bluuebunny/crossref_metadata_2025 huggingface dataset:
Strategic Talent Management in Emerging Markets: An Empirical Study in Indian organisation
Authors: Purohit Priyanka
Published: May 2023

Effective talent management is a pivotal component in facilitating sustainable growth and achievement for organizations. The advent of emerging markets, such as India, has mandated that companies adopt strategic talent management practices to remain competitive. Indian companies place significant emphasis on talent acquisition, retention, and development as critical areas in the strategic management of talent. In order to secure and maintain top-tier talent. Companies leverage diverse methods such as employee recommendations, social media recruitment, and initiatives focused on engagement with employees. Strategic talent management in Indian organizations encompasses a comprehensive approach to talent management, from attraction and recruitment to development, retention, and planning for succession. Effective talent management necessitates the harmonization of the organization's objectives and strategies with the capabilities, skills, and aspirations of its workforce, and bestowing upon them opportunities to grow and develop. The Indian terrain of strategic talent management is further complicated by a convoluted talent landscape, with acute scarcities of skilled personnel in specific industries and an evolving demographic profile of the workforce, and fierce competition. Organizations must remain adaptable to these challenges and employ technology and data to optimize their talent management practices. Successfully managing talent can have a myriad of benefits for Indian organizations, including augmented workforce engagement, productivity, and innovation, as well as enhanced organizational performance and competitiveness. However, attaining such outcomes mandates persistent and proactive efforts, requiring cross-functional and organizational cooperation and a steadfast commitment to continuous improvement.
Examining the Impact of E-Commerce on the Retail Industry: A Comparative Study of Shopping Malls and Independent Retail Stores
Authors: Kothari Smitha
Published: May 2023

The emergence of e-commerce has significantly impacted the retail sector. The popularity of online shopping has led to a decrease in foot traffic at conventional brick-and-mortar establishments, such as shopping malls and independent retail stores. Decreased foot traffic brought on by the expansion of e-commerce has reduced sales for many mall-based stores. This has led to the closure or partial vacancy of some malls, which has diminished investor interest and caused a reduction in property values. To draw customers, some malls have modified the designs by adding more immersive features like dining options and entertainment centers. Others have adopted e-commerce themselves, developing online markets that provide a greater selection of goods and services. Customers can now purchase goods from big merchants more easily and frequently at lower prices than small enterprises can. Independent retailers are under pressure because of this to change and stand out from more established rivals. Some people have started e-commerce businesses to access markets outside of the immediate area. Others have concentrated on offering distinctive products and individualized customer service that are unavailable at bigger businesses. The retail industry has been significantly impacted by e-commerce overall. To stay relevant in a market that is changing quickly, it has required established merchants to innovate and adapt. Some merchants have found it difficult to keep up with the changes, while others have succeeded by embracing e-commerce and incorporating it into their business plans. The study had considered sample of 211 respondents (owners of shopping malls and independent retail store) to compare and examine the impact of e-commerce on the retail industry and concludes that there is a significant impact of e-commerce on the retail industry.
Job Satisfaction among BPO Employees: A Comparative Study of Males and Females
Authors: Punj Divya
Published: May 2023

This study investigates the job satisfaction levels of female and male employees working in the Business Process Outsourcing (BPO) industry. The purpose of the study is to identify any differences in job satisfaction between male and female employees and explore the elements that lead to these differences. Job satisfaction is an essential factor in the retention and motivation of employees in any industry. Several studies have investigated the factors that contribute to JS among employees in different industries. However, few studies have explored JS among BPO employees, particularly the differences between female and male employees. Possible outcomes of the research can aid BPO companies yearn for advances in the work culture, job characteristics, and work-life balance policies to increase employee job satisfaction levels. Furthermore, the area itself highlights the need for gender-specific interventions to address the different needs and preferences of male and female employees in the BPO industry. The study had considered sample of 206 respondents (employees of BPO) to compare the level of job satisfaction among male and female employees of BPO and concludes that there is a significant difference in job satisfaction level among male and female employees of BPO.
Contribution of Social Entrepreneurship in Creating Value for Society And Business: A Cross-Sectional Studies of Experts Opinion
Authors: Agarwal Pankaj
Published: May 2023

A key driver for financial and advancements in technology, the generation of fresh employment opportunities and changes in society is entrepreneurship by both startup and existing businesses. The method of economic invention known as social entrepreneurship is founded on the creation of value, is flexible enough to occur in a variety of societal settings, and follows its own set of principles and reasoning. It appears to be a strategy that can help business and deal with some of the biggest problems facing modern society. The capacity of social entrepreneurship to mix components of the commercial and voluntary spheres has proven the most controversial and startling aspect, but this blend could also represent the biggest barrier to the area's categorization. Both academics and practitioners need to outline the crucial issues and fundamental characteristics of social entrepreneurship, which has origins in both business and government policy. We offer an assortment of prospective topics for study since we think it is possible for scholars to examine this developing trend beyond boundaries of disciplines.
Preparing Herbal Formulations through Indigenous and Modern Methods: An Experimental Study
Authors: Lall Shatakshi
Published: May 2023

Traditional medicines can treat many problems because they are based on natural treatments. Because of their historical applications and useful treatments, numerous of these conventional medications have been the subject of extensive pharmacological research of their antibacterial, antiviral, and anti-inflammatory effects. Natural resources are frequently used as a primary or secondary source by academics and pharmaceutical corporations when developing new drugs. A wide variety of plants have long been used as a source of traditional medicine by people in many different cultures. Numerous research have examined the possible antibacterial and antiviral properties of these plants. Since there are so many different kinds of natural sources, including plants, choose the proper one as a starting point is crucial for accurate screening results. Due to their, “effectiveness in treating diseases and lower risk of side effects than synthetic treatments, the usage of plant-based medications has significantly expanded in the modern world. The current study was aimed to confirm the identity, quality and purity of some locally available potential medicinal plants such as Drymaria cordata (whole plant), Alstonia scholaris (bark), Hydrocotyle sibthorpioides (whole plant), Centella asiatica (whole plant), Senna hirsuta (leaf), Oroxylum indicum (bark),

 

Senna occidentalis (leaf), Stephania japonica (tuber) and Solanum indicum (root) in powdered form”. 

The powdered plant components underwent preliminary phytochemical analysis as well as pharmacognostic tests, physical evaluation and heavy metal analysis. Initial phytochemical study of the various extracts indicated that triterpenoids were absent, but alkaloids, phenolics, carbohydrates and amino acids were present. The powder was studied under a microscope to reveal its, “distinguishing characteristics, including calcium oxalate crystals, fibres, stone cells, trichomes, stomata, xylem vessels, pitted spiral vessels, etc. The colour, smell, fragrance, and texture of the ground plant were all acceptable. The physical characteristics that affect the flow rate of the powder with respect to Carr's index and Hausner's ratio were found to be good to passable, with the exception of Hydrocotyle sibthorpioides (the complete plant) and Oroxylum indicum (bark), which were not easily passable. During the heavy metal test, lead, cadmium, and bismuth were not found.  As a result, the current study may be utilised as a benchmark reference for the quality control analysis of the herbal medicine, either alone or in combination”.
Iot In Healthcare: Challenges and Opportunities for Improved Patient Outcomes
Authors: Chattopadhyay Saumitra
Published: May 2023

The Internet of Things (IoT) has emerged as a promising technology to revolutionize healthcare by transforming the way medical services are delivered, improving patient outcomes, and reducing costs. IoT-enabled devices and systems offer immense potential for enhancing patient outcomes, improving healthcare delivery, and reducing costs.  This paper presents an overview of the challenges and opportunities associated with IoT adoption in healthcare, emphasizing its potential to enhance patient care and streamline medical processes. This paper highlights the crucial role of IoT in transforming healthcare systems and emphasizes the need for multidisciplinary collaboration among stakeholders to ensure the successful implementation of IoT in healthcare.
Factors affecting the Growth and Development of MSME Sector in India: An Opinion Survey of Start-ups
Authors: Rawat Monika
Published: May 2023

The micro, small, and medium enterprises (MSMEs) sector in India is considered a crucial contributor to the country's economic growth, employment, and poverty alleviation. However, the sector faces various challenges that hinder its growth and development. This paper explores the factors that influence the growth and development of the MSME sector in India The study is based on a review of existing literature and secondary data analysis. The literature review reveals that government policies, access to finance, infrastructure, technology, human resource development, and market access are the primary factors that affect the MSME sector's growth and development in India. The identified factors include government policies, access to finance, infrastructure, technology, human resource development, and market access. The paper highlights the significance of these factors and their interrelationships in shaping the MSME sector's growth and development.
Factors determining the FinTech Adoption among Women: An Empirical Investigation
Authors: Batola Megha
Published: May 2023

The adoption of financial technology (FinTech) has grown rapidly in recent years, transforming the way people manage their finances. However, there is evidence that women have been slower to adopt FinTech than men. The purpose of this study is to investigate the elements that affect women's adoption of fintech. To uncover relevant elements that might have an impact on women's adoption of FinTech, the study starts by reviewing the available literature and expert perspectives. Based on this analysis, the report pinpoints several variables, such as trust, "perceived usefulness" (PU), "perceived ease of use" (PEOU), "social influence" (SI), and financial literacy, that may affect women's adoption of FinTech. The study concludes that addressing these concerns through targeted marketing and educational campaigns is necessary to increase the adoption of FinTech among women. The results of this study have significant ramifications for decision-makers, financial organisations, and FinTech firms working to advance gender equality and financial inclusion.
Analyzing the Relationship Between Shg and Micro Finance
Authors: Pratap Singh Bhanu
Published: May 2023

Micro-finance has developed as a need-based program for empowering women and reducing poverty as part of the development paradigm. One of the most successful strategies for improving the lives of the underprivileged via increased economic opportunity is microfinance. To reduce unemployment and end poverty, microfinance programs like the one discussed in this article attempt to help people start small businesses and start earning an income right away. To help small and medium-sized enterprises (SHGs), NABARD funds several MFIs. In order to help the world's poorest people and give them a better chance to survive in today's uncertain economy, India recognized the need of a poverty reduction program that used microfinance. Over 26 percent of India's population lives in rural poverty, according to the country's economic data, making it a top priority for the government and society at large to find solutions.
Machine Learning Techniques in the Detection of Drowsiness in Drivers
Authors: Garg Navin
Published: May 2023

Drowsiness is one of the major risk factors that contributes to numerous accidents. With a variety of strategies and accurate categorization procedures that identify different drowsy stages and alert the individual at particular periods, this study seeks to lessen its effects. To improve the detection effectiveness of the current methods, Fractional Fourier Transform (FrFT) has been employed for feature extraction, ABC (Artificial Bee Colony) for optimisation, and NN and sparse classifiers for classification. The two solutions exhibit high efficiency when it comes to improving the system's accuracy and other performance measures. The two strategies are also contrasted, with the latter producing better results.
Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets
Authors: Patra Rakesh
Published: May 2023

Data mining is the process of extracting new, possibly useful information from vast data bases that is not straightforward. Market basket analysis, a kind of data mining used in retail research, is used to analyse client transactions. The association between the things that occur in transactions more frequently was the focus of earlier data mining techniques. They don't take an item's significance or utility into account while often mining an itemset. Utility mining is a new field that has emerged as a result of the limits of common mining goods. When mining, the profitability or utility of an object is taken into account. In a transaction, an item's utility refers to its significance or financial gain. Finding the item set with utility values over the specified threshold is the main goal of mining high utility goods. We give a literature review on several mining methods in this work. The support-confidence framework, used in traditional association rule mining, gives users an objective way to quantify the rules that are important to them. Despite several research being done, the mining operation may perform worse with regard to of processing speed as well as memory. efficiency when consumers are presented with too many high utility itemsets by existing approaches. In this research, the system proposes a unique framework for mining closed high utility itemsets that acts as a lossless yet compact representation of high utility itemsets.
Improving the Performance of Primary Storage Systems Using Leveraging Data Deduplication
Authors: Pattanshetti Mahantesh K
Published: May 2023

Data deduplication is a specialised data compression technique used in computers to get rid of redundant copies of repeated data. Intelligent (data) compression and single-instance (data) storage are words that are similar and partly interchangeable. This method may be used to increase storage efficiency and the quantity of data that has to be sent should be reduced over networks. Unique data chunks, or byte patterns, are found and saved throughout the deduplication process through a process of analysis. Recent studies have demonstrated that main storage systems in the Cloud offer moderate to high levels of data redundancy. It demonstrates that as a result of the small I/O requests to redundant data's comparably high temporal access locality, data redundancy displays a substantially greater degree of intensity on the I/O path than that on discs.   The system in this project suggests using a performance-oriented I/O deduplication technique known as POD. It is used to increase the I/O performance of main storage systems in the Cloud without compromising the latter's capacity savings.
Enabling Fine-Grained Multi-Keyword Search Supporting Classified Sub-Dictionaries Over Encrypted Cloud Data
Authors: Vats Satvik
Published: May 2023

Abstract

Huge numbers of papers are transferred to the cloud for easier access and lower administration costs as cloud computing gains popularity. Individuals can utilize cloud computing to store their data on distant servers and grant public users access to the data via the cloud servers. Outsourced data are normally encrypted before being posted to the clod since they are likely to include sensitive privacy information. The difficulty of sifting through the encrypted data, however, severely restricts the utility of the outsourced data. In this research, we introduce the fine-grained multi-keyword search algorithms over encrypted clod data to overcome this problem. We made three original contributions. In order to provide exact keyword search and a tailored user experience, we initially present the relevance source plus preference factor upon keywords. Furthermore, we create a useful and very effective multi-keyword search strategy. The suggested system can enable complex logic searches using mixed "AND," "OR," and "NO" keyword operations. Thirdly, in order to improve the efficiency of index building and query generation, we also use the classified sub-dictionaries technique. Finally, we examine the security of the suggested strategies with regard to of document confidentiality, index alongside trapdoor privacy protection, as well as trapdoor unlinkability. We confirm that the suggested method performs better when it comes to of functionality, query complexity, yet efficiency than the ones currently in use plus can reach the same security level through rigorous tests on a real-world dataset.  We confirm that the suggested method performs better with regard to functionality, query complexity, and efficiency than the existing ones and can reach the same security level through rigorous tests on a real-world dataset.
Exploring the Power of Transformer Models in Hospitality Domain
Authors: Parsola Jyoti
Published: May 2023

ABSTRACT

Despite decades of medical advancements and a rising interest in precision healthcare, the great majority of diagnoses are made after patients start to exhibit observable symptoms of sickness. However, early disease indication and detection can give patients and caregivers the opportunity for early intervention, better disease management, and effective use of healthcare resources. Deep learning and other recent advancements in machine learning provide a fantastic chance to fill this unmet demand. Transformer designs are very expressive because they encode long-range relationships in the input sequences via self-attention methods. The models we offer in this work are Transformer-based (TB), and we provide a thorough description of each one in contrast to the Transformer's typical design. This study focuses on text-based task (TB) models used in Natural Language Processing (NLP). An examination of the key ideas at the core of the effectiveness of these models comes first.  NLP's flexible architecture allows it to incorporate various heterogeneous concepts (such as diagnoses, treatments, measurements, and more) to further improve the accuracy of its predictions. Its (pre-)training results in disease and patient representations can also be helpful for future studies (i.e., transfer learning).
Measure the Awareness of Individual Investors on Investment Practice in Land Investments
Authors: Rawat Monika
Published: May 2023

As India's economy as a whole has become more technologically advanced, so too has the land industry. Demand for commercial real property to construct homes and factories has increased as a consequence of rising business opportunities and the movement of workers to these sectors. Real estate projects are affected by demand from individuals, hotels, and the entertainment sector. The focus of this study is on the factors that drive, inform, and reward individual land investors. The research method is descriptive, and it's used to look at many different aspects of real estate investing.
Predicting Equipment Failure in Manufacturing Plants: An AI-driven Maintenance Strategy
Authors: Dhyani Bijesh
Published: May 2023

Failure of equipment can lead to costly repairs and downtime for manufacturing facilities. Traditional maintenance techniques can result in unexpected failures, which can cause revenue losses and production delays. Artificial Intelligence (AI)-driven methods use machine learning technologies to predict equipment malfunctions before they happen, which has gained widespread interest. Artificial intelligence-based maintenance strategies can help improve the reliability of equipment and reduce the cost of repairs in manufacturing facilities. This study explores the use of Kaggle's predictive maintenance dataset to predict equipment failure in a manufacturing plant. The research involves collecting and evaluating data, selecting machine learning models, and evaluating metrics.  The findings of this study revealed the application of AI-driven maintenance techniques to predict equipment failure, highlighting their potential to improve the efficiency of the manufacturing process and reduce costs. This study serves as a valuable contribution to the field of predictive maintenance and provides relevant implications for the industry. The paper explores the application of AI-based maintenance strategies to predict the failure of equipment in manufacturing facilities. The study utilizes the Kaggle dataset, which contains machine sensor readings and process variables from a manufacturing facility that processes batches of a specific product. The research methodology includes data gathering, engineering, and model selection. It also compared the effectiveness of AI-driven and traditional maintenance approaches. The results indicated that the former led to better predicted equipment failure rates and lower costs. The study's findings provide valuable insight into the subject of predictive maintenance and the implications it has for the manufacturing sector. It also offers future research directions.
Exploring the Impact of Data Augmentation on Deep Learning Models for Plant Disease Detection in PlantVillage Dataset
Authors: Negi Charu
Published: May 2023

Plant diseases are a vital part of ensuring the safety of agricultural crops and the protection of food security. With the development of deep learning techniques, it has been able to automate the detection of these diseases. Unfortunately, one of the main challenges that researchers face when it comes to training models for this field is the lack of labeled training data. The study analyzed the effects of the data augmentation on the deep learning models' performance when it came to identifying plant diseases in the PlantVillage dataset. Here focused on two common ailments affecting tomato and potato plants. To address this issue, we trained CNN models on the PlantVillage dataset using different augmentation techniques, such as cropping, rotation, scaling, and flipping. The results of the study were evaluated using various metrics, including accuracy, recall, area under the curve, and F1 score. Data augmentation can improve a deep learning model's performance when it comes to detecting plant diseases. Our study revealed that CNN models, which were trained using the same methods, achieved higher accuracy and recall metrics when compared to the original models. Furthermore, our findings show that cropping techniques, which are commonly used in augmentation, were more effective at improving the models' performance. Even though the training data is limited, data augmentation can still help improve deep learning models' performance when it comes to detecting plant diseases. The use of such techniques could significantly enhance the models' accuracy and speed in identifying plant diseases, which are important for global food security and the agricultural industry.
Smart Farming: Implementation of IoT-based Technology for Crop Monitoring and Management
Authors: Chandran S Nisha
Published: May 2023

The field of agriculture has undergone considerable transformations over the past couple of decades. As the world’s population continues to increase, the need for more food production is becoming more critical. With the emergence of smart farming, which utilizes the Internet of Things (IoT), it can provide farmers with an effective and efficient means of managing their crops, increasing their yield, and minimizing their environmental impact. This study explores the use of IoT-based technology in the monitoring and management of crops. It also provides a study that highlights the potential of this technology. The study utilized IoT devices and sensors to collect data on different environmental factors like temperature, humidity, and soil moisture. Through machine learning techniques, the collected information can be used to provide farmers with valuable information on the status of their crops. In addition, the researchers created a dashboard that lets them make informed decisions and improve their crop production. According to the study, IoT-based technology can enhance the efficiency and productivity of agricultural operations by allowing them to monitor and control their crops. Farmers can identify early signs of diseases and stress, as well as make better decisions regarding the use of pesticides and fertilizers. The study emphasized the importance of having reliable and accurate data in making decisions. It also highlighted the need for continuous research to develop better systems for monitoring and managing crops using IoT technology.
Optimized Traffic And Enhanced Lifetime Routing Algorithm In WSN
Authors: Painuly Sakshi
Published: May 2023

Abstract:

Wireless Sensor Networks (WSNs) have gained significant traction as a viable solution for a diverse range of applications that necessitate the monitoring and regulation of distant environments. Wireless sensor networks (WSNs) are frequently constrained by the energy resources of the individual sensor nodes, resulting in network failures and reduced operational lifetimes. In response to this challenge, we present a proposed optimised traffic and lifetime routing algorithm for Wireless Sensor Networks (WSNs). The algorithm has been specifically designed to minimise network traffic and ensure equitable energy consumption across all sensor nodes. The algorithm utilises a fusion of clustering and network coding methodologies to leverage spatial diversity and redundancy within the network. The performance of the proposed algorithm is assessed through simulation experiments and is juxtaposed with various contemporary routing algorithms. The findings indicate that the algorithm we employed exhibits superior performance compared to other algorithms with respect to network longevity, network flow, and energy usage. The algorithm under consideration exhibits potential for utilisation in diverse Wireless Sensor Network (WSN) applications that necessitate dependable and energy-efficient data transmission.
Weighted Boost Spearman Correlative Dual Cluster Head for Robust Transmission in Wsn
Authors: Bhatt Neha
Published: May 2023

Wireless Sensor Networks (WSNs) are widely used in various applications, but they are often subject to environmental factors that result in poor network performance and packet loss. To address this challenge, correlation-based routing algorithms have been proposed. However, the performance of these algorithms degrades when the correlation between nodes is low. In this paper, we propose a Weighted Boost Spearman Correlative Dual Cluster Head (WBSCDCH) approach for robust transmission in WSNs. The proposed approach uses weighted boosting to assign higher transmission power to cluster heads with low correlation and utilizes dual cluster heads for efficient data collection and transmission. Our simulation results show that the proposed approach outperforms existing algorithms in terms of packet delivery ratio, energy consumption, and end-to-end delay. This paper contributes to the field of WSNs by providing a novel approach for improving the robustness of data transmission in WSNs.
Investigating the Use of Artificial Intelligence for Fault Detection in Electronic Circuits
Authors: Dubey Dheeraj
Published: May 2023

Many control systems and industrial applications rely heavily on analogue electrical circuits. Diagnosing problems with such circuits the old-fashioned manner can be erroneous and time consuming, which can have a detrimental impact on the industrial product. Also Keeping up with the ever-increasing sophistication of modern electronics has made testing these systems a formidable challenge. Due to the complexity and amount of individual parts on a PCB, traditional simulation-based methods cannot be used, and instead, artificial intelligence-based methods must be prioritization intriguing area of study that has the potential to improve the safety and reliability of electronic systems is the use of artificial intelligence (AI) to the task of detecting faults in electronic circuits. Real-time defect identification, diagnosis, and localization are made possible by AI-based solutions, which can assist prevent system breakdowns and boost performance. Fault detection in electronic circuits has been approached using a wide variety of artificial intelligence (AI) based methods, each with their own set of benefits and drawbacks. The availability of labelled training data, the generalizability of AI models, and the interpretation of AI-based fault detection outcomes are just a few of the obstacles that must be overcome. Despite these limitations, problem detection based on AI has many potentials uses in industries like transportation, energy, healthcare, and more. In this study, we survey the landscape of available AI-based fault detection systems for electronic circuits, discussing their merits and shortcomings, difficulties, and possible applications. This study details an inquiry into the viability of utilising an AI approach to failure prediction and early detection in power systems. A machine-learning detector has been created to keep an eye on specific parts of electricity grids and anticipate any problems before they occur.The AI failure prediction system can quickly anticipate a system failure.
Bending Behaviour of Concrete Beams with Composite Reinforcement
Authors: Nainwal Ankit
Published: May 2023

Massive quantities of finite resources have been depleted due to the construction and building sectors. Using bio-products to reduce raw materials use is seen as a major step toward a sustainable building sector. In this regard, the current research developed a novel composite beam reinforced and double-confined using a flexural and shears continuous FRP-Jacket. The suggested method involves swapping out conventional Portland cement-based concrete with a greener variant made with a different mix percentage that includes dry clay and hydraulic lime. Experimental testing and theoretical modeling based on shear stress and deformation composite beams are used to assess the bends & shear performance of the suggested design with four-point flexural load. The double-confinement approach used resulted in a noticeable improvement in strength and deformability compared to conventional concrete beams, as shown by strong agreement between experimental and modeling results in terms of effective stress, pressure concentration, and cement reduction.
Water tank Seismic Analysis Noting Effect on time Period
Authors: Bahuguna Anoop
Published: May 2023

In the case of an earthquake, an elevated water tank is a crucial building component. In order to store water at a constant level, an elevated water tank is built and height for water distribution system pressurization. Uplifted water tanks, as was learned the hard way, suffered significant damage or collapsed during earthquakes. As a result of the raised water tanks' extreme sensitivity to earthquake parameters including peak ground acceleration, frequency contents, and duration records. The purpose of this study is to gain insight into the dynamic behavior of an elevated water tank fitted with a UG sump subjected to various recorded ground vibrations from earthquakes. An RCC square water tank with 14-, 17-, and 20-meter staging heights was subjected to a Time History Analysis using StaadPro software to simulate the effects of five distinct seismic ground movements. Roof displacement, velocity, acceleration, base shear, drift, and natural frequency were measured and compared for three different tank fill levels: empty, halfway, and full. Bhuj earthquake ground motion was the strongest, whereas Kobe earthquake ground motion was the weakest.
An Innovative Condition Assessment Method for Wastewater Treatment Facilities to Promote Long-Term Sustainability in Management and Operations
Authors: Danu Ritiksha
Published: May 2023

Conventionally, the success of a wastewater treatment plant is evaluated by the quantity of chemical oxygen demand (COD), dissolved organic matter (BOD), total suspended solids (TSS), and other consequences of wastewater treatment that are removed during the treatment process and thereafter. Environmental engineers consider a number of parameters at the plant's discharge, including pH, NH4-N, NTotal, fecal coliform, and others. The traditional approach to performance assessment fails because it does not directly compare the effluent's distribution of these characteristics throughout the output stage of the process to the standards or specification limits issued by the Central Pollution Control Board (CPCB). To fill this knowledge gap, we propose and implement the probability-based Process Capability Indices (PCIs) and Multi - variate Process Flow Indices (MPCIs) in this research. These indices measure the effectiveness of a wastewater treatment process by contrasting the observed results with the predicted ones. PCIs have been widely used as a baseline against which manufacturing processes may be evaluated and tweaked to increase efficiency. The focus of this effort is on PCIs and MPCIs from the standpoint of environmental engineers, with the hope that their use would grow. Using capacity indicators accurately measures the effectiveness of the process of wastewater treatment, which is essential for reducing pollution and permitting the reuse of treated water. This study provides an analysis of the treating wastewater process's capacity by applying appropriate capability indices, using additional information acquired from case studies via literature research. Findings suggest that appropriate capacity indices may allow for more precise assessments of sewage treatment system performance than are presently possible.
Using Data Mining to Improve Healthcare Decision-Making: A Systematic Review
Authors: Mishra Amit Kumar
Published: May 2023

Data mining is a process utilized to analyze large databases to find patterns and relationships. It involves using various techniques such as machine learning and statistical analysis to identify trends and patterns. Due to the massive amount of medical data that is available, data mining has become an increasing trend in healthcare. This process involves extracting knowledge from these data sources. This review aims to analyze the current state of the research regarding the use of data mining in the healthcare industry. This review thoroughly searched for studies related to various aspects of healthcare diagnosis and prognosis. The results indicated that data mining techniques can help improve the accuracy of diagnoses and the prediction of treatment outcomes.  Although the review acknowledged the potential of data mining in improving the quality of healthcare, it also highlighted various issues that need to be resolved in order for it to be successful. These include the need for standardization and privacy concerns. Despite these obstacles, the potential of this technology is still immense. The findings of this review provide a comprehensive analysis of the current state of the art in data mining in healthcare. It shows the potential of this technology to improve the quality of healthcare and inform clinical decisions. Future research must address the issues identified in this review in order to advance the use of this technology in the field.
A Decentralized and Privacy-Preserving Data Sharing Framework using Blockchain Technology
Authors: Garg Umang
Published: May 2023

Sharing data has evolved into an essential component of today's society as a direct result of the proliferation of new technologies and the growing prevalence of digitalization. Traditional methods of data sharing, on the other hand, frequently encounter considerable hurdles in terms of both privacy and security.With its immutability and indestructibility, blockchain technology has emerged as the most reliable all-in-one cryptosystem for protecting online transactions. Some businesses, IT managers, and programmers have taken an interest in the blockchain network because of its potential as a decentralized infrastructure. In addition to safeguarding transactions from modification, this solution also serves as a historical audit trail for the network. This paper presents a decentralized data sharing architecture that uses blockchain technology and protects users' privacy. The goal is to address the problems that have been identified. The framework that has been proposed makes use of a combination of cryptographic methods and the technology of blockchains in order to enable secure, efficient, and decentralized data sharing while still maintaining users' anonymity. In order to improve data privacy and scalability, the framework makes use of a hybrid blockchain method. This technique incorporates the positive aspects of both public and private blockchains. The usefulness of the suggested framework in maintaining data privacy and security while also enabling efficient and decentralized data exchange is demonstrated by the results of an evaluation that uses a prototype implementation as the basis for the evaluation.
Medical Data Analytics: Techniques, Challenges, and Opportunities
Authors: Agnihotri Aditya
Published: May 2023

The use of medical data analytics has evolved into an indispensable tool in the healthcare industry, with the goals of enhancing patient outcomes, lowering costs, and accelerating medical research. This article gives an overview of the many methods, problems, and possibilities that are involved in medical data analytics. The diagnosis of diseases, the search for new medications, and the treatment of patients are just some of the areas of medicine that have benefited from the use of a variety of data mining and machine learning methods, such as clustering, decision trees, and deep learning. Yet, there are still obstacles to overcome, such as issues around privacy and the interpretability of the data. Possibilities for medical data analytics include the combination of human and artificial intelligence in medical treatment as well as the incorporation of large-scale data analysis into clinical practise. Moreover, case studies on medical data analytics as well as future developments in the area are presented in this study. Overall, medical data analytics has a great deal of untapped potential to revolutionise healthcare; nevertheless, more work is required to surmount existing obstacles and fully capitalise on the opportunities it presents.
IoT-based Smart Farming Systems: Techniques, Challenges, and Applications
Authors: Bhatt Chandradeep
Published: May 2023

The ability of IoT-based smart farming systems to increase crop yields, decrease waste, and increase sustainability is causing them to gain popularity in the agricultural sector. The methods utilized in IoT-based smart farming systems, the difficulties involved in putting them into practice, and successful applications in agriculture are all covered in this research paper's summary. The report also examines new developments in IoT-based smart farming technology and their prospective effects on the agricultural sector. IoT-based smart agricultural systems have many advantages, but they also have drawbacks in terms of cost, technical know-how, connectivity, data privacy, and security. This study explores the advantages and difficulties of IoT-based smart agricultural systems and emphasizes the necessity of cooperation between farmers, technology companies, and governments to meet these difficulties. IoT-based smart agricultural systems appear to have a bright future, but further study is required to fully realize their potential in agriculture.
Thermodynamic Analysis and Comparison of Two Marine Steam Propulsion Turbines
Authors:   University of Zadar, Department of Maritime Sciences Zadar, Croatia, Poljak Igor , Mrzljak Vedran
Published: May 2023

This paper presents thermodynamic (energy and exergy) analysis and comparison of two different marine propulsion steam turbines based on their operating parameters from exploitation. The first turbine did not possess steam reheating and had only two cylinders (high-pressure and low-pressure cylinders), while the second turbine possesses steam reheating and has one additional cylinder (intermediate-pressure cylinder). In the literature at the moment, there cannot be found a direct and exact comparison of these two marine steam turbines and their cylinders based on real exploitation conditions. Along with energy and exergy analyses, the research it is investigated the sensitivity of exergy parameters to the ambient temperature change for both turbines and each cylinder. It is also presented the influence of the steam reheating process on the energy and exergy efficiency of the entire power plant. For both observed turbines and their cylinders it is valid that relative losses and efficiencies (both energy and exergy) are reverse proportional. The operation of an intermediate pressure cylinder from a steam turbine with reheating is the closest to optimal. Due to the different origins of losses considered in energy and exergy analyses, each analysis detects different turbine cylinders as the most problematic ones. The steam reheating process decreases losses and increases efficiencies (both energy of each turbine cylinder and the whole turbine. The whole turbine with reheating has an energy efficiency equal to 81.46% and an exergy efficiency equal to 86.48%, while the whole turbine without reheating has energy and exergy efficiencies equal to 76.47% and 80.94%, respectively. Exergy parameters of a steam turbine without reheating as well as its cylinders are much more influenced by the ambient temperature change in comparison to the steam turbine with reheating and its cylinders. The steam reheating process will increase the efficiency of the whole power plant in real exploitation conditions between 10% and 12%.
Hindcast of Significant Wave Heights in Sheltered Basins Using Machine Learning and the Copernicus Database
Authors:   University of Zagreb, Faculty of Civil Engineering, Bujak Damjan , Carević Dalibor , Bogovac Tonko , Kulić Tin
Published: May 2023

Long-term time series of wave parameters play a critical role in coastal structure design and maritime activities. At sites with limited buoy measurements, methods are used to extend the available time series data. To date, wave hindcasting research using machine learning methods has mainly focused on filling in missing buoy measurements or finding a mapping function between two nearshore buoy locations. This work aims to implement machine learning methods for hindcasting wave parameters using only publicly available Copernicus data. Ensemble regression and artificial neural networks were used as machine learning methods and the optimal hyperparameters were determined by the Bayesian optimization algorithm. As inputs, data from the MEDSEA reanalysis wave model were used for the wave parameters and data from the ERA5 atmospheric reanalysis model were used for the wind parameters. The results of this study show that the normalized RMSE of the test data improved by 29% for Rijeka and 12% for Split compared to the original MEDSEA wave hindcast at buoy locations. The proposed method was extremely efficient in removing bias in the original MEDSEA hindcasts (e.g., NBIAS = -0.35 for Rijeka) to negligible values for both Split and Rijeka (NBIAS < 0.03).
University Student Satisfaction A Systematic Literature Review
Authors: Khairusy Mirza Abdi , Febriani Reni
Published: May 2023

Student satisfaction can indicate the overall university experience, making it a contentious topic in the higher education literature. For this reason, the author conducted systematic literature discussing Student Satisfaction. This review aims to provide the available constructive literature on student satisfaction with a theoretical and empirical background published during the past 5 years, from January 2017- January 2022, to identify student satisfaction definitions and factors influencing student satisfaction. The author searched for articles in three databases (Scopus, Ebsco, and Proquest). The contribution of this research becomes a reference in understanding and determining the factors of student satisfaction in higher education.