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In this research, we introduce a comprehensive epidemiological model that accounts for multiple strains of an infectious disease and two distinct vaccination options. Vaccination stands out as the most effective means to prevent and manage infectious diseases. However, when there are various vaccines available, each with its costs and effectiveness, the decision-making process for individuals becomes paramount. Furthermore, the factor of waning immunity following vaccination also plays a significant role in influencing these choices. To understand how individuals make decisions in the context of multiple strains and waning immunity, we employ a behavioral model, allowing an epidemiological model to be coupled with the dynamics of a decision-making process. Individuals base their choice of vaccination on factors such as the total number of infected individuals and the cost-effectiveness of the vaccine. Our findings indicate that as waning immunity increases, people tend to prioritize vaccines with higher costs and greater efficacy. Moreover, when more contagious strains are present, the equilibrium in vaccine adoption is reached more rapidly. Finally, we delve into the social dilemma inherent in our model by quantifying the social efficiency deficit (SED) under various parameter combinations.

The heat transfer by natural convection of a nanofluid, which is ethylene glycol−Al2O3 has been analyzed in an open cavity numerically using the multiple-relaxation-time - lattice Boltzmann method by the graphics processing unit high-performance parallel computing. The right side of the cavity is open, and different boundary conditions have been applied to all the walls. Besides, one adiabatic fin has been installed on each side of the enclosure’s top and bottom sides. Here, the Prandtl number is fixed at 16.6, and the Rayleigh number changes from 104−106 with the nanoparticle volume fraction from 0%−5% has been used for numerical simulations. Besides, in this work, the power-law index is an important parameter as well, and 0.7, 0.8, 1, 1.2, and 1.4 are the values of this parameter. Results are presented concerning both the average and local Nusselt numbers in the form of streamlines, isotherms, temperature distributions, velocity distributions, heat transfer rate, and entropy production. It is observed when increases, average Nusselt number increases 607.94%, and for this reason, the overall heat transfer rate rises because of buoyancy force. In addition, the average Nusselt number falls by 83.28% when the power-law index rises; as a result, the total heat transfer rate falls because fluid viscosity increases with the power-law index. It is also observed that for shear-thickening fluids, the temperature gradient is higher. On the contrary, the temperature started decreasing with the increase of the power-law index. Additionally, the local Nusselt number value rises as power-law index falls. Moreover, the heat transfer rate increases by 7.08% when volume fraction increases. The intensity of buoyancy force reduces with the increase of volume fraction. Besides, the overall entropy generation rises when the Rayleigh number and the volume fraction increase, but it decreases when the power-law index increases. So, when the Rayleigh number is 106, the power-law index is 0.7, and the volume fraction is 0.00 then the entropy generation is the highest. This current research has many applications for example heat exchangers, electronic cooling equipment, solar heating systems, aerospace applications, medical devices, and entropy generation-related systems.

Abstract Antimicrobials are molecules that prevent the formation of microorganisms such as bacteria, viruses, fungi, and parasites. The necessity to detect antimicrobial peptides (AMPs) using machine learning and deep learning arises from the need for efficiency to accelerate the discovery of AMPs, and contribute to developing effective antimicrobial therapies, especially in the face of increasing antibiotic resistance. This study introduced AMP-RNNpro based on Recurrent Neural Network (RNN), an innovative model for detecting AMPs, which was designed with eight feature encoding methods that are selected according to four criteria: amino acid compositional, grouped amino acid compositional, autocorrelation, and pseudo-amino acid compositional to represent the protein sequences for efficient identification of AMPs. In our framework, two-stage predictions have been conducted. Initially, this study analyzed 33 models on these feature extractions. Then, we selected the best six models from these models using rigorous performance metrics. In the second stage, probabilistic features have been generated from the selected six models in each feature encoding and they are aggregated to be fed into our final meta-model called AMP-RNNpro. This study also introduced 20 features with SHAP, which are crucial in the drug development fields, where we discover AAC, ASDC, and CKSAAGP features are highly impactful for detection and drug discovery. Our proposed framework, AMP-RNNpro excels in the identification of novel Amps with 97.15% accuracy, 96.48% sensitivity, and 97.87% specificity. We built a user-friendly website for demonstrating the accurate prediction of AMPs based on the proposed approach which can be accessed at http://13.126.159.30/ .

Infectious fungi have been an increasing global concern in the present era. A promising approach to tackle this pressing concern involves utilizing Antifungal peptides (AFP) to develop an antifungal drug that can selectively eliminate fungal pathogens from a host with minimal toxicity to the host. Accordingly, identifying precise therapeutic antifungal peptides is crucial for developing effective drugs and treatments. This study proposed MLAFP-XN, a neural network-based strategy for accurately detecting active AFP in sequencing data to achieve this objective. In this work, eight feature extraction techniques and the XGB feature selection strategy are utilized together to present an enhanced methodology. A total of 24 classification models were evaluated, and the most effective four have been selected. Each of these models demonstrated superior accuracy on independent test sets, with respective scores of 97.93 %, 99.47 %, and 99.48 %. Our model outperforms current state of the art methods. In addition, we created a companion website to demonstrate our AFP recognition process and use SHAP to identify the most influential properties.

This article investigates the convective thermal and solutal exchange from the active walls of a trapezium chamber which is filled with multi-walled carbon nanotubes (MWCNT)-silicon dioxide (SiO2)-ethylene glycol-water hybrid nano-coolant. The hybrid nano-coolant exhibits non-Newtonian shear-thinning rheology and is modeled by the power-law viscosity as per an exploratory report. The convection is generated by both the thermal and solutal buoyancy forces in the presence of a magnetic field. Thermophysical properties of the particular nano-coolants are estimated from the temperature-dependent empirical correlation. An in-house FORTRAN code based on the finite volume method (FVM) has been utilized to simulate the non-dimensional controlling equations representing the physical model. By systematically varying the strength of the magnetic field (Ha=0−50) and its direction (δ1=0−90), the volume fraction of nano-coolant (ϕ=0−0.04) and solutal-to-thermal buoyancy ratio (N=−2 to 2), we thoroughly analyzed their impact on the flow field, thermal, and solutal transmission behavior. Significant impacts arising from the shear-thinning nature (n

Quorum Sensing Peptides (QSP) are small molecules crucial for microbial communication, enabling bacterial populations to coordinate behaviors such as biofilm formation and virulence. The identification of QSP is vital for understanding these biological processes. While existing clinical and lab-based methods are available, they can be costly and time-consuming. This study introduces DeepQSP, a novel technique for QSP identification, which combines Latent Semantic Analysis (LSA), a word embedding feature extraction method, with classical amino acid-based extraction Pseudo Amino Acid Composition (PAAC), and a convolutional neural network (CNN) classifier. The DeepQSP model was evaluated using a dataset of 440 peptide sequences, achieving impressive performance metrics: 0.9697 accuracy, 0.9655 sensitivity, 0.9730 specificity, and a Matthews correlation coefficient (MCC) of 0.9385. The LSA combined with PAAC improves peptide sequence representation, while the CNN effectively captures complex patterns, leading to accurate QSP identification. These quantified results demonstrate the effectiveness of the DeepQSP method, offering a powerful tool for advancing the study of microbial interaction and quorum sensing. The enhanced identification of QSPs is critical for microbiology and bioengineering, aiding in the understanding of cell-to-cell communication in microorganisms.

This study employs finite difference modelling to investigate the natural convection of a two-phase hybrid nanofluid consisting of Al[Formula: see text]O[Formula: see text] and Cu nanoparticles dispersed in water along a vertically heated wavy surface. The hybrid nanofluid has unique features that influence convective heat transfer. Numerical solutions analyze fluid flow patterns and temperature distributions, providing insights into heat exchange and thermal management. The research highlights the importance of hybrid nanofluids, notably those containing Al[Formula: see text]O[Formula: see text] and Cu in a water base, in improving overall heat transfer efficiency. The mathematical model accounts for laminar and incompressible fluid flow with a Prandtl number of Pr = 6.2, Lewis number Le = 10, and maximum [Formula: see text] concentration of hybrid nanoparticles. After transforming the governing equations into a non-dimensional form, the implicit finite difference method is used to solve them. The solution employs the in-house FORTRAN 90 code and compares its outcomes with the benchmark results. Various parameters, such as the Schmidt number (Sc = 1 to 10), volume fraction of nanoparticles ([Formula: see text] to 0.1), wavy amplitude (A = 0.0 to 0.3), and [Formula: see text] = (0.05 to 0.2), are investigated regarding temperature, velocity, local skin friction coefficient [Formula: see text], local Nusselt number (Nu), streamlines, and isotherms. Elevated volume fraction generally decreases skin friction, increases temperature, and may reduce velocity. Meanwhile, higher skin friction, temperature, velocity, and local Nusselt numbers often correlate with elevated Schmidt numbers. Changes in amplitude and [Formula: see text] affect skin friction, temperature, velocity, and local Nusselt numbers, providing information on the dynamics of fluid systems. For example, when the volume fraction (ϕ) increases from 0 to 0.1 at Y = 2, the temperature rises by [Formula: see text], while the velocity gradually decreases by [Formula: see text]. This is in contrast to findings according to when the [Formula: see text] rises from 0.05 to 0.2 at Y = 1. In this case, the temperature drops by [Formula: see text], followed by a [Formula: see text] fall in fluid velocity. Understanding these interactions enhances comprehension of heat transfer and fluid dynamics. By investigating these interactions, we not only improve our understanding of heat transfer and fluid dynamics, but also open the door to new strategies for improving thermal systems and manufacturing procedures.

This numerical analysis investigates the behavior of magnetohydrodynamics (MHD) thermosolutal convection in a hybrid nanofluid made up of multi-wall carbon nanotube (MWCNT)-Fe3O4 and water along a vertical wavy surface with wall heat and mass flux conditions. The mathematical model is developed using the conservation equations of mass, momentum, energy, and energy. The governing equations are converted into a non-dimensional form using the appropriate transformation, and the implicit finite difference technique is used to solve them. The effects of various parameters, such as the Schmidt number (Sc), nanoparticles volume fraction (ϕ), wavy amplitude (a), magnetic parameter (M), buoyancy ratio (Br), on the velocity, temperature, concentration distribution (Φ), streamlines, isotherms, local skin friction coefficient (Cf), local Nusselt (Nu) number, local Sherwood number (Sh) are investigated. Results show that higher amplitude and the addition of hybrid nanofluids volume fraction slow down the heat and mass transport rate, as indicated by declining local Nusselt and Sherwood numbers. However, enhancing the buoyancy ratio increases the fluid flow’s skin friction, heat, and mass transport. The velocity, temperature, concentration distribution, skin friction, and convective heat transport within the wavy surface is weakened by the addition of the magnetic parameter M.

Abstract The emergence of mobile edge computing (MEC) has brought cloud services to nearby edge servers facilitating penetration of real-time and resource-consuming applications from smart mobile devices at a high rate. The problem of task offloading from mobile devices to the edge servers has been addressed in the state-of-the-art works by introducing collaboration among the MEC servers. However, their contributions are either limited by minimization of service latency or cost reduction. In this paper, we address the problem by developing a multi-objective optimization framework that jointly optimizes the latency, energy consumption, and resource usage cost. The formulated problem is proven to be an NP-hard one. Thus, we develop an evolutionary meta-heuristic solution for the offloading problem, namely WOLVERINE, based on a Binary Multi-objective Grey Wolf Optimization algorithm that achieves a feasible solution within polynomial time having computational complexity of $$O(M^3)$$ O ( M 3 ) , where M is an integer that determines the number of segments in each dimension of the objective space. Our experimental results depict that the developed WOLVERINE system achieves as high as 33.33%, 35%, and 40% performance improvements in terms of execution latency, energy, and resource cost, respectively compared to the state-of-the-art.

The cross-diffusion via Soret and Dufour impacts on natural convection in an L-shaped enclosure full of multi-walled carbon nanotubes (MWCNTs)-H2O (water) nanofluids has been studied numerically utilizing the multiple-relaxation-time (MRT) lattice Boltzmann method (LBM) through graphics processing unit (GPU) computing. The indented part of the L-shape is cold (Tc), while the left and bottom walls of the chamber maintain a heated (Th) temperature. The adiabatic boundary condition is present on the cavity’s remaining walls. The thermal conductivity and viscosity of the nanofluids are the function of fluid temperature, and the corresponding correlation was derived from the experimental data. The current methodology and the experimental data based (MWCNTs)-H2O (water) nanofluids outcomes are wholly noble. Numerical simulations have been done for numeroust relevant pertinent such as Rayleigh number, 3×103≤Ra≤3×105, buoyancy ratio, −2≤Br≤2, volume fraction, 0.0≤ϕ≤0.01, Soret number, 0.0≤Sr≤0.2, Dufour number, 0.0≤Df≤0.2, while Prandtl number, Pr=6.2, and Lewis number, Le=2 are fixed. The results are presented regarding the streamlines, isotherms, isoconcentration, velocity and temperature distribution, local Nusselt and average Nusselt number, local Sherwood and average Sherwood number, and local and total entropy production. It is discovered that with growing Rayleigh numbers, volume fractions, and Soret numbers, the Nu¯ and the Sh¯ rise. For various Rayleigh numbers Ra = (3×103, 3×104, 3×105), the Nu¯ grew by 29.95%, 19.86%, and 1.77% respectively when ϕ increased from 0.0 to 0.01. The Nu¯ graph reaches its maximum for Ra=3×105. The streamlines grow as Ra enhances. In addition, when the volume fraction rises from 0.0 to 0.02, the local Nu rises because of more potent thermal conductivity, but the local Sh falls. The highest entropy formation is found by Ra=(3×105), and the general rate of entropy formation improves with enlarged Rayleigh numbers while the Bejan number stays constant. Finally, a response surface analysis has been done, and the output closely matches the original simulation results.

The emergence of a novel strain during a pandemic, like the current COVID-19, is a major concern to the healthcare system. The most effective strategy to control this type of pandemic is vaccination. Many previous studies suggest that the existing vaccine may not be fully effective against the new strain. Additionally, the new strain's late arrival has a significant impact on the disease dynamics and vaccine coverage. Focusing on these issues, this study presents a two-strain epidemic model in which the new strain appears with a time delay. We considered two vaccination provisions, namely preinfection and postinfection vaccinations, which are governed by human behavioral dynamics. In such a framework, individuals have the option to commit vaccination before being infected with the first strain. Additionally, people who forgo vaccination and become infected with the first train have the chance to be vaccinated (after recovery) in an attempt to avoid infection from the second strain. However, a second strain can infect vaccinated and unvaccinated individuals. People may have additional opportunities to be vaccinated and to protect themselves from the second strain due to the time delay. Considering the cost of the vaccine, the severity of the new strain, and the vaccine's effectiveness, our results indicated that delaying the second strain decreases the peak size of the infected individuals. Finally, by estimating the social efficiency deficit, we discovered that the social dilemma for receiving immunization decreases with the delay in the arrival of the second strain.

In this study, an epidemiological model with the provaccination and antivaccination susceptible groups is proposed, and the social dilemma of the model is analyzed. During a pandemic, such as the current COVID-19, many individuals get confused to choose the option of adopting a provaccination or antivaccination strategy based on the number of infected people and the payoff of being infected. In the proposed model, people can obtain immunity either through vaccination or by getting infected with the disease which is known as natural immunity. In addition, increasing the waning immunity influences the choice of adopting the provaccination or antivaccination strategy. We used the behavior model to analyze the choice of the two strategies, where any individual can choose a strategy based on the number of infected individuals from each group. Moreover, individuals who are already infected can choose their strategy based on the payoff of their disease cost or vaccination cost. Our results show that, at Nash equilibrium, individuals in both groups behave the same. Further, from our numerical results, increasing the number of vaccinations can reduce the social dilemma whereas an increase in the waning immunity rate increases the social dilemma.

Background and Objective. Motorbikes are a popular mode of transportation in developing nations, especially in places where public transportation is not easily accessible. This research aims to investigate the prevalence of road traffic crashes (RTCs) among motorcyclists in Noakhali, Bangladesh, and to identify the factors that play a role in these crashes. Methods. A cross-sectional survey was conducted using a structured questionnaire involving 402 motorcyclists from four major southeastern towns, comprising 350 (86.07%) males and 52 (12.93%) females. The chi-square test was applied in bivariate analysis, and binary multivariable logistic regression was performed to determine the risk factors of road traffic crashes. Results. This study’s findings revealed that the overall reported prevalence of road traffic crashes involving motorcycle drivers over one year was 68.66%. Multivariable logistic regression analysis revealed several factors that significantly impacted road traffic crashes. These factors included driving without a valid driving license, the young age (

This research looked at a viscoelastic Bingham hybrid Al2O3-Ag/water nanofluid’s natural convection in an inverted-T-shaped enclosure. It has been done using the mesoscopic simulation via the multiple-relaxation-time (MRT)-lattice Boltzmann method (LBM). And it employed highly effective graphics processing unit (GPU) computing to proceed with the investigation. The hybrid nanofluid’s viscosity is determined by the size of the nanoparticles and the velocity gradient, and the thermal conductivity is the function of fluid temperature. A uniform temperature boundary condition is used to heat the middle of the enclosure’s bottom wall. Other portions of the bottom and top walls are adiabatic, where the right and left walls are kept cold. Different parameters, namely the Bingham number (0≤Bn≤1), Rayleigh number (Ra=104, 105, 106), and nanoparticles volume fraction (ϕ=0,0.02,0.04), and heated sources’ size (ϵ=1/5,2/5,3/5,4/5) are applied to investigate and observe the natural convection of the fluid. Numerical outcomes are attained from the simulation and used to generate contours, figures, graphs of velocity temperature, average Nusselt number, local Nusselt number, isotherms, and streamlines. Based on the outcomes, it is observed that enhancements in Rayleigh number, heated sources’ size (ϵ), and volume fraction (ϕ) speed up the flow of the fluid. On the other hand, the Bingham number’s increment slows down the flow and decreases the value of Nu¯. The average Nusselt number increases 170.25% while increasing the length of bottom heater from ϵ=1/5 to ϵ=4/5 at ϕ=0.02, Bn=1, and Ra=106. This configuration can be applicable to electrical system cooling, ventilation, heat-exchanging system and in many other practical implements.

This paper focuses on the natural convection of heat transfer using magnetohydrodynamic (MHD) Bingham nanofluid. Utilizing the multiple-relaxation-time (MRT) lattice Boltzmann method (LBM) within a C-shaped enclosure and the NVIDIA graphics processing unit (GPU)-based compute unified architecture (CUDA) C/C++ platform, the simulation is carried out numerically. Inside the cavity, the base fluid is water and the nanofluid is Al2O3. Boundary conditions are presented in accordance with the heated, cold, and adiabatic conditions present in the cavity's various walls. Several parameters including Bingham number (Bn=0,0.5,1,1.5,2), Rayleigh number (Ra=104,105,106), Hartmann number (Ha=0,10,20,30), and nanoparticle volume fraction (ϕ=0,0.01,0.02,0.03,0.04). The results of the numerical simulation are shown using streamlines and isotherms, velocity-temperature, Local Nusselt number, and average Nusselt number. From the obtained results it is found that for the variation of Bn, Ha, and ϕ with different Ra the rate of heat transfer decreases along the bottom wall and increases for the left and top walls. The average Nusselt number decreases while Bn, Ha rises. On the other contrary, the average Nu increases as ϕ increases. Response surface methodology (RSM) is added here to have better understanding of the effects of parameter used. RSM includes statistical table for the combination of data set and their graphs to understand the accuracy. Moreover, regression analysis shows how average Nu increases or decreases with the variation of different parameters. The C-shaped geometry provides an excellent option in heat exchanging or electronic cooling equipment the chip designing technology. This study is only for two-dimensional laminar flow.

Menopause is an obligatory phenomenon in a woman’s life. Some women face mental and physical issues during their menopausal period. Depression is one of the issues some women struggle with during their menopausal period. The scarcity of specialists, lack of knowledge, and awareness is the motivating factor in this research to predict depression among menopausal women and enhance their quality of life. The prediction of depression symptoms among menopausal women with machine learning techniques is promising and challenging in artificial intelligence. This study develops a system with significant accuracy using a supervised machine-learning approach. Various classification algorithms are used to determine the best-performing classifier by evaluating multiple parameters, including accuracy, sensitivity, specificity, precision, recall, F-Measure, Receiver Operating Characteristic (ROC), Precision–Recall​ Curve (PRC), and Area Under the Curve (AUC). We found that Random Forest and XGBoost classifiers are the performers with 99.04% accuracy employing the 14 most significant features.

In this study, we propose machine learning (ML) for risk factors analysis and survival prediction of Heart Failure (HF) patients using a survival dataset. Five supervised ML methods are applied to the dataset: Decision Tree (DT), Decision Tree Regressor (DTR), Random Forest (RF), XGBoost, and Gradient Boosting (GB) algorithms. We compare the applied algorithms’ performances based on accuracy, precision, recall, F-measure, and log loss value and show RF provides the highest accuracy of 97.78%. The analysis of the risk factors shows the most predictive features based on coefficients and feature importance. The top six risk factors for HF patients are serum creatinine (SC), age, ejection fraction (EF), platelets, creatinine phosphokinase (CPK), and SS (SS). Further analysis of these factors shows significant clustering of the features. The survival analysis finds that the increment of SC, age, and SS and the decrement of EF are the most significant risk factors for HF patients. Our results suggest that HF survival prediction is possible with higher accuracy using the proposed model. Our ML models are useful in clinical settings for screening patients with HF probability.

The generalized Hirota-Satsuma coupled KdV system with fractional-order derivative plays a significant role to simulate the interaction of nearby identical-weight particles in a crystal lattice structure, two long waves interaction with different dispersion relationships, the description of shallow-water wave propagation, ion-acoustic waves, plasma physics and some other fields. In this study, diverse analytical wave solutions in general and standard structures are established of the stated equation by introducing two viable techniques, namely, the generalized Kudryashov scheme and the two variables G'/G,1/G-expansion approach. The solutions are established in terms of elementary functions, specifically trigonometric functions, rational functions, exponential functions, and hyperbolic functions. For definite values of free parameters, the obtained analytical wave solutions transform into solitary wave solutions. The graphical patterns of the wave solutions with there-, two-, and contour plots are depicted magnificently to elucidate the internal structure of the phenomenon. The methods contribute as powerful mathematical tools and appear to be further effective, computerized, and user-friendly to investigate nonlinear fractional equations as well as comprehensive analytical solutions of nonlinear evolution equations in engineering, technology, and mathematical physics.

Background: Abdominal aortic aneurysms (AAAs) present a formidable public health concern due to their propensity for localized, anomalous expansion of the abdominal aorta. These insidious dilations, often in their early stages, mask the life-threatening potential for rupture, which carries a grave prognosis. Understanding the hemodynamic intricacies governing AAAs is paramount for predicting aneurysmal growth and the imminent risk of rupture. Objective: Our extensive investigation delves into this complex hemodynamic environment intrinsic to AAAs, utilizing comprehensive numerical analyses of the physiological pulsatile blood flow and realistic boundary conditions to explore the multifaceted dynamics influencing aneurysm rupture risk. Our study introduces novel elements by integrating these parameters into the overall context of aneurysm pathophysiology, thus advancing our understanding of the intricate mechanics governing their evolution and rupture. Methods: Conservation of mass and momentum equations are used to model the blood flow in an AAAs, and these equations are solved using a finite volume-based ANSYS Fluent solver. Resistance pressure outlets following a three-element Windkessel model were imposed at each outlet to accurately model the blood flow and the AAAs’ shear stress. Results: Our results uncover elevated blood flow velocities within an aneurysm, suggesting an augmented risk of future rupture due to increased stress in the aneurysm wall. During the systole phase, high wall shear stress (WSS) was observed, typically associated with a lower risk of rupture, while a low oscillatory shear index (OSI) was noted, correlating with a decreased risk of aneurysm expansion. Conversely, during the diastole phase, low WSS and a high OSI were identified, potentially weakening the aneurysm wall, thereby promoting expansion and rupture. Conclusion: Our study underscores the indispensable role of computational fluid dynamic (CFD) techniques in the diagnostic, therapeutic, and monitoring realms of AAAs. This body of research significantly advances our understanding of aneurysm pathophysiology, thus offering pivotal insights into the intricate mechanics underpinning their progression and rupture, informing clinical interventions and enhancing patient care.

A numerical study on the forced convection of water-Cu power-law non-Newtonian nanofluid in a wavy channel has been done. The temperature of the horizontal walls is larger than that of the input fluid. The governing equations are solved by the SIMPLE algorithm-based finite volume method. The effects of pertinent parameters power-law index, Reynolds number, and nanoparticle volume fraction have been varied. The obtained results are presented in terms of the local and average Nusselt number (Nu) for the rate of heat transfer, streamlines, and isotherms. The correlation for the average Nusselt number is obtained using the Response Surface Analysis (RSM) for the flow parameters. The relationship between input parameters and output was observed by plotting the response surface and contours that were obtained by RSM. The sensitivity of the output response to all the input parameters is also observed. This study concludes that heat transfer enhanced in wavy channels by adding nanoparticles and increasing the Reynolds number. It is also observed that shear-thinning non-Newtonian nanofluids can be more effective in heat transfer enhancement than shear-thickening fluids.

Navigation and traffic services such as Google Maps, Bing Maps, and Apple Maps have become increasingly popular for their ability to calculate the shortest path, provide real-time traffic updates, recommend nearby points of interest, and suggest multi-modal route options based on user constraints. However, while these services offer convenience and efficiency, they may not always prioritize user safety. In response to this concern, recent research have begun to address safety issues in navigation and traffic services. To the best of our knowledge, none of these are capable of adapting to dynamic, conflicting safety features and real-time user feedback. A recent algorithm called SPaFE has been introduced to incorporate crowd-sourced and historical data, but it does not prioritize the most recent feedback or consider updated crime reports. It also does not account for distance and performs poorly in areas with insignificant or zero feedback. In light of the preceding, we introduce CrowdSPaFE, a population-based algorithm that adapts to dynamic crime reports, the most recent feedback, navigation in locations with negligible feedback, and a tradeoff between distance and safety considerations. Lastly, our empirical results demonstrate that the CrowdSPaFE algorithm outperforms the state-of-the-art.

Energy Harvesting technology contributes significantly to green cellular networking by ensuring self-sustainability and extinguishing environmental hazards. Due to the imbalance between the harvested energy and traffic load of the base stations (BSs), energy cooperation has become a crucial requirement. However, the decision of optimal energy cooperation among the BSs in a multi-operator cellular network is a challenging task due to the consideration of various factors, such as cost, loss of energy, future information of traffic load, and harvested energy of the BSs, etc. The two conflicting objectives are minimizing the energy buying cost and the loss of energy while transferring through the power links. In this work, we present an optimal energy cooperation framework, formulated as a multi-objective linear programming (MOLP) problem which brings a trade-off between the two above-mentioned conflicting objectives considering the harvested energy and load of the BSs at future time slots. For the prediction of harvested energy of the BSs, we develop a Deep Q-Learning-based prediction method that intelligently increases measurement accuracy through continuous exploration and exploitation. The results of simulation experiments carried out in MATLAB depict that the proposed multi-operator energy cooperation framework outperforms state-of-the-art works in terms of cost, performance, and energy-loss reduction.

The loss and degradation of habitat, Allee effects, climate change, deforestation, hunting-overfishing and human disturbances are alarming and significant threats to the extinction of many species in ecology. When populations compete for natural resources, food supply and habitat, survival to extinction and various other issues are visible. This paper investigates the competition of two species in a heterogeneous environment that are subject to the effect of harvesting. The most realistic harvesting case is connected with the intrinsic growth rate, and the harvesting functions are developed based on this clause instead of random choice. We prove the existence and uniqueness of the solution to the model. Theoretically, we state that, when species coexist, one may drive the other to die out, so both species become extinct, considering all possible rational values of parameters. These results highlight a worthy-of attention study between two populations based on harvesting coefficients. Finally, we solve the model for two spatial dimensions by using a backward Euler, decoupled and linearized time-stepping fully discrete algorithm in a series of examples and observe a match between the theoretical and numerical findings.

The numerical investigation focuses on the phenomenon of the power-law non-Newtonian magnetohydrodynamic (MHD) natural convection heat transfer in a C-shaped cavity that is filled with ethylene glycol-Al2O3 and Cu hybrid nanofluids with a heating block by utilizing the MRT (multiple-relaxation-time) lattice Boltzmann method (LBM). The heating block is maintained in a constant heat flux condition and placed in the middle of the right wall, and the top two right sides are considered as lower temperature conditions. The other walls of the cavity are in the adiabatic boundary condition. Hydro-thermal analysis of nanofluids is performed for different Rayleigh numbers (Ra), power-law index (n), Hartmann numbers (Ha), magnetic field inclination angle (γ), and nanoparticle volume fraction (ϕ). Results obtained from the numerical simulation are presented by plotting streamlines and isotherms, average Nusselt numbers, and velocity temperature. The results found that when the Hartmann number increases average rate of heat transfer decreases. In addition, the average heat transfer rate decreases for the higher value of the power-law index. For nanofluids like a hybrid, Cu, and Al2O3 nanofluids, the average Nusselt number decreases with the increasing values of the power-law index and Ha number. Here heat transfer is higher in Cu than in hybrid and Al2O3 nanofluids. Total entropy is in increasing band decreasing condition due to higher Rayleigh number. Total entropy decreases when the power-law index rises, and for Ra=104,106.

Skin cancer poses a significant healthcare challenge, requiring precise and prompt diagnosis for effective treatment. While recent advances in deep learning have dramatically improved medical image analysis, including skin cancer classification, ensemble methods offer a pathway for further enhancing diagnostic accuracy. This study introduces a cutting-edge approach employing the Max Voting Ensemble Technique for robust skin cancer classification on ISIC 2018: Task 1-2 dataset. We incorporate a range of cutting-edge, pre-trained deep neural networks, including MobileNetV2, AlexNet, VGG16, ResNet50, DenseNet201, DenseNet121, InceptionV3, ResNet50V2, InceptionResNetV2, and Xception. These models have been extensively trained on skin cancer datasets, achieving individual accuracies ranging from 77.20% to 91.90%. Our method leverages the synergistic capabilities of these models by combining their complementary features to elevate classification performance further. In our approach, input images undergo preprocessing for model compatibility. The ensemble integrates the pre-trained models with their architectures and weights preserved. For each skin lesion image under examination, every model produces a prediction. These are subsequently aggregated using the max voting ensemble technique to yield the final classification, with the majority-voted class serving as the conclusive prediction. Through comprehensive testing on a diverse dataset, our ensemble outperformed individual models, attaining an accuracy of 93.18% and an AUC score of 0.9320, thus demonstrating superior diagnostic reliability and accuracy. We evaluated the effectiveness of our proposed method on the HAM10000 dataset to ensure its generalizability. Our ensemble method delivers a robust, reliable, and effective tool for the classification of skin cancer. By utilizing the power of advanced deep neural networks, we aim to assist healthcare professionals in achieving timely and accurate diagnoses, ultimately reducing mortality rates and enhancing patient outcomes.

Growing energy demand has exacerbated the issue of energy security and caused us to necessitate the utilization of renewable resources. The best alternative for promoting generation in Bangladesh from renewable energy is solar photovoltaic technology. Grid-connected solar photovoltaic (PV) systems are becoming increasingly popular, considering solar potential and the recent cost of PV modules. This study proposes a grid-connected solar PV system with a net metering strategy using the Hybrid Optimization of Multiple Electric Renewables model. The HOMER model is used to evaluate raw data, to create a demand cycle using data from load surveys, and to find the best cost-effective configuration. A sensitivity analysis was also conducted to assess the impact of differences in radiation from the solar (4, 4.59, 4.65, 5 kWh/m2/day), PV capacity (0 kW, 100 kW, 200 kW, 300 kW, 350 kW, 400 kW, 420 kW), and grid prices (＄0.107, ＄0.118, ＄0.14 per kWh) upon that optimum configuration. Outcomes reveal that combining 420 kW of PV with a 405-kW converter and connecting to the utility grid is the least expensive and ecologically healthy configuration of the system. The electricity generation cost is estimated to be 0.0725 dollars per kilowatt-hour, and the net present value is 1.83 million dollars with a payback period of 6.4 years based on the system's 20-year lifespan. Also, compared to the existing grid and diesel-generator system, the optimized system, with a renewable fraction of 31.10%, provides a reduction in carbon dioxide emissions of 191 tons and 1,028 tons, respectively, each year.

This article reports an investigation of the Soret and Dufour effects on the double-diffusive natural convection of Al2O3-H2O nanofluids in a U-shaped porous enclosure. Numerical problems were resolved using the multiple-relaxation-time (MRT) lattice Boltzmann method (LBM). The indented part of the U-shape was cold, and the right and left walls were heated, while the bottom and upper walls were adiabatic. The experimental data-based temperature and nanoparticle size-dependent correlations for the Al2O3-water nanofluids are used here. The benchmark results thoroughly validate the graphics process unit (GPU) based in-house compute unified device architecture (CUDA) C/C++ code. Numeral simulations were performed for a variety of dimensionless variables, including the Rayleigh number, (Ra = 104,105,106), the Darcy number, (Da = 10−2,10−3,10−4), the Soret number, (Sr = 0.0,0.1,0.2), the Dufour number, (Df = 0.0,0.1,0.2), the buoyancy ratio, (−2≤Br≤2), the Lewis number, (Le = 1,3,5), the volume fraction, (0≤ϕ≤0.04), and the porosity, ϵ = (0.2−0.8), and the Prandtl number, Pr = 6.2 (water) is fixed to represent the base fluid. The numerical results are presented in terms of streamlines, isotherms, isoconcentrations, temperature, velocity, mean Nusselt number, mean Sherwood number, entropy generation, and statistical analysis using a response surface methodology (RSM). The investigation found that fluid mobility was enhanced as the Ra number and buoyancy force increased. The isoconcentrations and isotherm density close to the heated wall increased when the buoyancy force shifted from a negative magnitude to a positive one. The local Nu increased as the Rayleigh number increased but reduced as the volume fraction augmented. Furthermore, the mean Nu (Nu¯) decreased by 3.12% and 6.81% and the Sh¯ increased by 83.17% and 117.91% with rising Lewis number for (Ra=105 and Da=10−3) and (Ra=106 and Da=10−4), respectively. Finally, the Br and Sr demonstrated positive sensitivity, and the Ra and ϕ showed negative sensitivity only for higher values of ϕ based on the RSM.

Diabetes is one of the fatal diseases that play a vital role in the growth of other diseases in the human body. From a clinical perspective, the most significant approach to mitigating the effects of diabetes is early-stage control and management, with the aim of a potential cure. However, lack of awareness and expensive clinical tests are the primary reasons why clinical diagnosis and preventive measures are neglected in lower-income countries like Bangladesh, Pakistan, and India. From this perspective, this study aims to build an automated machine learning (ML) model, which will predict diabetes at an early stage using socio-demographic characteristics rather than clinical attributes, due to the fact that clinical features are not always accessible to all people from lower-income countries. To find the best fit of the supervised ML classifier of the model, we applied six classification algorithms and found that RF outperformed with an accuracy of 99.36%. In addition, the most significant risk factors were found based on the SHAP value by all the applied classifiers. This study reveals that polyuria, polydipsia, and delayed healing are the most significant risk factors for developing diabetes. The findings indicate that the proposed model is highly capable of predicting diabetes in the early stages.

Vitamin D deficiency is prevalent in adults and is associated with cognitive impairment. However, the mechanism by which adult vitamin D (AVD) deficiency affects cognitive function remains unclear. We examined spatial memory impairment in AVD-deficient BALB/c mice and its underlying mechanism by measuring spine density, long term potentiation (LTP), nitric oxide (NO), neuronal nitric oxide synthase (nNOS), and endothelial NOS (eNOS) in the hippocampus. Adult male BALB/c mice were fed a control or vitamin D deficient diet for 20 weeks. Spatial memory performance was measured using an active place avoidance (APA) task, where AVD-deficient mice had reduced latency entering the shock zone compared to controls. We characterised hippocampal spine morphology in the CA1 and dentate gyrus (DG) and made electrophysiological recordings in the hippocampus of behaviourally naïve mice to measure LTP. We next measured NO, as well as glutathione, lipid peroxidation and oxidation of protein products and quantified hippocampal immunoreactivity for nNOS and eNOS. Spine morphology analysis revealed a significant reduction in the number of mushroom spines in the CA1 dendrites but not in the DG. There was no effect of diet on LTP. However, hippocampal NO levels were depleted whereas other oxidation markers were unaltered by AVD deficiency. We also showed a reduced nNOS, but not eNOS, immunoreactivity. Finally, vitamin D supplementation for 10 weeks to AVD-deficient mice restored nNOS immunoreactivity to that seen in in control mice. Our results suggest that lower levels of NO and reduced nNOS immunostaining contribute to hippocampal-dependent spatial learning deficits in AVD-deficient mice.

A numerical analysis of magnetohydrodynamic natural convection along a thin vertical cylinder with a sinusoidal heat flux at the wall immersed in copper (Cu) and aluminum-oxide (Al2O3) hybrid nanofluids has been studied. A 2D vertical thin cylinder shape geometry has been considered with a radius of R. The fluid flow is considered laminar and incompressible with the Prandtl number of Pr = 6.2 and 10% concentration of hybrid nanoparticles. The nondimensional governing equations have been solved numerically by using the implicit finite difference method. An in-house FORTRAN 90 code is used for solving this problem and the code is validated with the available benchmark results. Numerical simulations have been performed for a wide range of governing parameters, Hartmann number from Ha = 0 to Ha = 4, nanoparticles volume fractions ϕ = 0.0 to ϕ = 0.1, and the amplitude of the wall heat flux ε = 0.0–0.3. The findings have been illustrated in terms of streamlines, isotherms, local skin friction coefficients, local Nusselt numbers, velocity, and temperature distributions. The flow field and temperature distribution within the boundary layer are deceased by the effects of the wall heat flux amplitudes. It is also noted that the rate of heat transfer increases with particle volume fraction and the amplitude of the wall heat flux. According to the findings, Nu increases by 24.72% as ϕ increases from 0 to 0.1 while ε = 0.3, and 27.66% while ε increases from 0.0 to 0.3 at 5% hybrid nanoparticles. The local skin frictions and Nusselt number diminish with the increment of the Hartman number due to the effects of the Lorenz force. The findings of this study can lead to a better understanding of the fundamental principles regarding the behavior of hybrid nanofluids under complex conditions, such as a vertical thin cylinder with a sinusoidal wall heat flux. Understanding the behavior of hybrid nanofluids in the presence of a magnetic field and a nonuniform wall heat flow can also lead to the development of innovative heat transfer enhancement strategies.

This research investigated the service quality of government secondary schools (GSS) in Bangladesh. A mixed methods research design combining quantitative and qualitative methods was applied for gathering and analyzing data. Quantitative data was collected from 601 randomly selected clients using a structured interview schedule developed based on the five-dimensional SERVQUAL instrument. Qualitative data was obtained through focus group discussions and interviews of students, teachers, headteachers, guardians, assistant school inspectors, and the deputy director of secondary and higher education working in the selected region. Quantitative data were analyzed using descriptive statistics and bivariate analysis, while qualitative data was transcribed and labeled into emerging themes. The study found that the quality of secondary education in the GSSs of the study area failed to meet the demand of the clients in terms of all the quality dimensions of SERVQUAL, where t-values revealed a significant gap between expectation and perception in Tangibles, Responsiveness, Reliability, Assurance and Empathy dimensions. Data from qualitative methods showed that all the stakeholders agreed with the gap in tangibles and empathy dimension claimed by the respondents in the quantitative survey. However, teachers and guardians showed differential opinions regarding the gaps in responsiveness, reliability, and assurance. It was also found that 40% of the respondents were either neutral or dissatisfied with the education service of GSSs. Logistic regression output shows that among the dimensions of SERVQUAL, tangibles, responsiveness, assurance, and empathy can significantly influence a respondent’s overall satisfaction with GSS services. One-way ANOVA results show that schools from at least two districts differ significantly in terms of the mean score of overall satisfaction and the perceived mean score of tangibles, responsiveness, reliability, assurance, and empathy dimensions. The schools lack teaching, learning and recreational facilities such as laboratory, library, common room, and sports. The students did not receive individual need-based care for academic performance improvement and did not participate in lesson planning, preferring teaching methods and deciding assessment strategies. The teachers were not sufficiently skilled in delivering intelligible lectures and didn’t give time to students outside of formal classes. A sizeable gap in the SERVQUAL dimensions indicates that a comprehensive all-out effort is necessary to improve the service quality of GSSs. Consequently, improving teaching learning facilities, student participation in the lesson plan and deciding on teaching methods, teachers' training in pedagogy, and school-specific quality improvement measures are imperative.

The study examined the impact on bankers' mental health in terms of fear and anxiety and perceived job insecurity during COVID-19. It also evaluated the effectiveness of combating steps against COVID-19 taken by government and employers to reduce bankers' mental stress. Data were collected from 252 banking professionals in Bangladesh through questionnaire survey. Goodness of fit has been analyzed using Structural Equation Modeling (SEM). Furthermore, Exploratory and confirmatory factor analyses were applied for reliability test. The sampling adequacy was tested using KMO and Bartlett's Test, and the results showed the sample is significant. Cronbach's alpha and composite reliability (C.R) results indicate that the construct is reliable. The results showed fear & anxiety and perceived job insecurity have significant and positive impact on bankers' mental health implying their increase deteriorates bankers' mental health. It is observed that the study and its concerned subject matter absolutely unleashed the inner scenario of banking professionals in Bangladesh. According to this study, lockdown limitations should be used more wisely, and the mental health of the bankers should receive more immediate and consistent attention. In the event that workforce reduction measures are required to address the financial crisis, it is advised that banking sector give furloughs preference over layoffs.

The numerical investigation of magneto-hydrodynamic (MHD) mixed convection flow and entropy formation of non-Newtonian Bingham fluid in a lid-driven wavy square cavity filled with nanofluid was investigated by the finite volume method (FVM). The numerical data-based temperature and nanoparticle size-dependent correlations for the Al2O3-water nanofluids are used here. The physical model is a two-dimensional wavy square cavity with thermally adiabatic horizontal boundaries, while the right and left vertical walls maintain a temperature of TC and TH, respectively. The top wall has a steady speed of u=u0. Pertinent non-dimensional parameters such as Reynolds number (Re=10,100,200,400), Hartmann number (Ha=0,10,20), Bingham number (Bn=0,2,5,10,50,100,200), nanoparticle volume fraction (ϕ=0,0.02,0.04), and Prandtl number (Pr=6.2) have been simulated numerically. The Richardson number Ri is calculated by combining the values of Re with a fixed value of Gr, which is the governing factor for the mixed convective flow. Using the Response Surface Methodology (RSM) method, the correlation equations are obtained using the input parameters for the average Nusselt number (Nu¯), total entropy generation (Es)t, and Bejan number (Beavg). The interactive effects of the pertinent parameters on the heat transfer rate are presented by plotting the response surfaces and the contours obtained from the RSM. The sensitivity of the output response to the input parameters is also tested. According to the findings, the mean Nusselt numbers (Nu¯) drop when Ha and Bn are increased and grow when Re and ϕ are augmented. It is found that (Es)t is reduced by raising Ha, but (Es)t rises with the augmentation of ϕ and Re. It is also found that the ϕ and Re numbers have a positive sensitivity to the Nu¯, while the sensitivity of the Ha and Bn numbers is negative.

This study aims to consider lattice Boltzmann method (LBM)–magnetohydrodynamics (MHD) data to develop equations to predict the average rate of heat transfer quantitatively. The present approach considers a 2D rectangular cavity with adiabatic side walls, and the bottom wall is heated while the top wall is kept cold. Rayleigh–Bénard (RB) convection was considered a heat-transfer phenomenon within the cavity. The Hartmann (Ha) number, by varying the inclination angle (θ), was considered in developing the equations by considering the input parameters, namely, the Rayleigh (Ra) numbers, Darcy (Da) numbers, and porosity (ϵ) of the cavity in different segments. Each segment considers a data-driven approach to calibrate the Levenberg–Marquardt (LM) algorithm, which is highly linked with the artificial neural network (ANN) machine learning method. Separate validations have been conducted in corresponding sections to showcase the accuracy of the equations. Overall, coefficients of determination (R2) were found to be within 0.85 to 0.99. The significant findings of this study present mathematical equations to predict the average Nusselt number (Nu¯). The equations can be used to quantitatively predict the heat transfer without directly simulating LBM. In other words, the equations can be considered validations methods for any LBM-MHD model, which considers RB convection within the range of the parameters in each equation.

Research question: What is the perceived use of accounting information and current practices of accounting in micro enterprises? Motivation: Business decisions are more accurate and focused if they are made based on reliable information, for instance, accounting information. However, the use of accounting information in decision-making varies among the business entities, which is even poor in micro enterprises in developing countries. Idea: This study aims to examine perceived use of accounting information in decision-making and current practices of accounting in micro enterprises of Bangladesh. Data: The data include the characteristics of owner and business, perception on accounting systems, the use of accounting information (ACI) in decision-making and the current accounting practices collected from 148 respondents. Tools: The data are analyzed using standard descriptive statistical tools and multivariate regression. Findings: We find that the perception regarding the use of ACI in decision-making is low. Furthermore, the existing practices of accounting in surveyed entities are very low. The key practices, in terms of weighted mean, are cash reconciliation and practices of budgeting. Furthermore, the number of employees in the enterprises has a positive significant relationship with the perception regarding the use of ACI in decision-making. Besides, enterprises practicing accounting have higher performance. In addition, the size, and the nature of business have statistically significant positive effects on turnover. Contribution: This study may help stakeholders in understanding the role of accounting in business decision-making and its impacts on financial performance of the micro enterprises in developing countries.

In this article, the influence of applied magnetic field on the non-Newtonian power-law nanofluid in a rectangular-shaped enclosure has been studied and investigated by deploying the multiple-relaxation-time (MRT) lattice Boltzmann method (LBM). The enclosure is filled with water containing Cu nanoparticles. For the proper investigation, the equation for mass, momentum, and energy has been governed by the lattice Boltzmann method for non-Newtonian power-law nanofluid. The code validation with the previous studies is presented in the paper. The study presented here takes consideration of a wide range of magnetic field angles (γ = 0°, 30°, 45°, 90° and 120°), thermal Rayleigh number (Ra = 104 and 105), power-law index (n = 0.6, 1.0 and 1.4) of non-Newtonian power-law fluid. The study found that the average Nusselt number rises with the increment of magnetic angle up to γ = 90°, then it starts to exacerbate when γ = 120°. The average Nusselt number also increases for the higher values of the power-law index and maximum when n = 1, then it starts to decrease when the fluid becomes shear-thickening. Furthermore, finally, the entropy profile of the study is thoroughly investigated in detail.

The problem of maximizing lifetime of a sensor network is still challenging mainly due to the stringent delay-deadline of real-time applications and heterogeneity of sensor devices. The problem is further complicated when the network contains many obstacles. In maximizing network lifetime, existing literature works either merely address issues of application delay-deadline and presence of obstacles, or analyze primitive data collection approaches for such an environment. In this paper, we formulate optimal data collection schedule of a mobile sink in an obstructed sensor network as a mixed-integer linear programming (MILP) problem. The proposed data collection scheduling finds an optimal set of rendezvous nodes over a preformed Starfish routing backbone, and corresponding sojourn duration so as to maximize the network lifetime while maintaining delay-deadline constraint in an obstructed network. The proposed Starfish-scheduling ensures a loop-free traveling path for a mobile sink across the network. The results of performance evaluation, performed in network simulator-2, depict the suitability of Starfish scheduling as it outperforms state-of-the-art-works in terms of extending network lifetime and data delivery throughput as well as reducing average end-to-end delay.

Objectives: This study aimed to determine the impact of the COVID-19 pandemic on the psychological, mental health and quality of life among Bangladeshi residents. Study design: A purposive cross-sectional study of quality of life during the COVID-19 pandemic was performed. Methods: Respondents completed a modified questionnaire that determined the Impact of Event Scale (IES), indicators of psychological distress impact, impact on government strategies, awareness and lifestyles, and impact on expectation of quality life change. A total of 465 (male = 330 and female = 135) respondents participated in this study. Results: The overall mean age of respondents was 28.42 ± 7.07 years, and 63.4%, 44.1% and 50.3% were unmarried, were in the middle-income family group and had a masters or PhD qualification, respectively. The overall mean IES score of respondents was 80.89 ± 8.91, which reflects a stressful impact of the COVID-19 pandemic on physical and mental health problems. Only 27.75% of respondents had an IES score ≥75. More than half of respondents (57.8%) reported that they did not feel lonely and hopeless. In terms of preventative measures, the majority of the respondents (80.2%) reported that they did not wash their hands frequently with soap and sanitiser for at least 20 s to reduce spread of the virus. During the pandemic, more than half of the respondents (56.8%) claimed that they faced serious problems in education. Conclusions: The ongoing COVID-19 pandemic has resulted in significant mental and physical health problems.

The flow field, thermal field, and solutal field exposed to thermal and solutal buoyancy forces have been investigated in detail within a wavy enclosure filled with copper(Cu)–water nanofluid incorporating the non-Newtonian characteristics predicted by the power-law viscosity model. During the convection process, the random motion of ultrafine Cu-nanoparticles causing an enhanced energy exchange rate is determined using the thermal dispersion model. The governing equations in a dimensionless form are numerically solved utilizing the finite volume method incorporated with the semi-implicit method for pressure linked equations-revised algorithm. The simulations are carried out with different pertinent parameters, such as the Rayleigh number, Lewis number, power-law index, volume fraction, and buoyancy ratio. The effect of the above parameters on the local Nusselt number (Nu) and the local Sherwood number (Sh) is analyzed to understand the heat and mass transfer properties from the heated wavy surface. Results show that the heat transfer rate from the wavy surface declines, but the mass transfer rate gets stronger with growing Lewis number. Both the heat and mass transfer rates become optimum when the nanofluid behaves as a shear thinning fluid. The distribution of Nu and Sh is found to be periodically attenuated from the lower end to the upper end along the hot wavy surface. The distribution of Nu and Sh is observed to be locally maximum at the crest point of the wavy surface. New correlations to predict the average heat and mass transfer rate concerning the studied parameters are proposed with remarkable accuracy.

Pollutant dispersion in urban areas has been a key concern in densely populated cities because pollutants can negatively impact human health and the environment. Topography and urban obstructions, such as buildings, greatly affect the atmospheric fluid flow, leading to the dispersion process. Street canyons are usually formed in highly populous cities due to the close proximity of buildings to streets to serve commercial purposes. It can also be termed as pollutant traps that exact adverse effects on urban life. Street canyons cause changes in pollutant dispersion, particularly in the case of vehicle exhaust air pollutants, which cannot be transported by the wind owing to the presence of buildings acting as impediments regardless of the wind flow. Hence, it is imperative to completely understand the behavior of pollutants within the confined urban surroundings for further improving the urban air quality. This study explores the effects of street canyons on the busiest street of Dhaka city in Bangladesh. The Reynolds-averaged Navier–Stokes based dispersion model is used to investigate the air quality phenomenon. Building height, width, building distance, and road width have been taken into consideration in the simulation. It is expected that the findings of the current study would be helpful for urban planners and designers of Dhaka city.

The atmospheric flow and dispersion of traffic exhaust were numerically studied in this work while considering a model street canyon intersection of a city. The finite volume method (FVM)-based large-eddy simulation (LES) technique in line with ANSYS Fluent have been used for flow and pollutant dispersion modelling through the consideration of the atmospheric boundary layer (ABL). Hexahedral elements are considered for computational domain discretization in order to numerically solve problems using FVM-LES. The turbulence parameters were superimposed through a spectral synthesizer in the existing LES model through ANSYS Fluent as part of ’damage control’ due to the unsteady k−ϵ simulation. Initially, the code is validated with an experimental study of an urban street canyon where the width and height ratio is in unity. After validation, a model urban street canyon intersection was investigated in this work. The model shows a high pollutant concentration in the intersecting corner areas of the buildings. Additionally, the study of this model intersection shows a high level of pollutant concentration at the leeward wall of downwind building in the case of increased height of an upwind building. Most importantly, it was realized from the street intersection design that three-dimensional interconnection between the dominating canyon vortices and roof level flow plays a pivotal role in pollutant concentration level on the windward walls. The three-dimensional extent of corner eddies and their interconnections with dominating vortices were found to be extremely important as they facilitate enhanced ventilation. Corner eddies only form for the streets towards the freeway and not for the streets towards the intersection. The results and key findings of this work offer qualitative and quantitative data for the estimation, planning, and implementation of exposure mitigation in an urban environment.

Largemouth bass (Micropterus salmoides) is one of the world’s most notorious fish species, rapidly invaded in the aquatic ecosystems because of high fecundity and recruitment rate across the world, including South Korea, and is a major threat to native fish biodiversity and ecosystems. Knowledge about invasion range of largemouth bass has immense importance for conservation and management of native fish species. This study was conducted to predict a potential spatial spread in the future for the national-level risk assessments and successful management of largemouth bass in Korea. Using the MaxEnt (Maximum Entropy) model, 1109 streams and rivers of largemouth bass along with 24 climatic environmental variables of Korea were used to predict the future spatial distribution patterns and habitat suitability area. The precision of the MaxEnt model was highly significant, with an area under the curve value of 0.854 and 0.829 in 2050 and 2100, respectively. Overall, our study suggested that the output of MaxEnt model is useful for understanding how climatic variables affect the distribution range of invasive alien fish species and generating a map of potential risk. Keywords: Geographical distribution, Invasive alien species, Largemouth bass, Maximum entropy model, South Korea

There have been numerous studies about the health implication of COVID-19 on patients, but little attention has been paid to the impacts of the pandemic on physicians. Our paper attends to this gap by exploring the mental health of physicians in Bangladesh during the COVID-19 pandemic. This is particularly important since the mental health of physicians impacts not only on themselves, but also their professional performance and hence the care of patients. This study examined physicians' mental health outcomes by evaluating the prevalence and associated potential risk factors of anxiety and depression. Using a web-based cross-sectional survey, we collected data from 114 physicians. Seven-item Generalized Anxiety Disorder (GAD-7) scale and Nine-item Patient Health Questionnaire (PHQ-9) were used to measure the anxiety and depression, respectively. Multivariate logistic regression models were used to explore the potential risk factors related to anxiety and depression. The prevalence of anxiety and depression were 32.5 and 34.2%, respectively. Findings revealed that marital status, work per day and current job location were the main risk factors for anxiety while sex, age, and marital status were the main risk factors for depression. Our results highlight the need to implement policies and strategies for positively impacting the mental health of physicians during and after the COVID-19 pandemic.

This study investigated the current practice of learning, teaching and assessment in science, particularly in classrooms at primary level in a division of Bangladesh. In addition, this study also focused on the role of DPEd program in the development of the professional preparation of teachers' for improving their quality of science teaching. For the purpose of this study, a mixed method research design which used both quantitative and qualitative method to gather and analyse data. A random stratified sampling process was used to select 150 primary science teachers and 60 students in grade 5 (10 + years) students for collecting quantitative data while a purposeful sampling process was used for qualitative inquiry. Qualitative data was obtained through analysis of the national documents and the interviews of Head teachers of primary schools, Assistant Upazila (Sub-district) Education Officers and Instructors of Primary Training Institutes (PTI) and Upazila Resource Centers. Quantitative data was analysed by using descriptive statistics and qualitative data was transcribed and labelled into emerging themes. A triangulation technique was used to establish the findings of this study. The findings of this study revealed that the present practice of learning and teaching science is mostly teacher centred with little active pupil participation, limited opportunity for hands on activity and discussion. The major barriers for teachers' to teach science effectively were large class sizes, insufficient materials, lack of teacher's knowledge and skills, absence of assessment for learning strategies, inadequate opportunities for professional development and poor quality of support from the stakeholders. The current DPEd program has consist of limited focus on hands of activities, insufficient learning assessment capacity of the PTI instructors, and a lack of teacher monitoring in placement schools during training. The basic recommendation suggested by this research is to enhance the use of an inquiry oriented science learning and teaching approach with associated assessment for learning strategies for ensuring the quality of science learning and teaching. For preparing a better quality teacher, ensuring subject based training and to review the current teacher training program is further suggested. The result of this study can be used for future development of science learning and teaching practice at elementary level in Bangladesh and similar other developing countries.

Background Lung cancer is the leading cause of cancer-related mortality worldwide. Demographic differential has been linked with the treatment outcome and survival in recent literature, mostly from the developed world. Considering diversity in population characteristics across income strata, it’s worth assessing the link in low- and middle-income population as well. Current study aimed to assess the association of demographic characteristics with lung cancer survival in Bangladeshi lung cancer patients. Methods & results All newly diagnosed primary lung cancer cases attending the national institute of cancer research & Hospital (NICRH), a tertiary cancer care center in Dhaka, Bangladesh between 2018 and 2019 were considered for the study. Demographic information and clinical data were obtained from the patients’ medical records by a trained physician. Survival estimate was generated using the Kaplan-Meier method and compared across demographic and clinicopathological categories using the log-rank test. Hazard ratio and 95% CI for treatment options are generated fitting multivariable Cox proportional hazard regression. Among 1868 patients, 84.6% were males and 15.4% were females, average (± standard deviation) age at diagnosis was 59.6±10.9 years, only 10.8% had not consumed tobacco of any form. Around two-thirds of the patient had Eastern Cooperative Oncology Group (ECOG) performance score ≥2, 29.5% had at least one comorbidity and 19.4% had metastasis at the time of presentation. Higher survival was associated with institutional education (HR 0.9; 95% CI 0.77, 0.99), and receipt of combined radiotherapy and chemotherapy (HR 0.56; 95% CI 0.46, 0.65; p Conclusion Older age, presence of one or more comorbidity, poorer performance status, and treatment with only RT appeared as a significant predictor of poorer prognosis of lung cancer in Bangladeshi patients. In contrast, having institutional education and treatment with combined Radiotherapy and Chemotherapy appeared as a predictor of a better prognosis. The finding of this study could serve as a basis for future studies inquiring into novel approaches for certain subgroups of patients believed to be challenged in limited resources.

The aim of this study is to investigate the effects of temperature-dependent viscosity on the natural convection flow from a vertical permeable circular cone with uniform heat flux. As part of numerical computation, the governing boundary layer equations are transformed into a non-dimensional form. The resulting nonlinear system of partial differential equations is then reduced to local non-similarity equations which are solved computationally by three different solution methodologies, namely, (i) perturbation solution for small transpiration parameter (ξ), (ii) asymptotic solution for large ξ, and (iii) the implicit finite difference method together with a Keller box scheme for all ξ. The numerical results of the velocity and viscosity profiles of the fluid are displayed graphically with heat transfer characteristics. The shearing stress in terms of the local skin-friction coefficient and the rate of heat transfer in terms of the local Nusselt number (Nu) are given in tabular form for the viscosity parameter (ε) and the Prandtl number (Pr). The viscosity is a linear function of temperature which is valid for small Prandtl numbers (Pr). The three-fold solutions were compared as part of the validations with various ranges of Pr numbers. Overall, good agreements were established. The major finding of the research provides a better demonstration of how temperature-dependent viscosity affects the natural convective flow. It was found that increasing Pr, ξ, and ε decrease the local skin-friction coefficient, but ξ has more influence on increasing the rate of heat transfer, as the effect of ε was erratic at small and large ξ. Furthermore, at the variable Pr, a large ξ increased the local maxima of viscosity at large extents, particularly at low Pr, but the effect on temperature distribution was found to be less significant under the same condition. However, at variable ε and fixed Pr, the temperature distribution was observed to be more influenced by ε at small ξ, whereas large ξ dominated this scheme significantly regardless of the variation in ε. The validations through three-fold solutions act as evidence of the accuracy and versatility of the current approach.