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Application of green logistics in supply chain of auto parts: A south indian scenario
Green supply chain concept is used to reduce environmental degradation and emissions of air, water, and waste by incorporating green practices into business operations. Growing impacts of global warming, climate change, waste, and air pollution problems have prompted experts all over the world to think more environment friendly and find the best possible approach for "Green" solutions. green supply chain management is one of the factors that motivates organizations to be more sustainable. This study focuses on the green supply chain management in the auto parts industry in South Indian. Data from three green initiatives: recyclable packaging, green warehouse management and milk run approach for logistics is taken and compared with nongreen approaches. It is found that there is significant reduction in costs by adopting the green approaches. With environmental issues growing all the time, green supply chain deserves to be a long-term community concern in developing countries. 2023 by IGI Global. All rights reserved. -
Vocational training course preferences among Sikkimese youth
Unemployment is one of the major issues in modern times. High unemployment rates affect a country's economic growth, mental wellbeing of an individual and his/her family members, and create unrest in society. Vocational training is one of the most crucial elements in the competitive and developing world. Through the provision of real-world experience, vocational training aids in developing skills. This study aims to highlight the aspirations of the people of Sikkim concerning vocational training and find its challenges and hindrances. With the help of a structured questionnaire, responses were taken from the youth of Sikkim, India and their perception about opting for different vocational training courses were taken. Upon analyzing the data, it was found that males are more inclined towards cooking and baking classes, repair of mobiles, laptops and other electronic accessories, and repair of bikes and automobiles. Females, on the other hand, wanted to focus on makeup and beautician courses, jewelry design, floriculture, and towards repair of mobile and computers. 2023, IGI Global. -
Customer perspective on a curated gift-box service: A study in Sikkim, India
Due to the proliferation of choices and brands, accessibility to information, and new communication mediums, consumer behavior, particularly decision-making processes, has been altered by the spending power of various segments. In the Indian environment, although product appearance has been identified as a significant factor in influencing customer behavior, its effect on decision making when combined with other factors such as cost, features, and intrinsic psychological factors has not been studied thoroughly. This study aims to highlight consumers' perspective on a curated gift-box service in Sikkim. Focusing on gifting during special occasions, impulse buying, and self-gift opportunities, this study stands on the possibility that there is a need for such service in the market. 2023, IGI Global. -
Analyzing the interactions among delay factors in construction projects: A multi criteria decision analysis
The construction industry is a crucial sector that drives economic growth and facilitates socio-economic development. However, construction projects often get delayed due to multiple controllable and uncontrollable circumstances. In this scenario, the construction industry is striving for potential solutions to resolve project delays. Thus, the present study objectives to analyze the delay factors that affect the timely accomplishment of construction projects in the context of emerging economies. The study adopts a mixed methodology comprising of Delphi, Total Interpretive Structural Modelling (TISM) and Matrice d' Impacts Croises Multiplication Applique a Classement (MICMAC) method to model the identified delay factors. A Delphi analysis was conducted to finalize the most crucial delay factors to the on-time completion of building projects. The causal relationships and expert interpretations for each identified delay factor were then determined using multi-criteria decision analysis, TISM and MICMAC analysis. The study results highlight that lack of knowledge of newer construction methodologies and lack of project monitoring tools and techniques are positioned at the bottom level of model, which suggests that this delay factor influences others. The study results will help managers resolve the issues of project delays by selecting the most suitable approach. The findings from the study suggest adopting advanced technologies for effective communication, use of analytical tools for resource allocation and waste-scrapping approaches for eliminating delays in construction projects. The Author(s) 2023. -
Correlation Between Evaluative Beliefs of Patients, Reminder and Medication Adherence
Patients often fail to comply with the instructions given by their physicians. They miss the timing, forget, neglect, or procrastinate taking their medication. This deteriorates the health and causes financial burden to the patient and family. Reminders have been successfully used in many phases of day-to-day activities, increasing the efficiency and productivity. This paper tries to identify the relationship between reminder and the perception of importance of medication based on 15 different factors. These factors have been further assessed to find their relationship with adherence of medication. Hence, with a two-way approach, the studies use exploratory factor analysis method to identify the latent factors, and these latent factors have been used to find the correlation between reminder and adherence through confirmatory factor analysis. It was found that there is positive and significant correlation between reminder and the latent factors and also between the latent factors and adherence. 2022 IGI Global. All rights reserved. -
Study of micro and small enterprises' readiness in implementing industry 4.0: A study in marathwada district of maharashtra, india
Industry 4.0 aims tp transform the development of global value chains and the development of a digital revolution, with intelligent machines capable of communicating via wireless connections and a connection thought system, resulting in autonomous decision-making. Although large sized firms are adopting Industry 4.0, the small and micro enterprises are facing great difficulties in adopting them. This study aims to identify the areas in which Enterprises need to focus for improving their level of readiness and develop strategies and plans to adopt Industry 4.0 technologies successfully. 219 samples were collected using snowball sampling from Marathwada District in Maharashtra, India. factor analysis was conducted using SPSS and different factors acting as barriers to implementation of Industry 4.0 technologies were identified. 2023 by IGI Global. All rights reserved. -
Probability of Medication Adherence When Alarm Is Used as a Reminder
The main objective of this research is to find the effect of alarm as a form of reminder in improving medication adherence rate. Medication non-adherence is a problem that adversely impacts patients' health, finances, and longevity. Several factors are associated with medication non-adherence. This research uses the method of probability estimates, risk difference, relative risk, and odds ratio to analyze the probability of an increase in medication adherence among patients who use the alarm as a form of reminder. By clustered sampling and a structured questionnaire, 525 responses were obtained from patients suffering from different types of diseases in the state of Sikkim, India. It has been observed that using the alarm as a form of reminder significantly improves adherence rates. The odds of not missing a dose reduces to 49.3%. At a personal level, the chance of not missing the dose reduces by 32.6%, and if the total population is considered, 16.4% of people will not skip the dose if a reminder in the form of an alarm is used. 2022 International Journal of Reliable and Quality E-Healthcare. All rights reserved. -
Advancements in Electronic Healthcare: A Bibliometric Analysis
Electronic healthcare has changed the traditional form of medical treatment. The integrated approach of interconnected devices had enhanced the process of record keeping and dissemination, benefitting Doctors, patients, and other stakeholders. This study aims to highlight the research carried out in the field of electronic healthcare from the year 2011 to 2020. Metadata of 821 publications from Scopus database was extracted and analyzed. VOS viewer was used to generate the network diagrams and link strengths. It was found that Harvard Medical School and European Commission were the top publication affiliation and funder, respectively. United Stated dominated with the maximum number of publications till 2017 but was surpassed by publications from India from 2018 onwards. Publications inclined toward Internet of things, network security, retrospective study, and authentication toward the end of this decade indicating the shift in trend for the future. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Job Satisfaction Among Health-Care Practitioners: A Bibliometric Analysis
Purpose: The study aims to throw light on job satisfaction among health-care practitioners based on the metadata of published literature from Scopus database with the help of bibliometric analysis. Design/methodology/approach: Metadata of 6,998 publications from the Scopus database were extracted. Bibliometric analysis was done with country-based co-authorship analysis, all keywords-based co-occurrence analysis, sources-based citation analysis, cited authors-based co-citation analysis, and term co-occurrence based on text data. Findings: It was found that United States had the highest number of publications at 2,037. The Journal Of Nursing Management had the highest number of publications (332). Term co-occurrence based on text data reveals that job satisfaction, turnover intention, work engagement, compassion fatigue, job stress, organizational commitment, job demand, job performance, workplace violence, job burnout, career satisfaction, safety climate, organizational support, transformational leadership, leadership style, discrimination, workplace bullying, and job strain are the most prominent terms. The paper also highlights the factors affecting job satisfaction of employees in the health-care sector. Conclusion: The paper tries to highlight the publication trends on the job satisfaction among health-care practitioners. Since health care is a primary sector, prosperity of other sectors in the society depends on the job satisfaction level of employees in this sector. 2024 Association for Radiologic & Imaging Nursing -
Taming theComplexity ofDistributed Lag Models: A Practical Approach toMulticollinearity, Outliers, andAuto-Correlation inFinance
This research investigates the application of robust estimators within the finite distributed lag model (DLM), a critical framework in finance research capturing temporal dependencies between lagged explanatory variables and a response variable. Traditional Ordinary Least Squares (OLS) estimation faces challenges when dealing with high lag counts, multicollinearity, and outliers, potentially compromising parameter estimates and model reliability. Employing real-world data from the RBI, spanning the years 20222023 encompassing budgetary and economic variables of Indian states and Union Territories, the study demonstrates that the MMS estimator emerges as the most efficient estimator, showcasing enhanced robustness against outliers and multicollinearity. Additionally, the study reveals positive autocorrelation in residuals, underscoring the importance of robust methods in addressing such issues in financial modeling. This research contributes valuable insights to financial analysts and offers a more accurate understanding of dynamic relationships in financial systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Living with Coronavirus outbreak in India
The present paper focuses on living with coronavirus outbreak in India. This piece emphasizes on various policies adopted by the government of India to face the coronavirus crisis. It brings into perspective what financial strides the economy is going through, the mental health of the citizens, and the current situation of health care in the country. The current commentary reflects the learnings from COVID-19, the role of defined governmental policies, and support in surviving such an unforeseen situation. 2020 American Psychological Association. -
Interpreting Scope of Predictive Analytics in Advanced Driving Assistant System
Distracted driving, caused by various factors such as human emotions or reading distracting messages on the roadside, has become a leading cause of traffic accidents today. Ensuring the safety of both individuals and vehicles while minimizing maintenance costs poses a significant challenge for the automotive industry. Fortunately, recent advancements in machine learning offer a potential solution. One promising method is the further development of Advanced Driver Assistance Systems (ADAS), for which machine learning serves as an ideal solution. The proposed model develops an advanced predictive learning enabled driving assistance system with prediction capabilities like traffic light behavior and parking availability detection. The model gave an optimum accuracy of 98.2% with 50 epochs count and the validation loss retains a constant value of 0.3 over epochs. 2023 IEEE. -
Star formation around three co-moving HAeBe stars in the Cepheus Flare
Context. The presence of three more Herbig Ae/Be (HAeBe) candidates in the Cepheus Flare within a 1.5 radius centered on HD 200775 suggests that star formation is prevalent in a wider region of the LDN 1147/1158, LDN 1172/1174, and LDN 1177 clouds. A number of young stellar objects (YSOs) are found to be distributed toward these cloud complexes along with the HAeBe stars. Various star formation studies clearly indicate ongoing low-mass star formation inside the clouds of this region. Sources associated with less near-infrared excess and less H? emission raise the possibility that more low-mass YSOs, which were not identified in previous studies, are present in this region. Aims. The aim is to conduct a search for additional young sources that are kinematically associated with the previously known YSOs and to characterize their properties. Methods. Based on the Gaia DR2 distances and proper motions, we found that the HAeBe candidates BD+681118, HD 200775, and PV Cep are all spatially and kinematically associated with previously known YSOs. Based on the Gaia DR2 data, we identified a number of co-moving sources around BD+681118. These sources are characterized using optical and near-infrared color-color and color-magnitude diagrams. Results. We estimated a distance of 3407 pc to the whole association that contains BD+681118, HD 200775, and PV Cep. Based on the distance and proper motions of all the known YSOs, a total of 74 additional co-moving sources are found in this region, of which 39 form a loose association surrounding BD+681118. These sources are predominantly M-type sources with ages of ?10 Myr and no or very little near-infrared excess emission. The distribution of co-moving sources around BD+681118 is much more scattered than that of sources found around HD 200775. The positive expansion coefficients obtained via the projected internal motions of the sources surrounding BD+681118 and HD 200775 show that the co-moving sources are in a state of expansion with respect to their HAeBe stars. A spatiooral gradient of these sources toward the center of the Cepheus Flare Shell supports the concept of star formation triggered by external impacts. 2021 ESO. -
A census of young stellar population associated with the Herbig Be star HD 200775
The region surrounding the well-known reflection nebula, NGC 7023, illuminated by a Herbig Be star, HD 200775, located in the dark cloud L1174 is studied in this work. Based on the distances and proper motion values from Gaia DR2 of 20 previously known young stellar object (YSO) candidates, we obtained a distance of 335 11 pc to the cloud complex L1172/1174. Using polarization measurements of the stars projected on the cloud complex, we show additional evidence for the cloud to be at ?335 pc distance. Using this distance and proper motion values of the YSO candidates, we searched for additional comoving sources in the vicinity of HD 200775 and found 20 new sources, which show low infrared excess emission and are of age ?1 Myr. Among these, 10 YSO candidates and 4 newly identified comoving sources are found to show X-ray emission. Three of the four new sources for which we have obtained optical spectra show H ? in emission. About 80 per cent of the total sources are found within ?1 pc distance from HD 200775. Spatial correlation of some of the YSO candidates with the Herschel dust column density peaks suggests that star formation is still active in the region and may have been triggered by HD 200775. 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society. -
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Effect of COVID-19 on ETF and index efficiency: evidence from an entropy-based analysis
We examine the informational efficiency of domestic equity ETFs vis-a-vis their underlying market indices during the COVID-19 pandemic. To do so, we employ a multiscale entropy-based methodology. Our findings indicate that the informational efficiency of all ETFs as well as the indices fall sharply during the COVID induced market crash in February-March 2020. Having said so, we find disproportionate deterioration in market efficiency of ETFs and indices pertaining to USA and Canada as compared to those of China, Hong Kong and Taiwan. Interestingly, ETFs and indices pertaining to certain developed markets were found to be less efficient than their emerging market counterparts even during the pre-covid timeline. Lastly, there is a discernible difference between the efficiency of ETFs vis-a-vis their underlying indices. These findings should nudge investors to exercise caution while dealing with ETFs, for domestic ETFs do not exactly mimic the dynamics of their underlying indices. 2021, Academy of Economics and Finance. -
Fake News Detection using Machine Learning and Deep Learning Hybrid Algorithms
Spreading misinformation or fake news for personal, political, or financial gain has become very common these days. The influence of this misinformation on peoples opinions can be significant, i.e., the 2016 presidential election in the United States was a perfect illustration of how false news may be used to deceive people. In todays fast-paced world, automatic detection of fake news has become an importantrequirement. In this paper, multiple machine learning algorithms have been implemented to perform classification. A proposition of a hybrid architecture consisting of CNN along with LSTM has also been made. The proposed model outperforms the other traditional approaches. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Pixels to Pathogens: A Deep Learning Approach to Plant Pathology Detection
It is known that accurately identifying, early and timely treatment and elimination of the plant diseases is essential for crop protection and healthy crop growth. In traditional or conventional methods, identification and classification were done by testing in laboratories or through visual inspection by farmers. Now going through the testing in labs is very time consuming, while the visual inspection requires enough experience and knowledge. To solve this problem, our study proposes a robust plant pathogen detection method based on a Deep Learning approach on a large dataset containing about 38 categories of different species like Maize, Potatoes, Tomatoes, Bell Pepper, Peach, Strawberry etc. and diseases like rust, molds, blight (late and early). This crop disease detection model leverages the power of the EfficientNetB3 architecture, a state-of-art convolutional neural network(CNN). The main backbone is served by EfficientNetB3and then it is fine-tuned using different hyperparameters and other regularization techniques like weight decay, dropout method and optimizers like RAdam,to enhance the overall accuracy coupled with dynamic learning rate adjustment. In the testing set of the dataset, the proposed model shows encouraging accuracy of about 99.25%, high precision of about 97.35%. A thorough evaluation of the model's functionality is given by the help of training and validation line chart and loss chart that gives the in-depth information on the prediction. And then we implemented the detection model in our mobile application whose interface screen shots are given below. In the application the image can be taken by camera or fed from folders and it will detect the type of disease. 2024 IEEE. -
Learning foreign languages: A comparative analysis of online learning process vs. traditional educational processes /
Internet has pervaded every aspect of the life of the modern person today in the contemporary world. The fact that the education sector is undergoing vast amount of change in terms of the digital revolution via the internet medium is exemplary of the powerful aspect of the internet medium. This is also the case with the practice of foreign languages by many people, especially the urban educated youth. -
Risk management of future of Defi using artificial intelligence as a tool
This chapter explores AI's pivotal roles in managing risks within DeFi, emphasizing strategic implementation to enhance risk assessment, management, and decisionmaking processes for a better user experience. The convergence of AI and DeFi presents unprecedented opportunities, fostering transparency and decentralization. Drawing from diverse sources, the study evaluates AI's effectiveness, particularly in machine learning, in addressing emerging risks. It focuses on how AI can guide DeFi's future while managing market and credit risks through tasks like data preparation, modeling, stress testing, and validation. Additionally, AI aids in data quality assurance, text mining, and fraud detection. Emphasis is placed on identifying and managing risks that could hinder DeFi's future, highlighting key AI techniques. Given the financial industry's ongoing transformation, these insights are increasingly vital. 2024, IGI Global. All rights reserved.