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UV-Promoted Metal- and Photocatalyst-Free Direct Conversion of Aromatic Aldehydes to Nitriles
Abstract: An efficient, simple, and catalyst-free UV-induced functional group transformation of aromatic aldehydes to nitriles has been reported. The developed strategy delivers various functionalized aromatic nitriles with high yields and purity. The UV irradiation activates the carbonyl group of aldehydes and leads to the formation of aldoxime intermediate, further resulting in the generation of nitriles. The striking highlights of the reported methodology are simple reaction conditions, good yields, UV-promoted transformation, and catalyst-free synthesis. Due to the above-mentioned advantages, the methodology provides a whip hand toward environmentally friendly chemical synthesis. 2022, Pleiades Publishing, Ltd. -
UV-C and gamma radiation mediated L-DOPA production from in-vitro cultures of Mucuna pruriens (L.) DC
This is the first report on UV-C and gamma rays mediated in-vitro elicitation of L-DOPA from Mucuna pruriens (L.) DC. cell suspension cultures. Gamma and ultraviolet rays are used on plants to induce mutations which results in activation of defence cascades and production of secondary metabolites due to this abiotic stress. The in-vitro callus developed from 0.5mg/L picloram was suspended into liquid medium and exposed to different time intervals (0, 15, 30, 45 and 60min) of UV-C radiations. On the other hand, the seeds were directly exposed to different doses (25, 50, 100, 150 and 200Gy) of gamma radiations and these irradiated seeds were grown in-vitro from which callus and cell cultures were established. From all these in-vitro cultures, the anti-Parkinsons drug L-DOPA was quantified using HPLC. 60 and 30-minute exposure of UV-C radiations resulted in highest biomass (193.27g/L FW) and L-DOPA production (5.13mg/g DW) respectively both showing a 1.5-fold increase than the control. In gamma radiation studies, 100Gy (Gy) dose showed the highest (83%) seed germination rate, 150Gy increased the in-vitro root and shoot length, while 100Gy increased the biomass of the cell cultures. Also, 150Gy dose showed a 6.1, 2.6 and 2.4-fold increase in L-DOPA production in the in-vitro roots, in-vitro shoots, and cell suspension culture respectively when compared to the control. UV light exposure of 30min and 150Gy doses of gamma radiation showed a significant increase in L-DOPA production. The Author(s) under exclusive licence to Society for Plant Research 2024. -
UV-C and gamma radiation mediated L-DOPA production from in-vitro cultures of Mucuna pruriens (L.) DC
This is the first report on UV-C and gamma rays mediated in-vitro elicitation of L-DOPA from Mucuna pruriens (L.) DC. cell suspension cultures. Gamma and ultraviolet rays are used on plants to induce mutations which results in activation of defence cascades and production of secondary metabolites due to this abiotic stress. The in-vitro callus developed from 0.5mg/L picloram was suspended into liquid medium and exposed to different time intervals (0, 15, 30, 45 and 60min) of UV-C radiations. On the other hand, the seeds were directly exposed to different doses (25, 50, 100, 150 and 200Gy) of gamma radiations and these irradiated seeds were grown in-vitro from which callus and cell cultures were established. From all these in-vitro cultures, the anti-Parkinsons drug L-DOPA was quantified using HPLC. 60 and 30-minute exposure of UV-C radiations resulted in highest biomass (193.27g/L FW) and L-DOPA production (5.13mg/g DW) respectively both showing a 1.5-fold increase than the control. In gamma radiation studies, 100Gy (Gy) dose showed the highest (83%) seed germination rate, 150Gy increased the in-vitro root and shoot length, while 100Gy increased the biomass of the cell cultures. Also, 150Gy dose showed a 6.1, 2.6 and 2.4-fold increase in L-DOPA production in the in-vitro roots, in-vitro shoots, and cell suspension culture respectively when compared to the control. UV light exposure of 30min and 150Gy doses of gamma radiation showed a significant increase in L-DOPA production. The Author(s) under exclusive licence to Society for Plant Research 2024. -
Utilizing Transforming Portfolio Management Through Automation Using Advanced Deep Reinforcement Learning Algorithms for Optimized Investment Strategies
This paper focuses on the future possibility of enhancing the applications of DRL in autonomously managing a portfolio for better investment plans. Having used past financial data and a highly developed case of DRL, the proposed system shows better performance compared to conventional investment strategies and indices. This process includes data gathering from the financial databases, the steps of preprocessing and feature extraction, and the use of the DQN structure. After that, the system's training and validation are done by a finite portion of real-world data and a large number of synthesized data to improve stability. The result shows that the new method achieves superior cumulative return, Sharpe ratio, maximum drawdown, and annualized volatility; therefore, it suggests that the proposed system can flexibly predict the fluctuating stock market trends and make appropriate investment decisions. Thus, the present research adds importance to the use of DRL in improving return potential and risk management in portfolio management. Thus, this study adds to the existing literature and practice by allowing for the automation of the optimization and testing for investment solutions at a larger scale, while opening up opportunities for future developments in the application of financial technology and investment tools. 2025 IEEE. -
Utilizing social psychology to drive financial policy solutions: Addressing female feticide and infanticide
Female feticide and infanticide, are two of the most serious problems confronting Indian society. This issue is largely caused by the identification of female fetuses through technology, which frequently results in the termination of a pregnancy. Despite the governments efforts to curb these practices, progress has been limited. There are facilities in cities for determining the gender of an unborn child. The financial difficulty of raising a girl child is a key element in the preference for male offspring. The aim of this study is to propose innovative financial solutions that the government can implement to address this longstanding and complex issue. By exploring various financial inclusion strategies, this study seeks to identify effective measures that can bring about social change and promote gender equality. 2024 by author(s). -
Utilizing Machine Learning for Sport Data Analytics in Cricket: Score Prediction and Player Categorization
Cricket is a popular sport with complex gameplay and numerous variables that contribute to team performance. In recent years, sports analytics has gained significant attention, aiming to extract valuable insights from large volumes of cricket data. Cricket has many fans in India. With a strong fan following, many try to use their cricket intuition to predict the outcome of a match. A set of rules and a points system govern the game. The venue and the performance of each player greatly affect the outcome of the match. The game is difficult to predict accurately as the various components are closely related. The CRR (Current Run Rate) approach is used to predict the final score of the first innings of a cricket match. Total points are calculated by multiplying the average number of runs scored in each over by the total number of overs. For ODI cricket, these methods are useless as the game can change very quickly regardless of the current run rate. The game may be decided by 1 or 2 overs. For more accurate score predictions, a system is needed that can more accurately predict the outcome of an inning. This research paper explores the application of machine learning techniques to predict scores and classify players based on their roles in the squad. The study utilizes a comprehensive dataset comprising various attributes of cricket matches, including player statistics, match conditions, and historical performance. Linear Regression, Logistic Regression, Naive Bayes, Support Vector Machines (SVM), Decision Tree, and Random Forest regression models are employed to predict scores. Additionally, player categorization is performed using a classification approach. The results demonstrate the effectiveness of machine learning techniques in enhancing performance analysis and decision-making in the game of cricket. 2023 IEEE. -
Utilizing Machine Learning for Advanced Natural Language Processing and Sentiment Analysis in Social Media Platforms
Social media is increasingly regarded as one of the most abundant online resources for information gathering and knowledge exchange. Among the most widely used social media sites is Twitter available today. When attempting to comprehend the information in any unknown word-based data (such as social media), natural language processing (NLP) techniques are crucial since they help remove noise from data, identify stem words, etc. It also helps with comprehension of the sentiment or semantic contents. Using social media, we apply machine learning techniques (clustering and classification) to determine the viewpoint's polarity in the information. Several classifiers and clusters, including SVM, RF, Naive Byes, and KNN, are used to detect content on social media. Sentiment analysis is the process of automatically classifying user-generated content as neutral, negative, or positive. It is possible to utilize the text, sentence, feature, or aspect as criteria to group feelings into distinct categories. This study demonstrates the application of machine learning techniques to the analysis of emotions expressed on the Twitter network. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Utilizing Highly Reactive Lewis Pairs Generated by Oxygen Vacancies in the Cu3Mo2O9 Solid Catalyst for Cycloaddition of CO2 to 1,2-Propanediol
This work emphasizes generating highly reactive Lewis pair sites on CuMo oxides for CO2 activation and utilization in the cyclization reaction to produce propylene carbonate from 1,2-propanediol. The CuMo oxides were synthesized by enabling the oxygen vacancies that enhance the catalytically active sites, resulting in the formation of metastable cations (Mo5+ and Cu1+) and oxygen vacancies. Under ethanol-PEG-400 medium, the pure phase of Cu3Mo2O9 obtained at 500 C exposed maximum defects without any secondary phase compared to other screened catalysts. The experimental and theoretical investigations provide evidence for determining and correlating the characteristics of active sites with catalytic performance. The catalysts were extensively characterized along with density functional theory (DFT) studies, which revealed the presence of defect centers as one of the key factors in the enhanced activity. From the chemical bonding analysis, i.e., Crystal Orbital Hamiltonian Population (COHP) and Electron Localization Function (ELF), the CO2 molecule is known to form a strong chemisorption interaction with the catalyst surface that is facilitated by the oxygen vacancy/Lewis pairs. The Cu-Mo oxide catalyst achieved 99% conversion of 1,2-propanediol and 97% yield of propylene carbonate, outperforming previously reported catalysts. Thus, Cu-Mo oxide was shown to be highly efficient catalyst with good recyclability for 1,2-propanediol and the CO2 reaction. 2025 American Chemical Society. -
Utilizing GIS for Crime Mapping to Identify Crime Hotspots in the Urban Context of Kerala
This study employs Geographic Information Systems (GIS) for crime mapping to pinpoint hotspots within the Museum police stations jurisdiction in Trivandrum, Kerala, which saw the highest number of crimes reported in 2022, as per the National Crime Records Bureau (NCRB), making it an important issue to be tackled. Trivandrum was chosen for its high population density, aligning with criminological theories linking dense urban areas to increased criminal activity. The jurisdiction of the Museum police station area was explicitly selected due to its significant incidence of various crimes as per the data available from the Kerala police website while comparing it with the overall 21 jurisdiction boundaries. Data collection encompassing seven crime categories as per the analysis of previous literature studies, which includesrape, theft, molestation, kidnapping, murder, hurt, auto theft, and robberywas meticulously gathered from the Museum police station and organized using Excel, then analysed through GIS techniques. These methods included Average Nearest Neighbours Analysis to identify crime pattern types, Kernel Density Estimation to visualize crime density maps, Choropleth mapping to highlight wards with heightened crime rates, and Standard Deviation Ellipse Analysis to explore trends in crime distribution. These analytical approaches and their comparison with buffered maps facilitated a comprehensive spatial examination, uncovering distinct crime hotspots and illuminating factors contributing to their concentration. The study concludes by pointing out the main vulnerable areas of the study with the help of the previously mentioned mapping analysis, helping in providing suitable areas to be focused on to provide design strategies to curb crime. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Utilizing Deep Learning Techniques for Lung Cancer Detection
Deep learning can extract meaningful insights from complex biomedical statistics, which includes Radiographs and virtual tomosynthesis. Traits in contemporary deep studying architectures have enabled faster and more correct mastering of the functions gifted in clinical imagery, main to better accuracy and precision in medical analysis and imaging. Deep studying strategies may be used to pick out patterns within the pics which may be indicative of illnesses like lung cancer. Those ailment patterns, which include small lung nodules, can be used for early detection and prognosis of the sickness. Recent studies have employed deep learning strategies consisting of Convolutional Neural Networks (CNNs) and switch learning to come across most lung cancers in CT pictures. The first step in this manner is to generate datasets of pictures of the lungs, each from wholesome people and those with most lung cancers. Those datasets can then be used to teach a deep knowledge of a set of rules that may be optimized to it should locate those styles. Once educated, the version can be used to come across styles indicative of lung most cancers from new take a look at images with high accuracy. For further accuracy and reliability, extra up-processing techniques, along with segmentation and records augmentation, may be used. Segmentation can be used to detect a couple of lung nodules in a photo, and records augmentation can be used to lessen fake high quality outcomes. 2024 IEEE. -
Utilizing Deep Learning Features to Categorize WBCs in Blood Smear Images
Automated categorization of white blood cells (WBCs) is essential not just to identify infections, autoimmune ailments, and blood-related disorders, but also in the pivotal decision-making process concerning patient treatment and the efficient management of diseases. In this paper, an advanced approach for WBC type classification using smear images is proposed. The VGG16 model is utilized to capture intricate features of the images, which are then provided to an XGBoost classifier. This integration enables precise classification into 5 distinct WBC types. Our model shows a significant accuracy score of 92.3%, demonstrating its capability in accurately identifying WBC types from smear images. Proposed technique provides a promising pathway for automating WBC classification, thereby enhancing efficiency in disease diagnosis and decision-making within clinical settings. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Utilizing brain-computer interfaces for personalized marketing strategies
Through the provision of direct insights into the preferences and emotional responses of consumers, the objective of this research paper is to evaluate the potential for Brain-Computer Interfaces (BCIs) to bring about a change in the approaches of personalized marketing. Brain-computer interfaces, also known as BCIs, are devices that allow for a link to be made between the brain and external equipment. This allows marketers to have access to real-time neurological data that is truly unequalled. In turn, this makes it possible for marketers to develop marketing strategies that are extremely focused on the population that they are trying to reach. The objective of this project is to examine how brain-computer interfaces (BCIs) can be leveraged to evaluate the responses of consumers to advertisements, product designs, and brand messages. To refine their plans, this would make it possible for marketers to make use of subconscious reactions rather than the conventional survey methods. Some significant challenges are also discussed, including the difficulty of decoding brain signals. 2025, IGI Global Scientific Publishing. -
Utilizing Artificial Intelligence-Powered Chatbots for Enhanced Customer Support in Online Retail
In many e-commerce contexts, live chat interfaces have become popular as a way to communicate with consumers and provide real-time customer support. Conversational software agents, commonly known as Chatbots, are systems created to converse with users in natural language and are often based on artificial intelligence (AI). These systems have replaced human chat service agents in many cases. Although AI -based Chatbots have been widely used due to their time and cost savings, they have not yet met consumer expectations, which may make users less likely to comply with chatbot requests. We empirically study, through a randomized online experiment, the impact of verbal humanoid design cues and a direct approach on compliance with user requirements, based on Social Reactions and Attachment Commitment Theory. Our results show that consumers are more likely to cooperate with chatbot service response requests when there is humanity and consistency. Furthermore, the results demonstrate that social presence plays a mediating role between humanoid design cues and user compliance. 2024 IEEE. -
Utilization of Iron Ore Tailings for the Production of Fly Ash - GGBS-Based Geopolymer Bricks
In India, million tons of manufacturing ravages such as ground-granulated blast furnace slag (GGBS), fly ash and mine tailings, are endangering. These ravages turn out to be injurious as they are landfilled close to the production sites and somewhere else. Since these manufacturing ravages include silica, alumina, calcium, etc., it is probable to formulate these as unprocessed resources to produce building substance which diminishes the carbon trace. In this circumstance, this analysis observes on utilizing iron ore tailings and slag sand as a substitution for clay or natural sand for the construction of steady geopolymer obstruct. Furthermore, in this analysis, geopolymer is utilized as a binder rather than cement. Expansion of geopolymer binder-oriented bricks with fly ash and GGBS has been implemented in this study. The analysis consists of automatic possessions of the geopolymer bricks. Sodium silicate (Na2SiO3) and sodium hydroxide (NaOH) resolution have been employed as alkaline activators. The proportion of alkaline liquid to aluminosilicate solid quotient and fraction of binder encompass foremost control on the force of brick. The bricks were casted and cured at ambient warmth. The compressive strength was tested at 7, 14 and 28 days. 2017 World Scientific Publishing Company. -
Utilization of IoT-Based Healthcare System and Vital Data Monitoring of patients
The next generation of technology, known as the Internet of Things (IoT), will provide a comprehensive system that connects different domains, functions, and innovations. With the increasing demand for elderly care due to the growing ageing population, health monitoring systems have become increasingly important. Continuous monitoring is required in ICU to monitor the health conditions of patients. In cases where patients are released from the hospital, they are advised to rest and observed for a certain period, and the IoT system is very helpful in such cases. This article primarily discusses the implementation of a precise autonomous medical facility management system using IoT. In the past, only current data was displayed, and the patients history could not be accessed. In this study, we propose an IoT-based healthcare system for continuous monitoring of a patients health conditions. The healthcare system focuses on measuring and monitoring various biological parameters of the patients body, such as heart rate, blood oxygen saturation level, and temperature, using a web server and an Android application. Doctors can continuously monitor the patients condition on their smart phones using the Android application. Moreover, the patients history will be stored on the web server, and doctors can access the information from anywhere without being physically present. RJPT All right reserved. -
Utilization of industrial and agricultural waste materials for the development of geopolymer concrete- A review
Concrete is a highly consumed construction material. Cement is the first and foremost ingredient in the manufacture of concrete. Manufacturing of cement results in emission of an equal amount of carbon dioxide. These greenhouse gases cause global warming. The utilization of environment-friendly construction materials has been identified to be most essential to overcome environmental issues. An ecofriendly concrete such as geopolymer concrete founds to be an alternative for cement concrete. Geopolymer concrete (GPC) is a sustainable construction material as it can reduce carbon dioxide emission by utilizing industrial and agricultural waste by-products. Hence in this context, to reduce global warming, usage of cement can be minimized by replacing it with other materials such as Fly ash, Silica fume, Red mud, Ground granulated blast furnace slag, Metakaolin, Rice husk ash, Corncob ash, Sugarcane bagasse ash etc. These materials have been utilized to prepare geopolymer concrete with good mechanical strength, durability and thermal resistivity. A lot of research has gone into the development of sustainable geopolymer concrete utilizing various industrial and agricultural waste. This review paper is on the research on the utilization of industrial and agricultural waste materials to produce sustainable geopolymer concrete. 2022 -
Utilization of CO2 for Electrocarboxylation of Benzophenone Using MXene-Based Electrodes: A Sustainable Approach
The significant rise in atmospheric carbon dioxide (CO2) levels has prompted the need to develop efficient methods for CO2 conversion and fixation methods. Electrocarboxylation reaction is a highly efficient and sustainable method for activating and utilizing CO2, yielding essential carboxylic acids and their analogues, which are important intermediates in the pharmaceutical and fuel industries. This research demonstrates the efficiency of 2D Ti3C2Tx and Ta2CTx MXene-modified carbon fiber paper electrodes (Ti3C2Tx/CFP and Ta2CTx/CFP) for CO2 fixation with benzophenone in a tetrabutylammonium bromide/acetonitrile (TBABr/CH3CN) medium, yielding benzilic acid. Ti3C2Tx/CFP exhibited superior electrocatalytic activity with a lower reduction potential for benzophenone at ?1.0 V and achieved a 72% yield of benzilic acid at an optimum current density of 50 mA cm-2. In comparison, Ta2CTx/CFP exhibited a cathodic peak at ?1.08 V, producing a 66% yield at 70 mA cm-2. The electron paramagnetic resonance spectrum substantiates the generation of reactive radical intermediates during the reaction. Ti3C2Tx/CFP showed robust structural stability with ?88% Faradaic efficiency and a turnover frequency of 1.90444 10-5 s-1, indicating its potential for CO2 fixation. 2024 American Chemical Society. -
Utilization of aluminum dross: Refractories from industrial waste
Aluminum oxide (Al2O3) and Magnesium-Aluminum oxides (MgAl2O4) are well known refractory materials used in engineering industries. They are built to withstand high temperatures and possess low thermal conductivities for greater energy efficiency. Dross, a product/byproduct of slag generated in aluminum metal production process is normally comprised of these two oxides in addition to aluminum nitride (AlN). Worldwide, thousands of tons of aluminum dross are generated as industrial wastes and are disposed of in landfills causing serious environmental hazard. This paper explores the potential to synergize the characteristics of the favourable contents of aluminum dross and its availability (in tons) via synthesis of refractories and thereby develop a value added product useful for the modern industries. In this work, Al-dross as-received from an aluminum industry which comprised of predominantly Al2O3, MgAl2O4 and AlN, was used to develop the refractories. AlN possesses high thermal conductivity values and therefore was leached out of the dross to protect the performance of the developed refractory. The washed dross was calcined at 700 and 1000C to facilitate gradual elimination of the undesired phases and finally sintered at 1500C. The dross refractory pellets were subjected to thermo-physical and structural properties analysis: XRD (structural phase), SEM (Microstructure), EDS (chemical constituents) and thermal shock cycling test by dipping in molten aluminum and exposing to ambient (laboratory). The findings include the favourable prospects of using aluminum dross as refractories in metal casting industries. Published under licence by IOP Publishing Ltd. -
Utilisation of Virtual Assistant and Its Impact on Retail Industry
Virtual assistant is nothing but an independent contractor, who offers administrative services to the clients of a particular organisation while operating outside of the office of the client. Generally, a virtual assistant operates from a home-based office. This virtual assistant application has the ability to access the required planning documents, such as shared calendars. The contemporary retail organisations like e-commerce companies in this competitive global business environment are using virtual assistant to enhance omnichannel experience, 24/7 customer service, order tracking, and product recommendations. Overall, virtual assistant helps the organisations in enhancing social media management activities. This concept of the use of virtual assistant has been significantly emerged after the increase in demands for e-commerce business activities in this decade. Research objectives related to the title of this research are developed and listed. Relevant theories on virtual assistant are applied in the literature review section of this study. The researcher has decided to adopt qualitative research methodology to achieve the objectives of the research. Moreover, the researcher has considered secondary data analysis approach to conduct this research. In terms of findings, it has been identified that virtual assistant has a positive impact on the business operation activities of retail organisations. Authentic secondary sources are considered to collect and analyse the data. Some challenges associated with the utilisation of virtual assistant also have been identified in the findings section. Some valuable recommendations are suggested for the future researchers to overcome those identified associated challenges. 2022 IEEE. -
Using Time series analysis, analyze the impact of the wholesale price index on the price escalation in the automotive industry
The automobile industry is a crucial sector of the economy, contributing significantly to employment and economic growth. One of the major challenges faced by this industry is the problem of price escalation, which can affect both consumers and manufacturers. In this project, we explore the impact of wholesale price index (WPI) on the price escalation of automobiles using time series analysis. We analyze the historical data of WPI and automobile prices in India from 2010 to 2022. We use statistical techniques like stationarity tests, autocorrelation analysis, and Granger causality tests to understand the relationship between WPI and automobile prices. Furthermore, we employ a SARIMA model in predicting WPI value and Vector Auto regression (VAR) model to analyze the dynamic interactions between WPI and CPI value. Our findings suggest that WPI has a significant impact on the price escalation of automobiles in India. The VAR model shows that there is a positive feedback loop between WPI, CPI and automobile prices, implying that an increase in WPI leads to a corresponding increase in automobile prices and vice versa. This feedback loop can create an inflationary spiral in the automobile industry, which can be detrimental to the economy. Our project highlights the importance of monitoring WPI and its impact on the automobile industry. Policymakers and industry experts can use our findings to develop effective strategies to manage price escalation in the automobile industry and mitigate its negative impact on the economy. 2023 ACM.