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Profit function Optimization for Growing Items Industry
The economy of a country depends on many industries; growing item industries are one of them. Growing items also exhibit mortality in the growth period, which creates a complex environment for the procurement decision. A practical inventory model is required to overcome this situation, which provides the optimum solution. This work describes an economics ordering quantity model for growing items with constant demand and mortality. We also take into consideration that one of the real-life management practices for businesses is the allowance of a delay in payment. There is a solution procedure with a numerical example. We have discussed analytical results to verify the concavity of the profit function. Sensitivity analysis provides us with some very useful information. . 2023 IEEE. -
An Inventory Model for Growing Items with Deterioration and Trade Credit
Growing items industry plays a vital role in the economy of most of the countries. Growing item industries consists of live stocks like sheep, fishes, pigs, chickens etc. In this paper, we developed a mathematical model for growing items by considering various operational constraints. The aim of the present model is to optimize the net profit by optimizing decision variables like time after growing period and shortages. Also, the delay in payment policy has been used to maximize the profit. A numerical example is provided in support of the solution procedure. Sensitivity analysis provides some important insights. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Efficient cationic dye removal from water through Arachis hypogaea skin-derived carbon nanospheres: a rapid and sustainable approach
The present study investigates the potential of Arachis hypogaea skin-derived carbon nanospheres (CNSs) as an efficient adsorbent for the rapid removal of cationic dyes from aqueous solutions. The CNSs were synthesized through a facile, cost-effective, catalyst-free and environmentally friendly process, utilizing Arachis hypogaea skin waste as a precursor. This is the first reported study on the synthesis of mesoporous carbon nanospheres from Arachis hypogaea skin. The structural and morphological characteristics of the CNSs were confirmed by different nano-characterization techniques. The adsorption performance of the carbon nanospheres was evaluated through batch adsorption experiments using two cationic dyes-methylene blue (MB) and malachite green (MG). The effects of the initial dye concentration, contact time, adsorbent dosage, and pH were investigated to determine the optimal conditions for dye removal. The results revealed that the obtained CNSs exhibited remarkable adsorption capacity and rapid adsorption kinetics. Up to ?98% removal efficiency was noted for both dyes in as little as 2 min for a 5 mg L?1 dye concentration, and the CNSs maintained their structural morphology even after adsorption. The adsorption data were fitted to various kinetic and isotherm models to gain insights into the adsorption mechanism and behaviour. The pseudo-second-order kinetic model and Redlich-Peterson model best described the experimental data, indicating multi-layer adsorption and chemisorption as the predominant adsorption mechanism. The maximum adsorption capacity was determined to be 1128.46 mg g?1 for MB and 387.6 mg g?1 for MG, highlighting the high affinity of the carbon nanospheres towards cationic dyes. Moreover, CNS reusability and stability were examined through desorption and regeneration experiments, which revealed sustained efficiency over 7 cycles. CNSs were immobilised in a membrane matrix and examined for adsorption, which demonstrated acceptable efficiency values and opened the door for further improvement. 2024 RSC. -
Exploring the efficiency of green synthesized silver nanoparticles as photocatalysts for organic dye degradation: unveiling key insights
Silver nanoparticles (AgNPs) have received a lot of interest for their several applications, including their remarkable potential as photocatalysts for organic dye degradation. This research thoroughly investigates the efficacy of ecologically friendly, green-synthesized AgNPs in the treatment of synthetic dye-contaminated wastewater. The synthesis of AgNPs from various biological substrates is investigated, emphasizing their economic viability, significant conductivity, and considerable biocompatibility. The improper disposal of synthetic dyes in wastewater poses severe environmental and health risks due to their non-biodegradable nature and persistent chemical features. In response to this challenge, this review paper investigates the capability of AgNPs to serve as effective photocatalysts for degrading a range of organic dyes commonly found in industrial effluents. Specific dyes, including methyl orange, congo red, nitrophenol, methylene blue, and malachite green, are studied in the context of wastewater treatment, providing insights into the efficacy of AgNPs synthesized from diverse biological sources. The review sheds light on the photocatalytic degradation methods used by green-synthesized AgNPs, shedding light on the transition of these synthetic dyes into less hazardous compounds. It also delves into the toxicity aspect of the AgNPs and its possible remediation from the environment. The ecologically friendly synthesis procedures investigated in this work provide an alternative to traditional methods, highlighting the importance of sustainable technologies in solving modern environmental concerns. Furthermore, a comparative examination of various biological substrates for AgNPs synthesis is presented, evaluating their respective dye degradation efficiencies. This not only helps researchers understand the environmental impact of synthetic dyes, but it also directs them in choosing the best substrates for the production of AgNPs with enhanced photocatalytic activities. 2024 The Author(s). Published by IOP Publishing Ltd. -
Brain Tumor Classification: A Comparison Study CNN, VGG 16 and ResNet50 Model
Brain tumors pose a severe threat to global health and may be lethal. Early detection and classification of brain tumors are essential for successful therapy and better patient outcomes. The good news is that advances in deep learning techniques have shown tremendous promise in medical image analysis, particularly in the detection and classification of brain tumors. Convolutional Neural Networks (CNN), a class of deep learning models, are used to process and analyze visual input, notably images, and movies. They excel in computer vision tasks like object detection, image segmentation, and categorization. Popular and efficient image analysis methods include CNNs. VGG 16 and ResNet 50 are two examples of deep convolutional neural network architectures used for image categorization applications. A number of image identification problems have been successfully solved using the 16 layer VGG 16. ResNet50, a well known 50 layer architecture, employs residual connections to get over the vanishing gradient issue and permits the training of deeper networks. A proprietary CNN model, VGG 16, and ResNet50 were compared in studies to see how well they performed on a dataset. The VGG 16, ResNet50, and the tailored CNN model were the most precise models. As a consequence, VGG 16 accurately detects brain cancers in the dataset that was supplied. Overall, this study highlights the value of deep learning techniques for medical image processing and their potential to improve the accuracy and efficacy of brain tumor diagnosis and treatment. 2023 IEEE. -
Pattern Recognition: An Outline of Literature Review that Taps into Machine Learning to Achieve Sustainable Development Goals
The sustainable development goals (SDGs) as specified by the United Nations are a blueprint to make the Earth to be more sustainable by the year 2030. It envisions member nations fighting climate change, achieving gender equality, quality education for all, and access to quality healthcare among the 17 goals laid out. To achieve these goals by the year 2030, member nations have put special schemes in place for citizens while experimenting with newer ways in which a measurable difference can be made. Countries are tapping into ancient wisdom and harnessing newer technologies that use artificial intelligence and machine learning to make the world more liveable. These newer methods would also lower the cost of implementation and hence would be very useful to governments across the world. Of much interest are the applications of machine learning in getting useful information and deploying solutions gained from such information to achieve the goals set by the United Nations for an imperishable future. One such machine learning technique that can be employed is pattern recognition which has applications in various areas that will help in making the environment sustainable, making technology sustainable, and thus, making the Earth a better place to live in. This paper conducts a review of various literature from journals, news articles, and books and examines the way pattern recognition can help in developing sustainably. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Study to assess attitudes towards statistics of business school students: An application of the SATS-36 in India
Students attitudes towards Statistics are pivotal to their learning process as positive attitudes lead to highly satisfactory course achievement and lead to positive outcomes outside class as well. In this paper we are exploring the perception of students of management apropos Statistics, familiarity with which is imperative in todays world of Analytics. The quantitative approach was used to compare attitudes of the students using the two versions of the SATS-36 instrument validated and copyrighted by Candace Schau. A Google form was used to collect responses and was sent to all the students who were enrolled in the Business Statistics course. 172 students responded for the pre-test study while 71 students responded for the post-test study. Data was analysed to see if gender, specialisation choices and previous math experiences accounted for differences in perceptions towards Statistics. It was found that students overall perception of statistics is positive and surprisingly they were more positive towards the beginning of the semester. These results are important as they can lead towards understanding of business students attitudes towards statistics and a way to refine the teaching learning process so that students are in a strong position to exploit the supply demand gap in the Analytics domain and deliver value to organisations. 2021 Eskisehir Osmangazi University. All rights reserved. -
A ratiometric luminescence thermometer based on lanthanide encapsulated complexes
Lanthanide-containing complexes have been widely developed as ratiometric luminescence thermometers, which are non-invasive, contactless and accurate. The synthesis of these Ln complexes generally requires high temperatures, multiple steps and other harsh conditions. Moreover, bimetallic lanthanide complexes, which have been reported to be better thermometers, are even more challenging to synthesize. This complexity can be simplified by preparing a host-guest complex of lanthanides. In this work, Tb or both Tb and Eu are encapsulated in an MOF host, making them emissive. The ratio of Tb/Eu was also easily tuned by simply changing their ratio in the solution, resulting in a tunable emission. Accordingly, we were able to synthesise both the emissive Tb complex and Tb/Eu complexes at different ratios using a single host. The complexes were found to be suitable as ratiometric luminescent thermometers in the temperature range of 160-380 K, with reasonably good sensitivity and uncertainty. The thermometer's sensitivity and uncertainty were significantly improved using bimetallic Tb and Eu host-guest complexes. Calculations using the host and Eu emission ratio were found to provide better thermometer parameters than the commonly reported Tb and Eu emission ratio. Thus, using a single host, we were able to synthesise different lanthanide complexes that can sense temperature, and we improved the thermometer parameters by incorporating multiple lanthanides in a single host. This research will enable the scientific community to reexamine the applicability of unexplored host-guest lanthanide complexes. 2025 The Royal Society of Chemistry. -
Powering Ahead: Navigating Opportunities and Challenges in the Electric Vehicle Revolution
The technology is clearing ways for buzz in the market brimming with innovative items and new prospects. The government has planned to shift to electric vehicles by 2030, whether it is for personal or commercial use. As innovative improvements are developing quickly, it is blasting the market with the EVs industry which expected to transform the future (Rajkumar S, in Indian electric vehicle conundrum: a tale of opportunities amid uncertainties, 2020). Volvo company has also announced that it will be fully electric by 2030 (https://gadgets.ndtv.com, in Volvo to go all electric by 2030, sell exclusively online, 2021). It is expected that EVs will generate more demand for electricity and help in settling the focus on resources problem. It will also help in improving the financial feasibility of power sector projects. In India, there is more dependency on renewable energy so this is a chance to be independent and provide cheap power to the people. The EVs are more economical than petrol or diesel vehicles. The government is also giving incentives to the makers of electric vehicles. GST on electric vehicles is 12% as compared to petrol and diesel vehicles with 28% GST. As per the Electricity Act, 2003, a distribution license is needed to supply power from respective state electricity regulatory commissions. Another challenge is that charging the EVs will lead to a rise in the demand of electricity which is risky for the electricity distribution companies (www.livemint.com, in Indias electric vehicle drive: challenges and opportunities, 2017). Indians are very price conscious. A recent study revealed that Indians are ready to compromise on more charging time, but they are not ready to pay higher price for EVs (Gupta NS, in Electric vehicle adoption in India: study reveals three tipping points, 2020). From Fig.1, it can be seen that in 2014 investment in EVs was $2.2 billion which has increased to $406 billion in 2019 (Shanti S, in The road to green: what makes electric vehicle adoption a challenge for India. 2020). This shows that people are shifting toward EVs. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Responding to the pandemic: A case of the indian hotel industry
The chapter presents a case study on how Indian hotel industry was affected by COVID-19. Three hotels-Lemon Tree, Oyo Rooms, and Taj Hotels-were selected to elaborate. The study found that the hotel industry developed various policies to keep running their hotels during the pandemic. Lemon Tree joined various hospitals to provide rooms to COVID patients, provided free food and face masks to individuals. Oyo Rooms gave employee stock ownership plans of Rs 130 crore to its COVID-hit employees. Taj Hotels did not cut down the salaries of their employees and reduced its seating capacity by 50%. The study concluded that as the hospitality sector battled hard to continue during the pandemic, modernization would become an imperative tool in the post-COVID period to beat obstructions and spotlight advancement. So, the companies should minimize fixed costs and maximize variable costs. They should preferably have liquid cash available that could enable them to mitigate the risk. 2022, IGI Global. -
EVALUATION OF THE ENVIRONMENTAL EFFECTS OF MEDICAL WASTE AND ITS INCREASE AFTER COVID-19 PANDEMIC
Medical waste is a special course of harmful contaminants. Improper treatment would cause tributary environmental pollution, expressly when countering to communal health tragedies. However, there are quite few explores on the peer group of medical waste, and there is a deficiency of basic considerate of its spatial-temporal heterogeneity. The purpose of this study is to conduct a systematic estimation of the effectiveness of these incongruous discarding procedures in expressions of water eminence and wellbeing. The research is centred on municipal areas characterised by vital medical waste production, which has the probable to taint groundwater and water sources. A complex approach is exploited in the procedure, which comprises of water sample collection, laboratory analysis, field surveys, and GIS-based spatial mapping. Medical waste disposal hotspots, such as healthcare facilities, waste collection points, and disposal sites, will be acknowledged through field surveys. Inspects will be showed on water samples poised from a variability of sources, including lakes, rivers, and groundwater wells, to find pathogens, medical residues, heavy metals, and organic pollutants, which are all gauges of medical waste contamination. The test centre analysis will utilise chic policies to portion the deliberation of pollutants in water samples, thereby gauging the likely hazards they pose to marine ecosystems and human health. Longitudinal visualisation of uncleanness distribution through GIS-based mapping facilitates the credentials of vulnerable areas and potential pathways for pollutant transport. The findings of this research will offer significant helps to our understanding of the extent of environmental deterioration resulting from the inadequate disposal of medical refuse into urban water sources. The results of this study will provide valuable insights for the creation of alertness campaigns, regulatory frameworks, and mitigation strategies that are operative in talking this urgent environmental concern and shielding the truthfulness of water in municipal regions. 2024, Scibulcom Ltd.. All rights reserved. -
Research & development premium in the Indian equity market: An empirical study
This article aims to investigate the research and development (R&D) premium and explore the three most prominent asset pricing models: capital asset pricing and the three-and five-factor models (Fama & French, 1993; 2015). The results show that India's annualized average R&D premium is significantly higher than the existing value, market, profitability, size and investment premiums, implying that the R&D premium is a more significant concern for Indian investors, particularly for high R&D firms. It was also observed that by applying the GRS test and the Fama and MacBeth (1973) two-pass procedure, the R&D risk factor augmented the CAPM, FF3F and FF5F models outperforming the existing CAPM, FF3F and FF5F models, respectively. We can also report that R&D is, unquestionably, a priced ingredient and a critical factor in developing pricing models for developing markets such as India. The paper's conclusions add to the current literature in R&D and asset pricing and assist investment professionals in developing better investment and trading strategies. 2021 AESS Publications. All Rights Reserved. -
Financial Distress and Value Premium using Altman Revised Z-score Model
In the stock market, investors and value managers desire to be safe. Estimating equity returns and evaluating potential financial distress risk are essential for investment and trading decisions. The link between distress risk and stock return is controversial, and current literature yields contradicting results. A variety of models may be used to evaluate distress risk-return trade-offs. This paper employs a revised Altman Z-score to examine financial distress and value premiums. Using univariate and multivariate techniques, we examine firm- and industry-level portfolio returns, encompassing all Indian companies listed on the Bombay Stock Exchange (BSE). Results confirm the existence of the distress factor effect found in industry and firm-level portfolios. It shows that the distress risk factor significantly determines stock returns as an independent systematic risk factor. This result is consistently found in most industries. The study demonstrates the existence of a value premium in both distressed and safe zones. The study also used a multivariate GRS test and the Fama-Macbeth procedure to validate the reliability of the distress factor and pricing models. Results confirm that Altman model-based distress factor augmented models improve the performance of existing pricing models with higher reliability and accuracy. 2023 MDI. -
A closer look at industry-associated value premium: evidence from India
This paper examines whether the academic literature-promised value premium has any industry association in the Indian equity market and the relationship between stock returns, value, and size within and across industries. We examine all listed firms trading at BSE India between 1999-2020, using CAPM and Fama-French three-factor models on each firm-levels and industry-level portfolio. The positive and significant value effect was found to exist in 17 out of 21 industry groups. Both industry and firm-level value effects are identified; however, the firm-level effect seems more prominent. Furthermore, the value effect is most substantial in small-cap value stocks of value- and growth-oriented industries, large-cap value stocks of value-oriented industry groups, then small-cap growth stocks of value- and growth-oriented industries and large-cap growth stocks of value- and growth-oriented industries. We also show evidence confirming the claim that value premium results from investors challenging higher returns from firms and industries operating in higher risk and distressing constraints. Copyright 2022 Inderscience Enterprises Ltd. -
Size, Value Effects and the Explanatory Power of Pricing Models: Evidence from BSE listed Indian Industries
The firm size and value anomalies are the global-level counterpart for explaining the cross-sectional variations of equity returns. This paper aims to examine the size, value effects and explanatory power of three well-known pricing models - CAPM, three-and five-factor- across and within 15 Indian industries. The study considers all firms listed on Indian largest stock exchange, BSE (Bombay stock exchange), between 1999-2021 by developing portfolios using firm size/value, size/investment and size/profitability risk characteristics. The study employs both univariate and multivariate methods, including time series, GRS statistics, and cross-sectional models within and across industries portfolios. Results indicated that size and value effects exist in almost all industries, presenting that size and value anomalies are the most prominent determinants for industry-level equity returns. In addition, the profitability and investment effects were also investigated; however, the results are mixed from industry to industry. In the case of the explanatory power of pricing models, the five-factor performs much better within and across industry portfolios than other pricing models; however, the models' effectiveness varies by industry. We also reported that investors who seek to allocate funds within and across industries tend to be expected reasonably stable returns and conceivably predictable; the findings of this study contribute to the existing literature on asset pricing and portfolio management in emerging markets. The Author(s) 2022. -
Is Industry-Specific Value Premium Declining? Evidence from India
This article examines whether the literature promised value effect exists and the changing nature of value premium at the industry level. It also determines the value premiums strength by controlling the January effect within and across the regulated industry groups. This is done by utilizing the two most prominent pricing models: FamaFrench three- and five-factor, considering all listed firms trading at BSE India between 1999 and 2020. The results show that a significant value effect exists in 15 of the 17 regulated industry groups over 21.5 years, while sub-period analysis revealed variation in the value effect at industry-based portfolio returns. We developed quintile and multivariate portfolios within and across the industries. Results show that the industry-specific value premium has been relatively low in the current decade due to decreasing industry portfolio returns and increasing P/B ratios within industry groups. The study also used the GRS test to explore the explanatory power of models. Results indicated that the explanatory power of models has declined in post-crisis periods. While controlling the January effect, the value premium has slightly diminished within and across the industry groups in the recent decade. We also observed that investors who seek to allocate assets within and across industries are likely to have potentially predictable and pretty stable returns. While other countries have found industry-specific value premiums, no such study has been conducted in India. As a first attempt, these findings are relevant for investors and academia. 2022 Management Development Institute. -
Comparing keyframe extraction for video summarization in CPU and GPU
Most of the information is captured through multimedia techniques. Videos contain many frames which might be redundant. Since processing of many frames is involved, these redundant frames must be removed for better and efficient results. Summarizing these frames by removing similar frames can speed up processing. In this paper video summarization is achieved by generating key frames. Key frames are generated using discrete wavelet transforms (DWT) technique and we subtract background from the keyframes to get region of interest. A video of 920&Times;720 resolution and length 120 second was used as test video and the run-time was 111 second in CPU and 60 second in GPU. The speed up is nearly 100%. A HD video which took 23 minutes in serial implementation to extract foreground object from key frames generated was reduced to 7 minutes using GPU acceleration. 2015 IEEE. -
Radon transform processed neural network for lung X-ray image based diagnosis
A novel method for image diagnosis with artificial learning is presented-ray images tuberculosis patients is subjected to neural network learning for prediction of diagnosis. The X-ray images of lungs are normally difficult for diagnosis, since its similarity to lung cancer. Under and over diagnosis of lung X-ray images is a difficult medical problem to resolve. In the present work radon transform of the x-ray images is fed to back propagation neural network trained with Levenberg algorithm. The present methodology gives sharp results, distincting the normal and abnormal images. 2014 IEEE. -
Parallelizing keyframe extraction for video summarization
In current era, most of the information is captured using multimedia techniques. Most used methods for information capturing is through images and videos. In processing a video, large information needs to be processed and a number of frames could contain similar information which could cause unnecessary delay in gathering the required information. Video summarization can speed up video processing. There are different techniques for video summarization. In this paper key frames are used for summarization. Key frames are extracted using discrete wavelet transforms. Two HD videos having 356 frames and 7293 frames were used as test videos and the runtime was 17 seconds and 98 seconds respectively in CPU and 11 seconds and 53 seconds respectively in GPU. 2015 IEEE. -
Humanizing technology: The impact of emotional intelligence on healthcare user experience
This investigation underscores the importance of humanizing technology within the healthcare sector, with a specific focus on the significant role of emotional intelligence in shaping the interactions between patients and healthcare providers, particularly in the context of advancing healthcare technology. By integrating empathy into medical interfaces and devices, the user experience is fundamentally grounded in human aspects. The study delves into firsthand experiences of patients using emotionally intelligent healthcare solutions that not only meet their medical needs but also address the emotional complexities of illness and recovery. The integration of emotional sensitivity in medical technology strives to enhance patient comfort and foster more open and communicative relationships between healthcare providers and recipients. Moreover, the research presents a framework for emotional intelligence in healthcare technology, encompassing elements such as emotional recognition, response, and management. This framework is designed to promote a culture of patient understanding and support, enabling healthcare technology to adapt to the emotional requirements of patients. In the ever-evolving healthcare landscape,it is essential to recognize the profound impact of embedding empathy in medical technology, ultimately shaping a more empathetic future for healthcare interactions. 2024 by IGI Global.