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Risk is inherent in all parts of life and brings consequences, but when it specifically emerges in supply chains, it is susceptible. Therefore, this study aims at identifying and assessing supply chain risks and developing criteria for managing these risks. Supply chain (SC) risks consist of complex, uncertain, and vague information, but risk assessment techniques in the literature have been unable to handle complexity, uncertainty, and vagueness. Therefore, this study presents a holistic approach to supply chain risk management. In this paper, neutrosophic (N) theory is merged with the analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) to deal with complexity, uncertainty, and vagueness. Then the proposed methodology is practically implemented through a case study on the automotive industry. SC resilience, SC agility, and SC robustness were selected as criteria for managing supply chain risks and analyzed using N-AHP. Furthermore, seventeen risks were identified and assessed by using N-TOPSIS. Results suggest supply chain resilience is the most important criterion for managing supply chain risks. Moreover, supplier delivery delays, supplier quality problems, supplier communication failures, and forecasting errors are the most vulnerable risks that occur in supply chains of the automotive industry in Pakistan.

Despite significant infrastructure improvements, cloud computing still faces numerous challenges in terms of load balancing. Several techniques have been applied in the literature to improve load balancing efficiency. Recent research manifested that load balancing techniques based on metaheuristics provide better solutions for proper scheduling and allocation of resources in the cloud. However, most of the existing approaches consider only a single or few QoS metrics and ignore many important factors. The performance efficiency of these approaches is further enhanced by merging with machine learning techniques. These approaches combine the relative benefits of load balancing algorithm backed up by powerful machine learning models such as Support Vector Machines (SVM). In the cloud, data exists in huge volume and variety that requires extensive computations for its accessibility, and hence performance efficiency is a major concern. To address such concerns, we propose a load balancing algorithm, namely, Data Files Type Formatting (DFTF) that utilizes a modified version of Cat Swarm Optimization (CSO) along with SVM. First, the proposed system classifies data in the cloud from diverse sources into various types, such as text, images, video, and audio using one to many types of SVM classifiers. Then, the data is input to the modified load balancing algorithm CSO that efficiently distributes the load on VMs. Simulation results compared to existing approaches showed an improved performance in terms of throughput (7%), the response time (8.2%), migration time (13%), energy consumption (8.5%), optimization time (9.7%), overhead time (6.2%), SLA violation (8.9%), and average execution time (9%). These results outperformed some of the existing baselines used in this research such as CBSMKC, FSALB, PSO-BOOST, IACSO-SVM, CSO-DA, and GA-ACO.

Maintaining accuracy in load balancing using metaheuristics is a difficult task even with the help of recent hybrid approaches. In the existing literature, various optimized metaheuristic approaches are being used to achieve their combined benefits for proper load balancing in the cloud. These approaches often adopt multi-objective QoS metrics, such as reduced SLA violations, reduced makespan, high throughput, low overload, low energy consumption, high optimization, minimum migrations, and higher response time. The cloud applications are generally computation-intensive and can grow exponentially in memory with the increase in size if no proper effective and efficient load balancing technique is adopted resulting in poor quality solutions. To provide a better load balancing solution in cloud computing, with extensive data, a new hybrid model is being proposed that performs classification on the number of files present in the cloud using file type formatting. The classification is performed using Support Vector Machine (SVM) considering various file formats such as audio, video, text maps, and images in the cloud. The resultant data class provides high classification accuracy which is further fed into a metaheuristic algorithm namely Ant Colony Optimization (ACO) using File Type Formatting FTF for better load balancing in the cloud. Frequently used QoS metrics, such as SLA violations, migration time, throughput time, overhead time, and optimization time are evaluated in the cloud environment and comparative analysis is performed with recent metaheuristics, such as Ant Colony Optimization-Particle Swarm Optimization (ACOPS), Chaotic Particle Swarm Optimization (CPSO), Q-learning Modified Particle Swarm Optimization (QMPSO), Cat Swarm Optimization (CSO) and D-ACOELB. The proposed algorithm outperforms them and provides good performance with scalability and robustness.

-Uncontrolled exposure of active and passive smokers to trace metals causes increase in health risks. The primary objective of this study was to determine whether local and imported cigarette brands used in Pakistan, have elevated levels of metals or not. Six metals manganese (Mn), cobalt (Co), copper (Cu), cadmium (Cd), lead (Pb) and zinc (Zn) were determined in tobacco of twenty cigarette contamination chances and for complete digestion of analytes a microwave digester was used. The analytical results showed highest concentration of Mn (84.78 μg/g dry weight), Cd (0.525 μg/g dry weight) and Zn (14.34 μg/g dry weight) metals in imported brands in relation to counterparts from the local brands. Certain elevated levels were observed for Co (3.344 μg/g dry weight), Pb (14.16 μg/g dry weight) and Cu (7.889 μg/g dry weight) metals in local brands. The inter-metal relationships in the tobacco of local and imported cigarette brands showed some integrated variation in the selected metal levels. In view of health risk associated with the above metals, there should be a strict quality control over monitoring of heavy metals during growing, processing and smoking of tobacco. Therefore, it is prudent to minimize exposure to toxic substances whenever possible because smoking and exposure to cigarette smoke is a confounder to be taken into account when carrying out epidemiological studies on human exposure to metals.

In today’s emerging environment sustainable supply chain risks play a vital role in firms’ performance more than ever, because risks tend to disrupt sustainable operations, which ultimately reduces a firm’s performance, but these risks can be managed through supply chain integration practices, which leads to higher firms’ performance. Therefore, this paper examines the relationship between sustainable supply chain risks, supply chain integration, and firm’s financial performance. This study employs 296 survey observations along with financial data of published annual statements to estimate the quantitative causal-effects of three dimensions of sustainable supply chain risks on supply chain integration and financial performance. The findings of the study suggest that sustainable internal business process risks, sustainable supply risks, and sustainable demand risks have a negative relationship with supply chain integration. Furthermore, results of the study explored that all the three supply chain integration practices have a positive impact on firms’ financial performance, which suggests that implementing supply chain integration practices reduces the effect of supply chain risks and increases the firm’s performance.

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