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Emerging challenges for the agro-industrial food waste utilization: A review on food waste biorefinery
Modernization and industrialization has undoubtedly revolutionized the food and agro-industrial sector leading to the drastic increase in their productivity and marketing thereby accelerating the amount of agro-industrial food waste generated. In the past few decades the potential of these agro-industrial food waste to serve as bio refineries for the extraction of commercially viable products like organic acids, biochemical and biofuels was largely discussed and explored over the conventional method of disposing in landfills. The sustainable development of such strategies largely depends on understanding the techno economic challenges and planning for future strategies to overcome these hurdles. This review work presents a comprehensive outlook on the complex nature of agro-industrial food waste and pretreatment methods for their valorization into commercially viable products along with the challenges in the commercialization of food waste bio refineries that need critical attention to popularize the concept of circular bio economy. 2022 -
Commercialization potential of PET (polyethylene terephthalate) recycled nanomaterials: A review on validation parameters
Polyethylene Terephthalate (PET) is a polymer which is considered as one of the major contaminants to the environment. The PET waste materials can be recycled to produce value-added products. PET can be converted to nanoparticles, nanofibers, nanocomposites, and nano coatings. To extend the applications of PET nanomaterials, understanding its commercialization potential is important. In addition, knowledge about the factors affecting recycling of PET based nanomaterials is essential. The presented review is focused on understanding the PET commercialization aspects, keeping in mind market analysis, growth drivers, regulatory affairs, safety considerations, issues associated with scale-up, manufacturing challenges, economic viability, and cost-effectiveness. In addition, the paper elaborates the challenges associated with the use of PET based nanomaterials. These challenges include PET contamination to water, soil, sediments, and human exposure to PET nanomaterials. Moreover, the paper discusses in detail about the factors affecting PET recycling, commercialization, and circular economy with specific emphasis on life cycle assessment (LCA) of PET recycled nanomaterials. 2024 Elsevier Ltd -
Origin, ecotoxicity, and analytical methods for microplastic detection in aquatic systems
Over the decades, interaction of microplastics with other pollutants in a dynamic environmental set up was observed to increase their toxic effects. This scenario is worse in aquatic environments due to the entry of huge loads of microplastic components into the waterbodies through direct plastic dumping and those delivered from effluents originating from various treatment plants. Although extensive research was done to understand the issue associated with microplastic contamination of various aquatic environments, a huge research gap still exists in areas like their ecotoxicology, fate, distribution, and detection methods in aquatic environments. Moreover, the combined deleterious effect of microplastics in association with other environmental pollutants is not widely studied and requires more research focus. The major scope of this review is to present a comprehensive outlook on the recent studies carried out to understand the types, origin, distribution, transport, fate, and toxicity of microplastics in aquatic environments, both fresh water and marine. The review summarizes the ecotoxicological effects of microplastic contaminants in aquatic environment like the oxidative damage, neurotoxicity and decreased reproductive potential. An in-depth discussion regarding the ability of the microplastics in combination with other pollutants to serve as potentially hazardous agents in aquatic environment is also elaborated in the review. Further a summary on various microplastic detection methods, challenges associated with microplastic detection and management is carefully reviewed and compiled in this work. The need for proper awareness programs to general public highlighting the toxicity of microplastics and strict regulations regarding their continuous assessment and management in waterbodies are essential factors in controlling their adverse effect on aquatic environment. 2023 Elsevier B.V. -
A Retrospection on Mercury Contamination, Bioaccumulation, and Toxicity in Diverse Environments: Current Insights and Future Prospects
Owing to various industrial applications of mercury (Hg), its release into the environment at high concentration is becoming a great threat to living organisms on a global scale. Human exposure to Hg is greatly correlated with contamination in the food chain through cereal crops and sea foods. Since Hg is a non-essential component and does not possess a biological role and exhibits carcinogenic and genotoxic behaviour, biomonitoring with a focus on biomagnification of higher living animals and plants is the need of the hour. This review traces the plausible relationship between Hg concentration, chemical form, exposure, bioavailability, bioaccumulation, distribution, and ecotoxicology. The toxicity with molecular mechanisms, oxidative stress (OS), protein alteration, genomic change, and enzymatic disruptions are discussed. In addition, this review also elaborates advanced strategies for reducing Hg contamination such as algal and phytoremediation, biochar application, catalytical oxidation, and immobilization. Furthermore, there are challenges to overcome and future perspectives considering Hg concentrations, biomarkers, and identification through the nature of exposures are recommended. 2023 by the authors. -
Bioengineering of biowaste to recover bioproducts and bioenergy: A circular economy approach towards sustainable zero-waste environment
The inevitable need for waste valorisation and management has revolutionized the way in which the waste is visualised as a potential biorefinery for various product development rather than offensive trash. Biowaste has emerged as a potential feedstock to produce several value-added products. Bioenergy generation is one of the potential applications originating from the valorisation of biowaste. Bioenergy production requires analysis and optimization of various parameters such as biowaste composition and conversion potential to develop innovative and sustainable technologies for most effective utilization of biowaste with enhanced bioenergy production. In this context, feedstocks, such as food, agriculture, beverage, and municipal solid waste act as promising resources to produce renewable energy. Similarly, the concept of microbial fuel cells employing biowaste has clearly gained research focus in the past few decades. Despite of these potential benefits, the area of bioenergy generation still is in infancy and requires more interdisciplinary research to be sustainable alternatives. This review is aimed at analysing the bioconversion potential of biowaste to renewable energy. The possibility of valorising underutilized biowaste substrates is elaborately presented. In addition, the application and efficiency of microbial fuel cells in utilizing biowaste are described in detail taking into consideration of its great scope. Furthermore, the review addresses the significance bioreactor development for energy production along with major challenges and future prospects in bioenergy production. Based on this review it can be concluded that bioenergy production utilizing biowaste can clearly open new avenues in the field of waste valorisation and energy research. Systematic and strategic developments considering the techno economic feasibilities of this excellent energy generation process will make them a true sustainable alternative for conventional energy sources. 2023 Elsevier Ltd -
An Integration of Satellite A Based Network with Higher Level Type Network with the use of P-P Connection: A Deep Review
The Aerial Access 6g Network (AAN) is seen as a way to access remote and sparsely populated areas not served by traditional terrestrial networks, especially with the advent of 6G technology. This study presents a new approach for efficient data collection and transmission in point to point access networks using low earth orbit (LEO) satellites and high altitude platforms (HAPS). Incorporating LEO satellites as backlinks and HAPs as airborne base stations, the system provides low-bandwidth transmission to ground users. A Time Augmented Graph (TEG) model is proposed to represent the dynamic topology of the air access network according to time slots. With this example, this study can create an entire programming problem with the goal of maximizing data transfer to the country's data processing centre (DPC) while respecting resource constraints. Benders' decomposition-based algorithm (BDA) is proposed to solve the NP-hardness of the problem and is shown to perform well in producing near-optimal solutions. The effectiveness and efficiency of the proposed strategy is verified through simulation results performed in a realistic environment, showing high speed and performance comparable to search methods. By informing the design and optimization of future communication systems, this study will provide a better understanding of how HAP and LEO satellites work together in aerial access networks for the collection and delivery of remote terrain data. 2024 IEEE. -
Investigation on Constraints and Recommended Context Aware Elicitation for IoT Runtime Workflow
Various technological and application challenges arise in the advancement of Internet of Things. Apart from Design and Deployment, Security also falls as a primary challenge to overcome. Device Management is becoming more complex as numerous network services to be handled. Inter-device communication for technical aspects appears to be underappreciated. There is a critical necessity to include this area's requirements and challenges. By abstracting data models and operations and expressing them using semantics, M2Mcommunication and interoperability and may be made simple. A thorough investigation of the foregoing is preparing the way for various approaches. Along with Semantics, a high-level language construct is suggested that can enable run-time workflow construction. Things Markup Language is the name of the concept (TML). 2024, Ismail Saritas. All rights reserved. -
Data-Driven Transformation of Hospitality Supply Chains Using AI-Powered Segmentation
The increasing complexity of supply chain operations in the hospitality sector demands data-driven strategies for efficient resource utilization and service delivery. This study proposes an artificial intelligence (AI)-driven framework leveraging unsupervised machine learning to uncover hidden patterns in patient-related operational data sourced from a publicly available dataset. The research applies clustering algorithmsK-Means, DBSCAN, and Agglomerative Hierarchical Clusteringto segment patient prof iles based on key variables such as length of stay, procedure type, room category, equipment usage, and staffing needs. Principal Component Analysis (PCA) was employed for dimensionality reduction and cluster visualization. The optimal number of clusters was identified using the Elbow Method, with K-Means yielding the highest silhouette score. Comparative analysis of all clustering models revealed varying strengths in noise detection, interpretability, and handling of sparse features. The results demonstrate how intelligent segmentation can support dynamic resource planning, targeted supply allocation, and improved operational responsiveness in hospital-based hospitality systems. This work contributes to the growing domain of AI-enabled supply chain analytics and of fers a practical pathway for enhancing decision-making in smart hospitality environments. 2025 IEEE. -
Predictive Modeling of Student Learning Outcomes Through Cognitive and Emotional Skill Integration
The interplay of factors, including both cognitive and non-cognitive, plays a significant role in the learning patterns of students. However, the majority of the research conducted on such issues mainly puts forward the role of cognitive skills but forgets that a very important role is played by the non-cognitive factor, specifically motivation and emotional intelligence. Therefore, this study focuses on bridging that gap by investigating the combined influence of cognitive and non-cognitive factors on the learning capacities of engineering students during their transition to higher education. A two-year longitudinal study on engineering students of AITAM, Tekele, India was considered in relation to their academic performance, learning preference, and socio-emotional aspects. The approach adopted makes use of predictive analytics. It is deployed here as machine learning algorithms in the form of Logistic Regression (LR), Naive Bayes, k-Nearest Neighbors (k-NN), Decision Trees (DT), and Support Vector Machines (SVM) to classify the learners into very fast, fast, average, and slow learners. The algorithm of k-NN also achieved the highest accuracy classification and showed good robustness for learning the students' learning rates. This study underscores the combination of new teaching approaches as well as personalized self-learning methods to enhance learning performance, especially for slow learners. Indeed, the outcome gives avenues for much more extensive studies done on large datasets using advanced algorithms which can be applied across a range of educational fields to support tailored learning interventions. 2025, Iquz Galaxy Publisher. All rights reserved. -
Kashmir and Conflict: Objectivity and Balance in News Sourcing
Journalistic balance and objectivity have been critical concepts of scholarly debate. While balance traditionally meant giving equal space to opposing views, newer models of impartiality aim to represent a broader range of perspectives. Using a quantitative content analysis, this chapter analyses news published in the two leading English dailies, Rising Kashmir from Kashmir and Daily Excelsior from Jammu of the Indian Union Territory of Jammu and Kashmir from 1 to 31 October 2022. As many as 62 newspaper editions comprising 987 pages of broadsheets are examined and conflict-related news articles are sampled for analysis. A manual analysis is used to analyse the conflict news articles and identify the sources quoted in them. Study findings indicate the dominance of elite political sources in the news reports. Drawing from seminal studies in journalistic sociology, such as Gans Deciding Whats News, this chapter discusses the implications of the high prevalence of elite voices, comprising political, social and economic. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. -
HEART FAILURE DETECTION USING OPTIMIZATION ALGORITHMS
Heart failure (HF) remains a significant global health challenge, requiring early and precise detection to improve clinical outcomes and reduce mortality rates. Traditional diagnostic approaches often fail to capture the complexity of HF pathophysiology, necessitating advanced computational methods for accurate prediction. In this study, we propose a novel optimized Stacked Support Vector Machine (S-SVM) framework, integrating multiple SVM classifiers with diverse kernel functions to enhance predictive accuracy. A genetic algorithm (GA) is employed to fine-tune hyperparameters, ensuring model robustness and generalizability across patient populations. The model is rigorously evaluated on the UCI Heart Failure Clinical Records Dataset and the Framingham Heart Study Dataset, demonstrating superior performance in accuracy (95.7%), precision (0.90), recall (0.87), and AUC (0.96) compared to conventional machine learning techniques. The proposed system effectively balances computational efficiency with clinical interpretability, making it a promising tool for early-stage HF detection and risk stratification. This research advances the intersection of machine learning and cardiovascular diagnostics, offering a scalable and adaptive solution for real-world healthcare applications. Little Lion Scientific. -
Psychology Teaching and Learning: Innovations, Trends, and Best Practices
Deliver effective psychology education with proven strategies for diverse learners As psychology education evolves amid global shifts, educators need practical, evidence-based strategies that work across contexts. Psychology Teaching and Learning: Innovations, Trends, and Best Practices brings together international experts from twelve countries to address these challenges. Editors Aneesh Kumar and Rituparna Chakraborty have assembled leading voices in psychology education to provide actionable guidance for enhancing teaching effectiveness while promoting equity and inclusion. This volume explores the International Competencies for Undergraduate Psychology Model, culturally responsive pedagogy, online learning strategies, and innovative assessment approaches. Discover how to integrate artificial intelligence and cognitive science into teaching, implement open science pedagogy, and apply the megastudy method. The book addresses student well-being through emotional intelligence, resilience, self-compassion, and creating safe learning environments. Readers will discover: Evidence-based teaching strategies and assessment models proven to enhance effectiveness across diverse learning environments and cultural contexts worldwide International perspectives from psychology educators in twelve countries offering varied approaches to common challenges in contemporary psychology education Practical guidance on integrating emerging technologies like AI and intelligent tutoring systems into psychology teaching and learning experiences Frameworks for promoting student well-being including emotional intelligence development, resilience training, self-compassion, and creating psychologically safe classrooms Reflection questions and supplementary classroom resources in each chapter to facilitate immediate application and adaptation to your teaching context Whether you teach undergraduate courses, develop curricula, or train future educators, this book equips you with forward-thinking approaches to prepare students for real-world challenges. By bridging contemporary educational challenges with actionable strategies, it empowers you to transform your teaching practices. 2026 by John Wiley & Sons, Ltd. All rights reserved. -
Viscosity dissipation and BrinkmanBard convection with thermal anisotropy: stability studies in both linear and nonlinear
This study presents both linear and nonlinear stability analyses of BrinkmanBard convection in a porous medium, considering the effects of thermal anisotropy. The flow occurs between two walls maintained at uniform but different temperatures. The critical Rayleigh number is examined, including variations in the Darcy number, porosity, Prandtl number, and anisotropic thermal conductivity, with both linear and nonlinear stability regimes analyzed. Contour plots of streamlines and isotherms are provided to visualize fluid and heat flow directions. The results demonstrate that the presence of the porous medium inhibits convection and reduces the cell size at the onset of instability. Additionally, thermal anisotropy stabilizes the system, with the region of subcritical instability shrinking as the anisotropy parameter increases. While the linear stability analysis does not reveal any significant impact of viscous dissipation, the nonlinear stability analysis shows that viscous dissipation destabilizes the system. These findings contribute to a deeper understanding of the interplay between thermal anisotropy, porosity, and convection behavior in porous media, with implications for various engineering and geophysical applications. The Author(s), under exclusive licence to SocietItaliana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
The talent compass: guiding workforce realignment at Vertex Engineering
Learning outcomes After discussing this case, students will be able to apply the Competency Mapping framework to design redeployment and reskilling strategies for displaced employees; analyze workforce realignment challenges through the lens of Strategic Workforce Planning (SWP) models; evaluate HRs strategic role in balancing financial constraints with talent retention using evidence-based reasoning; assess the risks and outcomes of alternative workforce planning models and propose suitable HR interventions; and design an integrated competency assessment and performance evaluation framework to prepare the organization for future transitions. Case overview/synopsis In early 2023, Suresh Nair, Deputy General Manager of Human Resources at Vertex Engineering, a medium-sized firm in Indias construction equipment sector, faced a major workforce challenge. The company decided to sell its Core Manufacturing Plant, whose capacity utilization had fallen from 85% in 2010 to 50% in 2023. Out of 500 affected employees, 250 were transferred to the acquiring company, while the remaining 250 required redeployment, reskilling or release. This case study explores how Vertex can strategically realign its workforce through the application of Strategic Workforce Planning (SWP) and Competency Mapping frameworks. It emphasizes the HR functions evolving role from administrative redeployment to strategic capability building within an environment of technological change, cost pressure and labor volatility. The protagonist, Suresh Nair, must navigate conflicting priorities between cost reduction advocated by the CFO and the COOs emphasis on talent retention as a long-term competitive asset. Complexity academic level Graduate and under-graduate level Supplementary material Teaching notes are available for educators only. Subject code CSS 6: Human Resource Management. 2026 Emerald Publishing Limited -
The Smart Detection of Ovarian Cancer in Complex Medical Images Using Deep Learning
Ovarian cancer is a challenging disease to detect and diagnose, especially in complex medical images where the cancerous lesions may be small and difficult to differentiate from surrounding healthy tissue. The use of deep learning algorithms has shown promising results in computer-aided diagnosis of various cancers. This study aims to develop a smart detection system for ovarian cancer in complex medical images using deep learning techniques. The proposed system will have the ability to accurately and efficiently identify cancerous lesions, leading to earlier detection and improved treatment outcomes. Through the use of advanced computer vision and machine learning methods, the system will be able to learn from a large dataset of medical images and make accurate predictions. This research has the potential to significantly improve the diagnosis and treatment of ovarian cancer, ultimately saving lives. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Hybrid CMNV2: DeepFake faces classification and recognition using deep learning methods
Deepfake detection has become a critical component of digital forensics and security, as manipulated images and videos increasingly threaten trust in visual media. However, existing methods often struggle with robustness under post-processing operations such as JPEG compression, Gaussian blur, scaling, and filtering, and with the growing diversity and realism of face image modification (FIM) forgeries. This work proposes CMNV2, a hybrid architecture that integrates MobileNetV2 with a custom CAFFE block to enhance feature extraction and classification accuracy. By adding five additional layers to a pre-trained structure, the model demonstrates superior resilience against complex real-world conditions and achieves 99.10% accuracy across multiple datasets, outperforming 13 baseline CNN models. The study trained and tested CMNV2 on 5,000 images (real and deepfake faces), using a combination of deep neural networks (DNNs), transfer learning (TL), and deep learning (DL) techniques. Compared to 13 CNN-based architectures, the proposed model achieved superior performance across some important evaluation metrics, including accuracy, precision, recall, F1-score, error rate, and computational efficiency. These results highlight hybrid CMNV2 as a robust and efficient solution for deepfake face detection and classification, with potential applications in security, healthcare, and education. 2025 -
Design of Heart Rate Monitoring System Using Visual Simulation Tool
In this research paper, a wireless heart rate and temperature monitoring system using Packet Tracer is proposed and put into operation. A wearable bracelet with sensors that can track temperature and heart rate in real time is integrated into the system. The patient and the attending physician receive the gathered data wirelessly for in-depth health monitoring and analysis. This system is used to provide a practical and effective way to track vital signs in real-time, especially temperature and heart rate. The system's functionality and performance can be thoroughly evaluated and validated in a controlled environment thanks to this simulation. Additionally, the system includes functions for data analysis and visualization to help medical professionals make well-informed decisions. To increase the accuracy and accessibility of heart rate monitoring, which would eventually enhance patient outcomes and general wellbeing, the proposed initiative aims to address these challenges. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Blockchain Technology in Higher Education: Opportunities, Applications, and Network Security Challenges
Blockchain technology has garnered attention beyond its origins in cryptocurrency, positioning itself as a robust solution to the security and administrative hurdles faced by higher education establishments. This paper delves into how the decentralized nature and unchangeable ledger of blockchain could enhance the management of academic records, bolster data security, and streamline administrative processes in institutions. In the contemporary era of digitization, higher education increasingly depends on digital platforms for record-keeping, offering advantages like accessibility and efficiency, yet also presenting susceptibilities to cyber threats. Conventional centralized databases are prone to tampering, breaches, and unauthorized entry, jeopardizing the confidentiality of student information. Blockchain emerges as a feasible remedy by furnishing cryptographic security that ensures the immutability and openness of data. This research scrutinizes blockchains potential to elevate the security of academic records, diminish fraud, and refine administrative workflows within establishments. It scrutinizes real-life cases and practical applications of blockchain to assess their efficacy in safeguarding student data and upholding academic honesty. Moreover, it tackles the challenges and apprehensions related to implementation that are vital for the successful integration by educational institutions. By addressing these issues, the research aids in elucidating how blockchain could enhance the record-keeping processes in higher education. It showcases blockchains capability to authenticate educational accomplishments adequately, thus safeguarding the reputation of institutions and fostering confidence in academic qualifications. Ultimately, this research pinpoints blockchain as an indispensable technology for modernizing school management and preserving the validity of educational information in an increasingly digital landscape. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Transforming Food Waste Management with Blockchain: A Sustainable, Consensus Driven Framework
Food waste management is a stern issue that is prevalent around the word, affecting multiple countries and cultures. In a technology driven era, blockchain has gained intense attention because of its various beneficial facets. Blockchain is defined as a technology with its features like security, transparency and immutability, where only the authorized members in a network are given access. The decentralized technology enables transparency and traceability which can be used in food supply chain in conjunction with the various consensus mechanisms to validate transactions. This would ensure consistency and reliability, ensuring the stakeholders to track vivid stages such as food production, processing and distribution. Wastage of food is a cosmopolitan issue conducive to social, economic and environmental challenges. Our proposed work provides substantial benefits in a way to tackle inefficiencies in the food supply chain, Blockchain based business process reengineering can further automate these processes. The paper presents five popular consensus mechanisms that can be used for a sustainable food waste management. The work focuses on providing a blockchain based solution that is low powered and scalable, which in turn increases sustainability and reduces global food waste. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.