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An Overview of Nano-Catalysts in Biodiesel Production
Energy consumption and dependence on non-renewable resources is increasing over the years. The combustion of fossil fuels resulting in the emission of substantial amounts of CO2, NOX, SOX and some greenhouse gases. Biofuels are evolving as the primary alternatives to fossil fuels since they can be readily synthesised from discarded bioresources and yield lesser emission during the combustion process. However, the extraction of biofuels has thrown up new challenges that have widened the scope of the use of nano-particles in the synthesis of biofuels. From the literature, distinct findings concerning the use of nano-particles as a catalyst and process reactant during biodiesel production have been identified; this is majorly attributed to the fact that nano-catalysts enhance thermophysical properties, reaction speed and mass transport properties. Henceforth, the present paper aims to review, summarise and provide an insight into the research findings of effectively using nanocatalysts in biofuel production and consider the significance and its relevance for further researchers in the domain of biofuels. 2022, Books and Journals Private Ltd.. All rights reserved. -
An Overview of Material Waste Management in Construction Projects
Wastage of construction materials has long been a persistent issue within construction projects. The improper planning and management of construction materials during the performance of construction activities is a critical issue that negatively impacts the performance of construction projects. Encouraging sustainable waste management involves minimizing waste generation and promoting the reuse, recycling, and recovery of resources. This paper provides a broad overview of construction waste minimization and management, as well as mitigation factors for sustainable construction waste management. It integrates sustainability principles into waste management practices, including the adoption of a waste management hierarchy to advance environmental friendliness within the building industry. Also this delves into the significance of material waste, taking into account its environmental, economic, and social repercussions. It identifies various sources of material waste across the construction lifecycle, shedding light on the factors contributing to waste generation and inefficiencies. It evaluates existing practices and strategies utilized for waste minimization and management, encompassing approaches like reuse, recycling, and disposal. It emphasizes the crucial need to tackle material waste in construction projects to foster sustainability and optimize resource utilization in the built environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
An overview of maritime research, its significance, challenges, and opportunities: Navigating the future
Maritime research is an essential field that addresses the complex interactions between human activities and marine environments. As global trade continues to expand, the significance of understanding maritime systems cannot be understated. Research in this domain encompasses various areas, including marine ecology, maritime logistics, and oceanography, aiming to promote sustainable practices and technological advancements. Climate change is altering oceanic conditions, resulting in shifts in biodiversity and marine resources, which poses significant threats to ecosystems and coastal communities. Emerging technologies, such as artificial intelligence and autonomous vessels, can enhance operational efficiency and safety in marine transport. As we navigate the future of maritime research, it is crucial to engage stakeholders from various sectors to create a synergy that addresses the pressing issues facing our oceans, while simultaneously unlocking their vast potential for future generations. 2025, IGI Global Scientific Publishing. -
An Overview of Augmenting AI Application in Healthcare
Artificial intelligence (AI) is showing a paradigm shift in all spheres of the world by mimicking human cognitive behavior. The application of AI in healthcare is noteworthy because of availability of voluminous data and mushrooming analytics techniques. The various applications of AI, especially, machine learning and neural networks are used across different areas in the healthcare industry. Healthcare disruptors are leveraging this opportunity and are innovating in various fields such as drug discovery, robotic surgery, medical imaging, and the like. The authors have discussed the application of AI techniques in a few areas like diagnosis, prediction, personal care, and surgeries. Usage of AI is noteworthy in this COVID-19 pandemic situation too where it assists physicians in resource allocation, predicting death rate, patient tracing, and life expectancy of patients. The other side of the coin is the ethical issues faced while using this technology like data transparency, bias, security, and privacy of data becomes unanswered. This can be handled better if strict policy measures are imposed for safe handling of data and educating the public about how treatment can be improved by using this technology which will tend to build trust factor in near future. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An overview of AI applications in wildlife conservation
The integration of artificial intelligence (AI) into wildlife conservation has revolutionized methodologies for monitoring species, enhancing habitat management, and combating poaching. This chapter examines various AI applications that contribute to the protection and preservation of biodiversity. Remote sensing technologies, powered by machine learning algorithms, assist in assessing habitat health and tracking changes over time. AI- driven image recognition tools enable the identification of individual animals from camera trap photos, facilitating more accurate population estimates and behavioral studies. Moreover, predictive analytics play a crucial role in forecasting human- wildlife conflicts and informing proactive management strategies. This synthesis of AI technologies demonstrates their potential to enhance conservation efforts, optimize resource allocation, and ultimately foster more effective wildlife protection initiatives. The ongoing advancement of AI in this field promises to create innovative solutions to some of the most pressing challenges. 2025, IGI Global Scientific Publishing. All rights reserved. -
An overlap-based human gait cycle detection
Identification of a person by his/her style of walking is referred as gait recognition. Gait is one among the biometric used for human identification. In gait recognition, an inevitable step for accurate feature extraction is gait cycle detection. In this paper, a novel gait cycle detection algorithm based on the concept of overlap between legs during locomotion is proposed. To identify overlap, zero-crossing counts of silhouette frames as well as bottom halves of silhouette frames are considered. The efficiency of this algorithm is tested using normal walking sequence of subjects with 90 viewing angle from CASIA B as well as TUM-IITKGP human gait databases. The results obtained shows that gait cycle can be easily and efficiently detected with zero-crossing count of silhouette frames. Further zero-crossing counts taken from bottom halves of silhouette frames gives better performance. Copyright 2019 Inderscience Enterprises Ltd. -
An outlook on zero-dimensional nanocarbons as components of DSSC
Solar energy is an abundant source of energy, and harnessing the suns radiation with an efficient solar cell can be a promising technology for a limitless supply of sustainable energy. The amount of solar power that reaches the earth is beyond the worlds energy consumption. But, the main cause for minimal usage of the suns energy is the complicated technology, restricted band gap, high-temperature instability, and high cost of production. Likewise, the usage of space and infrastructure required for the installation of solar cells is yet another reason for limited usage. Upon comparing the emerging photovoltaics, DSSC (dye-sensitized solar cells) can be a solution for the drawbacks faced by the older generation solar cells which has greater future scope as an energy harvester. Rapid technological growth over the years, usage of affordable materials, and capability of working efficiently in low lighting conditions make DSSC a commercially viable and potent solar energy harvester. Furthermore, its efficiency can be improved with the inclusion of low-dimensional nanocarbons in various components of DSSC. Therefore, this review describes the mechanisms of improving the performance of zero-dimensional nanocarbons and their application in components of DSSC alternative to conventional materials. The significant impact of surface functionalization of low-dimensional nanocarbon on the performance of dye-sensitized solar cells is also discussed. Graphical abstract: (Figure presented.) The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. -
An Outlook on Sustainable Business Practices through Virtual Reality Marketing
Technologies and businesses blend progressively and work towards creating a sustainable future through the company's marketing strategies. The purpose of the study is to find out the various sustainable outcomes of Virtual Reality Marketing (VRM). The exploratory research identified immersive experience, experiential economy, positive image creation, positive travel decisions, and repeat purchase as the constructs of VRM, and a total of 418 people were surveyed to analyze those constructs. The data were analyzed through statistical tests such as t-test, One-way ANOVA, and Chi-square with the help of SPSS software. The study shows a positive relationship between customers and virtual reality marketing. The results predict that businesses that have incorporated VRM tend to likely have a high-profit margin and more sustainable returns compared to their peer competitors. 2024 IEEE. -
An Outlook of Gender Differential Happiness in India
Studies on happiness and subjective wellbeing, in general, are aplenty, but applying a gender lens to it is comparatively rare, especially in the Indian context. The social construction of gender roles will influence happiness being a subjective matter. This paper explores this idea of gender differential happiness in light of India's peculiar social and cultural context. Using the World Value Survey (WVS) for India (Wave 6) in 2012 and Ordinary Least Square (OLS) regression analysis, the study finds that self-reported happiness is gender differential in India. Factors such as marital status, educational attainments, managerial roles and thrust on women empowerment were found to be vital for happiness for all. However, there are visible patriarchal gender stereotype notions with factors such as individual autonomy and homemaking. 2024 IEEE. -
An outlook in blockchain technology- Architecture, applications and challenges
Blockchain is mechanism which stores and exchange data in a peer-peer network serving as an immutable ledger allowing transactions to take place in decentralized method which neglects the role of intermediaries. The technology reduces greater complexity by combining three key features; security, decentralization and transparency. This paper is an attempt explaining the concepts, structure, applications and challenges the technology has. The paper introduces blockchain taxonomy, reviews applications and discussed technical challenges and way of handling these challenges. Blockchain technology is springing up with promising applications in various fields and the authors have explored about three emerging field of blockchain say; Education, Government and Healthcare. Finally the paper concludes by stating other emerging fields of applications where further research can be explored. International Research Publication House. -
An organocatalytic C-C bond cleavage approach: A metal-free and peroxide-free facile method for the synthesis of amide derivatives
A facile organocatalytic approach has been devised towards the synthesis of amide derivatives using 1,3-dicarbonyls as easily available acyl-sources under peroxide-free reaction conditions. This transformation was accomplished by the cleavage of the C-C bond in the presence of TEMPO as an organocatalyst and excludes the use of transition-metals and harsh reaction conditions. A broad range of substrates with diverse functional groups were well tolerated and delivered the products in high yields. The Royal Society of Chemistry and the Centre National de la Recherche Scientifique. -
An ordered ideal intuitionistic fuzzy software quality model
Software is one of the major factors in the development of computer - based systems and products. Measurement of the software quality is thus the key factor that has to be taken into account while developing a software system. Many software quality models with numerous quality parameters are under use to measure the performance of a software system, on the basis of which the software is valued. This study intends to make available a fuzzy multiple criteria decision making (FMCDM) approach to measure software quality and to propose new similarity measures between ordered ideal intuitionistic fuzzy sets (OIIFSs). The proposed model is applied to five live software projects so as to quantify the software quality of each project under fuzzy environment. IAEME Publication. -
An optimized technique to foster omnichannel retail experience leveraging key technology dimensions in the context of an emerging digital market
Customers approach towards shopping has transformed, as a result of their reduced tolerance, increased technology usage and being well informed than ever before. As customers expect a seamless shopping experience regardless of where they are engaged within a retailers network, the line between physical and digital retailing is blurring. Retailers across the world are contemplating on transforming into Omnichannel hubs to deliver an elevated experience anytime anywhere. And, experts have often indicated that an Omnichannel strategy delivers a unified shopping experience than a mere channel experience. However, the true Omnichannel experience is still not evident in India with minimal action in this space, indicating a subverted outlook towards building necessary Omnichannel Capabilities. This paper examines the most essential and significant technology dimensions that are imperative towards fostering a seamless Omnichannel Retail Experience. The findings of this study serve as a basis for retailers in India to evaluate their strategies towards adopting these technology dimensions and respective capabilities, using an optimized approach. The study employed a quantitative research involving survey of executives from major retailers in India. The quantitative data was analyzed applying Structural Equation Modeling, to ascertain the technology dimensions that emerged and their significance in deriving Omnichannel Retail Experience. BEIESP. -
An optimized method for mulberry silkworm, Bombyx mori (Bombycidae:Lepidoptera) sex classification using TLBPSGA-RFEXGBoost
Silkworm seed production is vital for silk farming, requiring precise breeding techniques to optimize yields. In silkworm seed production, precise sex classification is crucial for optimizing breeding and boosting silk yields. A non-destructive approach for sex classification addresses these challenges, offering an efficient alternative that enhances both yield and environmental responsibility. Southern India is a hub for mulberry silk and cocoon farming, with the high-yielding double-hybrid varieties FC1 (foundation cross 1) and FC2 (foundation cross 2) being popular. Traditional methods of silkworm pupae sex classification involve manual sorting by experts, necessitating the cutting of cocoons a practice with a high risk of damaging the cocoon and affecting yield. To address this issue, this study introduces an accelerated histogram of oriented gradients (HOG) feature extraction technique that is enhanced by block-level dimensionality reduction. This non-destructive method allows for efficient and accurate silkworm pupae classification. The modified HOG features are then fused with weight features and processed through a machine learning classification model that incorporates recursive feature elimination (RFE). Performance evaluation shows that an RFE-hybridized XGBoost model attained the highest classification accuracy, achieving 97.2% for FC1 and 97.1% for FC2. The model further optimized with a novel teaching learning-based population selection genetic algorithm (TLBPSGA) achieved a remarkable accuracy of 98.5% for FC1 and 98.2% for FC2. These findings have far-reaching implications for improving both the ecological sustainability and economic efficiency of silkworm seed production. 2024. Published by The Company of Biologists Ltd. -
An Optimized Convolutional Neural Network Model for Real-Time Object Detection in Drones
The capacity of drones to perform item detection in actual-time is crucial for applications inclusive of surveillance, seek and rescue, and environmental tracking. This look at investigates how convolutional neural networks (CNNs) can beautify object detection in aerial imagery by enhancing both accuracy and speed. CNNs excel at extracting spatial info, permitting drones to apprehend objects even in relatively complicated environments. by adopting light-weight CNN architectures and optimization strategies, we acquire advanced performance with minimum computational requirements, ensuring green operation on embedded drone platforms. Our findings verify that CNN-based fashions considerably decorate detection accuracy and responsiveness, allowing the improvement of smarter and more self reliant drones. 2025 IEEE. -
An Optimized Convolutional Neural Network Model for Real-Time Object Detection in Drones
The capacity of drones to perform item detection in actual-time is crucial for applications inclusive of surveillance, seek and rescue, and environmental tracking. This look at investigates how convolutional neural networks (CNNs) can beautify object detection in aerial imagery by enhancing both accuracy and speed. CNNs excel at extracting spatial info, permitting drones to apprehend objects even in relatively complicated environments. by adopting light-weight CNN architectures and optimization strategies, we acquire advanced performance with minimum computational requirements, ensuring green operation on embedded drone platforms. Our findings verify that CNN-based fashions considerably decorate detection accuracy and responsiveness, allowing the improvement of smarter and more self reliant drones. 2025 IEEE. -
An optimized back propagation neural network for automated evaluation of health condition using sensor data
Ships and other large equipment must meet strict standards for equipment integrity and operational dependability in order to perform missions. To meet this demand, one of the essential linkages is to guarantee the long-term safe and healthy functioning of their power transmission equipment. The Optimized Back Propagation Neural Network (OBPNN) technique used in this study introduces a unique method for monitoring sensor data and evaluating the health state, with the SVM being optimized using the fish swarm algorithm (FSA). A major problem that maintenance is facing nowadays is reliable fault prediction. One of the trickiest difficulties is arguably automatically modelling typical behaviour from condition monitoring data, particularly when there is little information about actual failures. A data-driven learning framework with the best bandwidth selection is suggested to address this challenge. It is based on nonparametric density estimation for outlier identification and OBPNN for normality modelling. The distance to the separating hyper plane's log-normalization is used to provide a health score that is also available. The algorithm's viability is shown by experimental findings while evaluating the progression of a major defect over time in a marine diesel engine. Improved prediction capabilities and low false positive rates on healthy data are realized. 2023 The Authors -
An Optimized Approach for Spam Message Detection Using C4.5 Classifier with Stochastic Hill Climbing and Genetic Algorithm for Feature Selection
In the mobile industry, text messaging is a popular feature that is mainly intended to make money for service providers. But spam, which is defined as unsolicited bulk messages that contain commercial content, has become a widespread problem. These spam texts are frequently used to spread phishing links or advertise goods and services in order to make money. The phone alerts the user whenever spam text messages arrive in their inbox. When the user discovers that the message is unsolicited, these unsolicited texts not only take up storage space and waste their time, but they also irritate them. Even with the development of numerous sophisticated algorithms to identify spam, users are still impacted by text message spam. Thus, the mobile sector needs to implement efficient filtering methods. The proposed study uses the C4.5 Decision Tree as the classification model and combines a Genetic Algorithm and Stochastic Hill Climbing to find optimal features in order to detect spam in text messages. This method uses metaheuristic techniques to find the best features, which are then categorized using decision trees. This hybrid model performs better than current classification methods. 2026 IEEE. -
An Optimized Algorithm for Selecting Stable Multipath Routing in MANET Using Proficient Multipath Routing and Glowworm Detection Techniques
Mobile Ad Hoc Networks (MANETs) depend on the selected and constant path with an extended period and the flexibility of the battery power condensed in searching end nodes, leading to numerous link failures. This kind of link damages occurs, and it also affects the packet success rate. We presented a Proficient Multipath Routing and Glowworm detection (PMGWD) technique to overcome such a Manets failure. Initially, a proposed Proficient Multipath Routing (PMR) technique identifies the damaged or failure routes and continues communication inefficiently. Secondly, the Glowworm detection node technique is implemented for both fault node identification and for extending the nodes network lifetime. Another reason to select the glowworm optimization is to update the node based on the glow to improve its neighbor its search space. Lastly, the PMGWD technique is utilized for identifying an optimal route and fault nodes in the manet. It is achieved to correct the identification of fault nodes using the glowworm detection node technique, and it helps to explore more paths for the optimal route by using proficient multipath routing. Hence, this proposed PMGWD technique is used to perform a problem-free communication process in a network system. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
AN OPTIMIZATION AND PREDICTIVE MODELING TO ENHANCE THE WEAR AND MECHANICAL PERFORMANCE OF Al 5054 ALLOY FOR DEFENSE APPLICATIONS WITH TiO2 NANOPARTICLES
This study examines the effects of 2%, 4%, and 6% additions of TiO2 nanoparticles on the wear and mechanical characteristics of Al 5054 alloy reinforcement. The results demonstrate that the addition of TiO2 nanoparticles considerably increases the alloys tensile and impact strengths. Tensile strength reaches a peak of 221 MPa at 6% reinforcement and it rises gradually as the percentage of TiO2 reinforcement increases. Similarly, impact strength rises with time and, with TiO2 reinforcement, it reaches a maximum of 63 Joules at 6%. Wear analysis using Taguchi-based design determines the optimal combination of composition, disc rotation speed, load, and sliding distance to minimize a given wear rate and friction force. The SEM analysis validates that the composites exhibit enhanced wear resistance due to the uniform distribution of TiO2 nanoparticles. An Artificial Neural Network (ANN) model is also developed to predict the responses, and it achieves an overall accuracy of 83.549%. The mechanical properties and wear resistance of TiO2-reinforced Al 5054 composites can be enhanced, as it is demonstrated by these results. This information is crucial for material design and optimization across a range of engineering applications. 2024, Scibulcom Ltd.. All rights reserved.