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A road map on synthetic strategies and applications of biodegradable polymers
Biodegradable polymers have emerged as fascinating materials due to their non-toxicity, environmentally benign nature and good mechanical strength. The toxic effects of non-biodegradable plastics paved way for the development of sustainable and biodegradable polymers. The engineering of biodegradable polymers employing various strategies like radical ring opening polymerization, enzymatic ring opening polymerization, anionic ring opening polymerization, photo-initiated radical polymerization, chemoenzymatic method, enzymatic polymerization, ring opening polymerization and coordinative ring opening polymerization have been discussed in this review. The application of biodegradable polymeric nanoparticles in the biomedical field and cosmetic industry is considered to be an emerging field of interest. However, this review mainly highlights the applications of selected biodegradable polymers like polylactic acid, poly(?-caprolactone), polyethylene glycol, polyhydroxyalkanoates, poly(lactide-co-glycolide) and polytrimethyl carbonate in various fields like agriculture, biomedical, biosensing, food packaging, automobiles, wastewater treatment, textile and hygiene, cosmetics and electronic devices. 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
A road map on synthetic strategies and applications of biodegradable polymers /
Polymer Bulletin, Vol.79, ISSN No: 0170-0839 ( print) 1436-2449 (Online).
Biodegradable polymers have emerged as fascinating materials due to their non-toxicity, environmentally benign nature and good mechanical strength. The toxic effects of non-biodegradable plastics paved way for the development of sustainable and biodegradable polymers. The engineering of biodegradable polymers employing various strategies like radical ring opening polymerization, enzymatic ring opening polymerization, anionic ring opening polymerization, photo-initiated radical polymerization, chemoenzymatic method, enzymatic polymerization, ring opening polymerization and coordinative ring opening polymerization have been discussed in this review. -
A Road to Become Successful in The Fashion Industry of China: A Case Study of Zara
In this research, it was found that Zara is facing issues while maintain its profitability and also while maintaining its large stores. Existing information collected from websites and articles show that Zara provides inferior quality products, does not have factory in China, focuses less on e-commerce activities and contributes directly to environment pollution through waste generation in China. These are reasons that the organization is losing its brand image in China. To improve its current condition, it is recommended that Zara should improve its products, focus more on marketing, develop factories in China and reduce environment pollution. The Electrochemical Society -
A robust explainable machine learning pipeline for transformer health index prediction addressing data pathologies and redundancy
Power transformers are critical infrastructure assets where unexpected failures incur severe technical and economic penalties. This study proposes a robust, explainable machine-learning (ML) pipeline for predicting the transformer Health Index (HI) using routinely collected dissolved gas analysis (DGA) and dielectric measurements. To ensure model reliability, the pipeline specifically addresses data pathologiesnamely extreme skewness and heavy tailsusing YeoJohnson transformations, while mitigating multicollinearity through hierarchical correlation clustering (|r| ? 0.85) followed by a Variance Inflation Factor (VIF) screening (VIF ? 5). Four high-performance ensemblesRandom Forest, XGBoost, LightGBM, and CatBoostwere optimized via randomized cross-validation. Experimental results on a dataset of 470 records demonstrate consistent generalization across all models (RMSE ? 0.022), with Random Forest providing superior accuracy (MAPE ? 1.24%). A Taylor diagram confirmed consistent generalization (correlation ? 0.730.78 and matched variance), while residual analysis showed minimal bias. SHAP explanations indicated that dibenzyl disulfide (DBDS) and interfacial tension (Interfacial V) were the most influential positive drivers of HI; water content tended to depress HI; and several gases (e.g., methane, hydrogen, acetylene, CO) contributed positively at higher concentrations. The proposed workflow was robust to skew/heavy tails and multicollinearity, required no feature scaling, and produced transparent, practitioner-ready insights that support condition-based maintenance at fleet scale. 2026 Elsevier B.V. -
A Robust Model for Quantum-Resistant Cryptography to Tackle Quantum Risks
As quantum computing advances, conventional cryptographic algorithms face developing threats, necessitating the improvement of quantum-resistant security mechanisms. Winternitz One-Time Signature (WOTS) is a promising cryptographic scheme that offers robust resistance in competition to quantum attacks. This paper explores the software of WOTS in enhancing the protection of digital communications and information integrity in a quantum computing generation. By manner of analysing the fundamental standards, sensible implementations, and ability demanding situations of WOTS, this research dreams to provide insights into its effectiveness as a quantum-resistant protection solution. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
A Sampling-Based Stack Framework for Imbalanced Learning in Churn Prediction
Churn prediction is gaining popularity in the research community as a powerful paradigm that supports data-driven operational decisions. Datasets related to churn prediction are often skewed with imbalanced class distribution. Data-level solutions, like over-sampling and under-sampling, have been commonly used by researchers to address this problem. There are limited number of case studies that attempt to evolve these data-level solutions by integrating them with computationally advanced frameworks, like ensembles. Ensembles primarily employ algorithmic diversity using a fixed set of training instances to achieve superior performance. This study aims to introduce algorithmic diversity in ensembles by modifying the fixed set of training instances using diverse sampling strategies to increase predictive performance in imbalanced learning. Data is acquired from the world's largest open hotel commerce platform company. A four-part series of experiments is conducted to analyze the effectiveness of sampling techniques and ensemble solutions on model performance. A new sampling-based stack framework called 'Stacking of Samplers for Imbalanced Learning' is proposed. The framework combines the prediction capabilities of sampling solutions to stimulate the information gain of the meta features in ensemble. It is observed that the proposed framework leads to improvement in model performance with AUC of 86.4% and top-decile lift of 4.7 for customers of the hotel technology provider. Additionally, results show that the framework records a higher information gain for meta features used in a stack, compared to commonly used stack frameworks. 2013 IEEE. -
A scalable scheduling and resource management framework for cloud-native B2B applications
In modern cloud computing environments, customers increasingly depend on on-demand resource provisioning to handle dynamic workloads. However, fluctuations in job arrival rates can result in prolonged queue times, which negatively affect overall system performance. Although existing scheduling algorithms provide efficient job management, they often fail to account for the combined impact of queue delays and the need for flexible resource provisioningparticularly in business-critical applications. In order to tackle these issues, the paper proposes a new Optimized Job Scheduling and Resource Scaling (OJSRS) algorithm designed to improve job execution efficiency and support elastic resource management in cloud environments. The OJSRS algorithm integrates two key components: Tree-based Job Scheduling (TJS) and Automated Resource Scaling and Scheduling (ARSS). The TJS component constructs a hierarchical structure that concurrently maps incoming jobs to the most suitable Virtual Machines (VMs), thereby minimizing queue delays. Meanwhile, ARSS adjusts resource allocation dynamically, increasing or decreasing capacity according to workload requirements and cloud service provider policies, enabling responsive and adaptive provisioning. Experimental results show that the OJSRS algorithm increases resource utilization by approximately 510% and accelerates job completion through proactive resource scaling. This approach provides a significant performance advantage for cloud-native business applications that require both efficiency and scalability. The Author(s) 2025. -
A Scientometric Analysis of Research Studies on 43 Years of Leadership in Online Education
Leadership in online education involves strategically managing digital learning environments to ensure effective instruction and engagement. This research aims to identify the publication trends and highlight trending research topics and scientific conversations in this field of leadership in online education. Using 947 records from the Scopus database, the evolution of leadership discourse in online education was examined using scientometric analysis to find the trends, most influential authors, institutions, publishing platforms, and countries. The extracted data spanned publications from 1981 to 2023. The trending topics evolved from knowledge management and school administration to e-learning and higher learning, and, after 2020, to challenges faced in imparting education due to Covid-19. The United States, China, and the United Kingdom emerged as leading contributors to this field. Co-authorship analysis highlighted international collaborations, which emphasised the growing global interest in leadership within virtual learning environments. These research findings could be helpful for researchers and managers in the field of education for adapting to the digital age. 2025, Commonwealth of Learning. All rights reserved. -
A scientometric analysis of social entrepreneurship
Impactful studies in social entrepreneurship area has garnered attention of the researchers in recent times. The interest and importance is generated in this area because of its nature in addressing social problems and welfare of the communities and societies. The study aims at providing insight on scientometric analysis in the domain of social entrepreneurship. The study further identifies researchers exploring sub domai ns considering parameters like publication language, outlook of publication patterns that changed every year, contextual journals to perform a literature review, primary subject areas in which research is being conducted, most productive institutes/universities, most productive countries where research is being conducted in the domain of social entrepreneurship and the most prolific authors in the area of social entrepreneurship. This study is a pathfinder for researchers with plans to conduct studies in social entrepreneurship domain by leading them to relevant scholarly journals and authors for greater impact. IJSTR 2019. -
A Scoping review of Deep Reinforcement Learning methods in Visual Navigation
Reinforcement Learning (RL) is a subset of Machine Learning that trains an agent to make a series of decisions and take action by interacting directly with the environment. In this approach, the agent learns to attain the goal by the response from its action as rewards or punishment. Recent advances in reinforcement learning combined with deep learning methods have led to breakthrough research in solving many complex problems in the field of Artificial Intelligence. This paper presents recent literature on autonomous visual navigation of robots using Deep Reinforcement Learning (DRL) algorithms and methods. It also describes the algorithms evaluated, the environment used for implementation, and the policy applied to maximize the rewards earned by the agent. The paper concludes with a discussion of the new models created by various authors, their merits over the existing methods, and a briefing on further research. 2023 IEEE. -
A Scoping Review of Formal Care to Children with Special Needs during the Covid-19 Pandemic
The Covid-19 pandemic caused an unprecedented closure of direct service for children with special needs (CSNs), which shifted service to remote mode. This scoping review analyzed the strategies adopted by different formal care services for CSNs, their strengths and weaknesses, and the challenges faced by the formal care providers (FCPs). This study identified relevant articles through academic databases and Google searches using appropriate search strings and keywords. It included ten journal articles (n=10) and eight pieces (n=8) of grey literature through a meticulous selection process and extracted data. This review drew results by collating the descriptive numerical data analysis and qualitative thematic analysis and interpreting them. Reporting incor-porated all the possible items recommended by the PRISMA-ScR guidelines. This review demonstrated that pediatric rehabilitation adopted the telehealth approach and that special education changed to remote learning. When childcare programs in the USA functioned according to specific guidelines, residential care in South Asian countries faced a financial crunch. FCPs faced personal and professional challenges that required systematic training to deal with pandemic situations. This scoping review made suggestions for relevant policy formulations for equitable and effective service delivery to CSNs during pandemic situations, and it exposed new avenues for research. 2022 Authors. -
A Scoping Review on Integration of Electroencephalogram Neurofeedback Training for Alcohol Use Disorder: Clinical and Neurocognitive Outcomes
Background. The conventional treatment for alcohol use disorder (AUD) consists of dual treatment encompassing pharmacotherapy and psychotherapy. Nonetheless, the impact of these treatments on clinical and neurocognitive outcomes is only low to medium efficacy. Research studies substantiate the integration of electroencephalogram neurofeedback training (EEG-NFT) as an add-on tool with significant improvements in clinical and neurocognitive outcomes. Methods. A scoping review of the existing literature on EEG-NFT and AUD, which are open access, including review papers and empirical studies in the English language, and with human subjects are deemed worthy of the scope of this study. The keywords electroencephalogram neurofeedback training, alcohol use disorder, stress, neurocognition, and relapse were used. The primary sources of the literature search were Science Direct, Scopus, PubMed, and Google Scholar. A total of 35 articles have been included in the scoping review. Studies from the last 15 years were considered for the same. Results. This review revealed that EEG-NFT is a promising tool with significant improvements in stress levels, cognitive deficits, and relapse rates for individuals with AUD when used in integration with conventional treatments. Conclusion. Chronic alcohol use affects cognitive functions, escalates relapse rate, and increases stress experienced by the individual. The present study highlights the significance of NFT as a potent add-on treatment modality to improve clinical and cognitive outcomes, thereby facilitating abstinence and reducing relapse rates in individuals with AUD. Copyright: 2023. -
A Scoping Review on the Factors Affecting the Adoption of Robo-advisors for Financial Decision-Making
Robo-advisors have recently gained popularity as an algorithm-based method of simplifying financial management. The present study explores the factors that lead many potential consumers to use Robo-advisors in financial decisions. Adopting a scoping review approach formulated by Arksey and O'Malley, the study examines the factors affecting the acceptance and usage of financial Robo-advisors in different parts of the world. The results suggest that performance expectancy, effort expectancy, trust in technology, financial knowledge, investing experience, cost-effectiveness, facilitating conditions, and intrinsic motivation are positively related to adopting Robo-advisors. On the contrary, anxiety, risk perception, investor age, data security, and behavioral biases negatively influence the investor attitude toward Robo-advisors. This creates a barrier to the diffusion of financial Robo-advisors among the investors. The study concludes by providing recommendations to service providers, policymakers, and marketers for the speedy distribution and acceptance of algorithms for the public's financial decision-making. The study identifies gaps in the existing literature and suggests areas for future research for aspiring academics. 2024 University of Pardubice. All rights reserved. -
A Search for X-Ray/UV Correlation in the Reflection-dominated Seyfert 1 Galaxy Markarian 1044
Correlated variability between coronal X-rays and disk optical/UV photons provides a very useful diagnostic of the interplay between the different regions around an active galactic nucleus (AGN) and how they interact. AGNs that reveal strong X-ray reflection in their spectra should normally exhibit optical/UV to X-ray correlation consistent with reprocessingwhereas the optical/UV emission lags behind the X-rays. While such correlated delay has been seen in some sources, it has been absent in others. Mrk 1044 is one such source that has been known to reveal strong X-ray reflection in its spectra. In our analysis of three long XMM-Newton and several Swift observations of the source, we found no strong evidence for correlation between its UV and X-ray lightcurves both on short and long timescales. Among other plausible causes for the nondetection, we posit that higher X-ray variability rather than UV and strong general relativistic effects close to the black hole may also be responsible. We also present results from the spectral analysis based on XMM-Newton and NuSTAR observations, which show the strong soft X-ray excess and iron K? line in the 0.3-50 keV spectrum that can be described by relativistic reflection. 2023. The Author(s). Published by the American Astronomical Society. -
A secure and light weight privacy preserving data aggregation algorithm for wireless sensor networks
WSN is a collection of sensors, which senses critical information related to military, opponent tracking, patient health details etc. These sensed critical and private data will be collected and aggregated by aggregators and forward it to the base station. Due to the involvement of sensitive data, there is a demand for secure transmission and privacy preserving data aggregation. In this paper, we propose a light weight, secure, multi party, privacy preserving data aggregation scheme, in which one or more sensors share their private data with aggregator securely without revealing the original content. The aggregators also perform the aggregation operation without knowing the original content. 2020 Alpha Publishers. -
A secure bio-hash-based multiparty mutual authentication protocol for remote health monitoring applications
Remote health monitoring can benefit a large number of stake holders in healthcare industry, and it has the potential to make healthcare facilities available to a large number of masses at a reduced cost. Wireless Body Area networks (WBAN) comprising of sensors, capable of capturing and transferring physiological parameters of patients, provide an efficient and cost-effective solution for remote health monitoring. Data security is one among the major concerns preventing the widespread adoption of this technology by patients and healthcare sector. This chapter on remote health monitoring, presents a biometric-based authentication protocol. The work also proposes a multiparty mutual authentication protocol for authenticating the entities, such as users, sensors, personal devices, and medical gateway, participating in a WBAN. In the proposed protocol, a verifier table is not required to store the password of users. Formal security analysis and verification of the discussed protocols are performed using Scyther tool, and the results reveal that the protocols are resistant to privileged-administrator resilience attack, man-in-the-middle attack, replay attack, and impersonation attack. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. -
A Secure Communication Gateway with Parity Generator Implementation in QCA Platform
Quantum-Dot Cellular Automata (QCA) has arisen as a potential option in contrast to CMOS in the late time of nanotechnology. Some appealing highlights of QCA incorporate incredibly low force utilization and dissemination, high gadget pressing thickness, high velocity (arranged by THz). QCA based plans of normal advanced modules were concentrated broadly in the ongoing past. Equality generator and equality checker circuits assume a significant part in blunder discovery and subsequently, go about as fundamental segments in correspondence circuits. In any case, not very many endeavors were made for an efficient plan of QCA based equality generator as well as equality checker circuits up until now. In addition, these current plans need functional feasibility as they bargain a ton with normally acknowledged plan measurements like territory, postponement, intricacy, and manufacture cost. This article depicts new plans of equality generator and equality checker circuits in QCA which beat every one of the current plans as far as previously mentioned measurements. The proposed plans can likewise be effortlessly reached out to deal with an enormous number of contributions with a straight expansion in territory and inactivity. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
A Secure Data Encryption Mechanism in Cloud Using Elliptic Curve Cryptography
Cloud computing is undergoing continuous evolution and is widely regarded as the next generation architecture for computing. Cloud computing technology allows users to store their data and applications on a remote server infrastructure known as the cloud. Cloud service providers, such Amazon, Rackspace, VMware, iCloud, Dropbox, Google's Application, and Microsoft Azure, provide customers the opportunity to create and deploy their own applications inside a cloud-based environment. These providers also grant users the ability to access and use these applications from any location worldwide. The subject of security poses significant challenges in contemporary times. The primary objective of cloud security is to establish a sense of confidence between cloud service providers and data owners inside the cloud environment. The cloud service provider is responsible for ensuring user data's security and integrity. Therefore, the use of several encryption techniques may effectively ensure cloud security. Data encryption is a commonly used procedure utilised to ensure the security of data. This study analyses the Elliptic Curve Cryptography method, focusing on its implementation in the context of encryption and digital signature processes. The objective is to enhance the security of cloud applications. Elliptic curve cryptography is a very effective and robust encryption system due to its ability to provide reduced key sizes, decreased CPU time requirements, and lower memory utilisation. 2024 IEEE. -
A Secure Deep Q-Reinforcement Learning Framework for Network Intrusion Detection in IoT-Fog Systems
IoT-Fog system security depends on intrusion detection system (IDS) since the growing number of Internet-of-Things (IoT) devices has increased the attack surface for cyber threats. The dynamic nature of cyberattacks often makes it difficult for traditional IDS techniques to stay up to date. Because it can adapt to changing threat landscapes, deep Q-reinforcement learning (DQRL) has become a potential technique for ID in IoT-Fog situations. In this paper, an IDS system for IoT-Fog networks based on DQRL is proposed. The suggested solution makes use of fog nodes' distributed computing power to provide real-time IDS with excellent accuracy and minimal latency. With feedback from the network environment, the DQRL agent learns to recognize and categorize network traffic patterns as either normal or intrusive. Adaptive exploration techniques, effective reward functions, and deep neural networks for feature extraction are adopted by the system to improve predictive performance. The evaluation findings show that, in terms of detection accuracy, precision, recall and f-measure, the proposed DQRL provides flexibility to changing threat patterns as compared to conventional IDS techniques. A vast array of cyberattacks, such as malware infections, denial-of-service (DoS) attacks, and command-and-control communications, are successfully recognized and categorized by the system. It is possible that the suggested solution will be crucial in safeguarding IoT-Fog networks and preventing cyberattacks 2024 IEEE. -
A secure image-based authentication scheme employing DNA crypto and steganography
Authentication is considered as one of the critical aspects of Information security to ensure identity. Authentication is generally carried out using conventional authentication methods such as text based passwords, but considering the increased usage of electronic services a user has to remember many id-password pairs which often leads to memorability issues. This inspire users to reuse passwords across e-services, but this practice is vulnerable to security attacks. To improve security strength, various authentication techniques have been proposed including two factor schemes based on smart card, tokens etc. and advanced biometric techniques. Graphical Image based authentication systems has received relevant diligence as it provides better usability by way of memorable image passwords. But the tradeoff between usability and security is a major concern while strengthening authentication. This paper proposes a novel twoway secure authentication scheme using DNA cryptography and steganography considering both security and usability. The protocol uses text and image password of which text password is converted into cipher text using DNA cryptography and embedded into image password by applying steganography. Hash value of the generated stego image is calculated using SHA-256 and the same will be used for verification to authenticate legitimate user. 2015 ACM.