Engineering Science and Technology-An International Journal-Jestech最新文献

Addressing the expanding Advanced Persistent Threat (APT) landscape is crucial for governments, enterprises and threat intelligence research groups. While defenders often rely on tabular formats for assets like logs, alerts, firewall rules; attackers leverage a graph-based mindset. In this work, we propose a novel multistage framework named APT-Scope which employs a comprehensive approach to Cyber Threat Intelligence (CTI) analysis on qualified real-world data. APT-Scope workflow consists of data gathering, enrichment, and analysis stages, where relationships between entities are used to construct a Heterogeneous Information Network (HIN). We applied CTI enrichment using additional active data collection techniques like DNS and Whois lookups, port scans, SSL footprinting, named entity recognition via SpaCy, and constructed a machine learning pipeline to predict relationships between entities using FastRP and Logistic Regression. By analyzing the resulting HIN, we discovered aliases for APT groups and predicted threat actors of APT attacks with unknown perpetrators. We observed AUCPR metrics as train score = 96.57% and test score = 92.36%. Our work is beneficial to oversee the entire APT landscape, steer ongoing and future CTI operations and make strategic decisions.

On 6 February 2023, Türkiye suffered two major earthquakes with magnitudes of Mw = 7.7 and Mw = 7.6 at 04:17 and 13:24 local time in Pazarcık and Elbistan districts of Kahramanmaraş, which were called as the disaster of the century. On 20 February 2023 at 20:04 local time, another major earthquake with Mw = 6.4 occurred in Yayladağı, Hatay. This disaster caused great loss of life and heavy destruction. The burden of the earthquake on the Turkish economy is extremely heavy. The purpose of earthquake early warning systems (EEWS) is to protect people and property, save time for precautions to be taken and prepare emergency response teams. An effective EEWS has a major role in reducing life, structural and economic losses. This study is an implementation of the original California optimized EPIC, an early warning algorithm, in the region of the 2023 Kahramanmaraş earthquakes. A total of 212 earthquakes in the region are replayed to derive magnitude scaling equations based on peak displacement amplitude ($\mathrm{Pd}$) and predominant period ($T_p^{\max }$). The performance of the modified equations is tested for three large earthquakes. By comparing the magnitudes estimated by $\mathrm{Pd}$ and $T_p^{\max }$, it is observed that Pd gives more suitable results. The 2023 Kahramanmaras earthquakes real-time tests suggested a warning time of 32 s for the first earthquake in Antakya (Hatay), where the heaviest destruction and loss of life was observed. In the other earthquakes, the warning times were interpreted as quite useful to minimize the loss of life. The geography of the 2023 Kahramanmaraş earthquakes is a seismically active region and will be subject to earthquakes in the future. As a result of this study, a region-specific scaling of the magnitude, which is the most important parameter if an early warning system is established in the region, is presented. It is also a good example of how an effective earthquake early warning system can reduce potential hazards.

In this paper, a kind of superconductor ceramics metastructure-photonic crystals (SCMPC) is proposed to investigate the absorption and transmission properties of electromagnetic waves (EW) by combining a metastructure with multiple degrees of freedom regulation and strong energy localization characteristics of photonic crystals. Firstly, for the periodically aligned SCMPC, EW mainly realizes absorption in forward propagation and transmission in backward case. The relative bandwidth (RB) for both forward absorptivity and backward transmittance greater than 0.9 is 2.7 %, and the operating bandwidth (OB) is 696 ∼ 715 terahertz (THz), which is an asymmetric absorption-transmission (AAT) characteristics. Importantly, the periodically aligned SCMPC can realize the double-band rasorber phenomenon, and the forward EW exhibits an absorption-transmission-absorption phenomenon with OBs of 644.2 ∼ 671.1 THz, 700.9 ∼ 742.1 THz, and 766.8 ∼ 784.2 THz. RBs with absorption and transmissivity greater than 0.8 are 4.1 %, 5.7 %, and 2.2 %, respectively, and the backward EW one is mainly transmitted. To optimize AAT, a quasi-periodic Octonacci sequence-aligned SCMPC is introduced. The results show that the maximum OB of forward absorption and backward transmission is 428.3 ∼ 670.5 THz and RB is 44.1 %, achieving favorable broadband AAT. In addition, the effects of temperatures, dielectric thicknesses, and stacking numbers on AAT are also investigated in detail. In conclusion, AAT has promising applications in unidirectional optical transmission, photodiodes, optical isolators, etc.

In general, gearbox is prone to occur compound fault frequently because of its harsh working environment, its fault vibration signal often contains polymorphic-oscillatory components and is corrupted by heavy background noise, which brings great difficulty to diagnose fault. Sparse decomposition is often utilized to extract weak fault feature among heavy background noise. In order to solve the problems of traditional sparse decomposition method, such as lacking signal fidelity, causing local optimal solution by using the non-convex objective function, and presenting poor universality, a multi-source sparse optimization objective function with convexity is constructed based on the generalized mini-max concave penalty function. By using forward–backward splitting algorithm combination with Laplace wavelet dictionary, Morlet wavelet dictionary and DFT dictionary, the sparse coefficients corresponding to polymorphic-oscillatory components can be computed efficiently and each oscillatory component can be extracted accurately. Finally, simulation and experimental signal validate that the proposed method can decompose fault signal according to oscillatory property and diagnose gearbox compound fault without the prior knowledge of specific fault numbers.

For the past decade, a number of robotic catheters have been developed for precise and repeatable insertion of endovascular catheters and guidewires. However, using the existing descriptions of a variety of designs and functions it is impossible to systematically evaluate the development of robotic catheters and follow their modifications that lead to improvements in their performance. This systematic literature review focuses on the evolution of robotic catheters in order to define the current results and trends in this field. We have followed the PRISMA guidelines and conducted a comprehensive search of publications reporting design and feasibility tests from January 2011 to December 2023 in the Google Scholar database. The evolution of each model was described in terms of the implemented mechanisms and features. The accuracy, ranges of motion and speed of robotic catheters were compared. The most common number of degrees of freedom of the slave unit of robotic catheters is 2-3. Robotic catheters usually have unlimited translation and rotation ranges, and the speed range varies from 2 to 20 mm/s for translation and from 8 °/s to 20 °/s or from 50 °/s to 70 °/s for rotation. The major part of robotic catheters has the maximum translational trueness between 0.5 and 1.1 mm, and the maximum rotational trueness is between 0.8°and 1.2°, which is enough to execute a precise endovascular procedure. No commercially available robotic catheter has force feedback, however, many of the catheters are developing various elements for it, which suggests that such systems will appear in the future.

This study critically investigates analysis and control of non-isolated boost three-port DC to DC converter (TPC) for standalone PV system. The converter is controlled such that regulates flow of power from PV array to load and batteries as storage devices. According to PV power, there are four operating modes of operation of converter. Simple reduced-order dynamic models of the converter in various modes of operation are obtained using state-space averaging and small-signal techniques. In this work, the controllers of the closed-loop scheme are designed and only two controllers are used to achieve output voltage regulation, and to extract maximum power from PV array under different operating conditions. Closed-loop system simulation is studied to verify the operation of converter with the designed controllers in different modes. Transition between modes is presented with solar radiation variation and change of connected loads. Experimental verification of control systems at different modes of operation is investigated. The experimental results show that the controllers can regulate the power flow between TPC three ports and regulate output voltage under PV irradiance variation and load changing. The controller shows good performance measures such as maximum settling time of 0.06 s, maximum steady-state ripples of ±0.8 %, and 99.3 % power efficiency.

In this study, fluid flow predictions using three different methods were compared: DeepCFD, an artificial intelligence code; computational fluid dynamics (CFD) using Ansys Fluent and OpenFOAM; and two-dimensional, two-component particle image velocimetry (PIV) measurements. The airfoils under investigation were the NACA 0012 with a 10° angle of attack and the NACA 6412 with a 0° angle of attack. To train DeepCFD, 763, 2585, and 6283 OpenFOAM simulations based on primitives were utilized. The investigation was conducted at a free stream velocity of 10 m/s and a Reynolds number of 82000. Results show that once the DeepCFD network is trained, prediction times are negligible, enabling real-time optimization of airfoils. The mean absolute error between CFD and DeepCFD, with 6283 trained primitives, for NACA 0012 predictions resulted in velocity components $U_x$ = 1.08 m/s, $U_y$ = 0.43 m/s, and static pressure p = 4.57 Pa. For NACA 6412, the corresponding mean absolute errors are $U_x$ = 0.81 m/s, $U_y$ = 0.59 m/s, and p = 7.5 Pa. Qualitative agreement was observed between PIV measurements, DeepCFD, and CFD. Results are promising that artificial intelligence has the potential for real-time fluid flow optimization of NACA airfoils in the future. The main goal was not just to train a network specifically for airfoils, but also for variant shapes. Airfoils are used since they are highly sophisticated in fluid dynamics and experimental data was available.

Shrink-fit, wire-winding, and autofrettage processes are commonly utilized to enhance fatigue strength and durability of thick-walled cylinders across various mechanical applications. In this study, a novel practical design optimization methodology has been developed to determine the optimal configuration of a thick-walled cylinder, incorporating different combinations of shrink-fit, wire-winding, and autofrettage techniques. The objective is to identify the optimal layer thickness, shrink-fit interference, conventional autofrettage pressure, and reverse autofrettage pressure, if applicable, to maximize the compressive residual stress and minimize the tensile residual stress, thereby extending fatigue lifetime of the cylinder. First, different configurations of thick-walled cylinders, subjected to various combinations of reinforcement processes, are identified. A dataset of residual hoop stress profiles through the cylinder thickness is subsequently generated for these configurations based on the same manufacturing process. Neural network regression is effectively utilized to construct a single fitting function for the residual hoop stress profiles. A parametric study is performed to determine the optimal training functions, activation functions, and hyperparameters, achieving a remarkable agreement with the dataset, indicated by a coefficient of determination of over 0.97. A combination of Genetic Algorithm and Sequential Quadratic Programming algorithms is utilized to determine the accurate optimal values. Fatigue life analysis is subsequently conducted to estimate the fatigue lifetime of the optimal configuration. Results suggest that the optimal configuration, involving conventional autofrettage of the inner layer followed by shrink-fitting with a virgin layer and wire-winding the entire assembly, achieves a maximum fatigue life of 88 × 10⁶ cycles under cyclic pressure load of 300 MPa.

In response to the escalating global CO2 emissions and the urgent need to reduce dependence on fossil fuels, this study diverges from prior research that predominantly focuses on intentions or attitudes towards renewable energy. It investigates the actual uptake of residential solar photovoltaic (PV) systems in regions rich in solar radiation, where, despite the potential, renewables remain a minor part of the energy mix. Incorporating psychological and functional factors and employing the innovation resistance theory (IRT), the study comprehensively examines solar PV technology’s resistance aspects. Utilizing a robust methodological framework that uses partial least squares-structural equation modeling (PLS-SEM) with fuzzy set qualitative comparative analysis (fsQCA), the research evaluates responses from a comprehensive questionnaire survey of 758 households. The advantages of this method lie in its ability to capture both symmetric and asymmetric relationships, thereby offering a richer and more detailed analysis compared to traditional single-method approaches. PLS-SEM results identify significant barriers: image barriers (β = −0.131, t = 3.418, p < 0.001), traditional barriers (β = −0.084, t = 2.143, p < 0.05), and risk barriers (β = −0.124, t = 4.172, p < 0.001). Positive influences include environmental benefits (β = 0.166, t = 3.108, p < 0.001), environmental concern (β = 0.364, t = 6.341, p < 0.001), and government incentives (β = 0.159, t = 2.767, p < 0.01). Conversely, usage barriers and value barriers appeared non-influential. Conversely, fsQCA revealed that all factors may have a role in the uptake of residential solar PV systems. The novelty of this research is evident in its application of IRT to the context of solar PV adoption and the use of a hybrid analytical method, which together provide new insights into consumer behavior and policy implications. These findings offer actionable recommendations for policymakers and practitioners to promote the adoption of residential solar PV systems.

There are some researches in the literature on the mechanical characteristics of dual-phase (DP) steels used in the automotive industry, but there is no comprehensive research on the corrosion behavior of these steels. In this work, the corrosion behavior of DP steels (DP440, DP590, DP980) exposed to two cycles of accelerated corrosion testing in accordance with Ford CETP 00.00-L-467 was observed. Raman and X-ray diffraction (XRD) techniques were used to classify the corrosion products, and the morphology of the samples was studied using a scanning electron microscope (SEM). Goethite and haematite were the primary chemical compounds determined. In high-mechanical strength DP steels, akaganeite was also identified in corroded specimens. The compounds formed due to corrosion were revealed by SEM images. In this work, according to the results of Raman spectroscopy, which was employed for the first time to reveal corrosion products in high-strength dual-phase steels, it was discovered that corrosion products increased with increasing mechanical strength due to an increasing martensite phase volume percentage. Polarization tests were carried out to support the electrochemical data reported by the Raman analysis. Similarly, an increase in the amount of martensite phase in the microstructure led to a decrease in the material’s corrosion resistance. Polarization experiments were carried out to support the electrochemical data interpreted by Raman analysis. In addition, an increase in the amount of martensite phase in the microstructure led to a decrease in the corrosion resistance of the material. In addition, information regarding the material’s electrochemical performance was obtained through Raman analysis. As shown by Raman, XRD, and polarization tests, the increase in corrosion products formed due to the increase in the amount of martensite led to a decrease in corrosion resistance.