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Advances in Type II Diabetes Prediction: A Comprehensive Review of Machine Learning Techniques
Type II diabetes mellitus, on the other hand has been regarded as one of the growing concerns globally and thus clearly raises the need for making accurate forecasts of diabetes. The risk for Type II diabetes can be predicted using Ma-chine Learning as well as any other form to make the predictions much more enhanced than the traditional methods. This paper aims to give a broad overview of literature that has so far been available on the ML algorithms used in the management of Type II diabetes including such supervised algorithms as logistic regression, alphabet regression, random forest, support vector regression along with other methods such as, ensemble learning, deep learning, and hybrid. Analysis of the main aspects for the performance model such as parameter selection, the way to face and cope with imbalance parameters, interpretability and generalizability across different populations, another aspect that was regarded is the possibility of using real-time data collected with wearable devices and applying tissue and other biomarkers for better prediction. Finally, the key obstacles and future directions towards developing ML algorithms and models explainable and clinically relevant have been introduced to help researchers and practitioners toward effective, personalized, and scalable interventions. 2025 IEEE. -
Harnessing the Power of Cloud Computing for Advanced Business and Economic Research
Cloud computing has surfaced as a significant influence in the domain of business and economic research. Its ability to deliver vast computational resources, scalable storage, and unparalleled accessibility has revolutionized the way researchers analyze complex datasets, conduct simulations, and collaborate on ground-breaking projects. This paper delves into the myriad ways cloud computing is empowering researchers to unlock unprecedented economic insights. This research article delves into the key dimensions of leveraging cloud computing for advanced business and economic research. It investigates the scalability and flexibility of cloud-based infrastructure, enabling researchers to process and analyze extensive datasets, conduct complex simulations, and implement machine learning algorithms for predictive modeling. Moreover, the cloud facilitates real-time collaboration and data sharing, fostering a global research community that transcends geographical boundaries. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Building a sustainable relationship between customers and marketers
Finding new customers is costlier than retaining the existing customers for the business. Therefore, building a strong relationship with customers helps marketers to retain their existing customers. Incorporating ethical and moral values into marketing activities offer a way to build a strong relationship. This study identifies the factors that bind customers and marketers into a sustainable relationship in the Indian context. This study constitutes a framework to understand and apply sustainable relationship marketing in the personal care industry. This study touches certain marketing disciplines such as marketing mix policy, transparency in trades, building trust, product delivery, promises delivery, and sustainable relationship. The convenience sampling technique used for the selection of respondents from the Mohali City of Punjab, and interview them. The finding suggests that promises delivery is the most important factor for a sustainable relationship. If promises are delivered effectively then the life of the relationship will be longer. Copyright 2024 Inderscience Enterprises Ltd. -
Development and characterization of Fe2O3 nanoparticles coated with chitosan and folic acid for biomedical applications
Polymeric inorganic nanoparticles have emerged as promising nanomedicines due to their unique properties, offering enhanced antibacterial and anticancer effects. Thus, the study focus on the synthesis of Fe2O3 and Fe2O3 coated with chitosan and folic acid nanoparticles (Fe2O3-CS-FA NPs) mediated by Tagetes erecta (T. erecta) extract and assess their biological effects. The synthesized NPs are analysed by various characterisation techniques. FTIR spectroscopy of Fe2O3 and Fe2O3 -CS-FA NPs revealed characteristic peaks corresponding to Fe2O3, chitosan, and folic acid molecules. The XRD pattern confirmed the successful synthesis of Fe2O3 NPs and Fe2O3 -CS-FA NPs, indicating a rhombohedral structure. FESEM demonstrated spherical structures for both Fe2O3 and Fe2O3 -CS-FA NPs. Antimicrobial activity was assessed against various pathogens using the disk diffusion method, showing that Fe2O3-CS-FA NPs demonstrated superior antibacterial activity compared to Fe2O3 NPs. In terms of antioxidant activity, Fe2O3 -CS-FA NPs showed the highest scavenging activity against DPPH, outperforming Fe2O3 NPs. The anticancer activity of both Fe2O3 NPs and Fe2O3 -CS-FA NPs was tested against the HCT-116 human colon cancer cell line, where Fe2O3 -CS-FA NPs demonstrated greater anticancer activity with an IC50 value of 10.2 ?g/mL compared to Fe2O3 at 13.8 ?g/mL. Based on the findings of this research, there is a strong indication that Fe2O3 -CS-FA NPs hold significant potential as a nanomaterial well-suited for advanced biomedical applications in the industry. 2025 Indian Chemical Society -
Theoretical Studies ond(?,p)n atAstrophysical Energies
The photonuclear reactions using deuterium target finds application in nuclear physics, laser physics and astrophysics. The studies related to deuteron photodisintegration using polarized photons has been the focus of interest since 1998 which influenced many experimental studies which were carried out using 100% linearly polarized photons at Duke free electron Laser laboratory. Theoretical study on deuteron photodisintegration was carried out and in these studies the possibility of 3 different E1v amplitudes leading to the final n-p state in the continuum was discussed. As there is experimental evidence about the splitting of 3 E1vp- wave amplitudes at slightly higher energies, we hope that the same may be true at near threshold energies also. As the spin dependent variables are more sensitive to theoretical inputs and the data obtained on polarization observables are more sensitive to theoretical calculations, there is a considerable interest on studies related to the reaction. More recently, neutron polarization in d(?,n)p was studied at near threshold energies. In this regard the purpose of the present contribution is to extend this study to discuss proton polarization in d(?,p)n reaction using model independent irreducible tensor formalism at near threshold energies of interest to astrophysics. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Model independent approach to proton polarization in photodisintegration of deuteron
In addition to other photonuclear reactions, the study of photonuclear reactions on deuterium targets is important for laser physics, nuclear physics, astrophysics, and a number of applications, including nondestructive testing of nuclear materials. In this paper, we have carried out a model independent analysis of proton polarization in photodisintegration of deuterons with initially unpolarized beam and unpolarized target. The angular dependence of the polarization is studied by expressing it in terms of multipole amplitudes. 2023 Elsevier Ltd. All rights reserved. -
Data-driven triumph: CRM sales insights revolutionize customer retention
Context: The examination of the CRM data is anchored in a comprehensive analysis of sales performance metrics, with a significant role played. It was found a gap in the literature, considering the scarcity of pertinent case studies within the academic literature. Method: The geographical factor is paramount in this analysis, as it unveils divergent results across different regions. Moreover, the venture into predictive analytics for sales forecasting, capitalizing on CRM primary data spanning from 2018 to 2023, facilitating more informed decision-making. The sample comprises around 1500 Business to Business customer clusters for in-depth analysis is considered. Findings: From the Business Intelligence analysis, it was found the presence of long-standing customers with a lower purchase rate, favouring average industrial product models preferred by the customers. Conclusion:The study also explores the link between CRM can shape business strategies, enhance customer relationships, and boost organizational performance and customer retention. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Chatbots in health care: AI-based personalization and EHR integration in patientdoctor communication
The artificial intelligence (AI)-driven chatbots in healthcare integration revolutionizes patientprovider interactions for real-time support, communication streamline, and patient engagement. These chatbots connected to natural language processing (NLP) and machine learning provide medical queries resolution, chronic condition management, and scheduling appointments. Despite the advancements, there are gaps remain in the chatbot personalization interactions and Electronic Health Records (EHR) seamless integration. Personalization is crucial for satisfied patient and medical advice. EHR integration enables context-aware responses, error reduction, and better healthcare outcomes. This study effectiveness fosters the evaluation of AI-driven chatbots in healthcare communication personalization and potential benefits examination and EHR integration challenges. Using a mixed-methods approach includes sentiment analysis for patient satisfaction sentiments understanding, thematic analysis for key themes and findings from Patient Message, regression analysis for personalization, EHR integration, and patient outcomes understanding, and structural equation modeling (SEM) highlights the personalization and EHR integration impact on patient satisfaction, engagement, and trust in chatbot technology. The findings reinforcing the healthcare providers need to adopt AI-driven solutions and personalized communication priorities and seamless data integration for patient experience improvement and overall healthcare efficiency. 2026 Elsevier Inc. All rights reserved. -
Workforce Transformation and Value Creation in the Era of Industry 4.0, 5.0 and 6.0: Challenges and Enablers
Industry 4.0, Industry 5.0, and Industry 6.0 have been empowered by the acceptance of several advanced technologies, including the Internet of Things (IoT), artificial intelligence, robotics, and human-centric innovation in releasing industries. Comprehensive studies that can include barriers as well as enablers are difficult to conduct. This study employs a mixed-methods research approach, integrating of AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to evaluate key challenges and enablers in workforce transformation. The Findings indicate that leadership vision, digital investment, and employee up-skilling play a crucial role in transformation navigation. Additionally, automation and AI adoption present both opportunities and challenges for workforce adaptability. The study provides strategic insights for organizations to enhance their workforce resilience, competi-tiveness in the evolving industrial landscape. The study offers actionable advice for businesses, policymakers, and educators, and suc-cessfully adaptation the paradigm and sustainable development in the modern era. Authors. -
Eco-friendly operations in Reality: Analysis of key factors of sustainability performance in manufacturing companies
Sustainable product design (SPD) focuses on eco-friendly products. The energy efficiency (EE) optimizes energy use in buildings, transportation, and industry. The policies that reduce the consumption at the macro-level rebound effects are debated. The evidence leans toward waste management (WM) needing strategies like recycling and source reduction in developing countries. This study examines the sustainable product design, energy efficiency, and waste management on overall sustainability performance in manufacturing companies. The study used correlation analysis, descriptive statistics, and multiple linear regression to quantify the significance of these factors. Results show that all three variables significantly contribute to sustainability performance and support the hypotheses. SPD, EE, and WM positively impact overall sustainability performance, and evidence leans toward strong relationships among variables, but multicollinearity could complicate findings. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Adaptive consumer psychology navigating trust and scepticism in automated retail experiences
The rapid deployment of automated retail technologies has brought both opportunities and challenges for consumer acceptance. Trust is an important factor for benefits determination of the automation systems. Sometimes Skepticism often acts as a barrier to adoption. This chapter enlightens into the psychology of consumer trust, perceived security, reliability, and transparency in automated systems. The research identifies key psychological triggers lead to skepticism in such a way that the data misuse or system failures and solution mitigation. The chapter highlights the role of adaptive design in consumer hesitations and positive interactions. Using empirical evidence and practical approach emphasizes technological efficiency balance with emotional reassurance and consumer comfort in automated environments. 2026, IGI Global Scientific Publishing. -
Synthetic Biology Tools for Genome and Transcriptome Engineering of Solventogenic Clostridium
Strains of Clostridium genus are used for production of various value-added products including fuels and chemicals. Development of any commercially viable production process requires a combination of both strain and fermentation process development strategies. The strain development in Clostridium sp. could be achieved by random mutagenesis, and targeted gene alteration methods. However, strain improvement in Clostridium sp. by targeted gene alteration method was challenging due to the lack of efficient tools for genome and transcriptome engineering in this organism. Recently, various synthetic biology tools have been developed to facilitate the strain engineering of solventogenic Clostridium. In this review, we consolidated the recent advancements in toolbox development for genome and transcriptome engineering in solventogenic Clostridium. Here we reviewed the genome-engineering tools employing mobile group II intron, pyrE alleles exchange, and CRISPR/Cas9 with their application for strain development of Clostridium sp. Next, transcriptome engineering tools such as untranslated region (UTR) engineering and synthetic sRNA techniques were also discussed in context of Clostridium strain engineering. Application of any of these discussed techniques will facilitate the metabolic engineering of clostridia for development of improved strains with respect to requisite functional attributes. This might lead to the development of an economically viable butanol production process with improved titer, yield and productivity. Copyright 2020 Kwon, Paari, Malaviya and Jang. -
Hybrid bimetallic sulfide (FeCoS)-doped conductive polymer as efficient oxygen evolution reaction electrocatalyst for direct seawater electrolysis
Seawater electrolysis is critical for sustainable hydrogen production, especially in regions facing freshwater scarcity. However, chloride ions compete through parasitic reactions, such as the chlorine evolution reaction, creating a serious challenge that reduces catalytic activity and durability. Herein, a hybrid electrocatalyst composed of FeCoS embedded in a polyaniline matrix (FCS-PANI) is synthesized using a simple hydrothermal method. This fabricated composite combines the benefits of the high catalytic activity of FeCoS and the corrosion resistance of the conductive polymer (PANI). Structural analysis establishes the formation of a uniform nanocomposite with strong metalsulfur and metalnitrogen interactions. Advanced oxygen evolution reaction (OER) performance with a low overpotential of 327?mV at 30?mA?cm?2 and a Tafel slope of 38.67?mV dec?1 is achieved through electrochemical testing in alkaline seawater. High stability, low degradation (0.3?mV?h?1) over 500?h of operation, and 99.97% hydrogen purity are observed upon integration into an anion exchange membrane water electrolyzer (AEMWE), indicating its practical potential for seawater electrolysis. 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Executives Perception about Project Management Practices in BEML Bangalore
International Journal of Research in Commerce, IT & Management Vol.2, No. 7, pp 69-74, ISSN No. 2231-5756 -
Relationship between Emotional Intelligence and Academic Achievement among College Students
Indian Journal of Applied Psychology Vol. 50, pp. 78-81, ISSN No. 0019-5073 -
Chlorella vulgaris-mediated sustainable biogenic synthesis of silver nanoparticles for wastewater remediation and antibacterial applications
This investigation examines the green synthesis of silver nanoparticles (SNPs) using Chlorella vulgaris as a reducing and stabilizing agent. Algae-mediated SNPs (ASNPs) were tested for the potential application in sewage water remediation and as an antibacterial agent. Biogenic ASNPs demonstrated excellent stability and a surface plasmon resonance (SPR) peak at 440 nm. Energy dispersive X-ray (EDAX) spectroscopy analysis and Fourier transform infrared (FTIR) spectroscopy investigation indicated the role of biomolecules originating from the algal extract, which play a crucial role in the green synthesis process of ASNPs. Dynamic light scattering analysis yielded a hydrodynamic mean particle size of 200 nm and a zeta potential of around 18 mV. Observation under electron microscopy presented the morphological diversity with a prominent signature of elemental silver in ASNPs. A domestic waste sewage sample collected from a sewage treatment plant presented elevated levels of alkalinity, salinity, and biological oxygen demand (BOD). ASNP treatment normalises most of the water parameters, while algal extract alone could produce minimal effects. The antibacterial evaluations against Staphylococcus aureus and Escherichia coli, well-known opportunistic pathogens responsible for a wide range of hospital and community-acquired infections, showed dose-dependent effects. These findings highlight the dual functional role of C. vulgaris-mediated SNPs as an effective, eco-friendly solution for both wastewater remediation and antibacterial application. 2025 Elsevier Ltd -
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The birth of Be star disks: I. from localized ejection to circularization
Context. Classical Be stars are well known to eject mass to build up a disk, but the details governing the initial distribution and subsequent evolution of this matter into a disk are in general poorly constrained through observations. Aims. By combining high-cadence time-series spectroscopy with contemporaneous space photometry from the Transiting Exoplanet Survey Satellite (TESS), we have sampled about 30 mass ejection events in 13 Be stars. Our goal is to constrain the geometrical and kinematic properties of the ejecta as early as possible, facilitating the investigation into the material's initial conditions and evolution, and understanding its interactions with preexisting material. Methods. The photometric variability is analyzed together with measurements of the at-times rapidly changing emission features in order to identify the onset of outburst events and obtain information about the geometry of the ejecta and how it changes over time. Short-lived line asymmetries display oscillation cycles (tefl frequencies), which are compared to photometric and stable spectroscopic frequencies. Results. All Be stars observed with sufficiently high cadence during an outburst are found to exhibit rapid oscillations of line asymmetry with a single frequency in the days following the start of the event. For a given star this circumstellar frequency may differ only slightly from event to event even when the outbursts they are associated with have different properties. These circumstellar frequencies are typically between 0.5 to 2 d- 1, and are generally near photometric frequencies. They are slightly below prominent (generally stable) spectroscopic frequencies seen in photospheric absorption lines. The emission asymmetry cycles break down after roughly 5- 10 cycles, with the emission line profile converging toward approximate symmetry shortly thereafter. In photometry, several frequencies typically emerge at relatively high amplitude at some point during the mass ejection process. Conclusions. In all observed cases, freshly ejected material was initially constrained within a narrow azimuthal range, indicating it was launched from a localized region on the stellar surface. The material orbits the star with a frequency consistent with the near-surface Keplerian orbital frequency. This material circularizes into a disk configuration after several orbital timescales. This is true whether or not there was a preexisting disk at the time of the observed outburst. We find no evidence for precursor phases prior to the ejection of mass in our sample. The several photometric frequencies that emerge during outburst are at least partially stellar in origin. The Authors 2025. -
Novel Hybrid Machine-Learning Algorithms for Resource Optimization in Cloud
The resource optimization process in the cloud is crucial and can be achieved through the ideal Load Balancing (LB) mechanism. The cloud undergoes several challenges with resource optimization due to poor LB mechanism, where its Virtual Machines (VMs) are either overloaded or idle. The main aim of this experimental-based research is to enhance the LB mechanism of the cloud by implementing and comparing the performance of novel hybrid LB algorithms RLFCFS and RLSJF to optimize the resources. The RLFCFS and RLSJF novel LB algorithms are designed by combining the Reinforcement Learning (RL) technique with the heuristic FCFS and SJF algorithms. The proposed algorithms improve resource optimization in terms of cost and time by facilitating enhanced LB mechanism through RL intelligence mechanism. The performance of RLFCFS and RLSJF LB algorithms is compared with respect to the average (avg.) load managed by the VMs and the avg. percentage (perc.) of deviation observed against the expected load in each experimental stage. The experimental throughput conveys that the RLFCFS LB algorithm managed an aggregate avg. load of 968.77 tasks against the RLSJF LB algorithm, which managed 999.08 tasks aggregately across all experimental stages. Concerning the avg. perc. of deviation, the RLFCFS LB algorithm deviated by 63.44% against the ideal expected load to manage against the RLSJF LB algorithm, which deviated by 64.60%. This shows that the RLFCFS LB algorithm gave better resource optimization results than the RLSJF LB algorithm. Lastly, these results are mathematically validated using the Simple Linear Regression model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
EM-ACO-ARM: An Enhanced Multiple Ant Colony Optimization Algorithm for Adaptive Resource Management in Cloud Environment
Ant Colony Optimization (ACO) is an intelligent algorithm ensuring optimal resource management in cloud environments. This paper proposes an enhanced version of the ACO algorithm called Enhanced Multiple Ant Colony Optimization for Adaptive Resource Management (EM-ACO-ARM). Our approach uses multiple ant colonies undergoing several iterations of optimizations to find the optimal Virtual Machine (VM) and adapt to the convergence uncertain-ties, unlike a single ant colony in the existing ACO, which can hinder Quality of Service (QoS)-based performance parameters. We conducted experiments in a cloud-simulated environment to evaluate EM-ACO-ARM in two phases. In the first phase, we computed real-time Montage tasks using the existing ACO algorithm on VMs across ten scenarios. To ensure an unbiased comparison, the same cloud configuration was maintained in the second phase, and the same tasks were computed using the proposed EM-ACO-ARM algorithm in all ten scenarios. The experimental results demonstrate that EM-ACO-ARM improves Execution Cost and Execution Time, leading to a 14.73% increase in Resource Utilization. This ultimately improves the management of cloud resources. Additionally, a stability evaluation was conducted using regression models, and it outputted EM-ACO-ARM to provide more stability than the existing ACO algorithm. The cloud can provide better QoS with the proposed EM-ACO-ARM algorithm while abiding by Service Level Agreements. 2025 The Authors. Published by Elsevier B.V.