In recent years, penetration testing (pen-testing) has emerged as a crucial process for evaluating the security level of network infrastructures by simulating real-world cyber-attacks. Automating pen-testing through reinforcement learning (RL) facilitates more frequent assessments, minimizes human effort, and enhances scalability. However, real-world pen-testing tasks often involve incomplete knowledge of the target network system. Effectively managing the intrinsic uncertainties via partially observable Markov decision processes (POMDPs) constitutes a persistent challenge within the realm of pen-testing. Furthermore, RL agents are compelled to formulate intricate strategies to contend with the challenges posed by partially observable environments, thereby engendering augmented computational and temporal expenditures. To address these issues, this study introduces EPPTA (Efficient POMDP-Driven Penetration Testing Agent), an agent built on an asynchronous RL framework, designed for conducting pen-testing tasks within partially observable environments. We incorporate an implicit belief module in EPPTA, grounded on the belief update formula of the traditional POMDP model, which represents the agent’s probabilistic estimation of the current environment state. Furthermore, by integrating the algorithm with the high-performance RL framework, Sample Factory, EPPTA significantly reduces convergence time compared to existing pen-testing methods, resulting in an approximately 20-fold acceleration. Empirical results across various pen-testing scenarios validate EPPTA’s superior task reward performance and enhanced scalability, providing substantial support for efficient and advanced evaluation of network infrastructure security.

The increasing number of private cars, public transportation vehicles, and pedestrians, as well as the absence of adequate space for these ground amenities, are the primary causes of traffic congestion and accidents in the Kathmandu Valley. Investigations have indicated that the Kathmandu Valley has the greatest traffic accidents despite the heavy presence of government and its agencies there. Most teens and young adults suffer injuries while using motor vehicles. The study’s primary objective is to foresee and prevent such complications by planning for sufficient subsurface infrastructure for the Kathmandu valley’s transportation network. Overlying pressure, lateral earth pressure, live load, uplift pressure, and live surcharge are some of the forces acting on the tunnel, creating unique stress and moment zones. The tunnel meets the following geometric requirements: a) each of the tunnel’s two cells has a clear span of 10 meters and a clear height of 5.5 meters. The side walls, inner walls, top slab, and bottom slab are all 700 mm thick. Soil has built up to a height of 4m over the tunnel’s roof. Construction sequencing, the application of various loads during construction, and expected service life are all taken into account during the design process. Analytical and computer software (SAP 2000) are both used in the tunnel segment’s analysis. Furthermore, the designed tunnel has been evaluated for stability, considering the deflection and shear resistance. The analysis indicates that the tunnel meets the stability requirements, as the checks performed for deflection produce satisfactory results. This implies that the structure is capable of withstanding the applied forces without excessive deflection.

This paper proposes a hierarchical distributed voltage control (HDVC) scheme for active distribution networks (ADNs) with high penetration of photovoltaics based on distributed model predictive control (DMPC) and alternating direction method of multipliers (ADMM). The reactive power outputs of several photovoltaic clusters (PVCs) and photovoltaic (PV) units within each PVC are optimally coordinated to keep PV terminal voltages and the voltages of all critical buses of ADNs within the feasible range and mitigate voltage fluctuations. In the ADN layer, a distributed reactive power control scheme based on DMPC is designed for the PVC, which regulates the voltages of all critical buses to be closed to the rated value and mitigates the reactive power variations. In the PVC layer, the reactive power outputs of PV units are optimized based on ADMM to minimize the voltage deviation of each PV terminal and track the reactive power reference from the PVC control. The proposed HDVC scheme requires communication only between neighboring PVC controller, while each PV controller only communicates with the corresponding PVC controller. This regulates the voltages in a completely decentralized manner and effectively reduces the computation burden of the PVC and PV controllers. A modified Finnish distribution network with 10 PVCs was used to validate the control performance of the proposed HDVC scheme.

At present, the main way to deal with the gas well effusion is to use the over-effusion prediction model to calculate the critical fluid carrying velocity and other factors, which provides data support and theoretical basis for the drainage process, so as to achieve the effect of bringing the effusion out of the wellbore. When the prediction results of the hydrops prediction model are biased, hydrops will be generated at the bottom of the wellbore, resulting in a decrease in gas well productivity. Aiming at the problem that the drag coefficient of the wellbore droplet movement model changes greatly in the process of natural gas production, which leads to the error of the wellbore effusion prediction, the commonly used droplet models and the common drag coefficient models are analyzed and evaluated. Considering that the fitting method of the commonly used drag model has different applicability for each Reynolds number region, the literature review, calculation and verification methods are used. The area with the highest fitting accuracy of each method is divided and sorted, and the model is selected. Compared with the model obtained by the partition and the existing drag model and the experimental value, it is found that the model can effectively reduce the average error rate between the calculated results and the experimental value, and can be better applied to the turbulent area and the highly turbulent area, and is more consistent with the actual working condition.

Off-grid solar systems provide clean and affordable energy.Adoption of off-grid solar energy is becoming increasingly popular in Kenya as a source of renewable energy,with an estimated 6 million people now using off-grid solar power systems.However, the rising off-grid solar systems technology uptake comes with a growing amount of solar e-waste, which can have harmful environmental and health effects if not managed properly.Current data on the exact amount of solar e-waste being generated in Kenya is unavailable and this amount will continue to rise with the expiry of many of these off-grid solar systems lifespans.This study through stakeholder's workshop engagement and document analysis approaches,established that the country has robust general waste policy,legal and institutional framework but there is no specific policies and regulations on off grid-solar electronic waste management,just like it is the case in many developing countries. In addition, there is a lack of awareness on hazardous nature of off-grid solar systems e-waste to both consumers and institutions of governance.Furthermore,there is little enforcement of the general regulations in addition to inadequate management infrastructures.This calls for development of effective off-grid solar e-waste management policies and regulations within the backdrop of the rising uptake of off-grid solar energy systems in Kenya.

In this paper, the vector extension operation is proposed to replace the de Boor-Cox formula for a fast algorithm to B-spline basis functions. This B-spline basis function based on vector extending operation is implemented in the class and shape transformation (CST) parameterization method in place of the traditional Bezier polynomials to enhance the local ability of control and accuracy to represent an airfoil shape. To calculate the k-degree B-spline function’s nonzero values, the algorithm can improve the computing efficiency by 2k+1 times.

Compared with traditional technology, bonding technology is more suitable for civil structure reinforcement because of its cost-efficiency and superior mechanical properties. As one of the simplest forms of adhesive joints, numerous studies have been conducted on the performance of single-lap joints (SLJs). However, research on the long-term performance of SLJs requires better organization and comprehension. This paper aims to investigate the long-term performance and optimization design of SLJs. The main factors influencing the long-term performance of SLJs from both material and component levels are discussed. The moisture diffusion mechanisms of bulk adhesives and the degradation mechanisms of SLJs are explored. Moreover, the optimization design of SLJs focuses on evaluating the overlap length, adhesive layer thicknesses, and changes in adhesives along the overlap length based on available literature. This paper can be employed to improve the shear strength and long-term performance of SLJs and to provide insights into their challenges and prospects.

Epilepsy is a condition that disrupts normal brain function and sometimes leads to seizures, unusual sensations, and temporary loss of awareness. Electroencephalograph (EEG) records are commonly used for diagnosing epilepsy, but traditional analysis is subjective and prone to misclassification. Previous studies applied Deep Learning (DL) techniques to improve EEG classification, but their performance has been limited due to dynamic and non-stationary nature of EEG structure. In this paper, we propose a multi-channel EEG classification model called LConvNet, which combines Convolutional Neural Networks (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) for capturing temporal dependencies. The model is trained using open source secondary EEG data from Temple University Hospital (TUH) to distinguish between epileptic and healthy EEG signals. Our model achieved an impressive accuracy of 97%, surpassing existing EEG classification models used in similar tasks such as EEGNet, DeepConvNet and ShallowConvNet that had 86%, 96% and 78% respectively. Furthermore, our model demonstrated impressive performance in terms of trainability, scalability and parameter efficiency during additional evaluations.

The main challenge faced by many mechanical engineering educators is the implementation of real solutions during their courses. One alternative can be a project-based learning, where the students can be engaged in the development and analysis process currently applied in the industry. This kind of teaching process not only can be used to improve the quality of teaching-learning process but also the students can have opportunities to solve real engineering problem. This paper therefore reports a project-based learning implemented in mechanical engineering courses given in bachelor´s and master’s degree. The component under evaluation has been selected considering the student’s interest, this criterion was also taken to involve several students into a real project process to their learning development. During the semester have been delivered activities to solve the same problem using different approaches based on student´s skills. This procedure can be replicated for students and teachers by following steps. To begin this process, the first step is present the theoretical basis for modal analysis. The second step is to apply theorical knowledge to structure a numerical finite element model. Then, it is used Hypermesh, Optistruct and HyperView software to solve and simulate. The final step was to perform experimental using a three-dimensional scanning vibrometer on 60 samples. It is well noted that the implementation of engineering software commonly used at the industry would increase students’ confidence. This allows students to perform real problem-solving activities to develop outcomes as establish goals, plan task, meet deadlines. Therefore, this paper shows an engineering solution process to provide a learning alternative to teach the modal finite method solutions correlation with experimental solutions.

Engineering systems have been designed to facilitate society. These systems can be seen everywhere in our daily lives ranging from electrical systems to mechanical systems, and from bio-medical systems to industrial systems. With tight coupling with information and communication technology (ICT), these engineering systems can be even controlled and monitored remotely. These systems are supported massively with sensors through which they capture enormous data, which is then used to improve the performance of the systems. Moreover, complex processes are involved in the overall functioning of these engineering systems. The management of data and processes within these engineering systems has been done through traditional ways such as database management systems or spread sheets, however, involvement of multiple parties makes these engineering systems more complex to operate, track, and audit. Blockchain technology has the potential to replace traditional database systems and offers a level of trust in an untrusted environment. With features of immutability, traceability, transparency, availability, and decentralization, blockchain technology is a good match for engineering systems. Blockchain technology can help in supply chain in these engineering systems, but it can also be used to facilitate data, process, and parties. Considering enormous applications of blockchain technology in engineering systems, this Special Issue in Wiley Engineering Reports invited for the original scientific and technical contributions.

Current study examined the magnetohydrodynamic (MHD) Prandtl nanofluid of a thermal double-diffusive flow through an exponentially vertical surface in association with heat generation, and thermophoresis effect. The novelty of this study is due to the analysis of Prandtl nanofluid model with Soret mechanism and chemically responding fluids. The fluid flow phenomenon is characterized by nonlinear coupled differential equations involving two or more independent variables. A suitable numerical technique is used to handle the set of governing equations along with a stability and convergence analysis. According to recent study, the fluid velocity increases since all the parameters are set to higher levels. For the various parametric values, isotherms and streamlines have been explored. This suggested model is beneficial since it can significantly advance the domains of thermal and industrial engineering. For instance, thermal radiation is crucial in designing sophisticated energy-transformed systems that operate at high temperatures. On the other hand, the phenomenon of Soret is useful in separating isotopes in chemical engineering. These studies have several applications in the manufacturing and biomedical fields, petrochemical industries, automobiles, medical sciences, and various production processes in industries.

Bilge and oily water (BOW) during vessel’s operation are the most large-tonnage type of waste and for their treatment all ships, in accordance with regulatory requirements [14], have to be equipped with special equipment – oily water separators. At sea vessel’s operating conditions three main directions of BOW cleaning are now used: physical, chemical and biological. The analysis of BOW separation methods based on these three directions has shown that it is very difficult to obtain secondary petrochemical products. In the article authors offer a new method for BOW separation which is based on the use of a hydrodynamic process of supercavitation with artificial ventilation of the cavitational cavern. With local origin in the flow of a supercavitating cavern, there will always be saturated water vapor inside of it. The process of permanent water vapor selection from the cavern will ultimately contribute to the production of highly concentrated mixture of secondary petroleum products from initial mixture of BOW. During the study of BOW separation process it was found that decreasing of the working pressure inside the working chamber of the cavitation separator have to be always compensated by an increase in the temperature of the processed multiphase flow.

To ensure that the crane can smoothly calibrate and align the lifting rod with the beam body lifting hole, it is necessary to use image processing technology to locate and detect the corner coordinates of the crane’s lifting rod. Traditional corner detection methods are not suitable for this scene. This article proposes a new idea for corner positioning, which locates corner coordinates through the intersection of straight lines. Firstly, using the R and G channels of the RGB color space to construct a grayscale difference map is beneficial for Otsu’s threshold segmentation; Secondly, this article proposes an optimal adaptive threshold determination method to filter the number of votes in the clustering results, eliminate interfering straight lines, and improve the clustering centroid calculation method based on the weight calculation formula of different voting proportion, replacing the original clustering centroid as the basis for line fitting; Finally, calculate the corner coordinates of the crane’s grab boom based on the straight line fitting results, and compare the recognition accuracy under different lighting conditions. This method is significantly superior to traditional corner detection methods, providing a method basis for solving the algorithm accuracy and robustness problems of port cranes under multiple environmental variables.

This paper presents a framework combining Monte Carlo Simulation (MCS) and the Newmark sliding block model with Representative slip surfaces (RSS) (model II) and Multiple response surfaces method (MRSM) to conduct seismic reliability analysis and risk assessment of soil slopes. An empirical threshold is introduced to define the limit state function to identify the failure samples in MCS and the sliding area and Newmark sliding displacement are multiplied to quantify the failure consequence. The proposed methodology is illustrated through a soil slope with multiple layers. The calculation results demonstrate that traditional Newmark sliding block model (model I) tend to underestimate the variations of yield acceleration. Both the failure probability and landslide risk exhibit decreasing trends with the increase of threshold. Significant discrepancy in failure probability and landslide risk between two models is found even for a small threshold. It is therefore, the proposed methodology is highly recommended in seismic reliability analysis and risk assessment. The contributions of RSSs to the failure probability and landslide risk are insensitive to the variation of displacement thresholds.

Aiming at the problem of crosstalk between microstrip lines, a method of reducing crosstalk by using Cross-Shape Resonators (CSR) structures is proposed. On the premise of not changing the spacing of microstrip lines, this method adds CSR structures between the coupled microstrip lines to increase the capacitive coupling and thus to suppress the far-end crosstalk. Based on the analysis of the equivalent circuit of CSR structure, the parameters simulation and verification are carried out by ADS and HFSS software. Through HFSS simulation and physical test of the designed CSR structure, the results show that: the CSR structure can significantly reduce the far-end crosstalk by about 15 dB in the frequency of 0~10GHz, and the maximum can reach 43 dB. Compared with 3W crosstalk reduction method and RectangularShape Resonators (RSR) crosstalk reduction method, the crosstalk reduction effect is improved.

The study’s foundation is a scenario analysis of a textile mill’s weaving department, with the goal of determining the necessity of a reliable and comprehensive plan for scheduling maintenance time. According to the background information and problem statement, incidents of Run failure maintenance and lengthy downtime (up to 60 days) undermine the machines’ availability (Schmidt, Galar & Wang, 2016).The desired efficiency and production are 90% and 194.76 m, respectively, however, the preliminary result indicate less. This indicates a gap that must be closed by implementing regular and appropriate maintenance plans. Additionally, the the incoherent and inconsistencies points at a lack of an efficient maintenance plan. It was established that the current strategy is not optimized and does not ensure machine availability because there are disparities and irregularities in the maintenance of crucial equipment. The objective of the study was to map out the critical equipment and collect data on the number and time between failures encountered in the weaving section of the textile manufacturing processes. Failure mode and effect analysis and fish-bone diagram were used in the analysis of the data. Mapping results indicates downtime up to 60 days, the productivity was estimated at 194.76 meters, and efficiency was 90%. The results showed that weaving looms were the essential piece of machinery.

The failure mechanisms caused by electrostatic discharge (ESD) effects at ambient temperatures ranging from -75℃ to 125℃ are investigated by Silvaco TCAD simulator. The devices are NMOS transistors fabricated with 28nm fully depleted silicon-on-insulator (FDSOI) technology. Results indicate that with an increase in temperature, the first breakdown voltage of the device decreased by 27.32%, while the holding voltage decreased by approximately 8.49%. The total current density, lattice temperature, and potential etc. were extracted for a detailed insight into the failure process. These findings provide valuable references for the design and development of ESD protection devices applied at different temperature ranges.

Background: Students’ academic achievement is regarded as the scholastic standing of students at the end of a given study period that is expressed in terms of grades. The key to bridging the attainment gap at the end of their study period is through their cumulative grade points over the duration of the study. Predictive validity study on students first-year GPA as a predictor of their final-year CGPA was carried out to predict the students’ academic performance in Chemical, Civil, Electrical, and Mechanical Engineering. Purpose/Hypothesis: This study examined the relationship between first-year GPA and final-year CGPA, as well as the relationship between Age, Gender and Geopolitical zones on first-year GPA and CGPA of Engineering students in the Faculty of Engineering students University of Abuja, Nigeria. The data obtained from the four Departments; Chemical, Civil, Electrical and Mechanical were analyzed. Two hypotheses were formulated to guide the study. Design/Method: An ex-post factor research approach was adopted, and Pearson’s correlation and Regression Analysis were fitted with the data using Minitab software. Results: The results of the study highlighted that first-year GPA had a strong positive relationship with final-year CGPA. Age, Gender and Geopolitical zones have no correlation with students’ final-year CGPA. The regression equations can be used to predict students’ CGPA to bridge the attainment gap at the end of their studies. Conclusions: Finally, the study emphasized the need to admit more female students in Engineering studies as they constitute 12.9% of the population.