (MOMILP) optimization model was designed under uncertain demand and disruption caused by natural disasters in Iran's home appliance industry. The study also considered changes in the capacities of online and marketplace sales channels, revealing a significant reduction in costs at each stage. The results show that the increase in demand has a direct impact on the production level, warehousing, and transportation costs, leading to social impacts on the model. However, the current system cannot handle an increase in demand of more than 20%, requiring managers to make decisions to increase production capacity or build new factories. Thus, the study highlights the importance of considering online markets as a means to adapt to disruptions caused by the pandemic and maintain a competitive edge.

Designing the home healthcare supply chain during a health crisis

During the COVID-19 pandemic, sectoral contributors to home healthcare supply chain (HHCSC) corporations highlighted the role of home care services. Pharmacies are located where patients are allocated to them, and nurses are routed and scheduled according to their patients' needs. It is the first study to propose an integrated location-allocation-routing model, which includes all preliminaries necessary to make these decisions. We implement the LP-metric and epsilon-constraint methods to solve this model, and then we discuss the results of these methods. A comparison is also made regarding the objective function values and the time taken to solve the problem. The average, mean ideal distance (MID) (3.74; 3.19), the rate of achievement of two objectives simultaneously (RAS) (1.71; 3.56), and computational time (CPU time) (1.92; 24.92) for two ɛ-constraint and LP-Metric methods is calculated. The superior technique is finally selected by utilizing the TOPSIS. To solve the study’s mathematical model, the LP-metric method is worth implementing. Based on these results, the suggested model for HHCSC companies, and employees’ performance, is efficient during the COVID-19 pandemic.

Effects of transport-carbon intensity, transportation, and economic complexity on environmental and health expenditures

Health and the environment are complex components of the countries, influenced by several factors, especially transport, and economics. Thus, this paper assesses the role of transportation and economic complexity in the environment and public health for the Organization for Economic Co-operation Development (OECD) countries from 2001 to 2020. This study also focuses on the relationship between transport and economic complexity with environmental and healthcare expenditures. Precisely, transport and economic activities stimulate healthcare expenditures through multiple channels. The current study employs the STIRPAT model to investigate the association with transportation, economic complexity, transport-carbon intensity, and healthcare expenditure. Besides, the current research confirms the plausible cross-sectional dependency across countries, and it adopts a second-generation technique. Analytical findings suggest that transportation-carbon intensity is positively and significantly associated with healthcare expenditures. Healthcare and transport-household expenditures increase transport-carbon intensity (TCI) by 75% and 45%, respectively, in the long run. In the contrast, TCI and transport-household expenditures have also a remarkable impact on healthcare expenditures and are estimated approximately 95% in the long run. Moreover, economic growth also upsurges TCI and healthcare expenditures through multiple economic activities. Besides, transport-household expenditures (THE) drastically impact transport-carbon intensity and healthcare expenditures (HEX) through passenger traffic (PTF). Diagnostic upshots unveil that the joint effect of THE and PTF increases TCI and HEX by 4 and 3%, respectively. Finally, findings recommend some policy implications and future research directions for the countries based on empirical outcomes. Countries should regulate economic activities to reduce transport carbon emissions.