Chittagong University of Engineering & Technology

This study investigates the effects of epitaxial strain on the crystal structure, magnetization, and spin wave propagation characteristics of rare-earth iron garnet thin films. Thin films of Y3Fe5O12 (YIG) and Lu3Fe5O12 (LuIG), each with identical thicknesses, are grown on Gd3Ga5O12 (GGG), substituted Gd3(GaMgZr)5O12 (SGGG), and Y3Al5O12 (YAG) substrates, causing systematic strain variation in films based on the substrate lattice constants. Exceeding a certain critical thickness for epitaxial strain relaxation, the strained epitaxy transitions to a relaxed state, and dislocations form at the boundary between the strained and relaxed phases. High-resolution scanning transmission electron microscopy reveals dislocations and missing atomic lines in relaxed films with large mismatches, which correlates with a reduction in saturation magnetization and an increase in Gilbert damping. Consequently, the spin wave transmission drops significantly more in relaxed YIG/YAG (large mismatch of +2.96%) and LuIG/YAG (+2.2%) films than in their strained counterparts. Remarkably, the tensile-strained films maintain their strain without relaxation, even with a large mismatch, confirming efficient spin-wave transmission. These findings emphasize the significant advantages of strained epitaxy for advanced spin wave-based magnonic device applications.

In this paper, four new MAX phases M2AB2 (M = Mo, Ta; A = Ga, Ge) are explored, and the elastic, electronic, thermal, and optical properties are studied to anticipate their potential applications. The stability is confirmed by calculating formation energy (EF), formation enthalpy (ΔH), phonon dispersion curve (PDC), and elastic constant (Cij). The study reveals that M2AB2 (M = Mo, Ta; and A = Ga, Ge) exhibits significantly higher elastic constants, elastic moduli, and Vickers hardness values than their counterpart 211 borides. Higher Vickers hardness values of Ta2AB2 (A = Ga, Ge) than Mo2AB2 (A = Ga, Ge) are explained in terms of bond overlap population (BOP). The density of states (DOS) and electronic band structure (EBS) reveal the metallic nature of the titled borides. The melting temperature (Tm), Grüneisen parameter (γ), minimum thermal conductivity (Kmin), Debye temperature (ΘD), and other parameters of M2AB2 (M = Mo, Ta; A = Ga, Ge) are computed. These findings suggest that the studied compounds exhibit superior thermal properties compared to 211 MAX phases and are suitable for thermal barrier coating (TBC) applications. The optical characteristics are examined, and the reflectance spectrum indicates that the materials have the potential to mitigate solar heating.

Syngonium (S.) podophyllum L. is recognized for its diverse applications. This study evaluated the in vivo anti‐inflammatory and neuropharmacological properties of its methanolic flower extract (SPF‐ME) using Swiss albino mice and it’s in vitro antioxidant capabilities. The anti‐inflammatory activity was assessed through the xylene‐induced ear edema method, while antioxidant properties were evaluated using DPPH and ABTS radical scavenging assays. Analgesic efficacy was tested with the acetic acid‐induced writhing and hot plate methods. Antidepressant effects were examined using the forced swimming test (FST) and the tail suspension test (TST), and anxiolytic activity was measured through the elevated plus maze (EPM) and hole board tests. Gas chromatography‐mass spectrometry (GC‐MS) was utilized to identify bioactive compounds, alongside in silico investigations using online tools for pass prediction, ADME/T, and molecular docking. Results showed that SPF‐ME exhibited significant antioxidant activity, reduced edema (p < 0.05), and provided a notable analgesic effect (200 mg/kg, p < 0.001). It also demonstrated anxiolytic effects and reduced immobility time in both FST and TST (400 mg/kg, p < 0.001), and SPF‐ME compounds showed greater binding affinity to target human receptors, according to molecular docking analysis. Overall, SPF‐ME is a promising natural source of antioxidants, anti‐inflammatory, and neuropharmacological properties.

The COVID-19 pandemic has affected millions of people globally, with respiratory organs being strongly affected in individuals with comorbidities. Medical imaging-based diagnosis and prognosis have become increasingly popular in clinical settings for detecting COVID-19 lung infections. Among various medical imaging modalities, ultrasound stands out as a low-cost, mobile, and radiation-safe imaging technology. In this comprehensive review, we focus on AI-driven studies utilizing lung ultrasound (LUS) for COVID-19 detection and analysis. We provide a detailed overview of both publicly available and private LUS datasets and categorize the AI studies according to the dataset they used. Additionally, we systematically analyzed and tabulated the studies across various dimensions, including data preprocessing methods, AI models, cross-validation techniques, and evaluation metrics. In total, we reviewed 60 articles, 41 of which utilized public datasets, while the remaining employed private data. Our findings suggest that ultrasound-based AI studies for COVID-19 detection have great potential for clinical use, especially for children and pregnant women. Our review also provides a useful summary for future researchers and clinicians who may be interested in the field.

To understand the nonlinear behavior of the dust acoustic solitary waves (DASWs) and the production of rogue waves (RWs) in the Jupiter environment at distances greater than 15 RJ , a five-component relativistic dusty plasma system comprising positive dust particles, streaming positive ions, isothermal ions, electrons, and solar wind electrons has been considered. To analyze this system, the Korteweg–de Vries (KdV) equation is derived using the reductive perturbation method (RPM), and the nonlinear Schrödinger equation (NLSE) is derived by employing the derivative expansion method. Both analytical and numerical (Adomian decomposition scheme) solutions of the KdV equation are studied. It is found that the nonlinearity and amplitude of dust-acoustic solitary waves (DASWs) as well as the width of the electric field structure increase, while the rogue wave (RW) amplitude decreases with the growing effect of the relativistic streaming positive ion factor. The analytic solution of the KdV equation provides only the rarefactive DASWs, while its numerical solution gives both compressive and rarefactive DASWs. This investigation may help not only to comprehend the space plasma phenomena, particularly in the Jupiter atmosphere, but also to validate laboratory investigations where concerned plasma species are present.

The compressive behavior of concrete-filled steel (CFS)-reinforced cement concrete (RCC) columns with square, rectangular, and circular cross sections is investigated in this study using COMSOL Multiphysics models. The simulations’ dependability is confirmed by the nonlinear 3D finite element models that were developed and verified using experimental data, displaying a maximum deviation of 5%. The study carefully examined the effects of load eccentricity, cross-sectional shape, and RCC thickness on column performance. The findings demonstrated that circular columns outperform square and rectangular columns in terms of energy absorption and load carrying capacity. However, despite having a less uniform stiffness distribution, square and rectangular columns showed higher starting stiffness. For all column types, the ultimate load capacity and energy absorption were enhanced by increasing RCC thickness. These results provide insightful information for enhancing RCC column design, resulting in safer and more robust structural systems in civil engineering applications.

This study investigates the effects of plasma parameters on shock and periodic waves, along with their dynamic behaviors, in unmagnetized strongly coupled plasmas (SCPs), particularly with the presence of super-critical values (SCVs). To achieve this, the research extends the Burgers equation (BE) by incorporating higher-order nonlinear terms to explore the nonlinear characteristics of heavy ion acoustic (HIA) shock waves and periodic waves, along with their dynamics in SCPs. First, a BE with quartic nonlinearity is derived, which is used to analyze the propagation features of HIA shock waves near and at critical values (CVs) and SCVs. Based on the analytical solutions, the effects of plasma parameters on electrostatic shocks, anti-shocks, and their dynamic behaviors are examined. Second, the study investigates the propagation features of shock and periodic waves and their dynamics, not only near but also at SCVs, by deriving a new nonlinear evolution equation that incorporates both cubic and quartic nonlinearities. Since integrable nonlinear evolution equations are often solvable analytically, the research also assesses the need for numerical methods in studying acoustic wave phenomena in such plasmas. This approach provides a comprehensive understanding of both the theoretical and practical aspects of wave propagation in SCPs. The findings of this study may aid in understanding and predicting the behavior of HIA wave dynamics in space environments, such as stellar polytropes, hadronic matter, quark-gluon plasma, dark-matter halos, and contribute to future experimental studies in plasma laboratories.

Early detection of anxiety disorders in a non-intrusive manner is crucial, as these conditions can profoundly impact an individual’s health and daily functioning. Traditional approaches relying solely on unimodal data often fall short, potentially introducing bias and inaccuracies. TI-Fusion is a novel late multimodal fusion technique that integrates text and image data for a unified reliable outcome, overcoming limitations in existing methods. The primary advantage of TI-Fusion is its non-intrusive nature, ensuring patient comfort by avoiding invasive methods while still delivering robust diagnostic capabilities. The study utilizes six advanced machine learning algorithms (Gaussian Naive Bayes, XGB Classifier, K-Neighbors, SVM, Decision-Tree, and RandomForest) for data classification, pattern recognition, and predictive accuracy. Concurrently, image data from the KDEF and CK+ datasets was processed through a Convolutional Neural Network (CNN) enhanced with a Real Gabor filter, which is particularly adept at capturing textures, edges, and complex visual patterns necessary for precise image analysis and recognition. By employing a late multimodal fusion approach, TI-Fusion integrates the outcomes of models trained on distinct data modalities, yielding a more comprehensive and accurate prediction than unimodal methods. This technique not only surpasses existing multimodal approaches but also achieves a commendable final accuracy rate of 92.38%, demonstrating its effectiveness in enhancing the early detection of anxiety disorders.

Online learning continues to expand due to globalization and the COVID-19 pandemic. However, maintaining student engagement in this new normal has become increasingly difficult. Conventional techniques, such as self-reports and manual observations, often fall short of capturing the subtle behaviors that indicate attentiveness. This emphasizes the necessity for sophisticated tools to assess engagement effectively. The proposed system introduces an innovative approach to monitoring student attention in online learning environments by integrating computer vision techniques with a Gradient Boosting classifier (GBC). It conducts a multimodal analysis of behavioral cues captured through a standard webcam, such as facial expressions, hand movements, mobile phone usage, and head poses, to enable a comprehensive and accurate evaluation of student engagement. With thorough validation on a dataset of 6,000 records, the GBC model outperformed traditional approaches and other machine learning algorithms by attaining an accuracy of 99.13%. Through the utilization of Explainable AI (XAI) tools such as LIME and SHAP, we increased the transparency and interpretability of our model. This allows educators to gain a better understanding of the elements that influence student engagement, hence promoting trust among all stakeholders involved. The system's focus on resource efficiency and scalability makes it adaptable to diverse educational settings without extensive infrastructure. The user-friendly web interface facilitates real-time monitoring, seamlessly integrating with popular e-learning platforms and providing detailed, anonymized reports. This enables instructors to make data-driven interventions to enhance teaching strategies and offers actionable insights to improve learning outcomes. Non-identifiable data collection meets ethical requirements while maintaining privacy and producing insightful engagement metrics.

Land use/land cover (LULC) dynamics play a crucial role in understanding the complex interactions between ecosystems and climate. This study demonstrates the effective integration of Google Earth Engine (GEE) and machine learning (ML) algorithms for monitoring LULC changes in two rapidly urbanizing cities in Bangladesh. By combining Landsat imagery with classification and regression trees, random forest (RF), and support vector machine algorithms within the GEE platform, we analyzed LULC changes from 2001 to 2021. Our analysis revealed significant urban expansion in both cities, with built-up areas showing the highest increase, while natural land covers experienced notable declines. The RF classifier consistently demonstrated superior performance, with the overall accuracy exceeding 93%. The GEE-based approach significantly reduced the processing time compared to traditional methods, while the integration of multiple ML algorithms enhanced the classification accuracy. This research advances environmental monitoring by showcasing the effectiveness of cloud-based geospatial analysis for rapid and accurate LULC change detection. The methodology presented herein offers valuable insights for urban planners and policymakers, particularly in rapidly urbanizing regions, contributing to Sustainable Development Goals 11 (Sustainable Cities and Communities) and 15 (Life on Land).

Software‐defined networking (SDN), a cornerstone of future‐generation networks, is adopted in Named Data Networks (NDN) for large‐scale deployment. The forwarding strategies proposed for SDN‐based NDN primarily use the centralized controller to optimize Interest forwarding and Data delivery. The nodes direct the Interests to the controller to discover the content source(s) and suppress the suboptimal responses. To support such content discovery and delivery, the controller experiences frequent path calculation and trades excessive control messages to install the paths in the nodes due to rapid cache admission and replacement. Besides, the typical NDN forwarding solutions are not viable to realize or need considerable modifications in SDN‐based NDN. To that end, the proposed strategy optimizes Interest forwarding and Data delivery using a Single‐State Q‐learning‐based technique, namely, SDN‐Q. In SDN‐Q, each content source learns to suppress the suboptimal responses, with the learning task offloaded to the controller. The controller communicates the learning decision to the nodes. Each node only retains the action (decision) to entertain an incoming Interest. Once an Interest hits, the source either replies with the Data or remains silent and sends the Interest's information (meta‐data) to the controller for the learning task. Thus, SDN‐Q enables the NDN nodes to remain light‐loaded. Each node can instantly answer an Interest request without redirecting it to the controller. Additionally, we optimize Interest forwarding using a hop‐based scoped‐flooding approach. The proof‐of‐concept implementation in software (simulation) reveals that the proposed system outperforms the competing strategies by reducing the traffic load, latency, and control messages in SDN‐based NDN (at most by 40%, 7%, and four (4) times respectively), without negotiating packet delivery ratio.

Concrete is susceptible to cracks that may arise from shrinkage under tensile stress, which reduces its mechanical strength and endangers the durability of structures. Superabsorbent polymers (SAP) have emerged as promising self-healing agents as they can reduce crack closure and help mitigate plastic shrinkage. However, incorporating SAP may increase porosity, reduce workability, and negatively influence the mechanical properties of cementitious composites. This study utilized micro-silica (MS) and fly ash (FA) to counteract the reduction in compressive strength and workability caused by SAP while enhancing the self-healing performance of mixtures incorporating these supplementary cementitious materials (SCMs). Thirteen mix proportions with varying SAP, MS, and FA replacements were analyzed to assess their effect on self-healing efficiency, mechanical performance, and shrinkage resistance. These specimens were preloaded to generate micro-cracks, and these pre-cracked specimens were exposed to a wet–dry cycle. The results demonstrate that the combination of SAP (0.2–0.4%) with FA (15–30%) accelerates crack closure and recovers workability up to 28%, while MS (3–5%) densifies the matrix, reducing permeability and recovering compressive strength up to 17%. Notably, a mixture of 0.4% SAP, 5% MS, and 15% FA achieves a maximum crack closure ratio of 97%, recovering the lost workability and restoring full compressive strength in the long term. This study represents a practical way to incorporate SAP for self-healing quality while maintaining workability and strength with the optimum proportions of MS and FA. The findings can contribute to developing long-lasting, self-healing concrete for real-world implications.

Short-term load forecasting (STLF) is a paramount progression in effective resource allocation, strategic infrastructure planning, and efficient operation of power systems. Accurate STLF ensures optimized power plant operations, grid stability, and energy conservation that positively impact the economic growth of a country. STLF is critical for any developing country like Bangladesh because accurate STLF can reduce drastically the power system operation and planning expenses. Dhaka Power Distribution Company (DPDC) Limited is a public limited company under the Power Division of the Ministry of Power, Energy and Mineral Resources of the Government of Bangladesh that manages the distribution of electricity to the customers in Dhaka City Corporation area. DPDC regularly provides one-day-ahead daily load consumption information to the National Load Dispatch Centre (NLDC), which is engaged in the management of critical functionalities of the power system network including electrical load demands across the country. DPDC mostly relies on assumption-based forecasting technique which often leads to significant errors in daily load demand estimation resulting in improper utilization of energy resources. This manuscript focuses on enhancing accuracy of one-day-ahead STLF for the DPDC by employing machine learning algorithms. Eight machine learning algorithms have been utilized in this study to assess the model accuracy and to identify the best-suited machine learning model for the one-day-ahead STLF. Among all the machine learning models, LightGBM model proved to be the most effective approach for STLF for DPDC dataset. Results suggest that LightGBM model exhibits average 5.08% reduction in MAPE, 47.76 MW decreases in RMSE, 5268.46 reductions in MSE, and 45.25 MW decreases in MAE from current assumption-based DPDC predicted method. These accuracy enhancements hold substantial economic benefits of Bangladesh aiding in the minimization of energy losses and optimization of power system operations. To validate the reliability of the used models, another dataset from the East Kentucky Power Cooperative (EKPC) Ltd. in the USA has been analysed by all the used ML models where LightGBM model exhibits the superior performance as observed in the case of DPDC. This study contributes to advancing the utilization of machine learning models in load forecasting techniques and thus offering valuable insights for improving efficiency and accuracy in power system management.

Background: Cheilocostus speciosus (J. Koenig) C. Specht, commonly known as “Crepe‐ginger”, is a traditional plant with edible flowers utilized in folk medicine. This study employs crepe‐ginger flowers to evaluate their role in boosting liver immunity, hepatoprotective actions through oxidative stress management. Methods: Cheilocostus speciosus flower’s methanolic extract (CSF‐ME) was subjected to In‐vitro anti‐oxidant effects were evaluated using DPPH and ABTS and in‐vivo by catalase (CAT) assays which ameliorated CCl4‐induced hepatic injury evident by histopathological analysis. The chemical assay was evaluated via phytochemical screening and GC‐MS/MS analysis followed by in‐silico studies. Results: The antioxidant assay DPPH (IC50 =179.36 µg/ml) and ABTS (IC50 = 198.27 µg/ml) showed remarkable scavenging activity. Hepatotoxicity experiments demonstrated that CSF‐ME improved liver function by positively regulating AST, ALT, ALP, bilirubin, creatinine, LDL, CHO, TG, HDL, and catalase levels. Besides, histopathological analysis revealed normal hepatocyte integrity and microstructures after treatment. Besides, phytochemical screening revealed prospective phytochemical groups while GC‐MS/MS analysis recognized forty compounds resulting in auspicious outcomes employing computer‐aided studies. Conclusion: The findings indicated that the CSF‐ME possesses promising hepatoprotective, and antioxidant prospects which demand further extensive research to develop novel lead compounds from this natural source.

Fuel is a major regulator in the current global instability. It is safe to say that energy is the silent hero of the fourth industrial revolution. Because of its environmentally favorable function and cost-effective sustainability, biofuel can be an ideal alternative to fossil fuels. Microalgae-based biofuel is gaining popularity among scientists and entrepreneurs due to its high biomass yield. There are some growth factors like strain properties, light, temperature CO2, P H , nutrients (N, P, Mg, Mn, etc.), culture systems, etc. to increase biomass growth. In this present research, a growth model based on nitrogen intake is considered to investigate the effect on optimal biomass growth. The other kinematic parameters are taken from an experiment for our simulation. The local light intensity is taken into account for a geographical location. Microalgae culture shows a very significant growth in biomass concentration while nitrogen intake gradually decreases. The simulated results were also compared with a reference model and the experimental one and found a good agreement. The rate of biomass concentration within the first 100 hours in our present research work, using 1.575 gL-1 acetate and 0.0735gL-1 N, is revealing upper than the compared experimental results. The nitrogen consumption by the culture shows a similar pattern to the reference study.

Correct detection of plant diseases is critical for enhancing crop yield and quality. Conventional methods, such as visual inspection and microscopic analysis, are typically labor-intensive, subjective, and vulnerable to human error, making them infeasible for extensive monitoring. In this study, we propose a novel technique to detect tomato leaf diseases effectively and efficiently through a pipeline of four stages. First, image enhancement techniques deal with problems of illumination and noise to recover the visual details as clearly and accurately as possible. Subsequently, regions of interest (ROIs), containing possible symptoms of a disease, are accurately captured. The ROIs are then fed into K-means clustering, which can separate the leaf sections based on health and disease, allowing the diagnosis of multiple diseases. After that, a hybrid feature extraction approach taking advantage of three methods is proposed. A discrete wavelet transform (DWT) extracts hidden and abstract textures in the diseased zones by breaking down the pixel values of the images to various frequency ranges. Through spatial relation analysis of pixels, the gray level co-occurrence matrix (GLCM) is extremely valuable in delivering texture patterns in correlation with specific ailments. Principal component analysis (PCA) is a technique for dimensionality reduction, feature selection, and redundancy elimination. We collected 9014 samples from publicly available repositories; this dataset allows us to have a diverse and representative collection of tomato leaf images. The study addresses four main diseases: curl virus, bacterial spot, late blight, and Septoria spot. To rigorously evaluate the model, the dataset is split into 70%, 10%, and 20% as training, validation, and testing subsets, respectively. The proposed technique was able to achieve a fantastic accuracy of 99.97%, higher than current approaches. The high precision achieved emphasizes the promising implications of incorporating DWT, PCA, GLCM, and ANN techniques in an automated system for plant diseases, offering a powerful solution for farmers in managing crop health efficiently.

The shift to online learning during the COVID-19 pandemic presented significant challenges and opportunities, particularly in developing nations like Bangladesh, where digital infrastructure is limited. This study compares the effectiveness of online and offline learning for Bangladeshi engineering students using the Best Worst Method (BWM), a robust decision-making tool that simplifies the analysis by focusing on the best and worst criteria, ensuring consistency and accuracy. Eight key factors, including cost, flexibility, learning effectiveness, and technological challenges, were evaluated to identify the distinct strengths and limitations of each learning modality. The findings highlight that online learning is favored for its affordability and adaptability, with cost (25.74%) and flexibility (19.38%) emerging as the highest-priority criteria. In contrast, offline learning is valued for its hands-on practicality and structured environments, where technological challenges (19.25%) and concentration (18.47%) ranked as the most critical factors. Sensitivity analysis confirms the robustness of these rankings, reinforcing the reliability of the results. This research uniquely applies BWM to a resource-constrained educational context, addressing gaps in the literature. Its findings have broader implications for educational policy and resource allocation, providing actionable insights for designing blended learning strategies. By integrating the flexibility of online platforms with the immersive, practical benefits of offline learning, this study proposes a scalable framework for improving learning outcomes in Bangladesh and similar developing regions.

This study synthesized eighteen phenyl and furan rings containing thiazole Schiff base derivatives 2(a–r) in five series, and spectral analyses confirmed their structures. The in vitro antibacterial activities of the synthesized analogs against two gram-positive and two gram-negative bacteria were evaluated by disk diffusion technique. Compounds (2d) and (2n) produced prominently high zone of inhibition with 48.3 ± 0.6 mm and 45.3 ± 0.6 mm against B. subtilis, respectively, compared to standard ceftriaxone (20.0 ± 1.0 mm). However, the antibacterial potency of the compounds with furan ring was more notable than that of phenyl ring-containing derivatives. Molecular docking and dynamic study were performed based on the wet lab outcomes of (2d) and (2n), where both derivatives remained in the binding site of the receptors during the whole simulation time with RMSD and RMSF values below 2 nm. In silico ADMET prediction studies of the synthesized compounds validated their oral bioavailability. A more detailed study of the quantitative structure-activity relationship is required to predict structural modification on bioactivity and MD simulation to understand their therapeutic potential and pharmacokinetics.

Single-use plastics are a major environmental concern in developing countries like Bangladesh due to their non-biodegradable nature. Finding sustainable alternatives is crucial to reduce reliance on these harmful plastics and mitigate pollution. This study aims to explore the public opinions on plastic pollution and investigate the challenges and potential for substituting plastics with jute. The study also provides essential recommendations for addressing these challenges and fostering the successful substitution of plastics with jute-based alternatives. A thorough social study was carried out in two major cities of Bangladesh, involving 212 participants through face-to-face questionnaire surveys. The selected participants represented diverse demographics in terms of age, gender, occupation, and education level. The findings reveal broad support for plastic recyclability, with many participants favoring jute and paper bags as alternatives to plastics. However, most individuals show little concern for reusing plastic products. Moreover, more than half of the total participants, spanning various demographics, have been exposed to plastic waste reduction campaigns. Furthermore, two-thirds of participants from diverse age groups, occupations, education levels, and genders support the introduction of higher pricing, such as additional tax, as measures to reduce plastic pollution. The correlation and principal component analysis (PCA) plot reveal clustering patterns aligning plastic recycling, extra charges on plastic, and the availability of jute products with socio-demographic variables. Despite favorable views on jute, participants highlight high prices and limited availability as major barriers to adopting jute alternatives. Most of the participants call for additional support to the jute sector, with consensus favoring increased subsidies from the Government of Bangladesh and recognition of the significance of investing in research.