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Impact of the operational parameters of a dual fuel engine operating on a blend of Water Hyacinth biodiesel and Mesua ferrea biodiesel with hydrogenA clean development mechanism
The study was conducted to uncover the emission, combustion, and performance features of the blend of Water Hyacinth biodiesel and Mesua Ferrea seed oil biodiesel with Hydrogen addition on a diesel engine in dual fuel. Pilot fuel is a blend of 50% Water Hyacinth biodiesel and 50% Mesua Ferrea seed oil biodiesel. A single-cylinder compression ignition engine was modified to operate on dual fuel mode with hydrogen. Variations of engine operating parameters such as injection timing, and engine load were performed. The study was conducted with three pilot fuel injection timings (23, 26, and 29bTDC) and variable engine loadings (20%100% with an increment of 20%) at an injection pressure of 200 bar and compression ratio of 18. The results indicated that the maximum brake thermal efficiency of 28.11% and a replacement of liquid fuel by 85% was obtained for the WHMF blend powered dual fuel diesel engine at pilot fuel injection timings of 26bTDC at 100% load. HC, CO, and smoke emissions are reduced with hydrogen due to faster combustion. On the other hand, there was a slight increase in NOx emissions noticed with hydrogen enrichment. 2024 Hydrogen Energy Publications LLC -
Creation of Bookshelf Using Autodesk 3ds Max: 3D Modelling and Rendering
The step-by-step process of creating a bookshelf design is specified, including the ProBoolean compound primitive, applying edit poly modifier, using detach option, making use of lattice modifier, using bend modifier, using twist modifier. The manner in which materials are added to the model, together with environment lighting and renderer configuration, is defined. Procedures and methods for rendering are also defined. What we aim to achieve through our research is to create a Bookshelf design that uses materials to enhance the models. The shapes used in the model were Box, Teapot, sphere, chamfercyl, Oiltank, ProBoolean compound. The modifiers used were edit poly, bend, twist, lattice. Afterwards we used the Arnold light and material editor to enhance and glorify the model. 2023 IEEE. -
A Non-Linear Approach to Predict the Salary of NBA Athletes using Machine Learning Technique
Every sportsman traded/drafted receives monetary compensation in accordance with their contract. In this study, we propose a nonlinear approach based on performance and other aspects to determine the salary of a basketball player. We estimate the salary based on four regressive models. Whilst predicting we also Figure out the important features impacting the salary. Comparatively speaking, random forest outperformed other algorithms. Furthermore, we consider that our findings might benefit discussions between basketball teams and players. This model can also help set a benchmark for salary expectations by the players in accordance. 2022 IEEE. -
RCBAM-CNN: Rebuild Convolution Block Attention Module-based Convolutional Neural Network for Lung Nodule Classification
Lung cancer remains the leading cause of cancer-related deaths worldwide. Pulmonary nodules, indicative of tumor growth, present significant diagnostic challenges due to their varying sizes and shapes. Computed Tomography (CT) is commonly used for lung cancer screening due to its high sensitivity and efficacy in detecting these nodules. However, differentiating between benign and malignant nodules can be difficult due to their overlapping characteristics. To address this challenge, we propose a Rebuild Convolution Block Attention Module-based Convolutional Neural Network (RCBAM-CNN) designed to accurately classify lung nodules from CT scans. The RCBAM-CNN integrates a Rebuild Convolution Block Attention Module (RCBAM), which includes reshaped layers and redefined spatial attention mechanisms to enhance the networks focus on relevant features while minimizing noise. The performance of the proposed method is evaluated using the LIDC-IDRI dataset. Data augmentation techniques, including rotation, rescaling, and both vertical and horizontal flips, are applied to improve the models robustness and generalization. Subsequently, U-Net is employed for precise image segmentation, ensuring accurate delineation of nodule regions. The proposed RCBAM-CNN demonstrates exceptional performance, achieving an accuracy of 99.72%, surpassing existing methods such as adaptive morphology with a Gabor Filter (GF) and Capsule Network-based CNN. This approach represents a significant advancement in lung nodule classification, offering improved diagnostic accuracy and reliability. 2024 River Publishers. -
Alkali-Activated Materials - A Review for Sustainable Construction
New, sustainable low-Carbon Dioxide (CO2) construction materials must be developed for the global building sector to decrease its environmental impact. During the last several decades, Alkali-activated Materials (AAMs) is a Portland cement-free form, have been intensively researched as a potential alternative for ordinary Portland cement concrete (OPCC), with the objective of lowering CO2 emissions while repurposing a large volume of industrial waste by-products. The suitability of using AAMs made up of industrial waste by-products such as blast furnace slag (BFS), calcined clay (metakaolin), and fly ash (FA) was investigated in this study utilizing a performance-based approach that was unaffected by binder chemistry, history, or environmental effect, Binder paste microstructural assessment and influence on engineering effectiveness, including fresh and hardened characteristics of these materials, In the Viewpoints area, we analyze specific premature phase and long-phase performance of AAMs, as well as Upcoming scientific breakthroughs are also discussed in the Viewpoints section. 2022 American Institute of Physics Inc.. All rights reserved. -
Blockchains Transformative Potential in Healthcare
Blockchains transformative potential, its current applications, and the path forward for its integration into the healthcare ecosystem are all explored in the journal Blockchain in Healthcare Today. The healthcare industry is facing significant challenges and opportunities after COVID-19. As we navigate the complexities of increasing healthcare costs and technological updates for better patient outcomes, innovative technologies are emerging as pivotal tools for healthcare transformation. Healthcare digital platforms have witnessed revolutionizing the dynamics of healthcare systems using disruptive technologies. However, while these technologies have garnered extensive attention for their transformative potential, there remains a critical gap in our understanding of the impact of digital technology on the healthcare industry. Population health management has critical challenges in data protection, sharing, and interoperability, where personalized medicines and wearable devices are highlighted as a concern. Patients and medical personnel need a safe and simple way to record, transmit, or access information through networks without concern for their safety. Using blockchain technology can help address these problems. Blockchain technology enhances medical data security by providing a decentralized, immutable ledger that ensures data integrity, transparency, and privacy. It enables fine-grained access control, improves interoperability, and resists cyber-attacks. Streamlining regulatory compliance allows patients and medical personnel to safely record, transmit, and access sensitive information across networks. 2024, Partners in Digital Health. All rights reserved. -
Effective time context based collaborative filtering recommender system inspired by Gowers coefficient
The fast growth of Internet technology in recent times has led to a surge in the number of users and amount of information generated. This substantially contributes to the popularity of recommendation systems (RS), which provides personalized recommendations to users based on their interests. A RS assists the user in the decision-making process by suggesting a suitable product from various alternatives. The collaborative filtering (CF) technique of RS is the most prevalent because of its high accuracy in predicting users' interests. The efficacy of this technique mainly depends on the similarity calculation, determined by a similarity measure. However, the traditional and previously developed similarity measures in CF techniques are not able to adequately reveal the change in users' interests; therefore, an efficient measure considering time into context is proposed in this paper. The proposed method and the existing approaches are compared on the MovieLens-100k dataset, showing that the proposed method is more efficient than the comparable methods. Besides this, most of the CF approaches only focus on the historical preference of the users, but in real life, the people's preferences also change over time. Therefore, a time-based recommendation system using the proposed method is also developed in this paper. We implemented various time decay functions, i.e., exponential, convex, linear, power, etc., at various levels of the recommendation process, i.e., similarity computation, rating matrix, and prediction level. Experimental results over three real datasets (MovieLens-100k, Epinions, and Amazon Magazine Subscription) suggest that the power decay function outperforms other existing techniques when applied at the rating matrix level. 2022, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden. -
Performance Evaluation of Time-based Recommendation System in Collaborative Filtering Technique
The Collaborative Filtering (CF) technique is the most common neighbourhood-based recommendation strategy, that provides personalized recommendation to a user for the items using a similarity measure. Hence, the selection of the appropriate similarity measure becomes crucial in the CF based recommendation system. The traditional similarity measures merely focus only on the historical ratings provided by the users to compute the similarity, completely ignoring the fact that preferences change over a period of time. Considering this, the paper aims to develop an effective Recommendation System that uses temporal information to capture the changes in the preferences over a period of time. For this, the existing exponential and power time decay functions are integrated with Cosine, Pearson Correlation, and Gower's similarity measures to compute similarity. The similarity is computed at the similarity computation and prediction levels of recommendation processes. Experimental findings in terms of MAE and RMSE on the MovieLens-100k demonstrate that performance of Gower's coefficient is better when applied with the exponential function at the similarity computation level of the recommendation process. 2022 Elsevier B.V.. All rights reserved. -
TD?DNN: A Time Decay?Based Deep Neural Network for Recommendation System
In recent years, commercial platforms have embraced recommendation algorithms to provide customers with personalized recommendations. Collaborative Filtering is the most widely used technique of recommendation systems, whose accuracy is primarily reliant on the computed similarity by a similarity measure. Data sparsity is one problem that affects the performance of the similarity measures. In addition, most recommendation algorithms do not remove noisy data from datasets while recommending the items, reducing the accuracy of the recommendation. Further-more, existing recommendation algorithms only consider historical ratings when recommending the items to users, but users tastes may change over time. To address these issues, this research presents a Deep Neural Network based on Time Decay (TD?DNN). In the data preprocessing phase of the model, noisy ratings are detected from the dataset and corrected using the Matrix Factorization approach. A power decay function is applied to the preprocessed input to provide more weight-age to the recent ratings. This non?noisy weighted matrix is fed into the Deep Learning model, con-sisting of an input layer, a Multi?Layer Perceptron, and an output layer to generate predicted rat-ings. The models performance is tested on three benchmark datasets, and experimental results con-firm that TD?DNN outperforms other existing approaches. 2022 by the authors. Li-censee MDPI, Basel, Switzerland. -
A Cognitive Similarity-Based Measure to Enhance the Performance of Collaborative Filtering-Based Recommendation System
Advances in technology and high Internet penetration are leading to a large number of businesses going online. As a result, there is a substantial increase in the number of customers making online purchases and the number of items available online. However, with so many options available to choose from, users have to face the information overload problem. Several techniques have been developed to handle this, but the performance of the recommendation system (RS) has been recorded unprecedentedly. The collaborative filtering (CF) of RS is the most prevalent technique, which suggests personalized items to users based on their past preferences. The efficacy of this technique mainly depends on the similarity calculation, which the traditional or cognitive approach can ascertain. In the traditional approach, a similarity measure utilizes the user's ratings on an item to compute the similarity. Most similarity measures in this approach suffer from either data sparsity and/or cold-start problems. To address both of them, a new similarity measure based on the Jaccard and Gower coefficients, the efficient Gowers-Jaccard-Sigmoid Measure (EGJSM), is proposed in this article. It also includes a nonlinear sigmoid function to penalize the bad ratings. The performance of EGJSM is evaluated by conducting experiments on benchmark datasets, and the results depict that the proposed technique outperforms several existing methods. Along with this, a cognitive similarity (CgS) measure has been proposed, which considers cognitive features such as genre and year of release along with rating information, to calculate similarity. The CgS method also outperforms the proposed EGJSM method and produces almost 4% and 1% lower mean absolute error (MAE) and root-mean-squared error (RMSE) values than that. 2014 IEEE. -
Clustering-Based Recommendation System for Preliminary Disease Detection
The catastrophic outbreak COVID-19 has brought threat to the society and also placed severe stress on the healthcare systems worldwide. Different segments of society are contributing to their best effort to curb the spread of COVID-19. As a part of this contribution, in this research, a clustering-based recommender system is proposed for early detection of COVID-19 based on the symptoms of an individual. For this, the suspected patients symptoms are compared with the patient who has already contracted COVID-19 by computing similarity between symptoms. Based on this, the suspected person is classified into either of the three risk categories: high, medium, and low. This is not a confirmed test but only a mechanism to alert the suspected patient. The accuracy of the algorithm is more than 85%. 2022 IGI Global. All rights reserved. -
Wideband Compact Two-Element Millimeter Wave MIMO Antenna for Communication Systems
This article presents the wide band two-element MIMO antenna with an I-shaped decoupling structure in the ground plane. It is to enhance the isolation on the MIMO antenna. The dimension is 7.5 17.5 mm2. The measured bandwidth is 2 GHz (22.25-24.25 GHz) with a maximum gain of 4.5 dBi and bidirectional radiation. MIMO antenna satisfies three diversity metrics. 2024 IEEE. -
Delayed in sensorimotor reflex ontogeny, slow physical growth, and impairments in behaviour as well as dopaminergic neuronal death in mice offspring following prenatally rotenone administration
The environment is varying day by day with the introduction of chemicals such as pesticides, most of which have not been effectively studied for their influence on a susceptible group of population involving infants and pregnant females. Rotenone is an organic pesticide used to prepare Parkinson's disease models. A lot of literature is available on the toxicity of rotenone on the adult brain, but to the best of our knowledge, effect of rotenone on prenatally exposed mice has never been investigated yet. Therefore, the recent work aims to evaluate the toxic effect of rotenone on mice, exposed prenatally. We exposed female mice to rotenone at the dose of 5mg/Kg b.w. throughout the gestational period with oral gavage. We then investigated the effects of rotenone on neonate's central nervous systems as well as on postnatal day (PD) 35 offspring. In the rotenone group, we observed slow physical growth, delays in physical milestones and sensorimotor reflex in neonates and induction of anxiety and impairment in cognitive performances of offspring at PD-35. Additionally, immunohistochemical analysis revealed a marked reduction in TH-positive neurons in substantia nigra. Histological examination of the cerebellum revealed a decrease in Purkinje neurons in the rotenone exposed group as compared to the control. The data from the study showed that prenatally exposure to rotenone affects growth, physical milestones, neuronal population and behaviour of mice when indirectly exposed to the offspring through their mother. This study could provide a great contribution to researchers to find out the molecular mechanism and participating signalling pathway behind these outcomes. 2023 International Society for Developmental Neuroscience. -
Emotional Landscape of Social Media: Exploring Sentiment Patterns
Sentiment analysis, a pivotal research area, involves exploring emotions, attitudes, and evaluations prevalent in diverse public spheres. In the contemporary era, individuals extensively share their perspectives on various subjects through social media platforms. Twitter has emerged as a prominent microblogging site, facilitating users to express opinions and insights globally. However, disrespectful or unfair comments have prompted specific platforms to restrict user comments, highlighting the need to foster productive discourse on social media. This study addresses this imperative by analyzing sentiments using data from Twitter. This work employed various deep learning algorithms and methods to classify elements as negative or positive. The Sentiment140 dataset, sourced from Twitter, serves as the training data for the models to identify the most accurate classification approach. By delving into sentiment analysis on Twitter, the study contributes to a better understanding of the nuances of online expressions. It aims to enhance the overall quality of discourse in social media. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Lightweight Multi-Chaos-Based Image Encryption Scheme for IoT Networks
The swift development of the Internet of Things (IoT) has accelerated digitalization across several industries, offering networked applications in fields such as security, home automation, logistics, and quality control. The growth of connected devices, on the other hand, raises worries about data breaches and security hazards. Because of IoT devices' computational and energy limits, traditional cryptographic methods face issues. In this context, we emphasize the importance of our contribution to image encryption in IoT environments through the proposal of Multiple Map Chaos Based Image Encryption (MMCBIE), a novel method that leverages the power of multiple chaotic maps. MMCBIE uses multiple chaotic maps to construct a strong encryption framework that considers the inherent features of digital images. Our proposed method, MMCBIE, distinguishes itself by integrating multiple chaotic maps like Henon Chaotic Transform and 2D-Logistic Chaotic Transform in a novel combination, a unique approach that sets it apart from existing schemes. Compared to other chaotic-based encryption systems, this feature renders them practically indistinguishable from pure visual noise. Security evaluations and cryptanalysis confirm MMCBIE's high-level security properties, indicating its superiority over existing image encryption techniques. MMCBIE demonstrated superior performance with NPCR (Number of Pixel Changing Rate) score of 99.603, UACI (Unified Average Changing Intensity) score of 32.8828, MSE (Mean Square Error) score of 6625.4198, RMSE (Root Mean Square Error) score of 80.0063, PSNR (Peak Signal to Noise Ratio) score of 10.2114, and other security analyses. 2013 IEEE. -
Balance of payment crisis in India: What the figure say
Volume 2, Issue 5, September-October 2013 -
Predictive Analytics for Network Traffic Management
It examines how this can be applied to monitoring network traffic and carrying out predictive analysis to improve the functionality and effectiveness of network management. The study uses historical data of the network traffics and uses machine learning techniques such as the Long Short Term Memory based models and the Ensemble Methods to predict the traffic patterns in the future. It includes data gathering, data pre-processing, feature selection, model choice, model training, model validation, and the architectural setup of the machine learning solution in a real-time stream processing pipeline using Apache Kafka and Apache Flink. It is evident from the results that the proposed models yield a high level of accuracy in terms of prediction and that the Ensemble method alone gives a slightly higher accuracy than LSTM in the specific metrics. Real-time values closely followed actual traffic level, thus allowing real-time adjustments in network usage. In light of this, there is a clear understanding of the significance of having reliable data preprocessing, feature engineering, and model optimization process. The study also notes the need in prediction concerning data quality and scalability issues taking into account that current and future networks are characterized as dynamic and highly complex to offer more effective solutions for intelligent and proactive networking. 2024 IEEE. -
Greenpreneurship pioneering solutions for climate change: An Indian perspective
In an era marked by pressing global climate challenges, the role of green entrepreneurship, or "greenpreneurship, " has gained paramount significance. The present chapter begins by elucidating the inherent connection between greenpreneurship and climate change, followed the contributions of the greenpreneurs appreciating their efforts to make a difference to the society. The chapter mentions certain skills and traits required by greenpreneurs to run their business. It states the challenges that the greenpreneurs face. The chapter then takes a dive into the realm of greenpreneurship from an Indian perspective. India, with its unique blend of environmental challenges and a burgeoning entrepreneurial ecosystem, presents an intriguing case study. The chapter illustrates how Indian greenpreneurs are contributing to a cleaner, greener future. It states the policies which the government has made to support greenpreneurs in the country. Finally, recommendations are given for existing greenpreneurs, budding entrepreneurs, the public, and the government to collectively drive green solutions. 2024, IGI Global. All rights reserved. -
Environmental cost of food wastage: Integrated response through a mix of environmental policy instruments
Food, when wasted, reaches landfills and emits greenhouse gases. The impact of greenhouse gases (GHGs), in turn, is felt by even those who do not waste food in the place. Externalities thus created are known to distort market efficiency and the most widely discussed externality is climate change. This study takes the case of United States of America (USA) to ascertain the GHGs resulting due to food wastage. The difference between cost per capita due to emissions from animal-based products and emissions from plant-based products comes out to be $122. In the year 1997 total GHG emission for the entire population of the USA due to food wastage was 401.98 billion kgCO2eq, costing the country 45.42 billion US dollars. Two decades later, in 2017, the food waste costs went up by 6 billion US dollars amounting to 51.14 billion US dollars and 452.64 billion kgCO2eq of GHG emissions The novelty of this research lies in highlighting the carbon footprints of food wastage in terms of GHG's and monetizing these emissions. The study proposes an integrated response through a mix of environmental policy instruments of economic incentives, command and control and moral suasion. 2023 ERP Environment and John Wiley & Sons Ltd. -
User Sentiment Analysis of Blockchain-Enabled Peer-to-Peer Energy Trading
A new way for the general public to consume and trade green energy has emerged with the introduction of peer-to-peer (P2P) energy trading platforms. Thus, how the peer-to-peer energy trading platform is designed is crucial to facilitating the trading experience for users. The data mining method will be used in this study to assess the elements affecting the P2P energy trading experience. The Natural Language Processing (NLP) approach will also be used in this study to evaluate the variables that affect the P2P energy trading experience and look at the role of topic modeling in the topic extraction using LDA. The findings show that the general public was more interested in the new technology and how the energy coin payment system operated during the trade process. This explanation of energy as a CC is an outlier that fits well with the conventional literature. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.