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Cloud computing, a kind of web service provisioning model, provides immense benefits over traditional IT service environments with the help of virtualization technology. As cloud computing is not a fully matured paradigm, it poses many open issues to be addressed. The key research problem in cloud computing is efficient resource provisioning which is due to its complex and distributed architecture. Graph-based representations of complex networks provide simpler views and graph theoretical techniques provide simpler solutions for lot of issues inherent in networks. Hence, this paper begins with exploration of graph theory applications in computer networks with specific focus on graph theory applications in cloud computing. This work pays attention to basic resource provisioning problems arise in cloud computing environments and presents some conceptual graph theoretical suggestions to address these issues.

PurposeThe purpose of this study is to present a large-scale real-world comparative study using pre-COVID lockdown data versus post-COVID lockdown data on predicting shipment times of therapeutic supplies in e-pharmacy supply chains and show that our proposed methodology is robust to lockdown effects.Design/methodology/approachThe researchers used organic data of over 5.9 million records of therapeutic shipments, with 2.87 million records collected pre-COVID lockdown and 3.03 million records collected post-COVID lockdown. The researchers built various Machine Learning (ML) classifier models on the two datasets, namely, Random Forest (RF), Extra Trees (XRT), Decision Tree (DT), Multi-Layer Perceptron (MLP), XGBoost (XGB), CatBoost (CB), Linear Stochastic Gradient Descent (SGD) and the Linear Naïve Bayes (NB). Then, the researchers stacked these base models and built meta models on top of them. Further, the researchers performed a detailed comparison of the performances of ML models on pre-COVID lockdown and post-COVID lockdown datasets.FindingsThe proposed approach attains performance of 93.5% on real-world post-COVID lockdown data and 91.35% on real-world pre-COVID lockdown data. In contrast, the turn-around times (TAT) provided by therapeutic supply logistics providers are 62.91% accurate compared to reality in post-COVID lockdown times and 73.68% accurate compared to reality pre-COVID lockdown times. Hence, it is clear that while the TAT provided by logistics providers has deteriorated in the post-pandemic business climate, the proposed method is robust to handle pandemic lockdown effects on e-pharmacy supply chains.Research limitations/implicationsThe implication of the study provides a novel ML-based framework for predicting the shipment times of therapeutics, diagnostics and vaccines, and it is robust to COVID-19 lockdown effects.Practical implicationsE-pharmacy companies can readily adopt the proposed approach to enhance their supply chain management (SCM) capabilities and build resilience during COVID lockdown times.Originality/valueThe present study is one of the first to perform a large-scale real-world comparative analysis on predicting therapeutic supply shipment times in the e-pharmacy supply chain with novel ML ensemble stacking, obtaining robust results in these COVID lockdown times.

The waste disposal issues were the most severe problems that could cause global warming, which depletes the environment. The research hypothesis was to find the suitability and sustainability of utilizing the waste by-products in the invention of green geopolymer concrete to eliminate the tremendous effects caused by the wastes. Due to the increased demand for fly ash in recent years, the requirement of high alkaline activators, and elevated temperature for curing, there was a research gap to find an alternative binder. The novelty of this research was to utilize the waste wood ash, which is available plenty in nearby hotels and has an inbuilt composition of high potassium that can act as a self alkaline activator. Waste wood ash procured from the local hotels was replaced with fly ash by 0 to 100% at 10% intervals. The setting and mechanical characteristics were found on the prolonged ages to understand the influence of waste wood ash. Microstructural characterization was found using Scanning Electron Microscope and X-Ray Diffraction Analysis to define the impact of waste wood ash in the microstructure. The research findings showed that replacing 30% waste wood ash with fly ash attained better performance in setting properties and all mechanical parameters. The obtained optimum mix could provide the best alternative for fly ash in geopolymer to eliminate the economic thrust by the requirement of alkaline activators and deploy the environmental impact caused by the waste wood ash.

PurposeThis paper aims to address the pressing problem of prediction concerning shipment times of therapeutics, diagnostics and vaccines during the ongoing COVID-19 pandemic using a novel artificial intelligence (AI) and machine learning (ML) approach.Design/methodology/approachThe present study used organic real-world therapeutic supplies data of over 3 million shipments collected during the COVID-19 pandemic through a large real-world e-pharmacy. The researchers built various ML multiclass classification models, namely, random forest (RF), extra trees (XRT), decision tree (DT), multilayer perceptron (MLP), XGBoost (XGB), CatBoost (CB), linear stochastic gradient descent (SGD) and the linear Naïve Bayes (NB) and trained them on striped datasets of (source, destination, shipper) triplets. The study stacked the base models and built stacked meta-models. Subsequently, the researchers built a model zoo with a combination of the base models and stacked meta-models trained on these striped datasets. The study used 10-fold cross-validation (CV) for performance evaluation.FindingsThe findings reveal that the turn-around-time provided by therapeutic supply logistics providers is only 62.91% accurate when compared to reality. In contrast, the solution provided in this study is up to 93.5% accurate compared to reality, resulting in up to 48.62% improvement, with a clear trend of more historic data and better performance growing each week.Research limitations/implicationsThe implication of the study has shown the efficacy of ML model zoo with a combination of base models and stacked meta-models trained on striped datasets of (source, destination and shipper) triplets for predicting the shipment times of therapeutics, diagnostics and vaccines in the e-pharmacy supply chain.Originality/valueThe novelty of the study is on the real-world e-pharmacy supply chain under post-COVID-19 lockdown conditions and has come up with a novel ML ensemble stacking based model zoo to make predictions on the shipment times of therapeutics. Through this work, it is assumed that there will be greater adoption of AI and ML techniques in shipment time prediction of therapeutics in the logistics industry in the pandemic situations.

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Purpose Demand for Geopolymer concrete (GPC) has increased recently because of its many benefits, including being environmentally sustainable, extremely tolerant to high temperature and chemical attacks in more dangerous environments. Like standard concrete, GPC also has low tensile strength and deformation capacity. This paper aims to analyse the utilization of incinerated bio-medical waste ash (IBWA) combined with ground granulated blast furnace slag (GGBS) in reinforced GPC beams and columns. Medical waste was produced in the health-care industry, specifically in hospitals and diagnostic laboratories. GGBS is a form of industrial waste generated by steel factories. The best option to address global warming is to reduce the consumption of Portland cement production and promote other types of cement that were not a pollutant to the environment. Therefore, the replacement in ordinary Portland cement construction with GPC is a promising way of reducing carbon dioxide emissions. GPC was produced due to an alkali-activated polymeric reaction between alumina-silicate source materials and unreacted aggregates and other materials. Industrial pollutants such as fly ash and slag were used as raw materials. Design/methodology/approach Laboratory experiments were performed on three different proportions (reinforced cement concrete [RCC], 100% GGBS as an aluminosilicate source material in reinforced geopolymer concrete [GRGPC] and 30% replacement of IBWA as an aluminosilicate source material for GGBS in reinforced geopolymer concrete [IGRGPC]). The cubes and cylinders for these proportions were tested to find their compressive strength and split tensile strength. In addition, beams (deflection factor, ductility factor, flexural strength, degradation of stiffness and toughness index) and columns (load-carrying ability, stress-strain behaviour and load-deflection behaviours) of reinforced geopolymer concrete (RGPC) were studied. Findings As shown by the results, compared to Reinforced Cement Concrete (RCC) and 100% GGBS based Reinforced Geopolymer Concrete (GRGPC), 30% IBWA and 70% GGBS based Reinforced Geopolymer Concrete (IGRGPC) (30% IBWA–70% GGBS reinforced geo-polymer concrete) cubes, cylinders, beams and columns exhibit high compressive strength, tensile strength, flexural strength, load-carrying ability, ultimate strength, stiffness, ductility and deformation capacity. Originality/value All the results were based on the experiments done in this research. All the result values obtained in this research are higher than the theoretical values.

A mixture of hazardous waste from various places, including hospitals, testing facilities, clinics, etc., is a biomedical waste. It is possible that eventually, there could be a significant risk to human lives and the survival of plant and animal life on this planet from hazardous biomedical waste disposal. Biomedical waste is usually burnt at an incineration plant and produces ash called incinerated biomedical waste ash (IBWA). If IBWA is not disposed correctly, it may contaminate the groundwater because heavy metals not eliminated during the incineration process. The groundwater contamination problem due to IBWA may be minimised using geopolymer concrete (GPC). When discussing pollution and the environment, another big issue involves waste glass. There was a rise in the quantity of waste glass generated each day, and the recycling region is restricted. To minimise this issue, we can exploit the waste glass powder in the construction sectors. The substitute for cement concrete is geopolymer concrete which was predominantly manufactured from renewable resources. This research work aims to produce the geopolymer concrete at room temperature. The ground-granulated blast-furnace slag (GGBS) with IBWA ratio is fixed as 70:30, and the fine aggregate (M-Sand) is replaced with varying the glass powder as 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45% and 50% have been done and cured under ambient curing. The addition of IBWA and glass powder has been performed to increase the reactivity and performance of geopolymer concrete. KeywordsGeopolymer concrete • Incinerated biomedical waste ash • GGBS • Waste glass powder • Mechanical properties • Rheological properties