Varendra International Journal for Interdisciplinary Research (VIJIR)
Farm-Level Profitability and Determinants of Maize Production in Northern Bangladesh
This study examines the farm-level profitability of maize production and identifies the key determinants of gross revenue among maize producers in Northern Bangladesh. Using primary data collected from 100 farmers through a multistage sampling technique during the 2024–2025 production season, the analysis integrates descriptive statistics, cost–return analysis, and a Cobb–Douglas production function estimated by the ordinary least squares (OLS) method. The results indicate that maize cultivation is economically viable, with an average profit of Tk. 14,462.6 per bigha, reflecting strong income potential for smallholder farmers. The regression findings represent that hired labor, machinery, seed, and pesticide costs exert statistically significant positive effects on gross revenue, highlighting the importance of modern inputs and efficient farm management practices. In contrast, plowing cost shows a negative association with revenue, suggesting inefficiencies in land preparation, while irrigation, fertilizer, and harvest–transport costs are found to be statistically insignificant, possibly due to uniform usage patterns or suboptimal application. The model demonstrates strong explanatory power, accounting for a substantial proportion of variation in farm revenue. Despite profitability, farmers face several constraints, including limited access to credit, inadequate extension services, and high input costs, which hinder optimal resource utilization. Therefore, this study recommends strengthening institutional support, improving access to quality inputs and affordable credit, and enhancing extension services to promote sustainable and efficient maize production in Bangladesh.
Copyright (c) 2026 Varendra International Journal for Interdisciplinary Research (VIJIR)
Exploring the Dimensions of Artificial Intelligence in Enhancing Language Learning and Creativity of Authors in Literature
In the age of science and technology, Artificial Intelligence is a globally recognized phenomenon. The rapid growth of Artificial Intelligence is having influence significantly on various domains including language and literature. The intriguing question of whether Artificial Intelligence (AI) can replace human creativity in literature or can prove as a tool that helps to enhance and reshape the creativity of authors will be explored in this paper. The article will also consider whether AI can help in language learning. Artificial Intelligence has attracted the notion that it may replace the human creativity and originality because of its capability of creating anything and everything. However, the creativity produced by the intricate expression of human mind is irrefutable. Conflict may develop between artificial intelligence and human thought processes resulting from this. This study looks at the pros and cons of using AI algorithms as a means of simulating creativity while being aware of the many nuances of human expression, culture, and emotion that exist. The intent of this study is to stimulate discussion on the evolving relationship between human creativity and AI. In particular through the views from various disciplines this study will elicit critical thoughts on the way in which the relationship between AI and human creativity in the arts is changing.
Exploring the Link between Societal Norms-Customs and Female Adolescents’ Well-being in Rural Area
This exploratory study examines how social norms and customs influence the well-being of married female adolescents in rural Bangladesh. The research, conducted in Bagmara Upazila, used a mixed-methods approach, combining qualitative and quantitative data from 150 randomly selected married female adolescents and 15 focus group discussions. Findings reveal a strong link between socioeconomic status and adherence to social norms and customs. The majority of respondents were from the middle-income class (60%), who followed norms with moderate flexibility, while those from the lower-income class (23.33%) adhered to them more strictly. The study found that a significant portion of the girls (90%) experienced health problems resulting from these social norms and customs, including menstrual taboos and gender-based food inequality. These practices led to prevalent issues like malnutrition, anemia, and mental health disorders. Most adolescents (80%) mentioned care from local village doctors, indicating limited access to formal healthcare services. The findings suggest that restrictive social norms and customs significantly impact the health and overall well-being of rural adolescent girls.
85th Birthday Celebration of Professor Arun K. Basak: The Legacy of a Dedicated, Nobel Emeritus Physics Professor
Since this is a commemorative paper, the 'Abstract' part is not available.
This paper is a continuation of our study of anti-Hausdorff spaces started in [3]. An anti-Hausdorff space is a topological space which is completely opposite of Hausdorff space. Some more characterizations of anti-Hausdorff spaces have been established here.
Enhanced Reverse Intersystem Crossing: Quantum Insights into Boron Containing Emitters for Organic Light Emitting Diode Application
Thermally activated delayed fluorescence (TADF) materials have emerged as promising candidates for next-generation organic light-emitting diodes (OLEDs) due to their ability to harvest both singlet and triplet excitons without requiring heavy metals. In this work, a series of boron-based emitters were investigated to explore the effect of donor substitution on their electronic and excited-state properties. The parent molecule, a boron-oxygen fused molecule 5,9-dioxa-13b-boranaphtho[3,2,1-de]anthracene (BA), was systematically modified with various donor moieties like acridine and carbazole derivatives to form TBA-Ac, TBA-3Cz, TBA-P3Cz, BBAC-Ac and BBAPCAc. The structural optimization, frontier molecular orbital analysis and excited-state characterization of these emitters collectively revealed that donor incorporation strongly influences the singlet-triplet energy separation and spin-orbit interaction, which are critical for efficient reverse intersystem crossing (RISC). The parent compound BA exhibited a large singlet-triplet gap, prohibiting RISC and eliminating TADF behaviour. In contrast, the donor-substituted derivatives showed significantly reduced ΔEST values enabling effective thermal upconversion. TBA-Ac demonstrated the smallest ΔEST (0.12 eV) and the highest SOCME (1.54 cm⁻¹), resulting in the fastest kRISC (1.26 × 10⁶ s⁻¹), establishing it as the most efficient TADF candidate. BBAPC-Ac also showed strong SOC (1.19 cm⁻¹) and high kRISC (4.04 × 10⁵ s⁻¹), confirming that acridine-carbazole donor combinations effectively promote singlet-triplet conversion. The results demonstrate that strategic donor engineering around the boron–oxygen core can effectively accelerate the RISC process. Ultimately, this work establishes a clear structure-property relationship, offering a rational design strategy for developing high-performance, metal-free TADF emitters for next-generation OLED applications.
Insight into the Structural, Mechanical, Thermophysical and Optoelectronic Properties of K2X3 (X = S, Se) by DFT Investigation
The structural, mechanical, thermophysical, and optoelectronic properties of K2X3 (X = S, Se) were systematically explored using the full-potential linearized augmented plane wave (FP-LAPW) method within the framework of density functional theory (DFT), as implemented in WIEN2k. The optimized structural parameters confirm the stability of both compounds. Mechanical analysis, including elastic constants, bulk modulus, shear modulus, Young’s modulus, Poisson’s ratio, and Pugh’s ratio, reveals that K2S3 and K2Se3 are mechanically stable yet brittle, with pronounced elastic anisotropy. Phonon dispersion and density of states further validate their dynamical stability. Thermophysical evaluation shows that K2S3 exhibits a higher Debye temperature and lattice thermal conductivity than K2Se3, reflecting stronger covalent bonding. Electronic structure calculations indicate semiconducting behavior, with band gaps reduced by ~42% upon substitution of S with Se. The density of states is dominated by S 3p orbitals in K2S3 and Se 4p orbitals in K2Se3. Optical investigations reveal strong anisotropy, with maximum absorption and photoconductivity in the ultraviolet (UV) region. The strong UV absorption highlights its potential for applications as an efficient UV-absorbing material.
From Laboratory Foundations to Sustainable Materials Solutions: A Scientific Journey
In the twenty-first century, the defining challenges facing humanity—clean energy, environmental protection, and sustainable development—are inseparable from advances in materials science. From renewable energy integration and high-voltage energy storage to wastewater purification and green hydrogen production, materials form the backbone of technological progress. Yet for developing nations, meaningful participation in this transformation requires more than scientific ideas; it demands laboratories, institutions, and trained minds capable of turning knowledge into enduring solutions. When I returned to Bangladesh after completing my doctoral training in Materials and Condensed Matter Physics, we faced a fundamental question: How can globally relevant research flourish within limited infrastructure and resources? Rather than perceiving constraints as barriers, we chose to see them as catalysts for resilience and innovation. Since then, my journey has been guided by a simple conviction—that scientific excellence, grounded in integrity and societal purpose, can emerge from anywhere. This journey began in 2013 with the establishment of the Nanotechnology Research Laboratory at the Bangladesh University of Engineering and Technology (BUET). Over more than a decade, that laboratory has matured into a well-established research ecosystem. In 2025, with financial support from the Bangladesh Energy and Power Research Council, this foundation was further expanded through the establishment of the Energy Materials and Supercapacitor Research Laboratory. Together, these two laboratories now form complementary pillars of sustainable materials research for energy and environmental applications.
Superstring Theory: Motivation, Structure, and Prospects for a Unified Description of Nature
The quest for a unified theoretical framework capable of describing all fundamental interactions has long guided the development of fundamental physics. While the Standard Model of particle physics provides a highly successful quantum field theoretic description of electromagnetic, weak, and strong interactions, it neither incorporates gravitation nor explains the origin of its numerous free parameters. General relativity, by contrast, offers an elegant geometric description of gravitation but resists conventional quantization. Superstring theory has emerged as a leading candidate for resolving this conceptual tension by providing a consistent quantum framework in which gravity arises naturally alongside gauge interactions. In this article, we present a critical and comprehensive review of superstring theory, emphasizing its physical motivation, mathematical structure, and unifying power. We discuss supersymmetry, extra dimensions, D-branes, and dualities, review key theoretical achievements such as black hole entropy and gauge–gravity duality, and examine phenomenological as well as conceptual challenges, including the landscape problem. A mathematical appendix is included to make the presentation self-contained at the graduate level.
Inter-Rater Reliability in Social Sciences: A Comprehensive Review of Methods, Challenges, and Implications
Inter-rater reliability (IRR) is a crucial aspect of social science research, ensuring consistency and reproducibility across different evaluators. This review explores key statistical methods, including Bland-Altman Analysis, Intraclass Correlation Coefficient (ICC), Cohen’s kappa, Fleiss’ kappa, Lin’s Concordance Correlation Coefficient (CCC), and Gwet’s AC1, which are widely employed in social science studies. The paper highlights major challenges, such as subjective biases, variability in training, and contextual influences that affect evaluator agreement. Furthermore, the implications of unreliable ratings on research validity, policy-making, and practical applications are discussed. Addressing these challenges through standardization, structured training protocols, and advanced computational techniques is essential for enhancing the reliability and credibility of assessments in social sciences. This paper provides a comprehensive discussion of the aforementioned measures, accompanied by real-life examples, ensuring that social science researchers can effectively apply these methods in their own studies.
Simulations provide accessible approximate solutions and verify advanced exact solutions. Oftentimes, it also generates questions worthy of theoretical developments. We illustrate these roles of simulation using a couple of examples and R codes.
Lattice Path Counting and Optimal Utilization of Multi-Channel Multi-Server Resources: Decorating the Math Magic Flower
Inspired by a recent study of a Math Magic Circle [MMC], we undertake a similar study of a mathematical construct -- called Math Magic Flower [MMF]-- involving the first 81 natural numbers {1, 2, ..., 81} - each one being used exactly once. Of these numbers, only one viz., the number 41 acts as an isolate and it occupies a special position in the MMF. The geometry exhibits the number 41 holding a central position, better identified as one acting as the centre of six concentric circles. Along the circumferences of these circles are placed the natural numbers - each circle holding 10 of the numbers. As a matter of fact, the third and the fourth circles accommodate 20 numbers each. Thus we have the count 81 (= 1 + 10 + 10 + 20 + 20 + 10 + 10). The placement of the numbers is very special. There are intersecting straight lines with each line supporting exactly 3 of the 80 numbers and more over, the sum of the three numbers is the same (=123) for all lines. Our application of the magic flower deals with a navy pier operating from the central position towards outwardly navigation of ships. Or else, it could as well be in the opposite direction - from the farthest position on the sixth circle towards the centre touching each of the circles in between. There is a sense of Lattice path counting towards optimal utilization of Multi-Channel Multi-Server Resources.
Quantum Physics and its Profound Impact on Human Society
Quantum physics emerged from the failures of classical physics to explain many physical phenomena. Quantum physics has contributed immensely to science and underpinned modern technologies. The UN-designated International Year of Quantum Science and Technology (2025) recognizes the centenary of the birth of quantum mechanics as a complete theory and its profound contribution to the first quantum revolution, which has given us computers, mobile phones, lasers, GPS and MRI. Also a second quantum revolution is poised to provide unprecedented computing ability through quantum computers, secure communication with quantum cryptography and ultrasensitive quantum sensing devices for various diagnostic tools.
An Assessment of g-ray Emission Probabilities in the Decay of 238U
We have deduced the 49.55- and 113.6-keV g-ray emission probabilities in the decay of 238U from the intensity balance of a and g transitions feeding and depopulating the levels of 234Th. The deduced value of the 49.55-keV g-ray emission probability is in good agreement with most of the data available in the literature. A precision of 1.4% has been obtained, which is significantly better compared to the earlier values. The 113.6 g-ray emission probability of this work is in excellent agreement with one of the literature values, however, the precision of 14% remains comparable to the earlier one. This work highlights the need to remeasure the emission probability of the 49.55-keV g ray to obtain a high-confidence measurement, given that there is significant spread in the current data by as much as 10%. From this work, we also recommend to remeasure the energy of the 49.55-keV g ray for improved reconciliation between the deduced and directly measured data.
Brief Lifesketch of Professor Arun Kumar Basak
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.
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.