International Journal of Engineering
Volume:38 Issue: 4, Apr 2025

Since the constant power loads (CPLs) have negative-impedance characteristics, the system damping of DC microgrid is reduced, which will lead to the collapse of bus voltage. In addition, the errors of current sharing within parallel-connected DC-DC converters amplify due to different line impedances. To address these issues, a hybrid coordination control strategy is proposed for parallel-connected boost converters, which realizes the stable control and maintains the accuracy of current distribution. Firstly, a passivity-based control (PBC) with a proportional-integral (PI) regulator is developed for the boost converter with CPL. The virtual damping based PBC enhances the system damping and PI regulator eliminates the steady-state error caused by the variation of load. On this basis, a secondary voltage control (SVC) featuring in simplicity and weak dependence on communication is introduced to remove the errors of current distribution. Finally, a RT-LAB-based hardware in the loop (HIL) experimental platform is established and the experimental results verify the effectiveness of the proposed hybrid coordination control strategy.

Predicting herb-target interactions is crucial for advancing traditional Chinese medicine (TCM), but the existing methods often struggle with incomplete datasets and fail to fully leverage the network structure.   The objective of this study is to develop a novel network-based approach that integrates both network topology as well as molecular data to further improve the accuracy of herb-target interaction predictions. We have constructed a heterogeneous network encompassing herbs, targets and symptoms, incorporating various network measures to assess edge weights. Molecular data for herbs has also been integrated into the model. Using six ensemble supervised learning models (GBM, XGBoost, LightGBM, CatBoost, etc.),  the model has been trained to predict herb-target interactions. The proposed  model achieved an AUROC of 88% and an AUPR of 90% on the HIT2 dataset, significantly outperforming the existing approaches. This research highlights the potential of integrating network structure and molecular data for accurate herb-target prediction, opening new avenues for drug discovery and personalized medicine in TCM.

The article considers the issue of managerial decision-making in an oil and gas company, which is related to the class of multi-criteria choice problems. To improve the validity of decisions in the context of companies’ sustainable development it is proposed to implement a Decision Support System (DSS). A brief review of the existing methods of assessment and choice of alternatives is given. A structural model of the DSS based on the concept of a distributed problem-oriented solving network is introduced. It includes tools for extracting and processing the raw data, a module for data analysis based on ESG indicators, a visualisation subsystem and a coordinating unit. A functional model of the DSS coordinating unit that includes a logical system planner and a task planner has been developed. The hypothesis that the utilisation of the proposed DSS improves the validity of managerial decisions on the sustainable development of oil and gas companies has been statisticaly verified.

This article analyzes the effects of combustion type (conventional diesel, dual-fuel, and gas-fueled), structural material (Ductile cast iron and Gray cast iron), and thermal boundary conditions on the sensitive points of the heavy diesel engine cylinder head. The target engine in this numerical and experimental simulation is a 12-cylinder V-shaped heavy-duty medium-speed diesel engine with a turbocharger. The experiment begins by installing 14 sensors on cylinder head No. A6 due to the worst working condition of the engine. 3D simulation is performed using the Finite Volume Method (FVM) within the framework of ANSYS-Fluent 2021 software under time-variable conditions (18 min) and k-ε turbulent flow model. The outputs are as follows: with an increase of 24% of the TGas, the temperature of the bridge between the exhaust-exhaust and Inlet-Inlet valves for the Gray and Ductile cast iron cylinder head increases by about 20%. Meanwhile, with an increase of 36% from the heat transfer coefficient (HTC), the temperature of the bridge between the valves increases by 14%. The ratio of the effect of changes in the TGas to the HTC on the temperature of the bridge between the valves is about 1.9 for the cylinder head made of ductile cast iron and about 1.8 for the gray cast iron. Subcooled flow boiling will probably occur in the initial areas of the cylinder head, i.e. sensors 2, 5, 8, and 11 due to the temperature increase above 447.33K.

The ever-increasing growth of amoral content in cyberspace has caused the idea of detecting such contents in images, then creating an alarm or limiters in different age ranges around the world. This content not only detects harmful visual items, but also it detects instances of abusing individuals. This study has represented a new approach to detect images with amoral content. The suggested model includes two deep models based on inception blocks and attention and residual based convolutional layer which acts with two different approaches: the first model is a five-class recognition model and the second one is an estimator model which maps the image into levels of inappropriateness between 0 and 1. Combining these two models by an aggregator based on first-order rules leads to representing a model which improved 2.1% detection precision in not suitable for work (NSFW) dataset and 2.3% on TI-UNRAM dataset in comparison with other state-of-the-art models. Our approach also shows promising results to model culturally based definition of NSFW, especially in Iran.

Groundwater pollution from arsenic is one of the biggest hindrances for billions of people to get cheap and easy access to potable water. Searching for novel remediation techniques is prompted by the cost of installations and maintenance associated with existing purification technologies. Because of landfill issues and secondary pollution, solid waste from the construction and agricultural sectors is difficult to handle. In this study, a gypsum-based porous filter media block (PFMB) has been introduced which can be a cheap arsenic (III) removal technology introduced using agricultural and construction solid waste. The PFMB was made of gypsum (CaSO4) using aluminium sulphate (Al2(SO4)3), Ferric chloride hexahydrate (FeCl3.6H2O, Sodium bicarbonate (NaHCO3), bio-adsorbent and biochar prepared from waste vegetables. The physical properties of PFMB were evaluated, and hydraulic conductivity was found suitable for applying this in the water filtration system. In the column study using NaOH-treated PFMB, arsenic (III) removal was achieved 100% in the range of 0.1-1.0 mg/L. In batch experiments, the removal rate was more than 80%. Isotherms were evaluated as batch experiment was fitted with Langmuir isotherm and column study was compiled with Thomas model. The maximum adsorption capacity was for column experiments, 5.471 µg/g, and for batch experiments was 28 µg/g. Both the adsorbents and metal salts were found responsible for arsenic (III) adsorption. Variation of pH in adsorption was found insignificant. This PFMB can be used as filter media for arsenic (III) treatment in developing countries using solid waste.

In an era where underground transportation infrastructure is increasingly vital, the construction of tunnels across fault lines has become a necessary challenge. This study addresses the critical issue of assessing the impact of fault ruptures on shallow tunnels, with a particular emphasis on the variability of soil parameters. We employ the Stochastic Finite Element Method (SFEM), providing a robust framework for simulating the unpredictable nature of soil properties in shallow tunnels under the impacts of surface fault rupture hazards. Our approach highlights the significant influence of soil parameter variations in the analysis of tunnel vulnerability during fault ruptures. The findings offer valuable insights for the design and safety assessment of tunnels in seismically active regions, contributing to the advancement of geotechnical engineering practices in the context of fault rupture hazards. Specifically, the maximum stress values demonstrated substantial increases of 77 and 100% when compared to the 0.5-meter case for the 1.0-meter and 2.0-meter scenarios, respectively.

The global energy sector faces an important task of ensuring the energy security of states. In this regard, oil and gas producing companies need to master and use potentially technologically effective solutions in the development of hard-to-recover reserves (HTR), whose share is on an upward trend. It should be noted that one of the key problems in well production of hydrocarbons is heavy oil with a high content of asphalt-resin-paraffin components. Combined steam cyclic treatments of bottomhole formation zones are the most popular solution, and there are ways to significantly increase their efficiency. The purpose of this study is to substantiate the efficiency of using aquathermolysis catalysts in the implementation of combined cyclic steam stimulation (CSS). In the present article the numerical model was built in the software product for geological and hydrodynamic modeling. The model demonstrates the distribution of high-molecular-weight oil components in the bottomhole formation zone during solvent assisted CSS with and without the use of a molybdenum-based oil-soluble aquathermolysis catalyst and formic acid hydrogen donor. The results obtained show that pre-injection of the catalyst and hydrogen donor contribute to the reduction of high molecular weight components of oil by 1.5 times, while the average daily oil production rate increased by about 4.3 times. This confirms the technological efficiency of the technology under consideration. The obtained relationship between the nature of the distribution of high-molecular components of oil in the bottomhole formation zone and the improvement of reservoir properties has not been previously considered in similar studies.

The effects of lightweight aggregate type, content, and maximum size, as well as the water-to-binder ratio, on the fresh and hardened characteristics of lightweight self-compacting concrete (LWSCC), were investigated. Fifteen LWSCC mixtures were prepared with varying proportions of LECA and scoria (100-0, 50-50, and 0-100%) and divided into two groups based on water-to-binder ratios and dmax values. The slump flow, T50, V-funnel flow time, and L-box were the fresh properties that were investigated, while the measurements of compressive strength, splitting tensile strength, flexural strength, modulus of elasticity, ultrasonic pulse velocity, and drying shrinkage were carried out in the hardened state. The results indicated that higher scoria content generally declined fresh state properties and improved hardened ones. An increase in dmax resulted in a reduction in the amount of superplasticizer needed to maintain the slump flow between 70 and 75 cm. The impact of the dmax on the mechanical characteristics of LWSCC was negligible. With increasing the dmax from 9.5 to 19 mm, the fc merely decreased by 7, 4, and 5% for S100-L0, S50-L50, and S0-L100 mixes, respectively. For ft, fr, and E, the decrease was between 3.2 and 0.6%. The correlation between compressive strength and splitting tensile strength was close to the CEB-FIP proposed relationship for conventional concrete, whereas the relationship between compressive strength and the modulus of elasticity was appropriately estimated using the ACI 318-19 suggested expressions. None of the proposed relations in ACI 209.2R-08 demonstrated adequate accuracy to predict the drying shrinkage of LWSCC at all ages.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder which impacts individuals in various ways. It is distinguished by confined and repetitive behaviors as well as difficulties in interaction and social communication. Neuroimaging techniques such as resting-state functional magnetic resonance imaging (rs-fMRI) have impacted our knowledge of the brain's function by enabling researchers to view brain activity with respect to time. These technologies have offered useful information on how brain regions are engaged in numerous cognitive processes. Functional connectivity (FC) features extracted from the rs-fMRI scans are widely used for classifying individuals with ASD using deep learning methods. However, developing an effective approach to increase ASD classification accuracy from typically developing controls remains an important challenge. Therefore, we introduced a new deep learning architecture for ASD classification. The architecture is composed of four main steps: 1. Convolutional-max pooling 2. First fully connected layers 3. Concatenating 4. Second fully connected layers. We have evaluated the architecture for classification using rs-fMRI data from the publicly available dataset Autism Brain Imaging Data Exchange I (ABIDE I) with a 10-fold cross-validation method. We utilized the Pearson correlation coefficient to calculate the FC matrices, which served as the input for the architecture. Then, the proposed architecture classifies the subjects with ASD. We achieved an average classification accuracy of 72.46%, which outperformed the existing methods in ASD classification.

The widespread use of Global Navigation Satellite Systems (GNSS) in geodesy necessitates the transformation of coordinates from global to national systems, a process that can introduce spatial errors. This research focuses on evaluating the accuracy of coordinate transformations to the national coordinate system of Lebanon, which uses the double stereographic projection on the CLARKE 1880 ellipsoid. Specifically, the study examines the horizontal plane accuracy of this transformation process. The Lebanese map was divided into five zones for this study. Transformation parameters were generated for each zone by combining International Terrestrial Reference Frame (ITRF) 2014 coordinates (obtained using an online precise point positioning service) with CLARKE 1880 coordinates calculated from known geodetic coordinates. The newly calculated parameters were then tested against the adopted parameters using 39 check points, and positional errors in the horizontal plane were analyzed. Additionally, the inverse distance weight method was employed to interpolate transformation parameters at the boundaries between zones. The applied method significantly reduced positional errors in projected coordinates at most check points. Furthermore, the successful application of the inverse distance weight method for interpolation proved effective in minimizing positional discrepancies when transforming coordinates in areas located along zone boundaries.

In this paper, the effects of administration strategies on the design of multi-drug chemotherapy treatments were studied. Two separate approaches were suggested, such that in the first one, drugs are administered sequentially, and on the second approach, simultaneous injection of drugs was suggested. A multi-drug two-compartment pharmacokinetic model and a multi-drug cell- cycle nonspecific tumor growth model are expanded to investigate how the tumor behaves when applying the simultaneous strategy. The chemotherapy design is structured as a nonlinear optimization problem, aiming to maximize the treatment’s effectiveness and minimize the quantity of cancerous cells remaining after the completion of the therapy. By developing an optimal control problem and employing a genetic algorithm to solve it, the most appropriate drug regimens are determined. This study seeks to determine whether or not the way of drugs administration may affect the treatment outcome. The simulation results are reported for several scenarios. To verify the robustness of the resultant findings, various virtual patients with uncertainty in the model’s parameters were also evaluated. The obtained results showed that simultaneous administration strategy can provide better results.

Despite the global trend for the development and implementation of green energy, petroleum is still the main source of energy in the society. In this regard, the issue of increasing efficiency and reducing the cost of construction and operation of oil and gas wells is still relevant. Drilling into the pay-zone is one of the most important stages of the whole field development. This paper is devoted to the consideration of the possibility of applying the effect of changing the wettability of oil-saturated rock to increase the relative permeability of oil and improve the overall drill-in operation. For this purpose, the authors analyzed international experience in the use of surfactants in technological fluids. Further, a methodology for testing and selection of the most effective surfactant samples using KRUSS DSA 100 tensiometer and regression analysis of the collected data was prepared. As a result of this work, it was found that surfactants can change the wettability of saturated rock in hydrocarbon environment and due to adsorption of surfactant molecules on the surface of saturated rock, the adhesion of hydrocarbons decreases, which leads to an increase in their mobility on the rock surface.

With industrialization, economic growth, and increasing demand for minerals, mining raw materials from deep and low-grade deposits is increasing, highlighting the need to evaluate the environmental impacts of these mines. This study aims to provide a comprehensive sustainability assessment (SA) model for deep and large-scale open pit mines (DLSOP). The proposed model evaluates a total of 44 environmental factors in 11 groups. Z-numbers plus Delphi Fuzzy Analytical Hierarchy Process (ZFDAHP) and scenarios, evaluate DLSOP mine conditions with minimal uncertainty. Since the mining impacts spread over time (short-, medium-, and long-term) and space (local, regional, national, and global), the spatiotemporal scale (ST) of factors is used to calculate the dynamic sustainability score. This model considers the impact of the mine's depth and scale on the intensity of the positive and negative environmental impacts of mining and provides a final sustainability score on a scale of 0-10. Verification was done in Sungun Copper Mine (SCM) in northwest Iran to show the effectiveness of the proposed model. Waste, water, and climate change with 23.9%, 16.5%, and 15.6% importance weight, respectively, accounted for the greatest environmental impact in DLSOP mines. The static and dynamic environmental sustainability scores for SCM were 3.110 and 3.200 out of 10, respectively, showing that SCM should pay more attention to its activities towards environmental sustainability. The combined technique of this model increases the reliability and accuracy of previous models in DLSOP mines by considering a wide range of impact factors and taking into account the ST scale of impacts.

Optimizing time, cost, quality and risk are the four main factors in gas project management. By considering the significance of gas projects in the country's gas development as one of the top sources of revenue and economic advantages, efficient and targeted management in gas distribution network projects implementation is believed essential. The objective of this research is to develop a multi-objective robust probabilistic programming model using the improved ε - constraint method while adhering to the objectives of minimizing time, cost and risk functions and maximizing the expected quality function. To this end, a sample size of 36 experts and specialists in the gas industry has been selected. The results of this research enable gas project managers to select the best solution among various scenarios based on effective management with different values between the four objective functions of time, cost, quality and risk, thus achieving strong optimization. Additionally, the results indicate that the two objective functions derived from the Improved ε - constraint method have 99% confidence in better exploring the problem-solving space in producing superior solutions and Pareto charts of the risk and quality objective functions exhibit the optimal solution. The findings demonstrate the accuracy of the model and the efficiency of the proposed method.

Sales forecasting is an essential task for businesses as it enables suppliers to analyze customer preferences, thereby optimizing profits, reducing costs, and minimizing product returns.  Confronting the complexities of sales forecasting, this research introduces a new hybrid model for sales forecasting that combines classic time series analysis with advanced deep learning techniques to address the limitations present in existing forecasting models. This model combines Long Short-Term Memory (LSTM), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Triple Exponential Smoothing (Holt-Winters) to capture complex patterns, handle linear trends and seasonal patterns, and emphasize recent sales trends. A comparative analysis using the Mean Absolute Percentage Error (MAPE) metric demonstrates the enhanced performance of the hybrid model over the individual components. The findings indicate that the hybrid model surpasses LSTM, SARIMA, and Holt-Winters models by 9%, 39%, and 43%, respectively. This improvement in forecasting accuracy significantly benefits marketplace management by offering more reliable sales predictions. Applying this model facilitates the prediction of sales for the next ‘n’ days, informing inventory management, pricing strategies, and promotional planning to optimize sales performance.

The huge amount of information has forced researchers to find a solution to face this fundamental problem called data overload. Recommender systems try to suggest the required information to the user by examining the user's preferences directly or based on the behavior of other similar users in a way that best matches the user's needs. Meanwhile, the use of textual information hidden in the user's biography or comments can be very useful. Declarative systems try to find similarities by examining each word in users' comments with the comments of other users, this is if different meanings for a word are ignored. In this way, the use of auto-encoder networks in order to check the semantic relationship of words in a sentence with respect to the opinions of other users can overcome this challenge. In this article, a personalization approach is presented based on the recommendation system in social networks using the combination of collaborative filter and deep auto-encoder networks. In proposed recommendation system, the information in the user profile and user comments to each website is used as the input of the presented combined deep auto-encoder network and the collaborative filter method in order to find similar users accurately and predict the website's rating by users. Finally, after finding similar users, it provides recommendations to visit and personalize the web page of serious users based on the favorite websites of similar users. Due to the convolutional layers of proposed deep auto-encoder network, the training process in the middle layer has performed on semantic relationship of words in a sentence to find similar comments and users. This method implemented on two standard datasets titled TripAdvisor and Yelp. The proposed method has improved in terms of statistical accuracy of about 39%, the ratio of successful recommendations to useful recommendations of about 6%, and the accuracy of recognizing similar users is about 18% from other classification methods.

Bridge is important infrastructure that supports societal connectivity, mobility, and the smooth flow of goods as well as services in Indonesia. In the last 3 years, 60% of bridges in Indonesia have become old and deteriorated, necessitating the need for proper maintenance and preservation. Implementation challenges often arise due to policy issues, inefficient organizational structures, and limited adequate Funding. Therefore, this research aimed to evaluate the policies, organizational structures, and Funding related to bridge maintenance and preservation in Indonesia. Secondary data obtained from current policy regulations, present organizational structures, and budget allocations for bridge maintenance using Partial Least Square Structural Equation Modeling (SEM-PLS) – Artifical Neural Network (ANN) Method were qualitatively and quantitatively analyzed. Surveys and interviews with stakeholders, such as government entities, relevant institutions, and contractors involved in bridge maintenance were conducted. The result provided insights into the challenges faced in bridge maintenance and preservation implementation. Furthermore, it provided policy recommendations needed to enhance the effectiveness of policies, organizational structures, and Funding related to bridge maintenance and preservation.

In this research, a hybrid method of a simple shear extrusion (SSE) process is proposed with simultaneous application of ultrasonic vibrations at the beginning of the deformation zone in pure copper. A cylindrical-conical-cylindrical horn was designed to amplify and transmit the ultrasonic vibrations using modal analysis in Abaqus. The resonant frequency of 20.332 kHz was used with two amplitudes of 15 and 25 micrometers. Then, the produced ultra-fine grain copper samples after the first pass with and without ultrasonic vibrations were compared. A 54% and 65% reduction in grain size were reported in ultrasonic-assisted simple shear extrusion (USSE) with two amplitudes of 15 μm and 25 μm, respectively. Also, a significant increase in microhardness values in the USSE method compared to the SSE method indicated that the hardness increases significantly by increasing the amplitudes under the influence of acoustic hardening. In addition, the required force to extrude the samples with the presence of ultrasound was reduced under the effect of acoustic softening. In addition, finite element simulation of both SSE and USSE processes was performed in Abaqus/Explicit software. Higher equivalent plastic strain and plastic deformation along the length of the sample were reported in the USSE method. Additionally, in the USSE method compared to the SSE method, the maximum plastic strain distribution was improved by applying ultrasonic vibrations.

The effective monitoring of environmental contamination demands the use of small, cost-effective, and rapid equipment, aligning with the prevailing trend in biosensor development for pollutant identification. This comprehensive overview succinctly summarizes recent developments in biosensor applications for detecting various environmental pollutants. Different kinds of biosensors are talked about, such as enzyme, antibody, aptamer, whole-cell, DNA-based, and biomimetic sensors. Their uses in finding contaminants and unique traits are also considered. The choice of the sensitive element significantly influences selectivity and detection limits. Notably, biomimetic biosensors are gaining popularity due to their superior performance. This manuscript further delves into the applications of biosensors in monitoring specific environmental pollutants, including biochemical oxygen demand, pesticides, phenols, heavy metals, and polluting gases. It highlights the critical role biosensors play in real-time monitoring and early detection of environmental hazards. The integration of nanotechnology techniques and materials in biosensors is explored, focusing on key materials such as carbon nanotubes, graphene, quantum dots, and chitosan. The synergy of nanotechnology and biosensors enhances sensitivity, selectivity, and overall performance. Future perspectives stress the need for ongoing research to create strong biosensing devices that can find contaminants in complex mixtures without having to prepare samples in great detail. The continual pursuit of knowledge remains paramount for advancing environmental monitoring technology to new heights and ensuring a sustainable and ideal environment.

A circulation sub is used during drilling process, as bypass circulation is necessary. The research is dedicated to the search and optimization of hydraulic parameters for planned bench testing of the piston flow assembly of the circulation sub to reduce operational costs and advance the technical level. Flowing parameters, affecting hydraulic drag, are poorly studied, that has greatly hindered the research. The importance of the study lies in researching the hydraulic impact on local resistance, which relates to the prospective method of hydraulic activation of well equipment. The research results confirm that increasing fluid rate is a more efficient option to achieve the necessary forces values and pressure differentials, compare to changing fluid density and viscosity, as it’s more effortful. The highest pressure differential does not exceed 1.6 MPa. It is recommended to adjust fluid density during device maintenance for optimal operation. Fluid viscosity changes significantly affect hydraulic drag and pressure differential, especially with higher fluid density and flow rate. An increase in viscosity by a factor of 25 leads to an increase in pressure differential of no more than 8.2 %. Thus, it is necessary to use a detachable part of the piston flow assembly in the form of a diaphragm, which allows for device adjustment, increases reliability, and repairability. Subsequently, the results of the work will be applied to the development of a test bench for similar devices, enabling the selection of optimal design and determining recommendations for its application in specific geo-tenchnical conditions.

Successful civil projects are one of the key factors in the economic development. Civil projects are always criticized for time and budget wastage. Delays in the execution of these projects sometimes not only waste national resources and cause social damages, but also may ultimately render the project economically unjustifiable, to the extent that the direct and indirect damages from delays can sometimes exceed the actual value of the project. However, despite all government efforts, projects in Iran still suffer from delays. Therefore, managing the delays in construction projects in Iran for optimal utilization and proper management is essential. This research proposes a delay management model for construction projects based on evaluating the effective factors on the delay of construction projects in Iran using system dynamics and optimization methods. Finally, a model suitable for the managerial situation in top-ranked contracting companies in Tehran province was presented. The validation and credibility assessment of the proposed model in managing the delay of construction projects in Iran indicated that, by combining these two approaches, project managers can improve project performance under dynamic system conditions. This innovation can be utilized in different industries, especially in the construction industry. In fact, this study provides an optimal response to the management of delays in construction projects by changing the planning horizon using integrated tracking methods and identifying the best strategy and planning horizon for policymakers. Through this, an optimal planning horizon for managing delays in construction projects in the real world is achieved.