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Promoting Net-Zero Economy for Sustainable Development: Practice-Based View
The present research investigates the utilization of various resources, including tangible assets, human expertise, and intangible assets, in a cohesive set of established procedures, which impact the development and implementation of net-zero practices. It also explores the effect on the environmental performance of SME enterprises operating in business markets. Additionally, the study explores whether digitalization plays a moderating role in this relationship. The samples of 291 were used in the study. Data were analyzed using partial least square structural equation modeling. For a sustainable net-zero economy (SNZE), it is essential for managers to acknowledge the importance of resource and capabilities management. While the management of tangible assets and human skills is vital, greater emphasis should be placed on intangible resources like organizational culture and learning. Furthermore, the capacity of small-sized enterprises (SMEs) to process and implement knowledge could prove to be instrumental in accomplishing net-zero targets. Consequently, managers should leverage Industry-4.0-based technological solutions to enhance resource and capabilities management effectively. This research pioneers an exploration into the influence of human capital and various assets (tangible and intangible), on the development and implementation of a SNZE in organizations, underpinned by empirical data. The study broadens the understanding of the practice-based view (PBV) framework in realizing SNZE, particularly within SME B2B enterprises. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Promoting photocatalytic hydrogen evolution rates in layered graphitic carbon nitride through integrated non-noble CoB co-catalyst
Despite being one of the most widely studied metal-free semiconductors, graphitic carbon-nitride (gC3N4) shows meaningful photocatalytic activities only when loaded with noble-metal co-catalysts. The present work reports an alternative to noble metals in the form of cobalt boride (CoB) co-catalyst that can be easily integrated within the gC3N4 framework with facile fabrication strategies. The optimized CoB-gC3N4 composite showed ?60 times higher hydrogen generation rate compared to bare gC3N4 nanosheets, with good stability. Detailed morphological, structural, chemical, electrochemical and spectroscopic investigations revealed the key aspects of CoB-gC3N4 composite that unanimously led to higher photocatalytic activity. Computational investigations not only corroborated the experimental results but also established that the surface Co and B sites in CoB provided the most energetically favoured sites for hydrogen evolution reaction. Based on the experimental and computational investigations, a generic reaction mechanism was formulated that will prove as a guiding light for future studies on similar photocatalytic systems. 2024 The Authors -
Promoting Sustainability Through Corporate Social Responsibility: Insights and Barriers of Medium-Sized Manufacturing Enterprises in India
The current study aims to explore if effective corporate social responsibility leads to corporate sustainability in medium-sized manufacturing enterprises. Using the factors, an exploratory examination was performed to assess their suitability in Indian context, and data were collected from 121 manufacturing companies using structured questionnaire based on pretested scale, and the proposed relationships were tested through partial least square structural equation modelling (PLS-SEM). The results show overall model fit and empirical examinations support causal relationships between effective corporate social responsibility and corporate sustainability (CS). The results indicated that effective CSR mediated the relationships between corporate sustainability and integration of CSR into corporate policy and priority of the board towards CSR. The results of this study are useful for medium-sized enterprises to establish a formal approach towards CSR and meet the needs of business and society in the 21st century. Copyright 2022, IGI Global. -
Propagation of SH Waves in Piezo-Flexoelectric-Layered Structures with Imperfect Interfaces: Analytical Formulation and Data-Driven Surrogates
This study presents an analytical and data-driven investigation of shear-horizontal (SH) wave propagation in layered piezo-flexoelectric (PFE) materials with imperfect interfaces. The novelty lies in integrating flexoelectric coupling and interfacial defects within a unified dispersion framework, supported by physics-consistent machine-learning surrogates for efficient parametric analysis. Governing electromechanical equations are formulated and solved under mechanical, electrical, and interfacial continuity conditions to derive dispersion relations linking phase velocity with wavenumber, flexoelectric parameters, and interface stiffness under electrically open and short-circuited boundary conditions. The results show that flexoelectric effects strongly influence dispersion at short wavelengths, while interface imperfections significantly reduce phase velocity and increase attenuation sensitivity. Electrically open conditions enhance electromechanical coupling, whereas short-circuit conditions suppress dispersion sensitivity. To accelerate large-scale evaluations, surrogate models are developed that accurately reproduce analytical dispersion behavior with substantially reduced computational cost. The proposed hybrid framework provides improved insight into guided wave mechanics in stratified smart materials and offers an efficient tool for the analysis and design of piezo-flexoelectric structures in sensing and MEMS applications. World Scientific Publishing Europe Ltd. -
Propensity Score Matching and a Difference in Difference Approach to Assess ESGs Influence on Indian Acquirer Performance
This research involves an in-depth analysis of the intricate relationship between Environmental, Social, and Governance scores and the financial and operational performance of Indian acquirers. The research methodology employed herein entails a meticulously crafted design, incorporating a blend of the Propensity Score Matching and Difference-in-Differences model. This strategic amalgamation serves to rigorously assess the impact of ESG factors on the performance outcomes of Indian acquirers involved in M&As. The empirical findings of this study reveal a robust and statistically significant correlation between M&A endeavours and ESG considerations. Notably, the research discerns that M&A activities tend to exert an adverse influence on ESG performance metrics within the Indian corporate landscape. This nuanced insight underscores the multifaceted interplay between strategic corporate actions and the broader sustainability and governance landscape, thereby offering valuable implications for scholars and practitioners in finance and corporate strategy. 2024, University of Wollongong. All rights reserved. -
Properties and Occurrence Rates for Kepler Exoplanet Candidates as a Function of Host Star Metallicity from the DR25 Catalog
Correlations between the occurrence rate of exoplanets and their host star properties provide important clues about the planet formation process. We studied the dependence of the observed properties of exoplanets (radius, mass, and orbital period) as a function of their host star metallicity. We analyzed the planetary radii and orbital periods of over 2800 Kepler candidates from the latest Kepler data release, DR25 (Q1-Q17), with revised planetary radii based on Gaia DR2 as a function of host star metallicity (from the Q1-Q17 (DR25) stellar and planet catalog). With a much larger sample and improved radius measurements, we are able to reconfirm previous results in the literature. We show that the average metallicity of the host star increases as the radius of the planet increases. We demonstrate this by first calculating the average host star metallicity for different radius bins and then supplementing these results by calculating the occurrence rate as a function of planetary radius and host star metallicity. We find a similar trend between host star metallicity and planet mass: the average host star metallicity increases with increasing planet mass. This trend, however, reverses for masses >4.0 M J: host star metallicity drops with increasing planetary mass. We further examined the correlation between the host star metallicity and the orbital period of the planet. We find that for planets with orbital periods less than 10 days, the average metallicity of the host star is higher than that for planets with periods greater than 10 days. 2018. The American Astronomical Society. All rights reserved. -
Properties of alkali-activated concrete (AAC) incorporating demolished building waste (DBW) as aggregates
This study was undertaken to evaluate the potential of demolished building waste (DBW) as aggregates in alkali-activated concrete (AAC). A recent road-widening activity led to the demolition of commercial buildings along National Highway 275, Bangalore-Bantwal, India. DBW was collected from these sites and processed manually at the laboratory facility of CHRIST (Deemed to be University). Processing of DBW was done to obtain both waste coarse and fine aggregates from demolished concrete and brick waste units, respectively. AAC was synthesized by fly ash, ground granulated blast furnace slag, sodium hydroxide, sodium silicate, along with waste aggregate replacement rates of 0, 25, 50, and 75% by weight of natural aggregates. Fresh and hardened properties of developed concrete mixtures were experimentally determined. Results of the study indicate that 28-day compressive strength of 30.4 and 21 MPa was obtained for AAC with 25 and 50% DBW aggregates, which was 8.6 and 36.9% lower than control mix, respectively. Further, there was an increase in the water absorption and a reduction to acid resistance for all the AAC mixes with DBW aggregates. Based on the results obtained, it was observed that AAC with 25 and 50% DBW aggregates find great potential in civil engineering applications. 2021 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Properties of high strength concrete with reduced amount of Portland cement a case study
In the last 15years Bangalore city has systematically modernized its concrete production process with the help of ready-mix concrete (RMC) facility. However, one of the present requirements of these facilities is to lower its carbon footprint by reducing consumption of Portland cement in the concrete production process. Further, the demand for high-strength concrete (HSC) has increased due to construction of high-rise buildings and other major infrastructure projects in the urban areas of the city. Therefore, this study presents the experimental test results of HSC mixes proportioned with reduced consumption of Portland cement. Four types of concrete mixes with 50% of Portland cement replaced by ground granulated blast furnace slag (GGBS) were considered. Additionally, two control mixes without GGBS replacement were also tested. Fresh, hardened, and durability properties of all the mixes were experimental determined and presented. The results showed that concrete mixes proportioned with 50% GGBS obtained a maximum 28-day compressive strength of 77 MPa. Further, all the mixes with GGBS exhibited superior durability properties when compared to control mixes. Thus, concrete mixes with 50% GGBS replaced for Portland cement are favourable for producing HSC at RMC facilities at Bangalore city. 2021 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Properties, Synthesis and Emerging Applications of Graphdiyne: A Journey Through Recent Advancements
Graphdiyne (GDY) is a new variant of nano-carbon material with excellent chemical, physical and electronic properties. It has attracted wide attention from researchers and industrialists for its extensive role in the fields of optics, electronics, bio-medics and energy. The unique arrangement of spsp2 carbon atoms, linear acetylenic linkages, uniform pores and highly conjugated structure offer numerous potentials for further exploration of GDY materials. However, since the material is at its infancy, not much understanding is available regarding its properties, growth mechanism and future applications. Therefore, in this review, readers are guided through a brief discussion on GDYs properties, different synthesis procedures with a special focus on surface functionalization and a list of applications for GDY. The review also critically analyses the advantages and disadvantages of each synthesis route and emphasizes the future scope of the material. Graphical abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Properties, Synthesis, and Characterization of Cu-Based Nanomaterials
Copper-based nanomaterials offer a fascinating array of properties that make them pivotal in various technological applications. These materials, when scaled down to the nanoscale, exhibit enhanced electrical conductivity, surpassing their bulk counterparts. This book chapter primarily focuses on the properties, synthesis, and characterization of copper nanoparticles while also discussing metal and metal oxide nanoparticles. Their large surface-to-volume ratios enable efficient electron transport, making them valuable components in electronics and conductive inks for flexible devices. Furthermore, copper nanomaterials possess exceptional thermal conductivity, making them crucial for efficient heat management in electronics and advanced thermal interface materials. Copper and copper oxide have positive economic and environmental effects. Their catalytic properties render them important in diverse chemical reactions and as components in energy storage systems like batteries and supercapacitors. Additionally, the tunability of their optical properties makes them suitable for various photonic and optoelectronic applications, ranging from sensors to light-emitting devices. The multifaceted properties of copper-based nanomaterials continue to drive innovation across a broad spectrum of industries. 2024 American Chemical Society. -
Prophesying Credit Card Frauds Using Predictive and Deep Transfer Learning: A Comprehensive Experimental Perspective
Credit card fraud has become a major issue in the online financial environment, requiring the implementation of smart and automated tools for real-time detection of frauds. Machine Learning (ML) has been an important asset in this area because of its capability to discover underlying patterns, learn new fraud methods, and offer scalable solutions. This study investigates the usage of different classical machine learning and deep transfer learning based on predictive models for credit card fraud detection with a focus on their comparative performance on six important parameters: time elapsed, accuracy, precision, recall, TNR and F1 score. The investigation makes use of a PCA transformed benchmark dataset with a total of 2,84,807 credit card transactions to train models. In depth experimentation is performed using five classical ML models named Random Forest, Logistic Regression, Linear SVM; Non-Linear SVM; XGBoost and four classical Deep Learned models named MLP, Shallow ANN, ID CNN, and LSTM. To enhance experimental validity, prediction capability of four GNN based CNN models such as Boosting-GNN, Jump-Attentive GNN, GNN and PC-GNN are also tested. Deep learning based neural network models are analysed using seven different activation functions and each model is fit using 10 epochs of batch size 512. Testing results point out that overall best performance in classical ML models is shown by Non-Linear SVM with best recall score depicted by ANN on RBG kernel and GPU. In the ensemble category, Random Forest model exhibits overall best performance with best recall for XGBoost. Precision, accuracy and F1 score of random forest and XG boost are highest. Results have shown that in case of Random Forest the accuracy, precision, recall and F1score are 99.9%, 97.7%, 81.9% and 89.12% respectively whereas for XG boost the values for accuracy, precision and F1 score are 99.96%, 92.63%, 83.81% and 88% respectively. Deep Learned models showed high accuracies, however they were significantly utilized computational resources in respect to elapsed time. The study provides a roadmap to financial institutions for efficient model selection while deciding on implementing automated and trustworthy fraud detection systems and helps shape the dynamic world of intelligent financial security solutions to reduce financial losses. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Proposal of smart home resource management for waste reduction and sustainability using AI and ML
A research indicated that electricity is obliterating extra non-renewable sources for its production. In that, as per Centre for Policy Research (CPR), about 25% of the total production is diverted to meet the daily consumption in an Indian household. Not only this but also, waste management has become an important issue to deal with. According to Municipal Solid Waste (MSW) of India, waste generation in Indian urban communities extends between 200 - 870 grams per day, contingent on the localities' standard of living and the area of the city. Therefore, in this paper we propose a concept that focuses on a sustainable solution using Artificial Intelligence and Machine Learning algorithms for waste and carbon footprint reduction in a home. This concept explains a solution availed with the help of a proposed model called Home Resource Management (HoReM) that is imbibed in a Smart home. 2019 IEEE. -
Proposing an AI-Enabled Waste Segregation System for Domestic Settings
This paper proposes an innovative AI-based system for automated domestic waste segregation. Utilizing Teachable Machine and MobileNet, the system accurately categorizes waste into dry and wet components, laying the foundation for sustainable waste management practices. Embedded in a Raspberry Pi 4, the system integrates real-time image processing with various sensors to streamline the sorting process. While the model has been simulated due to budgetary constraints, future implementation envisions real-world application. Potential advancements include expanding the dataset, enabling multi-category waste classification, and exploring low-power alternatives. This research contributes to the evolving landscape of smart waste management, addressing environmental sustainability and the pressing need for automated, efficient waste segregation at the domestic level. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Prospective applications of two-dimensional materials beyond laboratory frontiers: A review
The development of nanotechnology has been advancing for decades and gained acceleration in the 21st century. Two-dimensional (2D) materials are widely available, giving them a wide range of material platforms for technological study and the advancement of atomic-level applications. The design and application of 2D materials are discussed in this review. In order to evaluate the performance of 2D materials, which might lead to greater applications benefiting the electrical and electronics sectors as well as society, the future paradigm of 2D materials needs to be visualized. The development of 2D hybrid materials with better characteristics that will help industry and society at large is anticipated to result from intensive research in 2D materials. This enhanced evaluation might open new opportunities for the synthesis of 2D materials and the creation of devices that are more effective than traditional ones in various sectors of application. 2023 The Authors -
Prospective memory in early and established psychosis: An Indian perspective
Individuals affected by psychosis often have deficits in several neurocognitive functions. Prospective memory (PM), the ability to remember to do things, is crucial for activities of daily living, social and occupational functioning, but very few studies have attempted to examine this domain of functioning in people with psychosis, particularly in India. A total of 71 patients with psychosis, (both early and established psychosis), and 140 age, gender and education-matched healthy controls were assessed using the Positive and Negative Symptom Scale, Hospital Anxiety and Depression scale, and Addenbrooke's Cognitive Examination. PM was assessed using the Cambridge Prospective Memory Test and the Prospective and Retrospective Memory Questionnaire (PRMQ). Group differences were evaluated using MannWhitney U-tests. Significantly greater cognitive deficits, higher anxiety and depression were evident in the psychosis group compared with controls. The psychosis group performed significantly poorer on both time- and event-based tests in CAMPROMPT than controls. These differences remained when controlling for age, education, general cognitive functioning and mood. The subjective measure of PM (PRMQ) did not differentiate the two groups. The PM performance of early and established psychosis patients was similar. Comparisons with cross-cultural data (PRMQ UK norms and CAMPROMPT and PRMQ Chinese data) revealed important differences in PM performance. Individuals with psychosis have significant deficits in both time- and event-based PM. CAMPROMPT emerged as a more sensitive PM measure compared with PRMQ. Results from cross-cultural comparisons underscore the need for cultural contextualization of assessments. 2023 The British Psychological Society. -
Prospects of CSR: An Overview of 500 Indian Companies
The IUP Journal of Corporate Governance, ISSN No. 0972-6853 -
Prospects of Green Finance for a Sustainable Future: A Critical Study
Global initiatives for economic development started with the Industrial Revolution. However, it caused serious environmental issues. It necessitated to focus on ecofriendly developmental projects. Accordingly, the sustainable development paradigm has evolved to balance both environmental and developmental models. Although the UN affirmed sustainable development from 1987 onwards, its scope was elaborated with MDGs and SDGs. The declaration of SDGs in 2015 provided a framework for a global sustainable future. However, developing countries face financial hurdles in responding positively to the demands of SDGs that prioritize sustainable development and environmental protection. The OECD (2021) notes that developing countries experience 2.5 to 3.7 trillion dollars shortfall to meet the demands of SDGs. To address this complexity, developed countries, international institutions, and multinational and transnational corporations promoted green finance. From this perspective, this chapter critically evaluates the scope and functioning of green finance in light of a sustainable future. Copyright 2026, IGI Global Scientific. -
Prospects of Medical Tourism - A Study on the Management Trends and Practices of the Prominent Participants of Hospital Sector in South India
International Journal of Research in Commerce & Management, Vol-3 (12), pp. 73-76. ISSN-0976-2183 -
Protecting Cultural Knowledge in the Digital Era: Legal Dimensions of Intellectual Property in AI- Powered Immersive Learning and the Metaverse
The rapid expansion of AI- driven metaverse environments has challenged traditional intellectual property frameworks, leaving indigenous and cultural heritage vulnerable to unauthorized use and misrepresentation. This study analyzes legal gaps in the protection of cultural knowledge in immersive digital platforms, highlighting risks posed by generative AI and decentralized governance. We evaluate emerging solutions such as blockchain- based ownership verification and NFT licensing-against ethical concerns of profit- sharing and moral rights. Drawing on comparative legal research, we propose an international framework integrating digital rights management and sui generis protections for cultural assets. Our recommendations stress collaboration among governments, international organizations, and technology developers, alongside the inclusion of digital heritage law in academic curricula, to empower indigenous communities to safeguard their cultural rights. 2026 by IGI Global Scientific Publishing. All rights reserved. -
PROTECTING DATA AND PRIVACY: CLOUD-BASED SOLUTIONS FOR INTELLIGENT TRANSPORTATION APPLICATIONS
The interaction between transportation networks and intelligent transportation systems has been revolutionized by cloud computing. However, the reliance on cloud-based solutions raises security and privacy concerns. This article examines the challenges of safeguarding data and privacy in intelligent transportation applications and emphasizes the potential of cloud-based solutions to resolve these issues. Organizations can protect sensitive data and user privacy by employing encryption, access controls, threat detection mechanisms, and privacy protection measures. Adopting these cloud-based solutions will encourage the extensive adoption of intelligent transportation applications while infusing users and stakeholders with confidence. 2023 SCPE.