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Biofortification and Sustainable Intensification of Soil: Perspectives on Rice Cultivation in India
Biofortified crops have inherently been known to acquire climate-smart traits and stress resilience. Climate-smart agriculture integrates climate change into the planning and implementation of sustainable agricultural strategies. Biofortification is a climate-smart concept that enhances crop nutrient quality and quantity through conventional breeding, agronomic practices, or genetic engineering. It will enrich food availability, stability, accessibility, and utilization and positively impact the health, livelihood, production, and distribution of food crops. The system of rice (Oryza sativa L.) intensification involves a set of agronomic principles to improve the structure and functioning of the soil system by fortifying it with organic matter and micronutrients. With the exceeding urbanization and population explosion, food security is a primary concern for policymakers all around the globe. Widespread zinc, iodine, iron, and selenium micronutrient malnutrition is a significant cause of numerous health problems in human populations where rice is part of the staple diet. Climate-smart biofortification is a durable and effective option to reach the vast numbers of malnourished populations scattered across the world sustainably. Approaches have been strategized worldwide under rice biofortification research projects for maintaining, increasing, and introducing new micronutrients in rice grain. Biofortification has been safely implemented as an environmentally friendly approach to produce higher yields at low costs without undesirable soil effects. Prospective advancements can be achieved by integrating mineral and organic fertilizers with superior germplasm, promoting improved nutrient uptake and localization in the consumed parts of the crop. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Improved Photocatalytic Activity of g-C3N4/ZnO: A PotentialDirect Z-Scheme Nanocomposite
In this study, a Z-scheme g-C3N4/ZnO nanocomposite was synthesized using exfoliation process, which was further characterized using XRD, FT-IR, UV-DRS, SEM-EDAX, PL, EIS, and TGA techniques. The properties of g-C3N4 were enhanced when fabricated with ZnO resulting in a better electron mobility, high redox potential, and excellent semiconducting properties. The performance of this heterostructure was evaluated by photocatalytic degradation of malachite green (MG) under visible light irradiation. The g-C3N4/ZnO heterostructure achieved a degradation of 84.3 % within 60 min under visible light irradiation. The degradation reaction follows a pseudo first-order kinetic model with a reaction rate constant of 0.0329 min?1. The nanocomposite demonstrated outstanding stability and recyclability. 2020 Wiley-VCH GmbH -
Exploring the potential application of Cr2AlC MAX phase as an emerging electrocatalyst for overall water splitting
A three-dimensional (3D) chromium carbide ceramic type, H-phase (211) categorized as Cr2AlC, MAX phase has garnered enormous attention in recent times due to its unique structure and bonding, surface area, thermal stability, and thermo-electrical conductivity, and hydrophilicity. A simple synthesis approach is proposed for obtaining layered Cr2AlC, MAX phase, with X-ray diffraction data and SEM morphology confirming the formation of the H-phase. The electrocatalyst Cr2AlC is being utilized for electrocatalytic water splitting application. The Cr2AlC is observed to exhibit an overpotential and Tafel slopes of 215 mV/88.3 mV dec? 1 for the hydrogen evolution reaction (HER) and 376 mV/96.5 mV dec? 1 for the oxygen evolution reaction (OER), respectively, demonstrating good stability for up to 7200s. This study establishes a straightforward method for producing emergent material, Cr2AlC MAX phase, and highlights its promising applications in water electrolysis, hydrogen evolution, and oxygen evolution reactions. Qatar University and Springer Nature Switzerland AG 2024. -
Exploring the potential application of Cr2AlC MAX phase as an emerging electrocatalyst for overall water splitting
A three-dimensional (3D) chromium carbide ceramic type, H-phase (211) categorized as Cr2AlC, MAX phase has garnered enormous attention in recent times due to its unique structure and bonding, surface area, thermal stability, and thermo-electrical conductivity, and hydrophilicity. A simple synthesis approach is proposed for obtaining layered Cr2AlC, MAX phase, with X-ray diffraction data and SEM morphology confirming the formation of the H-phase. The electrocatalyst Cr2AlC is being utilized for electrocatalytic water splitting application. The Cr2AlC is observed to exhibit an overpotential and Tafel slopes of 215 mV/88.3 mV dec? 1 for the hydrogen evolution reaction (HER) and 376 mV/96.5 mV dec? 1 for the oxygen evolution reaction (OER), respectively, demonstrating good stability for up to 7200s. This study establishes a straightforward method for producing emergent material, Cr2AlC MAX phase, and highlights its promising applications in water electrolysis, hydrogen evolution, and oxygen evolution reactions. Qatar University and Springer Nature Switzerland AG 2024. -
Exploring the Influence of Etching media on the Electrochemical Behavior of Cr2CTx MXene
MXenes, a class of 2D materials, have garnered significant attention for energy applications due to their unique properties.This study investigates the influence of different etching media on the synthesis of 2D Cr2CTx MXenederived from cost-effective Cr2AlC MAX phase. Three etching solutions- hydrofluoric acid(HF), HF-forming (lithium fluoride + Hydrochloric acid, LiF+HCl), and non-fluoride (sodium hydroxide, NaOH) have been used to treat ternary carbide Cr2AlC MAX phase under varied reaction conditions. The MXenes, Cr2CTx-HF, Cr2CTx-LiF/HCl, and Cr2CTx-NaOH are structurally, and morphologically characterized using XRD, Raman spectroscopy, TGA, XPS, SEM-EDX, and BET-BJH analysis. Theelectrochemical performance of Cr2CTx MXene is assessed, focusing on its performance in water splitting and supercapacitive applications. The materials exhibitlower overpotential values for hydrogen evolution reaction (HER), oxygen evolution reaction (OER), and demonstrate improved pseudocapacitive behavior, with enhanced energy and power densities. The introduction of surface termination groups in Cr2CTx MXene (Tx = ?F, ?OH, ?O) resulted in a more open and accessible layered structure with an appreciable surface area, without any modifications. This enhancedelectrochemical kinetics, improved ion transport, diffusion, and storage capacity, which are beneficial for electrochemical energy storage and production. 2025 Wiley-VCH GmbH. -
Nanostructured Carbon-Coated Barium Ferrite for Efficient Cr(VI) Adsorption: Synthesis and Performance Evaluation
The study reports the synthesis of nanostructured carbon-coated barium ferrite via a combined in-situ pyrolysis and co-precipitation approach. The resulting material was characterized using XRD, TEM, and BET analysis, confirming its nanostructure and high surface area. The adsorbent demonstrated efficient removal of hexavalent chromium (Cr(VI)) from aqueous solutions, achieving up to 92% removal under optimal conditions. Adsorption followed a multilayer process with pseudo-second-order kinetics, and the material retained significant efficiency over six reuse cycles. These findings highlight the potential of carbon-coated barium ferrite as a promising adsorbent for water purification applications. 2025 Wiley-VCH GmbH. -
Towards connected government services: A cloud software engineering framework
Cloud computing technologies are being used highly successfully in large-scale businesses. Therefore, it is useful for governments to adopt cloud-driven multi-channel, and multiple devices to offer their services such as e-tax, e-vote, e-health, etc. Since these applications require open, flexible, interoperable, collaborative, and integrated architecture, service-oriented architecture approach can be usefully adopted to achieve flexibility and multi-platform and multi-channel integration. However, its adoption needs to be systematic, secure, and privacy-driven. In this context, micro services architecture (MSA), a direct offshoot of SOA, is also a highly attractive mechanism for building and deploying enterprise-scale applications. This chapter proposes a systematic framework for cloud e-government services based on the cloud software engineering approach and suggests a cloud adoption model for e-government, leveraging the benefits of MSA patterns. The proposed model is based on a set of evaluated application characteristics that, in turn, support emerging IT-based technologies. 2021 by IGI Global. All rights reserved. -
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Scientific competence and acquisition challenges in education managed by analytics
Integration of instructional, informational, and communication technology underpins modern higher education. After decades without computer networks, these technologies have transformed learning. E-learning has transformed the education sector, solving its problems. The similarities between technology and cognition make this change noteworthy. Artificial intelligence-inspired model-based reinforcement learning lets agents predict states and outcomes across activities and settings to modify their behaviour. The human brain has similar mechanisms, especially in model selection, which is a fascinating mystery. This study examined the brains model selection process and found that sensory prediction errors motivate the brain to choose between computational models. The theory was contrasted with internal modelling and incentive predictive performance to show how prediction errors influence computational model selection. The brain can choose an internal validation learning model based on incentive prediction mistakes, as empirical evidence demonstrates that the policy gradient method matches these models. These models were intended to address higher education issues like administration, academic delivery, instructional design, and ethics. The report also suggested that e-learning could help solve industry issues like student concentration on campuses, brain drain, and resource shortages. This research shows how technology can change higher education and the future of learning. Copyright 2025 Inderscience Enterprises Ltd. -
Can generative AI serve as the modern-day white-collar knowledge laborer?
Purpose: There has been a growing debate on whether generative AI can serve as the modern-day equivalent of white-collar knowledge workers. In a recent post, technology magnate Bill Gates boldly proclaimed that ChatGPT would soon become the quintessential white-collar worker of tomorrow (Dean, 2023). This is indeed an exciting prospect, as generative AI advances at breakneck speed. Need for the Study: This research delves upon the implications of such advancements for industries reliant on skilled employees. It raises questions about how these individuals will adjust their skillset going forward, given the proliferation of generative AI solutions poised to disrupt traditional roles previously occupied by humans. Methodology: The study uses an exploratory framework to understand AI's implications on job roles, productivity, and skill requirements. It introduces generative AI and its relevance, focusing on how it could transform whitecollar jobs. Findings: One thing seems clear: its impact on future employment opportunities. However, this technology still has limitations, potentially leading to unintended consequences. While capable of performing certain functions precisely and accurately, it cannot fully replace the reasoning abilities or cognitive flexibility innate in human workers tasked with knowledge-based work. Practical Implications: The potential implications for workforce development, policy-making, and future research in AI and labor economics are highlighted. This will also help gain insights into the integration process, benefits, challenges, and the changing nature of white-collar work due to generative AI. 2025 by Ram B. Ramachandran and Chabi Gupta. All rights reserved. -
Content-Based Product Recommendation SystemsReview
Content-based recommendation systems have become essential for improving user experiences in e-commerce and various digital platforms. This review paper examines the recent advancements in content-based recommendation systems, focusing on machine learning techniques and models used to personalise user interactions. The paper also explores the role of deep learning and hybrid approaches in increasing the accuracy and relevance of recommendations. Despite significant progress, the product recommendation systems face challenges such as capturing complex user preferences, ensuring scalability, addressing the cold start problem, and improving explainability which remains crucial and requires further research. This paper offers a comprehensive overview of current methodologies, identifies existing limitations, and suggests future directions to optimise content-based recommendation systems to provide more effective and reliable recommendations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A constrained multi-period portfolio optimization model based on quantum-inspired optimization
Multi-period portfolio optimization (MPO) is one of the most important problems to be solved to help investors select optimal portfolios for investment plans. The portfolios are influenced by the risk factors in the market and it is important to select optimal portfolios that can maximize the returns with minimum risk values. Other than the risk factor, there are several other influential factors that reduce the optimality of the portfolios. Therefore, by considering all possible constraints, this study proposes a multi-constraint MPO model that selects the optimal portfolio based on the asset returns. To solve the multi-constrained problem, a novel quantum-inspired whale optimization algorithm (QWOA) is introduced in this paper. The proposed algorithm enhances the traditional optimization model to work in a multi-constrained scenario. Here, quantum entanglement is adapted to reduce the slow convergence issue of whale optimization. Apart from considering only the risk factors, this paper also considers certain higher-order moments (HOM), such as skewness, kurtosis, transaction cost, diversification, boundary and budget constraints. These factors affect the portfolios as the market is dynamic, and timely changes are always seen. Thus, optimizing the mentioned factors aids in attaining an optimal portfolio. Empirical evaluations are performed, and the results suggested that the proposed model provided beneficial outcomes as compared with other algorithms like whale optimization algorithm (WOA), gray wolf optimization (GWO), fruitfly optimization algorithm (FOA), particle swarm optimization (PSO) and fruitfly algorithm (FA). The overall net return rate of the proposed model is always above 0.85% for different values of upper bounds, and the obtained Sharpe ratio, Sortino ratio, STARR ratio, information ratio, Shannon entropy, and downside deviation values of the proposed algorithm are 5.016254, 0.89327, ? 0.01987, 0.103826, 3.04452 and 0.2854. Hence, the proposed approach is highly effective for optimizing the constrained MPO. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
An Intelligent Portfolio Management Scheme Based On Hybrid Deep Reinforcement Learning and Cumulative Prospective Approach
Stock markets retain an extensive role towards economic growth of diverse countries and it is a place where investors invest assured amount to earn more profit and the issuers pursue the investors for project investing. However, it is deliberated as a challenging task to buy and sell because of its explosive and complex nature. The existing portfolio optimization models are primarily focused on just improving the returns whereas, the selection of optimal assets is least focused. Hence, the proposed research article focuses on the integration of stock prediction with the portfolio optimization model (SPPO). Initially, the stock prices for the next period are predicted using the hybrid deep reinforcement learning (DRL) model. Within this prediction model, the gated recurrent unit network (GRUN) model is utilized to simulate the interactions of the agent with the environment. The best actions in the prediction model are determined throughout the prediction process using the quantum differential evolution algorithm (Q-DEA). After the prediction of best assets, the optimal portfolio with the best assets is selected using the cumulative prospect theory (CPT) model. The work will be implemented in python and evaluated using the NIFTY-50 Stock Market Data (2000 -2021) dataset. Minimal error rates of 0.130, 0.114, 0.148 and 0.153 is obtained by the proposed model in case of MSE, MAE, RMSE and MAPE. 2024 IEEE. -
Empowering energy access: the influence of Islamic banking and Fintech on sustainable energy in MENA
Purpose This study aims to examine the impact of financial inclusion through Islamic banking and fintech-driven digital financial services on sustainable energy access (SEA) in the Middle East and North African (MENA) region. Despite the regions abundant energy resources, persistent challenges of energy poverty remain. This study explores how complementary financial mechanisms can address financing gaps in clean energy initiatives. Design/methodology/approach This study explores the influence of Islamic banking and fintech on energy poverty in the MENA region. Using the entropy weight approach, the authors uniquely construct composite SEA indices for individual MENA countries and ascertain how these SEAs are influenced by Islamic banking (FI1) and fintech-driven financial inclusion (FI2). Findings The findings reveal an average SEA value of 0.25, indicating that MENA countries face energy poverty. The analysis shows the influence of banking infrastructure and Islamic financial services on SEA in the Islamic MENA region, highlighting the complexity of the issue. From an aggregate view, both aspects of financial infrastructure improve SEA. The results show that unemployment, trade openness, urbanization and inflation significantly predict SEA. Originality/value The findings apply to the Islamic MENA region because of the larger sample of countries than the single-country studies primarily found in the relevant literature. The findings underscore the complementary roles of Islamic banking and fintech-based financial inclusion in addressing energy poverty, presenting essential implications for policymakers and governments in the MENA region. 2025 Emerald Publishing Limited -
Energy efficiency and conservation using machine learning
This chapter explores the fascinating nexus between machine learning (ML), energy efficiency, and conservation, concentrating on a captivating case study that makes use of the oneAPI framework. Optimizing energy consumption has become crucial due to the increased interest in sustainable practices. By investigating the use of oneAPI in energy efficiency projects, we examine the possibility of ML techniques to overcome this difficulty. We demonstrate how ML algorithms can accurately model and anticipate energy usage patterns through a thorough analysis of real-world data. Additionally, we discuss the importance of feature engineering, algorithm selection, and data pretreatment in creating accurate energy consumption models. The case study emphasizes the wider implications of utilizing ML to support energy-saving initiatives in addition to demonstrating the effectiveness of oneAPI. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Orality, Literacy, and Modernity: A Reading of The Legends of Khasak
What is the relationship between literacy and culture? It is not possible to give a simple answer to this question. Eric Havelock, while commenting on ancient Greek culture and literacy, observes that the classic culture of Greece had attained an advanced stage even before the emergence of Greek script. It continued to exist as an oral culture for a long time (Havelock 1963, 117120). A culture without a script is not uncivilized or underdeveloped. Havelock observes: One can propose with assurance that the pre-Homeric epoch the Dark Age yields for the historian what might be called a controlled experiment in non-literacy. Here, if anywhere, we can study those conditions on which a total culture, and a very complex one, relied for its preservation upon oral tradition alone. (pp. 11718) 2025 selection and editorial matter, E.V. Ramakrishnan and K.C. Muraleedharan; individual chapters, the contributors. -
Enhanced Process Model and Analysis of Risk Integration in Software effort estimation
The development of software within the estimated effort is remaining as a challenging task. The process of effort estimation is a critical activity in a software project, where effort estimates are utilized to arrive at the schedule, resources, and cost. Though many software effort estimation techniques exist, effort overrun occurs in a project. Identification of risks and their consideration in software lifecycle activities play a significant role in the successful execution of a software project. It would be required to account for uncertainty and the key factors that contribute to it. This study focuses on the need to include project risk score in the software effort estimation process to arrive at better effort estimates. This paper depicts the standard and enhanced process frameworks for estimation of software development efforts. A multi-layer perceptron model was built and the results indicated the relevance of considering project risk score in the effort estimation process. The usage of an enhanced gradient boosting technique for predictive modelling revealed a decrease in standard deviation of the residuals, thus indicating a better fit for the effort estimation model through integration of risks. 2019 IEEE. -
Exploring digital twins: Attributes, challenges and risks
The recent approach to digitalization and digital transformation is based on the focus of every industry to develop systems and practices for optimizing the operational phase of the product lifecycle and beyond. Digital twins have become the buzzword in the domain of digital transformation. These Digital twins, which are a virtual representation of real-world occurrences such as processes, services, or products offer a new perspective to digitalization. It has emerged from Industry 4.0 and involves a mapping of the real physical world and the virtual world through Digital Twinning. Artificial Intelligence, Cryptography, Blockchain, Big Data technologies, and IoT act as technology enablers for Digital Twins. The capability of Digital Twin is its ability to cater to diverse applications. Within a decade, it has penetrated deeply into every functional aspect of business right from Patient Health Information Systems to remote control and maintenance of satellites/ space stations and to agriculture. This chapter has a focus on the key attributes, challenges, and risk factors that pertain to digital twin technologies and provides adequate examples from diverse sectors. The key challenges of digital twin technologies include Modeling the unknown, Transparency, Interpretability, Interactions with physical assets, Large-scale computation, Physical realism, Future projections, Data management, Privacy, Security and Quality. The four facets of risks related to Digital Twins include restrictions in access to system resources, theft of intellectual property, lack of compliance, and integrity issues in data/information. Hence, additional efforts and a holistic approach towards privacy and security are required to manage these risks. The holistic approach should cover hardware, software, and firmware together with the information that passes between them. Further, it is required to ensure that system, assets and data are adequately protected. Digital Twin technologies provide enormous competitive advantage for an organization, and a more pragmatic approach for mitigation of risks associated with digital twins is required. This would involve co-creation of Digital Twins with clients along with combined extensive knowledge of physical assets, disruptive technologies and appropriate security measures. 2023 Nova Science Publishers, Inc. All rights reserved. -
Ecofriendly Approaches for Ameliorating the Adverse Effects of Cadmium in Plants by Regulating Physiological and Defense Responses: An Overview
Mitigating cadmium stress in agricultural plants becomes extremely critical in order to assure food sufficiency in the scenario of a rapidly growing population. An extensive review of environmentally friendly methods for reducing cadmium toxicity in plants is provided in this chapter, with special attention to a variety of tactics like phytohormones, polyamines, melatonin, mineral ions, nanoparticles, and transgenic techniques. Nanoparticles are capable of changing the distribution of cadmium, activating antioxidant defense mechanisms, and boosting physiological processes that are crucial for plant resilience and growth. Microorganisms greatly increase plant resistance to cadmium stress by modifying phytohormones and regulating defense-related proteins. Phytohormones can increase a plants adaptability to cadmium stress through a number of mechanisms, such as the regulation of gene expression and physiological processes. Melatonin and polyamines provide protection against oxidative stress and heavy metal toxicity, while mineral ions such as silicon, calcium, zinc, iron, and selenium increase plant resistance to cadmium, minimizing pollution-related harm. Transgenic plants that are tolerant to cadmium exhibit enhanced detoxification processes and reduced metal accumulation. These findings provide important insights for long-term plant cadmium mitigation and highlight the significance of interdisciplinary approaches in managing heavy metal stress in agricultural systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Salvia officinalis L. resilience under chromium stress: An integrated study of growth, physiology, biochemical changes and rosmarinic acid production
Medicinal plants are increasingly challenged by rising chromium (Cr) levels in agricultural soil and water bodies due to industrialization and human activities. This research examines the impact of various chromium concentrations on Salvia officinalis L., a medicinal herb, over 3 specific time periods: 30, 60 and 90 days. As the duration of Cr exposure increases, various growth parameters showed an upward trend at the lowest concentrations, with the most robust growth observed in the 20 ppm Cr treatment group after 90 days. However, higher chromium concentrations resulted in reduced plant growth compared to untreated plants. Chromium primarily accumulates in the roots, stems and leaves, with the highest accumulation observed at 100 ppm. However, chlorophyll content declined with prolonged Cr exposure, particularly at higher concentrations. Carbohydrate levels initially increased at lower Cr concentrations but decreased with greater exposure, while protein content consistently decreased with elevated Cr levels. Proline levels exhibited mixed responses, rising at lower concentrations and declining at higher ones. Malondialdehyde (MDA) content increased with higher Cr levels and extended exposure. The enzymatic antioxidant system showed an initial increase followed by a decline with prolonged exposure. Rosmarinic acid content increased with chromium (Cr) exposure upto 60 ppm but subsequently decreased beyond that threshold. In the first 30 days, plants treated with Cr demonstrated a 17 % increase in rosmarinic acid production compared to the control (48.9 mg/g DW). However, with continued Cr exposure, there was a decline in rosmarinic acid production ranging from 10 % to 20 % compared to the control level (67.02 mg/g DW) at 90 days post-treatment. These findings underscore the complex and contrasting responses of Salvia officinalis to Cr toxicity, highlighting the necessity for extended study into the core mechanisms governing these responses and the development of strategies to alleviate heavy metal stress in plants. The Author(s).