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Enhancement in air-cooling of lithium-ion battery packs using tapered airflow duct
Temperature uniformity and peak-temperature reduction of lithium-ion battery packs are critical for adequate battery performance, cycle life, and safety. In air-cooled battery packs that use conventional rectangular ducts for airflow, the insufficient cooling of cells near the duct outlet leads to temperature nonuniformity and a rise in peak temperature. This study proposes a simple method of using a converging, tapered airflow duct to attain temperature uniformity and reduce peak temperature in air-cooled lithium-ion battery packs. The conjugate forced convection heat transfer from the battery pack was investigated using computational fluid dynamics, and the computational model was validated using experimental results for a limiting case. The proposed converging taper provided to the airflow duct reduced the peak temperature rise and improved the temperature uniformity of the batteries. For the conventional duct, the boundary layer development and the increase in air temperature downstream resulted in hotspots on cells near the outlet. In contrast, for the proposed tapered duct, the flow velocity increased downstream, resulting in improved heat dissipation from the cells near the outlet. Furthermore, the study investigated the effects of taper angle, inlet velocity, and heat generation rate on the flow and thermal fields. Notably, with the increase in taper angle, owing to the increase in turbulent heat transfer near the exit, the location of peak temperature shifted from the exit region to the central region of the battery pack. The taper-induced improvement in cooling was evident over the entire range of inlet velocities and heat generation rates investigated in the study. The peak temperature rise and maximum temperature difference of the battery pack were reduced by up to 20% and 19%, respectively. The proposed method, being effective and simple, could find its application in the cooling arrangements for battery packs in electric vehicles. 2023 Y?ld?z Technical University. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). All Rights Reserved. -
AI and Machine Learning Enabled Software Defined Networks
The telecommunications industry has not been exempt from the technology sectors massive artificial intelligence (AI) and machine learning (ML) boom in recent years. Artificial intelligence (AI) and machine learning (ML) provide advanced analytics and automation that are in line with modern networking concepts like software-defined networking (SDN) and software-defined wide-area networks (SD-WAN). Work is being done to determine how AI/ML can benefit SD-WAN and to demonstrate these benefits in a real SD-WAN network using a workable example. Modern ML techniques and algorithms are the extent of AI/ML. Todays Internet is under constant threat from DDoS (Distributed Denial of Service) attacks. As the volume of Internet traffic grows, its getting harder and harder to tell whats legitimate and whats malicious. The DDoS attack was detected using a machine learning approach that makes use of a Random Forest classifier. To better detect DDoS attacks, we tweak the Random Forest algorithm. The proposed machine learning approach outperforms, as demonstrated by our results. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Potent of sales-persons, impact on the channel of distribution in lighting industry in bangalore
Its found in array of literature on the roles, functioning of the sales persons and also illuminates how these are measured on effectiveness of channel of distribution. This study made with objective for better understanding of various variables, and out of which primary factors that could be focused for effectiveness of channel of distribution in lighting industry in Bangalore from the perceptive of intermediaries. This study draws the responses from intermediaries who are pivotal force (opinion leaders) in the market, which could prove more deep understanding for strategizing the channels in the said industry. From the review of literature we streamlined the functions performed for potent of sales persons. Further analysed with vivid using various statistical tools to understand loads (Eigen value), hence, prompting with Principal Component Analysis. This study is uses all normative way to analyse of the results reframed pivotal factors, in classifying, draining out insignificant factors. By regrouping based on the array of load, we come to understand 3 vital ingredients viz., 1) intermediaries appointment criteria 2) sales training& communication 3) concern for cost and needs of intermediaries, and urging to business institutions to opt for better channel strategy. Notwithstanding, the relationship with intermediaries are charismatic in nature, and dynamics of channel strategy would and will be determinant for success of any business organisation. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Biomass-derived carbonaceous materials: Synthesis and photocatalytic applications /
Novel Applications of Carbon Based Nano-materials, 1st ed., pp.412-429, eBook ISBN : 9781003183549. -
Flavonol based surface modification of doped chalcogenide nanoflakes as an ultrasensitive fluorescence probe for Al3+ ion
A highly selective novel fluorescent probe was prepared by using surface modified ZnS:Mn nanoparticles, functionalized with morin, a flavonol. SEM investigations of the heterostructures prepared using wet chemical precipitation technique revealed a nanoflake type of morphology. HR-TEM and powder XRD analysis confirmed the crystalline planes corresponding to Wurtzite ZnS. The functionalized nanoparticles were characterized using Raman, XPS and FTIR which confirms the binding of morin to the nanoparticles via surface coordination. The prepared probe selectively interacts with Al3+ ions which has been used as an ultrasensitive analytical tool for determination of Al3+ ions. A major advantage of the proposed method is that the other metal ions closely associated with Al3+ did not interfere with the analysis. The detection limit and the quantitation limit were found to be 0.07 nM and 0.20 nM respectively with a linear dynamic range 0.20 nM80 nM. The method was successfully applied to environmental water samples and other complex matrices. 2017 Elsevier B.V. -
Biomass-derived carbonaceous materials: Synthesis and photocatalytic applications
[No abstract available] -
Mathematics Self-Efficacy, Utility Value and Well-Being Among School Students in India: Mediating Role of Student Engagement
Teaching and learning mathematics has many challenges, including student engagement, attitudes and beliefs toward mathematics. Students experience stress and anxiety while learning mathematics. Mathematics is perceived as a complex subject. Student self-efficacy and a sense of utility value of mathematics topics can impact student learning and well-being. The current study aims to examine the mediating role of student engagement on the relationship between mathematics self-efficacy, utility value and well-being among students. A cross-sectional survey of 774 eighth-grade students (491 male and 283 female) from India was carried out using standardized scales to measure the study variables. The mediation analysis tested two conceptual models. The findings indicate that student engagement mediates the relationship between self-efficacy and student well-being (model 1), and student engagement mediates the relationship between utility value and student well-being (model 2). The structural equation model results indicate an acceptable fit of the tested conceptual models. The study findings call for focusing on socio-emotional aspects of mathematics learning to improve the well-being of students. 2023 Research Council on Mathematics Learning. -
Student Perceptions and Experiences in Mathematics Classrooms: A Thematic Analysis
Classroom experiences contribute to learners' perceptions and interest in a particular subject. The present study aims to understand students' perception of mathematics learning by exploring their classroom experiences. The study sample consisted of 17 eighth-grade students in English-speaking urban schools in South India. The data was collected through a semi-structured interview schedule. The thematic analysis presents five themes student personal factors, teacher-related, content-related, classroom environment and utility value. Teachers characteristics and mathematics content were the essential factors contributing to students' perceptions and experiences. The study highlights the utility value of the content to help students see the application of the subject in real-world situations. Understanding students' perception of mathematics learning would help to choose appropriate content and teaching methods in the curriculum. The study highlights the need for educational and psychological interventions, focusing on student-teacher engagement and curriculum development to enhance mathematics learning. (2023). All Rights Reserved. -
Classroom mathematics learning: Association of joy of learning and school connectedness among high school students in India
Mathematics learning experiences can influence the overall academic and socio-emotional development of a child. The present study investigates the mediating effect of mathematics anxiety and emotional engagement on the relationships between teacherstudent interaction, the joy of learning, and school connectedness. Two mediation models were tested for the dependent variables: the joy of learning and school connectedness, using Hayes' process macro in SPSS on a sample of 774 eighth-standard students from Indian schools. The study's results indicate the presence of a serial mediation effect on the relationship between teacherstudent interaction and joy of learning, teacherstudent interaction, and school connectedness through mathematics anxiety and emotional engagement. The study emphasized the role of mathematics learning within the overall framework of joy of learning and school connectedness.. 2024 Wiley Periodicals LLC. -
Python's role in predicting type 2 diabetes using insulin DNA sequence
This chapter examines how Python can assist in predicting type 2 diabetes using insulin DNA sequences, given the substantial problem that biologists face in objectively evaluating diverse biological characteristics of DNA sequences. The chapter highlights Python's various libraries, such as NumPy, Pandas, and Scikit- learn, for data handling, analysis, and machine learning, as well as visualization tools, such as Matplotlib and Seaborn, to help researchers understand the relationship between different DNA sequences and type 2 diabetes. Additionally, Python's ease of integration with other bioinformatics tools, like BLAST, EMBOSS, and ClustalW, can help identify DNA markers that could aid in predicting type 2 diabetes. In addition, the initiative tries to identify unique gene variants of insulin protein that contribute to diabetes prognosis and investigates the risk factors connected with the discovered gene variants. In conclusion, Python's versatility and functionality make it a valuable tool for researchers studying insulin DNA sequences and type 2 diabetes prediction. 2023, IGI Global. All rights reserved. -
Automatic fertilizer dispenser robot /
Patent Number: 354319-001, Applicant: Ravikumar R. -
Systematic Literature Review on Industry Revolution 4.0 to Enhance Supply Chain Operation Performance
Industry 4.0 is a notion in which industries automate systems and processes, innovate digitally, and share information. It aims to obtain a smart factory in an attempt to lessen required time in responding to consumer demand or unexpected circumstances and to enhance organizational productivity. The integration of Industry 4.0 and supply chain management (SCM) ensures immense development opportunities for manufacturing firms. This article provides a systematic literature review and formulation of the existing research on Industry 4.0 in SCM, resulting in some intriguing analyses that will be useful to academics and industry, particularly top managers. The content of the article is classified into three categories: exploratory vs. confirmatory, qualitative vs. quantitative, and management level vs. technology level. The findings will benefit managers in understanding the significance of Industry 4.0 and its relationship with SCM. The formation of clusters and their affiliations has resulted in the emergence of new areas requiring managerial attention. The article concludes by examining the possibilities of the present and future research. 2022 ACM. -
Future Innovation in Healthcare by Spatial Computing using ProjectDR
Spatial Computation is the next step in the continuing convergence between the digital and physical realms. It is a set of inventions and developments that can better our lives through learning the real world, acknowledging and connecting our connection to, and traveling through various locations in the world. The lack of modern, precise, and effective diagnosis limits the rehabilitation of patients, despite technical advancements in medicines. The capabilities of spatial computing are expanded in a healthcare framework during the care and treatment of the patient. In this article, our purpose is to clarify the function of ProjectDR in the field of healthcare, which enables the display of medical images, such as CT scans and MRI results, directly on the patient's body in a manner that moves as patients do. 2021 IEEE. -
COBOTS: Vital role in significant domains
The term COBOT refers to "collaborative robot, " which is created by combining humans and robots to increase the efficacy and efficiency of industrial processes. Cobots have extensive applications in various sectors, including healthcare, motoring, production, electronics, space exploration, logistics, and astronomy. Industry 5.0 is a development that aims to combine human specialists' creativity with accurate, intelligent, and efficient technologies to revolutionize manufacturing processes worldwide. Therefore, in the age of Industry 5.0, there is a great demand for Cobots with high, quick advancement, and low costs. Industry evolution, fundamentals of Cobots, how they differ from robots, key features, basic components, the significant role of Cobots in Industry 5.0, challenges and limitations, future scope, and ethical aspects of Cobots are covered in this chapter. This book chapter is a comprehensive manual for academic researchers and corporate executives to learn about Cobots completely. 2024, IGI Global. All rights reserved. -
Blurred Image Processing and IoT Action Recognition in Academy Training Sport
Smart wearable technologies utilising devices connected to the web (IoT) are on the rise, and many of these new applications involve the identification of athletic performance. Many people across the world participate in soccer, also called football in some regions. Soccer players practise discrete actions (like shooting and passing) in order to ingrain them in muscle memory and speed up their reflexes during actual games. There is always a compromise between blur and noise when processing images. Denoising naturally softens an image because noise is high-frequency information. Deblurring, on the other hand, causes additional noise in the final product. The need to brighten an image in low-light conditions only adds to the difficulty. Noise is introduced into the image during the brightening process itself. Images taken while moving, especially those of wildlife (though not exclusively), will have more blur than those taken while still. Many previous projects have focused on a single problem, but very few have attempted to address the entire set of problems simultaneously. So, we set out to make a way to turn these lowlight, fuzzy images into high-contrast, clear images. A fuzzy invariant space is the result of the union of several fuzzy invariant spaces. After numerous iterations of processing a blurred image, the final stage is to utilise a progressive restoration procedure. The experimental findings demonstrate the effectiveness of the suggested technique in reducing calculation error, improving the recovery effect, and avoiding the noise caused by numerous deconvolutions. This work introduces new concepts and methods for recognition research by applying fuzzy image processing to the study being human mobility and the detection of activities in the realm of IoT. Using the Kinect, an IoT somatosensory camera, we are able to collect 15 3D skeletal elements via its software development kit (SDK). This led to the study of kinesiology and the creation of a motion resolution model that works well with the Internet of Things. 2022 IEEE. -
Improved Deep Learning Model for Detection and Classification of Pneumonia from X-Ray Images
Pneumonia is a severe respiratory disease that can lead to inflammation, fluid accumulation in lungs and breathing difficulties, which needs immediate and accurate diagnosis. Chest X-Ray images are a necessary tool to diagnose pneumonia because manual interpretation poses challenges, particularly for radiologists with less expertise. Artificial intelligence (AI), specifically Convolutional Neural Networks (CNNs), has become a significant in the field of pneumonia detection within chest X-Ray images in recent years. This research presents SarNet, a neural network model developed for the identification of pneumonia in chest X-Ray images. The study involved the compilation of dataset containing chest X-Ray images categorized as normal, pneumonia, and COVID-19 pneumonia cases, each accompanied by appropriate annotations. This dataset was employed as the basis for training and assessing SarNet's performance, underscoring its promise in transforming the diagnosis of pneumonia. SarNet proved highly effective, achieving good accuracy, sensitivity, and specificity compared to traditional diagnostic methods. The model's simplicity, with 41 layers, strikes a balance between depth and computational complexity, enhancing efficiency and ensuring accurate pneumonia detection. Furthermore, the study expanded its scope to include COVID-19 pneumonia detection. SarNet achieved an accuracy of 99.15% in binary classification and 94.9% in multiclass classification, including healthy, pneumonia, and COVID-19 pneumonia cases. -
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Facial emotion recognition using convolutional neural networks
Emotional expressivity has always been a simple job for people, but computer programming is much harder to accomplish. Image emotions may be recognised by recent developments in computer vision and machine learning. In this article, we present a new method to detect face emotion. Use neural networks convolutionary (FERC). The FERC is based on a CNN network of two parts: the first portion removed the backdrop of the image, the second part removed the face vector. The expressional vector (EV) is utilised in the FERC model to detect the fve different kinds of regular facial expressions. The double-level CNN is continuous and the weights and exponent values of the final perception layer vary with each iteration. In that it improves accuracy, FERC varies from widely utilised CNN single-level technology. Moreover, EV generation prevents the development of a number of issues before a new background removal process is used (for example distance from the camera). 2021 -
The Latest Technology and its Integration for the Development of Healthcare(Medical)
Healthcare advances that use Artificial Intelligence (AI) to analyze data, use devices, and identify patients offer new possibilities for better patient care, cutting costs, and growing the medical sector. The age of specialized human health tests has begun. It uses noninvasive instruments, sound, visual the use of photography, electronic health tools, embedded health instruments, fluidic diagnostic tracking, and combined data analysis to provide people with tailored medical suggestions. These technologies contribute to early identification and comprehending of health issues linked to chronic illnesses and general health using information analysis and AI-driven ideas. Notable uses include a Parkinson's and Huntington's Under certain circumstances, diabetes, cancer, kidney disease, heart problems, elderly care, and a number of healthcare areas. Industry changes are expected as a result of the latest breakthroughs in outdoor monitors, AI-driven evaluation of data, and healthcare testing technologies. AI systems give data to people and health workers, possibly better their way of life and cutting healthcare costs. These include: tracking the effectiveness of medicines, finding chronic illnesses early, and offering individualized care using medical trends and DNA. In relation to healthcare studies and sensor tracking, this study explains new technologies and advances in diverse fusion methods, materials, and processes. Precise diagnostic info, small merchandise dimensions, and cost are high considerations. Healthcare workers, patients, consumers all benefit from more personal health care services thanks to the merging of AI with information streams. The text highlights both advantages and hurdles while showing the way toward upcoming displays and academic papers that follow a path of growth in the industry. 2024 IEEE.