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Light-induced advanced oxidation processes as pfas remediation methods: A review
PFAS substances, which have been under investigation in recent years, are certainly some of the most critical emerging contaminants. Their presence in drinking water, correlated with diseases, is consistently being confirmed by scientific studies in the academic and health sectors. With the aim of developing new technologies to mitigate the water contamination problem, research activity based on advanced oxidation processes for PFAS dealkylation and subsequent mineralization is active. While UV radiation could be directly employed for decontamination, there are nevertheless considerable problems regarding its use, even from a large-scale perspective. In contrast, the use of cheap, robust, and green photocatalytic materials active under near UV-visible radiation shows interesting prospects. In this paper we take stock of the health problems related to PFAS, and then provide an update on strategies based on the use of photocatalysts and the latest findings regarding reaction mechanisms. Finally, we detail some brief considerations in relation to the economic aspects of possible solutions. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Smart Portable Neonatal Intensive Care for Rural Regions
Every year, an increasingly large number of neonatal deaths occur in India. Premature birth and asphyxia are being two of the leading causes of these neonatal deaths. A well-regulated thermal environment is critical for neonatal survival. In the current scenario, it is impossible for the health centers in the rural areas of India to afford a neonatal incubator for every newborn due to its price and transportability. The successful delivery of neonates is hampered in India due to its increasing population along with limited technology and resources. Thus, a prototype of an incubator has been designed that is affordable, transportable, and energy saving for the health centers in the rural regions, with an AI-based decision support system. Springer Nature Singapore Pte Ltd 2020. -
A Multicriteria Decision-Making Approach to Building Resilience Along the Indian Medical Equipment Supply Chain
The presence of risks that lead to potential disruptions is evident along the Indian medical equipment supply chain. Identifying and prioritising the supply chain risks is pivotal in enhancing supply chain resilience, surplus, and sustainability. This study uses multicriteria Decision-Making to prioritise supply chain risks in the Indian medical equipment industry. Unstructured interviews were conducted with industry experts from six medical equipment firms to identify supply chain risks. The identified risks were prioritised using the Analytic Hierarchy Process (AHP), Fuzzy AHP, and Analytic Network Process (ANP). AHP outlines the relative importance and ranks the risks. Finally, a simulation using ANP ranks the risks under different circumstances, considering the magnitude of impact and frequency of occurrence. A total of nine iterations were run to obtain a generalised rank for the identified supply chain risks under a combination of different scenarios of risk magnitude and frequency. The AHP results indicated that the industry experts considered inventory management risks as the most significant factor, followed by digitalisation and technological infrastructure. The Fuzzy AHP results revealed the triangulated weights in the same rank which was used to reiterate the findings from the AHP results with added dynamics in the form of the nearest neighbouring values. The ANP iterations revealed that supply and demand uncertainties must be managed first amidst any given risk scenario, followed by inventory and technological risks. The originality of this study is that the ANP results derived from nine iterations provide an overall decision matrix that can be generalised across the Indian medical equipment sector. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A systematic literature network analysis approach to assess the topology of modern-era supply chain risk management research
Over the past decade, there has been a significant increase in research on supply chain risk management (SCRM). This review uses a systematic literature network analysis to provide an overview of the SCRM research landscape, with emphasis on optimisation approaches, mathematical modelling tools, and the identification of seminal studies and relevant keywords used in SCRM research. However, there are few quantitative models that represent the relationship between supply chain surplus, sustainability, and resilience in SCRM literature. The study has limitations since it only sources from a single database, and more clarity is needed on the effectiveness of optimisation in SCRM, which can be further evaluated through case studies and empirical studies. Copyright 2025 Inderscience Enterprises Ltd. -
Thermal and solutal stratified Heimanz flow of AA7072-deionized water over a wedge in the presence of bioconvection
The bioconvective Heimanz flow of nanofluid across a wedge with thermal stratification is analyzed in this article. The wedges are often seen in glider aircraft, rocket climbing frames, etc. The nanofluid considered in this study is composed of aluminum alloys of AA7072 and deionized water. The AA7072 alloys are specially manufactured materials composed of Aluminum and Zinc in the ratio of (Formula presented.) along with metals like silicon, ferrous, and copper so that they possess enhanced heat transfer features. The mathematical model is formed using the modified Buongiornos model that includes the discussions related to slip mechanisms and volumetric analysis in terms of the weight of the nanoparticle. The model is in the form of partial differential equations and is later converted to ordinary differential equations with the assistance of similarity transformation. This set of equations is solved by the Differential Transformation Method (DTM) and the outcomes are discussed through graphs.,. 2024 Taylor & Francis Group, LLC. -
Analysis of the Thomson and Troian velocity slip for the flow of ternary nanofluid past a stretching sheet
In this article, the flow of ternary nanofluid is analysed past a stretching sheet subjected to Thomson and Troian slip condition along with the temperature jump. The ternary nanofluid is formed by suspending three different types of nanoparticles namely TiO 2, Cu and Ag into water which acts as a base fluid and leads to the motion of nanoparticles. The high thermal conductivity and chemical stability of silver was the main cause for its suspension as the third nanoparticle into the hybrid nanofluid Cu-TiO 2/ H 2O. Thus, forming the ternary nanofluid Ag-Cu-TiO 2/ H 2O. The sheet is assumed to be vertically stretching where the gravitational force will have its impact in the form of free convection. Furthermore, the presence of radiation and heat source/sink is assumed so that the energy equation thus formed will be similar to most of the real life applications. The assumption mentioned here leads to the mathematical model framed using partial differential equations (PDE) which are further transformed to ordinary differential equations (ODE) using suitable similarity transformations. Thus, obtained system of equations is solved by incorporating the RKF-45 numerical technique. The results indicated that the increase in the suspension of silver nanoparticles enhanced the temperature and due to density, the velocity of the flow is reduced. The slip in the velocity decreased the flow speed while the temperature of the nanofluid was observed to be increasing. 2023, The Author(s). -
Effects of activation energy and chemical reaction on unsteady MHD dissipative DarcyForchheimer squeezed flow of Casson fluid over horizontal channel
The impact of chemical reaction and activation energy plays a vital role in the analysis of fluid dynamics and its thermal properties. The application of the flow of fluid is significantly considered in nuclear reactors, automobiles, manufacturing setups, electronic appliances etc. This study explores the impacts of activation energy and chemical reaction on the magnetohydrodynamic DarcyForchheimer squeezed Casson fluid flow through a porous material across the horizontal channel where the two parallel plates are assumed to be in motion. By using similarity variables, partial differential equations are converted to ordinary differential equations. Numerical method is applied using MATLAB to solve the problems and acquire the data for velocity field, thermal distribution, and concentration distribution. The graphs indicate that fluid velocity and temperature increases as the plates are brought closer. In addition, there was a correlation between a rise in the Hartmann number and a decrease in the fluid's velocity because of the existence of strong Lorentz forces. The temperature and the concentration of the liquid will increase due to the Brownian motion. When the DarcyForchheimer and activation energy parameters are both increased, the velocity and concentration decreases. 2023, The Author(s). -
Thermal and solutal stratified Heimanz flow of AA7072-deionized water over a wedge in the presence of bioconvection
The bioconvective Heimanz flow of nanofluid across a wedge with thermal stratification is analyzed in this article. The wedges are often seen in glider aircraft, rocket climbing frames, etc. The nanofluid considered in this study is composed of aluminum alloys of AA7072 and deionized water. The AA7072 alloys are specially manufactured materials composed of Aluminum and Zinc in the ratio of (Formula presented.) along with metals like silicon, ferrous, and copper so that they possess enhanced heat transfer features. The mathematical model is formed using the modified Buongiornos model that includes the discussions related to slip mechanisms and volumetric analysis in terms of the weight of the nanoparticle. The model is in the form of partial differential equations and is later converted to ordinary differential equations with the assistance of similarity transformation. This set of equations is solved by the Differential Transformation Method (DTM) and the outcomes are discussed through graphs.,. 2024 Taylor & Francis Group, LLC. -
Optimized Handoff Strategy for Vehicular Ad-hoc Network based Communication
The dissertation titled ???Optimized Handoff Strategy for Vehicular Ad-hoc Network based Communication??? is the compilation of all efforts taken and tasks completed in order to implement an optimal handoff method in Vehicular Ad-hoc Network communication.Wireless communication technologies have been improving exponentially. Ad-hoc networks can form a network of wireless nodes anywhere and they are not bound by the limitations of a static infrastructure. This enhances the ability of mobile nodes to communicate with each other even in situations where a defined architecture is absent. Vehicular Ad-hoc Networks (VANETs) has its applications in dynamic environments that involve nodes with high mobility. The nodes frequently move between the coverage areas of different access points. This increases the chance of link breakage and new link formation in communication network. Handoff is a process that helps in transferring the session details between one access point to another whenever the node is about to move away from a currently serving access point. Many handoff methods have been proposed but a majority of them utilize just a particular attribute of a network to employ the channel selection process. This process of network selection would be skewed as other attributes of a network play important roles in improving its overall efficiency. Multiple Attributes Decision Making (MADM) methods make use of different attributes in order to perform the network selection process. Use of MADM methods help in selecting optimal access points that can provide services to the nodes for a longer duration. In the proposed system, MADM methods have been utilized to modify existing protocols in order to optimize their approach for handoff operations. Various scenarios involving vehicular nodes and different access points have been considered in order to improve the efficiency of the proposed system across applications. The proposed handoff mechanism follows a proactive approach where the target access points are selected before the mobile node reaches the edge of its coverage area. This leads to a seamless transition of the communication channels. Based on the client/access point information stored in the data log, optimal access points which are situated along the direction of the node???s movement can be selected. NS2 and SUMO have been implemented to simulate mobile environments that accommodate handoff operations. -
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Season of Hate and Intolerance: Revisiting Ambedkar and Periyars Path to Social Justice
While India celebrates 75 years of being an independent republic, the rise in hate crimes fueled by caste and religious prejudices poses a serious threat to the constitutional values and secular fabric of the nation. Despite the constitutional safeguards, Dalits and religious minorities continue to face discrimination, violence and social exclusion, making social justice an elusive goal. Dr. B. R. Ambedkar and E. V. Ramasamy Periyar (hereafter referred to Periyar) promoted the idea of self-respect, arguing that it is crucial for social cohesion and the realization of social justice. Drawing on recent cases of hate crimes reported in Tamil Nadu, this study examines the complexities of caste dynamics and the emergence of religious intolerance in contemporary times. It argues that rejuvenating the concept of self-respect offers a transformative pathway toward achieving social justice. It also offers a few constructive possibilities to strengthen the application of self-respect in the pursuit of social justice. It concludes by highlighting the scope for a scholarly engagement on the idea of self-respect and exploring it as a broader, emancipatory framework for social justice beyond the socio-political context of Tamil Nadu. , Copyright Taylor & Francis Group, LLC. -
LP norm regularized deep CNN classifier based on biwolf optimization for mitosis detection in histopathology images
Mitosis detection, a crucial biomedical process, faces challenges like cell morphology variability, poor contrast, overcrowding, and limited annotated dataset availability. This research presents a novel method for mitosis detection in histopathological images highlighting two important contributions using a Bi-wolf optimization-based LP norm regularized deep Convolutional neural network (CNN) model. This hybrid optimization protocol is the key to the precise calibration of model parameters and effective training, which translates into optimal classifier performance. The results reveal that this model achieves high accuracy, sensitivity, and specificity values of 96.69%, 91.89%, and 97.74% respectively. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Efficient Mitosis Segmentation and Detection in Breast Cancer Histopathological Images Using YOLOv5 Model
Mitosis count serves as a critical biomarker in breast cancer research, aiding in the prediction of aggressiveness, prognosis, and grade of the disease. However, accurately identifying mitotic cells amidst shape and stain variations, while distinguishing them from similar objects like lymphocytes and cells with dense nuclei, presents a significant challenge. Traditional machine learning methods have struggled with this task, particularly in detecting small mitotic cells, leading to high inter-rater variability among pathologists. In recent years, the rise in deep learning has reduced the subjectivity of mitosis detection. However, Deep Learning models face challenges with segmenting and classifying mitosis due to its intricate morphological variations, cellular heterogeneity, and overlapping structures. In response to these challenges, this study presents an Intelligent Mitosis Segmentation and Detection in Breast Cancer Histopathological Images Using Deep Learning (IMSD-BCHIDL) Model. The purpose of the IMSD-BCHIDL technique is to segment and classify mitosis in the histopathological images. To accomplish this, the IMSD-BCHIDL technique mainly employs YOLO-v5 model, which proficiently segments and classifies the mitosis cells. In addition, InceptionV3 is applied as a backbone network for the YOLO-v5 model, which helps in capturing extensive contextual details from the input image and results in improved detection tasks. For demonstrating the greater solution of the IMSD-BCHIDL method of the IMSD-BCHIDL technique, a wide range of experimental analyses is made. The simulation values portrayed the improved solution of the IMSD-BCHIDL system with other recent DL models. 2024 by the authors. -
Analysis of U-Net and Modified VGG16 Technique for Mitosis Identification in Histopathology Images
One of the most frequently diagnosed cancers in women is breast cancer. Mitotic cells in breast histopathological images are a very important biomarker to diagnose breast cancer. Mitotic scores help medical professionals to grade breast cancer appropriately. The procedure of identifying mitotic cells is quite time-consuming. To speed up and improve the process, automated deep learning methods can be used. The suggested study aims to conduct analysis on the detection of mitotic cells using U-Net and modified VGG16 technique. In this study, pre-processing of the input images is done using stain normalization and enhancement processes. A modified VGG16 classifier is used to classify the segmented results after the altered image has been segmented using U-Net technology. The suggested method's robustness is evaluated using data from the MITOSIS 2012 dataset. The proposed strategy performed better with a precision of 86%,recall of 75% and F1-Score of 80%. 2024 IEEE. -
Combined Antimicrobial Activity of Chromolaena odorata, Azadirachta indica Leaf Extract, against Streptomyces scabiei (Plant pathogen) and Paenibacillus polymyxa, and their interaction
Streptomyces scabiei is a gram-positive soil dwelling actinobacteria, that causes the scab disease in many plants, especially in potatoes. This causes black blotches on the tuber that negatively impacts the economic and market worth of potatoes. Plant species like Chromolaena odorata and neem are known for their antibacterial potential against common human microflora and agricultural pathogens. Aiming to find natural solutions to plant pathogens, the current study analyzes, the antimicrobial effect of these plant extracts at different concentrations using well diffusion, MIC and MBC. Combination study was also performed to test the synergistic antimicrobial effect against Streptomyces scabiei. In addition, another soil dwelling bacteria, Paenibacillus polymyxa, known for its plant growth promoting potential was also tested against the plant pathogen to understand microbe-microbe interactions through cross streak assay. Both plant extracts showed promising antimicrobial effect against Streptomyces scabiei, however, they showed very less antimicrobial potential against the plant growth promoting bacterium. Moreover, cross streak assay showed that both the bacterium coexisted together. Hence, further studies can be conducted to formulate a biofertilizer containing the two-plant extract in optimum concentrations and Paenibacillus polymyxa to prevent scab disease and also enhance plant growth. 2025, Crop Protection Research Centre. All rights reserved. -
Deep Learning Model with Enhanced Segmentation and Combined Feature Activation for Mitosis Classification
Mitosis is a cell division mechanism vital for the growth of tissues and repair, Histopathological images are used by pathologists to diagnose cancer, but mitosis classification plays an important role in disease diagnosis. The mitotic counts are a proliferative indicator to find the aggressiveness of breast cancer. Detecting the mitotic tumor cells in tissue areas is a critical marker in cancer prognosis. Various researchers have focused on developing an automatic detection framework to identify mitotic figures, but detecting and classifying mitosis accurately remains a significant challenge in the medical field. Moreover, this research has designed a proposed Aggressive Tracing Seeking Optimization (ATSO) based Deep Convolutional Neural Network (Deep CNN) for the mitosis classification framework. The proposed framework uses less memory and increases the convergence rate; hence, it is globally efficient in achieving optimal solutions in the search space. The inspiration for considering the ATSO is its excellent behavior, as well as its scalable and adaptable mechanism, which allows optimization to move away from local optima. Moreover, it is computationally faster and exhibits higher global convergence capability in searching for the best solution. ATSO optimally trains a Deep CNN to generate higher classification accuracy by minimizing the false rate using the loss function. More explicitly, the proposed ATSO-Deep CNN model attained higher performance with an accuracy of 96.31%, an F1-score of 96.3%, precision of 96.84%, and recall of 95.78% with a 90% training percentage for the BreCaHAD dataset. 2025 Inventive Research Organization. -
A Potential Review on Self-healing Material Bacterial Concrete Methods and Its Benefits
Building plays an important role for survival of human being in a safe place to live and store basic requirements. The building can be constructed for any purpose and the architecture of each building (official, residential) differs according to the plan. Beyond the plan for a building, it is also significant in designing plans for the construction of bridges, dams, canals, etc. In all the construction, the key goal is the strength of a building which completely depends on the materials that are chosen for each work. Hence, it is essential to prefer high quality materials for the construction of a building and the major materials are such as cement, concrete, steel, bricks, and sand. Among these materials, the concrete is often used for construction which enables to harden the building by combining cement, sand, and water. The concrete looks like a paste that reinforce to prolong life of the building. In this paper, we discuss a review on the use of bacteria in concrete that has the ability of self-healing cracks in the building. The procedural process behind the activation and reaction of bacteria into concrete is studied with the benefits of this process. This bacterial concrete usage assures to enhance the property of durability and but still it is yet to be introduced in the industries. Hereby, we review the recent research works undergone in concrete using bacteria. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Tailored Garment Recommendations Using Computer Vision and Machine Learning
In the realm of online shopping, despite the many advancements made in this specific field, the dilemma of finding the best clothing items that are right fit and meet the style preference of a consumer and avoid returns is yet to be completely solved. This problem has often resulted in customer dissatisfaction. The proposed system intends to deal with this problem by analyzing the users body size and suggesting the best fitting garment that suits their size and style with the help of advanced computer vision and machine learning technologies. This approach not only provides an improved online shopping experience with personalized recommendations, it furthermore contributes toward reducing the environmental impact caused by the return of ill-fitting clothing hence promoting sustainability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
MALL-Based Writing Instruction: Assessing the Effectiveness of Digital Platforms Among ESL Learners
Nowadays, mobile-assisted language learning (MALL) has emerged as a globally adopted approach that builds on the earlier development of computer-assisted language learning (CALL) by utilizing the accessibility and flexibility of mobile devices to promote independent and self-directed learning. It enables learners to extend lan guage practice beyond classroom boundaries and provides authentic opportunities to engage with English as a Second Language (ESL). This study investigates the potential of digital platforms, specifically WordPress and Hem ingwayEditor, in enhancing the writing skills of non-native English learners within a MALL framework. WordPress offers a collaborative digital space where learners can publish, share, and receive feedback on their writing, while Hemingway Editor provides real-time analytical feedback to improve readability, grammar, and stylistic accuracy. The research adopted a quantitative design with both control and experimental groups to examine the effective ness of these platforms. Participants included ESL learners who engaged in structured writing tasks, with their progress assessed through pre- and post-tests. The findings of the study reveal that learners using WordPress and Hemingway Editor demonstrated notable improvements in writing performance when compared to the control group. The integration of these tools not only improved grammatical accuracy and stylistic clarity but also encour aged active participation, reflection, and learner autonomy. The results emphasize the pedagogical value of incor porating MALL strategies into language instruction, particularly for developing essential writing skills among ESL learners. In conclusion, this research affirms that mobile technologies, when strategically integrated into teaching, significantly enhance learning outcomes and offer sustainable pathways for improving ESL writing proficiency. 2025, Digital Technologies Research and Applications. All rights reserved.