Engineering and Applied Science Letters (EASL)

Traffic congestion presents a critical challenge in contemporary urban environments, necessitating the development of effective traffic management systems. Microscopic traffic flow models, which offer detailed insights into individual vehicle dynamics such as car-following and lane-changing behaviors, are pivotal in addressing these challenges. However, a comprehensive review synthesizing the advancements and research trends in this field has been lacking. This paper presents a systematic review of major microscopic traffic flow research from 1950 to 2023. Our extensive search across multiple academic databases identifies significant methodologies and model equations, highlighting notable advancements in the field. The presentation reveals critical trends, including the integration of connected and autonomous vehicles, the application of machine learning techniques, and the increasing reliance on real-time data for traffic management. This paper provides a foundation for future research directions and contributes to the ongoing development of more efficient and sustainable urban traffic management strategies.

In this article, we present a new asymmetric distribution, the Topp-Leone modified Weighted Rayleigh (TLMWR) distribution, which extends the well-known Topp-Leone distribution. We derive several of its properties, including the probability density function, cumulative distribution function, survival function, failure (hazard) rate, moments, generating functions, quantile function, and order statistics. The model parameters are estimated by the method of maximum likelihood, and a simulation study is conducted to examine the finite-sample behavior of the estimators. We summarize key characteristics of the data using graphical displays and diagnostic procedures, including normality assessments and model-selection criteria. These analyses are performed on real-world data to assess the level and direction of skewness and kurtosis. The proposed distribution is then evaluated with a real-life dataset, and its performance is compared with existing and newly proposed distributions. The results support the validity of the proposed model and highlight its effectiveness relative to existing alternatives.

With rapid economic development and urbanization, many cities, particularly in China, face serious PM\(_{2.5}\) pollution issues. In this study, the city of Hefei is selected as the research area to investigate the factors influencing PM\(_{2.5}\) concentrations. Data on electricity consumption of major PM\(_{2.5}\)-emitting industries, meteorological factors (temperature, wind speed, wind direction, relative humidity), and atmospheric pollutant concentrations (NO\(_{2}\),SO\(_{2}\),O\(_{3}\),CO) are utilized to explore PM\(_{2.5}\) concentrations in Hefei from 2020 to 2021 using a generalized additive model (GAM). The aims are to identify the main influencing factors and potential control pathways for particulate matter pollution. Results reveal that CO accounts for 69% of the variation in PM\(_{2.5}\) mass concentration, suggesting it as the dominant factor in Hefei in 2020. Additionally, the major PM\(_{2.5}\)-emitting industries contribute to a 16% change in PM\(_{2.5}\) mass concentration, with a significant impact from smelting industries, which exhibit an increase in electricity consumption associated with an increase in PM\(_{2.5}\) mass concentration. Model fitting indicates that a 50% reduction in electricity consumption within the iron and steel making industries can lead to a 37% decrease in PM\(_{2.5}\) mass concentration compared to pre-reduction levels. Moreover, targeted control measures in winter result in higher reductions in PM\(_{2.5}\) pollution within a 40% reduction compared to consistent emission reductions throughout the year. These findings highlight the effectiveness of more focused control strategies based on localized circumstances. Implementing measures to restrict electricity use by key industries during high pollution seasons and in cities with high pollution levels can effectively address local PM\(_{2.5}\) pollution concerns.

Aging is a complex, systemic process often driven by both intrinsic and extrinsic factors. Recent evidence suggests that secretions from aging organs may influence the function of distant tissues. This study investigates the impact of liver-derived secretions from oxidatively aged cells on induced pluripotent stem cell-derived cardiomyocytes (iCMs). Here, we exposed HepG2 liver cells to hydrogen peroxide at varying concentrations (25–300 \(\mu\)M) and durations with regular intervals to model aging. Post-treatment validation confirmed increased oxidative stress, lipofuscin accumulation, p21 expression, and senescence, particularly in the 15-day 100 \(\mu\)M group. Conditioned media from aged HepG2 cultures were then applied to healthy, differentiated iCMs at various dilutions including undiluted, 1:1, and 1:3 with iCM media. iCMs exposed to aged liver secretions exhibited significantly increased aging phenotypes, including elevated lipofuscin and p21 expression, as well as increased senescent cell populations, with the strongest effects observed in undiluted and 1:1 treatment conditions. While senescence levels peaked at the 1:1 dilution rather than in undiluted media, a dose-dependent response to secreted stress factors was observed. Control experiments with untreated liver media showed no significant effects, confirming that the aging phenotypes observed in iCMs were driven specifically by the secretome of aged liver cells. These findings reveal a clear mechanism by which hepatic aging can promote cardiac aging and dysfunction, offering insight into liver-heart crosstalk in the context of human aging.

This study presents a semi-analytical investigation of transient free convection flow of a viscous, incompressible fluid within a vertical channel subjected to third-kind thermal boundary conditions. These boundary conditions, representing convective heat exchange between the fluid and surroundings, offer a more realistic thermal model for practical systems such as heat exchangers and insulated enclosures. The governing partial differential equations are transformed into the Laplace domain using the Laplace transform technique, and closed-form solutions are obtained. These are subsequently inverted to the time domain via Riemann-sum approximation. To capture memory and hereditary effects inherent in complex fluid behavior, the model incorporates fractional derivatives in the Caputo-Fabrizio and Atangana-Baleanu senses. The study analyzes temperature distribution, velocity profiles, Nusselt number, and skin friction, with results validated numerically using MATLAB. Graphical and tabular analyses reveal the influence of key parameters, including Biot number, buoyancy forces, and various Prandtl numbers. The findings contribute to the broader understanding of transient free convection under realistic thermal conditions and have potential applications in the design and optimization of thermal systems in engineering and industry.

University campuses pose unique challenges in terms of environmental pollution and crowd management due to increasing human activity, expansive physical areas, and diverse sources of waste generation. Traditional monitoring systems often fall short in addressing these issues, as they lack the ability to deliver location-based, detailed, and real-time information. Situations such as waste accumulation and high crowd density present serious environmental and safety risks, demanding more sensitive, comprehensive, and dynamic solutions. This study presents an integrated drone-based monitoring system capable of real-time, location-aware tracking of environmental pollution and human density. The system consists of a drone that captures high-resolution imagery, a YOLOv8x model for waste detection, a YOLOv11n model for human detection, geolocation algorithms that utilize image metadata, and density maps generated using Kernel Density Estimation. Leveraging various open-source datasets, the models accurately detected waste and human objects from field-captured images. Experimental evaluations demonstrated detection accuracies of 85.87% for waste and 73.36% for humans. The detections were interactively visualized on the campus map, providing decision-makers with real-time, data-driven insights for sanitation and safety operations. The proposed system serves not only as a standalone object detection platform but also as a multi-layered decision support infrastructure that includes spatial and temporal analysis. Results indicate that the integration of UAV technology with AI-powered object detection offers a highly effective tool for environmental monitoring and operational planning in campus settings.

The Taguchi Orthogonal Method was used in the study to improve biodiesel production from Jatropha oil in a single pot. This method predicted the conversion (%) from Jatropha oil transesterification by optimizing four critical process variables. Using the hydrothermal-sulphonation method, a special bio-functionalized catalyst made from agricultural waste, such as cocoa pods, eggshells, orange peels, and snail shells, was used to accelerate the reaction. The ideal conditions of MTOR (15:1), CW (3 wt%), RTime (60 minutes), and RT (65 ◦C) resulted in an optimal conversion of 95.20%. Furthermore, at MTOR of 15:1, CW of 2 Wt.%, RTime of 120 minutes, and RT of 60\(\mathrm{{}^\circ}\)C, a 99.08% product yield was obtained. Nine (9) experimental runs that assessed the FAME yield and the FFA conversion showed coefficients of variation (1.2000 and 0.1083), R\({}^{2}\) values (0.9821 and 0.9981), adjusted R\({}^{2}\) values (0.9641 and 0.9923), and projected R\(^{2}\) values (0.9091 and 0.9539), respectively. The goal of this research was to increase biodiesel yield from Jatropha oil by improving the attribute and conversion of the yielding transformation. The renewable fuel generated under peak conditions met the necessary conditions for manufacturing.

Adsorption of organic compounds on surfaces plays a decisive role in corrosion inhibition, especially on steel materials. The interaction of the sites on the organic molecule with the active sites on the surface remains a complex phenomenon that is very challenging to explain from purely experimental investigation. The integration of computational intelligence through computer algorithms and softwares reduces the laborious and time consuming trial and error stages of laboratory experiments. In this study, density functional theory was deployed to expound the adsorption of benzothiazole and four of its derivatives, namely: benzothiazol-2-ol (BZT-OH), benzothiazol-2-amine (BZT-NH\(_2\)), benzothiazol-2-carboxylic acid (BZT-COOH) and benzothiazol-2-thiol (BZT-SH) on Fe(110) surface. Energy and quantum chemical calculations were performed to determine the positions and orientations of molecular orbitals, molecular reactivity, most preferable sites for nucleophilic and electrophilic attack as well as potential adsorption sites. Molecular dynamics simulation were performed to understand the configuration of the adsorbed molecules on the surface and to predict the mechanism of adsorption. Results reveal that the adsorption sites were mostly domiciled around N, O and S atoms of the amine, carboxyl/hydroxyl and thiol groups, respectively. Adsorption energy decreased following the trend BTZ-COOH > BTZ-SH > BTZ-NH\(_2\) > BTZ > BTZ-OH whereas binding energy decreased following the trend BTZ-SH > BTZ-COOH > BTZ-OH > BTZ-NH\(_2\) > BTZ. Overall, adsorption of BTZ-COOH and BTZ-SH respectively was most enhanced and strongest on Fe(110) surface. All the studied molecules would exhibit good adsorption characteristics on steel surface, making them potential efficient ingredients for formulation of corrosion inhibitors.

Assessment of breast cancer at all stages is of great importance in medicine because in addition to predicting the growth rate, treatment planning must also be considered. In this study, the new maps named the M\(^{*}_{new}\)(k)-map besides a proper signal-to-noise ratio (SNR) under the corresponding theory as well as new parameters such as Ti and FWHM have been introduced to investigate breast cancer performance. In other words, a new function on the SNR from convolutional combination of the relaxation times in MRI as FD\(_{SNR}\) is suggested which utilizes Fourier transform and differentiating operator. This function may be computed for all T1- and T2- weighted images towards prediction of the growth rate of abnormal tissues. These maps and the parameters may contribute to better diagnosis of breast cancer.

This study explores the electro-magneto-hydrodynamic (EMHD) flow, heat and mass transfer of a Jeffrey nanofluid between two horizontal plates under the combined influence of electroosmotic flow (EOF), velocity slip, and an induced magnetic field. The base fluid is water with dispersed copper (Cu) nanoparticles. The governing nonlinear partial differential equations are solved using a finite difference method (FDM), complemented by an analytical approach via the method of undetermined coefficients. The results show that nanofluid velocity increases with higher Grashof numbers and permeability parameter, driven by buoyancy and porous medium effects. A magnetic field lowers fluid velocity but enhances the induced magnetic field near the lower wall; velocity slip reduces wall shear stress but increases velocity farther from the boundary; the Prandtl number improves heat transfer by reducing thermal diffusivity; the Darcy number facilitates flow through porous media; and an increase in Reynolds number sharpens the velocity profile and slightly enhances heat and mass transfer. These findings offer important insights into the coupled dynamics of EMHD nanofluid flow with potential applications in microfluidic and biomedical fields.