Search Results (138,628)

SQL injection (SQLI) black-box detection, which simulates external attack scenarios, is crucial for assessing vulnerabilities in real-world web applications. However, existing black-box detection methods rely on predefined rules to cover the most common SQLI cases, lacking diversity in vulnerability detection scheduling and payload, suffering from limited efficiency and accuracy. Large Language Models (LLMs) have shown significant advancements in several domains, so we developed SqliGPT, an LLM-powered SQLI black-box scanner that leverages the advanced contextual understanding and reasoning abilities of LLMs. Our approach introduces the Strategy Selection Module to improve detection efficiency and the Defense Bypass Module to address insufficient defense mechanisms. We evaluated SqliGPT against six state-of-the-art scanners using our SqliMicroBenchmark. Our evaluation results indicate that SqliGPT successfully detected all 45 targets, outperforming other scanners, particularly on targets with insufficient defenses. Additionally, SqliGPT demonstrated excellent efficiency in executing detection tasks, slightly underperforming Arachni and SQIRL on 27 targets but besting them on the other 18 targets. This study highlights the potential of LLMs in SQLI black-box detection and demonstrates the feasibility and effectiveness of LLMs in enhancing detection efficiency and accuracy. Full article

The multivariate random coefficient autoregression (RCAR) process is widely used in time series modeling applications. Random autoregressive coefficients are usually assumed to be independent and identically distributed sequences of random variables. This paper investigates the issue of coefficient constancy testing in a class of static multivariate first-order random coefficient autoregressive models. We construct a new test statistic based on the locally most powerful-type test and derive its limiting distribution under the null hypothesis. The simulation compares the empirical sizes and powers of the LMP test and the empirical likelihood test, demonstrating that the LMP test outperforms the EL test in accuracy by 10.2%, 10.1%, and 30.9% under conditions of normal, Beta-distributed, and contaminated errors, respectively. We provide two sets of real data to illustrate the practical effectiveness of the LMP test. Full article

CO₂ geological storage, as a large-scale, low-cost, carbon reduction technology, has garnered widespread attention due to its safety. Monitoring potential leaks is critical to ensuring the safety of the carbon storage system. Geochemical monitoring employs methods such as gas monitoring, groundwater monitoring, tracer monitoring, and isotope monitoring to analyze the reservoir’s storage state and secondary changes after a CO₂ injection. This paper summarizes the recent applications and limitations of geochemical monitoring technologies in CO₂ geological storage. In gas monitoring, the combined monitoring of multiple surface gasses can analyze potential gas sources in the storage area. In water monitoring, pH and conductivity measurements are the most direct, while ion composition monitoring methods are emerging. In tracer monitoring, although artificial tracers are effective, the environmental compatibility of natural tracers provides them with greater development potential. In isotope monitoring, C and O isotopes can effectively reveal gas sources. Future CO₂ geological storage project monitoring should integrate various monitoring methods to comprehensively assess the risk and sources of CO₂ leakage. The incorporation of artificial intelligence, machine learning technologies, and IoT monitoring will significantly enhance the accuracy and intelligence of numerical simulations and baseline monitoring, ensuring the long-term safety and sustainability of CO₂ geological storage projects. Full article

A thick-lens, structured-light measurement model is introduced to overcome the oversights in traditional models, which often disregard the impact of lens thickness. This oversight can lead to inaccuracies in Scheimpflug camera calculations, causing systematic errors and diminished measurement precision. By geometrical optics, the model treats the camera as a thick lens, factoring in the locations of its principal points and the spatial shifts due to image plane tilting. The model deduces the positional relationship of the thick lens with a tilted optical axis and establishes a linear-structured-light measurement model. Simulations confirm that the model can precisely calculate the 3D coordinates of subjects from image light strip data, markedly reducing systematic errors across the measurement spectrum. Moreover, experimental results suggest that the refined sensor model offers enhanced accuracy and lower standard deviation. Full article

A novel Gm-C structure of a second-order continuous-time filter is proposed that allows for the independent control of the filter’s natural frequency (ω₀) and quality factor (Q). The structure consists of two capacitors and four transconductors. Two transconductors together with the capacitors form a lossless second-order circuit with tunable ω₀. The other two transconductors form a variable gain amplifier (VGA) which realizes an adjustable loss and thereby adjustable Q. The proposed solution can be used to implement low-voltage and low-power tunable front-end filter banks for fully integrated CMOS cochlear implants and edge intelligence accelerators. An example filter bank powered by 0.5 V and consuming 40 nW of power per single filter is designed and simulated using a 180 nm CMOS process. Circuitries for the adaptive control of transistor bias at a reduced supply voltage are proposed. The ω₀ and Q control circuitries are also proposed: a delay-locked loop (DLL)-based system for fine ω₀ tuning and a binary-weighted current mirror for Q adjustment. The proposed solution allows for the independent regulation of ω₀ and Q within the ranges of 0.25–8 kHz and 1–14, respectively, with a relative tolerance of up to 5% across a filter bank. Full article

In the context of autonomous driving, the augmentation of existing data through simulations provides an elegant solution to the challenge of capturing the full range of adverse weather conditions in training datasets. However, existing physics-based augmentation models typically rely on single scattering approximations to predict light propagation under unfavorable conditions, such as fog. This can prevent the reproduction of important signal characteristics encountered in a real-world environment. Consequently, in this work, Monte Carlo simulations are employed to assess the relevance of multiple-scattered light to the detected LiDAR signal in different types of fog, with scattering phase functions calculated from Mie theory considering real particle size distributions. Bidirectional path tracing is used within the self-developed GPU-accelerated Monte Carlo software to compensate for the unfavorable photon statistics associated with the limited detection aperture of the LiDAR geometry. To validate the Monte Carlo software, an analytical solution of the radiative transfer equation for the time-resolved radiance in terms of scattering orders is derived, thereby providing an explicit representation of the double-scattered contributions. The results of the simulations demonstrate that the shape of the detected signal can be significantly impacted by multiple-scattered light, depending on LiDAR geometry and visibility. In particular, double-scattered light can dominate the overall signal at low visibilities. This indicates that considering higher scattering orders is essential for improving AI-based perception models. Full article

There is an increasingly rich literature on the decarbonisation of heat and the evolution of heat networks. This paper investigates whether a novel fifth Generation District Heating and Cooling Network (5GDHC) could be retrofitted to an existing National Health Service (NHS) hospital campus for the purpose of heating and cooling. The building load was simulated and input into a custom-written script to carry out a series of parametric studies and optimise design options. The model was calibrated against site data available from hospital facilities management. The research found that it is feasible to use a 5GDHC consisting of a large single mass of water to utilise inter-seasonal thermal storage. A natural water resource such as an aquifer was not required. The model tested sizing options and found that larger thermal storage, heat pumps and chillers reduce operating costs and improve flexibility. The paper closes with a discussion of the practical factors in retrofitting 5GDHC networks to a densely occupied and highly constrained campus environment. The findings are novel in further describing the circumstances for which 5GDHC networks are suitable. Full article

The quality of woven carbon fiber fabric/polycarbonate thermoplastic composites after thermoforming and demolding was investigated using finite element simulation and the Taguchi orthogonal array. The simulation utilized a discrete approach with a micro-mechanical model to describe the deformation of woven carbon fabric, combined with a resin model. This simulation was validated with bias extension tests at five temperatures. The thermoforming process parameters considered were blank temperature, mold temperature, and blank holding pressure, with three levels for each factor. Optimal values for the fiber-enclosed angle, spring-back angle, mold shape fitness, and the strain of the U-shaped workpiece were desired. The results indicated that the comparison of the stress-displacement curve of bias extension tests verified the application of the discrete finite element method. Results from the Taguchi array indicated that blank holding pressure was the dominant parameter, with the optimal value being 1.18 kPa. Blank temperature was the second most significant factor, effective in the range of 160 °C to 230 °C, while mold temperature had a minor effect. Furthermore, the four quality values are dependent and have a similar trend. The best combination was identified as a blank holding press of 1.18 kPa, a blank temperature of 230 °C, and a mold temperature of 190 °C. Full article

An intelligent optimization technique has been presented to enhance the multiple structural performance of PA6-20CF carbon fiber-reinforced polymer (CFRP) plastic injection molding (PIM) products. This approach integrates a deep neural network (DNN), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Monte Carlo simulation (MCS), collectively referred to as the DNN-GA-MCS strategy. The main objective is to ascertain complex process parameters while elucidating the intrinsic relationships between processing methods and material properties. To realize this, a numerical study on the PIM structural performance of an automotive front engine hood panel was conducted, considering fiber orientation tensor (FOT), warpage, and equivalent plastic strain (PEEQ). The mold temperature, melt temperature, packing pressure, packing time, injection time, cooling temperature, and cooling time were employed as design variables. Subsequently, multiple objective optimizations of the molding process parameters were employed by GA. The utilization of Z-score normalization metrics provided a robust framework for evaluating the comprehensive objective function. The numerical target response in PIM is extremely intricate, but the stability offered by the DNN-GA-MCS strategy ensures precision for accurate results. The enhancement effect of global and local multi-objectives on the molded polymer–metal hybrid (PMH) front hood panel was verified, and the numerical results showed that this strategy can quickly and accurately select the optimal process parameter settings. Compared with the training set mean value, the objectives were increased by 8.63%, 6.61%, and 9.75%, respectively. Compared to the full AA 5083 hood panel scenario, our design reduces weight by 16.67%, and achievements of 92.54%, 93.75%, and 106.85% were obtained in lateral, longitudinal, and torsional strain energy, respectively. In summary, our proposed methodology demonstrates considerable potential in improving the, highlighting its significant impact on the optimization of structural performance. Full article

The paper deals with the dynamics of a resistance spot welding system. At the core of this system is a transformer, which is powered on the primary side by a pulse-width modulated inverter and has a full-wave output rectifier on the secondary side that provides a direct welding current. The entire system is nonlinear, due to magnetic hysteresis and electronics. The electronics prevent the current from flowing in all parts of the welding transformer at separate time intervals during the voltage supply period; therefore, not all the parameters affect the dynamic of currents and voltages all the time so the system is also time-variant. To design a high-performance welding system and to predict the maximum possible welding current at a specific load, it is necessary to know the welding and primary currents. The leakage inductances of the system can reduce the maximum welding current significantly at higher frequencies and the same load. There are several methods to determine these currents, each with its drawbacks. Measurements are time-consuming, using professional software is expensive and requires time to learn and free open-source software has many limitations and does not guarantee the correctness of the results. The article presents a new, fourth option—a theoretical derivation of analytical expressions that facilitate straightforward and rapid calculation of the welding and primary currents of the resistance spot welding system with symmetrical secondary branches. The derivation of the mathematical expressions is based on the equivalent circuits that describe the system in different operating states. The results of the numerical simulations confirmed the derived expressions completely. Full article

Subalpine forests provide crucial ecosystem services and are increasingly threatened by human alterations like bare-cut slopes from highway construction. External soil spray seeding (ESSS) is often employed to restore these slopes, but the cement it introduces can negatively affect soil fungi, which are vital for the ecological sustainability of restored slopes. Despite previous extensive discussions about ESSS-restored slopes, fungal dynamics and their underlying ecological mechanisms during ESSS-based restorations still remain elusive. Here, we conducted a 196-day simulation experiment using natural soils from a subalpine forest ecosystem. By using nuclear ribosomal internal transcribed spacer (ITS) sequencing, we revealed soil fungal dynamics and their ecological mechanisms during simulated ESSS-based restorations. Results showed a decline in fungal α-diversity and significant shifts in community structures from the initial day to day 46, followed by relative stabilities. These dynamics were mainly characterized by ectomycorrhizal, plant pathogenic, and saprotrophic fungi, with ectomycorrhizal fungi being depleted, while saprotrophic and pathogenic fungi showed enrichment over time. Shifts in nitrate nitrogen (NO− 3−N) content primarily regulated these dynamics via mediating homogeneous selections. High NO− 3−N levels at later stages (days 46 to 196, especially day 46) might exclude those poorly adapted fungal species, resulting in great diversity loss and community shifts. Despite reduced homogeneous selections and NO− 3−N levels after day 46, fungal communities did not show a recovery but continued to undergo changes compared to their initial states, suggesting the less resilient of fungi during ESSS-based restorations. This study highlights the need to manage soil NO− 3−N levels for fungal communities during ESSS-based restorations. It provides novel insights for maintaining the ecological sustainability of ESSS-restored slopes and seeking new restoration strategies for cut slopes caused by infrastructure in subalpine forests. Full article

To meet the strong coupling characteristics of the MSCSG deflection channel and the demand for high control accuracy, a two-degree-of-freedom deflection channel model is firstly established for the structure and working principle of the MSCSG; to meet the strong coupling between the two channels, the inverse system method is used to decouple the model; then, the operation principle of the MSCSG system is introduced, and the modeling of the power amplifier is carried out; to meet the demand for high-precision control of the MSCSG rotor system, the RBF neural network is improved using the fuzzy method to achieve high-precision estimation of the residual coupling terms and deterministic disturbances, and the adaptive sliding mode controller is designed. For the high-precision control of the MSCSG rotor system, the fuzzy method is used to improve the RBF neural network to realize the high-precision estimation of the residual coupling term and uncertain perturbation, and the adaptive sliding mode controller is designed, and the convergence of the controller is proved on the basis of the Lyapunov stability criterion. Simulation analysis shows that the method has a large improvement in decoupling performance and anti-disturbance performance compared with the traditional method, and finally, the experiment verifies the effectiveness of the present method and achieves the optimization of the deflection channel controller. The method can be extended to other magnetic levitation actuators and related fields. Full article

This paper presents the results of computer simulations of fracture in three laboratory tests: the three-point bending of a notched beam cut from sandstone, the pull-out test of a self-undercutting anchor fixed in sandstone, and the pull-out test of a bar embedded in concrete. Five material failure criteria were used: Rankine, Coulomb–Mohr, Drucker–Prager, Ottosen–Podgórski, and Hoek–Brown. These criteria were implemented in the Abaqus^® FEA system to work with the crack propagation modeling method—extended finite element method (X-FEM). All criteria yielded similar force–displacement relationships and similar crack path shapes. The improved procedure gives significantly better, close-to-real crack propagation paths than can be obtained using the standard subroutines built into the Abaqus^® system. Full article

With the continuous advancement of petroleum extraction technologies, the importance of horizontal and inclined wells in reservoir exploitation has been increasing. However, accurately predicting oil–water two-phase flow regimes is challenging due to the complexity of subsurface fluid flow patterns. This paper introduces a novel approach to address this challenge by employing extreme gradient boosting (XGBoost, version 2.1.0) optimised through Bayesian techniques (using the Bayesian-optimization library, version 1.4.3) to predict oil–water two-phase flow regimes. The integration of Bayesian optimisation aims to enhance the efficiency of parameter tuning and the precision of predictive models. The methodology commenced with experimental studies utilising a multiphase flow simulation apparatus to gather data across a spectrum of water cut rate, well inclination angles, and flow rates. Flow patterns were meticulously recorded via direct visual inspection, and these empirical datasets were subsequently used to train and validate both the conventional XGBoost model and its Bayesian-optimised counterpart. A total of 64 datasets were collected, with 48 sets used for training and 16 sets for testing, divided in a 3:1 ratio. The findings highlight a marked improvement in predictive accuracy for the Bayesian-optimised XGBoost model, achieving a testing accuracy of 93.8%, compared to 75% for the traditional XGBoost model. Precision, recall, and F1-score metrics also showed significant improvements: precision increased from 0.806 to 0.938, recall from 0.875 to 0.938, and F1-score from 0.873 to 0.938. The training accuracy further supported these results, with the Bayesian-optimised XGBoost (BO-XGBoost) model achieving an accuracy of 0.948 compared to 0.806 for the traditional XGBoost model. Comparative analyses demonstrate that Bayesian optimisation enhanced the predictive capabilities of the algorithm. Shapley additive explanations (SHAP) analysis revealed that well inclination angles, water cut rates, and daily flow rates were the most significant features contributing to the predictions. This study confirms the efficacy and superiority of the Bayesian-optimised XGBoost (BO-XGBoost) algorithm in predicting oil–water two-phase flow regimes, offering a robust and effective methodology for investigating complex subsurface fluid dynamics. The research outcomes are crucial in improving the accuracy of oil–water two-phase flow predictions and introducing innovative technical approaches within the domain of petroleum engineering. This work lays a foundational stone for the advancement and application of multiphase flow studies. Full article

Existing Super-Resolution (SR) methods are typically trained using bicubic degradation simulations, resulting in unsatisfactory results when applied to remote sensing images that contain a wide variety of object shapes and sizes. The insufficient learning approach reduces the focus of models on critical object regions within the images. As a result, their practical performance is significantly hindered, especially in real-world applications where accuracy in object reconstruction is crucial. In this work, we propose a general learning strategy for SR models based on expert knowledge supervision, named EKS-SR, which can incorporate a few coarse-grained semantic information derived from high-level visual tasks into the SR reconstruction process. It utilizes prior information from three perspectives: regional constraints, feature constraints, and attributive constraints, to guide the model to focus more on the object regions within the images. By integrating these expert knowledge-driven constraints, EKS-SR can enhance the model’s ability to accurately reconstruct object regions and capture the key information needed for practical applications. Importantly, this improvement does not increase the inference time and does not require full annotation of the large-scale datasets, but only a few labels, making EKS-SR both efficient and effective. Experimental results demonstrate that the proposed method can achieve improvements in both reconstruction quality and machine vision analysis performance. Full article