University of Aden

We propose a novel class of generalized complex It & ocirc;-type Hermite polynomials and examine their algebraic and operational structure. Connections with classical special functions and polynomials are established, demonstrating links to hypergeometric-type functions. A quasi-monomial structure approach is employed by introducing appropriate raising and lowering operators, leading to operational formulas and second-order differential relations. Rodrigues-type and Burchnall-type operator images are constructed, offering concise expressions and generating relations. Further supplementary results, including generating functions and functional expansions, are derived. Furthermore, the paper demonstrates the applied utility of these generalized polynomials by establishing explicit solutions to one-and two-dimensional heat-type partial differential equations. Graphical representations of the generalized complex Hermite polynomials are also included. The results extend the framework of complex Hermite polynomials and provide a flexible toolset for operational and applied analysis in mathematical physics.

This study assesses soil contamination in areas adjacent to a crude oil pipeline in Shabwah Governorate, Yemen. A total of 22 surface soil samples were collected from three representative sites along the pipeline corridor using a systematic sampling design. Soil physicochemical properties were determined using standard methods, while major and trace elements were analyzed by inductively coupled plasma - mass spectrometry (ICP-MS). Polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) were quantified using gas chromatography - mass spectrometry (GC - MS), with appropriate quality assurance and quality control procedures. The soils were predominantly alkaline and sandy. Major elements generally reflected lithogenic background conditions, while selected trace metals locally exceeded international soil quality guideline values. Average PAH concentrations ranged from 1.084 to 3.529 mg/kg, and PCB concentrations ranged from 0.070 to 0.150 mg/kg, with consistently higher levels observed at sites located near documented oil spill areas. The spatial distribution of PAHs and PCBs suggests a petroleum-related influence potentially associated with pipeline leakage and spill recurrence. These findings highlight localized soil contamination along the pipeline corridor and underscore the need for continued environmental monitoring and targeted remediation measures to mitigate potential environmental and human health risks in the study area.

Background: In Yemen, there is a paucity of research on substance use in school-aged males. Objective: This study aimed to assess the prevalence and patterns of substance use among male secondary school students in Aden Governorate, Yemen. Methods: A school-based cross-sectional study was undertaken in Aden Governorate from January to March 2024. A structured questionnaire was administered to assess students' knowledge of drugs, familial neglect, and aggressive tendencies linked to substance use. Urine samples were tested using rapid diagnostic kits. Result: The study revealed notable prevalence rates of substance experimentation among students, particularly with cigarettes (43.6%), khat (15.4%), and shamma (13.9%). Laboratory screening confirmed positive results for cotinine (49.3%), benzodiazepines (2.5%), Cannabis (marijuana) (2.1%), amphetamines (1.4%), and tramadol (0.7%), no traces of cocaine were detected. A statistically significant association was observed between students' age and their level of drug-related knowledge, particularly for amphetamines, marijuana, heroin, and cocaine (p < 0.001), and tramadol (p < 0.001). Family neglect emerged as a critical factor, showing a significant link to increased substance use (p < 0.001). Conclusion: The findings indicate a high prevalence of substance use, with cotinine emerging as the most frequently detected substance, underscoring the need for targeted prevention and intervention strategies within the community.

Ternary hybrid nanofluids(THNFs) are a revolutionary advancement in thermal management, offering remarkable thermal conductivity that significantly boosts heat transfer efficiency. THNFs are ideal for cooling systems, solar energy applications, and electronic device regulation, where effective heat management is crucial. By fine tuning their composition, researchers can tailor these nanofluids to meet specific industrial needs, leading to improved efficiency and energy savings. The goal of current study is twofold: first, to examine the improvement of heat, mass, and motile density of THNF, and secondly, to investigate the irreversibilities in bioconvective dihybrid and trihybrid nanofluids. The THNF is formulated by suspension of nanoparticles of cobalt ferrite ( CoFe_2O_4) , disulfide (dithioxo) molybdenum ( MoS_4) , and copper (Cu) into pure engine oil ( C_8H_18) . For dihybrid nanofluid the volume fraction of copper is taken as zero. Maxwell fluid model is utilized to analyze the performance of THNF and dihybrid nanofluid(DHNF). The flow in THNF and DHNF is induced due to an impermeable stretched sheet. Flow governing equations for DHNF and THNF are obtained considering diverse assumptions like electro-magnetohydrodynamic(EMHD), Dufour, Soret, chemical reaction, thermal radiation, and activation energy. Irreversibilities are modeled with the help of thermodynamics second law. The model equations are altered into ordinary system via transformation procedure. Numerical simulations are carried out through built-in function(NDSolve) of Mathematica. Impact of generated variables on DHNF and THNF velocity, thermal field, motile density profile and mass concentration are examined. Comparative analysis for THNF and DHNF is performed. Furthermore, local heat, mass, density, and skin friction coefficient for THNF and DHNF are numerically investigated. Numerical results show that the skin friction coefficient for THNF is 3.2

The Soret-Dufour effects and non-uniform heat generation on gyrotactic and oxytactic microorganisms in stagnation point flow of CNTs-water based hybrid nanofluid flow via a rotating sphere with convective boundary conditions. Through the utilization of gyrotactic and oxytactic microorganisms, the system improves nutrient delivery and fluid mixing, which raises reaction rates and sensor sensitivity. Accurate biosensor performance depends on the hybrid nanofluid improved heat dissipation and stability, which are facilitated by the carbon nanotubes it contains. This approach is useful for applications in environmental monitoring, bioprocess engineering, and medical diagnostics since machine learning also facilitates real-time prediction, optimization, and control of sensor conditions. This research provides a novel numerical solution to this problem using back-propagation intelligent Bayesian regularization in the neural network domain (BIBR-NNs), which has convergent stability. Using a dataset for the proposed (BIBR–NNs) for many MHD-BNF-DSTR scenarios, the Bvp4c numerical technique. To determine the accuracy of the suggested model, the data is processed, appropriately tabulated, and its validity is tested. The BIBR-NNs training, testing, and validation procedures were utilized to assess the estimated solutions for specific occurrences and compare the proposed model for verification.