Controlled Morphology and Its Effects on the Thermoelectric Properties of SnSe2 Thin Films

In the last few years, the thermoelectric properties of tin selenide (SnSe) have been explored in much detail due to its high efficiency and green nature, being free of Te and Pb. In the same chalcogenide family, SnSe2 is also a layered structured material, but its thermoelectric potential has not been widely explored experimentally. Since SnSe2 has the layered structure, its electrical transport properties may strongly be affected by its microstructure and morphology. Here, we report the effect of reaction time on the structure, phase, and morphology of the SnSe2 during solvothermal synthesis process. We have studied four SnSe2 samples with different reaction times. The sample obtained after 16 h of reaction time was named as M1, for 20 h M2, similarly for 24 h was M3 and for 48 hours’ time, the sample was named as M4. We investigated its thermoelectric properties and found that phase purity and morphology can affect the thermoelectric performance of the synthesized samples. The peak power factor (PF) value along the ab plane was (0.69 μWcm−1K−2) for the M4 sample at 575 K, which was the highest among all the measured samples. The comparatively larger PF value of sample M4 can be related to the increase in its electrical conductivity due to increase in phase purity and band gap reduction

Privacy-aware relationship semantics–based XACML access control model for electronic health records in hybrid cloud

State-of-the-art progress in cloud computing encouraged the healthcare organizations to outsource the management of electronic health records to cloud service providers using hybrid cloud. A hybrid cloud is an infrastructure consisting of a private cloud (managed by the organization) and a public cloud (managed by the cloud service provider). The use of hybrid cloud enables electronic health records to be exchanged between medical institutions and supports multipurpose usage of electronic health records. Along with the benefits, cloud-based electronic health records also raise the problems of security and privacy specifically in terms of electronic health records access. A comprehensive and exploratory analysis of privacy-preserving solutions revealed that most current systems do not support fine-grained access control or consider additional factors such as privacy preservation and relationship semantics. In this article, we investigated the need of a privacy-aware fine-grained access control model for the hybrid cloud. We propose a privacy-aware relationship semantics–based XACML access control model that performs hybrid relationship and attribute-based access control using extensible access control markup language. The proposed approach supports fine-grained relation-based access control with state-of-the-art privacy mechanism named Anatomy for enhanced multipurpose electronic health records usage. The proposed (privacy-aware relationship semantics–based XACML access control model) model provides and maintains an efficient privacy versus utility trade-off. We formally verify the proposed model (privacy-aware relationship semantics–based XACML access control model) and implemented to check its effectiveness in terms of privacy-aware electronic health records access and multipurpose utilization. Experimental results show that in the proposed (privacy-aware relationship semantics–based XACML access control model) model, access policies based on relationships and electronic health records anonymization can perform well in terms of access policy response time and space storage

On the Feature Selection Methods and Reject Option Classifiers for Robust Cancer Prediction

Cancer is the second leading cause of mortality across the globe. Approximately 9.6 million people are estimated to have died due to cancer disease in 2019. Accurate and early prediction of cancer can assist healthcare professionals to devise timely therapeutic innervations to control sufferings and the risk of mortality. Generally, a machine learning (ML) based predictive system in healthcare uses data (genetic profile or clinical parameters) and learning algorithms to predict target values for cancer detection. However, optimization of predictive accuracy is an important endeavor for accurate decision making. Reject Option (RO) classifiers have been used to improve the predictive accuracy of classifiers for cancer like complex problems. In a gene profile all of the features are not important and should be shaved off. ML offers different techniques with their own methodology for feature selection (FS) and the classification results are dependent on the datasets each having its own distribution and features. Therefore, both FS methods and ML algorithms with RO need to be considered for robust classification. The main objective of this study is to optimize three parameters (learning algorithm, FS method and rejection rate) for robust cancer prediction rather than considering two traditional parameters (learning algorithm and rejection rate). The analysis of different FS methods (including t-Test, Las Vegas Filter (LVF), Relief, and Information Gain (IG)) and RO classifiers on different rejection thresholds is performed to investigate the robust predictability of cancer. The three cancer datasets (Colon cancer, Leukemia and Breast cancer) were reduced using different FS methods and each of them were used to analyze the predictability of cancer using different RO classifiers. The results reveal that for each dataset predictive accuracies of RO classifiers were different for different FS methods. The findings based on proposed scheme indicate that, the ML algorithms along with their dependence on suitable FS methods need to be taken into consideration for accurate prediction

Calculating Completeness of Agile Scope in Scaled Agile Development

Flexible nature of scope definition in agile makes it difficult or impossible to measure its completeness and quality. The aim of this paper is to highlight the important ingredients of scope definition for agile projects and to present a method for agile projects in order to measure the quality and completeness of their scope definitions. The proposed method considers the elements that are retrieved as a result of the systematic literature review. An industrial survey is conducted to validate and prioritize these elements. Elements are then assigned weights according to their importance in scope definition to build a scorecard for calculating the score of user stories present in the product backlog. The proposed method is able to identify the clear and complete user stories that can better be implemented in the coming iteration. Formal experiments are performed for the evaluation of the proposed method, and it suggests that the method is useful for experts in order to quantify the completeness and quality of scope definition of an agile software project

Calculating Completeness of Agile Scope in Scaled Agile Development

Flexible nature of scope definition in agile makes it difficult or impossible to measure its completeness and quality. The aim of this paper is to highlight the important ingredients of scope definition for agile projects and to present a method for agile projects in order to measure the quality and completeness of their scope definitions. The proposed method considers the elements that are retrieved as a result of the systematic literature review. An industrial survey is conducted to validate and prioritize these elements. Elements are then assigned weights according to their importance in scope definition to build a scorecard for calculating the score of user stories present in the product backlog. The proposed method is able to identify the clear and complete user stories that can better be implemented in the coming iteration. Formal experiments are performed for the evaluation of the proposed method, and it suggests that the method is useful for experts in order to quantify the completeness and quality of scope definition of an agile software project

Fiction and Facts about BCG Imparting Trained Immunity against COVID-19

The Bacille Calmette-Guérin or BCG vaccine, the only vaccine available against Mycobacterium tuberculosis can induce a marked Th1 polarization of T-cells, characterized by the antigen-specific secretion of IFN-γ and enhanced antiviral response. A number of studies have supported the concept of protection by non-specific boosting of immunity by BCG and other microbes. BCG is a well-known example of a trained immunity inducer since it imparts ‘non-specific heterologous’ immunity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the recent pandemic. SARS-CoV-2 continues to inflict an unabated surge in morbidity and mortality around the world. There is an urgent need to devise and develop alternate strategies to bolster host immunity against the coronavirus disease of 2019 (COVID-19) and its continuously emerging variants. Several vaccines have been developed recently against COVID-19, but the data on their protective efficacy remains doubtful. Therefore, urgent strategies are required to enhance system immunity to adequately defend against newly emerging infections. The concept of trained immunity may play a cardinal role in protection against COVID-19. The ability of trained immunity-based vaccines is to promote heterologous immune responses beyond their specific antigens, which may notably help in defending against an emergency situation such as COVID-19 when the protective ability of vaccines is suspicious. A growing body of evidence points towards the beneficial non-specific boosting of immune responses by BCG or other microbes, which may protect against COVID-19. Clinical trials are underway to consider the efficacy of BCG vaccination against SARS-CoV-2 on healthcare workers and the elderly population. In this review, we will discuss the role of BCG in eliciting trained immunity and the possible limitations and challenges in controlling COVID-19 and future pandemics

Microstructure and Corrosion Behavior of Atmospheric Plasma Sprayed NiCoCrAlFe High Entropy Alloy Coating

High entropy alloys (HEAs) are multi-elemental alloy systems that exhibit a combination of exceptional mechanical and physical properties, and nowadays are validating their potential in the form of thermal sprayed coatings. In the present study, a novel synthesis method is presented to form high entropy alloy coatings. For this purpose, thermal sprayed coatings were deposited on Stainless Steel 316L substrates using atmospheric plasma spraying technique with subsequent annealing, at 1000 °C for 4 h, to assist alloy formation by thermal diffusion. The coatings in as-coated samples as well as in annealed forms were extensively studied by SEM for microstructure and cross-sectional analysis. Phase identification was performed by X-ray diffraction studies. The annealed coatings revealed a mixed BCC and FCC based HEA structure. Potentiodynamic corrosion behavior of SS316L sprayed as well as annealed coatings were also carried out in 3.5% NaCl solution and it was found that the HEA-based annealed coatings displayed the best corrosion resistance 0.83 (mpy), as compared to coated/non-annealed and SS 316 L that showed corrosion resistance of 7.60 (mpy) and 3.04 (mpy), respectively