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• The input data is pre-processed using Morphological filtering. • Extended Empirical wavelet transformation for removing the baseline. • The pre-processed data are given to Ternary pattern and discrete wavelet transform. • The proposed BiGRU-AE-AD-ECG method is implemented in python. • It is examined under sensitivity, precision, recall, f-measure, specificity and accuracy. Heartbeat irregularity or arrhythmia of a patient with serious congestive cardiovascular breakdown is distinguished in this article, and the class not really settled utilizing long haul ECG information. Several existing methods on arrhythmia detection are utilized, which does not accurately classify the Arrhythmia and it takes higher computational time. Morphological filtering and Extended Empirical wavelet transformation preprocess the input data to remove noises and base line. Then these pre-processed data is fed to Ternary Pattern and Discrete Wavelet Transform (TP-DWT) related feature extracting to take relevant feature. Then, the extracted features are given to Bidirectional Gated Recurrent Unit with Auto Encoder (BiGRU-AE) to classify Arrhythmia into Normal, Atrial Fibrillation (AF), First-degree atrioventricular block (1-AVB), Left Bundle Branch (LBBB), Right Bundle Branch Block (RBBB), Premature Atrial Contraction (PAC), Premature Ventricular Contraction (PVC), ST-segment depression (STD) and ST-segment elevation (STE). The proposed BiGRU-AE-AD-ECG is activated in Python and its efficiency is measured under certain metrics, like sensitivity, precision, f-measure, specificity, accuracy, error rate, ROC and computational time. Here, CPSC 2018 dataset is used to validate the efficiency of the BiGRU-AE-AD-ECG method. The performance of BiGRU-AE-AD-ECG method provides 23.40 %, 18.78 % and 28.05 % higher accuracy, 20.41 %, 27.07 % and 18.80 % higher precision, 32.01 %, 27.40 % and 28.95 % higher sensitivity and 28.05 %, 23.41 % and 31.34 % higher specificity analyzed with existing models, like automatic cardiac arrhythmias categorization based on CNN and attention-based RNN network (CNN-RNN-AD-ECG), a multiple lead-branch fusion network under 12-Lead ECG for multiple class arrhythmia categorization (MLBFNet-AD-ECG), analysis of ECG-based arrhythmia identification scheme using machine learning (SVM-AD-ECG)respectively. [ABSTRACT FROM AUTHOR]

• The role of operations and SCM in managing epidemics and pandemics is reviewed. • Five research areas are identified, providing an integrative view of the field. • Ten future research directions are developed. • A starting point for researchers to investigate this domain is provided. Epidemics have been posing significant challenges to health, existence, and continuity. From the emergence of an outbreak to its elimination, managing an epidemic/pandemic entails many operations and supply chain management decisions that can contribute to a lessening of its impact. With these decisions, epidemic-/pandemic-imposed challenges related to forecasting, planning, supply, manufacturing, storage, and transportation can be addressed in an effort to curtail and end the epidemic/pandemic. We have witnessed these disruptions first-hand during the COVID-19 pandemic, which has had a destructive effect on many well-established supply chains, threatening the existence of firms. The role of operations and supply chain management is thus pivotal for navigating epidemics/pandemics. Against this background, we present a systematic literature review on the role of operations and supply chain management during epidemics and pandemics, illustrating its potential and calling for future research. Leveraging bibliographic coupling analysis, we identify major research areas and contributions that serve as a foundation to propel these domains forward. We further critically review these research areas, identifying multiple themes of which many have been perennially relevant, while others have come to the fore only recently due to the COVID-19 pandemic. Our review provides an integrative view of the field, concurrently advancing theory, and offering ten distinct future research directions. Overall, this paper is meant to serve as a starting point for researchers in operations and supply chain management aiming to investigate this increasingly important domain. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) ability to monitor the surrounding environment promotes autonomous activities like determining a trajectory and responding accordingly. Security and transportation are vital components of today's technology-driven society. In addition to search and rescue, it may also be used for planetary exploration and material management. So, the Internet of Vehicles must drive independently in various dynamic situations. Unlocking the potential of the IoV navigation process, a dynamic access control architecture-based autonomous navigation system is proposed. It can perform assigned tasks while also reacting quickly to unexpected situations. The efficiency of AI-based IoV navigation is compared and studied using autonomous decision control systems. [Display omitted] [ABSTRACT FROM AUTHOR]

Among the various climate change causing agents, transportation alone is responsible for 19.2% of the carbon emissions. By proper implementation of various preventive measures, the impact of transportation on climate change can be drastically reduced. It is also necessary to determine the relationship between the transportation factors responsible for creating environmental degradation and causing climate change and to rank them based on the preventive measures impact to deal with them. In this paper, we have identified the transportation factors that affect climate change and the factors related to its preventive measures based on the 'environment', 'sociology' and 'modernity' elements of Ecological Modernization Theory (EMT). Given the context, this study focusses on development of an assessment framework that combines three techniques: Simple Additive Weighting (SAW), Interpretive Structural Modelling (ISM) and Interpretive Ranking Process (IRP). • We integrate SAW-ISM-IRP approach to understand the impact of transportation on climate change. • We have identified the factors based on Ecological Modernization Theory. • First, SAW has been used to reduce the number of factors in order to apply ISM and IRP easily. • We have collected data from 10 experts. [ABSTRACT FROM AUTHOR]

In recent years, municipal authorities especially in the developing nations are battling to select the best health care waste (HCW) disposal technique for the effective treatment of the medical wastes during and post COVID-19 era. As evaluation of various disposal alternatives of HCW and selection of the best technique requires considering various tangible and intangible criteria, this can be framed as multi-criteria decision-making (MCDM) problem. In this paper, we propose an assessment framework for the selection of the best HCW disposal technique based on socio-technical and triple bottom line perspectives. We have identified 10 criteria on which the best HCW disposal techniques to be selected based on extant literature review. Next, we use Fuzzy VIKOR method to evaluate 9 HCW disposal alternatives. The effectiveness of the proposed framework has been demonstrated with a real-life case study in Indian context. To check the robustness of the proposed methodology, we have compared the results obtained with Fuzzy TOPSIS (Technique of Order Preference Similarity to the Ideal Solution). The results help the municipal authorities to establish a methodical approach to choose the best HCW disposal techniques. Our findings indicate that incineration is the best waste disposal technique among the available alternatives. Even if the dataset indicates 'incineration' is the best method, we must not forget about the environmental concerns arising from this method. In COVID time, incineration may be the best method as indicated by the data analysis, but "COVID" should not be an excuse for causing "Environmental Pollution". • Health care waste disposal technique selection is a serious concern during COVID-19. • We propose Fuzzy VIKOR based method for selecting the best disposal technique. • This work has been grounded on Triple Bottom Line and Socio-Technical perspectives. • The proposed framework has been validated with a real-life case study. • The effectiveness of the proposed method has been compared with Fuzzy TOPSIS. [ABSTRACT FROM AUTHOR]

Sedimentological parameterization of rock heterogeneity and prediction of shallow-marine siliciclastic reservoir quality remains a challenge globally to date, especially in regions with complex depositional histories, in that, variations in petrographic and petrophysical properties can occur within the same sedimentary sequence of similar age and rock type. Because exploratory drilling strategies and reservoir models can significantly be augmented through direct observation of outcrops that are representative of equivalent reservoir stratigraphic intervals in the offshore zone, outcrop sedimentological characterization can help improve the understanding of subsurface reservoirs of similar strata in surrounding petroliferous basins. To this end, we analyzed well-exposed successions of the mid Mio–Pliocene shallow-marine sandstone deposits of the Sandakan Formation, Borneo, through conventional field investigation, petrographic and petrophysical studies of different sandstone facies types to predict reservoir quality and heterogeneity within different depositional settings. On the basis of these evaluations, the studied sandstone was grouped into three qualitative reservoir rock classes: Class I (Ø = 18.10–43.60%; K = 1265.20–5986.25 mD), Class II (Ø = 17.60–37%; K = 21.36–568 mD) and Class III (Ø = 3.4–15.7%; K = 3.21–201.30 mD). The petrographic and petrophysical results reveals a consistent rise in both gamma ray and permeability values due to a change in depositional environment from proximal lower shoreface to upper shoreface of some successions within the sequence. The change in depositional setting for each sandstone type establishes certain variation in classes within the same facies type. For instance, the permeability may indicate low values due to the presence of bioturbation and increased mud content owing to the burrows with bimodal grain classification, regardless of change in gamma ray readings. This phenomenon signifies that there exists no strict correlation between gamma ray and permeability profile despite containing good quality reservoir rocks and appropriate classification. Our study is of paramount significance to the development of initial exploration strategies in regions where similar siliciclastic strata can be potential reservoirs because it serves as analogs to offshore hydrocarbon bearing packages. Image 1 • S edimentological characterization and reservoir quality assessment of Sandakan Formation. • Reservoir quality affected by petrographic, petrophysical properties and depositional setting. • No strict correlation between GR and permeability despite having good quality reservoir rocks. • Thorough outcrop appraisal can be good analogs to understand offshore hydrocarbon bearing rocks. [ABSTRACT FROM AUTHOR]

• We compare a seller's disaster payment period policies to boost demand. • We consider Advance, Cash, Credit or Disaster Grace Period (ACCD) payment options. • The trade-off between default/disaster risk and demand is investigated. • We identify the optimum replenishment cycle and payments period for a seller's profit. Sellers can offer various payment schemes for buyers to boost demand, with the advance, cash, and credit payment policies being popular approaches. To provide further insight into the dynamics of these policies, we consider them in addition to a Disaster Grace Period payment option, which has become increasingly prevalent. The seller's motivation for offering Advance, Cash, Credit or Disaster Grace Period (ACCD) payment options include the desire for a stronger relationship to the buyer, a smoother cash flow throughout the supply chain, and a potential increase in demand. In addition, the disaster grace period payment scheme can help in better navigating worldwide emergencies, such as pandemics, but also natural emergencies like severe storms, floods, tornadoes, and landslides, which have been happening with an increasing frequency over the last decades. However, the increase in the payment period increases the default/disaster risk, rendering a trade-off between risk and demand, making these crucial parameters in influencing the seller's profit. Within this context, we aim to find the optimum replenishment cycle and payments period for a seller to increase their total profit. To do so, we perform numerical analysis to examine the impact of the demand coefficient, interest rate, default risk, disaster risk, and the disaster demand coefficient on profit. The results offer managerial insights on which payment option to choose in a particular setting to maximize profit. We for example find that sellers should offer a disaster grace period when the disaster demand increases and the risk of a disaster decreases. [ABSTRACT FROM AUTHOR]

• Best strategies for a large-scale COVID-19 vaccine distribution are developed. • Storage and transportation requirements, as well as costs are considered. • An equitable distribution is considered due to limited vaccine availability. • Best location and allocation decisions for cold storage facilities are provided. • The model is evaluated in a real-world case study. While cold chain management has been part of healthcare systems, enabling the efficient administration of vaccines in both urban and rural areas, the COVID-19 virus has created entirely new challenges for vaccine distributions. With virtually every individual worldwide being impacted, strategies are needed to devise best vaccine distribution scenarios, ensuring proper storage, transportation and cost considerations. Current models do not consider the magnitude of distribution efforts needed in our current pandemic, in particular the objective that entire populations need to be vaccinated. We expand on existing models and devise an approach that considers the needed extensive distribution capabilities and special storage requirements of vaccines, while at the same time being cognizant of costs. As such, we provide decision support on how to distribute the vaccine to an entire population based on priority. We do so by conducting predictive analysis for three different scenarios and dividing the distribution chain into three phases. As the available vaccine doses are limited in quantity at first, we apply decision tree analysis to find the best vaccination scenario, followed by a synthetic control analysis to predict the impact of the vaccination programme to forecast future vaccine production. We then formulate a mixed-integer linear programming (MILP) model for locating and allocating cold storage facilities for bulk vaccine production, followed by the proposition of a heuristic algorithm to solve the associated objective functions. The application of the proposed model is evaluated by implementing it in a real-world case study. The optimized numerical results provide valuable decision support for healthcare authorities. [ABSTRACT FROM AUTHOR]

• We investigate a location-allocation problem for convalescent plasma banks. • Optimal locations are determined, then collection facilities are allocated. • A MILP model considering transportation time and cost is solved via CPLEX. • Results are validated by a comparison study using NSGA-II and NSGA-III. • The model is implemented within the context of India and results are presented. With convalescent plasma being recognized as an eminent treatment option for COVID-19, this paper addresses the location-allocation problem for convalescent plasma bank facilities. This is a critical topic, since limited supply and overtly increasing cases demand a well-established supply chain. We present a novel plasma supply chain model considering stochastic parameters affecting plasma demand and the unique features of the plasma supply chain. The primary objective is to first determine the optimal location of the plasma banks and to then allocate the plasma collection facilities so as to maintain proper plasma flow within the network. In addition, recognizing the perishable nature of plasma, we integrate a deteriorating rate with the objective that as little plasma as possible is lost. We formulate a robust mixed-integer linear programming (MILP) model by considering two conflicting objective functions, namely the minimization of overall plasma transportation time and total plasma supply chain network cost, with the latter also capturing inventory costs to reduce wastage. We then propose a CPLEX-based optimization approach for solving the MILP functions. The feasibility of our results is validated by a comparison study using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) and a proposed modified NSGA-III. The application of the proposed model is evaluated by implementing it in a real-world case study within the context of India. The optimized numerical results, together with their sensitivity analysis, provide valuable decision support for policymakers. [ABSTRACT FROM AUTHOR]