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COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

The global impact of the COVID-19 pandemic has been well documented, but there is still limited insight into the complex interaction of factors that determine its longer-term effects on the most vulnerable sectors of society. The current study therefore develops an integrated conceptual framework to investigate how consumers' fear of mortality and the perceived risk of severe illness associated with COVID-19 act as critical determinants of consumer food choices and perceived well-being.

An online survey was utilized to collect data from a sample of 407 adult, low-income consumers across Trinidad and Tobago. The PROCESS macro was used to empirically test the hypothesized relationships in a moderated mediation model.

The results confirm that an increase in the perceived risk of severe illness has a significant negative effect on the consumption of healthy foods and perceived well-being. Moreover, consumers' choice of healthy foods mediates the negative relationship between consumers' perceived risk of severe COVID-19 illness and subjective well-being. Finally, the negative relationship between perceived risk of illness and healthy food choice weakens as an individual's fear of pandemic-related mortality increases.

This research integrates multiple related theoretical constructs to provide a more nuanced understanding of the lingering impact that risk perceptions and fear have on consumer food choices and associated well-being among a vulnerable Caribbean population. The changes identified have important implications for researchers interested in consumer food preferences as well as policymakers seeking to promote a healthy lifestyle among individuals coping with psychologically stressful circumstances.

During the coronavirus disease 2019 (COVID-19) pandemic, ubiquitous social media has become a primary channel for information dissemination, social interactions and recreational activities. However, it remains unclear how social media usage influences nonpharmaceutical preventive behavior of individuals in response to the pandemic. This paper aims to explore the impacts of social media on COVID-19 preventive behaviors based on the theoretical lens of empowerment.

In this paper, survey data has been collected from 739 social media users in China to conduct structural equation modeling (SEM) analysis.

The results indicate that social media empowers individuals in terms of knowledge seeking, knowledge sharing, socializing and entertainment to promote preventive behaviors at the individual level by increasing each person's perception of collective efficacy and social cohesion. Meanwhile, social cohesion negatively impacts the relationship between collective efficacy and individual preventive behavior.

This study provides insights regarding the role of social media in crisis response and examines the role of collective beliefs in the influencing mechanism of social media. The results presented herein can be used to guide government agencies seeking to control the COVID-19 pandemic.

The purpose of this paper is to present a model depicting the relationship between the behavioral intention of tourists in the conditions prevailing during a pandemic and other variables.

In constructing the research procedure, two measurements of tourist behavioral intention were taken into account, which were taken far apart in time. In verifying the developed model, the results of surveys of 1,615 people carried out in June 2021 and 917 people carried out in December 2021 were considered.

As a result of the habituation process, tourists show greater acceptance of the restrictions.

Information on the basis of which companies make management decisions plays a significant role in the creation of company value. In the tourism sector, the information concerns primarily consumer behavior.

Changes over time in risk perception, health protection motivation, and reactance due to perceived pandemic-related restrictions were taken into account in the context of behavioral intention towards tourism.

The purpose of this study is to solve the problem of healthcare waste management in developing countries. The buildup of medical waste has attracted the attention of all spheres of society due to the expanding population and developing economy. Timely collection and processing of medical waste are extremely important due to its potential hazards. Although the problem of planning medical waste management has been addressed in developed countries, it persists in several developing countries. This research is motivated by an example of a city in India characterized by a dense population, abundant health-care facilities and a lack of planning for managing large medical waste generated daily.

The authors address the problem of designing the network of collection and processing facilities for medical waste and optimizing the vehicle route that collects and transfers the waste between facilities. Due to distinct topographic restrictions in the considered city, the collection and transfer process needs to be conducted in two echelons – from hospitals to collection centers using smaller vehicles and then to the processing facilities using trucks. This work addresses these two problems as a two-echelon location-routing problem.

A mixed-integer programming model is developed to minimize the cost of opening the facilities and transporting medical waste. Several managerial insights are drawn up to assist planners and decision-makers.

This study follows a case study approach to provide a descriptive and prescriptive approach to hospital waste management in the ancient city of Varanasi. The city has witnessed unplanned growth over the years and is densely populated. The health-care facilities in the city have a large catchment area and attract patients from neighboring districts. The situation analysis based on secondary data and unstructured interviews of the stakeholders suggests that the ad hoc approach prevails in present hospital waste management in the city.

This research intended the utilization of Javanese turmeric (0.4% w/v) as a kombucha substrate and analysis of its hepatoprotective activity, in comparison against nonfermented Javanese turmeric beverage (JTB) and black tea kombucha.

Forty-two healthy male Balb/c mice (two- to three-week-old, 20–30 g) were divided into six groups with seven replicates each. The treatments were normal diet, normal diet + Javanese turmeric kombucha (JTK), normal diet + diethylnitrosamine (DEN), DEN + JTB, DEN + JTK, DEN + black tea kombucha. Kombuchas and JTB were given at 0.3 mL/20 g BW/d. DEN was induced intraperitoneally at a dose of 100 mg/kg. Observed biomarkers were blood serum glutamate pyruvate transaminase (SGPT) and serum glutamate oxaloacetate transaminase (SGOT) activity, serum malonaldehyde (MDA), as well as liver histology. Data were analyzed using analysis of variance.

Among DEN-induced groups, JTK significantly (p < 0.05) diminished the level of blood SGPT, SGOT and serum MDA. JTK also had lower blood SGPT (8.604 ± 2.195 U/L) and serum MDA levels (2.884 ± 0.083 nM/mL) compared to the normal group (8.604 ± 2.195 U/L and 5.050 ± 0.998 nM/mL, respectively). JTK also produced the least damaged liver-cell numbers.

JTK demonstrated better hepatoprotective activity compared to JTB.

This paper investigates the dynamics between state affect and trusting cognitive beliefs on post-adoptive information technology (IT) use behaviors in the form of intention to explore and deep structure usage. That state affect can influence behaviors is recognized in practice. In fact, some studies examine the impact of affective constructs, but the way state affect impacts how individuals use IT remains largely unexplored. The authors theorize that state affect, in the form of positive and negative affect, will influence trusting cognitive beliefs regarding an IT artifact (in terms of perceived helpfulness, capability and reliability) and, more importantly, directly influence intention to explore and deep structure usage.

The authors test the model using a sample of 357 IT users. Survey items were derived from established measures, and the data were analyzed using structural equation modeling.

Results of this study suggest that positive affect and negative affect impact trusting cognitive beliefs. Trusting cognitive beliefs positively impact intention to explore with an IT and deep structure usage of an IT. Even in the presence of trusting beliefs, though, positive affect directly impacts intention to explore. Positive affect and negative affect both also have various indirect, mediated effects on intention to explore and deep structure usage.

In order to maximize value from workplace IT, the results suggest managers foster an authentic, positive work environment in order to harness or redirect employees' emotional energies.

The COVID-19 outbreak reached a critical stage when it became imperative for public health systems to act decisively and design potential behavioral operational strategies aimed at containing the pandemic. Isolation through social distancing played a key role in achieving this objective. This research study examines the factors affecting the intention of individuals toward social distancing in India.

A correlation study was conducted on residents from across Indian states (N = 499). Online questionnaires were floated, consisting of health belief model and theory of planned behavior model, with respect to social distancing behavior initially. Finally, structural equation modeling was used to test the hypotheses.

The results show that perceived susceptibility (PS), facilitating conditions (FC) and subjective norms are the major predictors of attitude toward social distancing, with the effect size of 0.277, 0.132 and 0.551, respectively. The result also confirms that the attitude toward social distancing, perceived usefulness of social distancing and subjective norms significantly predict the Intention of individuals to use social distancing with the effect size of 0.355, 0.197 and 0.385, respectively. The nonsignificant association of PS with social distancing intention (IN) (H1b) is rendering the fact that attitude (AT) mediates the relationship between PS and IN; similarly, the nonsignificant association of FC with IN (H5) renders the fact that AT mediates the relationship between FC and IN.

The results of the study are helpful to policymakers to handle operations management of nudges like social distancing.

The research is one of its kind that explores the behavioral aspects of handling social nudges through FC.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

Heating, ventilating, air-conditioning and cooling (HVAC) systems are crucial in daily health-care facility services. Design-related defects can lead to maintenance issues, causing service disruptions and cost overruns. These defects can be avoided if a link between the early design stages and maintenance feedback is established. This study aims to use experts’ experience in HVAC maintenance in health-care facilities to list and evaluate the risk of each maintenance issue caused by a design defect, supported by the literature.

Following semistructured interviews with experts, 41 maintenance issues were identified as the most encountered issues. Subsequently, a survey was conducted in which 44 participants evaluated the probability and impact of each design-caused issue.

Chillers were identified as the HVAC components most prone to design defects and cost impact. However, air distribution ducts and air handling units are the most critical HVAC components for maintaining healthy conditions inside health-care facilities.

The unavailability of comprehensive data on the cost impacts of all design-related defects from multiple health-care facilities limits the ability of HVAC designers to furnish case studies and quantitative approaches.

This study helps HVAC designers acquire prior knowledge of decisions that may have led to unnecessary and avoidable maintenance. These design-related maintenance issues may cause unfavorable health and cost consequences.