Browse Items (14428 total)
Sort by:
-
Specialized Metabolites of Mangroves and Their Biological Activities
Mangroves are woody plants that are found in intertidal zones, where land meets the sea, especially in the tropical and subtropical regions of the world. They synthesize and accumulate diverse specialized metabolites that fall into major categories such as phenolics, terpenes, and alkaloids. Mangrove-derived chemical compounds have also been shown to exhibit a variety of biological properties including anticancer, antidiabetic, anti-inflammatory, antibacterial, antioxidant, and neuroprotective activities. In this chapter, we present the chemistry and biological activities of the mangrove-specialized metabolites. Springer Nature Switzerland AG 2025. -
Specialized Metabolites of Mangroves and Their Biological Activities
Mangroves are woody plants that are found in intertidal zones, where land meets the sea, especially in the tropical and subtropical regions of the world. They synthesize and accumulate diverse specialized metabolites that fall into major categories such as phenolics, terpenes, and alkaloids. Mangrove-derived chemical compounds have also been shown to exhibit a variety of biological properties including anticancer, antidiabetic, anti-inflammatory, antibacterial, antioxidant, and neuroprotective activities. In this chapter, we present the chemistry and biological activities of the mangrove-specialized metabolites. Springer Nature Switzerland AG 2026. -
Novel Secondary Metabolites from Mangrove Flora: Chemistry and Bioactivity
Secondary metabolites found in abundance in mangrove plants play a vital role in enabling these plants to withstand challenging environmental circumstances. Over the years, studies on the isolation and characterization of secondary compounds from mangroves have shown that they include a vast number of novel compounds that have not been previously described. Mangrove secondary compounds have been shown to feature unique carbon skeletons, unique ring systems, or peculiar structural moieties. These new substances have also shown a range of biological activity. We reviewed a variety of new compounds in this review, along with their structural variations and biological activity. Springer Nature Switzerland AG 2026. -
Edible Flowers: An Updated Review on Nutritional Composition, Phytochemicals, and Biological Activities
Edible flowers have been identified as a possible source of vitamins, minerals, protein, fat, and carbohydrates. Additionally, they are abundant in bioactive substances such as terpenoids, alkaloids, phenolics, flavonoids, glucosinolates, and essential oils. The pharmacological properties of edible flowers have been shown to include anti-aging, anti-cancer, antidiabetic, anti-inflammatory, antimicrobial, antioxidant, cardioprotective, hepatoprotective, and neuroprotective effects in recent years. The most recent information on the nutritional, phytochemical, and biological properties of agathi, lotus, moringa, and banana flowers is presented in this review. This analysis concludes by shedding light on the latest data regarding the use of edible flowers in the food, pharmaceutical, and cosmetic industries. 2026 Hosakatte Niranjana Murthy. -
An exhaustive examination of automatic speech recognition
Speech is nature gift to the human being to differentiate with other creatures on the earth. Speech research stands out as one of the most daunting fields amidst numerous challenging research domains. This current analysis of existing literature aims to provide insights for future endeavors within the global speech research community. The study delves into various challenges associated with speech corpora, front-end algorithms aimed at efficient speech representation, and back- end engines tasked with the recognition process. Thirteen speech corpora undergo scrutiny concerning factors such as language diversity, duration, developmental progress, and accessibility. Furthermore, this research review illuminates potent methodologies that foster the extraction of rich features and bolster robust speech recognition capabilities. This gives an idea on how various methods are available to recognize speech in an effective way. 2026 Author(s). -
Personal Cognitive Predictors Influencing Career Resilience Among Indian Women Information Technology Professionals
Career resilience, which is culturally and contextually determined, has been insufficiently explored in the literature regarding women, with an inadequate investigation into the factors predicting their resilience. This investigation offers fresh insights into the determinants of career resilience among women professionals in the Indian information technology sector by examining career-related personal cognitive factors. The study specifically explored the effects of career self-management skills, work volition, career salience, and occupational self-efficacy on career resilience (N = 306). Hierarchical multiple regression analysis revealed that career self-management skills, work volition, and occupational self-efficacy significantly predicted career resilience in women professionals. Additional analyses revealed occupational self-efficacy as a mediating factor. These findings contribute to formulating strategies to enhance career resilience through organizational support and targeted interventions. 2025 by the American Counseling Association. -
Career Resilience and Advancement: A Research Note on Women in Indian IT
Purpose: The representation of women in the Information Technology (IT) industry in India is higher than in other sectors. However, many women leave the industry after five years of employment. Organizations are concerned about the gender gap in leadership positions within this sector. The paper suggested that career resilience (CR) is a strategic method for retaining and advancing women in IT professions. Methodology/Approach: The paper employed an interpretive synthesis approach. Literature from peer-reviewed sources on CR, career development, and career advancement of women professionals was reviewed. Findings: The synthesis of literature highlighted the role of CR in improving women's continued participation in the industry. The findings suggested building resilience through organizational interventions as a way forward to creating a more gender-equitable workforce. Practical Implications: The strategies presented are practical and feasible for IT organizations to create a more inclusive workspace. These strategies are designed to empower organizations to take meaningful steps for an equitable workforce. Originality: The paper presented a comprehensive approach to sustaining and advancing women in the IT industry in India. 2025, Associated Management Consultants Pvt. Ltd. All rights reserved. -
Is Bitcoin a Safe Haven for Indian Investors? A GARCH Volatility Analysis
This paper attempts to understand the dynamic interrelationships and financial asset capabilities of Bitcoin by analysing several aspects of its volatility vis-a-vis other asset classes. This study aims to analyse the volatility dynamics of the returns of Bitcoin. An asymmetric GARCH model (EGARCH) is used to investigate whether Bitcoin may be useful in risk management and ideal for risk-averse investors in anticipation of negative shocks to the market (leverage effect). This paper also examines Bitcoin as an investment and hedge alternative to gold as well as NSE NIFTY using a multivariate DCC GARCH model. DCC GARCH models are also used to check whether correlation (co-movement) between the markets is time-varying, examine returns and volatility spillovers between markets and the effect of the outbreak of COVID-19 in India on the investigated markets. The results show that given the supply of Bitcoin is fixed, low returns realisation is equivalent to excess supply over demand wherein investors are selling off Bitcoin during bad times. The positive co-movement between Bitcoin and gold during the COVID-19 outbreak shows that investors perceived Bitcoin as a relatively safe investment. However, overall analysis shows that Bitcoin was not considered a safe hedge and an investment option by Indian investors during the study period. 2022 by the authors. -
Influence of high-shear exfoliation and the stabilizer on the formation of exfoliated graphene nanosheets and its supercapacitive performances
Despite the extensive research on the preparation of graphene nanosheets, there is no suitable and optimized procedure for the large-scale production of 'defect-free' graphene monolayers. In addition, there is only a few works on the eco-friendly shear exfoliation of graphite using water as the solvent. However, no work has been reported on optimizing the critical parameters such as rotation speed and time duration (rpm) of shear homogenizer and concentration of stabilizer that determine the quality of graphene nanosheets. In this paper, an eco-friendly and scalable approach to prepare graphene nanosheets from natural graphite flakes using a high-speed shear homogenizer and sodium dodecyl sulfate (SDS) as an ionic stabilizer has been reported. As a result, the obtained efficiency of exfoliated graphene corresponding to the optimized condition is found to be 89%. Later, the exfoliated graphene is characterized by both physical characterization (i.e., Raman spectroscopy and FE-SEM) and electrochemical characterization. The electrochemical analysis reveals that the prepared graphene nanosheets exhibit a specific capacitance of 60.24Fg?1. This method is simple, inexpensive, environmentally friendly, and easy to scale up. Iranian Chemical Society 2025. -
Wireless Communication for Robotic Process Automation Using Machine Learning Technique
Machine intelligence is what has been generated by programming computers with certain aspects of human intellect, like training, solving problems, and priority setting. A machine can solve a number of complicated issues using these capabilities. In major industries, such as customer support and manufacturing, machine intelligence is now being employed. The growth and quick development of digital technology and artificial intelligence (AI) technologies are becoming more and more difficult. At now, sophisticated manufacturing, the world of invention, and broad acceptance are undergoing a fast transition. Robotics is much more vital as it may now be related to the human brain by the connection between machine and brain, as AI develops. The world's economy faces substantial difficulties by increasing productivity in the manufacturing industry. This study examines the present progress of robotic communication styles of artificial intelligence (AI). In many specific applications, communication between members of a robotic group or even people becomes vital. The paper solves the problem of implementation of an independent industry mobile robot in all fields in the major business, live interactive, planning, mobile robot technologies, and intending. In order to identify the best solution to this issue, a mixed integer robotic model has been developed. 2022 C. Murugamani et al. -
Machine Learning and Signal Processing Methodologies to Diagnose Human Knee Joint Disorders: A Computational Analysis
Computer-aid diagnostic (CAD) has emerged as a highly innovative research topic in diverse fields which includes medical imaging systems, radiology diagnostics, and so on. These are the systems that majorly assist doctors by the way of interpretation of medical data or images. In the diagnosis of knee joint disorder technique, both time and frequency-based analysis can be done. These non-stationary and non-linear signals are processed into three important methods, namely VMD, TVF-EMD, and CEEMDAN. To analyze the vibroarthrographic (VAG) signal, the initial stage is to compute the mode strategies termed as intrinsic mode functions (IMFs) which can be attained only after performing the transformations. In our chapter, we analyzed Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for computing the mode signals. The CEEMDAN method utilized the time and frequency data for the available features. The feature extraction depends purely on pixel intensity and the statistical parameters. The classification of available data samples is done through the Least Square Support Vector Machine (LS-SVM) and SVM-Recursion of Feature Elimination (SVM-RFE) for the efficient analysis of healthy and unhealthy data samples. 2024 selection and editorial matter, Hemachandran K., Raul V. Rodriguez, Umashankar Subramaniam, and Valentina Emilia Balas; individual chapters, the contributors. -
An Adaptive Scriptless Behavior-Driven Development Automation Framework with Self-Healing Intelligence for Evolving Software Applications
Background: The high rate of user interface (UI) and source code changes in contemporary software development resulted in automated testing failures that augmented maintenance expenses and decreased the usefulness of automated testing. The current tools need regular updating by manual means, which is ineffective and expensive. Purpose: To present the Adaptive Scriptless Behavior-Driven Development (BDD) Automation Framework with Self-Healing Intelligence, which is an artificial intelligence (AI) and machine learning (ML)-driven framework of automatic test failure detection and resolution based on UI drift, broken locators, or timing. Approaches: The framework uses dynamic locator approaches, adaptive test generation, and reinforcement learning to allow updating test scripts in response to application changes. Such a self-healing feature will minimize human intervention and reduce maintenance expenses. An experimental case study was conducted in order to assess the performance of the framework in a practical context. Findings: The framework demonstrated significant advances in automated testing, such as a 30% drop in maintenance speed, reduced number of resources to update tests, a 25 % reduction in total cost of testing since less manual effort is needed, and a 40 % rise in stability of the test suites, which can execute its tests more reliably and with greater accuracy despite the presence of changes to the application. Conclusions: The Adaptive Scriptless BDD Automation Framework with Self-Healing Intelligence goes a long way to improving the flexibility, scalability, and efficiency of automated testing. It enhances the speed of testing, saves costs, and adds confidence in the quality of software, and is therefore valuable for ensuring high-quality standards in dynamic software landscapes. 2026, Innovative Information Science and Technology Research Group. All rights reserved. -
Preparation and Electrochemical Investigation of NiO Hollow Sphere from Bio Waste (Sugarcane Bagasse) Extract for Energy Storage Applications
This work describes how to easily make NiO hollow sphere composites using waste sugarcane bagasse for use in supercapacitor applications. NiO hollow spheres (NOHSs) nanomaterialis effectively synthesized through the nano carbon sphere (CS) template. A core-shell structure was created on the carbon spheres surface by NiO nanoparticles that were several nanometers in size. The structural and morphological of the synthesized materials were investigated by X-ray diffraction (XRD) and Scanning electron microscope (SEM). The energy-dispersive X-ray spectroscopy (EDS) was used to confirm the presence of the elements in NOHS. The electrochemical behaviour of hierarchical CSs and NOHSs electrode was examined through cyclic voltammetry (CV), Galvanostatic charge/discharge (SC) and electrochemical impedance spectroscopy (EIS). In GCD analysis, NOHSs electrode showed a concentrated specific capacitance (Csp) of 913.79F/g at 5A/g current density. The porous conductive carbon with macro pores that speeds up the transit of electron and electrolyte ions causes noticeably better capacitive behavior. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Scalable synthesis of 2D-layered Ti3C2 MXene by HF etching method; electrochemical investigations and device fabrication to enhancing capacitive nature
The goal of the current effort is aimed to synthesise the uniform exfoliated titanium carbide (Ti3C2) MXene sheets by utilising hydrofluoric (HF) acid to remove/etch aluminium from the parental Ti3AlC2 MAX phase. The Ti3C2 MXene was investigated by structural analysis using X-ray diffraction (XRD), Higher Resolution Transmission Electron Microscope (HRTEM), Scanning electron microscope (SEM), and EDS with mapping for morphological and elemental analysis, Moreover, the Ti3C2 MXene was studied its electrochemical properties to electrochemical energy storage application using cyclic voltammetry (CV), Galvanostatic chargedischarge (GCD) and electrochemical impedance spectroscopy (EIS) techniques. Since the GCD analysis of Ti3C2 MXene, a great specific capacitance (Csp) of 318F/g was attained with current density of 1 A/g and up to 90 % retentivity was attained after 7500 cycles. Besides, fabricated Ti3C2 MXene||Ti3C2-MXene symmetric supercapacitor device (SSD) has described the energy density (ED) of 27.78 Wh/kg at a power density (PD) of 400 W/kg and the capacitive retention existed attained 92.1 % after 7500 cycles with 5 A/g. 2024 Elsevier B.V. -
Exploration of low heat rejection engine characteristics powered with carbon nanotubes-added waste plastic pyrolysis oil
Compression ignition (CI)-powered alternative energy sources are currently the main focus due to the constantly rising worldwide demand for energy and the growing industrialization of the automotive sector. Due to their difficulty of disposal, non-degradable plastics contribute significantly to solid waste and pollution. The waste plastics were simply dropped into the sea, wasting no energy in the process. Attempts have been made to convert plastic waste into usable energy through recycling. Waste plastic oil (WPO) is produced by pyrolyzing waste plastic to produce a fuel that is comparable to diesel. Initially, a standard CI engine was utilized for testing with diesel and WPO20 (20% WPO+80% diesel). When compared to conventional fuel, the brake thermal efficiency (BTE) of WPO20 dropped by 3.2%, although smoke, carbon monoxide (CO), and hydrocarbon (HC) emissions were reasonably reduced. As a result, nitrogen oxide (NOx) emissions decreased while HC and CO emissions marginally increased in subsequent studies utilizing WPO20 with the addition of 5% water. When combined with WPO20 emulsion, nanoadditives have the potential to significantly cut HC and CO emissions without impacting performance. The possibility of incorporating nanoparticles into fuel to improve performance and lower NOx emissions should also be explored. In order to reduce heat loss through the coolant, prevent heat transfer into the cylinder liner, and increase combustion efficiency, the thermal barrier coating (TBC) material is also coated inside the combustion chamber surface. In this work, low heat rejection (LHR) engines powered by emulsion WPO20 containing varying percentages of carbon nanotubes (CNT) are explored. The LHR engine was operated with a combination of 10 ppm, 20 ppm, and 30 ppm CNT mixed with WPO20. It was shown that while using 20 ppm of CNT with WPO20, smoke, hydrocarbons, and carbon monoxide emissions were reduced by 11.9%, 21.8%, and 22.7%, respectively, when compared to diesel operating in normal mode. The LHR engine achieved the greatest BTE of 31.7% as a result of the improved emulsification and vaporization induced by CNT-doped WPO20. According to the study's findings, WPO20 with 20 ppm CNT is the most promising low-polluting fuel for CI engines. 2023 The Institution of Chemical Engineers -
Earlier Stage Identification of Bone Cancer with Regularized ELM
A major focus of current research in the field of image processing is the application of such methods to the field of medical imaging. While dealing with biological issues like fractures, canoers, ulcers, etc., image processing facilitated pinpointing the precise cause and tailoring a remedy. In the field of tumor identification, medical imaging has set a new standard by overcoming a number of challenges. Medical imaging is the practice of generating images of the human body for diagnostic or exploratory purposes. Because of its high image quality, MRI is the method of choice for detecting tumors. This research study proposes the integration of RLM to detect tumors and presents an automatic bone cancer detection system to assist oncologists in making early diagnosis of bone malignancies, which in turn allows patients to receive treatment as soon as possible. This research work also proposes to detect bone tumors by using a combination of the RELM based M3 filtering, Canny Edge segmentation, and the Enhanced Harris corner approach. When compared to other models like CNN, ELM, and RNN, the suggested technique achieves an accuracy of around 97.55%. 2023 IEEE. -
Catalyzing Green Mobility: Consumer Preferences for Green Energy Vehicles
Due to growing urbanization and the increase of vehicles, most Indian cities endure traffic congestion and significant air pollution. As a result, alternate technology in autos, such as electric vehicles, may become necessary (EV). This study aims to identify consumer preferences toward electric vehicles in the Indian market. This research conducted a survey and analyzed the opinions of people regarding their preferences for electric vehicles, demographics, and some of the demotivation which might be stopping them to switch to electric vehicles altogether. This research will help in determining different factors influencing the perception of consumers toward electric vehicles and what they expect when they think about purchasing a new electric vehicle. It is important to understand that electric vehicles are really getting popular now because of the rising fuel prices and environmental concerns. People are thinking about electric vehicles and replacing them with their regular petrol or diesel vehicles. In this research there might be some challenges or roadblocks in switching to electric vehicles. This research found out that despite a favorable attitude toward electric vehicles, individuals are hesitant to transition to electric vehicles due to different hurdles connected with them. This research found out that mostly the preferences of the consumers are good charging infrastructure, a good range of the electric vehicle, pocket-friendly vehicles are the most common preferences of consumers buying an electric vehicle. 2023 EDP Sciences. All rights reserved. -
A study of Artificial Intelligence impacts on Human Resource Digitalization in Industry 4.0
Artificial Intelligence (AI) has opened up tremendous opportunities in the workplace through robotics innovation, which envelops both AI and the Internet of Things (IoT). Precision, Efficiency, and Flexibility are considered the potential benefits of Industry 4.0. The implementation of Industry 4.0 requires a lot of changes, including the Human Resource (HR) function. In Industry 4.0, the HR capability is more critical and gives an upper hand to the organization. The HR capability should be more cautious and adaptable to adjust to the difficulties and requirements. We study the contributions of AI in HR digitalization and practices in Industry 4.0. 271 HR experts working in Information Technology (IT), Manufacturing, and administration are selected to participate in this review focusing on five AI applications in HR capability and three elements of HR readiness. The information collected was examined utilizing the Statistical Package for Social Sciences (SPSS) tool and Analysis of Moment Structures (AMOS). The results uncovered that hierarchical organization examination is a fundamental part of acquiring sustainable development. Adaptability and human asset capability are upheld by each of the five components of AI application areas of HR. Well-being and Safety improvement were viewed as vital components under the AI application in HR. 2023 The Author(s) -
Power Consumption Forecasting with AI and IOT
Electricity plays a fundamental and indispensable role in modern society, driving progress, development, and the overall quality of life. Electricity is profoundly ingrained in daily life. It powers homes, providing lighting, heating, cooling, and appliances that support, comfort, and convenience. From cooking meals to powering electronic devices and entertainment systems, electricity is vital for modern living, enhancing our quality of life and enabling various activities. Power forecasting is critical to the effective management and optimization of power generation, consumption, and distribution. Power consumption forecasting has evolved significantly with the introduction of advanced technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT). AI techniques, such as machine learning and deep learning, make use of the massive amounts of data produced by IoT devices like smart meters and energy monitoring devices. These devices continuously gather real-time data on power consumption, weather conditions, grid performance, and other relevant factors. AI algorithms can find patterns and correlations and provide accurate forecasts and important insights for power forecasting by processing and analyzing data. Machine learning algorithms, such as regression models, neural networks, and ensemble approaches, are trained using historical power consumption data and the features that have been chosen. The models discover the underlying patterns and correlations between input features and power consumption. These forecasts can be used for short-term load balancing, energy procurement planning, demand response management, and optimizing energy distribution. AI and IoT power usage projections give valuable data for decision-making and energy optimization techniques. These projections can be used by energy suppliers, grid operators, building managers, and consumers to plan energy usage, distribute resources efficiently, optimize demand response programs, and discover possibilities for energy saving. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Navigating resource scarcity in a changing climate: AI-powered perspectives on mental health
In the backdrop of the extensive global impact of the COVID-19 pandemic, environmental crises have, to a certain degree, taken a back seat. The pandemic-induced scarcity mindset, emphasizing immediate short-term needs over long-term considerations, has played a role in this shift in priorities. This scarcity mindset, prevalent during the pandemic, poses a risk to pro-environmental behavior and may contribute to environmental degradation, thereby heightening the likelihood of future pandemics. This chapter advocates for a reevaluation of pro-environmental actions, emphasizing their role in addressing various human needs, especially during periods of scarcity. AI-driven chatbots possess the capability to significantly enhance accessibility to affordable and efficient mental health services by complementing the efforts of clinicians. To safeguard pro-environmental behavior, we propose a reconceptualization that positions these actions not merely as value-laden or effortful but as pragmatic measures essential for resource conservation, particularly in times of scarcity. The study explores, the intricate dynamics of resource scarcity, climate change, and mental health, employing AI-powered perspectives to navigate this complex interplay. 2024, IGI Global. All rights reserved.