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Comparative study of various metals in the sewage samples of three major drains of the city-Patna, Bihar, India /
Mapana Journal Of Science, Vol.16, Issue 4, pp.23-35, ISSN: 0975-3303. -
Vappav /
Patent Number: 202241028305, Applicant: Sashi Kumar D.
The primary objective and the motto behind this invention of the device Vappav is for the safety of food items and food parcels which needs to have a minimum contact from people and to make sure that the delivery of these parcels are safe without many people touching it, which further ensures the safety and hygienic of the food items. -
A system and method for integrated monitoring, control and management of various parameters of agriculture and crop growth and a device for implementing the same /
Patent Number: 202041010535, Applicant: S Rakesh Kumar.
A system and method for integrated monitoring and management of vital agricultural parameters that are key for sustained plant growth and enhanced crop output, qualitatively and quantitatively is provided. The key aspects of sustained and healthy growth of agricultural crops, viz., temperature, moisture, humidity are monitored and controlled through means, comprising of temperature sensor, soil moisture sensor, motor pump progressively controlled by Arduino and the collated data are displayed through screen. -
Retail merchandising aided by shop display systems /
Patent Number: 202211009293, Applicant: Rakesh Kumar Yadav.
The present invention is a retail merchandising aided by shop display systems: comprises of, three or more display modules but at least 3 should be available therein, once side of the display structure should be open and provided space to access it, but the other side is closed thereby pivoted vertically to provide the provision for fastening it firmly, therein displaying the items have a better space along with facility is given to the system that it state the items placed in the display modules while pressing the corresponding button provided to the individual display unit. -
Device for analysis of market communication in tourism /
Patent Number: 202221009290, Applicant: Dr.Shilpa Bhakar.
The present invention is a device for analysis of market communication in tourism comprises of, a display unit is split into at the 4 parts to convert into a segmented display, thereby, an electronic kit is fitted into the display unit which is being getting the data therein, it is received by an analyser, this analysed data is send to the display unit to show the graphical representation of the tourism information under four different displaying ways. -
Design of an real time smart health care system through data mining and IOT /
"Patent Number: 201941041826, Applicant: Dr. Dinesh Kumar.
Due to the advancements in the medical field , the life expectancy has been i increased significantly .This project is mainly useful forthe elderly people who live without the support from others . They need the assistance for the various parameters. IoT in health careplays a major role in identifying the medical facilities for the patients and the doctors . Our Proposed system with various sensors todiagnose the patients health condition from the remote place" -
System and method for detecting presence of an intruder near a neonatal crib /
Patent Number: 201941028405, Applicant: Karthick Raghunath K M.
Present disclosure provides systems and methods for monitoring presence of an intruder near a neonatal crib in real time. A set of input signals are captured from the one or more sensors that are operatively coupled to the neonatal crib. Attributes pertaining to distance of the intruder from the neonatal crib are determined based on the received input signals. Based on the determined distance, an executable set of instructions generate a rule. -
A Review on Development and Properties of Ultra-High-Performance Concrete
This study presents a review of literature on ultra-high-performance concrete that brings in information regarding the preparation of UHPC, mix design of UHPC on the basis of various particle packing density models, microstructural analysis, durability studies, and strength characteristics. A data base is collected to study the performance, mechanical strength and durability of UHPC from various research works. UHPC can found to be a long-term solution for present day challenges that is faced in construction industry when conventional concrete is used and makes this concrete a novel concept in concrete technology. The advantages of UHPC are: less porosity, high abrasion resistance, greater mechanical properties, high packing density, and improvement in fatigue behavior though the cost of UHPC is high. The non-availability of a standard code for mix design of UHPC makes it difficult to arrive at consistent and comparable mix. The cost of UHPC can be controlled with the use of naturally available materials and utilizing agricultural and industrial waste materials in UHPC. From the data collected, it is observed that the binder content can be optimized and cement which is residual in UHPC can be replaced by industrial residue like fly ash, GGBS, glass powder, etc. and thereby brings down the cost without compromising on the strength and performance. The information shared in this paper will help the contractors, consultants, engineers, industry stakeholders and researchers to alleviate the confusions regarding the use of UHPC in construction industry and to encourage research to make this concrete construction-industry friendly. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Copper immobilized on a layered magnetite-based nanocatalyst for sustainable Ullmann cross-coupling reaction
This study demonstrates the efficient synthesis of diarylthioethers via CS cross-coupling between diverse aryl halides and arylthiols utilizing a magnetically retractable Fe3O4@SiO2PrNH2SACu(ii) nanocatalyst using K2CO3 as a base in DMF. The heterogeneous nanocatalyst was fabricated through a multistep process. The designed catalyst was characterized using various techniques, such as XRD, HRTEM, FESEM, STEM, EDAX, elemental mapping, TGA, VSM, XPS, ICP-OES and FT-IR. The catalyst design provides a dual role of the Schiff base-anchoring copper ions, to accelerate the oxidative addition and reductive elimination steps. This method makes use of ligand-free synthesis of diarylsulfides, enabling magnetic recovery and reuse of the catalyst for up to 6 cycles. The nanocatalyst exhibited high catalytic activity and a broad substrate scope. The magnetic nature of the nanocatalyst enabled easy separation from the reaction mixture using an external magnet, thus simplifying the workup. The synthesized nanocatalyst was then utilized for the synthesis of diarylthioethers and heterodiarylthioethers. The pure compounds were characterized using 1H and 13C NMR. This catalytic system offers a cost-effective, efficient, and simple protocol for the formation of the CS bond. This journal is The Royal Society of Chemistry, 2026. -
Surface bound copper- grafted TiO2 nanocatalyst for carbon-sulfur cross coupling reaction
This study reports the synthesis of TiO2-based nanocatalyst for efficient diarylsulfide synthesis via Ullmann-type reaction strategy, addressing challenges in conventional methods that are reliant on toxic reagents and harsh reaction conditions. The nanocatalyst comprises an amine-functionalized TiO2 core followed by copper doping. This nanocatalyst demonstrates exceptional performance in cross coupling reactions under mild conditions, achieving yields up to 5098 % with broad-substrate scope. The pure products were characterized using 1H NMR, 13C NMR, FT-IR, and mass spectrometry. The catalyst's heterogeneous nature enables easy recovery and reuse for upto 5 cycles without any significant activity loss. The synthesized nanocatalyst was characterized using various characterization techniques such as FT-IR, TGA, XRD, EDX, SEM, and STEM. This approach aligns with the green chemistry principles, minimizing waste and energy consumption and replacing highly expensive transition metal catalysts. The work highlights the potential of functionalized TiO2 nanomaterials in sustainable organic synthesis, contributing to SDGs 3 (Health through safer pharmaceuticals), 9 (industry innovation), and 12 (responsible production). 2025 Elsevier B.V. -
What fuels the employees in startups?: Data on hybrid/colocated/virtual working environment towards efficiency
Purpose: This article examines the concepts of workplace satisfaction and productivity using data. The data will be used to investigate the variables contributing to employee satisfaction to achieve optimum efficiency through various startup working environments. Design/ Methodology/ Approach: Descriptive causal investigation. A structured instrument scale questionnaire via the internet to 256 employees working for highly organized organizations in Bangalore, India, using Qualtrics. The researcher adopted a simple random sampling method. Findings: The respondents in the data believed that the pre-covid workplace was advantageous. The hybrid model's prevalence of autonomy and flexibility increases work productivity. When employees are given more responsibility, their job satisfaction and productivity increase. Research Limitations/ Implications: Collecting data in a startup was extremely difficult due to the difficulty of obtaining permission, and through the analysis, it was determined that businesses have a responsibility to provide supplemental benefits to remote employees, which may increase the level of job satisfaction and enjoyment experienced by these individuals. 2023 The Author(s) -
Attitude toward inter-religious marriage: interplay of generational shifts with religious affiliations and educational attainments
This quantitative research examined the interaction of generational shifts, religious affiliations, and educational attainments in shaping attitudes toward inter-religious marriage. Data were collected from 1231 Indian respondents from iGen/Gen Z, Xennials & Millennials, and Baby Boomers through a demographic response sheet and the Attitude Scale developed by Parker et al. where lower ratings signified positive attitudes and higher ratings indicated negative attitudes. The result revealed that generational shifts were significantly associated with religion (?2 = 96.6, p=<.001) and education (?2 = 279, p=<.001). Significant interaction effects were found between generational shifts and religious affiliations (F = 5.36, p <.001, ?2 p =.017) and generational shifts and educational attainments (F = 6.79, p <.001, ?2 p =.027) concerning attitudes toward inter-religious marriage. This study uncovered the interaction of the demographic variables in shaping the attitude toward inter-religious marriage. 2026 Taylor & Francis Group, LLC. -
Individual and Relational Outcomes of Inter-religious Marriage: A Scoping Review
Inter-religious marriages, where partners come from different religious affiliations, pose unique challenges and opportunities. This scoping review aims to examine the literature on individual and relational outcomes of inter-religious couples and their families, synthesising existing evidence on their social, psychological, and cultural aspects. While numerous studies exist on this topic, their findings have not yet been systematically synthesised. The question for this scoping review was how existing studies explore the individual and relational outcomes of interfaith marriages. A comparative search, following Arksey and OMalleys five-step framework and PRISMA-ScR guidelines, was conducted across Scopus, ScienceDirect, APA PsycNet, JSTOR, PubMed, Google Scholar, and ProQuest databases from 2004 to 2024. After screening 1,276 references based on inclusion criteria, 19 peer-reviewed articles were selected for the scoping review. Four key themes emerged: (1) Marital adjustment and tensions, (2) Psychological impacts, (3) Marital instability and dissolution, and (4) Strengths and opportunities. This scoping review emphasises the intricate challenges encountered by inter-religious couples, encompassing familial opposition, identity dilemmas, cultural and religious disputes, marital instability, and psychological distress. The review highlights the need for increased societal and institutional support and calls for further research into adaptive coping strategies across diverse cultural contexts. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Heart Disease Prediction Using Ensemble Voting Methods in Machine Learning
Heart disease is the leading cause of mortality globally according to the World Health Organization. Every year, it results in millions of mortalities and thus billions of dollars in economic damage throughout the world. Many lives can be saved if the disease is detected early and accurately. The typical methods to predict or diagnosis heart diseases require medical expertise. Such facilities and experts are relatively expensive and not very commonly available in under developed and developing countries. Recent times, much research is done on leveraging technology for the prediction as well as diagnosis of heart diseases. Machine Learning techniques have been extensively deployed as quick, inexpensive, and noninvasive ways for heart disease identification. In this work, we present a machine learning approach in detecting heart disease using a dataset that contains vital body parameters. We used seven different models and combined them with Soft-Voting and Hard-Voting ensemble approaches to improve accuracy in 7-model and various 5-model combinations. The ensemble combinations of 5 models achieved the highest test accuracy score of 94.2%. 2022 IEEE. -
Cardiovascular Disease Prediction through Ensembled Transfer Learning on Cardiac Magnetic Resonance Imaging
Cardiovascular Diseases (CVD) cause more deaths worldwide than most of the other diseases. The diagnosis of cardiovascular disease from Magnetic Resonance Imaging plays a major role in the medical field. The technological revolution contributed a lot to increase the effectiveness of CVD diagnosis. Many Artificial Intelligence methods using Deep Learning models are available to assist the cardiologist in the diagnosis of CVD from Magnetic Resonance Imaging (MRI). In this study, we leverage on the merits of deep learning, transfer learning, and ensemble voting to improve the accuracy of Artificial Intelligence-based CVD detection. VGG16, MobileNetV2, and InceptionV3, trained on ImageNet, are the models used and the dataset is the Automatic Cardiac Diagnosis Challenge dataset. We customized the classification layers of all three models to suit the CVD detection problem. The results from these models are ensembled using the soft-voting and hard-voting approaches. Test accuracies obtained are 97.94% and 98.08% from hard-voting and soft-voting respectively. The experimental results demonstrated that the ensemble of outputs from transfer learning-based Deep Learning models produces much improved results for CVD diagnosis from MRI images. 2022 Sibu Cyriac, Sivakumar R. and Nidhin Raju. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Comparison of Full Training and Transfer Learning in Deep Learning for Image Classification
The deep learning algorithms on a small dataset are often not efficient for image classification problems. Make use of the features learned by a model trained on large similar dataset and saved for future reference is a method to solve this problem. In this work, we present a comparison of full training and transfer learning for image classification using Deep Learning. Three different deep learning architectures namely MobileNetV2, InceptionV3 and VGG16 were used for this experiment. Transfer learning showed higher accuracy and less loss than full-training. According to transfer learning results, MobileNetV2 model achieved 98.96%, InceptionV3 model achieved 98.44% and VGG16 model achieved 97.405 as highest test accuracies. The full-trained models did not achieve as much accuracy as that of transfer learning models on the same dataset. The accuracies achieved by full-training for MobileNetV2, InceptionV3 and VGG16 are 79.08%, 73.44% and 75.62% respectively. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Pneumonia Detection using Ensemble Transfer Learning
Pneumonia is among the most common illnesses and causes to death among the young children worldwide. It is more serious in under-developed countries as it is hard to diagnose due to the absence of specialists. Chest X-ray images have essentially been utilized in the diagnosis of this disease. Examining chest X-rays is a difficult task, even for an experienced radiologist. Information Technology, especially Artificial Intelligence, have started contributing to accurate diagnosis of pneumonia from chest X-ray images. In this work, we used deep learning, transfer learning, and ensemble voting to increase the accuracy of pneumonia detection. The models utilized are VGG16, MobileNetV2, and InceptionV3, all pre-trained on ImageNet, and used the Kaggle RSNA CXR image dataset. The results from these models are ensembled using the weighted average ensemble approach to achieve better accuracy and obtained 98.63% test accuracy. The results are promising, and the proposed model can assist doctors in detecting pneumonia quickly and accurately from Chest X-Ray. 2022 IEEE. -
Seismic Activity-based Human Intrusion Detection using Deep Neural Networks
Human intrusion detection systems have found their applications in many sectors including the surveillance of critical infrastructures. Generally, these systems make use of cameras mounted on strategic locations for surveillance purposes. Cameras based detection systems are limited by line-of-sight, need regular maintenance and dependence of electricity for operations. These are all detrimental to the efficiency of these detection systems, especially in remote locations. To overcome these challenges, intrusion detection systems based on seismic activities have been in use. The seismic activities collected through geophones from the human footfalls can act as the input for these detection systems. This also poses a challenge as the data generated by the geophones for the seismic activities produced from footsteps are not always identical and hence not accurate. In this proposed work, a Deep Neural Network based approach has been used on the dataset collected from the geophones to effectively predict the presence of humans. The results gave a success rate with 94.86% accuracy with testing data and 92.00% accuracy with real-time data with the geophones deployed on an area covered with grass. 2022 IEEE. -
Application of LSTM Model for Western Music Composition
Music is one of the innate creative expressions of human beings. Music composition approaches have always been a focal point of music-based research and there has been an increasing interest in Artificial Intelligence (AI) based music composition methods in recent times. Developing an accurate algorithm and neural network architecture is imperative to the success of an AI-based approach to music composition. The present work explores the composition of western music through neural network using a Long Short-Term Memory (LSTM) algorithm. Compositions from seminal western composers such as J.S. Bach, W.A. Mozart, L.V. Beethoven, and F. Chopin were used as the dataset to train the neural network. Seven compositions were generated by the LSTM model and these outputs were presented to a group of thirty volunteers between 18-24 years of age. They were surveyed to identify the music piece as composed by a human or AI and how interesting they found the melodies of each piece. It was found that the LSTM model generated compositions that were thought to be made by a human and create melodies of interest from the perception of the volunteers. It is expected that through this study, more AI-based composition approaches can be developed which encompass more and more of the musical phenomenon. 2022 IEEE. -
Interaction of Generational Differences with Gender and Residential Nature in Attitudes Toward Interfaith Marriages
The present study examined the interaction effects of generations, gender, and residential nature on attitudes toward interfaith marriage in a sample of 1190 Indian participants from iGen, Xennials and Millennials, and Baby Boomers generations. Data were collected using a socio-demographic response sheet and the Attitude Scale, with lower ratings indicating positive attitudes and higher ratings indicating negative attitudes. The results of this study demonstrated that generational differences are significantly associated with gender and residential nature. There was a significant interaction between generation and gender and generation and residential nature on attitudes toward interfaith marriages. 2024 Taylor & Francis Group, LLC.