With the growth in food products’ usage, ensuring their quality and safety has become progressively difficult. Specifically, food traceability turns out to be a very critical task for retailers, sellers, consumers, surveillance authorities, and other stakeholders in the food supply chain system. There are requirements for food authenticity verification (correct declaration of cultivation, origin, and variety), quality checks (e.g., justification for higher prices), and preventing food products from fraudsters in the food industry. The ubiquitous and promising technology of blockchain ensures the traceability of food trade networks with high potential and handles the aforementioned issues. Blockchain makes the food industry more transparent at all levels by storing data immutably and enabling quick tracking across the stages of the food supply chain. Hence, commodities, stakeholders, and semi-finished food items can be recognized significantly faster. Motivated by these facts, in this paper, we present an in-depth survey of state-of-the-art approaches to the food industry’s security, food traceability, and food supply chain management. Further, we propose a blockchain-based secure and decentralized food industry architecture to alleviate security and privacy aspects and present a comprehensive solution taxonomy for a blockchain-based food industry. Then, a comparative analysis of existing approaches with respect to various parameters, i.e., scalability, latency, and food quality, is presented, which facilitates the end-user in selecting approaches based on the merits over other approaches. Finally, we provide insights into the open issues and research challenges with concluding remarks.
In this research work, hybrid polyamide 66–basalt fiber (10 wt%)–marble dust particulates (0–20 wt% with a variation of 5%) polymeric composites were designed and prepared through the injection molding method. Each composition sample was analyzed for its physical, mechanical, and thermal behavior. The Taguchi methodology was adopted to design experimental runs of dry sliding wear and for input operating parameter optimization, along with analysis of variance. Using a scanning electron microscope, worn-out surface micrograph examinations were carried out to comprehend wear mechanisms across the surface. Furthermore, a decision-making tool such as a hybrid Analytic Hierarchy Process – R method (hybrid AHP-R method) was applied to determine the ranking of the composites based on performance measures. The composition having polyamide 66 supplemented with 15 wt% marble dust particulate and 10 wt% basalt fiber tends to optimize overall performance measures. It shows voids content of 5.80%, water absorption of 2.54%, tensile strength of 117 MPa, flexural strength of 154 MPa, impact strength of 2.8 J, Rockwell hardness of 64 HRM, thermal conductivity of 1.11 W/mK, fracture toughness of 4.7 MPa√m, and specific wear rate of 7.05 × 10−4 mm3/Nm, respectively. Thus, it optimizes overall performance measures along with steady-state dry sliding wear behavior, which is in tune with the ranking results obtained by the hybrid AHP-R method.
Objective We present the unique administrative issues as well as specific patient and surgeon related challenges and solutions implemented while treating neurosurgical cases during the COVID pandemic vis-à-vis the pre COVID times at our tertiary care center. Methods This is a retrospective study comparing the outcome of the neurosurgical patients treated from the beginning of lockdown in India on 25 March 2020 to 30 November 2020 with that of same period in the previous year, 2019. Results We had a total of 687 admissions under neurosurgery this year during the study period as compared to 2550 admissions in 2019. The total number of surgeries done under neurosurgery also showed a similar trend with only 654 surgeries in 2020 compared to 3165 surgeries in 2019. During COVID-19 times, a total of 474 patients were operated including both trauma and non-trauma cases. Out of the 50 COVID-19 suspect/ indeterminate patients who were operated upon, 5 patients turned out to be positive for COVID-19. Significant differences were seen in the mortality (p<0.01) and morbidity (p<0.01) among trauma patients on comparing COVID and pre-COVID periods. Similarly, a significant difference was observed in the mortality (p<0.001) and morbidity (p<0.001) in non-trauma patients. Conclusions A higher mortality and morbidity during the COVID times is primarily attributable to poorer baseline clinical status. Our experience in this COVID period might not only help us in tackling subsequent waves but also help other institutions in developing world to be better prepared for the same.
In the healthcare domain, a transformative shift is envisioned towards Healthcare 5.0. It expands the operational boundaries of Healthcare 4.0 and leverages patient-centric digital wellness. Healthcare 5.0 focuses on real-time patient monitoring, ambient control and wellness, and privacy compliance through assisted technologies like artificial intelligence (AI), Internet-of-Things (IoT), big data, and assisted networking channels. However, healthcare operational procedures, verifiability of prediction models, resilience, and lack of ethical and regulatory frameworks are potential hindrances to the realization of Healthcare 5.0. Recently, explainable AI (EXAI) has been a disruptive trend in AI that focuses on the explainability of traditional AI models by leveraging the decision-making of the models and prediction outputs. The explainability factor opens new opportunities to the black-box models and brings confidence in healthcare stakeholders to interpret the machine learning (ML) and deep learning (DL) models. EXAI is focused on improving clinical health practices and brings transparency to the predictive analysis, which is crucial in the healthcare domain. Recent surveys on EXAI in healthcare have not significantly focused on the data analysis and interpretation of models, which lowers its practical deployment opportunities. Owing to the gap, the proposed survey explicitly details the requirements of EXAI in Healthcare 5.0, the operational and data collection process. Based on the review method and presented research questions, systematically, the article unfolds a proposed architecture that presents an EXAI ensemble on the computerized tomography (CT) image classification and segmentation process. A solution taxonomy of EXAI in Healthcare 5.0 is proposed, and operational challenges are presented. A supported case study on electrocardiogram (ECG) monitoring is presented that preserves the privacy of local models via federated learning (FL) and EXAI for metric validation. The case-study is supported through experimental validation. The analysis proves the efficacy of EXAI in health setups that envisions real-life model deployments in a wide range of clinical applications.
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