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Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy is required for health coverage tailored to needs and capacity. Therefore, this study aims to explore the adoption of a cognitive computing decision support system (CCDSS) in the assessment of health-care policymaking and validates it by extending the unified theory of acceptance and use of technology model.

A survey was conducted to collect data from different stakeholders, referred to as the 4Ps, namely, patients, providers, payors and policymakers. Structural equation modelling and one-way ANOVA were used to analyse the data.

The result reveals that the behavioural insight of policymakers towards the assessment of health-care policymaking is based on automatic and reflective systems. Investments in CCDSS for policymaking assessment have the potential to produce rational outcomes. CCDSS, built with quality procedures, can validate whether breastfeeding-supporting policies are mother-friendly.

Health-care policies are used by lawmakers to safeguard and improve public health, but it has always been a challenge. With the adoption of CCDSS, the overall goal of health-care policymaking can achieve better quality standards and improve the design of policymaking.

This study drew attention to how CCDSS as a technology enabler can drive health-care policymaking assessment for each stage and how the technology enabler can help the 4Ps of health-care gain insight into the benefits and potential value of CCDSS by demonstrating the breastfeeding supporting policy.

The best possible way for brick-and-mortar retailers to maximise engagement with personalised shoppers is capitalising on intelligent insights. The retailer operates differently with diversified items and services, but influencing retail atmospheric on personalised shoppers, the perception remains the same across industries. Retail atmospherics stimuli such as design, smell and others create behavioural modifications. The purpose of this study is to explore the atmospheric effects on brick-and-mortar store performance and personalised shopper's behaviour using cognitive computing based in-store analytics in the context of emerging market.

The data are collected from 35 shoppers of a brick-and-mortar retailer through questionnaire survey and analysed using quantitative method.

The result of the analysis reveals month-on-month growth in footfall count (46%), conversation rate (21%), units per transaction (27%), average order value (23%), dwell time (11%), purchase intention (29%), emotional experience (40%) and a month-on-month decline in remorse (20%). The retailers need to focus on three control gates of shopper behaviour: entry, browsing and exit. Attention should be paid to the cognitive computing solution to judge the influence of retail atmospherics on store performance and behaviour of personalised shoppers. Retail atmospherics create the right experience for individual shoppers and forceful use of it has an adverse impact.

The paper focuses on strategic decisions of retailers, the tactical value of personalised shoppers and empirically identifies the retail atmospherics effect on brick-and-mortar store performance and personalised shopper behaviour.

Any business that opts to adopt a recommender engine (RE) for various potential benefits must choose from the candidate solutions, by matching to the task of interest and domain. The purpose of this paper is to choose RE that fits best from a set of candidate solutions using rule-based automated machine learning (ML) approach. The objective is to draw trustworthy conclusion, which results in brand building, and establishing a reliable relation with customers and undeniably to grow the business.

An experimental quantitative research method was conducted in which the ML model was evaluated with diversified performance metrics and five RE algorithms by combining offline evaluation on historical and simulated movie data set, and the online evaluation on business-alike near-real-time data set to uncover the best-fitting RE.

The rule-based automated evaluation of RE has changed the testing landscape, with the removal of longer duration of manual testing and not being comprehensive. It leads to minimal manual effort with high-quality results and can possibly bring a new revolution in the testing practice to start a service line “Machine Learning Testing as a service” (MLTaaS) and the possibility of integrating with DevOps that can specifically help agile team to ship a fail-safe RE evaluation product targeting SaaS (software as a service) or cloud deployment.

A small data set was considered for A/B phase study and was captured for ten movies from three theaters operating in a single location in India, and simulation phase study was captured for two movies from three theaters operating from the same location in India. The research was limited to Bollywood and Ollywood movies for A/B phase, and Ollywood movies for simulation phase.

The best-fitting RE facilitates the business to make personalized recommendations, long-term customer loyalty forecasting, predicting the company's future performance, introducing customers to new products/services and shaping customer's future preferences and behaviors.

The proposed rule-based ML approach named “2-stage locking evaluation” is self-learned, automated by design and largely produces time-bound conclusive result and improved decision-making process. It is the first of a kind to examine the business domain and task of interest. In each stage of the evaluation, low-performer REs are excluded which leads to time-optimized and cost-optimized solution. Additionally, the combination of offline and online evaluation methods offer benefits, such as improved quality with self-learning algorithm, faster time to decision-making by significantly reducing manual efforts with end-to-end test coverage, cognitive aiding for early feedback and unattended evaluation and traceability by identifying the missing test metrics coverage.

Coronavirus disease (COVID-19) was declared as a pandemic since COVID-19's widespread outbreak and the hospitality industry has been the hardest hit due to lockdown. Consequently, hospitality workers are suffering from the negative aspects of mental health. In the event of such a crisis, this study aims to explore the link between unemployment and home isolation to the willingness to choose electronic consultation (e-consultation) by exploiting psychological ill-being and behavioural intention (BI) with marital status as a moderator.

A quantitative methodology is applied to primary data collected from 310 workers from the hospitality industry through an online survey.

Findings of this study suggest that the usage of the e-consultation service can be adopted using three levels. There are valid reasons to conclude unemployment and home isolation are linked to higher rates of psychological health behaviours, which can result in stigma, loss of self-worth and increased mortality. The adverse effect is higher for single individuals than for married people.

The study focussed on e-consultation, BI coupled with the Fishbein scale and a classification model for the prediction of willingness to choose e-consultation with the extension of Theory of Planned Behaviour (TPB).

The purpose of this paper is to propose and experiment a framework for analysing the tweets to find the basis of popularity of a person and extract the reasons supporting the popularity. Although the problem of analysing tweets to detect popular events and trends has recently attracted extensive research efforts, not much emphasis has been given to find out the reasons behind the popularity of a person based on tweets.

In this paper, the authors introduce a framework to find out the reasons behind the popularity of a person based on the analysis of events and the evaluation of a Web-based semantic set similarity measure applied to tweets. The methodology uses the semantic similarity measure to group similar tweets in events. Although the tweets cannot contain identical hashtags, they can refer to a unique topic with equivalent or related terminology. A special data structure maintains event information, related keywords and statistics to extract the reasons supporting popularity.

An implementation of the algorithms has been experimented on a data set of 218,490 tweets from five different countries for popularity detection and reasons extraction. The experimental results are quite encouraging and consistent in determining the reasons behind popularity. The use of Web-based semantic similarity measure is based on statistics extracted from search engines, it allows to dynamically adapt the similarity values to the variation on the correlation of words depending on current social trends.

To the best of the authors’ knowledge, the proposed method for finding the reason of popularity in short messages is original. The semantic set similarity presented in the paper is an original asymmetric variant of a similarity scheme developed in the context of semantic image recognition.

This paper aims to propose an approach for the analysis of user interest based on tweets, which can be used in the design of user recommendation systems. The extract topics are seen positively by the user.

The proposed approach is based on the combination of sentiment extraction and classification analysis of tweet to extract the topic of interest. The proposed hybrid method is original. The topic extraction phase uses a method based on semantic distance in the WordNet taxonomy. Sentiment extraction uses NLPcore.

The algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results and confirm the suitability of the approach combining sentiment and categorization for the topic of interest extraction.

The hybrid method combining sentiment extraction and classification for user positive topics represents a novel contribution with many potential applications.

The functionality of positive topic extraction is very useful as a component in the design of a recommender system based on user profiling from Twitter user behaviors.

The application of the proposed method in short-text social network can be massive and beyond the applications in tweets.

There are few works that have considered both sentiment analysis and classification to find out users’ interest. The algorithm has been extensively tested using real tweets generated by 1,000 users. The results are quite encouraging and outperform state-of-the-art results.

The changes in tensile behavior of polycrystalline nanocopper lattice with changes in temperature, average grain size (AGS) and strain rate, have been explored. The existence of a critical AGS has also been observed which shows that the Hall–Petch relationship behaves inversely.

Nanoscale deformation of polycrystalline nanocopper has been done in this study with the help of an embedded atom method (EAM) potential. Voronoi construction method has been employed for creating four polycrystals of nanocopper with different sizes. Statistical analysis has been used to examine the observations with emphasis on the polycrystal size effect on melting point temperature.

The study has found that the key stress values (i.e. elastic modulus, yield stress and ultimate tensile stress) are significantly influenced by the considered parameters. The increase in strain rate is observed to have an increasing impact on mechanical properties, whereas the increase in temperature degrades the mechanical properties. In-depth analysis of the deformation mechanism has been studied to deliver real-time visualization of grain boundary motion.

This study provides the relationship between required grain size variations for consecutive possible variations in mechanical properties and may help to reduce the trial processes in the synthesis of polycrystalline copper based on different temperatures and strain rates.

The Internet is used as a tool to seek health information by individuals. Mental health concerns are the high prevalence of the novel coronavirus disease 2019 (COVID-19) and preventive steps are required to curb the illness. Therefore, to gain more insight into health concerns, it is now a common practice to seek health information on the Internet. This study propose an integrated theoretical model to explore the relationship between COVID-19 protocols and perceived online trust with online health information seeking intention (OHISI) and a moderating effect of perceived severity and perceived urgency.

Data are collected from 325 athletes in the category of individual and team sports through an online survey in a Likert-scale questionnaire. The analysis is performed with a quantitative methodology.

The study reveals the bright side of online health information (OHI), which brings athletes together and has played out with virtual happy hours, meetings and events. The bright side of OHI reflects social, cultural, technological and economic benefits. An OHI chatbot offers bright personalised side information to the individual seeker, which is more convenient and efficient than human capabilities.

The pivotal contribution is the integrated theoretical framework that is derived from multidisciplinary literature to capture the complexity of OHI. Also, it conceptualises the constructs in the context of OHI and COVID-19.

Development of (1T-type) ferroelectric random access memory (FeRAM) has most actively progressed since 1995 and motivated by the physical limits and technological drawbacks of the flash memory. 1T-type FeRAM implements ferroelectric layer at the field effect transistor (FET) gate. During the course of the investigation, it was very difficult to form a thermodynamically stable ferroelectric-semiconductor interface at the FET gate, leading to the introduction of one insulating buffer layer between the ferroelectric and the silicon substrate to overcome this problem. In this study, Al₂O₃ a high-k buffer layer deposited by plasma enhanced atomic layer deposition (PEALD) is sandwiched between the ferroelectric layer and silicon substrate.

Ferroelectric/high-k gate stack were fabricated on the silicon substrate and pt electrode. Structural characteristics of the ferroelectric (PZT) and high-k (Al₂O₃) thin film deposited by RF sputtering and PEALD, respectively, were optimized and investigated for different process parameters. Metal/PZT/Metal, Metal/PZT/Silicon, Metal/PZT/Al₂O₃/Silicon structures were fabricated and electrically characterized to obtain the memory window, leakage current, hysteresis, PUND, endurance and breakdown characteristics.

XRD pattern shows the ferroelectric perovskite thin Pb[Zr_0.35Ti_0.65]O₃ film with (101) tetragonal orientation deposited by sputtering and PEALD Al₂O₃ with (312) orientation showing amorphous nature. Multiple angle analysis shows that the refractive index of PZT varies from 2.248 to 2.569, and PEALD Al₂O₃ varies from 1.6560 to 1.6957 with post-deposition annealing temperature. Increase in memory window from 2.3 to 8.4 V for the Metal-Ferroelectric-Silicon (MFS) and Metal-Ferroelectric-Insulator-Semiconductor (MFIS) structure has been observed at the annealing temperature of 500°C. MFIS structure with 10 nm buffer layer shows excellent endurance of 3 × 109 read-write cycles and the breakdown voltage of 33 V.

This paper shows the feature, principle and improvement in the electrical properties of the fabricated gate stack for 1T-type nonvolatile FeFET. The insulating buffer layer sandwiched between ferroelectric and silicon substrate acts as a barrier to ferroelectric–silicon interdiffusion improves the leakage current, memory window, endurance and breakdown voltage. This is perhaps the first time that the combination of sputtered PZT on the PEALD Al₂O₃ layer is being reported.

The new ways to complete financial transactions have been developed by setting up mobile payment (m-payment) platforms and such platforms to access banking in the financial mainstream can transact as never before. But, does m-payment have veiled consequences? To seek an answer, the research was undertaken to explore the dark sides of m-payment for consumers by extending the theory of innovation resistance (IR) and by measuring non-adoption intention (NAI).

Three hundred individuals using popular online m-payment apps such as Paytm, PhonePe, Amazon Pay and Google Pay were surveyed for the primary data. IBM AMOS based structural equation modelling (SEM) was used to analyse the data.

Each m-payment transaction leaves a digital record, making some vulnerable consumers concerned about privacy threats. Lack of global standards prevents consumers from participating in the m-payment system properly until common interfaces are established based on up-to-date standards. Self-compassion (SC) characteristics such as anxiety, efficacy, fatigue, wait-and-see tendencies and the excessive choice of technology effect contribute to the non-adoption of m-payment.

This study proposes a threat model and empirically explores the dark sides of m-payment. In addition, it also unveils the moderator's role of SC in building the structural relationship between IR and NAI.