https://vjtve.tvu.ac.ir/ Visual Journal of Technical and Vocational Education en daily 1 Wed, 01 Oct 2025 00:00:00 +0330 Wed, 01 Oct 2025 00:00:00 +0330 https://vjtve.tvu.ac.ir/article_209734.html In recent decades, wearable sensors have played a significant role in enhancing personal health management as advanced tools in the medical field and health monitoring. These sensors utilize microchannels to direct fluids for accurate physiological data collection. Microchannels are crucial in improving the precision and efficiency of sensors due to their ability to control fluid flow. Polymethyl methacrylate (PMMA) is one of the widely used materials in the fabrication of these microchannels; however, its hydrophobic nature can disrupt fluid flow. To address this issue, coating with agarose nanoparticles has been proposed as an effective solution. Agarose nanoparticles, due to their biocompatibility and ability to improve the hydrophilicity of surfaces, facilitate fluid flow and enhance sensor accuracy. Experimental testing showed that the optimized microchannel design, with a length of 10 millimeters, a depth of 300 microns, and a constant width of 200 microns for all microchannels, including the outlet microchannel Type 1, exhibited the highest fluid velocity of 62.5 millimeters per second. Additionally, computational simulations demonstrated that optimized geometrical shapes can significantly improve sensor performance. This study examines the effects of geometry, nanostructured materials, and integrated optimization methods in microchannel design, providing strategies to enhance the performance of wearable sensors. The results of this research could pave the way for the development of a new generation of high-performance wearable sensors in the healthcare field. https://vjtve.tvu.ac.ir/article_219645.html In hot forging process, a lubricant is added to the mold to increase the metal flow, reduce friction and wear, and help separate the final part from the mold. The lubrication in a hot forging process is the key to quality and productivity. One of the best ways to study the effect of lubricants in this process is ring compression testing (RCT). In this study, the effect of nanoparticles (Aluminum oxide (AL2O3) and Nano glass) with two different nanoparticles concentrations (0.5wt % and 1wt %) as an additive to base oil (SAE10) at 1050 °C on the friction behavior of 17-7 PH stainless steel using RCT and finite element analysis (FEA) has been studied. Finally, the importance of using Nano lubricants with different nanoparticles concentrations (0.5wt % and 1wt %) in the hot forging process has been investigated. The results showed that nanoparticles as lubricant additives performed better than conventional lubricants (such as graphite) for the frictional behavior of 7-17 PH stainless steel in the hot forging process So that at 1050 ° C the Friction factor by the addition of AL2O3 nanoparticles was reduced about 36% and by the addition of glass nanoparticles about 40%. It was also observed that by adding 0.5wt % Nano glass and AL2O3 nanoparticles to SAE10 base oil, the coefficient of friction (m) was significantly reduced, but with increasing nanoparticles concentrations to 1wt %, the coefficient of friction (m) was somewhat raised. https://vjtve.tvu.ac.ir/article_233969.html A band gap energy, a fundamental concept in electronic devices, governs both their electrical and optical behaviors. The structural, optical, and band structure properties of niobium and tantalum doped ZnO films deposited on glass substrates were studied for their potential application in the opto-electronic. The films were prepared using the pulse laser deposition technique and characterized by various spectroscopic and microanalytical tools. The samples were annealed at 400 in a controlled oxygen environment to improve crystalline quality. The X-ray diffraction analysis of doped ZnO thin films confirmed the incorporation of dopants into the ZnO lattice without altering its wurtzite structure.The crystallite size of ZnO, TaZ, and NbZ films was calculated from the Williamson-Hall method to be 86, 60, and 28 nm, respectivily .The optical bandgap energy was also computed as a function of photon energy using the Tauc formula, revealing a direct band gap of approximately 2.28, 2.38, and 2.40 eV for ZnO, TaZ, and NbZ films, respectivily . A small blue shift in the optical band gap, called the Burstein-Moss shift, was observed in the absorption spectrum, which is often expected in semiconductors with doping.The temperature dependence of the resistivity of the thin films was also investigated. The results showed that the electrical resistances of the films decrease with an increases in temperature. https://vjtve.tvu.ac.ir/article_234037.html In the metal structure industry, electroslag welding (ESW) is widely recognized as an effective method for joining thick sections, particularly in building box constructions and column stiffener assemblies. This study examines ST52 steel due to its widespread industrial applications and the lack of prior research investigating its mechanical properties when welded using the electroslag welding (ESW) technique. Two 20 mm thick St52 steel plates were joined via ESW at welding currents of 200 A and 250 A to simulate a beam-to-column connection. The welded joints were evaluated for hardness, impact resistance, tensile strength, and bending properties. Hardness testing revealed an inverse relationship between heat input and weld metal hardness, while the heat-affected zone (HAZ) exhibited increased hardness relative to the base St52 steel. The average impact energy of the St52 steel (81J) was found to be lower than the average impact energy of the weld metal. As the heat input increases, the grains get coarser, causing cavities to develop on the fracture surfaces and a reduction in fracture energy.The tensile sample fractured at the St52 base metal because the ultimate strength of the St52 steel connection in both samples exceeded 480 MPa. In light of the tensile and bending test findings, the ESW process was approved in full for joining the beam to the column. No defect was found that would compromise the mechanical properties of the weld. https://vjtve.tvu.ac.ir/article_234090.html This study experimentally examined the thermo-physical properties and thermal performance of CuO nanoparticles in water-based fluids and ethylene glycol. Four concentrations of nanofluids (1-4 volume percent) were prepared in the base fluids using an electric mixer, magnetic stirring, and ultrasonic oscillation, with a surfactant added to enhance stability.To measure the thermo-physical properties, the thermal conductivity was assessed.The findings demonstrated that adding 1% by weight of sodium dodecyl sulfate (SDS) to the CuO-water mixture stabilized the nanofluid for 20 days, resulting in a zeta potential of 37.7 mV, indicating good stability. Additionally, as the volume fraction of nanoparticles increased in the base fluid, there was an increase in thermal conductivity, density, steam pressure, and heating curve slope, while surface tension decreased. Moreover, with higher temperatures, the thermal conductivity and specific heat of water increased, whereas the density, viscosity, and specific heat of the nanofluid decreased with varying volume fractions. Such insights contribute to the broader understanding of nanofluid behavior, laying the groundwork for their application in enhanced thermal management systems. https://vjtve.tvu.ac.ir/article_234093.html The metal powder injection process is a combination of powder metallurgy and plastic injection in the manufacturing field. This process has both the economic advantages of the plastic injection process and the mechanical properties of the powder metallurgy process; In such a way that a metal piece with desirable mechanical properties can be economically produced. Hence, this process has spread in various industries, including the medical equipment industry. The Metal Injection Molding (MIM) process can be divided into four parts: feedstock, injection molding, debinding and sintering. The metal powder is first combined with the polymer and injected into the mold, and then the polymer is destroyed in the debinding process, and the part is finalized in the sintering process. In this study, the joint part of the laparoscopic stapler was manufactured with the MIM process, and the details of all four parts of the process were explained for the desired part. After making the sample, it was concluded that the method is competitive with CNC machining and the main joint and Driver of the laparoscopic stapler can be made and produced with the same method. https://vjtve.tvu.ac.ir/article_234095.html Minimum quantity lubrication (MQL) is a promising solution as an alternative to conventional flood cooling and dry machining. This study investigates the enhancement of drilling performance through the application of hybrid (Al2O3 + CuO) and unitary (Al2O3) nanofluids in the MQL system, focusing on thrust force, torque, friction coefficient, and the final surface quality. A full factorial design of experiments was employed to evaluate the effects of lubrication type, nozzle configuration (number, geometry, and outlet diameter), and their interactions under identical conditions. Results demonstrated that hybrid nanofluids outperformed unitary nanofluids, achieving reductions of 51% in thrust force, 56% in torque, and 42% in friction coefficient compared to dry machining when using four rectangular nozzles with a 1.5 mm outlet. Increasing the number of nozzles from one to four enhanced lubricant distribution, reducing thrust force, torque, and friction by 22%, 23%, and 38%, respectively. Rectangular nozzles with a 1.5 mm outlet proved effective due to superior spray coverage, while ANOVA identified number of nozzles and nozzle geometry as the most influential parameters. Surface quality improvements, including reduced burrs and cracks, were observed with hybrid nanofluids, enhancing precision and fatigue life. Multi-criteria optimization via TOPSIS confirmed the hybrid nanofluid MQL system with four rectangular nozzles (1.5 mm) as the most effective configuration. These findings underscore the potential of advanced MQL strategies to improve machining efficiency, tool life, and surface integrity in green manufacturing. https://vjtve.tvu.ac.ir/article_234135.html Shape memory alloys (SMAs), particularly NiTi-based systems, have garnered significant attention due to their exceptional functional properties, including the shape memory effect (SME) and superelasticity (SE). These characteristics can be tuned by modifying the transformation temperatures through ternary alloying. In this study, the main objective is to tune the transformation temperatures of NiTi alloys through ternary alloying by introducing a low hafnium content (3 at.%). The Ni48.4Ti48.6Hf3 ingots were subjected to various annealing treatments to optimize microstructural homogeneity and promote controlled precipitation of secondary phase precipitation. Dilatometry revealed that annealing at 1050°C for 48 h resulted in the most favorable transformation characteristics, with martensitic start (Ms), martensitic finish (Mf), austenitic start (As), and austenitic finish (Af) temperatures of approximately 65°C, 50°C, 90°C, and 110°C, respectively, measured for the homogenized sample. Transmission electron microscopy confirmed the presence of Ti2Ni precipitates. The incorporation of Hf effectively increased the transformation temperatures, attributed to a reduction in valence electron concentration (cv), thereby enhancing resistance to shear-induced martensitic transformation. Furthermore, mechanical testing demonstrated improved strength and thermal stability in the Hf-modified alloy compared to conventional NiTi, highlighting its suitability for high-temperature shape memory applications. Furthermore, thermomechanical processing including hot rolling, cold rolling, and wire-drawing to 0.3 mm diameter was successfully performed, confirming the alloy’s processability.” https://vjtve.tvu.ac.ir/article_220675.html Investigation of Von Karman rectangular plates nonlinear vibration : Optimal Ho-motopy Analysis Method: In the present study, the Optimal Homotopy Analysis Method (OHAM) is employed to solve the motion equation for a rec-tangular isotropic plate in the presence of the shear deformation and rotary inertia effects based on the Von Karman theo-ry. Suitable agreement between OHAM solution and previously published studies was observed as well as numerical results obtained by bvp function from Maple in the especial cases. The effects of some system parameters such as and initial amplitude on the amplitude oscillation have been checked and studied and also different mode shapes of oscillation for different values of physical parameters are illustrated.Keywords: Nonlinear vibration; Rectangular plate; Time function; Optimal Homotopy Analysis Method (OHAM)To accelerate solution convergence, HAM with two auxiliary parameters was applied to investigate the nonlinear vibration of a rectangular plate. The second auxiliary parameter increases the rate of convergence . In addition, the system parameters effects on the amplitude of oscillation are displayed and different mode shapes of the oscillation for various plate parameters are illustrated https://vjtve.tvu.ac.ir/article_234439.html Life-saving and amputation prevention systems in traditional cutting machines are designed to prevent serious accidents such as amputations and severe injuries. These systems usually include a set of sensors, emergency stop mechanisms, and specialized brakes that work together to stop the operation of the device quickly in case of dangerous conditions, but the stopping speed is sometimes not enough or the safety factor of the brake used in the device's engine. The mentioned is not enough and it leads to an error that leads to serious harm to the user. In this research, an advanced system to save life and prevent amputation has been designed. This safety system is designed for industrial cutting devices whose purpose is to prevent serious accidents such as amputations and severe injuries. The research method includes the design and evaluation of these systems using different sensors and emergency stop mechanisms. The results show that these systems can effectively prevent accidents and increase productivity. https://vjtve.tvu.ac.ir/article_234457.html The reliability of electronic devices can be significantly compromised by moisture condensation within enclosures, leading to corrosion, electrical faults, and pollution accumulation. To address this, a numerical simulation study investigated the impact of ventilation hole dimensions and placement, enclosure material (aluminum, polystyrene, and polyvinyl chloride), and enclosure size under realistic 24-hour weather conditions in humid (Nowshahr) and dry (Tabriz) regions of Iran. Analyzing 15 distinct models through 24-hour transient simulations, the research found that in humid winter conditions, enclosures experienced over 14 hours daily with relative humidity exceeding 95%. Key results indicate that placing ventilation at the top minimizes condensation, and while more holes don't guarantee improvement, their location is critical. Smaller ventilation holes increase condensation, while larger ones are more effective. Notably, polymeric enclosures reduced condensation risk by 19.6-20.6% compared to aluminum in humid climates, whereas increasing enclosure size from 50mm to 100mm only yielded a 2-3% reduction. These findings provide valuable guidance for designing electronic enclosures that effectively mitigate moisture and enhance device longevity. https://vjtve.tvu.ac.ir/article_234464.html Due to their variable speed operation, the use of switched reluctance generators (SRGs) in wind energy systems is more complex compared to conventional generators in this industry. The unique structure of SRGs makes their control more challenging. However, their simple and robust construction, along with lower cost, makes them an attractive option for wind turbines. This paper introduces a novel approach for controlling the speed of SRGs using an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. In this setup, the SRG is powered by a variable speed wind turbine and connected to the power grid through an asymmetric half-bridge converter, a DC-link, and a DC-AC inverter. Effective speed regulation is essential to maximize power output at varying wind speeds. We provide a comprehensive analysis of the SRG’s modeling and control strategies, along with detailed descriptions of the wind turbine, converter, and inverter components. The proposed system's performance is validated through simulations based on variable wind speed data with Matlab/Simulink demonstrate the efficacy and robustness of our control methodology. https://vjtve.tvu.ac.ir/article_208943.html At present time, hospitals and treatment centers are one of the most important places where people travel to receive health and treatment services in different countries of the world. But in some cases, the process of providing services by these centers inevitably leads to the transmission and spread and epidemic of diseases between people. This can even be life-threatening for some patients with vulnerable conditions. So, considering the sensitivity of the transmission of diseases between people and the risks of life threatening, the use of proper ventilation and air purification systems in medical centers, especially indoor and closed spaces such as elevators, where people gather in these spaces without observing social distance, can be help prevent the chain spread of diseases. Therefore, considering the existence of microorganisms in hospital environment, this paper, concentrated on designing a suitable ventilation system especially for air disinfection and purification in the elevators in hospital and medical centers. https://vjtve.tvu.ac.ir/article_209728.html Modern CPUs and GPUs come with multiple processing units, enabling the parallel execution of tasks, which enhances overall system performance. Various parallel programming models have been developed to take full advantage of this parallelism. For computation-heavy applications, such as matrix-matrix multiplication—a key operation in linear algebra frequently applied in scientific simulations and multimedia processing—achieving efficient implementation is essential to fully utilize hardware resources. This paper explores optimized implementations of matrix-matrix multiplication using several parallel programming techniques: SIMD, OpenMP, a Hybrid OpenMP-SIMD model, and OpenCL. Our experimental results highlight the effectiveness of these approaches, with observed speedups of up to 6.5x using SIMD, 3.2x with OpenMP, 16.7x via hybrid OpenMP-SIMD, and a notable 32x speedup with OpenCL, compared to a highly optimized serial baseline. These results demonstrate significant performance improvements when leveraging parallelism for matrix-matrix multiplication. This paper focuses on efficiently implementing matrix-matrix multiplication using popular parallel programming models: SIMD, OpenMP, Hybrid OpenMP-SIMD, and OpenCL. Our experimental results demonstrate the effectiveness of our implementations for different matrix sizes. Compared to an optimized serial implementation, we achieve significant speedups of up to 6.5x for SIMD, 3.2x for OpenMP, 16.7x for hybrid OpenMP-SIMD, and an impressive 32x for OpenCL implementations. https://vjtve.tvu.ac.ir/article_219642.html Nowadays, information security is a crucial issue in organizations. Security flaws are serious threats to an organization. Every organization has sensitive information that may be threatened by malicious adversaries and even employees (insiders). In order to limit security incidents, each organization must concentrate on employee’s behavior. Indeed, employees of an organization act as a natural safeguard for information. Therefore, predicting the behavior of internal employees against the information assets of the organization is very important. To this end, this paper proposes a game-theoretic modeling approach to capture the interaction between employees and organization to predict malicious employees (insiders) and organizations behavior with incomplete information. The game parameters are some important elements of security culture including influencing and organizational behavior factors, and security related factors. By solving the game model, Nash equilibriums are computed and best possible strategies for the participants are recognized. Quantitative evaluation of the information security of organization can be done according to the probability of performing unacceptable behavior by employees and the probability of organization investment in order to increase the organizational information security. https://vjtve.tvu.ac.ir/article_220676.html The present work concerns the analytical solution to estimate of added mass and damping coefficient of a large surface-piercing and bottom-mounted vertical circular cylinder undergoing an oscillating motion. For added mass coefficient, numerical results are presented for various order of solution (an order study). These results show that forth order of solution is agreeable generally. Some numerical results are compared with the previously published data. The comparison shows good agreement. In addition, effects of geometric parameters on the added mass and damping are investigated. Existence of the singular nature causes considerable difficulties in computing the added mass for higher frequencies than the corresponding computation of the damping coefficient. This is encountered when we compute infinite number of eigenvalues. Comparison with previous work indicates good agreement over a range of frequencies the added mass and damping coefficient are plotted along with the actual analytical solutions and performing of the solution until higher order cause that solution procedure be very long. https://vjtve.tvu.ac.ir/article_222045.html Breast cancer is the most commonly identified cancer among women and a major cause of death worldwide. Early detection significantly improves survival rates, but challenges remain in accurately differentiating between benign and malignant tumors. This study proposes a hybrid method combining machine learning algorithms, specifically Random Tree and J48, to improve diagnostic accuracy in breast cancer detection. The research used two datasets: the Ljubljana dataset, consisting of 286 patient samples with ten essential features such as tumor size, malignancy grade, and lymph node involvement, and the Wisconsin (WDBC) dataset, which contains 569 samples. Data preprocessing steps, including standardization, duplicate removal, and feature filtering, were applied to ensure data quality and relevance. The study compared the performance of multiple classifiers, including Naive Bayes, Support Vector Machines (SVM), and Random Tree, using metrics such as accuracy, precision, and recall. Results showed that the Random Tree algorithm achieved a remarkable accuracy of 97.9% on the Ljubljana dataset and 100% on the Wisconsin (WDBC) dataset, outperforming other single algorithms. Combined algorithms significantly increased accuracy. The hybrid method Random Tree + KNN and Random Tree + J48 achieved 96% and 99% accuracy, respectively, across the datasets. These combinations, with higher processing speed and improved accuracy, enhanced diagnostic efficiency and demonstrated greater suitability for clinical implementation and early breast cancer detection. This study highlights the potential of machine learning in advancing early breast cancer detection and sets a benchmark for scalable, accurate, and interpretable diagnostic tools in healthcare. https://vjtve.tvu.ac.ir/article_232191.html This paper presents the design and analysis of an advanced autopilot system for controlling the lateral and longitudinal dynamics of a custom-built unmanned aerial vehicle (UAV). The system regulates roll, pitch, and yaw angles through a unified control strategy, leveraging recursive least squares (RLS) estimation to model the UAV’s dynamic behavior with high precision. A key innovation lies in applying RLS to extract both lateral and longitudinal transfer functions, offering a level of adaptability rarely seen in comparable studies. To optimize multi-axis control, a digital linear quadratic regulator (DLQR) is developed, enabling simultaneous tuning of all three rotational degrees of freedom. This integrated approach marks a departure from conventional proportional, proportional-integral- derivative or fuzzy logic controllers, which often treat these axes independently. The simulation framework incorporates pseudo-random binary sequence (PRBS) inputs and white Gaussian noise (WGN), ensuring robustness under realistic operating conditions. The UAV platform is grounded in validated aerodynamic data, with stability derivatives obtained from DATCOM and JSBSIM, effectively bridging theoretical modeling with real-world flight dynamics. Simulation results reveal substantial improvements in rise time, settling time, and overshoot across all axes, demonstrating the DLQR controller’s effectiveness in achieving fast, stable, and responsive flight behavior. https://vjtve.tvu.ac.ir/article_237673.html Pipelines are a crucial component of the global transporting of petroleum products. Nonetheless, they are susceptible to leakage caused by aging and corrosion. It is therefore important to identify the leaks as soon as possible and have them repaired to avert serious ramifications. In this paper, a reliable robust leak detection system (LDS) that employs neural networks (NNs) in the analysis of pressure fluctuations and detection of leaks is proposed. The approach incorporates a novel data-set generation stage, preprocessing of the data, extraction of input features and design of the neural network to identify and localize leaks. Pressure drop ratios or changes (dP) are computed to check for deviations and establish a baseline for leak detection. Matlab and Simulink are employed to generate reliable operational scenarios with which real-life-like situations for creating the model are acquired. By optimizing the NN architecture and hyperparameters, nonlinear relationships in pipeline data were effectively modeled. Through careful preprocessing and logical decision-making processes, false alarms were significantly reduced, enhancing the reliability of the system in practical applications. The system demonstrated real-time monitoring with rapid leak identification. Adaptability to varying noise levels and operational conditions highlighted the robustness of the model. In testing, the proposed system demonstrated consistently low errors, with Mean Squared Error (MSE) reaching approximately 1.7e-05 for leak localization, ensuring robust real-time pipeline leak detection under diverse flow conditions. https://vjtve.tvu.ac.ir/article_237674.html This paper explores the nonlinear characteristics of coupled radial-tangential vibrations in single-walled carbon nanotubes (SWCNTs) using numerical methods. The authors derive two coupled partial differential equations governing these vibrations through the application of doublet mechanics (DM). It is the first time that coupled nonlinear radial-tangential vibration of SWCNTs studied using DM. On the other hands; the tangential vibration mode has been less studied in scientific researches due to the complexity of the governing equation. To calculate the nonlinear natural frequencies, they utilize the Homotopy Perturbation Method (HPM) to solve the derived equations. The analysis reveals that the coupling between the two vibration modes leads to complex natural frequency behaviors. The study examines how different boundary conditions, vibration modes, and geometrical parameters affect the nonlinear dynamics of these vibrations. Significantly, the research finds that the maximum vibration velocity has a considerable impact on the nonlinear coupled radial-tangential vibration response of SWCNTs. In contrast to linear models, the nonlinear natural frequencies are influenced by the maximum vibration velocity; specifically, an increase in this velocity results in higher natural frequencies. However, as the tube diameter increases, the influence of the maximum vibration velocity decreases. The variations in nonlinear natural frequencies become more pronounced in higher vibration modes. To demonstrate the effectiveness of this method, we compare their findings with results from the fourth-order Runge-Kutta method, observing a strong correlation between both sets of results. The outcomes presented in this paper are original and offer a valuable benchmark for future investigations in this field. https://vjtve.tvu.ac.ir/article_241473.html The rapid expansion of big data analytics has fundamentally transformed marketing research, enabling unprecedented insights into consumer behavior. However, the extensive collection, integration, and analysis of large-scale consumer data have also intensified ethical concerns that extend beyond traditional issues of privacy. This study aims to critically examine the ethical issues and research challenges associated with the use of big data analytics in marketing research.Drawing on contemporary literature in marketing ethics, data ethics, and digital governance, this conceptual paper identifies and categorizes key ethical risks in big data–driven marketing research. The findings indicate that ethical challenges are multidimensional and include violations of informed consent, secondary use of data beyond original purposes, lack of algorithmic transparency, potential biases and discrimination embedded in analytical models, and weaknesses in data governance and regulatory compliance.The study contributes to the marketing research literature by providing an integrated ethical framework that moves beyond a narrow focus on privacy and emphasizes accountability, fairness, and transparency in data-driven decision-making. From a practical perspective, the paper offers guidance for researchers, marketing practitioners, and policymakers to design ethically responsible big data research practices that align with emerging regulatory standards and societal expectations. https://vjtve.tvu.ac.ir/article_242456.html In gasoline engines, the fuel delivery system is one of the most precise and critical components. The design of fuel pathways, pumps, filters, and related features directly impacts fuel pressure a key parameter for combustion performance. In this study, in order to evaluate the combustion parameters and exhaust emissions of a gasoline engine, the effect of fuel injection pressure on lambda (ratio between the actual air-fuel ratio and the stoichiometric air-fuel ratio), ignition advance and exhaust emissions of the engine has been investigated using the response surface method. The test results indicated that the amount of Lambda, ignition advance, CO and HC increased with decreasing the fuel injection pressure. The highest values of Lambda, ignition advance and CO at 5000 rpm with fuel injection pressure of 2.4 bar was measured as 1.044, 37° and 0.88%, respectively. The highest value of CO2 gas was recorded at 1000 rpm with fuel injection pressure of 3.5 bar at 14.8%. The lowest amount of HC emission was reported at 5000 rpm engine and with fuel injection pressure of 3.5 bar as 86 ppm. The results of this study showed that fuel pressure in a gasoline engine is an effective parameter in engine performance and emissions. The study showed that proper design and maintenance of the fuel system maintain optimal fuel pressure, improve engine performance, and reduce exhaust emissions. This plays a key role in reducing air pollution in metropolitan areas https://vjtve.tvu.ac.ir/article_242833.html This study was conducted to design, construct, and evaluate a vibratory apple harvesting machine. The natural frequency, damping constant, damping coefficient, and stiffness of the branch are determined based on mechanical properties. In the next step, to evaluate the efficiency of the machine in terms of apple detachment, a factorial experiment (3 × 3) with a completely randomized design was conducted to investigate the amplitude and frequency for apple detachment. Three levels of frequency (4, 9, and 20 Hz) and three levels of amplitude (20, 32, and 40 mm) were investigated. Analysis of the variance of fruit detachment based on Duncan 5% shows that shaking frequency and shaking amplitude both have a highly significant effect on fruit detachment. The effects of shaking frequency and amplitude were both significant on fruit detachment, but no interacting effects were observed. The most suitable shaking frequency and amplitude for harvesting apples were 9 Hz and 40 mm, respectively, which resulted in a 94.37% fruit detachment rate. The static pulling force required to detach the fruit from the branch decreases with an increase in fruit mass or geometric size. The ratio of the static detachment force to fruit weight was determined to vary between 5.1 and 15.3 for the apple investigated. Therefore, in apple harvesting, the effect of increasing the shaking amplitude relative to the shaking frequency on fruit detachment was concluded. Applying oscilation for 8 seconds increased harvesting, but increasing the time from 8 seconds to above that did not significantly affect the outcome. https://vjtve.tvu.ac.ir/article_243374.html This study leverages Diffusion Tensor Imaging (DTI) data from the ADNI database to classify Alzheimer’s Disease (AD) stages—Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and AD—while distinguishing them from healthy controls (HC). A total of 228 features were extracted from 57 regions of interest (ROIs) and analyzed using advanced machine learning methods. Classification models were rigorously trained and validated through 10-fold cross-validation. The best-performing model achieved a test accuracy of 94.3% in distinguishing the four groups (HC, EMCI, LMCI, and AD), underscoring the importance of model selection and feature engineering. The Areas Under the Curve (AUCs) were 0.97, 0.96, 0.99, and 0.91 for HC, EMCI, LMCI, and AD, respectively. Feature ranking highlighted Axial Diffusivity (AxD) in the uncinate fasciculus as a key biomarker with strong discriminative power across all stages of AD. Moreover, regions such as the sagittal stratum and hippocampal cingulum were also implicated, reflecting their known roles in memory and executive function decline. Overall, these findings enhance understanding of the neuropathological mechanisms underlying AD and demonstrate the potential of DTI-based machine learning as a non-invasive tool for early diagnosis and personalized treatment planning. https://vjtve.tvu.ac.ir/article_243747.html This study develops a computational framework to predict the evolution of matrix cracking and the resulting interlaminar delamination in cross-ply composite laminates subjected to in-plane loading. By combining meso–macro multiscale modeling with continuum damage mechanics (CDM), an integrated approach is proposed to evaluate progressive damage in composite structures. The methodology employs quasi-simultaneous multiscale analysis, advanced finite element simulations, and image-processing-assisted machine learning to accurately characterize the initiation and propagation of matrix cracks and delamination. To quantify the influence of matrix cracking and interlaminar separation on the nonlinear response of multilayer laminates, a simplified AI-based formulation is developed to compute damage parameters linked to matrix cracking and induced delamination. A CDM-based user subroutine is implemented in the finite element environment to simulate the gradual evolution of transverse cracks, fiber breakage, and interlayer separation, enabling prediction of the ultimate failure load under monotonic loading. The accuracy of the proposed computational model is validated against available experimental datasets. The predicted stress–strain curves, damage chronology, and failure patterns show excellent agreement with experimental observations, confirming the method's ability to capture intralaminar and interlaminar damage mechanisms simultaneously in composite laminates. https://vjtve.tvu.ac.ir/article_243748.html The cooling system is essential for the optimal operation of internal combustion engines. Regular maintenance and monitoring can enhance its performance and extend the engine's lifespan. This study examined the effect of changes in coolant pressure on local boiling in the Ford MVH418 engine. Tests were conducted at eight different speeds and two loads of 15% and 30%. A Cussons dynamometer was used to measure load and torque. Coolant pressure was measured at three points: the inlet and outlet of the radiator and the engine inlet, using an MPS500 pressure sensor. Results indicated that at high RPMs and heavier loads, engine warm-up time is reduced. Additionally, the greatest pressure difference observed in all tests was between the radiator inlet and outlet, which increases with higher RPMs and applied loads. For example, at a load of 30%, this pressure difference reached a maximum of 10.6% at 1500 RPM and 38.3% at 3000 RPM. https://vjtve.tvu.ac.ir/article_243749.html In recent years, the significant increase in global energy demand, combined with the limited availability of non-renewable resources, has highlighted the critical need to optimize energy consumption and reduce waste, particularly in the building sector. In Iran, the building and housing sector accounts for a disproportionately high share of total energy consumption, exceeding global averages, which underscores the importance of targeted energy efficiency measures. This study investigates twelve key variables across 768 diverse building samples to identify and analyze the main factors influencing heating energy efficiency. Utilizing data mining techniques alongside artificial neural network (ANN) algorithms implemented in SPSS Modeler software, the study quantitatively evaluates the impact of these variables. Results indicate that overall building height, roof area, and window area are the most significant parameters affecting heating energy consumption, collectively explaining over 68% of the observed variance. The ANN model further demonstrates high predictive performance, achieving an accuracy rate of 96.3%. These findings provide practical, quantitative insights that can assist engineers, architects, and policymakers in developing effective strategies to improve energy efficiency in heating systems within buildings. https://vjtve.tvu.ac.ir/article_243750.html Electrical Impedance Tomography (EIT) is a promising imaging technique for real-time, radiation-free monitoring of thoracic function. Accurate image reconstruction in EIT critically depends on the forward problem solver, especially in dynamic applications like lung monitoring, where internal anatomical deformation occurs continuously. This paper presents a comparative study of the Finite Element Method (FEM) and the meshless Element-Free Galerkin Method (EFGM) for solving the forward and inverse problems in Applied Potential Tomography (APT)/EIT using the Complete Electrode Model (CEM). The novelty of this work lies in the integration of a fast polygonal-pixel-based Jacobian computation technique with EFGM for APT/EIT systems, extending previous work limited to Adaptive Current Tomography (ACT). We demonstrate that EFGM, owing to its meshless nature, offers higher flexibility and robustness under geometrical deformation compared to FEM. Simulation results using a realistic thoracic model validate the improved stability and reconstruction accuracy of the EFGM-based approach, highlighting its potential for clinical thoracic EIT applications. https://vjtve.tvu.ac.ir/article_243751.html Understanding and identifying the organization’s existing situation completely and finding its problems, known as organizational diagnosis, is a vital step to improve the organization’s internal situation. Organizational diagnosis is an effective method used to examine the organization and determine the gap between the current and desired performance and how to achieve goals. Gathering information and understanding the organizations’ problems are among the major factors for constructive development. In this way, the organization’s problems and their causes could be identified, so that the organization’s manager could plan effective solutions. The present research aims to identify the points that could be improved and prioritize the solutions to improve the project management system problems and, finally, examine the project management of Lavan Tablo Company in Kerman Province. Thus, project management system problems are first weighted by distributing a questionnaire among the relevant experts in the studied company using the best-worst method (BWM) and experts’ opinions working in the project management system and, then, prioritized. Finally, important problems affecting the project management system are identified based on their priority. https://vjtve.tvu.ac.ir/article_243752.html Air pollution is a major environmental challenge in densely populated cities, with serious impacts on human health and ecosystems. Accurate prediction of pollutant concentrations such as carbon monoxide (CO) and nitrogen dioxide (NO₂) is essential for environmental planning and air quality management. In this study, we propose an innovative and lightweight framework based on the Transformer architecture for simultaneous prediction of CO and NO₂ using time-series data. The methodology includes data cleaning, handling noisy values, normalization, and creating time windows. The model uses historical data in 24-hour sliding windows to predict pollutant levels for the next hour. Experimental results show that our model achieves high accuracy while being computationally efficient, outperforming conventional models such as LSTM and CNN-LSTM. With an R² score above 0.92 for both pollutants, the model demonstrates strong performance on multivariate environmental data. Additionally, comparative analysis highlights the potential of this lightweight Transformer model for use in real-time air quality monitoring systems and smart city applications. https://vjtve.tvu.ac.ir/article_243753.html Accurate forecasting of photovoltaic (PV) power generation is important to enhance the efficiency and reliability of renewable energy in the present power grids. Despite significant progress in predicting PV generation, the nature of PV data remains heterogeneous, non-linear, and dynamic, making modelling and predictive ability challenging in their own right. This dissertation proposes a self-adaptive hybrid approach that offers a novel development avenue within the renewable energy forecast paradigm. It utilizes a Nonparametric Dirichlet Process Gaussian Mixture Model (DPGMM) to identify the distinct predictive capabilities of machine-learning algorithms from a validation perspective, thereby informing their employment in the hybrid method. DPGMM was able to identify homogeneous subgroups from a ~3-year PV dataset of 300 households in Sydney, Australia, allowing the development of distinct predictive models for each subgroup. From a statistical validation perspective, contrast testing was completed using a Friedman and Wilcoxon signed-rank procedure. While there exist differences between the models (p < 0.05), it was clear that Gradient Boosting Regression (GBR) outperformed the support vector regression (SVR) and random forest regression (RFR) models on all clusters. Finally, metrics of the mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE) were employed to examine and measure improvements in accuracy compared to baseline methods. The hybrid approach yielded statistically significant improvement, which included increases of 12% for MAE, 22% for MSE, 15% for RMSE compared against single-stage convolution implementations. https://vjtve.tvu.ac.ir/article_243755.html In this study, considering that we can remove chromium (III) oxides from the surface of galvanized sheets using purification technologies such as electrolysis, active nanoparticles, photocatalysts, or electrochemical oxidation processes, one of these methods involves the use of photocatalysts such as titanium dioxide (TiO2). However, the application of single-component photocatalysts like TiO2 is limited due to issues in achieving strong oxidation and reduction capabilities and a limited broad light response. Among the various strategies to enhance photocatalytic performance, constructing a heterojunction is highly recommended due to its advantages in combining the individual benefits of each component and effectively separating the light-excited electron-hole pairs. The TiO2 nanophotocatalyst, with its wide bandgap, is effective in generating electron-hole pairs for photocatalytic reactions. On the other hand, the Sb2WO6 photocatalyst, with a more suitable bandgap, enhances visible light absorption and electron-hole pair generation. Combining these two photocatalysts in a nanocomposite improves the photocatalytic efficiency, allowing chromium (III) oxides to be removed from galvanized surfaces. The Sb2WO6/TiO2 nanocomposite was characterized using techniques such as FT-IR, UV-Vis, XRD, and FESEM for morphological analysis. https://vjtve.tvu.ac.ir/article_243756.html Hepatitis C is a serious and potentially life-threatening liver disease that often remains asymptomatic for years, making early diagnosis challenging. If left undetected, it can lead to severe complications such as cirrhosis, liver failure, and hepatocellular carcinoma. Given the limitations of traditional diagnostic methods, which often struggle with accuracy and reliability, there is a growing need for advanced computational techniques to improve early detection and classification. This study introduces an ensemble machine learning approach to enhance the accuracy of Hepatitis C classification. The proposed method integrates multiple classifiers, including Naïve Bayes, Support Vector Machine, Decision Tree, and Linear Regression, using a majority voting mechanism. By addressing class imbalance and optimizing feature selection, this ensemble approach enhances predictive performance. Experimental evaluations using the Hepatitis UCI dataset demonstrate that the proposed method achieves a remarkable accuracy of 97.5%, outperforming individual models and conventional techniques. These findings underscore the potential of ensemble learning in medical decision support systems, providing a reliable tool for early diagnosis, risk assessment, and timely intervention. https://vjtve.tvu.ac.ir/article_243757.html This paper introduces a novel two-stage algorithm for structural damage detection under static loading conditions, explicitly addressing uncertainties in both applied loads and sensor measurements. We rigorously demonstrate that the combined effect of load and sensor errors can be equivalently represented as sensor errors with a specifically defined covariance matrix. The first stage of the proposed method focuses on identifying the location of damaged elements. This is achieved by initially transforming the error covariance matrix into an identity matrix via a linear whitening transformation, effectively converting the ellipsoidal error probability density function (PDF) into a spherical form. Subsequently, the damaged elements are identified by locating those whose displacement sensitivity vectors most effectively span the subspace containing the displacement change vector. The second stage then quantifies the extent of damage using Tikhonov regularization, without requiring iterative model updating. The efficacy of the proposed Subspace Selection with Whitening and Tikhonov Regularization (SSWT) algorithm is rigorously validated through numerical simulations on a truss structure subjected to various damage scenarios and under the influence of uncertainties in both sensors and applied loads. The results unequivocally demonstrate the superior performance of SSWT compared to existing methods in the literature. https://vjtve.tvu.ac.ir/article_243758.html Cyber-physical security is essential for protecting critical infrastructure, such as water and electricity distribution networks. This study addresses false code injection attacks on cyber-physical systems (CPS) by proposing an adaptive optimal control strategy. The method ensures that system outputs remain within safe operational limits during attacks, preventing potential damage. An adaptive control system is developed to estimate and compensate for disturbed control signals in real-time using system data, thereby enhancing stability and resilience against cyber threats. The effectiveness of the approach is validated through graph theory, demonstrating its ability to improve CPS security and reliability. The research also identifies limitations in active cyber defense systems, particularly against zero-day attacks, and offers a practical solution using an adaptive optimal controller. Simulations highlight that the implemented FIR algorithm effectively filters noise and tracks reference signals, while the optimal controller autonomously derives control laws without requiring system dynamics equations. This work contributes to advancing adaptive control strategies for enhancing cyber-physical security in critical infrastructure. https://vjtve.tvu.ac.ir/article_243759.html This study investigates the critical role of organizational leadership in advancing green economic growth amid global environmental challenges. The primary objective is to evaluate sustainable leadership—a model emphasizing social responsibility and creating long-term value for stakeholders, future generations, and the environment while enhancing organizational performance and reducing costs. An applied research method was adopted, utilizing the Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) to assess and rank managers’ performance based on sustainable leadership dimensions. This method effectively addresses the inherent uncertainty and complexity involved in evaluating sustainability-related leadership. The statistical population includes 15 selected companies from the food industry in Alborz province, Iran. The sample was selected through purposive sampling, comprising five experts specialized in sustainability and green management within the food sector. These experts assessed the companies’ leadership performance based on three main indicators: environmental, economic, and social. The findings revealed that among the studied companies, Zar, Delpazir, and REZ Chocolate achieved the highest sustainable leadership scores, with values of 0.3645, 0.3533, and 0.3501, respectively. These results underscore the practical applicability of the IF-AHP method in identifying and ranking sustainable leaders within organizations. In conclusion, the research contributes to understanding sustainable leadership and offers a structured approach for evaluating managerial effectiveness in supporting green economic development. It highlights the importance of capable leaders who can align organizational goals with sustainability principles while fostering trust and long-term success. https://vjtve.tvu.ac.ir/article_243760.html This study examines the impact of Managers’ Perceived Organizational Paranoia (MPOP) -defined as an individual-level perceptual climate construct - on the quality of financial decision-making processes reported by managers in Iranian manufacturing firms. Data were collected from 285 financial managers (one per firm) using a descriptive survey design and snowball sampling, yielding a non-representative sample. Structural equation modeling in AMOS, cross-validated with PLS-SEM (SRMR = 0.049), showed that MPOP significantly reduces financial decision-making quality. Perceived risk partially mediates this negative relationship, while internal controls exert a modest but significant moderating effect (β = 0.18, p < 0.001) through latent variable interaction, despite lacking a direct main effect on decision quality. All measures exhibited high reliability (Cronbach’s α > 0.88), and tests confirmed that common method bias was not a concern (common latent factor explained 18.4% of variance; parameter shifts < 5%). Given the single-respondent, non-probability design, MPOP reflects individual perceptions rather than shared organizational cognition, and results are preliminary and not statistically generalizable. Nonetheless, this study provides early empirical evidence of how psychological climate, risk perception, and internal governance mechanisms jointly shape financial decision-making under conditions of uncertainty. https://vjtve.tvu.ac.ir/article_244134.html In this work , using quantum mechanics , the interaction of drug gentamicin(GEN) with natural nanotubes(NT) and COOH functionalized (NTCOOH) single carbon nanotubes (SWNTs) have been studied.All of the present calculations have been performed with the B3LYP hybrid density functional level using the GAUSSIAN03 package in gas and solution phases. Quantum molecular descriptors and frontiter orbital analysis in the drug- nanotube systems were studied . It was found that binding of gentamicin with pristine (GEN/NT) in gas phase and COOH functionalized (GEN/NTCOOH) carbon nanotubes in both phases is thermodynamically favorable.The solvation energies show that the solubility of GEN/NTCOOH is higher than GEN/NT. The rapid development of nanoscience has opened new ways in timely and prompt diagnosing of diseases and drug delivery. The use of carbon nanotubes in drug delivery is a new field which is rapidly developing.so far, different systems such as polymers, dendrimers, snd liposomes have been used for drug delivery, but carbon nanotubes provide more effective structures due to high drug loading capacities and good cell penetration qualities.