Varendra International Journal for Interdisciplinary Research (VIJIR)
Climate Change, Extreme Weather, and Mortality: A Review of Global Disparities
Extreme weather events are expected to become more often and severe as a result of climate change in many parts of the world. So, it is crucial to study and perceive the impacts of climate change- related extreme weather events on population especially on public health. This review article analyzed 35 research articles to understand how extreme weather events influence death rates. The study used thematic analysis to combine the data from the selected articles. The findings of the research show that climate change affects mortality both directly and indirectly. Among extreme weather events, extreme temperatures emerge as the most significant direct contributor to mortality disparities. Additionally, indirect factors such as floods, droughts, heavy precipitation, air pollution, food scarcity, malnutrition, and climate-sensitive diseases further exacerbate health risks. The impact of climate change on mortality is not uniform but varies based on geographical, socioeconomic, and demographic factors. Pregnant women, infants, young children, and older adults are at the highest risk. This paper also discusses strategies to reduce the impact of climate change on public health. Finally, the study underscores key considerations for policymakers in designing and refining policies that enhance public health resilience in the face of climate change.
Copyright (c) 2026 Varendra International Journal for Interdisciplinary Research (VIJIR)
An Exploration of Female Agency and Feminist Resistance in Rupi Kaur’s milk and honey
Human psychology as well as human relationship is the central element of both human life and literature. Since the father-child relationship is a fundamental aspect of human life, this study is going to explore this dynamic in two different texts from two distinct eras: Wuthering Heights from the 19th century and Sons and Lovers from the 20th century. Psychological upbringing of a child is, profoundly, influenced by the environment that comprises of family atmosphere in the house, cultural context, and, other relationships. Considering the factors, the first focus of this paper will be on assessing the psychology of the fathers and sons in Wuthering Heights, by applying the concepts of ‘unconscious’ (the unconscious is the area of the mind that contains memories, desires, thoughts, and sensations that are not conscious but yet have an impact on the behavior) and ‘defense’ (selective perception, selective memory, denial, avoidance displacement, and projection), and its impact on their relationship. Next concentration will be on to explore the mother controlled father-child relationship in Sons and Lovers. For this, Freud’s theory of Oedipus Complex will be applied to analyze William’s and Paul’s relationship with Mr. Morel. Additionally, the impact of industrial society on worker’s mind and relationship will be highlighted to explore Mr. Morel as a father figure. As the conditions of the two novels are different, a comparative study will be conducted between the mother-dominated father-child relation in Sons and Lovers and father-child bond in Wuthering Heights, which lacks the presence of the mother. As a healthy relationship between father-child is essential for the ideal psychological growth of a child, the outcome of this study will give some valuable suggestions in this regard for our real- life situations.
Revitalizing Environmental Governance: Examining the Limitations of the Bangladesh Environment Conservation Act 1995 and Pathways to Strengthen Legal Frameworks
This study reflects the gaps in the Bangladesh Environment Conservation Act (ECA) 1995 and suggests a pragmatic approach to deal with environmental governance. It emphasizes systemic gaps- at the level of laws, enforcement mechanisms and inter-institutional coordination that has impeded the ECA 1995 effectiveness in addressing the new generation issues like industrial pollution and climate change. The study is qualitative in nature integrating descriptive, analytical and comparative approaches based on archival data including legal documents, government records and international standards from Norway, as well as from other jurisdictions such as Costa Rica and India. The findings indicate that the Act is undermined by bad governance, antiquated regulations and a parallel institutional regime show case examples in the form of Hazaribagh Tanneries and the Approval of Rampal Power Plant. Further delays on accountability are also caused by a failure to provide administrative remedies and environmental courts. It aids policy debate by proposing complete reforms that incorporate penalties, climate change adaptation efforts, and the expansion of the Department of Environment’s capacity to perform its duties. As lessons learned from global efforts, the study underlines the need to harmonize the existing laws with international accords and clarify its language. The study highlights the imperative of legal and institutional reforms for aligning Bangladesh’s environmental governance with sustainable development objectives, as well as presents a road map to policy-level actors on how to tackle systemic gaps.
Human Emotion Recognition Utilizing Transfer Learning
Facial Emotion Recognition (FER) is a crucial area of research in the field of Computer Vision (CV) and Deep Learning (DL), aiming to identify and classify emotions such as happiness, sadness, anger, and surprise from facial expressions. This study focuses on developing a model for recognizing facial emotions using DL techniques along with Transfer Learning (TL). For this work, we have used CKPLUS dataset. We have used two pre-trained State-of-the-art (SOTA) Convolutional Neural Network (CNN) models, namely, InceptionV3 and MobileNet, to transfer their learning in the task of feature extraction from our image data. For the classification task, we have used extracted features from these models both individually and fusing them together. For classifying these sets of features, we have used multiple classifiers, namely, Logistic Regression, a custom 1-dimensional (1D) CNN, and hard voting classifier that ensembles the decisions of Linear Support Vector Machine (LSVM), Logistic Regression, perceptron, quadratically penalized SVM, and SVM with quadratically smooth loss. Moreover we have utilized a soft voting classifier that ensembles the decision of LSVM, and SVM with quadratically smooth loss. The fused features extracted from InceptionV3 and MobileNet achieve the highest performance when classified using the voting classifier, with a classification accuracy of 98.48%, a weighted precision of 99.53%, a recall of 98.57%, and an F1-score of 98.48%. This work indicates the ensemble learning technique outperforms individual models across the dataset, identifying and classifying human emotion from an image of facial expression more accurately.
FusionNet: A Comprehensive Framework for Automated Deepfake Detection
Advancement in deep generative models have led to the spread of deepfake media, which poses a serious danger to information authenticity, privacy, and trust in digital media forensics. In order to meet the increasing demand for reliable deepfake picture identification, this study uses the publicly accessible Kaggle Deepfake Dataset to investigate hybrid deep learning approaches. Initial experiments with standalone Keras models, including DenseNet121, RestNet50, Xception, NASNet-Mobile, VGG16, and InceptionV3, achieved accuracies ranging from 89% to 90% over 20 epochs. To push the boundaries of performance, we developed a novel hybrid model that concatenates feature maps extracted from DenseNet121 and DenseNet169. This method greatly improved classification performance, obtaining a 91.22% accuracy rate with high precision 90.82%, 94.37% recall, and 92.56% F1-score with error rate 8.78%. Confusion matrices, classification reports, and ROC curves were used to thoroughly assess the model, which showed that it was effective at differentiating real photos from ones that had been altered, with an AUC score of 0.98. Our findings underscore the effectiveness of combining pre-trained CNN architectures for deepfake detection and contribute to the advancement of scalable and reliable solutions for safeguarding digital media integrity.
Continuous Blood Pressure Estimation Without a Cuff Using Deep Learning on PPG and ECG Signals
Regular monitoring of blood pressure (BP) is essential for the early detection and management of hypertension and other cardiovascular diseases. However, traditional cuff-based BP measurement methods are bulky, uncomfortable, and unsuitable for continuous monitoring. This study proposes a novel, non-invasive, and cuffless approach for continuous BP estimation by leveraging physiological signals. The proposed framework utilizes photoplethysmogram (PPG) and electrocardiogram (ECG) signals to capture the mechanical and electrical activities of the cardiovascular system. It includes signal preprocessing, segmentation, statistical feature extraction, and pattern analysis to establish a correlation between signal characteristics and BP values. A variety of features are extracted from the signals, including time-domain, frequency- domain, and morphological attributes. A deep learning model is then trained on these features using a publicly available dataset containing synchronized PPG, ECG, and reference BP readings. The model is validated using key performance metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), which confirm its effectiveness in accurately predicting systolic and diastolic BP. The results meet established clinical standards, demonstrating that the proposed system can reliably estimate BP in real time. This study highlights the potential of integrating PPG and ECG signals for continuous, non-invasive BP monitoring, offering a foundation for wearable health technologies and remote healthcare solutions.
Detection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning
A mutation in the DNA of a single cell that compromises its function initiates leukemia. This leads to the overproduction of immature white blood cells, which encroach upon the space required for the generation of healthy blood cells. Leukemia is treatable if identified in its initial stages. Nonetheless, its diagnosis is both arduous and time-consuming. In this study, a novel approach for diagnosing leukemia across four stages—Benign, Early, Pre, and Pro—utilizing deep learning techniques. Firstly, we had employed two Convolutional Neural Network (CNN) models: MobileNetV2 with an altered head and a bespoke model. The custom model has multiple convolutional layers, each paired with corresponding max pooling layers. Nowwe have employed two more pretrained model: InceptionV2 and VGG16 with altered heads. We utilized MobileNetV2, InceptionV2 and VGG16 with ImageNet weights, and the head was adjusted to integrate the final results. Finally, we have utilized ensemble with soft voting technique to comprehend the results from multiple neural networks. The utilized dataset is a publicly available collection of blood cell smear images titled “Acute Lymphoblastic Leukemia (ALL) image dataset”, and then used the Synthetic Minority Oversampling Technique (SMOTE) to augment and balance the training dataset. Which attained an accuracy of 96.34% with the custom model, while MobileNetV2 and InceptionV2 achieved a superior accuracy of 99.39%. The VGG16 achieved an accuracy of 99.08%. Finally, the ensemble technique ensured more promising result with 99.70% accuracy. The pre-trained model exhibited encouraging results and an increased likelihood of real-world application.
Skin Disease Detection and Classification of Actinic Keratosis and Psoriasis Utilizing Deep Transfer Learning
Skin diseases can arise from infections, allergies, genetic factors, autoimmune disorders, hormonal imbalances, or environmental triggers such as sun damage and pollution. Skin diseases such as Actinic Keratosis and Psoriasis can be fatal. These are treatable if identified early. However, its diagnostic methods are expensive and not widely accessible. In this study, a novel and efficient method for diagnosing skin diseases using deep learning techniques has been proposed. This approach employs multiple modified Convolutional Neural Network (CNN) models like DenseNet169, DenseNet201 and VGG16. Soft voting ensemble strategy is applied to combine the strengths of individual models to get better result. These models include several convolutional layers. The models have been employed using ImageNet weights and modified top layers. The top layers are modified by fully connected layers and a final softmax activation layer to obtain the result. The dataset analyzed is publicly available and titled “Skin Disease Dataset”. The CNN architecture does not include augmentation by default; data augmentation is typically performed during preprocessing prior to model training. The proposed methodology achieved 93.11% accuracy using the ensemble strategy, demonstrating reliability in classifying skin diseases. The modified pre-trained models showed promising results, increasing its potential for real-world applications.
Firm-Level and Macroeconomic Drivers of Non-Performing Loans: Evidence from Bangladeshi State-Owned Banks
The study aims to analyze the firm-level and macroeconomic drivers of non-performing loans (NPLs) of the state-owned banks in Bangladesh. To meet the study objective, the 10 years’ of data—from 2013 until 2022, of five state-owned banks have been anlyzed using the loan-to-deposit ratio (LDR), loan-to-asset ratio (LAR), capital-to-risk-weighted asset ratio (CRWAR), profitability, inflation, and GDP growth metrics to study how these factors determine their impact on NPLs. Through using the correlation coefficient and random-effect regression model, the results revealed that LAR and CRWAR affect NPLs negatively. However, profitability and economic growth have significant negative effects, while the LDR and inflation play only a minor role in determining the state-owned banks’ NPL level. The research demonstrates that strong credit monitoring methods combined with sound regulation and a steady economy are needed to help solve this NPLs issue. The study suggests useful policies to mitigate bank system risks while enhancing the performance of state-owned banks to support Bangladesh's future economic growth.
Copyright (c) 2025 Varendra International Journal for Interdisciplinary Research (VIJIR)
Dysfunctional Father-Child Relationships in Wuthering Heights and Sons and Lovers: A Psychoanalytical Study
Human psychology as well as human relationship is the central element of both human life and literature. Since the father-child relationship is a fundamental aspect of human life, this study is going to explore this dynamic in two different texts from two distinct eras: Wuthering Heights from the 19th century and Sons and Lovers from the 20th century. Psychological upbringing of a child is, profoundly, influenced by the environment that comprises of family atmosphere in the house, cultural context, and, other relationships. Considering the factors, the first focus of this paper will be on assessing the psychology of the fathers and sons in Wuthering Heights, by applying the concepts of ‘unconscious’ (the unconscious is the area of the mind that contains memories, desires, thoughts, and sensations that are not conscious but yet have an impact on the behavior) and ‘defense’ (selective perception, selective memory, denial, avoidance, displacement, and projection), and its impact on their relationship. Next concentration will be on to explore the mother controlled father-child relationship in Sons and Lovers. For this, Freud’s theory of Oedipus Complex will be applied to analyze William’s and Paul’s relationship with Mr. Morel. Additionally, the impact of industrial society on worker’s mind and relationship will be highlighted to explore Mr. Morel as a father figure. As the conditions of the two novels are different, a comparative study will be conducted between the mother-dominated father-child relation in Sons and Lovers and father-child bond in Wuthering Heights, which lacks the presence of the mother. As a healthy relationship between father-child is essential for the ideal psychological growth of a child, the outcome of this study will give some valuable suggestions in this regard for our real- life situations.
Influence of Social Media on Public Opinion and Decision Making in Varendra Region: Analyzing Netizen’s Behavioral Patterns
Social media has become a dominant space for public discussions, which influence public opinion formation and decision-making processes worldwide. Our study examines the influence of social media in shaping public opinion formation and decision-making in Varendra Region of Bangladesh, an area previously unexplored. Combining a mixed method approach, including surveys and open-ended question, the sample size of the study was 1053. It identifies key behavioral patterns from the netizens. Findings reveal that 67% of the respondents use social media on a regular basis, with 73% acknowledging the influence it has on their perspectives, especially regarding political and social issues. Facebook emerged as the most popular social media platform, while video content is the most preferred format. Influencers (68%) and peer networks (57%) significantly influence opinion formation, while 43% of users showed concerns about misinformation. Our study also explores echo chambers, with 62% of users was exposed to content that aligns with their pre-existing beliefs, reinforcing ideological polarization. In decision-making, social media has a key role in career choice and consumer behavior, with influencer endorsements (72%) and peer suggestions (61%) were the major drivers. Urban users showed higher engagement with news and politics (67%) and stronger media literacy (46%), at the same time rural users rely more on peer recommendations (71%). Mostly they consume content for entertainment (58%). Despite its potential to amplify marginalized voices, social media also presents risks such as misinformation, algorithmic bias, and polarization. The study recommends enhancing media literacy, promoting algorithmic transparency, and developing region-specific interventions to create an informed digital public sphere.
Impact of Child Birth Related Social Norms and Customs on Rural Adolescent Mothers’ Health
Adolescent pregnancies and motherhood reveal risks to the health of both the mother and child. Childbirth-related social norms and customs reduce such kinds of risk in many rural areas. The primary aim of this study is to explore the health complications regarding the social norms and customs and their outcomes among adolescent mothers during childbirth and the postpartum period in rural areas. This study is a qualitative scrutiny in the field of sociology with descriptive analysis, where the survey and case study were also used for collecting the actual data from the respondents. The study is based on a survey of 150 adolescents who became mothers. A set of straightforward and fundamental interview questions was included in the agenda. Informal meetings, non-participant observation, spot observation, and the gathering of personal and family information through informal meetings were all employed to explore and gain a critical awareness of all aspects of adolescent health and its impacts in the research region. Both closed-ended and open-ended questions were on the schedule. In order to investigate the actual results of this study, focus group discussions (FGD) and in-depth interviews were also used as data collection techniques. A fascinating finding is that a larger proportion suffer postpartum health problems due to following some specific birth-related social norms and customs such as gender-based inequality, restriction on movement, home-based delivery practices, food taboos, distortion of religious codes, patriarchal dominance, maternal altruism, etc. Most of the adolescents are subjected to those social norms and customs by members of their in-laws’ families. Appropriate health consciousness, proper education, familial cooperation, etc. can be helpful in addressing this problem.
Assessment of Chemical Fertilizer Use in Mustard Production: Evidence from Kurigram District, Bangladesh
Agricultural practices in the Kurigram district of Bangladesh remain largely traditional, with most farmers relying on plows, ladders, and cattle for crop production and processing. As a result, this district of Bangladesh has been a scene of poverty and persistent food insecurity. This study examines the extent and determinants of chemical fertilizer overuse in mustard cultivation in Nageswari Upazila, Kurigram, Bangladesh. Based on a survey of 250 mustard farmers, the findings reveal that 78.33% applied chemical fertilizers in excess of the recommended doses. Specifically, 95.2% overused Urea, 94.8% TSP, 89.6% MP, 66% Gypsum, and 100% Zinc sulphate—while 24.4% applied Boric acid, which is not officially recommended. The average application per bigha was 36.98 kg for Urea, 34.20 kg for TSP, and 15.57 kg for MP, all significantly exceeding the standard guidelines. Ordinary Least Squares (OLS) regression analysis showed that age, education, farming experience, and agricultural training significantly reduce overuse (p < 0.01), while farm size and extension contact were not significant predictors. Additionally, 56.8% of farmers relied on personal experience to determine fertilizer use, with only 9.2% consulting specialists. Multicollinearity tests confirmed the robustness of the regression model. These findings highlight the urgent need for improved farmer education, effective extension services, and local policy enforcement to reduce excessive fertilizer application and mitigate its environmental and economic consequences.
Local Leadership and Sustainable Development: A Psychological Appraisal of Compatibility
This is a qualitative study that follows the phenomenological approach. Taking into account the literature that mentions the necessary attributes of sustainable leaders and the thematic analysis of interviews with Pourashave (Municipality) level local leaders, this paper argues that leaders have not yet fully grasped the concept of sustainability, although they possess some knowledge about sustainable development goals. The fact that sustainability is an eternal issue, not just the sustainable development goals, has yet to be fully understood by them. Among the leaders, there is no shortage of political obligations, as they are affiliated with the party whose banner they use to run in elections. However, no concrete vision, mission-driven plan, or specific political ideology has been identified among them.
Desire and Divide in La Nuit Bengali and Na Hanyate: A Deconstructive Analysis
This paper explores how Mircea Eliade’s La Nuit Bengali and Maitreyi Devi’s Na Hanyate paint rich tapestry of love, longing and cultural dislocations. These two stories, from the same lived experience, however, crafted from completely different vantage points, navigate the tensions between personal wants and historical reality. Eliade’s vision of a brief, intoxicating romance is pit against Devi’s righteous reclamation of agency, pushing back against the gaze which distorts and appropriates. At heart, this study inquires into how love, rather than being a straightforward or apolitical force, is vitally braided into arrangements of power, colonial nostalgia and the weight of memory. This paper examines this experience comprehensively and illustrates how longing transforms into a contentious arena for authorship and identity when the tenuous distinctions between East and West, fiction and reality, and self and other are disrupted. Ultimately, this research speaks to the conversation about postcolonial literature, how stories are gendered, early and recent, and how silent negotiations inform how but also why histories are remembered—and who gets to tell them.
Exploring the Determinants of Health Production at the Household Level: An Empirical Study in Naogaon District, Bangladesh
This study aims to estimate the health production function at the household level in Bangladesh, considering both medical and non-medical inputs, and to identify determinants affecting health production. The study employs a quantitative approach, using primary data collected from 200 randomly selected households in the Naogaon district of Bangladesh. Descriptive statistics, multiple regression model, and binary logistic regression model are employed to analyze the data. In the multiple regression analysis, variables such as age, family size, occupation, working environment, health insurance, lifestyle, genetic diseases, and health expenditure demonstrate significant associations with health status. Equally, the binary logistic regression model highlights the importance of education, working environment, access to healthcare services, environmental conditions, lifestyle, and genetic diseases in predicting health status. The study underscores the importance of socioeconomic factors in determining health outcomes and recommends policy measures to improve health production. This study recommended to ensure universal access to quality education and health education through academic curricula and community programs, to raise awareness for discouraging unhealthy behaviors like smoking and alcohol consumption, to create more employment opportunities for enhancing income and health status, to establish robust health surveillance systems, to implement nutritional interventions, and to increase the number of physicians in Bangladesh can be achieved by expanding medical education opportunities and reducing associated costs.
Integrating Indigenous Knowledge into Biodiversity Conservation Strategies
This study investigates the incorporation of Indigenous Knowledge (IK) and Traditional Ecological Knowledge (TEK) into biodiversity conservation, highlighting the crucial role these systems of wisdom play in maintaining ecological variety. The statement emphasizes the comprehensive comprehension of ecosystems that IK (Indigenous Knowledge) and TEK (Traditional Ecological Knowledge) offer, their contribution to adaptive management techniques, and their crucial role in incorporating cultural viewpoints into conservation efforts. The study emphasizes the significance of establishing strong collaborations among indigenous people, environmental organizations, and governmental entities. Furthermore, it promotes the implementation of policy improvements and legislative changes that recognize and safeguard the rights and knowledge of indigenous communities. Moreover, the study emphasizes the importance of promoting cross-cultural comprehension and admiration, which are crucial for developing conservation tactics that are not only efficient but also fair and culturally perceptive. By utilizing the valuable knowledge provided by IK (Indigenous Knowledge) and TEK (Traditional Ecological Knowledge), conservation efforts can attain enhanced sustainability and adaptability, guaranteeing the protection of biodiversity for future cohorts.
Mental Health Condition and Its Indicative Factors among University Students in Rajshahi City
This study explores mental health issues among university students in Rajshahi. For this purpose, a convenience sampling technique was applied, and a total of 375 responses were collected through an online survey in mid-December 2024. The collected data are sorted and coded in Excel. Then, simple descriptive statistics, such as frequency and percentage, are performed, along with econometric analysis using Gretl software, to determine the significant factors of different mental health indicators. The study firstly finds that there is diversity in the demographic features of the respondent students. The participants are mostly young and female, but rural-based inhabitants. Secondly, the percentage distribution indicates that university students in Rajshahi city experience poor mental health, as most of them have at least one symptom of degrading mental health condition. Finally, it finds that academic years and smoking habits are negatively associated with students' concentration ability. In contrast, personal income source and, interestingly, smoking habit are positively associated with the students' feeling under strain. Moreover, female students have a higher chance of being unhappy and depressed. As students get older and have no personal income source, they are more unhappy and depressed. Therefore, it is strongly recommended to launch mental health programs and highlight the distinct needs of female students and those enrolled in universities. Moreover, awareness campaigns within university settings can be crucial in reducing mental health issues.
An Anthropological Investigation of Individual and Contextual Barriers to Modern Healthcare Utilization Among Santal Women in the Barind Tract of Rajshahi
Indigenous women in low-resource settings face compounded barriers to healthcare access, yet limited research explores how cultural, structural, and gendered inequities intersect to shape these challenges. This study investigates of individual and contextual barriers to modern healthcare utilization among Santal women in the Barind Tract of Rajshahi, Bangladesh, a region marked by geographic remoteness and socio-economic marginalization. A qualitative approach was employed, combining 25 in-depth interviews, two focus group discussions (FGDs), and field observations with Santal women (October–December 2024). Purposive sampling ensured representation across age, marital status, and occupation. Secondary data from peer-reviewed articles, government reports, and gray literature were analyzed thematically to contextualize. Cultural beliefs (e.g., viewing pregnancy as a “natural event”), reliance on traditional healers, and patriarchal restrictions on mobility emerged as primary individual barriers. Structural barriers included geographic remoteness (nearest clinic: 15–20 km), economic constraints from daily wage labor, and institutional mistrust fueled by discriminatory healthcare staff. Women who are widowed or unmarried experienced significant marginalization, as they did not have male guardians to assist them in accessing clinics or hospitals. The exclusion of Santal women from healthcare is fundamentally linked to overlapping cultural and structural inequities, highlighting systemic shortcomings in addressing the health needs of Indigenous populations. Findings inform SDG 3 (health), SDG 5 (gender equality), and SDG 10 (reduced inequalities), urging policymakers to integrate traditional healers into primary care systems and deploy mobile clinics in remote Indigenous communities.
Reducing Power System Loss in Bangladesh: Historical Insights and Policy Recommendations
Power system loss is a major challenge for Bangladesh, affecting energy security, economic growth, and grid reliability. This study analyzes system loss trends from 2001 to 2024, evaluating the effectiveness of policy interventions, grid modernization, and technological advances. The findings reveal a significant decline from 24.5% in 2001 to 7.25% in 2024, driven by the adoption of smart metering, SCADA systems, underground cabling, and stricter regulations aimed at combating electricity theft. Despite this progress, challenges like aging infrastructure and enforcement gaps persist. To achieve further reductions, investment in grid automation, renewable energy integration, and stronger policy enforcement is essential. These strategies will support Bangladesh’s goal of a more efficient and sustainable power sector by 2030.
AI-Driven Biotelemetry: Enhancing IoT-Based Human Area Networking with RedTacton for Smart Healthcare
Biotelemetry based on the Internet of Things (IoT) and artificial intelligence (AI) is revolutionizing predictive health monitoring by making it possible to collect, analyze and diagnose physiological data in real time. With the use of ESP32 microcontrollers for effective data processing and RedTacton technology for secure data transmission, this study presents an AI-powered predictive health monitoring system. The system uses integrated biological sensors to continually monitor vital signs, such as blood glucose, heart rate, SpO₂, ECG, body temperature and stress levels. For remote analysis and visualization, sensor data is securely transmitted to an IoT cloud platform using RedTacton-based human body connection. Based on comparison, RedTacton outperforms Bluetooth, NFC and RF-based networks in terms of power efficiency, security and dependability. With its inexpensive cost and integrated wireless connectivity, the ESP32 microcontroller also shows to be a better option than gadgets like the Raspberry Pi Pico W and STM32F4. Support Vector Machines (SVM) for stress analysis, Convolutional Neural Networks (CNN) for ECG classification, Random Forest for glucose prediction and Long Short-Term Memory (LSTM) networks for anomaly detection are some of the machine learning models that are integrated into the AI framework. Prompt alarm generating and real-time anomaly identification are guaranteed via a Python-based solution. By demonstrating lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), experimental validation versus the ESP8266 demonstrates the efficacy of the ESP32. The system incorporates advanced security features like multi-factor authentication (MFA), AES-256 encryption and tamper-proof enclosures to mitigate cybersecurity threats.
The Evolution and Future of Telemedicine: Challenges, Innovations, and Global Adoption Trends
Telemedicine has revolutionized healthcare delivery, particularly in the wake of the COVID-19 pandemic. This paper explores the historical development of telemedicine, its rapid expansion, and its impact on healthcare accessibility and patient outcomes. The study also examines the challenges associated with telemedicine adoption, including technological barriers, regulatory inconsistencies, and data privacy concerns. Additionally, it highlights emerging trends such as artificial intelligence (AI), virtual reality (VR), and blockchain technology in telemedicine. The paper concludes with recommendations for improving telemedicine infrastructure and policies to ensure equitable access and efficient healthcare delivery worldwide.
The Structural Characterization of a C1q/TNF-α Domain Containing Lectin OXYL in the Feather Star Anneissia Japonica
The structure of a lectin, OXYL, derived from the feather star, Anneissia japonica (phylum Echinodermata), was analyzed from biochemistry, informatics, and 3D structural prediction. OXYL’s primary structure resembled the globular domain of complement C1q in vertebrates. Structural simulations identified OXYL as part of the C1q/TNF superfamily, characterized by a jellyroll β-sandwich fold. The subunits can form monomer to nonamer (9-mer), with trimers being the most stable. However, gel permeation chromatography indicated tetramers as the significant form. In hemagglutination inhibition tests, OXYL bound type-2 N-acetyllactosamine (LacNAc: Galβ1-4GlcNAc) and inhibited agglutination but did not bind lactose (Lac: Galβ1-4Glc). Glycan microarray analysis with 20 immobilized glycans confirmed OXYL’s specificity for type-2 LacNAc and its sialic acid α2-3 linkage. The structural simulation showed a suitable molecular docking between type-2 LacNAc and the lectin rather than lactose. OXYL bound to the LacNAc glycan on the microarray was also elucidated to capture human IgG. This finding suggests that the invertebrate C1q/TNF family lectin will mimic vertebrate C1qs, despite lacking evolutionally antibody systems, possess the potential to interact with antibodies.
Facial Image-Based Autism Detection: A Comparative Study Among Machine Learning, Deep Learning and Transfer Learning
For prompt intervention and better results, early autism screening is essential. This work investigates the use of face image databases for autism classification in a non-invasive, cost-effective manner. Support Vector Machine (SVM), Convolutional Neural Network (CNN), and EfficientNet were the three classification models used. With the greatest accuracy of 85.67% and superior performance across important performance parameters including precision, recall, and F1-score, EfficientNet surpassed the competition. The work demonstrates how EfficientNet may be used to detect minute face traits associated with autism, providing a good substitute for more traditional techniques including brain signal, imaging, and video analysis. This study highlights the potential of cutting-edge machine learning models for autism identification and promotes more investigation into bigger datasets to improve diagnostic accessibility and accuracy.
Vertex Labelling of a Half-Cube to Induce Fibonacci Face Labels
Label the ten vertices of a hexagonal half-cube with digits 0, 1, 2, …, 9 (using each digit exactly once) such that the induced labels of the six faces (other than the hexagon), given by the sums of the labels of the vertices around those faces, are all Fibonacci numbers. We want a complete list of all solutions to the puzzle using mathematical logic, without a complete search of all permutations of the digits.