Engineering and Applied Science Letters (EASL)

The analysis of the fractional time-derivative model of the Caputo-Fabrizio and Atangana-Baleanu in Caputo sense with the ramped temperature on transient free convection flow in the vertical plates with isothermal boundary conditions has been established. The Laplace transform scheme was adopted in solving the governing equations, and semi-analytical solutions were achieved through the inversion of the solutions from the Laplace purview to the time purview. It found that the temperature and velocity obtained via the CF model are higher than ABC model, and the fractional parameters reduced the temperature and slowed down the movement of the fluid. It is perceived that relaxation time parameters boost the temperature and heighten the fluid’s movement. Also noted that the negative augment in the value of the heat source/sink caused retardation in the movement of the fluid, while the positive increment of the heat source/sink heightened the movement of the fluid. Similarly, it is observable that the temperature produced through a ramped temperature is higher than the constant temperature.

Petroleum and petrochemical resources remain pivotal to the energy and revenue streams of many economies, with drilling operations constituting a fundamental phase in hydrocarbon production. Drilling mud and its associated additives are critical components in the drilling process. The escalating cost, pressure on foreign exchange and ecological footprint of some imported drilling mud additives have spurred interest in sustainable, locally derived alternatives. In this work, a water-based drilling mud (WBM) was formulated using only locally sourced materials from Akwa Ibom State, Nigeria. Key components such as beneficiated local clay, okra-ogbono biopolymer, cassava starch, wood-ash alkalinity agent, and coconut-husk filtrate controller were prepared and blended following API RP 13 B-1 protocols. The rheology (apparent and plastic viscosity, yield point), filtrate loss and cake thickness, pH, density, and thermal/biological stability were determined in the laboratory. Compared to commercial variants used as benchmarks, the local WBM met API rheological and density targets but exhibited elevated fluid loss (17.5 mL vs. ≤ 15 mL) and marginal pH buffering (initial pH 9.2 vs. ≥ 9.5). Thermal aging (90 ^∘C, 24 h) and degradation tests revealed viscosity loss and gel-strength decline, greater than polymer-based commercial variants. On implementation of clay micronization and starch modification, the fluid loss reduced below 15 mL while cake thickness was 2.0 mm. At an estimated 60 % – 65 % cost saving, this WBM demonstrates significant promise for eco-friendly, cost-effective drilling in the Niger Delta.

This paper develops a theory for stability analysis of semigroups of linear operators acting on variable Banach spaces—families of Banach spaces {X(t)}_t ≥ 0 whose norms may depend on time. We establish generation theorems under appropriate resolvent conditions, characterize exponential stability through Lyapunov-type functionals, and analyze spectral properties of evolution families in variable settings. Our approach systematically extends classical semigroup theory to accommodate time-dependent norms by transporting all objects to a fixed reference space. Applications include non-autonomous parabolic equations and reaction-diffusion systems with time-dependent coefficients. All proofs are provided with full mathematical rigor, addressing technical challenges unique to variable Banach spaces.

This study reports the synthesis of a sustainable biopolymer-based coating using cassava starch as the polymeric matrix, oil-palm empty fruit bunch (EFB) lignin as reinforcement, and acid-activated nano-bentonite clay with boric acid as a crosslinker. Coatings were applied to mild-steel substrates and characterized using FTIR, TGA, SEM/TEM, XRD, BET, thermal conductivity measurements, and ASTM E1321 lateral ignition and flame-spread tests. FTIR confirmed hydrogen bonding and strong interfacial adhesion, indicated by O–H and C=O stretching from borate crosslinking, aromatic C=C (lignin), and Si–O–Si (bentonite) vibrations, with an O–H peak shift from 3405 to 3378 cm⁻¹. TGA revealed three-stage decomposition with increased onset degradation temperature (300 ^∘C) and enhanced char yield (27.5%). SEM/TEM showed well-dispersed bentonite nanoparticles (<100 nm) and dense post-burn char morphology, while XRD indicated partial intercalation/exfoliation of clay, and BET confirmed increased surface area and mesoporosity. Optimized formulations (Sample B) exhibited superior fire performance: ignition delay of 126 ± 4 s, flame-spread rate of 1.0 ± 0.1 cm min⁻¹, and char continuity of 92 ± 3%, alongside a 35–45% reduction in thermal conductivity. Mechanical evaluations demonstrated improved adhesion, abrasion resistance, and hydrophobicity. These findings confirm that multi-component, bio-based coatings derived from locally available materials provide a low-cost, eco-friendly strategy for fire retardancy and thermal insulation, supporting sustainable development goals (SDGs 9, 11, and 13).

Classical lifetime models often struggle to accommodate skewed data, tail variation, and non-monotone hazard behaviour observed in engineering and reliability applications. Motivated by this limitation, this study considers the Topp–Leone New Weighted-Weibull (TLNWW) construction obtained by applying the Topp–Leone generator to the New Weighted-Weibull baseline distribution. The resulting model is developed in its original parameterization and, importantly, is also expressed through the composite rate parameter k = 2α(1 + β^θ), which yields an equivalent exponentiated Weibull representation and clarifies the model’s identifiable structure. On this basis, we derive the density, distribution, survival and hazard functions, quantile function, moments, and order statistics, and we discuss maximum likelihood estimation using the identifiable parameters. A Monte Carlo study is reported to examine finite-sample behaviour across several parameter settings. The simulation results show that estimation is satisfactory in some scenarios, although interval performance is uneven in others, indicating that numerical inference should be interpreted with appropriate caution. Two real datasets from materials and engineering applications are used to assess empirical performance and to compare the model with related competitors by means of likelihood-based criteria and goodness-of-fit summaries. Across these examples, the TLNWW model provides competitive fits and attains the best information criteria by small margins, while remaining comparable to closely related alternatives. The paper therefore contributes a careful reparameterized treatment of the TLNWW model, clarifies its connection with existing families, and provides a practically useful distributional form for positively skewed lifetime data.

This study presents the synthesis, application, and dosage optimization of nano-lead chromate as a microstructural modifier for cementitious composites. PbCrO₄ nanoparticles were incorporated into cement-paste specimens at addition levels of 5, 10, 20, 40, 80, and 100%, defined with respect to the stoichiometric SiO₂ content of the cement and also reported for comparison with conventional binder-based formulations. The resulting composites were evaluated by X-ray diffraction, FTIR, UV/Vis., TGA/DTA, SEM, EDX, and TEM in order to relate phase development and microstructural features to mechanical performance. Mechanical response was assessed from replicated loading tests at 7 and 28 days, and the recorded failure loads were used to estimate indirect tensile behavior for the prismatic specimens. The results indicate that nano-PbCrO₄ modifies the texture and compactness of the cementitious matrix and increases the measured load-bearing capacity within the investigated dosage range. The study therefore provides an exploratory assessment of PbCrO₄ as a nano-scale mechanical promoter, while practical implementation would require additional durability and environmental verification.

The growing thermal demands of modern energy conversion, chemical processing, and micro-scale electronic devices necessitate advanced heat and mass transfer strategies, particularly for smart fluids operating in reactive environments. In this work, the flow behavior of an electrically conducting Oldroyd-B nanofluid containing motile microorganisms is analyzed within the Cattaneo-Christov double-diffusion (CCDD) paradigm, which accounts for finite thermal and solutal relaxation times beyond the classical Fourier-Fick theory. The model incorporates mixed convection, and Arrhenius-type chemical reactions to capture complex transport interactions. By employing the Chebyshev Collocation Method (CCM), the coupled nonlinear ordinary differential equations governing the system are solved with high spectral accuracy. The parametric analysis reveals that buoyancy-induced forces significantly strengthen convective transport. Distinct and contrasting influences of relaxation and retardation parameters are observed in the velocity field, highlighting the viscoelastic nature of the fluid. Moreover, thermophoresis and Dufour mechanisms promote thermal and concentration diffusion, while increasing Prandtl and Schmidt numbers, thermal relaxation time, and chemical reaction rate diminish the associated boundary layers. The combined effects of non-Fourier diffusion, and chemical activity lead to transport characteristics unattainable under classical assumptions. These findings offer valuable physical insight for the design and optimization of nanofluid-based thermal systems in advanced industrial and technological applications.

The increasing prevalence of electric vehicles (EVs) in urban logistics presents challenges such as route planning, energy constraints, and demand management. EVs’ limited range, charging requirements, and sensitivity to traffic conditions necessitate advanced optimization strategies. Fleet management systems are thus evolving into intelligent, modular platforms that not only plan delivery tasks but also interact with real-time data and respond to dynamic disruptions. Among these, traffic congestion remains a critical factor that can severely affect route reliability and lead to time window violations. In this study, a modular fleet management system architecture is proposed, capable of real-time monitoring, dynamic rerouting, and traffic-aware decision-making. The system introduces a standardized data structure called the Routing Markup Language (RML), which formalizes the communication between components and supports various route outputs including simulation and vehicle-level execution. Adaptive Large Neighborhood Search (ALNS) is applied for route planning using real-world order data from a water distribution company operating in the Büyükdere district of Eskişehir. The system also features a dynamic reassignment mechanism that responds to vehicle failure scenarios, ensuring continued operation with minimal disruption. Traffic scenarios are evaluated through the Simulation of Urban Mobility (SUMO) environment to assess route robustness under varying conditions. The proposed approach integrates routing optimization, dynamic disruption handling, and simulation-supported fleet monitoring into a cohesive system, offering a responsive and data-driven solution for sustainable urban logistics.

This study investigates the effects of velocity slip and convective boundary conditions on heat transfer and entropy generation in steady magnetohydrodynamic flow of a viscous, incompressible, electrically conducting fluid with internal heat generation/absorption, offering conditions relevant to microchannel cooling, porous heat exchangers, and energy system thermal management. The governing equations were transformed into coupled ordinary differential equations and solved analytically using the method of undetermined coefficients. The analytical solutions showed strong agreement with existing results, validating the model. Parametric analyses, supported by MATLAB visualizations, examined the influence of the magnetic field, slip coefficients, Biot number, and other parameters on flow, temperature distribution, and thermodynamic irreversibility. Results indicate that velocity decreases with increasing suction, magnetic intensity, and upper-wall slip, while temperature diminishes with higher Peclet number or injection velocity. Entropy generation is primarily governed by viscous and Joule dissipation, whereas wall convection and slip act as controlling mechanisms. The Bejan number analysis reveals that heat-transfer irreversibility predominates at higher magnetic parameters, while larger slip and Biot numbers enhance viscous effects and lower Bejan values. These findings have the potential to offer practical guidelines for designing efficient porous-channel cooling system components, particularly where control over wall slip and convective heat exchange is critical to minimizing energy loss and enhancing thermal performance.

The world continues to experience rising levels of crime, particularly in regions affected by socioeconomic disparity and structural inequality. To better understand and control these dynamics, we develop a nonlinear dynamical system of ordinary differential equations describing the evolution of crime within a population. The model divides the total population into five interacting compartments: \(\mathcal{S}_1(t)\) (not-at-risk individuals), \(\mathcal{S}_2(t)\) (at-risk individuals), \(\mathcal{C}(t)\) (active criminals), \(\mathcal{H}(t)\) (habitual offenders who are resistant to rehabilitation), and \(\mathcal{R}(t)\) (rehabilitated or reformed individuals). The influence of key behavioural transition parameters—notably the crime initiation rate \((\alpha)\) and the rate of recovery from the at-risk group \((\beta)\)—on the temporal evolution of each compartment is examined using numerical simulations. Line and contour plots demonstrate that increasing \(\alpha\) enhances the recruitment of at-risk individuals into criminal activity, thereby expanding both the criminal \((\mathcal{C})\) and habitual \((\mathcal{H})\) populations. In contrast, higher \(\beta\) values promote reintegration and reduce the size of the at-risk group \((\mathcal{S}_2)\). These results emphasize the significance of prevention-based interventions (reducing \(\alpha\)) and rehabilitation-oriented strategies (enhancing \(\beta\)) in curbing persistent crime. Furthermore, the basic reproduction number \((\mathcal{R}_c)\) is derived using the next-generation matrix approach to serve as a threshold indicator for crime persistence. Analytical and graphical sensitivity analyses reveal that \(\mathcal{R}_c\) is strongly influenced by the crime transmission rate \((\beta)\), the recruitment fraction into the at-risk class \((p)\), the natural exit rate \((\mu)\), the conviction rate \((\sigma)\), and the rate of progression to habitual criminality \((\eta)\). Contour and three-dimensional surface plots identify parameter regimes for which \(\mathcal{R}_c < 1\) (crime eradication) and \(\mathcal{R}_c > 1\) (crime persistence). The study concludes that reducing recruitment into at-risk groups, increasing conviction and natural exit rates, and minimizing habitual offender influence can effectively suppress criminal propagation, providing a quantitative foundation for evidence-based crime mitigation policies.