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The purpose of this paper is to investigate the inhibitive performance of Coreopsis tinctoria (C. tinctoria) plant extract for the corrosion of mild steel in 0.5 M H₂SO₄.

The inhibition efficiency was studied by weight loss, electrochemical measurements and the surface analysis was done by Raman, scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM-EDS) and atomic absorption spectroscopy (AAS) analysis.

Maximum inhibition efficiency of C. tinctoria in 0.5 M H₂SO₄ on mild steel is 80.62 per cent (500 ppm) at 303 ± 1K. The adsorption of the C. tinctoria on the mild steel surface in 0.5 M H₂SO₄ was found to obey Langmuir adsorption isotherm. Temperature studies were carried out and the significant parameters, such as change in enthalpy (ΔH^°), change in entropy (ΔS^°) and change in free energy (ΔG°_ads) and heat of adsorption (Qads), were calculated. The productive layer formed on the mild steel surface in 0.5 M H₂SO₄ were confirmed by the Raman spectral analysis.

This paper provides information on the inhibitive properties of C. tinctoria plant extract which is found to be a good corrosion inhibitor for mild steel in 0.5 M H₂SO₄.

One of the fundamental tasks in the field of image processing is image denoising. Images are often corrupted by different types of noise and the restoration of images degraded with random-valued impulse noise is still a challenging task. The paper aims to discuss these issues.

This paper presents an adaptive threshold-based impulse noise detection following by a novel selective window median filter for restoration of RVIN pixels.

The proposed method emphasis a local image statistics using an exponential nonlinear function with an adaptive threshold is derived from the rank-ordered trimmed median absolute difference (ROTMAD) are deliberated to detect the noisy pixels. In the filtering stage, a selective 3×3 moving window median filter is applied to restore the detected noisy pixel.

Experimental result shows that the proposed algorithm outperforms the existing state-of-art techniques in terms of noise removal and quantitative metrics such as peak signal to noise ratio (PSNR), mean absolute error (MAE), structural similarity index metric (SSIM) and miss and false detection rate.

A detailed description will be provided of all the classification algorithms that have been widely used in the domain of medical science. The foundation will be laid by giving a comprehensive overview pertaining to the background and history of the classification algorithms. This will be followed by an extensive discussion regarding various techniques of classification algorithm in machine learning (ML) hence concluding with their relevant applications in data analysis in medical science and health care. To begin with, the initials of this chapter will deal with the basic fundamentals required for a profound understanding of the classification techniques in ML which will comprise of the underlying differences between Unsupervised and Supervised Learning followed by the basic terminologies of classification and its history. Further, it will include the types of classification algorithms ranging from linear classifiers like Logistic Regression, Naïve Bayes to Nearest Neighbour, Support Vector Machine, Tree-based Classifiers, and Neural Networks, and their respective mathematics. Ensemble algorithms such as Majority Voting, Boosting, Bagging, Stacking will also be discussed at great length along with their relevant applications. Furthermore, this chapter will also incorporate comprehensive elucidation regarding the areas of application of such classification algorithms in the field of biomedicine and health care and their contribution to decision-making systems and predictive analysis. To conclude, this chapter will devote highly in the field of research and development as it will provide a thorough insight to the classification algorithms and their relevant applications used in the cases of the healthcare development sector.

Big Data is one of the most promising area where it can be applied to make a change is health care. Healthcare analytics have the potential to reduce the treatment costs, forecast outbreaks of epidemics, avoid preventable diseases, and improve the quality of life. In general, the lifetime of human is increasing along world population, which poses new experiments to today’s treatment delivery methods. Health professionals are skillful of gathering enormous volumes of data and look for best approaches to use these numbers. Big data analytics has helped the healthcare area by providing personalized medicine and prescriptive analytics, medical risk interference and predictive analytics, computerized external and internal reporting of patient data, homogeneous medical terms and patient registries, and fragmented point solutions. The data generated level within healthcare systems is significant. This includes electronic health record data, imaging data, patient-generated data, etc. While widespread information in health care is now mostly electronic and fits under the big data as most is unstructured and difficult to use. The use of big data in health care has raised substantial ethical challenges ranging from risks for specific rights, privacy and autonomy, to transparency and trust.

The advent of information technology and the consequent access to Internet has led to significant changes in marketing practice where e-marketing has been the natural outcome of these technological changes and marketing innovations. For modernization and digital formation in India, marketing perception has been changing continually (“All business growth can only happen if business learners faster than the rate at which its customer changes” – William Charnock and Jonny Langden). E-marketing is currently the better element of the marketing mix. It has substantial benefits to the customer, marketers, and in society. Conscious customers have been increasing their purchase through e-marketing as it has a lot of benefits. It has opened a huge business opportunity for marketers. E-marketing is now tapping new markets. This paper is aimed at investigating the changing consumer perception and environment of e-marketing in rural India for consumer durables based on a primary survey. The primary data are collected from 200 households selected randomly in Howrah and Hooghly districts of South Bengal. We have used the chi-square tests to study the role of several demographic factors on e-marketing behavior. We have observed that demographic factors such as gender, family income, and education have an impact on e-marketing. This study also identifies the problems faced by rural customers with reference to payment, goods checking, language, etc., and the problems faced by marketers. In conclusion, appropriate suggestions have been made in this regard.

Dynamic economic and emission dispatch (DEED) aims to optimally set the active power generation with constraints in a power system, which should target minimum operation cost and at the same time minimize the pollution in terms of emission when the load dynamically changes hour to hour. The purpose of this study is to achieve optimal economic and emission dispatch of an electrical system with a renewable generation mix, consisting of 3-unit thermal, 2-unit wind and 2-unit solar generators for dynamic load variation in a day. An improved version of a simple, easy to understand and popular optimization algorithm particle swarm optimization (PSO) referred to as a constriction factor-based particle swarm optimization (CFBPSO) algorithm is deployed to get optimal solution as compared to PSO, modified PSO and red deer algorithm (RDA).

Different model with and without wind and solar power generating systems; with valve point effect is analyzed. The thermal generating system (TGs) are the major green house gaseous emission producers on earth. To take up this ecological issue in addition to economic operation cost, the wind and solar energy sources are integrated with the thermal system in a phased manner for electrical power generation and optimized for dynamic load variation. This DEED being a multi-objective optimization (MO) has contradictory objectives of fuel cost and emission. To get the finest combination of the two objectives and to get a non-dominated solution the fuzzy decision-making (FDM) method is used herein, the MO problem is solved by a single objective function, including min-max price penalty factor on emission in the total cost to treat as cost. Further, the weight factor accumulation (WFA) technique normalizes the pair of objectives into a single objective by giving each objective a weightage. The weightage is decided by the FDM approach in a systematic manner from a set of non-dominated solutions. Here, the CFBPSO algorithm is applied to lessen the total generation cost and emission of the thermal power meeting the load dynamically.

The efficacy of the contribution of stochastic wind and solar power generation with the TGs in the dropping of net fuel cost and emission in a day for dynamic load vis-à-vis the case with TGs is established.

Cost and emission are conflicting objectives and can be handled carefully by weight factors and penalty factors to find out the best solution.

The proposed methodology and its strategy are very useful for thermal power plants incorporating diverse sources of generations. As the execution time is very less, practical implementation can be possible.

As the cheaper generation schedule is obtained with respect to time, cost and emission are minimized, a huge revenue can be saved over the passage of time, and therefore it has a societal impact.

In this work, the WFA with the FDM method is used to facilitate CFBPSO to decipher this DEED multi-objective problem. The results reveal the competence of the projected proposal to satisfy the dynamic load demand and to diminish the combined cost in contrast to the PSO algorithm, modified PSO algorithm and a newly developed meta-heuristic algorithm RDA in a similar system.

This study aims to evaluate and compare the quality of work life (QWL) of nurses, in pre- and post-COVID-19 pandemic situations.

The study adopts a descriptive research design. Data were collected during the pre- and post-pandemic periods. The target sampling unit of the study comprises nurses working in Bangalore city, Karnataka, India. The minimum sample size was determined (Bartlett et al., 2001) as 385. The scale validation is carried out. The factors for the present study were explored using exploratory factor analysis and confirmed by confirmatory factor analysis. Model fitness (proposed measurement model) is ensured by using fit indices. The linear regression method was used to measure the level of QWL of nurses.

The present study noted that key factors that affects the QWL of nursing staff are work condition; work environment; work-life balance; compensation and reward; career development; job satisfaction and security; organization culture; relationship among co-workers and stress. Further, it is noticed that QWL of nurses pre-COVID-19 pandemic is 87.2%, while post-COVID-19 pandemic, it is 67%.

Present study can be extended to address the same research question by considering sampling unit such as therapist, technicians and sanitarians who have equally undergone tremendous pressure during pandemic.

The study outcome provides references for organizations engaged in health services to understand the extreme job conditions posed by pandemic. The constructive inspiration (physio-social and organizational support) reinforces the nurses to continue in their professions by decreasing negative impact.

The research paper extends the contributions of Hwang (2002), Nikeghbal et al. (2021), Howie–Esquivel et al. (2022) and Rania et al. (2023) and add to the existing body of the QWL literature. The outcome of the research records the prevailing conditions of pandemic and its effect on changes in work environment with specific reference to health-care sector.

Surface pretreatment of iron and its alloys to remove stains and inorganic contaminants on the metal surface undergoes dissolution by virtue of the strong acidic media thereby increasing its susceptibility to corrosion. The purpose of this study is to explore the corrosion mitigation prospects of green corrosion inhibitors on mild steel surface.

Corrosion inhibition performance of Garcinia gummi-gutta leaf extract (GGLE) was explored against mild steel in 1 M HCl solution using the weight-loss method, electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PDP) techniques. Surface characterization was carried out to study the mechanism of inhibitor action.

The concentration of GGLE varied from 100 to 6,000 ppm and the result indicates that corrosion inhibition efficiency was amplified by raising the inhibitor concentration. The maximum inhibition efficiency was 82.2% at 6,000 ppm concentration. EIS results show the development of a protective layer of inhibitor molecule over the metal surface and PDP demonstrates that the inhibitor operates as a mixed-type inhibitor. Scanning electron microscopy and atomic force microscopy were executed to assess the surface morphology and roughness, respectively.

To the best of the authors’ knowledge, so far, no studies have been reported on the corrosion inhibition performance of GGLE which is rich in many bioactive components especially hydroxyl citric acid. This work encompasses the corrosion inhibition capability of GGLE against mild steel in an acidic medium.

In this chapter, we investigate the role of the Internet of Things (IoT) for a more sustainable future. The IoT is an umbrella term that refers to an interrelated network of devices connected to the internet. It also encompasses the technology that enables communication between these devices as well as between the devices and the cloud. The emergence of low-cost microprocessors, sensors and actuators, as well as access to high bandwidth internet connectivity, has led to the massive adoption of IoT systems in everyday life. IoT systems include connected vehicles, connected homes, smart cities, smart buildings, precision agriculture, among others. During the last decade, they have been impacting human activities in an unprecedented way. In essence, IoT technology contributes to the improvement of citizens' quality of life and companies' competitiveness. In doing so, IoT is also contributing to achieve the Sustainable Development Goals (SDGs) that were adopted by the United Nations in 2015 as an urgent call to action by all countries to eradicate poverty, tackle climate change and ensure that no one is left behind by 2030. The World Economic Forum (WEF) recognises that IoT is undeniably one of the major facilitators for responsible digital transformation, and one of its reports revealed that 84% of IoT deployments are presently addressing, or can potentially address the SDGs. IoT is closely interlinked with other emerging technologies such as Artificial Intelligence (AI) and Cloud Computing, for the delivery of enhanced and value-added services. In recent years, there has been a push from the IoT research and industry community together with international stakeholders, for supporting the deployment and adoption of IoT and AI technologies to overcome some of the major challenges facing mankind in terms of protecting the environment, fostering sustainable development, improving safety and enhancing the agriculture supply chain, among others.