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Post-Operative Brain MRI Resection Cavity Segmentation Model and Follow-Up Treatment Assistance
Post-operative brain magnetic resonance imaging (MRI) segmentation is inherently challenging due to the diverse patterns in brain tissue, which makes it difficult to accurately identify resected areas. Therefore, there is a crucial need for a precise segmentation model. Due to the scarcity of post-operative brain MRI scans, it is not feasible to use complex models that require a large amount of training data. This paper introduces an innovative approach for accurately segmenting and quantifying post-operative brain resection cavities in MRI scans. The proposed model, named Attention-Enhanced VGG-U-Net, integrates VGG16 initial weights in the encoder section and incorporates a self-attention module in the decoder, offering improved accuracy for postoperative brain MRI segmentation. The attention mechanism enhances its accuracy by concentrating on a specific area of interest. The VGG16 model is comparatively lightweight, has pre-trained weights, and allows the model to extract incredibly detailed information from the input. The model is trained on publicly available post-operative brain MRI data and achieved a Dice coefficient value of 0.893. The model is then assessed using a clinical dataset of postoperative brain MRIs. The model facilitates the quantification of the resected regions and enables comparisons with each brain region based on pre-operative images. The capabilities of the model assist radiologists in evaluating surgical success and directing follow-up procedures. 2024 by the authors of this article. -
Post-Quantum Cryptography for Securing Next-Generation Communication Networks
Advancements in Quantum Key Distribution (QKD) and lattice-based encryption are paving the way for PQC adoption, but challenges remain, such as performance overhead and compatibility with existing infrastructure. It evaluates whether PQC schemes are feasible for real-time applications in high-speed, low-latency networks and analyzes the security-performance trade-offs. We investigate standardized candidates from NIST's PQC Project (e.g., CRYSTALS-Kyber, Dilithium) and their resistance to hybrid attacks. In addition, we also investigate the hardware acceleration (e.g., FPGA, ASIC) approach to alleviate the latency bottleneck. Transition strategies, such as hybrid cryptography (the coupling of classical and PQC algorithms) and zero-trust frameworks to maintain backward compatibility, are a key focus here. We further discuss side-channel vulnerabilities specific to PQC implementations and suggest mitigation strategies. These findings emphasize the need for a continued focus in areas such as scalability, standardization and quantum secure key distribution and the importance of collaboration between academia, industry and policymakers."By tackling these issues, PQC can secure next-gen networks from quantum dangers while aging to be efficient and trustworthy. 2025 IEEE. -
Post-quantum Cryptography in Practice: A Survey of Algorithms, Applications, and Deployment Challenges
As quantum computing becomes more practical, it significantly threatens the conventional cryptographic systems, particularly RSA and ECC, that are critical to worldwide digital security. Post-quantum cryptography (PQC) has emerged as a strong alternative as a response. NIST recently standardized algorithms such as CRYSTALS-Kyber and Dilithium. This survey brings together findings from ten key papers that examine PQC across different fields, including telecommunications, finance, healthcare, IoT, smart cards, and blockchain voting systems. The chosen studies include direct comparisons of digital signature schemes, real-world protocol integration on smart cards, hybrid cryptographic models using AES and blockchain, and strategies for transition based on policy frameworks like NIST CSF 2.0. The survey examines cryptographic flexibility, hardware practicality, readiness for adoption, and the social and economic effects of quantum breaches. It compares algorithm performance, deployment challenges, and specific needs for various areas. This paper is an overview of the current state and future directions for PQC implementation in critical infrastructure. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Post-road traffic injury experiences and challenges faced by college students: A qualitative study in Madurai district, Tamil Nadu, India
Road traffic injuries (RTIs) are a pressing public health concern in India, leading to a rise in injury-related deaths, hospitalizations, and disabilities. India accounts for a significant portion of the world's fatal traffic accidents, with two-wheelers being involved in the majority of these accidents. The impact of non-fatal injuries on individuals extends beyond the bodily consequences of the injury and includes both the physical and psychological dimensions of the injury. The literature indicates the need for policy cascades and implementation framework for the prevention of road traffic injury. This study aimed to investigate the post-RTI experiences and challenges faced by college students who experienced road traffic injury during their college life by using a qualitative research approach in Madurai district, Tamil Nadu, India. The study found that college students who experienced RTIs faced a wide range of physical, emotional, and social difficulties. The study highlights the need for a more comprehensive and holistic approach to RTI prevention that takes into account the complex interplay of individual, environmental, and societal factors that contribute to RTIs. The study also underscores the urgent need to improve the quality and availability of healthcare and rehabilitation services for RTI survivors. 2024 John Wiley & Sons Australia, Ltd. -
Posttraumatic relationship experiences in women in South India
Marriage is a socially binding intimate relationship between two individuals which is expected to be stable and enduring. In many cases, there can be severe difficulties questioning the quality of ones married life such as IPV or other kinds of abuse or exploitation which could lead to a divorce. Although divorce legally dissolves the relationship, studies suggest that the stress caused by a traumatic relationship may not end after terminating the relationship. The resemblance of these symptoms to PTSD led to the proposed diagnosis of PTRS. In this study, seven participants who have been divorced due to domestic violence for at least a year were identified and interviewed regarding their past and present life situations. The emergent themes in the data pointed to several factors that may influence ones married life, the decision of divorce and current life situations which can affect the amount of stress an individual might face concerning their past traumatic relationship. The factors influencing stress experienced during a traumatic marriage included involvement and support from ones family and in-laws, nature, and cause of abuse, stress-related to children, social support and the very decision to get a divorce. The process of overcoming fear, mistrust, and grief, social and family support, child custody, and related legal processes were factors that affected stress related to the process of divorce. The grief related to child custody, ability to rationalize the decision, career, remarriage and childrens future were some factors that influenced the stress these individuals experienced currently in their life. 2019, 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
Posture Classification Using a Hybrid Deep Learning Model
Automated posture detection is a critical task in ergonomics and healthcare, yet it presents significant challenges for standard computer vision models, particularly in handling class imbalance and understanding geometric constraints. This paper proposes an enhanced hybrid deep learning model that synergizes the feature extraction power of a pre-trained ResNet50 architecture with engineered geometric features derived from the Radon Transform and pre-calculated joint angles. Our approach utilizes a dual-balancing strategy, combining data upsampling with a custom weighted loss function, to effectively address the problem of underrepresented classes. By processing visual and geometric data streams in parallel and fusing them within a deep architecture, our model achieves a holistic understanding of the subject's posture. The fine-tuned model demonstrates strong performance on an unseen test set, achieving a final accuracy of 92% for wrist posture and 92% for neck posture. Crucially, it attains a robust F1-score of 0.74 for the challenging 'Bad Wrist Posture' minority class, a significant improvement compared to the ResNet50-only baseline (F1=0.24) and achieves excellent ROC-AUC scores of 0.9859 for wrist and 0.9838 for neck, proving the efficacy of our hybrid, dual-balancing methodology for realworld application. 2026 IEEE. -
Potassium tert-Butoxide-Mediated Synthesis of 2-Aminoquinolines from Alkylnitriles and 2-Aminobenzaldehyde Derivatives
KOtBu mediates the reaction between 2-amino arylcarbaldehydes and benzyl/alkyl cyanides toward the expeditious formation of 2-aminoquinolines under transition-metal-free conditions. The described transformation proceeds through in-situ generated enimine intermediate from benzyl/alkyl cyanides under KOtBu-mediated reaction conditions. The substituted 2-aminoquinolines were realized in excellent yields at room temperature and shorter reaction time. The designed process exhibits operational simplicity and broad functional group tolerance in delivering the products of high significance. 2022 Wiley-VCH GmbH. -
Potato Leaf Disease Identification using Hybrid Deep Learning Model
The potato is one of the most significant crops in the world. However, it is prone to several leaf diseases that can result in significant productivity losses, leading to economic challenges. Early and precise disease identification is essential for sensitive crops like potatoes. Deep learning approaches have demonstrated excellent potential in image-based disease classification tasks in recent years. This paper presents a hybrid strategy for classifying potato leaf image diseases by integrating Optimised Convolutional Neural Network (OCNN) and Long Short-Term Memory (LSTM) networks. The Adaptive Shark Smell Optimisation (ASSO) technique is used to optimize the weights of CNN models. The CNN component is initially used to extract pertinent characteristics from Potato leaves, capturing significant visual patterns related to various diseases. These extracted features are then fed into the LSTM model, which utilizes its sequential learning capability to model the temporal dependencies among the extracted features. The model performance is analyzed based on Accuracy, Precision, Recall, and F1-score criteria. Experimental results showed that the hybrid OCNN-LSTM model outperforms the individual CNN, LSTM, and MobileNet models. The proposed model results are compared with existing state-of-the-art work, and it was found that the OCNN-LSTM model performed better and received 99.02% accuracy. 2023 IEEE. -
Potent of sales-persons, impact on the channel of distribution in lighting industry in bangalore
Its found in array of literature on the roles, functioning of the sales persons and also illuminates how these are measured on effectiveness of channel of distribution. This study made with objective for better understanding of various variables, and out of which primary factors that could be focused for effectiveness of channel of distribution in lighting industry in Bangalore from the perceptive of intermediaries. This study draws the responses from intermediaries who are pivotal force (opinion leaders) in the market, which could prove more deep understanding for strategizing the channels in the said industry. From the review of literature we streamlined the functions performed for potent of sales persons. Further analysed with vivid using various statistical tools to understand loads (Eigen value), hence, prompting with Principal Component Analysis. This study is uses all normative way to analyse of the results reframed pivotal factors, in classifying, draining out insignificant factors. By regrouping based on the array of load, we come to understand 3 vital ingredients viz., 1) intermediaries appointment criteria 2) sales training& communication 3) concern for cost and needs of intermediaries, and urging to business institutions to opt for better channel strategy. Notwithstanding, the relationship with intermediaries are charismatic in nature, and dynamics of channel strategy would and will be determinant for success of any business organisation. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Potential applications of AI and IoT collaborative framework for health care
Digital technology has infiltrated the entire planet. Artificial Intelligence (AI) and the Internet of Things (IoT) are the two buzzwords that became popular in the current digital world, especially in recent decades. Both these technologies have their contribution in various domains. The existing frameworks will benefit from the AI-IoT collaborative system, which will assist them in having more intelligent or smart responses. Furthermore, these collaborative systems can provide improved devices with better decision-making capacity to facilitate the users. AI can work with IoT to increase functional precision in the healthcare domain by automating and tracking, monitoring, managing, optimizing, and predicting processes in 24x7 mode. Health professionals are the people involved in activities whose primary commitment is to improve the wellbeing of the community. They are a group of people who face various obstacles, including their health and safety concerns, especially during pandemic outbreaks. This book chapter aims to illustrate the impact of AI and IoT on the health care domain and the challenges that healthcare professionals face, especially when dealing with such an pandemic and suggests some potential health care advancements through AI and IoT. 2023 Bentham Science Publishers. All rights reserved. -
Potential flow simulation through Lagrangian interpolation meshless method coding
From the past many decades, mesh generation posed many challenges in the field of computational sciences for many researches. High rise in computational power has enabled many researches to tackle the problems of complex geometries. Due to the high need of computational power, computational cost also increased abruptly. In today's world, many academic and industry researches are willing to increase the use of present simulation technology; mesh generation plays a vital role in this aspect. we can say that many real-world simulation problems are dependent on mesh generation which has more chances in giving an inaccurate simulation results. In order to make the simulation process simpler, Meshless methods are introduced to the field of Computational Fluid Dynamics. This technique requires a less computational power compared to the computational power needed for generating the mesh. In the present dissertation, our main objective is to develop a scheme for Meshless method for the field of Computational Fluid Dynamics for flow over a blunt body. The performance of the present scheme is evaluated by comparing the simulation results with existing experimental data and also compared with the results obtained by generation of mesh using commercial CFD software. 2018 Isfahan University of Technology. -
Potential health, environmental implication of microplastics: A review on its detection
Microplastic contamination of terrestrial and aquatic environment has gained immense research attention due to their potential ecotoxicity and biomagnification property when enterer into food chain. Heterogenous nature of microplastics coupled with their ability to combine with other emerging pollutants have increased the severity of this crisis. Existing detection methods often fails to accurately quantify the amount of microplastic components present in environmental and biological samples. Thus, a great deal of research gap always exists in our current understanding about microplastics including the limitations in screening, detection and mitigation. This review work presents a comprehensive out look on the impact of microplastics on both terrestrial and aquatic environment. Furthermore, an in-depth discussion on various microplastic detection techniques recently used for microplastic quantification along with their significance and limitations is summarised in this review. The review also elaborates various physical, chemical and biological methods used for the mitigation of microplastics from environmental samples. 2024 Elsevier B.V. -
Potential health, environmental implication of microplastics: A review on its detection
Microplastic contamination of terrestrial and aquatic environment has gained immense research attention due to their potential ecotoxicity and biomagnification property when enterer into food chain. Heterogenous nature of microplastics coupled with their ability to combine with other emerging pollutants have increased the severity of this crisis. Existing detection methods often fails to accurately quantify the amount of microplastic components present in environmental and biological samples. Thus, a great deal of research gap always exists in our current understanding about microplastics including the limitations in screening, detection and mitigation. This review work presents a comprehensive out look on the impact of microplastics on both terrestrial and aquatic environment. Furthermore, an in-depth discussion on various microplastic detection techniques recently used for microplastic quantification along with their significance and limitations is summarised in this review. The review also elaborates various physical, chemical and biological methods used for the mitigation of microplastics from environmental samples. 2024 Elsevier B.V. -
Potential of banana based cellulose materials for advanced applications: A review on properties and technical challenges
Biocompatibility, biodegradability, and toxicity issues of synthetic polymers have propelled the search for environmentally friendly and non-toxic alternatives. In this context, biobased materials have gained much popularity due to their non-toxic, biodegradable, and sustainable nature. Bananas are considered as one of such natural material which fulfil the requirements to be tailored as a biocompatible biopolymer. Banana derived wastes can be used for extraction of commercially important biopolymers like starch, cellulose, nanocellulose and their subsequent utilization in wide variety of applications. Banana derived biopolymers and their bio composites and widely used for medical applications such as wound healing, fabrication of bone plates, cellulose based gate dielectrics, and capacitors for insulin pumps, and pacemakers. In addition, banana based nanocellulose can be used in tissue engineering, biosensing, drug delivery, bioimaging, wound healing, enzyme immobilization and preparation of tablets for oral administration. Moreover, banana-based polymers can be employed in applications such as food packaging, biofuel production, and production of multilayered papers. Considering the potential applications of banana-based nanomaterials, this review work is framed to understand the process of extraction of starch, cellulose, nanocellulose and biopolymers from banana derived wastes with specific emphasis on their extraction methods and composite preparation methods. In addition, it discusses in detail the promising and potential applications of the derived materials in health and environmental sectors. The presented review is a comprehensive discussion on banana-based waste conversion strategies to produce value added products useful in medical and environmental applications. 2023 The Author(s) -
Potential of insect-based milk moiety from Pacific beetle cockroach, Diploptera punctata (Blattodea): Insights as superfood
Diploptera punctata is a viviparous cockroach and secretes a concoction of glycosylated proteins as a basis of nutrition for developing embryos termed as cockroach milk. The milk proteins were identified as the lipocalin-like milk proteins (Lili-Mip) which are present in the brood sac of the gestated female D. punctata. Blotting techniques and RNA transcriptome assays have reported analogous Lili-Mip in the midgut of D. puncta embryos, which proved the ingestion during development inside brood sac. Thermodynamics and kinetic studies augmented the stability of the milk crystals and their controlled release mechanism of sustenance. Gene ontogeny reported the evolution of the ovoviviparous to viviparous due to selective pressures posed during the development stages of the embryos. Molecular simulation modelling studied unveiled the binding efficacies of the milk protein to build strong affiliates to the ligands to form a stable milk protein complex. Ahead of its nutritional benefits, cockroach milk also offers environmental advantages compared to traditional dairy and plant-based milk production. Nevertheless, there are still important hurdles to overcome to include this unconventional superfood as staple in human diets which are also discussed in the review. This review explored the existing molecular, evolutionary and biochemical insights for understanding the phenomena of the production of milk crystals and addressed the research gaps for developing a novel nutrient prospective from insect source. 2024 Korean Society of Applied Entomology -
Potential use of waste foundry sand with lateritic clayey soil in the construction of pavement sub-bases
The current study investigates the potential for use of waste foundry sand (WFS) in highway sub-base construction. Lateritic clayey soil (LCS) and waste foundry sand were combined in different proportions to examine its potential to use as construction material in sub bases. In this research, geotechnical investigation was conducted that is particle size distribution, specific gravity, Atterberg limits, OMC, density, un-soaked and soaked CBR were conducted to evaluate the properties and validity of WFS upon stabilizing with the LCS for usage in construction of sub bases. The study shows that WFS can be effectively used in highway sub-base construction upon stabilizing with the lateritic clayey soils. 2019 SERSC. -
Potential, prospects, and problems of textile tourism in Kerala
Tourism in the textile industry has tremendous potential for future growth and development of local economies and rural communities, building upon the services provided by local governments. This study focuses on Khadi textile business operations, with a particular emphasis on textile-based businesses, and provides visitor perceptions of textile tourism. It discusses the possibilities and challenges in the local textile industry, including the dificulties faced by the weavers in marketing their goods to tourists and exporters. The questionnaire survey applied to 120 visitors from three different khadi shops in Kozhikode, Kerala. According to the study's findings, textile business centres require strategic promotional approaches to boost textile tourism. The major hurdle for weavers and independent producers is a lack of direct access to visitors, as well as access to information controlled by producers, commercial interests, and gallery owners. more in the future to expand the reach of this type of tourism. The potential role that the government and tourism authorities may play an important role in designing policies that may grant this form of tourism increased visibility. 2022, Universidade de Aveiro. All rights reserved. -
Pothole Detection and Powertrain Control for Vehicular Safety
A new era of automotive technology has begun with the rapid advancement of electric vehicles (EVs), which promise efficiency and sustainability. With electric vehicles (EVs) becoming an integrated part of the traction systems, there is a growing need for novel safety and performance-enhancing features. The development of an Adaptive Cruise Control (ACC) system for autonomous powertrain control and pothole detection in electric vehicles is examined in this paper. The paper focuses on integrating an intelligent system that can detect potholes and autonomously regulate the powertrain to improve both the driving experience and safety of electric vehicles. The system makes use of Jetson Nano as the processing unit for regulation of the EV powertrain. This board enables quick and accurate reactions to changing road conditions by facilitating real-time data analysis and decision-making. The powertrain regulation will be performed by controlling the acceleration and braking signal provided to the powertrain. 2024 IEEE. -
Poverty and Social Insecurity Among the Unorganized Workers of Garment Industry: A Qualitative Study through the Lens of Human Security Approach
Poverty and social insecurities cause alarming inequalities. Employment is a secure mechanism to overcome these instabilities. However, formal and informal industrial sectors have insecure employment conditions affecting workers' well-b eing. Inequality is worse in the garment industrial sector as it has more unorganized workers. This is evident in developing countries like India and Bangladesh, which are demanding a human- centered security approach. This chapter explores, using qualitative methodology's semi- structured interview method, poverty and social insecurities of the unorganized garment workers in the Bommanahalli area of Urban Bangalore, India. The chapter also uses empirical study data from the Bangladesh Institute of Labor Studies (2023) to critically evaluate the garment sectors of Bangalore and Bangladesh through the lens of the human security paradigm. The study reveals that the unorganized garment workers of these developing countries face severe poverty and social insecurities. It requires urgent action from both governments using a human security approach. 2026, IGI Global Scientific Publishing. All rights reserved. -
Poverty Eradication via Sustainable Human Development: A Human Security Approach
Poverty has been one of the major human insecurities throughout history. It became more evident in the aftermath of the Industrial Revolution. Though economic growth was suggested as an effective mechanism for poverty eradication, it severely increased inequality together with poverty. Realizing the importance of poverty eradication for human well-being, the United Nations initiated many policies and programs including Millennium Development Goals (MDGs) and Sustainable Development Goals (SDGs). Based on the framework of MDGs, the SDGs was moving towards the achievement of no poverty by 2030. But the Covid-19 pandemic slowed this pace, demanding more earnest effort from the international community. This chapter, in this perspective, using critical analytical method examines the possibility of human security approachs sustainable human development model for the realization of SDG1. The chapter advances human securitys all-inclusive and comprehensive human-centered sustainable development model as an effective mechanism for poverty eradication in the entire globe. Copyright 2026, IGI Global Scientific Publishing. Copying or distributing in print or electronic forms without written permission of IGI Global Scientific Publishing is prohibited. Use of this chapter to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development.