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Powering the Future: The Role of Solar Energy in Indian Energy Transition
Energy has become one of the basic human needs, and the expanding population demands more energy for day-to-day needs. As the demand for energy increases, the easy solution for everyone to rely on is the employment of fuel-powered generation systems, which adversely affect the ecology and the environment. In order to address the energy needs of the time without harming the environment, we need considerable investments in the renewable energy sector. Government alone cannot perform this task. A collective effort from government, public, and private investors is required here. Energy conferences like Conferences of Parties (COP) focus on the transition of energy from non-renewable sources to renewable sources and on bringing down the loss of energy during transmission and distribution. This paper current Indian energy sector scenario, suggests solar energy as a solution to Indias energy crisis and discusses the reason behind the lack of motivation for people to invest in solar energy. Addressing these factors can attract more investors to the investment. The research in battery technology and solar panels can help the Indian energy sector focus on energy harvesting and the development of energy-independent systems which will solve the energy crisis to an extent. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Powerlessness in the moral self: a social cognitive perspective on drug users
Powerlessness resides in devalued self-images of drug users. This study, drawing on social and moral psychology, examined the moral functioning of drug users compared to non-drug users. Self-reported data concerning moral identity and moral judgment on drug use were assessed and compared between groups. Drug users appeared to have significantly weaker moral identity centrality and pro-drug moral judgment than non-drug users. They also showed dissociation in the relationship between moral identity and moral judgment. As a result, the study proposed a moral identity model of drug use to better approach social cognitive powerlessness in drug users moral self. 2021 Taylor & Francis Group, LLC. -
Prabhakar Anandrao Bhagwatwar (1934)
This chapter explores the initiatives undertaken by P.A. Bhagwatwar, a notable academic figure at the University of Mumbai, particularly in enhancing the practical application of psychology within the curriculum. It details the inception and development of a counselling centre, which began in 1988 and was officially established in 1995, under Bhagwatwars guidance. The centre provided comprehensive psychological services targeting a diverse range of demographics, from adults to the elderly, addressing issues such as family therapy and vocational guidance. Additionally, he is credited with authoring several influential books on psychology, including titles on general and organizational behaviour, as well as developing key assessment tools, such as aptitude tests and efficiency questionnaires, contributing significantly to the field of applied psychology. 2025 selection and editorial matter, Braj Bhushan; individual chapters, the contributors. -
Practical applications of self-service technologies across industries
Self-service technologies (SSTs) have practical applications across various industries, improving operational efficiency and customer satisfaction. In retail, self-checkout kiosks and mobile payment apps streamline the purchasing process, reducing waiting times and enhancing convenience. The hospitality industry utilizes SSTs through self-service check-in kiosks and digital concierge services. In healthcare, patients can use self-service portals to schedule appointments, access medical records, and complete pre-visit forms. In banking and finance, ATMs, mobile apps, and AI-powered chatbots offer access to essential services without the need for in-person assistance. These practical implementations demonstrate the versatility and importance of SSTs in modernizing service delivery across sectors. Practical Applications of Self-Service Technologies Across Industries explores self-service technology (SST) as a transformative force across industries. It examines practical applications of SST for improved customer service and business operations. This book covers topics such as smart technology, consumer behavior, and blockchain, and is a useful resource for business owners, computer engineers, academicians, researchers, and data scientists. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Practical Benefits of Using AI for More Accurate Forecasting in Mental Health Care
Artificial Intelligence (AI) is the general term for being able to make computers do things that require human-like intelligence. AI is the novel idea of the computer pioneers like Alan Turning and John von Neumann in the 1940s. Their novel intuition towards making machines think is the key start for this AI technology evolution. As shown in Fig. 1, the first milestone of AI happened in the year 1956 when it was proved by a group of researchers that a machine could solve any problem with the use of an unlimited amount of memory. Here they named this program General Problem Solver (GPS). 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar. -
Practices for measuring business in construction engineering organizations /
Patent Number: 202221034683, Applicant: Dr. Anil Zende.
The fundamental drives of every organization are profitability and achievement. The sustainability among these organizations relies on numerous elements that seem to have a substantial influence on performance. Estimating the implementation of sustainability organizations helps to discover weaknesses in terms of enhancing its productivity and profitability. Because of the enormous diversity of construction companies, it is harder for development organizations to develop or sustain a scientific approach for measuring their present effectiveness. Previous research utilized questionnaires and scientific and management consultations. -
Pratixa: A Cognitive Framework for Behavioral Decision-Making and Its Mathematical Formalization
The present study introduces pratixa, an internal cognitive structure that functions as a reference architecture guiding human decision-making. Pratixa is a dynamic, event-sensitive archive of anticipated outcomes of behavior, learned event-behavior-outcome associations, and adaptive behavioral responses, drawing on the theories from decision science, psychology, and behavioral adaptation. Past experiences shape pratixa, and iterative learning reinforces it. It supports predictive mental representations by enabling individuals to anticipate the outcomes of their own behavioral responses and adjust those responses when discrepancies arise between anticipated and actual outcomes. Pratixa supports anticipatory learning and real-time correction, making it a future-oriented cognitive structure for decision making. It matures in a spiral progression, from null pratixa, where no prior event-behavior-outcome associations exist, through quixotic pratixa, characterized by illusory or arbitrary associations, to realistic pratixa, where causal relationships are adequately approximated. This spiral maturation reflects how individuals adapt through experiential learning and reinforcement, transitioning from effortful reasoning to increasingly automatic and context-sensitive decision-making. By positioning decision-making within this evolving structure, pratixa offers a distinct perspective on predictive cognition in complex and ambiguous contexts, with implications for strategic foresight, behavioral economics, and adaptive behavioral decision making. The study also proposes a mathematical formulation to represent how this reference architecture evolves through reinforcement-based learning and guides decision-making, providing a computational basis for modeling human foresight and adaptation. 2025 John Wiley & Sons Ltd. -
Pre and Post Operative Brain Tumor Segmentation and Classification for Prolonged Survival
The aim of this research was to provide a detailed overview of the techniques in detecting and segmenting meningioma brain tumor in pre- and post-operative MRI images and classify for presence of meningioma thereby giving an early diagnosis to decrease the death rate. This study examines trending techniques for brain tumour segmentation and classification in Magnetic Resonance (MR) images of pre and post-surgery. For the segmentation and anomalies in the brain categorization, several approaches such as regular machine learning techniques (K-mean bunching, Fuzzy C mean grouping etc.), Deep Learning-based approaches (CNN, ResNET, Dense Net, VGG etc.), classical algorithms (Snake contour, watershed method etc.), and hybridization approaches were applied, according to the analysis. Information base, for example, BRATS, Fig-Share, EPISURG or TCIA can be taken to gather clinical pictures which principally contains of 2 classifications, pre and post pictures of Brain tumor. The multiple processes of brain tumour segmentation methodologies, such as preprocessing, feature extraction, segmentation, and classification, are also explained in this work. The task of segmenting residual and recurrent tumors differs greatly from that of segmenting tumors on baseline scans before surgery. This study shows that each approach has its own set of pros and limitations, as well as notable findings in terms of precision, sensitivity, and specificity, according to the comparison research. The use of segmentation approaches to determine success and reliability has been discovered. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Pre Packaged Insolvency - Exploring An Alternative Framework For Bankruptcy Resolution In India
This article is a review of literatures on the need for alternative bankruptcy resolution framework in India. The study explores the context & background to the recent initiation of limited Pre-Packaged Insolvency in India. The article makes a strong case for having a private & pre-negotiated mode of debt resolution along with the existing CIRP framework in India. The article provides a comparative perspective of CIRP and Pre-pack driven resolution model in India. The research paper also addresses some of the potential challenges & concerns related to initiation of pre-pack in India & accordingly discusses the relevant safeguards for the same. Lastly, the study also provide a brief view of pre-pack model currently practised in USA. The Electrochemical Society -
Pre-Service and In-Service Teachers Perceptions of Using Virtual Reality Tools in Teaching
This paper explores pre-service and in-service teachers perceptions of virtual reality (VR) technology as a teaching and learning tool in the classroom in India. The study aimed to answer four research questions, including the adoption rate of VR technology among teachers, their confidence levels in teaching using VR technologies compared to digital technologies, attitudes towards using VR technology, and the usefulness of different uses of VR technology. The survey conducted among 102 teachers found limited adoption of VR technology, lower confidence levels in using it, but willingness to use it in the future. The paper recommends providing adequate training and support to increase teachers confidence in using VR technology in their teaching practices. The study also suggests that strategies to promote VR technology should consider gender differences in attitudes towards it. Overall, the research concludes that teachers view VR technology as having potential benefits for learning and teaching across various uses. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Pre-Service and In-Service Teachers Perceptions of Using Virtual Reality Tools in Teaching
This paper explores pre-service and in-service teachers perceptions of virtual reality (VR) technology as a teaching and learning tool in the classroom in India. The study aimed to answer four research questions, including the adoption rate of VR technology among teachers, their confidence levels in teaching using VR technologies compared to digital technologies, attitudes towards using VR technology, and the usefulness of different uses of VR technology. The survey conducted among 102 teachers found limited adoption of VR technology, lower confidence levels in using it, but willingness to use it in the future. The paper recommends providing adequate training and support to increase teachers confidence in using VR technology in their teaching practices. The study also suggests that strategies to promote VR technology should consider gender differences in attitudes towards it. Overall, the research concludes that teachers view VR technology as having potential benefits for learning and teaching across various uses. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Precise cervical cancer cell boundary denoising and segmentation with adaptive wavelet-spectral enhancement
Accurate segmentation of cell nuclei in cervical cytology images is crucial for automated cervical cancer screening, yet existing methods struggle with blurred boundaries, noise-induced degradation, and topologically implausible predictions. The current research proposes Cell-Seg Tool, a novel triplet-branch diffusion AI tool that synergistically integrates three innovations to address these limitations. The Wavelet-Enhanced Contour Refinement Branch employs a learnable multi-scale discrete wavelet transform with adaptive coefficient attention to dynamically enhance boundary features across horizontal, vertical, and diagonal orientations. The Adaptive Spectral Noise Suppression module performs dual-domain processing using DCT-based filtering and uncertainty-guided fusion, coupled with bidirectional anchor semantic feedback to couple cross-branch information. The Topology-Aware Hybrid Loss integrates a focal Tversky loss, a persistent homology loss, a directional boundary loss, a skeleton completeness loss, and a diffusion-noise MSE loss for multi-objective optimization. Comprehensive experiments on multiple datasets demonstrate superior performance, achieving 94.45% Dice coefficient and 19.2% reduction in boundary localization error compared to state-of-the-art methods. Unlike prior work that applies these techniques independently, this work demonstrates that their adaptive, synergistic integration within a diffusion-based framework yields substantial improvements in boundary accuracy and topological correctness. 2026 The Author(s). -
Precise surface molecular engineering of 2D-Bi2S3 enables the ultrasensitive simultaneous detection of dopamine, epinephrine, serotonin and uric acid
Multiple biomolecule detection at a single read is an emerging and highly desirable technology in point-of-care diagnostics. Thus, functional nanoscale materials with high precision and stability at an affordable cost are required to fabricate adaptable multiplex biosensing devices with exceptional performance. Herein, an ultrasensitive molecularly engineered 2D-Bi2S3 biosensor is developed via a two-step synthetic approach. Simultaneous detection of dopamine (DA), epinephrine (EP), serotonin (ST), and uric acid (UA) is achieved at the nanomolar level. The surface molecular engineered 2D-Bi2S3 by 4-mercaptobenzoic acid (MBA) exhibits a well crystalline nature and consists of 36 stacked layers with creased-paper-like morphology after an MBA molecule has been precisely linked at the basal plane of Bi2S3. Bi2S3-MBA's surface/vibrational spectroscopic and scanning tunneling microscopic studies demonstrate the Bi2S3-MBA electronic nature and the linked molecule present on the Bi2S3 surface with a comparatively large random distribution of MBA molecules at the basal plane than the edge plane. The density functional theory (DFT) calculation verifies the proposed molecular interaction mechanism. The success of this unique surface molecular engineering strategy, which effectively modified the electronic and surface configuration of the 2D-Bi2S3, offers an exciting possibility for building different variants of the versatile biosensor for real-world diagnostic device applications. 2024 -
Precision agriculture takes flight: Drone technology in crop management
[No abstract available] -
Precision Corn Price Prediction with Advanced ML Techniques
In the ever-evolving corn market, accurate price prediction is imperative for informed decision-making. This research introduces an innovative predictive model that integrates and external factors to enhance forecasting accuracy in the corn market. By exploring historical trends, comparing machine learning algorithms, and employing advanced feature selection methods, the study addresses the complexities of the corn market, emphasizing economic indicators, geopolitical events, and demand-supply dynamics. Informed by a literature review, the research underscores the necessity of dynamic models in corn price forecasting. Utilizing machine learning models such as linear regression, random forest, SVM, Adaboost, and ARIMA, coupled with the interpretability of SHAP values, the study aims to improve prediction accuracy in the corn market. With a robust methodology and comprehensive evaluation metrics (MAE, RMSE, MAPE), the research contributes valuable insights into corn market dynamics, providing a variable dictionary for clarity and emphasizing the strategic implications of the superior random forest model for stakeholders in the corn sector. 2024 IEEE. -
Precision Farming on Sugarcane: Drone-Based Disease Detection Using YOLOv8 Neural Models
Precision agriculture is being revolutionized by the use of UAVs and AI, enabling more efficient and sustainable crop monitoring. This study presents a drone-based solution for real-time detection of sugarcane diseases such as Rust, Red Rot, Mosaic, and Yellow Leaf. A custom quadcopter, outfitted with a high-resolution camera and Raspberry Pi 4, is used to capture aerial imagery. The onboard YOLOv8 model processes images in real time, with data stored locally on an SD card for further evaluation. The paper covers the complete system setup, including hardware components, neural network deployment, and the end-to-end workflowfrom image capture to decision support. This integrated approach supports early intervention, better yield outcomes, and cost-effective disease management in sugarcane farming. 2025 IEEE. -
Precision Food Crop Mapping Using Deep Neural Networks and Improved Dipper Throat Optimization Techniques
In recent times, the use of Remote Sensing (RS) data obtained from Unmanned Aerial Vehicles (UAVs) has gained significant popularity in crop classification tasks, including crop mapping, yield prediction, and soil classification. The classification of food crops utilizing RS Imageries (RSI) is a major application of RS tools in crop growing. Meeting the conditions for investigating these data requires more difficult approaches, and Artificial Intelligence (AI) technologies offer the mandatory support. Because of the variation and division of crop planting, archetypal classification methods have fewer classification outcomes. This manuscript focuses on the design and execution of a Leveraging Enhanced Dipper Throat Optimization Algorithm with Dipper-Inspired Precision Classification for Remote-sensed Optimized (DIP-CROP) Processing methodology. The drive of the DIP-CROP algorithm is to classify distinct types of crops that exist in remote sensing. At first, the DIP-CROP model applies image processing using the Sobel Filter (SF) to eliminate the noise. Next, the presented DIP-CROP technique takes place SqueezeNet model is employed for the feature extractor. To classify the food crop types, the DIP-CROP approach utilizes a Multi-Head Attention-based Bi-directional Long Short Term Memory (MHA-BiLSTM) algorithm. For hyperparameter tuning of the MHA-BiLSTM classifier, the Enhanced Dipper Throat Optimization Algorithm (EDTOA) will be applied in this work. The optimization process utilizes Levy flight distribution, which is known for its faster convergence due to efficient exploration of the search space. Levy flights can be used to take larger steps in exploration, which prevents getting stuck in local minima and accelerates convergence. The performance of the DIP CROP method is examined experimentally using a benchmark database. Experimental results affirmed the superior solution of the DIP-CROP algorithm over existing methods. 2026 Seventh Sense Research Group. -
Precursor to employee engagement AMID knowledge workers
The main objective of this study is to critically analyze the precursor to employee engagement. The research methodology used in this research is descriptive research. In primary data, responses are collected through well framed questionnaires and direct interaction with the employees to selected sample of 550 respondents of information technology organisations in Bengaluru City. The questionnaire consists of 20 questions based on employee engagement precursor. To reduce the dimension of this an exploratory factor analysis was carried out and 3 factors explaining 65.26% of the variance were derived. The 3 precursors identified as professional contentment (Cronbach's alpha 0.940) career development (Cronbach's alpha 0.836) and job enrichment (Cronbach's alpha 0.826). The current study adds to the research pointing at precursors to employee's engagement among knowledge researcher. Medwell Journals, 2017. -
Predictability and herding of bourse volatility: An econophysics analogue
Financial Reynolds number works as a proxy for volatility in stock markets. This piece of work helps to identify the predictability and herd behavior embedded in the financial Reynolds number (time series) series for both CNX Nifty Regular and CNX Nifty High Frequency Trading domains. Hurst exponent and fractal dimension have been used to carry out this work. Results confirm conclusive evidence of predictability and herd behavior for both the indices. However, it has been observed that CNX Nifty High Frequency Trading domain (represented by its corresponding financial Reynolds number) is more predictable and has traces of significant herd behavior. The pattern of the predictability has been found to follow a quadratic equation. Bikramaditya Ghosh, Krishna M.C., Shrikanth Rao, Emira Kozarevi?, Rahul Kumar Pandey, 2018.