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ANALYSIS, ASSESSMENT, AND MANAGEMENT OF ENVIRONMENTAL AIR POLLUTION USING ENVIRONMENTAL ENGINEERING IN DEVELOPING COUNTRIES
Recent studies underscore the value of contemporary technology and gas emissions mitigation while overlooking the necessity of optimal fuel in Developing Countries (DC). DC's historical economic expansion has significantly depended on fossil fuels, resulting in severe environmental air pollution (EAP) challenges. The separation of economic progress from pollution has been the central emphasis in advancing environmental civilization in emerging countries. This study presents an analysis, assessment, and management of EAP using environmental engineering (EE) in DC. This work has examined the evolution of EAP regulations in DC, emphasizing a strategic shift from emission regulation to Air Quality Management (AQM). The regulation of Sulfur dioxide (SO2) emissions addressed the worsening of acid rain in DC. Since 2015, regulatory measures across several sources and industries have aimed to decrease the total amount of Fine Particulate Matter (FPM2.5), signifying a shift towards an AQM-focused policy. Escalating ozone (O3) pollution necessitates integrated management measures for O3 and FPM2.5, focusing on their intricate photochemical reactions. Significant enhancement of AQM in DC, as a crucial metric for the efficacy of sustainable economic development, necessitates the profound carbon reduction of the DC's energy infrastructure and the establishment of more integrated strategies to tackle EAP and climate change in DC concurrently. 2024, Rotherham Academic Press Ltd. All rights reserved. -
The Impact of Artificial Intelligence on Digital Employee Engagement
Purpose: This study aimed to investigate the impact of artificial intelligence (AI) on digital employee engagement, focusing on the roles of job autonomy and digital learning orientation. It sought to understand how these factors influenced employee engagement in a digital environment and the extent to which the meaningfulness of work mediated these relationships. Design/Methodology/Approach: Data were collected from 527 individuals performing administrative jobs in the private service sector. The study utilized partial least squares structural equation modeling (PLS-SEM) to test the proposed relationships among job autonomy, digital learning orientation, and digital employee engagement with mediation of meaningfulness of work. Findings: The findings indicated that job autonomy and digital learning orientation significantly and positively predicted digital employee engagement. However, the meaningfulness of work did not mediate the relationship between job autonomy, digital learning orientation, and digital employee engagement. The results of this study found that there was no significant relationship between the meaningfulness of work and digital employee engagement. This study also found that when the employees used digital tools, they often experienced feelings of loneliness and insecurity. Practical Implications: The study suggested that the organizations role should always be focused on promoting digital tools. Organizations should emphasize enhancing job autonomy and encourage employees to engage in digital learning orientation, boosting digital employee engagement in the workplace. Originality/Value: This study contributed to the literature considering the role of AI applications that directly influenced digital employee engagement by addressing the significant roles of job autonomy and digital learning orientation. It also emphasized the need for future research to explore the impact of the meaningfulness of work and the dependence on digital tools for employee performance. 2024, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Big Data and Competition Law: A New Challenge for Competition Authorities
Big data has become a key role player for almost all kinds of markets specifically in a digital economy. It is a raw material as well as a by-product of any process. It has very comprehensive inclusivity to cover all aspects of the market having direct as well as indirect market effects. These effects are inclined towards consumerism and market transparency. But it has inherent dangers that are somehow overlooked by competition authorities. Competition law has dealt with the brick-and-mortar economy that is traditional in a very efficient way. However, this is not the case with the digital economy. Traditional notions of the market, abuse of dominant position, anticompetitive practices, and regulation of combinations cannot be made applicable to the digital economy in the same manner. Big data analytics enables big giants or corporations to establish their dominance in their relevant market. Google, Amazon, Facebook, and Apple have been dominating almost digital economy; hence their strategies are being scrutinized under the lenses of competition law once again. This paper deals with the interplay between big data and competition law, and it is going to explore the impact of this unavoidable aspect of big data on a highly competitive digital economy. 2024 Taylor & Francis. -
Transformative pedagogy integrating bloom's taxonomy, David Kolb's experiential learning and neuro-systemic dynamics in learning
An application of Kolb's experiential learning theory (ELT) in real situations as a blend of Bloom's taxonomy of education is given as a case study conducted about more than 15 years ago. The world has changed since then with revolutionary developments in the techno-world. Student portfolio is regarded as the documentary evidence created by the student to diagnose the student's strengths and weaknesses to assist him or her in learning. In light of this case study and developments in technology, further scope in the application of the ELT model is discussed. It is also recognised that technology to facilitate experiential learning using virtual reality or augmented reality tools is to be made more user-friendly in terms of affordability and that neuroplasticity has an important role to play in this. A case for more research in neuroscience to analyse learning styles is raised. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. All rights reserved. -
Theoretical Framework for Integrating IoT and Explainable AI in a Smart Home Intrusion Detection System
Using IoT devices in smart homes brings benefits and security dangers. This study extensively examines various intrusion detection methods within smart home environments. It also suggests a novel hybrid intrusion detection theoretical framework integrating IoT data with Explainable Artificial Intelligence (XAI) approaches. Using information from multiple IoT devices, including motion sensors, door/window sensors, cameras, and temperature sensors, our theoretical framework can create a comprehensive image of the home environment. By effectively detecting new threats, it offers anomaly detection utilizing unsupervised learning approaches to discover potential breaches without tagged data. 2024 IEEE. -
A Comprehensive Investigation of Blockchain Technology's Role in Cyber Security
In recent years, blockchain has become an extremely trending technology, capable of solving a variety of problems. One of these domains is cybersecurity, where blockchain technology has a huge scope. To dive deeper into this topic, we first need to understand the cybersecurity domain, the need for this field, and how it has become crucial to the current Information-Technology industry. Once we have a good understanding of the field of cybersecurity, we next focus on blockchain technology, its basic working process, and what makes it a trending infrastructural technology in today's world. The basic idea about the field of cybersecurity and blockchain technology can help us understand how the two different fields can be integrated to solve several problems in the cybersecurity domain. Eventually, we discuss the pros and cons of blockchain technology in cybersecurity and how the integration of the two different fields can make a difference. This study aims to explore various possibilities where blockchain technology can be utilized in several applications to solve a variety of problems in the field of cybersecurity. 2023 IEEE. -
Functional Foods: Exploring the Health Benefits of Bioactive Compounds from Plant and Animal Sources
"Let food be the medicine"(Hippocrates) is a historic quote that became the basis of food science and nutraceuticals. Due to their possible therapeutic advantages, extracts from food have attracted much interest in the medical community. These extracts are abundant in bioactive compounds, which are natural molecules that may be found in various foods and have been demonstrated to affect health positively. Food components have lots of bioactive components, including primary and secondary metabolites and nutritional components, for example, carbohydrates, proteins, vitamins, minerals, fatty acids, antioxidants, phenolics, and flavonoids. This study's primary focus is on the make-up and purpose of these bioactive components found in food extracts. This review aims to give readers a thorough grasp of the bioactive substances found in food extracts and their possible physiological uses. These bioactive substances' functional traits, such as their antioxidant, anti-inflammatory, antibacterial, anticancer, and neuroprotective actions, are also studied. Further research is required to create new functional foods, nutraceuticals, and dietary supplements with specific health advantages that can benefit from understanding these molecules' structure and function. 2023 Versha Dixit et al. -
Effect of heavy metal stress on biochemical and antioxidant efficacy of Chamaecostus cuspidatus
Chamaecostus cuspidatus, commonly known as insulin plant is medicinally important and a rich source of several secondary metabolites which exhibit pharmacological properties. In the present study, three different heavy metals (Pb, Cu and Cr) with different concentrations (Pb and Cr-50, 100, 150, 200 and 250 ppm and for Cu 25, 50, 75, 100 and 125 ppm) was used for heavy metal treatment and its impact on several biochemical and antioxidant parameters was measured of the test plant along with control. Current study mainly focuses on the biochemical and antioxidants estimation of root and rhizome of C. cuspidatus. Protein, proline and carbohydrate content was increased in the treated groups. Total phenol and total flavonoid content were also found to be increased in all the treated groups. Both enzymatic (SOD, CAT, APX) and nonenzymatic antioxidants (DPPH, FRAP and total antioxidant activity) was measured. Antioxidant activity was also high in the treated groups. Highest DPPH activity was found in Cu 25 treated rhizome 91.8030.157 and lowest was observed in Pb 50 treated root 4.5530.240. Highest reducing power activity (FRAP) was observed in Cr 100 treated rhizome 0.75860.0008 and least was found in control root 0.2090.0005. Heavy metals accumulation was also measured and maximum heavy metal accumulation was found in soil following by root and rhizome of all the treated groups. 2024, Indian journals. All rights reserved. -
A Case Study on Zonal Analysis of Cybercrimes Over a Decade in India
Human intelligence has transformed the world through various innovative technologies. One such transformative technology is the internet. The world of the internet, known as cyberspace, though powerful, is also where most crimes occur. Cybercrime is one of the significant factors in cybersecurity, which plays a vital role in information technology and needs to be addressed with high priority. This chapter is a case study where we analyze cybercrimes in India. The data collected from NCRB for 2010 to 2020 are a primary source for the analysis. A detailed analysis of cybercrime across India is done by dividing locations into seven zones: central, east, west, north, south, northeast, and union territories. Cybercrimes reported in each zone are examined to identify which zone requires immediate measures to be taken to provide security. The work also identifies the top ten states which rank high in cybercrime. The main aim of this chapter is to provide a detailed analysis of crimes that occurred and the measures taken to curb them. Along with the primary data, secondary data from CERT-In are also used to provide an analysis of measures taken for handling cybercrime over a decade. The outcome facilitates various stakeholders to better bridge the gap in handling cybercrime incidences, thus helping in incidence prevention and response services as well as security quality management services. 2023 selection and editorial matter, Narasimha Rao Vajjhala and Kenneth David Strang; individual chapters, the contributors. -
Child mental health: The role of different attributional styles
Background: High prevalence of mental health issues in the twenty-first century accounts for a lion share in the worldwide burden of disease. There is an alarming decrease in the onset of half of the mental health problems. Hence, it is necessary to explore the current situation and figure out the causes and preventive measures as well as the appropriate mental health enhancement measures. Individual characteristics, such as thinking patterns and perception, have an impact on the mental health. Attributional style is one source of cognitive vulnerability which influences mental health disorders. Therefore, the present study examines whether there are any variations in the mental health of children with different attributional styles. Methods: The current research adopted a cross-sectional research design and selected 150 school going students [74 males and 76 females] between 10-13 years of age as participants. The Child Attributional Style Questionnaire [CASQ], Satisfaction with Life Scale-Children [SWLS-C], Brief Resilience Scale, and Revised Child Anxiety and Depression Scale [RCADS] are used to gather information. Results: The results indicated that children with a pessimistic attributional style experienced more depression and generalized anxiety than children with other two attributional styles. In terms of gender differences in mental health, female students with pessimistic attributional style significantly differed from their counterparts on depression [?2 [2] = 10.131, p = 0.006] and separation anxiety [?2 [2] = 6.456, p = 0.040]. Conclusion: Attributional style seems to have a significant role in depression and anxiety in female children. Although male children did not show any statistically significant results, they were more likely to be pessimistic in terms of their attributional style, which makes them vulnerable to mental health issues. 2020, Indian Association for Child and Adolescent Mental Health. All rights reserved. -
Grey Wolf Optimization Guided Non-Local Means Denoising for Localizing and Extracting Bone Regions from X-Ray Images
The key focus of the current study is implementation of an automated semantic segmentation model to localize and extract bone regions from digital X-ray images. Methods: The proposed segmentation framework uses a pre-processing stage which follows convolutional neural network (CNN) obtained segmentation stage to extract the bone region from X-ray images, mainly for diagnosing critical conditions such as osteoporosis. Since the presence of noise is critical in image analysis, the X-ray images are initially processed with a grey wolf optimization (GWO) guided non-local means (NLM) denoising. The segmentation stage uses a Multi-Res U-Net architecture with attention modules. Findings: The proposed methodology shows superior results while segmenting bone regions from real X-ray images. The experiments include an ablation study that substantiates the need for the proposed denoising approach. Several standard segmentation benchmarks such as precision, recall, Dice-score, specificity, Intersection over Union (IOU), and total accuracy have been used for a comprehensive study. The proposed architectural has good impact compared to the state-of-the-art bone segmentation models and is compared both quantitatively and qualitatively. Novelty: The denoising using GWO-NLM adaptively chose the denoising parameters based on the required conditions and can be reused in other medical image analysis domains with minimal finetuning. The design of the proposed CNN model also aims at better performance on the target datasets. 2023 Biomedical & Pharmacology Journal. -
Transfer Learning-Based Osteoporosis Classification Using Simple Radiographs
Osteoporosis is a condition that affects the entire skeletal system, resulting in a decreased density of bone mass and the weakening of bone tissues micro-architecture. This leads to weaker bones that are more susceptible to fractures. Detecting and measuring bone mineral density has always been a critical area of focus for researchers in the diagnosis of bone diseases such as osteoporosis. However, existing algorithms used for osteoporosis diagnosis encounter challenges in obtaining accurate results due to X-ray image noise and variations in bone shapes, especially in low-contrast conditions. Therefore, the development of efficient algorithms that can mitigate these challenges and improve the accuracy of osteoporosis diagnosis is essential. In this research paper, a comparative analysis was conducted Assessing the accuracy and efficiency of the latest deep learning CNN model, such as VGG16, VGG19, DenseNet121, Resnet50, and InceptionV3 in detecting to Classify Normal and Osteoporosis cases. The study employed 830 X-ray images of the Spine, Hand, Leg, Knee, and Hip, comprising Normal (420) and Osteoporosis (410) cases. Various performance metrics were utilized to evaluate each model. The findings indicate that DenseNet121 exhibited superior performance with an accuracy rate of 93.4% Achieving an error rate of 0.07 and a validation loss of only 0.57 compared with other models considered in this study. 2023, International journal of online and biomedical engineering. All Rights Reserved. -
Diagnosis of Osteoporosis from X-ray Images using Automated Techniques
Osteoporosis is Bone Disease most commonly seen in aged people due to various food habits and life style habits. The bone becomes so brittle and weak which may break just from a fall. So, it is required to attend this Issue as there are various challenges faced by medical domain to identify and treat Osteoporosis. In this paper we focus on identifying and detecting osteoporosis using X-ray images using modified U-net Architecture using Residual Block and skip connections and done comparison study with existing models, as per state-of-art our model outcomes issues in existing model and obtain better accuracy. 2022 IEEE. -
Assesment of bone mineral density in X-ray images using image processing
X-ray application in medical fields has given rise to various research challenges related to bone, due to its wide usage in finding out the disease related to human anatomy. It has lot of research challenges to solve using available wide application of medical imaging techniques and inspired by this, a novel X-ray images based survey was conducted to understand the role of Xray images in medical field. Bone mass density identification is the standard procedure to monitor the risk of fracture in bone using DEXA. Lot of research has been carried out to calculate BMD using X-ray images and it provided prominent results. Since Xray is economically affordable and very economical compared to DEXA, we have decided to work on X-ray images. This paper explains us about various current advancements and disadvantages with respect to X-ray image in medical sector and various techniques related to BMD calculation. X-ray images characteristics and its fundamentals in the medical field for identifying bone related diseases are also discussed. 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth. -
Effectiveness of Learning Management System (LMS) in Sustainable Learning and Development among Bank Employees
Learning Management Systems in the form of E-Learning platform is currently an evolving scenario for the primary means of delivering various courses across educational, business, industries and vocational learning environments in the form of Learning and Development activities in all the sectors. LMS is a challenging and resource-intensive task requires demanding substantial knowledge, time, and effort. Consequently, there emerged a necessity in both research and practical applications to establish the personalized usage process of an LMS. Despite its significant impact on the outcomes of such an Information System (IS), the usage process has to be analysed. The researcher developed a conceptual model to delineate with set of factors to influence LMS course in Learning and Development Practices in industry context. Researcher revealed specific set of factors such as interface design, content presentation format, transfer of learning, and feedback mechanisms significantly impact learner satisfaction among Bank employees in their Learning and Development activities. Moreover, learner satisfaction depends on the application platform and content. The findings offer a valuable insight to design a corporate education system, with the quality content delivery and practical delivery. By considering these results, designers can develop more integrated and effective LMS to cater the needs and satisfaction of Learning and Development activities among Bank employees. 2024, Creative Publishing House. All rights reserved. -
Analysis of Online In-Destination Booking Service Processes in the Travel Industry: A Case Study
This article presents a comprehensive analysis of the online in-destination booking service processes within the dynamic landscape of the travel industry. Utilizing a case study approach, the research investigates the various stages involved in providing travel-related services, focusing on the key players. The study employs a quantitative method to assess the information quality, system quality, service quality, customer satisfaction, and purchase intention of online in-destination booking. The research highlights the investigation of the usability of online travel booking systems and identifies the purchase intention of customers towards online travel booking websites. To address the research objectives, the participants are selected using a nonprobability sampling method. The sample size of the study is 225 from in and around Coimbatore. The sampling procedure used is convenience sampling. The sampling is selected based on convenience and accessibility to the residents. The findings reveal that there exists a significant difference in respondents opinions on quality criteria: system quality and service quality. Additionally, the study finds that the loading time of online travel booking websites is positively correlated with quality criteria and features of travel apps. By examining a specific case within the travel sector, this study contributes valuable insights that can inform strategic decision-making for businesses operating in the online in-destination booking space. The results aim to guide industry players in enhancing their operational efficiency, leveraging technology advancements, and aligning their services with evolving customer expectations, ultimately fostering sustainable growth in the competitive travel market. 2024, Bentham Books imprint. -
Zero forcing number of degree splitting graphs and complete degree splitting graphs
A subset Z V(G) of initially colored black vertices of a graph G is known as a zero forcing set if we can alter the color of all ver- tices in G as black by iteratively applying the subsequent color change condition. At each step, any black colored vertex has exactly one white neighbor, then change the color of this white vertex as black. The zero forcing number Z(G), is the minimum number of vertices in a zero forcing set Z of G (see [11]). In this paper, we compute the zero forcing num- ber of the degree splitting graph (DS-Graph) and the complete degree splitting graph (CDS-Graph) of a graph. We prove that for any simple graph, Z[DS(G)] k + t, where Z(G) = k and t is the number of newly introduced vertices in DS(G) to construct it. 2019 Sciendo. All rights reserved. -
3-Sequent achromatic sum of graphs
Three vertices x,y,z in a graph G are said to be 3-sequent if xy and yz are adjacent edges in G. A 3-sequent coloring (3s coloring) is a function ?: V (G) ?{1, 2,...,k} such that if x,y and z are 3-sequent vertices, then either ?(x) = ?(y) or ?(y) = ?(z) (or both). The 3-sequent achromatic number of a graph G, denoted ?3s(G), equals the maximum number of colors that can be used in a coloring of the vertices' of G such that if xy and yz are any two sequent edges in G, then either x or z is colored the same as y. The 3-sequent achromatic sum of a graph G, denoted a'3s(G), is the greatest sum of colors among all proper 3s-coloring that requires ?3s(G) colors. This research initiates the study of 3-sequent achromatic sum and finds the exact values of this parameter for some known graphs. Furthermore, we calculate the a'3s(G) of corona product, Cartesian product of the graphs and some important results have been proved and a comparative study is carried out. 2021 World Scientific Publishing Company. -
Cop-edge critical generalized Petersen and Paley graphs
Cop Robber game is a two player game played on an undirected graph. In this game, the cops try to capture a robber moving on the vertices of the graph. The cop number of a graph is the least number of cops needed to guarantee that the robber will be caught. We study cop-edge critical graphs, i.e. graphs G such that for any edge e in E(G) either c(G?e) < c(G) or c(G?e) > c(G). In this article, we study the edge criticality of generalized Petersen graphs and Paley graphs. 2023 Azarbaijan Shahid Madani University. -
Early prediction of lungs cancer by deep learning algorithms from the CT images with LBP features
The early prediction of the any type of cancer can save the lives of many especially if it is lung cancer which is one of the deadly diseases in the world. Thus the early prediction is implemented we can increase life expectancy and bring the mortality level low. Although there are various methods to detect the lung cancer cells by X-ray and CT scans, however the CT images are more preferred. The 2D images like CT scans are used to get medical results more accurate. The proposed method here will discuss how the LBP features are used to analyze the CT images with the support of Deep Learning methods. In this research work we will discuss how the image manipulation can be done to achieve better results from the CT images through various image processing methods. LBP features helps in estimating the distribution of local binary pattern of an image. A final result with 93% is achieved after the training of the processed images by LBP features. 2020 SERSC.