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The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud.

This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer.

The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia.

One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked.

Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

The built-transfer and operate (BOT) toll model has been a common approach for highway construction in India. Due to large amount of investment, many stakeholders and long concession period, Indian BOT toll roads become susceptible to different risks. Risk assessment is one of the essential and difficult steps of risk management that enables professionals to quantify and analyze the risks that may hamper the BOT toll project performance in terms of cost, quality, safety and time. The purpose of this study is to identify and assess the risk factors by the fuzzy probabilistic model.

The comprehensive literature review has been carried out for identification of Indian BOT toll roads projects' risk factors. A structured questionnaire was prepared which was then filled by contractors, government officers, academicians, project managers and consultants. For risk assessment, a systematic quantitative-based fuzzy probabilistic model is proposed with the help of lab-view, as a risk assessment technique to simulate the impreciseness of human judgment and to improve the assessment accuracy.

The risk assessment is one of the difficult tasks because BOT toll roads have complex structure. In this study, total 71 risks have been identified and categorized under 14 risk factors on a basis of case studies of Indian toll roads and literature review. All risks have been assessed by the fuzzy probabilistic model by lab-view. The land acquisition is the most crucial risk of BOT toll roads project which is subsequently followed by construction time and cost over-run. Also, Indian BOT toll roads are facing the traffic shortfalls critically, which became a reason toward declination trend for investment in BOT toll projects by the private players. Other risks like political risks, contractual and social risk badly are affecting the project performance. Early risk identification and assessment can be beneficial for the project, which is required to prepare the risk response strategy before the project commencement.

This study throws light on comprehensive risk assessment and framework modeling of the risk assessment for the BOT toll roads project in India. This comprehensive risk assessor model can be used for BOT toll roads in Indian scenario for prioritization of the critical risk. It is important for the public and private sectors to establish an effective risk assessor model for public–private partnership (PPP) projects to achieve win-win condition for both.

The case documents the evolution of an eye care hospital promoted by a not-for profit organization located in Mandvi, Surat, close to the tribal community of Gujarat state, India. In a short span of five years (as of 2016), the trust has evolved as a community hospital. The value proposition of the trust is a portfolio of activities, which includes awareness by education, prevention of eye care diseases through eye screening camps, treatment, and rehabilitation on need basis and addressing direct and indirect healthcare needs of the community. The managerial challenge before the board of trustees is to carefully balance (a) The purpose for which the trust was created, (b) the gap between the ground realities and the need in the relevant tribal community (c) the accomplishments of the eye hospital so far.

The purpose of this paper is to develop a scale for measuring benefits of third party logistics service providers for co‐operative dairies in an Indian context. The objective is to measure benefits of third party logistics service providers for co‐operative dairies.

A standard scale development research procedure recommended by experts was followed. First, the literature review of studies to measure benefits of third party logistics was undertaken. Later, Delphi method was used. Interviews were conducted of experts and customers for understanding and generating items for measuring benefits of third party logistics service providers for co‐operative dairies. A survey was then undertaken first for development of the scale and later for validation purpose.

A reliable and valid scale is developed to measure the five dimensions of benefits of using 3PLSPs for co‐operative dairies: responsiveness, accuracy, customization of service, inventory handling and order processing and information sharing.

This scale is developed to outsource logistics functions at operational levels in the context of co‐operative dairies in India. So, this scale can be tested for co‐operative dairies of countries other than India. The scale can also be tested where outsourcing of logistics activities is done at operational level, other than co‐operative dairies.

The proposed scale can be used as a diagnostic tool to identify important benefits to consider in outsourcing operational function of logistics management to 3PLSPs in co‐operative dairies.

Most relevant studies about benefits of third party logistics service providers do not have stable factor structure, especially for co‐operative dairies. The new scale fills the gap of the absence of a validated scale to measure benefits of 3PLSPs for co‐operative dairies at operational level.

This paper aims to establish and empirically investigate a research model examining the effect of four dimensions of the technology readiness index – optimism, innovativeness, discomfort and insecurity – on customer engagement that further influences purchase intention in the context of online shopping through artificial intelligence voice assistants (AI VAs).

Data were collected in India from 429 customers in a self-administered online survey. Data analysis uses the structural equation modelling technique.

Technology readiness dimensions, e.g. optimism, innovativeness, discomfort and insecurity, are critical factors driving customer engagement. Customer engagement further results in purchase intention in online shopping through AI VAs.

This study adds to the literature by understanding how customers’ technology readiness levels drive engagement and purchase intention. However, this study includes customer engagement as a unidimensional construct. Further research can consist of customer engagement as a multidimensional construct.

The findings offer guidelines for e-retailers to enhance customer engagement that matches their personality traits, thereby strengthening their purchase intention through AI VAs.

The research contributes to the literature by empirically investigating a research model, revealing optimism, innovativeness, discomfort and insecurity as crucial parameters for customer engagement and purchase intention.

This study aims to examine the impact of AI service robots on restaurant customers' engagement and acceptance and the moderating role of robot anthropomorphism on the relationship between AI robot service quality and customer engagement.

Using a three-wave time-lagged design, 416 customers of service robots-enabled restaurants participated in the study. Mplus was used to examine the hypotheses.

The results confirmed that customers' perception regarding automation, personalization, efficiency and precision of robot service quality determine customer engagement, which further influences customer acceptance of AI service robots. Additionally, robot anthropomorphism moderates the relationships between AI robot service quality in terms of automation, personalization, efficiency and precision and customer engagement. This study confirms that AI service robots-customer engagement contributes to better acceptance of AI service robots.

The proposed framework can be used as a diagnostic tool to enhance customer acceptance of AI service robots in restaurant settings. This research provides guidelines to restaurant owners to employ AI service robots in front-line services that provide better quality, ultimately enhancing customer engagement and acceptance.

This study fills the gap in the literature by investigating the influence of AI robot service quality on customer engagement and customer acceptance with the moderating effect of robot anthropomorphism in an emerging market context.

The purpose of the study is to explore and analyze m-car rental service quality dimensions.

Exploratory factor analysis method is applied to explore the m-car rental service quality dimensions. Further, confirmatory factor analysis is done to prove the reliability and validity of the factors using AMOS 22.0.

The results reveal the m-car rental service quality dimensions: ambient quality, technical quality, comfort, safety and employee service, mobile convenience, mobile responsiveness, mobile efficiency and reliability and mobile safety and billing.

The explored dimensions of car rental services are in Indian environment. So, these dimensions can be further validated in other similar cultural context.

The proposed measurements can also be applied to measure and compare the service quality performance of car rental firms.

Current literature does not confirm the stable factor structure of m-car rental service quality. This study confirms the reliable and valid dimensions of care rental service through mobile app.

Entrepreneurial and strategic actions are crucial for wealth creation, and the business opportunity is a critical factor in this process. The purpose of this paper is to explore the role of the firm’s strategic posture in the relationship between individual alertness and opportunity identification within an existing firm. This approach contributes to entrepreneurship theory building through a multilevel study.

The quantitative research focuses on understanding the mediating role of an organization’s strategic posture in the opportunity identification process. Using a sample of 276 firms, this study tests a two-level model to explain opportunity identification.

The findings provide empirical evidence that a firm’s strategic posture mediates the relationship between individual alertness and opportunity identification. Furthermore, this study finds differences in the mediating role of a firm’s strategic posture through which entrepreneurs and managers affect opportunity identification. Years after the creation of startup, the entrepreneurs still exhibit entrepreneurial characteristics that affect opportunity identification. The findings provide evidence that entrepreneurs foster an internal culture and set of values that are more favorable to radical innovation, compared to managers who favor incremental and less risky projects.

The findings suggest the possibility for new theory building that can improve the fields of entrepreneurship and management research. Moreover, the proposed model constitutes a new approach to analyze the mediating role of an organization’s strategic posture in the opportunity identification process.

This paper provides an original approach to literature in exploring the relationship between entrepreneurial alertness and firm’s strategic posture in explaining the opportunity identification process. This work will help expand the theory building that explores differences between managers and entrepreneurs in organizations.

The purpose of this paper is to explore various in-flight crew escape options of a prototype transport aircraft and finalize the option offering safest crew egress for different combinations of contingencies and flight conditions.

Various egress options were explored through simulation in a computational fluid dynamics (CFD) software using aircraft 3D CAD model and scalable digital mannequins. For this, certain important contingencies which best describe the extreme aircraft behaviour were identified. Crew escape options, which have least external interference in expected egress trajectory, were selected. Several test simulations representing each feasible combination of contingency, escape option and flight condition were simulated. The option which offers safe crew escape in each test case is deemed to be the safest egress option for the test aircraft.

Among five options explored, crew escape through forward ventral hatch provided the safest crew escape for all test cases. The selected option was validated for robustness with additional test cases modelling different anthropometric characteristics of 5th and 50th percentile pilot populations with different postures.

In-flight validation of safe crew escape option is infeasible by actual trial. Exploration of safe crew options for required number of test cases by any analytical method or by wind tunnels tests is tedious, time consuming and extremely expensive. On the other hand, exploration of safest crew option by CFD, besides being first of its kind, provides convenient option to configure, test and validate different test cases with unmatched benefits in time, cost and simplicity.