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This paper aims to explore the outcomes of an analysis on day by day task – journey planning conduct of senior citizens by using a modern dynamic model and a family unit travel overview, gathered in Bhubaneswar, Odisha, of India in 2018. The task-journey planning display assumes an unique time–space-constrained planning development.

The main commitment of this paper is to reveal day by day task – journey planning conduct through a comprehensive dynamic framework. Numerous behavioural subtleties are revealed by the subsequent empirical model. These incorporate the role that income plays in directing outside time consumption decisions of senior citizens. Senior citizens in the most elevated and least salary classes will in general have minor varieties in time consumption decisions than those in middle pay classifications. Generally speaking, the time consumption decisions become progressively steady with expanding age, demonstrating that more task durations and lower task recurrence become progressively predominant with increasing age.

Day by day task-type and area decisions reveal a reasonable irregular utility-amplifying level headed conduct of senior residents. Unmistakably expanding spatial availability to different task areas is an urgent factor in characterizing every day outside task interest of senior residents. It is likewise evident that the assorted variety of outside task-type decisions decreases with rise in age and senior citizens are major touchy to auto journey hour than to travel or non-mechanized journey hour.

The fundamental constraint to the dynamic structure is that the mode decision model was viewed as exogenic to the demonstrating framework. The essential purpose behind this supposition that was that senior citizens in the Bhubaneswar are overwhelmingly customers of the local car. Coordination of the mode decision display part inside this structure would deliver a full task-based journey request model that could catch trip age, starting times, outing circulation and mode decision using a solitary demonstrating framework.

In spite of the fact that literature shows that big data analytics (BDA) pass on a distinct corporate ability, little is thought about their performance impacts, specifically logical conditions. Establishing this research in the dynamic capability view (DCV) and corporate culture and dependent on an sample of 310 Indian production industries, the purpose of this paper is to experimentally study the impacts of BDA on corporate social performance (CSP) and corporate green performance (CGP) using variance-based structural equation modeling (for example, PLS).

A questionnaire was used to accumulate data sets to examine research hypothesis. The authors pre-examined the survey with six scholastics and six directors from production firms in India. With the help of their sources of data, the authors have adjusted their wordings to improve the transparency and guarantee that length of the survey is accurate. Finally, the questionnaire was prepared for definite data collection.

The authors conclude that BDA has noteworthy effect on CSP/CGP. Notwithstanding, the authors did not find proof for directing role of flexible direction and control direction in the connections among BDA and CSP/CGP. This research offers a more nuanced comprehension of the performance ramifications of BDA, and in this way, it is tending to the critical inquiries of how and when BDA can improve in supply chains.

This investigation makes helpful commitments to the BDA research and its effect on CSP/CGP. To the authors’ best of information, this is the first hypothesis-focused approach to clarify the effect of BDA on ecological and social supportability. Second, this investigation likewise gives empirical proof that BDA impact on CSP/CGP and is free of flexible or control direction of the industry.

Humanoids have become the center of attraction for many researchers dealing with robotics investigations by their ability to replace human efforts in critical interventions. As a result, navigation and path planning has emerged as one of the most promising area of research for humanoid models. In this paper, a fuzzy logic controller hybridized with genetic algorithm (GA) has been proposed for path planning of a humanoid robot to avoid obstacles present in a cluttered environment and reach the target location successfully. The paper aims to discuss these issues.

Here, sensor outputs for nearest obstacle distances and bearing angle of the humanoid are first fed as inputs to the fuzzy logic controller, and first turning angle (TA) is obtained as an intermediate output. In the second step, the first TA derived from the fuzzy logic controller is again supplied to the GA controller along with other inputs and second TA is obtained as the final output. The developed hybrid controller has been tested in a V-REP simulation platform, and the simulation results are verified in an experimental setup.

By implementation of the proposed hybrid controller, the humanoid has reached its defined target position successfully by avoiding the obstacles present in the arena both in simulation and experimental platforms. The results obtained from simulation and experimental platforms are compared in terms of path length and time taken with each other, and close agreements have been observed with minimal percentage of errors.

Humanoids are considered more efficient than their wheeled robotic forms by their ability to mimic human behavior. The current research deals with the development of a novel hybrid controller considering fuzzy logic and GA for navigational analysis of a humanoid robot. The developed control scheme has been tested in both simulation and real-time environments and proper agreements have been found between the results obtained from them. The proposed approach can also be applied to other humanoid forms and the technique can serve as a pioneer art in humanoid navigation.

To navigate humanoid robots in complex arenas, a significant level of intelligence is required which needs proper integration of computational intelligence with the robot's controller. This paper describes the use of a combination of genetic algorithm and neural network for navigational control of a humanoid robot in given cluttered environments.

The experimental work involved in the current study has been done by a NAO humanoid robot in laboratory conditions and simulation work has been done by the help of V-REP software. Here, a genetic algorithm controller is first used to generate an initial turning angle for the robot and then the genetic algorithm controller is hybridized with a neural network controller to generate the final turning angle.

From the simulation and experimental results, satisfactory agreements have been observed in terms of navigational parameters with minimal error limits that justify the proper working of the proposed hybrid controller.

With a lack of sufficient literature on humanoid navigation, the proposed hybrid controller is supposed to act as a guiding way towards the design and development of more robust controllers in the near future.

With the development of research toward damage detection in structural elements, the use of artificial intelligent methods for crack detection plays a vital role in solving the crack-related problems. The purpose of this paper is to establish a methodology that can detect and analyze crack development in a beam structure subjected to transverse free vibration.

Hybrid intelligent systems have acquired their own distinction as a potential problem-solving methodology adopted by researchers and scientists. It can be applied in many areas like science, technology, business and commerce. There have been the efforts by researchers in the recent past to combine the individual artificial intelligent techniques in parallel to generate optimal solutions for the problems. So it is an innovative effort to develop a strong computationally intelligent hybrid system based on different combinations of available artificial intelligence (AI) techniques.

In the present research, an integration of different AI techniques has been tested for accuracy. Theoretical, numerical and experimental investigations have been carried out using a fix-hinge aluminum beam of specified dimension in the presence and absence of cracks. The paper also gives an insight into the comparison of relative crack locations and crack depths obtained from numerical and experimental results with that of the results of the hybrid intelligent model and found to be in good agreement.

The paper covers the work to verify the accuracy of hybrid controllers in a fix-hinge beam which is very rare to find in the available literature. To overcome the limitations of standalone AI techniques, a hybrid methodology has been adopted. The output results for crack location and crack depth have been compared with experimental results, and the deviation of results is found to be within the satisfactory limit.

The purpose of this paper is to present a unique model for the production–recycling–reuse of aluminium refreshment cans. It is presumed that disposed-off 250-ml aluminium cans are collected from the retail outlet. The cans are thereafter arranged into non-tainted and tainted categories.

The current model considers all the factors, i.e. producing, recycling and remanufacturing, whereas the previous models provide emphasis only one factor. Six procedures were considered in the improvement of the mathematical model.

In this paper, a recycling–reuse model that remanufactures non-tainted aluminium beverage cans and uses regrind from damaged non-tainted aluminium beverage cans mixed with parent aluminium material in the production of new cans was developed and analysed to reduce the amount of aluminium beverage cans that are disposed off in a scrapyard. The model is assumed to have no shortcomings, and the different percentages regarding the classes of cans are taken to be deterministic.

The model incorporates several unique aspects, including accounting for the cost of land use and associated environmental damage through the calculation of a present value that is charged to the manufacturer.

The purpose of this paper is to provide an analysis on the relationship between information technology (IT) adoption and its usage and firm performance (banking and software firm) in India. Firm performance was measured with the help of three important variables: efficiency, effectiveness and productivity. Each one of the above has been described with a set of pretested questionnaires. Banks in India, in particular are geared for comprehensive banking solutions with extensive branch networks. Result from statistical analysis was validated with that achieved from ANN modeling.

Survey instrument was pilot tested. The pilot survey was administered to 20 randomly selected Indian service firms, whose Standard Industrial Classification codes were 6,021 (nationalized commercial Banks) and 7,371 (software firms). A 50 percent response rate was received. Internal reliability using Cronbach’s α was carried out for the entire set of responses from the pilot study. In addition, qualitative follow up from respondents was done. Unreliable items were deleted and modifications wherever necessary were made.

The research finds two important results with respect to IT adoption and firm performance. The first result is that service firms who implemented IT tools and techniques early achieved more turnover thereby greater market share from innovation/adoption (world-first and, to some extent India-first). These firms are able to better commercialize their service even if their most important innovations/adoptions supported by vendor’s to some extent. The second result is that service firms, which introduce new services, even if the service is already on the national or international front, derived more commercial sales from innovation, thus achieving more firm performance. Therefore, late followers (firm-first) would have higher sales from innovation by introducing services with high original content.

Performance is measured due to IT adoption.

Although artificial intelligence (AI) is increasing in influence across different fields, the subject of AI literacy in higher education (HE) remains inadequately explored. Through a systematic review of 19 peer-reviewed journal articles, this chapter fills that gap.

The findings of this study highlight the advancements in AI literacy education across different subject disciplines. It emphasises the importance of critical thinking, ethical reasoning, and practical skills in managing the increasingly complicated world driven by AI.

This study aims to investigate whether intellectual capital (IC) and its subcomponents enhance value and improve the profitability of real estate (RE) and infrastructure (INF) firms in India. In this study, IC is measured through the value-added intellectual coefficient (VAIC) model. The study further extends the VAIC model by incorporating an additional component of social welfare efficiency (SWE).

The study uses the panel data investigation based on the data of 63 firms (22 RE and 41 INF firms), for a period of 10 years (2008–2017). The dependent variables in the study are return on assets (ROA) and market price to book value ratio (PB), whereas the independent variables are VAIC and its components. The panel is tested for stationarity, heteroscedasticity and multicollinearity problems. Finally, to account for heteroscedasticity and endogeneity, Arellano and Bond's (1991) panel regression estimator with robust estimates are used.

The findings of the study suggest that IC has a significant influence on the profitability and value of infra firms, whereas capital-employed efficiency (CEE) positively affects the profitability of both RE and INF firms.

The study is an attempt to find the effect of IC and its components on profitability and value of RE and INF firms in India. The author has also extended the VAIC model, which was introduced by Pulic (2000), by adding an additional IC component, i.e. SWE. The study uses Arellano and Bond's (1991) panel regression estimator with robust estimates, which helps produce robust results.

The purpose of this paper is to analyze the long-term performance of construction sector initial public offers (IPO) made in India during 2006–2015. The study aims to compare the performance of the construction sector IPOs with the non-construction sector IPOs and finds the determinants of long-term performance of construction sector IPO with a time horizon of three years. The study also attempts to find out, if the long-term IPO underpricing that has been discussed in the literature, really exists or it is a myth.

The study uses data of IPOs listed on National stock exchange during 2006–2015. In total, 281 IPOs are considered for the study, among which 44 are construction sector IPOs. IPOs anniversary performance of three successive years is calculated from the date of listing, and a random effect panel regression model with clustered robust estimates using the maximum likelihood method is performed to find out the determinants of IPO performance. The data are also tested for multicollinearity, stationarity and heteroscedasticity to ensure the robustness of results.

The results show that in the long-run construction sector IPOs outperform the non-construction sector IPOs, though the performance is below average when compared to market returns. The IPO underpricing is a myth, and IPO underperformance is a reality in India. The performance of construction sector IPOs is driven positively by market return, size of the firm and negatively by liquidity of the firm.

The paper is the first attempt to analyze the performance of construction sector IPOs, and compare it with non-construction sector IPOs. The study uses a random effect panel regression model with robust estimates using the maximum likelihood method to ensure the robustness of results. This is the first time the performance of IPOs is studied with a panel data approach.