Abstract: Conventional first-year architectural pedagogy typically approaches spatial and volumetric literacy through externalized, abstract exercises such as composition, color, relief, and form, treated as disciplines to be mastered before applying them to any personal or lived context This sequence of academic structure leaves students technically fluent in formal manipulation while offering little space for spatial understanding to be grounded in lived, bodily experience. This study addresses this gap by examining an alternative model piloted as the final design project of a Level-l, Term-1 Design Studio, where the students first completed an online personality assessment and were then faced with a single question which shell do I live in? Using any material or medium of their choosing, each of the twelve students was required to design and construct a wearable, corporeal object, not a static model to be dJsplayed, but a structure to be inhabited, moved -with, and performed on the body, culminating in a public ramp-walk presentation.

Abstract: As architecture confronts accelerating social, ecological, and technological transformations and resultant uncertainty, the discipline is being asked to expand its role-from designing resolved forms to mediating complex, shifting, and often conflicting realities on the ground. This paper asks whether current studio training and evaluation, still organized around producing polished, often decontextualized static images and drawings, is preparing architects for that expanded role at all.rnStudio practices, professional competitions, and jury conventions have converged on static drawings and renders as the primary evaluative artifact-a mode of representation that abstracts the designed built form from the often messy physical, social, economic, and political ground it is meant to serve. This paper argues that such training enacts a disembodied, universalizing opjectivity-an epistemology structurally unsuited to the wicked, ill-structured problems (Rittel and Webber) that define real design situations, and even less equipped for a future in which computational tools and artificial intelligence may absorb routine, well-defined design tasks. In contrast, the reframing of ill-defined problems remains a distinctively human capacity.rnDrawing on Saloma\\\'s transformative pedagogy, Schon\\\'s reflective practitioner, and Awan et al\\\'s concept of spatial agency, this paper reframes the future architect\\\'s role as the system orchestrator-one who operates through agency exercised via mediation, negotiation, socio-political engagement, facilitation, and adaptation to conditions beyond their control. Under this reframing, the building or masterplan is no longer the architect\\\'s sole deliverable, but one possible outcome among several, produced through sustained engagement with a situation rather than imposed upon itrnMethodologically, the paper draws on five years of academic and professional practice across South Asian and European contexts, alongside a comparative reading of jury grading criteria, assessment structures, submission formats, project briefs, and jury sheet content across multiple programs, supplemented by informal student surveys and faculty conversations. This grounding is offered not as systematic empirical data, but as a practitioner-observed basis for the theoretical argument that follows.rnThe paper argues that, at a transitional point in the history of the profession, the academic studio model should be rethought to produce project briefs that emphasize designing for conflict, crisis, and vulnerability rather than sanitized sites. The design process should involve place-based knowledge production grounded in sustained community engagement and socio-political realities rather than aesthetic contextualism, and speculative imagination as a method for designing under uncertainty.

Traditional classroom-bound architectural pedagogy often lacks the mechanism to fully prepare graduates for the complex socio-ecological and professional realities of contemporary practice. This structural limitation challenges the efficacy of national shift toward Outcomes-Based Education (OBE), which mandates demonstrable contextual empathy and place-based problem-solving. This study addresses this academic-professional gap by defining the \\\'On-site Studio\\\' as a structured, curriculum-integrated framework that transitions architectural training into an immersive social practice. Eschewing the logistical and narrow focus of typical design-build exercises, the model champions action research through experiential site-writing, community dialogue, dnd multi-stakeholder collaborative inquiry.

House price prediction is crucial in real estate for informed decision-making. This paper presents an automated prediction system that combines genetic algorithms (GA) for feature optimization and Analysis of Variance (ANOVA) for statistical analysis. We apply and compare five ensemble machine learning (ML) models, namely Extreme Gradient Boosting Regression (XGBR), random forest regression (RFR), Categorical Boosting Regression (CBR), Adaptive Boosting Regression (ADBR), and Gradient Boosted Decision Trees Regression (GBDTR), on a comprehensive dataset. We used a dataset with 1000 samples with eight features and a secondary dataset for external validation with 3865 samples. Our integrated approach identifies Categorical Boosting with GA (CBRGA) as the best performer, achieving an R2 of 0.9973 and outperforming existing state-of-the-art methods. ANOVA-based analysis further enhances model interpretability and performance by isolating key factors such as square footage and lot size. To ensure robustness and transparency, we conduct 10-fold cross-validation and employ explainable AI techniques such as Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), providing insights into model decision-making and feature importance.

Bank loan prediction (BLP) analyzes the financial records of individuals to conclude possible loan status. Financial records always contain confidential information. Hence, privacy is significant in the BLP system. This research aims to generate a privacy-preserving automated BLP scheme. To achieve this, differential privacy (DP) is combined with machine learning (ML). Using a benchmark dataset, the proposed method analyzes two different DP techniques, namely Laplacian and Gaussian, with five different ML models: Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Logistic Regression (LR), and Categorical Boosting (CatBoost). Each of the DP techniques is evaluated by varying distinct privacy parameters with 10-fold cross-validation, and from the outcome analysis, optimal parameters are nominated to balance privacy and security. The analysis indicates that applying the Laplacian mechanism with a DP budget of 2 and the RF model achieves the highest accuracy of 62.31%. For the Gaussian method, the best accuracy of 81.25% is attained by the CatBoost model in privacy budget 1.5. Additionally, the proposed method uses explainable artificial intelligence (XAI) to show the conclusion capability of DP-integrated ML models. The proposed research shows an efficient method for automated BLP while preserving the privacy of personal financial information and, thus, mitigating vulnerability to scams and fraud.

Liver disease (LD) significantly impacts global health, requiring accurate diagnostic methods. This study aims to develop an automated system for LD prediction using machine learning (ML) and explainable artificial intelligence (XAI), enhancing diagnostic precision and interpretability.

Liver disease is any condition that negatively affects the liver’s function or structure, resulting in impaired liver function and various health complications. Abnormal conditions are rapidly increasing day by day. In this study, we used a dataset of key liver disease-related blood sample biomarkers to utilize various machine learning (ML) techniques to enhance the accuracy of liver disease prediction. Specifically, we integrated the artificial neural network (ANN) model with five ML models: Stacked Generalization (Stacking), Bootstrap Aggregating (Bagging), Adaptive Boosting (AdaBoost), Gradient-Boosted Decision Tree (GBDT), and Support Vector Machine (SVM)—resulting in five distinct hybrid models: Stacking with ANN (SANN), Bagging with ANN, AdaBoost with ANN (ABANN), GBDT with ANN (GANN), and SVM with ANN (SVMANN). We tested all these hybrid models with feature selection techniques, including linear discriminant analysis (LDA), principal component analysis (PCA), recursive feature elimination (RFE), and also without feature selection. Through extensive testing, we found that these five hybrid models performed best when combined with LDA rather than PCA, RFE, or no feature selection. This discovery led us to create a max voting ensemble (MVE) of these LDA-optimized hybrid models. Remarkably, our prediction accuracy increased from 79.15% to 98.38% using the MVE. Furthermore, we employ explainable artificial intelligence techniques such as Local Interpretable Model-agnostic Explanations, Shapley Additive Explanations, and Individual Conditional Expectations to analyze and enhance trust in the predictions. We also implemented 10-fold cross-validation to ensure the robustness and reliability of our results. This research underscores the significance of advancements in neural network systems and highlights the potential for hybrid models to improve predictive accuracy in liver disease diagnosis. Our findings pave the way for a new generation of computational technologies endowed with intelligence, ultimately contributing to better health outcomes and a deeper understanding of liver disease dynamics.

1. Introduction:rnrnDiabetes mellitus (DM) is a most important worldwidernhealth concern, which causes a range of long-term completernimpairments that have a signififcant influence on the patientrnand society, as the illness usually upsets people in their bestrnfruitful ages. As per the latest alarming statistical facts releasedrnby the International Diabetes Federation (IDF) DiabetesrnAtlas 2021 document [1], there are 537 million (1 inrn10) adults (aged 20u201379 years) living with diabetes mellitusrnworldwide in 2021. u009dis number is anticipated to rise to 643rnmillion (1 in 9 adults) by 2030 and 784 million (1 in 8 adults)rnby 2045. Also, 81% (4 in 5 adults) with diabetes are living inrnmiddle- and low-income countries. Around 6.7 millionrndeaths (1 every 5 seconds) occurred in 2021 due to DM. Anrnassessed 44% of adults (240 million) living with diabetes inrnmiddle and low-income countries are left undiagnosed. Inrn2021, the global health expenditure caused by diabetes alonernis evaluated at USD 996 billion with an increase of 316% overrnthe last 15 years. Around 541 million (1 in 10) adultsrnworldwide have been positioned at high risk of emergingrntype 2 diabetes due to diminished glucose tolerance levels.rnu009de top 10 countries with the maximum number of peoplernwith diabetes account for 68% of adults with diabetes [2].

Abstract: There has been an exponential rise in mobile data traffic in recentrntimes due to the increasing popularity of portable devices like tablets, smartphones,rnand laptops. The rapid rise in the use of these portable devices hasrnput extreme stress on the network service providers while forcing telecommunicationrnengineers to look for innovative solutions to meet the increasedrndemand. One solution to the problem is the emergence of fifth-generationrn(5G) wireless communication, which can address the challenges by offeringrnvery broad wireless area capacity and potential cut-power consumption. Thernapplication of small cells is the fundamental mechanism for the 5Gtechnology.rnThe use of small cells can enhance the facility for higher capacity and reuse.rnHowever, it must be noted that small cells deployment will lead to frequentrnhandovers of mobile nodes. Considering the importance of small cells in 5G,rnthis paper aims to examine a new resource management scheme that can workrnto minimize the rate of handovers formobile phones through careful resourcesrnallocation in a two-tier network. Therefore, the resource management problemrnhas been formulated as an optimization issue thatwe aim to overcome throughrnan optimal solution. To find a solution to the existing problem of frequentrnhandovers, a heuristic approach has been used. This solution is then evaluatedrnand validated through simulation and testing, during which the performancernwas noted to improve by 12% in the context of handover costs. Therefore,rnthis model has been observed to be more efficient as compared to the existingrnmodel.

Choosing a career is one of the most crucial decisions for business students, especially those completing graduation (Igere, 2017).

A firm\'s capital structure is crucial because it reflects how the firm finances its assets, which is often through a combination of equity and debt.

Child labor is one of the most widespread social problems in the world, perhaps more so in developing countries like Bangladesh.

Domestic violence is a pervasive issue in Bangladesh, impacting millions of persons, especially women, across all socioeconomic classes.

Tourism ranks as one of the largest and most rapidly expanding industry globally, representing approximately 7% of international exports and providing employment for one in ten workers (World Tourism Organization, 2022).

Air pollution is a critical environmental and public health challenge worldwide, exerting considerable detrimental consequences on human health and well-being.

Medicinal plants have evolved into a significant component of the basic medical system in the modern world because of the vast quantities of good natural products.

Before understanding the EB concept, both employer and branding concepts must be apprehending perfectly.

Attracting and retaining highly trained people has emerged as a strategic imperative in contemporary competitive job marketplaces.

The aim of this study is to investigate the impact of service quality of travel agencies on tourist satisfaction in Bangladesh.

“What factor should we consider to identify the best investment among the stream of investments?” has been considered as a burning question since the inception of the stock market.

Humans have relied on nature to provide for their most fundamental needs since the beginning of civilization.

Humans have relied on nature to provide for their most fundamental needs since the beginning of civilization.

Experience of boredom in learning indicates mental inability to experience positive attitudes towards rnlearning in class and outside classroom learning process.

In this era of the fourth industrial revolution, the changing needs of the teaching and learning culture promote teacherless classroom where the demands of the knowledge and skills are required by technological blessings and innovations.

Modern market evaluations of organizations assess their financial results alongside their environmental and social duty performance.

The pharmaceutical industry holds a strategically important status in the world economy since it has a direct effect on the health of the population, its economic performance, and social well-being. In comparison to other branches, pharmaceutical companies work in the area of scientific breakthroughs, ethical accountability, environmental regulations, and regulations (Mastrantonas et al., 2024).

It is not optimal to produce everything within the national border. As a result of depression in the 17th century, trade shifted its direction from the national economy to an economy with foreign trade surplus (Korkmaz, 2013).

Bangla literature is one of the most prevailing non-departmental courses of the English departments in Bangladesh, so there is a marginalized approach given to the teaching and learning of Bangla literature in most teaching and learning contexts of the English department.

his study explores how sustained interactions with AI systems may influence usersu2019 belief formation and cognitive processing. It focuses on the potential for feedback loops in AI-mediated communication to reinforce existing biases or generate new distortions over time. By analyzing patterns of engagement, response personalization, and user dependency, the research aims to identify conditions under which such distortions may emerge or intensify. The findings will contribute to a better understanding of humanu2013AI relational dynamics and inform the design of safer, more cognitively supportive AI systems.

This report provides a comprehensive comparative analysis of Bangladesh University (BU) rnacross several academic and financial indicators, benchmarked against a group of private rnuniversities that share similar institutional characteristics. Bangladesh University is used as rnthe central reference point, with all competitor institutions evaluated against BUu2019s rnperformance, cost structure, academic delivery environment, and market positioning.rnThe report examines the Business, CSE, English, Law, and Architecture departments across rnuniversities that operate within comparable student markets, fee structures, and historical rntimelines. Each institution included in this study has been selected based on clear, measurable rnreasons: similar establishment period, similar program offerings, and direct competition for rnstudent enrollment within Dhaka and the surrounding regions.rnThe objective is to provide the Board of Trustees with a holistic, data-driven assessment of rnthe universityu2019s position in the private higher-education marketplace and to identify rnactionable opportunities for strategic improvement.

Machine Learning-Enhanced SPR-Based Optical Biosensor for Cancer Cell Detection. Journal: Optics and Laser Technology.

Factors that affect potential investors in case of choosing optimal investment from the stream of investment alternatives: A case study on Bangladeshi investors. International Journal of Advanced Multidisciplinary Scientific Research, 1(10), 55u201364.

Exploring the Hidden Functionality of the Underneath Space of Moghbazar Flyover, Dhaka.u201drnPaper ID: ARCH 0160. Presented and published in 4th International Conference on Planning, Architecture and Civil Engineering (ICPACE 2023), RUET.