Search

Recent rank-based attacks have reduced the security of Rainbow, which is one of the multi-layer UOV signatures, below the NIST security requirements by speeding up iterative kernel-finding operations using classical mathematics techniques. If quantum algorithms are applied to perform these iterative operations, the rank-based attacks may be more threatening to multi-layer UOV, including Rainbow. In this paper, we propose a quantum rectangular MinRank attack called the Q-rMinRank attack, the first quantum approach to key recovery attacks on multi-layer UOV signatures. Our attack is a general model applicable to multi-layer UOV signature schemes, and in this paper, we provide examples of its application to Rainbow and the Korean TTA standard, HiMQ. We design two quantum oracle circuits to find the kernel in consideration of the depth-width trade-off of quantum circuits. One is to reduce the width of the quantum circuits using qubits as a minimum, and the other is to reduce the depth using parallelization instead of using a lot of qubits. By designing quantum circuits to find kernels with fewer quantum resources and complexity by adding mathematical techniques, we achieve quadratic speedup for the MinRank attack to recover the private keys of multi-layer UOV signatures. We also estimate quantum resources for the designed quantum circuits and analyze quantum complexity based on them. The width-optimized circuit recovers the private keys of Rainbow parameter set V with only 1089 logical qubits. The depth-optimized circuit recovers the private keys of Rainbow parameter set V with a quantum complexity of 2 174 , which is lower than the complexity of 2 221 recovering the secret key of AES-192, which provides the same security level as parameter set III. [ABSTRACT FROM AUTHOR]

Lane line images have the essential attribute of large-scale variation and complex scene information, and the similarity between adjacent lane lines is high, which can easily cause classification errors. And remote lane lines are difficult to recognize due to visual angle changes in width. To address this issue, this paper proposes an effective lane detection framework, which is a hybrid feature fusion network that enhances multiple spatial features and distinguishes key features throughout the entire lane line segment. It enhances and fuses lane line features at multiscale to enhance the feature representation of lane line images, especially at the far end. Firstly, in order to enhance the correlation of multiscale lane features, a multi-head self attention is used to construct a multi-space attention enhancement module for feature enhancement in multispace. Secondly, a spatial separable convolutional branch is designed for the jumping layer structure connecting multiscale lane line features. While retaining feature information of different scales, important lane areas in multiscale feature information are emphasized through the allocation of spatial attention weights. Finally, considering that lane lines are elongated areas in the image, and the background information in the image is much more abundant than lane line information, the flexibility of traditional pooling operations in capturing widely existing anisotropic contexts in actual environments is limited. Therefore, before embedding feature output branches, strip pooling is introduced to refine the representation of lane line information and optimize model performance. The experimental results show that the accuracy on the TuSimple dataset reaches 96.84%, and the F1 score on the CULane dataset reaches 75.9%. [ABSTRACT FROM AUTHOR]

Government policy in England aims for the elimination of bovine tuberculosis (bTB). This policy includes culling of European badger (Meles meles) to reduce cattle TB incidence. The rationale is based on a field trial, the Randomised Badger Culling Trial (RBCT) 1998–2005, which reported a substantial decrease in bTB herd incidence where badger culling had been implemented, in comparison to untreated control areas. The RBCT was undertaken because previous studies of reductions in badgers by culling, reported a possible association between bTB in badger and cattle, but none could directly show causation. The effect of intensive widespread (proactive) culling in the RBCT was reported in 2006 in the journal Nature. Analysis of an extensive badger removal programme in England since 2013 has raised concerns that culling has not reduced bTB herd incidence. The present study re-examined RBCT data using a range of statistical models. Most analytical options showed no evidence to support an effect of badger culling on bTB herd incidence ‘confirmed’ by visible lesions and/or bacterial culture post mortem following a comparative intradermal skin test (SICCT). However, the statistical model chosen by the RBCT study was one of the few models that showed an effect. Various criteria suggest that this was not an optimal model, compared to other analytical options available. The most likely explanation is that the RBCT proactive cull analysis over-fitted the data with a non-standard method to control for exposure giving it a poor predictive value. Fresh appraisal shows that there was insufficient evidence to conclude RBCT proactive badger culling affected bTB breakdown incidence. The RBCT found no evidence of an effect of culling on ‘total’ herd incidence rates. Total herd incidences include those confirmed as bTB at necropsy and those herds where there was at least one animal animal positive to the comparative intradermal skin test, the standard diagnostic test used for routine surveillance, but not confirmed at necropsy. This was also the case using the more suitable statistical models. Use only of ‘confirmed’ herd incidence data, together with a more recent (2013) published perception that RBCT data presented ‘a strong evidence base….with appropriate detailed statistical or other quantitative analysis’ should be reconsidered. The results of the present report are consistent with other analyses that were unable to detect any disease control benefits from badger culling in England (2013–2019). This study demonstrates one form of potential driver to the reproducibility crisis, in this case with disease control management in an increasingly intensified livestock industry. [ABSTRACT FROM AUTHOR]

With technological innovations, enterprises in the real world are managing every iota of data as it can be mined to derive business intelligence (BI). However, when data comes from multiple sources, it may result in duplicate records. As data is given paramount importance, it is also significant to eliminate duplicate entities towards data integration, performance and resource optimization. To realize reliable systems for record deduplication, late, deep learning could offer exciting provisions with a learning-based approach. Deep ER is one of the deep learning-based methods used recently for dealing with the elimination of duplicates in structured data. Using it as a reference model, in this paper, we propose a framework known as Enhanced Deep Learning-based Record Deduplication (EDL-RD) for improving performance further. Towards this end, we exploited a variant of Long Short Term Memory (LSTM) along with various attribute compositions, similarity metrics, and numerical and null value resolution. We proposed an algorithm known as Efficient Learning based Record Deduplication (ELbRD). The algorithm extends the reference model with the aforementioned enhancements. An empirical study has revealed that the proposed framework with extensions outperforms existing methods. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a significant impact on students' academic performance. The effects of the pandemic have varied among students, but some general trends have emerged. One of the primary challenges for students during the pandemic has been the disruption of their study habits. Students getting used to online learning routines might find it even more challenging to perform well in face to face learning. Therefore, assessing various potential risk factors associated with students low performance and its prediction is important for early intervention. As students' performance data encompass diverse behaviors, standard machine learning methods find it hard to get useful insights for beneficial practical decision making and early interventions. Therefore, this research explores regularized ensemble learning methods for effectively analyzing students' performance data and reaching valid conclusions. To this end, three pruning strategies are implemented for the random forest method. These methods are based on out-of-bag sampling, sub-sampling and sub-bagging. The pruning strategies discard trees that are adversely affected by the unusual patterns in the students data forming forests of accurate and diverse trees. The methods are illustrated on an example data collected from university students currently studying on campus in a face-to-face modality, who studied during the COVID-19 pandemic through online learning. The suggested methods outperform all the other methods considered in this paper for predicting students at the risk of academic failure. Moreover, various factors such as class attendance, students interaction, internet connectivity, pre-requisite course(s) during the restrictions, etc., are identified as the most significant features. [ABSTRACT FROM AUTHOR]

When conducting spine-related diagnosis and surgery, the three-dimensional (3D) upright posture of the spine under natural weight bearing is of significant clinical value for physicians to analyze the force on the spine. However, existing medical imaging technologies cannot meet current requirements of medical service. On the one hand, the mainstream 3D volumetric imaging modalities (e.g. CT and MRI) require patients to lie down during the imaging process. On the other hand, the imaging modalities conducted in an upright posture (e.g. radiograph) can only realize 2D projections, which lose the valid information of spinal anatomy and curvature. Developments of deep learning-based 3D reconstruction methods bring potential to overcome the limitations of the existing medical imaging technologies. To deal with the limitations of current medical imaging technologies as is described above, in this paper, we propose a novel deep learning framework, ReVerteR, which can realize automatic 3D Reconstruction of Vertebrae from orthogonal bi-planar Radiographs. With the utilization of self-attention mechanism and specially designed loss function combining Dice, Hausdorff, Focal, and MSE, ReVerteR can alleviate the sample-imbalance problem during the reconstruction process and realize the fusion of the centroid annotation and the focused vertebra. Furthermore, aiming at automatic and customized 3D spinal reconstruction in real-world scenarios, we extend ReVerteR to a clinical deployment-oriented framework, and develop an interactive interface with all functions in the framework integrated so as to enhance human–computer interaction during clinical decision-making. Extensive experiments and visualization conducted on our constructed datasets based on two benchmark datasets of spinal CT, VerSe 2019 and VerSe 2020, demonstrate the effectiveness of our proposed ReVerteR. In this paper, we propose an automatic 3D reconstruction method of vertebrae based on orthogonal bi-planar radiographs. With the 3D upright posture of the spine under natural weight bearing effectively constructed, our proposed method is expected to better support doctors make clinical decision during spine-related diagnosis and surgery. [ABSTRACT FROM AUTHOR]

The automated replacement of empty tubes in the yarn bank is a critical step in the process of automatic winding machines with yarn banks, as the real-time detection of depleted yarn on spools and accurate positioning of empty tubes directly impact the production efficiency of winding machines. Addressing the shortcomings of traditional methods, such as poor adaptability and low sensitivity in optical and visual tube detection, and aiming to reduce the computational and detection time costs introduced by neural networks, this paper proposes a lightweight yarn spool detection model based on YOLOv8. The model utilizes Darknet-53 as the backbone network, and due to the dense spatial distribution of yarn spool targets, it incorporates large selective kernel units to enhance the recognition and positioning of dense targets. To address the issue of excessive focus on local features by convolutional neural networks, a bi-level routing attention mechanism is introduced to capture long-distance dependencies dynamically. Furthermore, to balance accuracy and detection speed, a FasterNeck is constructed as the neck network, replacing the original convolutional blocks with Ghost convolutions and integrating with FasterNet. This design minimizes the sacrifice of detection accuracy while achieving a significant improvement in inference speed. Lastly, the model employs weighted IoU with a dynamic focusing mechanism as the bounding box loss function. Experimental results on a custom yarn spool dataset demonstrate a notable improvement over the baseline model, with a high-confidence mAP of 94.2% and a compact weight size of only 4.9 MB. The detection speed reaches 223FPS, meeting the requirements for industrial deployment and real-time detection. [ABSTRACT FROM AUTHOR]

In the realm of prognosticating the remaining useful life (RUL) of pivotal components, such as aircraft engines, a prevalent challenge persists where the available historical life data often proves insufficient. This insufficiency engenders obstacles such as impediments in performance degradation feature extraction, inadequacies in capturing temporal relationships comprehensively, and diminished predictive accuracy. To address this issue, a 1D CNN-GRU prediction model for few-shot conditions is proposed in this paper. In pursuit of more comprehensive data feature extraction and enhanced RUL prognostication precision, the Convolutional Neural Network (CNN) is selected for its capacity to discern high-dimensional features amid the intricate dynamics of the data. Concurrently, the Gated Recurrent Unit (GRU) network is leveraged for its robust capability in extracting temporal features inherent within the data. We combine the two to construct a CNN-GRU hybrid network. Moreover, the integration of data distribution alongside correlation and monotonicity indices is employed to winnow the input of multi-sensor monitoring parameters into the CNN-GRU network. Finally, the engine RULs are predicted by the trained model. In this paper, experiments are conducted on a sub-dataset of the National Aeronautics and Space Administration (NASA) C-MAPSS multi-constraint dataset to validate the effectiveness of the method. Experimental results have demonstrated that this method has high accuracy in RUL prediction tasks, which can powerfully demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

The problem of coal compression under buildings is common in underground mining of coal mines in China. The selection of traditional mining programme is subjective and lacks scientific rationality. In order to solve this problem, this paper studies the evaluation index system and model applicable to the selection of coal filling mining scheme under buildings. A multi-objective evaluation index system integrating economic, technical and adaptive factors is constructed. And an integrated optimization model is established, which is based on the traditional grey target model, combining the game theory optimal combination of weights with the hierarchical analysis method, entropy weight method, Critic method to determine the weights, and then introducing the TOPSIS model and the Mars distance to establish an improved grey target decision-making model. The validity of the evaluation index system and model is verified by taking the example of coal mining under pressure of buildings in five mining areas of a coal mine, which provides technical support for decision makers. This study helps to scientifically and reasonably carry out the preferred mining scheme of coal filling under building pressure. [ABSTRACT FROM AUTHOR]

In recent times, video inpainting techniques have intended to fill the missing areas or gaps in a video by utilizing known pixels. The variety in brightness or difference of the patches causes the state-of-the-art video inpainting techniques to exhibit high computation complexity and create seams in the target areas. To resolve these issues, this paper introduces a novel video inpainting technique that employs the Morphological Haar Wavelet Transform combined with the Krill Herd based Criminisi algorithm (MHWT-KHCA) to address the challenges of high computational demand and visible seam artifacts in current inpainting practices. The proposed MHWT-KHCA algorithm strategically reduces computation times and enhances the seamlessness of the inpainting process in videos. Through a series of experiments, the technique is validated against standard metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), where it demonstrates superior performance compared to existing methods. Additionally, the paper outlines potential real-world applications ranging from video restoration to real-time surveillance enhancement, highlighting the technique's versatility and effectiveness. Future research directions include optimizing the algorithm for diverse video formats and integrating machine learning models to advance its capabilities further. [ABSTRACT FROM AUTHOR]

Efficient specialized vehicle cooperative scheduling is significant for airport operations, particularly during times of high traffic, which reduces the risk of flight delays and increases customer satisfaction. In this paper,we construct a multi-type vehicles collaborative scheduling model with the objectives of minimizing vehicle travel distance and vehicle waiting time. Additionally, a three-layer genetic algorithm is designed, and the crossover and mutation operations are enhanced to address the scheduling model. Due to the numerous uncertainties and stochastic interferences in airport operations, conventional scheduling methods unable to effectively address these challenges, this paper combines improved genetic algorithm, simulation algorithm, and digital twins technology, proposing a multi-strategy scheduling framework for specialized vehicles based on digital twins. The scheduling framework utilises digital twins to capture dynamic data from the airport and continuously adjusts the scheduling plan through the scheduling strategy to ensure robust scheduling for specialized vehicles. In the event of severe delays at the airport, fast and efficient re-scheduling can be achieved. Finally, the effectiveness of the proposed scheduling framework is validated using domestic flight data, and extensive experiments and analyses are conducted in different scenarios. This research contributes to addressing the optimization problem of cooperative scheduling for multi-type vehicles at airports. [ABSTRACT FROM AUTHOR]

The energy management (EM) solution of the multi-microgrids (MMGs) is a crucial task to provide more flexibility, reliability, and economic benefits. However, the energy management (EM) of the MMGs became a complex and strenuous task with high penetration of renewable energy resources due to the stochastic nature of these resources along with the load fluctuations. In this regard, this paper aims to solve the EM problem of the MMGs with the optimal inclusion of photovoltaic (PV) systems, wind turbines (WTs), and biomass systems. In this regard, this paper proposed an enhanced Jellyfish Search Optimizer (EJSO) for solving the EM of MMGs for the 85-bus MMGS system to minimize the total cost, and the system performance improvement concurrently. The proposed algorithm is based on the Weibull Flight Motion (WFM) and the Fitness Distance Balance (FDB) mechanisms to tackle the stagnation problem of the conventional JSO technique. The performance of the EJSO is tested on standard and CEC 2019 benchmark functions and the obtained results are compared to optimization techniques. As per the obtained results, EJSO is a powerful method for solving the EM compared to other optimization method like Sand Cat Swarm Optimization (SCSO), Dandelion Optimizer (DO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and the standard Jellyfish Search Optimizer (JSO). The obtained results reveal that the EM solution by the suggested EJSO can reduce the cost by 44.75% while the system voltage profile and stability are enhanced by 40.8% and 10.56%, respectively. [ABSTRACT FROM AUTHOR]

This paper employs an innovative event study methodology to demonstrate the impact of climate change on the NASDAQ index from the unique perspective of extreme weather events. This is achieved through the application of the event study methodology to a total of 526 biological, climatic, geological, hydrological, and meteorological disasters of climate change occurring in the U.S. during the period of 2000–2019. The results of the study demonstrate that: ① it can be generally observed that the five dimensions of climate change have a significant impact on stock returns. ② Empirical evidence indicates that the impact of different climate change dimensions on the return rate of stocks from NASDAQ stocks varies. In contrast, the biological and hydrological dimensions have a significantly negative impact on the return rate of stocks from the NASDAQ index, while the climate dimension has a significantly positive impact on the return rate of stocks from the NASDAQ index. ③ From the perspective of time, the impact of the five dimensions of climate change on the stock yield exhibits certain non-linear characteristics. This can be observed in the phenomenon of shock reversal, which occurs before and after the event. [ABSTRACT FROM AUTHOR]

The Yangtze River (hereafter referred to as the YZR), the largest river in China, is of paramount importance for ensuring water resource security. The Yangtze River Basin (hereafter referred to as the YRB) is one of the most densely populated areas in China, and complex human activities have a significant impact on the ecological security of water resources. Therefore, this paper employs theories related to ecological population evolution and the Driving Force-Pressure-State-Impact-Response (DPSIR) model to construct an indicator system for the ecological security of water resources in the YRB. The report evaluates the ecological security status of water resources in each province of the YRB from 2010 to 2019, clarifies the development trend of its water resource ecological security, and proposes corresponding strategies for regional ecological security and coordinated economic development. According to the results of the ecological population evolution competition model, the overall indicator of the ecological security of water resources in the YRB continues to improve, with the safety level increasing annually. Maintaining sound management of water resources in the YRB is crucial for sustainable socioeconomic development. To further promote the ecological security of water resources in the YRB and the coordinated development of the regional economy, this paper proposes policy suggestions such as promoting the continuous advancement of sustainable development projects, actively adjusting industrial structure, continuously enhancing public environmental awareness, and actively participating in international ecological construction and seeking cooperation among multiple departments. [ABSTRACT FROM AUTHOR]

The intelligent appearance quality classification method for Auricularia auricula is of great significance to promote this industry. This paper proposes an appearance quality classification method for Auricularia auricula based on the improved Faster Region-based Convolutional Neural Networks (improved Faster RCNN) framework. The original Faster RCNN is improved by establishing a multiscale feature fusion detection model to improve the accuracy and real-time performance of the model. The multiscale feature fusion detection model makes full use of shallow feature information to complete target detection. It fuses shallow features with rich detailed information with deep features rich in strong semantic information. Since the fusion algorithm directly uses the existing information of the feature extraction network, there is no additional calculation. The fused features contain more original detailed feature information. Therefore, the improved Faster RCNN can improve the final detection rate without sacrificing speed. By comparing with the original Faster RCNN model, the mean average precision (mAP) of the improved Faster RCNN is increased by 2.13%. The average precision (AP) of the first-level Auricularia auricula is almost unchanged at a high level. The AP of the second-level Auricularia auricula is increased by nearly 5%. And the third-level Auricularia auricula AP is increased by 1%. The improved Faster RCNN improves the frames per second from 6.81 of the original Faster RCNN to 13.5. Meanwhile, the influence of complex environment and image resolution on the Auricularia auricula detection is explored. [ABSTRACT FROM AUTHOR]

The optimized layout of electric vehicle (EV) chargers is not only crucial for users' convenience but also a key element in urban sustainable development, energy transition, and the promotion of new energy vehicles. In order to provide a basis for the problem of localization and capacity determination of chargers and compare the merits of several mainstream algorithms, this paper first establishes an optimization model with the objective of minimizing the total investment cost of all the chargers and the constraint of meeting the charging demands of all electric vehicles. Optimizations were performed using genetic algorithm (GA), surrogate optimization algorithm (SOA), and mixed integer linear programming (MILP) algorithm, respectively. In the case of using MILP, the original nonlinear optimization problem was transformed into a linear problem. In the planning of city-level EV chargers, MILP took 14182.57 s to calculate the minimum cost of 34.62 million yuan. After retaining only 10% of the original data amount, SOA took 87651.34 s to calculate the minimum cost of 3.01 million yuan. The results indicate that GA is prone to falling into local optima and is not suitable for large-scale optimization problems. SOA, on the other hand, requires significant memory consumption, so the issue of memory usage needs to be carefully considered when using it directly. Although MILP is only applicable to linear programming problems, it has the advantages of lower memory usage and higher reliability if the problem can be transformed into a linear one. [ABSTRACT FROM AUTHOR]

Deep rock are often in a true triaxial stress state. Studying the impacts of varying unloading speeds on their strain energy (SE) density is highly significant for predicting rock stability. Through true triaxial unloading principal stress experiments and true triaxial stress equilibrium unloading experiments on sandstone, this paper proposes a method to compute the SE density in a true triaxial compressive unloading principal stress test. This method aims to analyze the SE variation in rocks under the action of true triaxial unloading principal stresses. Acoustic emission is used to verify the correctness of the SE density calculation method in this paper. This study found that: (1) Unloading in one principal stress direction causes the SE density to rise in the other principal stress directions. This rise in SE, depending on its reversibility, can be categorized into elastic and dissipated SE. (2)When unloading principal stresses, the released elastic SE density in the unloading direction is influence by the stress path and rate. (3) The higher the unloading speed will leads to greater increases in the input SE density, elastic SE density, and dissipative SE density in the other principal stress directions. (4) The dissipated SE generated under true triaxial compression by unloading the principal stress is positively correlated with the damage to the rock; with an increase in unloading rate, there is a corresponding increase in the formation of cracks after unloading. (5) Utilizing the stress balance unloading test, we propose a calculation method for SE density in true triaxial unloading principal stress tests. [ABSTRACT FROM AUTHOR]

The concept of volume fracturing has revolutionized the conventional limits of low permeability, expanded the effective resource space, and significantly enhanced oil well production in tight oil reservoir development. This paper elucidates the mechanism of volume fracturing technology for tight sandstone reservoirs by considering multiple factors such as the initiation range of multi-fractures, influence of far-well horizontal principal stress on fracture initiation and propagation, degree of natural fractures development, and mechanical parameters of reservoir rock. Through simulation based on the mechanical parameters of reservoir rock, a comparative analysis was conducted between the model-calculated rock fracture pressure value and measured data from fracturing construction wells in the study area. The results revealed that there was a discrepancy within 10% between the model calculations and actual data. By simulating the effects of different injection volumes of fracturing fluid, pumping rates, and perforation methods on the fracture geometry, optimal design parameters for volume fracturing technology were obtained. Additionally, we propose optimization ideas and suggestions for construction parameters applicable to field operations. The simulation results indicate that a minimum recommended fluid volume scale exceeding 1800 m3 is advised for the reservoir. Based on frictional calculations, it is recommended to have an on-site construction rate not less than 18.0 m3/min along with 36–48 holes/section for perforation purposes. The numerical simulation research presented in this paper provides a theoretical reference basis and practical guidance for the application of fracturing network technology in tight sandstone reservoirs. [ABSTRACT FROM AUTHOR]

Load frequency control (LFC) plays a critical role in ensuring the reliable and stable operation of power plants and maintaining a quality power supply to consumers. In control engineering, an oscillatory behavior exhibited by a system in response to control actions is referred to as "Porpoising". This article focused on investigating the causes of the porpoising phenomenon in the context of LFC. This paper introduces a novel methodology for enhancing the performance of load frequency controllers in power systems by employing rat swarm optimization (RSO) for tuning and detecting the porpoising feature to ensure stability. The study focuses on a single-area thermal power generating station (TPGS) subjected to a 1% load demand change, employing MATLAB simulations for analysis. The proposed RSO-based PID controller is compared against traditional methods such as the firefly algorithm (FFA) and Ziegler-Nichols (ZN) technique. Results indicate that the RSO-based PID controller exhibits superior performance, achieving zero frequency error, reduced negative peak overshoot, and faster settling time compared to other methods. Furthermore, the paper investigates the porpoising phenomenon in PID controllers, analyzing the location of poles in the s-plane, damping ratio, and control actions. The RSO-based PID controller demonstrates enhanced stability and resistance to porpoising, making it a promising solution for power system control. Future research will focus on real-time implementation and broader applications across different control systems. [ABSTRACT FROM AUTHOR]

The illicit traffic of cultural goods remains a persistent global challenge, despite the proliferation of comprehensive legislative frameworks developed to address and prevent cultural property crimes. Online platforms, especially social media and e-commerce, have facilitated illegal trade and pose significant challenges for law enforcement agencies. To address this issue, the European project SIGNIFICANCE was born, with the aim of combating illicit traffic of Cultural Heritage (CH) goods. This paper presents the outcomes of the project, introducing a user-friendly platform that employs Artificial Intelligence (AI) and Deep learning (DL) to prevent and combat illicit activities. The platform enables authorities to identify, track, and block illegal activities in the online domain, thereby aiding successful prosecutions of criminal networks. Moreover, it incorporates an ontology-based approach, providing comprehensive information on the cultural significance, provenance, and legal status of identified artefacts. This enables users to access valuable contextual information during the scraping and classification phases, facilitating informed decision-making and targeted actions. To accomplish these objectives, computationally intensive tasks are executed on the HPC CyClone infrastructure, optimizing computing resources, time, and cost efficiency. Notably, the infrastructure supports algorithm modelling and training, as well as web, dark web and social media scraping and data classification. Preliminary results indicate a 10–15% increase in the identification of illicit artifacts, demonstrating the platform's effectiveness in enhancing law enforcement capabilities. [ABSTRACT FROM AUTHOR]

The Hardening Soil-Brick model for soils is designed to carry out complex numerical analyses of soil-structure interaction problems taking into account strong stiffness variation in the range of small strains. However, to calibrate its parameters advanced triaxial and oedometric tests are required. In case of uncemented sands laboratory testing is usually difficult. Therefore, to facilitate calibration procedures, a CPTU based method, enhanced by an experimental evidence derived from advanced triaxial drained and oedometric tests, has been proposed and verified. It is shown in the paper that using exclusively the CPTU test results one can calibrate most important model parameters for sands with accuracy that is sufficient for solving real life problems. The major goal of this paper is to identify correlations between all reference stiffness moduli, then verify them, and finally link with the CPTU based identification procedures. It is shown in the paper that such correlations exist and they exhibit very high coefficients of determination. Moreover, as the seismic version of the CPTU test is not often available in the practice, an enhanced procedure for identification of very small strain shear stiffness modulus has been proposed and then verified, using set of the SCPTU tests conducted in Gdańsk sands (Poland). [ABSTRACT FROM AUTHOR]

One of the long-term goals of reinforcement learning is to build intelligent agents capable of rapidly learning and flexibly transferring skills, similar to humans and animals. In this paper, we introduce an episodic control framework based on the temporal expansion of subsequent features to achieve these goals, which we refer to as Temporally Extended Successor Feature Neural Episodic Control (TESFNEC). This method has shown impressive results in significantly improving sample efficiency and elegantly reusing previously learned strategies. Crucially, this model enhances agent training by incorporating episodic memory, significantly reducing the number of iterations required to learn the optimal policy. Furthermore, we adopt the temporal expansion of successor features a technique to capture the expected state transition dynamics of actions. This form of temporal abstraction does not entail learning a top-down hierarchy of task structures but focuses on the bottom-up combination of actions and action repetitions. Thus, our approach directly considers the temporal scope of sequences of temporally extended actions without requiring predefined or domain-specific options. Experimental results in the two-dimensional object collection environment demonstrate that the method proposed in this paper optimizes learning policies faster than baseline reinforcement learning approaches, leading to higher average returns. [ABSTRACT FROM AUTHOR]

As a research hot topic in the field of network security, the implementation of machine learning, such as federated learning, involves information interactions among a large number of distributed network devices. If we regard these distributed network devices and connection relationships as a complex network, we can identify the influential nodes to find the crucial points for optimizing the imbalance of the reliability of devices in federated learning system. This paper will analyze the advantages and disadvantages of existing algorithms for identifying influential nodes in complex networks, and propose a method from the perspective of information dissemination for finding influential nodes based on Kullback–Leibler divergence model within the neighborhood (KLN). Firstly, the KLN algorithm removes a node to simulate the scenario of node failure in the information dissemination process. Secondly, KLN evaluates the loss of information entropy within the neighborhood after node removal by establishing the KL divergence model. Finally, it assesses the damage influence of the removed node by integrating the network attributes and KL divergence model, thus achieving the evaluation of node importance. To validate the performance of KLN, this paper conducts an analysis and comparison of its results with those of 11 other algorithms on 10 networks, using SIR model as a reference. Additionally, a case study was undertaken on a real epidemic propagation network, leading to the proposal of management and control strategies for daily protection based on the influential nodes. The experimental results indicate that KLN effectively evaluates the importance of the removed node using KL model within the neighborhood, and demonstrate better accuracy and applicability across networks of different scales. [ABSTRACT FROM AUTHOR]

The innovative application of Crowd Intelligent Devices (CIDS) in edge networks has garnered attention due to the rapid development of artificial intelligence and computer technology. This application offers users more reliable and low-latency computing services through computation offloading technology. However, the dynamic nature of network terminals and the limited coverage of edge servers pose challenges, such as data loss and service interruption. Furthermore, the high-speed mobility of intelligent terminals in the dynamic edge network environment further complicates the design of computation offloading and service migration strategies. To address these challenges, this paper explores the computation offloading model of cluster intelligence collaboration in a heterogeneous network environment. This model involves multiple intelligences collaborating to provide computation offloading services for terminals. To accommodate various roles, a switching strategy of split-cluster group collaboration is introduced, assigning the cluster head, the alternate cluster head, and the ordinary user are assigned to a group with different functions. Additionally, the paper formulates the optimal offloading strategy for group smart terminals as a Markov decision process, taking into account factors such as user mobility, service delay, service accuracy, and migration cost. To implement this strategy, the paper utilizes the deep reinforcement learning-based CCSMS algorithm. Simulation results demonstrate that the proposed edge network service migration strategy, rooted in groupwise cluster collaboration, effectively mitigates interruption delay and enhances service migration efficiency. [ABSTRACT FROM AUTHOR]

Gestures are a common way people communicate. Gesture-based teleoperation control systems tend to be simple to operate and suitable for most people’s daily use. This paper employed a LeapMotion sensor to develop a mobile robot control system based on gesture recognition, which mainly established connections through a client/server structure. The principles of gesture recognition in the system were studied and the relevant self-investigated algorithms—GestureMoRo, for the association between gestures and mobile robots were designed. Moreover, in order to avoid the unstably fluctuated movement of the mobile robot caused by palm shaking, the Gaussian filter algorithm was used to smooth and denoise the collected gesture data, which effectively improved the robustness and stability of the mobile robot’s locomotion. Finally, the teleoperation control strategy of the gesture to the WATER2 mobile robot was realized, and the effectiveness and practicability of the designed system were verified through multiple experiments. [ABSTRACT FROM AUTHOR]

This article focuses on the cascading failure problem and node importance evaluation method in complex networks. To address the issue of identifying important nodes in dynamic networks, the method used in static networks is introduced and the necessity of re-evaluating node status during node removal is proposed. Studies have found that the methods for identifying dynamic and static network nodes are two different directions, and most literature only uses dynamic methods to verify static methods. Therefore, it is necessary to find suitable node evaluation methods for dynamic networks. Based on this, this article proposes a method that integrates local and global correlation properties. In terms of global features, we introduce an improved k-shell method with fusion degree to improve the resolution of node ranking. In terms of local features, we introduce Solton factor and structure hole factor improved by INCC (improved network constraint coefficient), which effectively improves the algorithm's ability to identify the relationship between adjacent nodes. Through comparison with existing methods, it is found that the KPDN–INCC method proposed in this paper complements the KPDN method and can accurately identify important nodes, thereby helping to quickly disintegrate the network. Finally, the effectiveness of the proposed method in identifying important nodes in a small-world network with a random parameter less than 0.4 was verified through artificial network experiments. [ABSTRACT FROM AUTHOR]

Freeze–thaw failure of frozen rock slope often occurs during engineering construction and mining in cold area, which poses a great threat to engineering construction and people's life safety. The properties of rock mass in cold region will change with the periodic change of temperature, which makes it difficult to accurately evaluate the stability of slope under the action of freeze–thaw cycle by conventional methods. Based on field investigation and literature review, this paper discusses the characteristics of frozen rock mass and the failure mechanism of frozen rock slope, and gives the types and failure modes of frozen rock slope. Then, the research status of frozen rock slope is analyzed. It is pointed out that the failure of frozen rock slope is the result of thermo-hydro-mechanical (THM) coupling. It is considered that freeze–thaw cycle, rainfall infiltration and fracture propagation have significant effects on the stability of frozen rock slope, and numerical simulation is used to demonstrate. The research shows that the safety factor of frozen rock slope changes dynamically with the surface temperature, and the safety factor of slope decreases year by year with the increase of freeze–thaw cycles, and the fracture expansion will significantly reduce the safety factor. Based on the above knowledge, a time-varying evaluation method of frozen rock slope stability based on THM coupling theory is proposed. This paper can deepen scholars' understanding of rock fracture slope in cold area and promote related research work. [ABSTRACT FROM AUTHOR]

Geophysical inversion usually involves ill-posed problem. Regularization is the most commonly used method to mitigate this problem. There are many regularization parameter selection methods, among which the adaptive regularization method can automatically update parameters during iteration, reducing the difficulty of parameter selection. Therefore, it is widely used in linear inversion. However, there are very few studies on the use of adaptive regularization methods in stochastic optimization algorithms. The biggest difficulty is that in stochastic optimization algorithms, the search direction of any iteration is completely random. Data fitting term and stabilizing term vary in a wide range, making it difficult for traditional methods to work. In this paper, we consider the contributions of the data fitting term and the stabilizing term in the objective function and give an improved adaptive regularization method for very fast simulated annealing (VFSA) inversion for transient electromagnetic (TEM) data. The optimized method adjusts the two terms dynamically to make them in balance. We have designed several numerical experiments, and the experimental results demonstrate that the method in this paper not only accelerates the convergence, but also the inversion results are very little affected by the initial regularization parameter. Finally, we apply this method to field data, and the inversion results show very good agreements with nearby borehole data. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) location is a significant research area. In complex environments like forests, inaccurate signal intensity ranging is a major challenge. To address this issue, this paper presents a reliable WSN distance measurement-positioning algorithm for forest environments. The algorithm divides the positioning area into several sub-regions based on the discrete coefficient of the collected signal strength. Then, using the fitting method based on the signal intensity value of each sub-region, the algorithm derives the reference points of the logarithmic distance path loss model and path loss index. Finally, the algorithm locates target nodes using anchor nodes in different regions. Additionally, to enhance the positioning accuracy, weight values are assigned to the positioning result based on the discrete coefficient of the signal intensity in each sub-region. Experimental results demonstrate that the proposed WSN algorithm has high precision in forest environments. [ABSTRACT FROM AUTHOR]

When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG signals will produce a large amount of data. At the same time, limited bandwidth, end-to-end delay, and memory space will bring great pressure on the effective transmission of data. The birth of compressed sensing alleviates this transmission pressure. However, using an iterative compressed sensing reconstruction algorithm for EEG signal reconstruction faces complex calculation problems and slow data processing speed, limiting the application of compressed sensing in EEG signal rapid monitoring systems. As such, this paper presents a non-iterative and fast algorithm for reconstructing EEG signals using compressed sensing and deep learning techniques. This algorithm uses the improved residual network model, extracts the feature information of the EEG signal by one-dimensional dilated convolution, directly learns the nonlinear mapping relationship between the measured value and the original signal, and can quickly and accurately reconstruct the EEG signal. The method proposed in this paper has been verified by simulation on the open BCI contest dataset. Overall, it is proved that the proposed method has higher reconstruction accuracy and faster reconstruction speed than the traditional CS reconstruction algorithm and the existing deep learning reconstruction algorithm. In addition, it can realize the rapid reconstruction of EEG signals. [ABSTRACT FROM AUTHOR]

Real-time online tracking of tool wear is an indispensable element in automated machining, and tool wear directly impacts the processing quality of workpieces and overall productivity. For the milling tool wear state is difficult to real-time visualization monitoring and individual tool wear prediction model deviation is large and is not stable and so on, a digital twin-driven ensemble learning milling tool wear online monitoring novel method is proposed in this paper. Firstly, a digital twin-based milling tool wear monitoring system is built and the system model structure is clarified. Secondly, through the digital twin (DT) data multi-level processing system to optimize the signal characteristic data, combined with the ensemble learning model to predict the milling cutter wear status and wear values in real-time, the two will be verified with each other to enhance the prediction accuracy of the system. Finally, taking the milling wear experiment as an application case, the outcomes display that the predictive precision of the monitoring method is more than 96% and the prediction time is below 0.1 s, which verifies the effectiveness of the presented method, and provides a novel idea and a new approach for real-time on-line tracking of milling cutter wear in intelligent manufacturing process. [ABSTRACT FROM AUTHOR]

Accurate prediction of sea level height is critically important for the government in assessing sea level risk in coastal areas. However, due to the nonlinear, time-varying and highly uncertain characteristics of sea level change data, sea level prediction is challenging. To improve the accuracy of sea level prediction, this paper uses a new swarm intelligence algorithm named the sparrow search algorithm (SSA), which can imitate the foraging behavior and antipredation behavior of sparrows, to determine optimal solutions. To avoid the algorithm falling into a local optimal situation, this paper integrates the sine–cosine algorithm and the Cauchy variation strategy into the SSA to obtain an algorithm named the SCSSA. The SCSSA is used to optimize the parameter values of the CNN-BiLSTM (convolutional neural network combined with bidirectional long short-term memory neural network) model; finally, a combined neural network model (named SCSSA-CNN-BiLSTM) is proposed. In this paper, the time series data of seven tidal stations located in coastal China are used for experimental analysis. First, the SCSSA-CNN-BiLSTM model is compared with the CNN-BiLSTM model to predict the time series data of SHANWEI Station. With respect to the training and test sets of data, the SCSSA-CNN-BiLSTM model outperforms the other models on all the evaluation metrics. In addition, the remaining six tide station datasets and five neural network models, including the SCSSA-CNN-BiLSTM model, are used to further study the performance of the proposed prediction model. Four evaluation indices including the root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and coefficient of determination (R2) are adopted. For six stations, the RMSE, MAE, MAPE and R2 of SCSSA-CNN-BiLSTM model are ranged from 20.9217 ~ 27.8427 mm, 9.4770 ~ 17.8603 mm, 0.1322% ~ 0.2482% and 0.9119 ~ 0.9759, respectively. The experimental analysis results show that the SCSSA-CNN-BiLSTM model makes effective predictions at all stations, and the prediction performance is better than that of the other models. Even though the combination of SCSSA algorithm may increase the complexity of the model, indeed the proposed model is a new prediction method with good accuracy and robustness for predicting sea level change. [ABSTRACT FROM AUTHOR]

In this article, the formation of laser-induced graphene on the two natural polymers, cellulose, and lignin, as precursors was investigated with molecular dynamics simulations and some experiments. These eco-friendly polymers provide significant industrial advantages due to their low cost, biodegradability, and recyclable aspects. It was discovered during the simulation that LIG has numerous defects and a porous structure. Carbon monoxide, H2, and water vapor are gases released by cellulose and lignin substrates. H2O and CO are released when the polymer transforms into an amorphous structure. Later on, as the amorphous structure changes into an ordered graphitic structure, H2 is released continuously. Since cellulose monomer has a higher mass proportion of oxygen (49%) than lignin monomer (29%), it emits more CO. The LIG structure contains many 5- and 7-carbon rings, which cause the structure to have bends and undulations that go out of the plane. In addition, to verify the molecular dynamics simulation results with experimental tests, we used a carbon dioxide laser to transform filter paper, as a cellulose material, and coconut shell, as a lignin material, into graphene. Surprisingly, empirical experiments confirmed the simulation results. [ABSTRACT FROM AUTHOR]

Master–slave blockchain is a novel information processing technology that is domain-oriented and uses efficient cryptography principles for trustworthy communication and storage of big data. Existing indexing methods primarily target the creation of a single-structured blockchain, resulting in extensive time and memory requirements. As the scale of domain data continues to grow exponentially, master–slave blockchain systems face increasingly severe challenges with regards to low query efficiency and extended traceback times. To address these issues, this paper propose a multi-level index construction method for the master–slave blockchain (MLI). Firstly, MLI introduces a weight matrix and partitions the entire master–slave blockchain based on the master chain structure, the weight of each partition is assigned. Secondly, for the master blockchain in each partition, a master chain index construction method based on jump consistent hash (JHMI) is proposed, which takes the key value of the nodes and the number of index slots as input and outputs the master chain index. Finally, a bloom filter is introduced to improve the column-based selection function and build a secondary composite index on the subordinate blockchain corresponding to each master block. Experimental results on three constraint conditions and two types of datasets demonstrate that the proposed method reduce the index construction time by an average of 9.28%, improve the query efficiency by 12.07%, and reduce the memory overhead by 24.4%. [ABSTRACT FROM AUTHOR]

Effective coping with acute stress is important to promote mental health and to build stress resilience. Interventions improving stress coping usually require long training periods. In this study, we present a hypnosis-based intervention that produces long-term effects after a single hypnosis session. In that session, we established a post-hypnotic safety suggestion that participants can activate afterwards with a cue, the Jena Safety Anchor. We tested 60 participants in our study who all received the hypnosis session and a stress task. The safety group used the Jena Safety Anchor during acute stress (Trier Social Stress Test, TSST). The control group used a neutral anchor. We measured subjective stress responses via self-reports and physiological stress responses via saliva and blood samples as well as heart rate. One week later, all participants filled in an online survey to measure long-term effects of the post-hypnotic safety suggestion. We found that participants using the Jena Safety Anchor during the TSST reported significantly lower stress compared to the control group. The safety group also reported significantly fewer negative thoughts concerning their TSST performance than the control group during the stress recovery phase and 1 week later. All participants indicated that the Jena Safety Anchor still worked 1 week after its establishment. Suggestibility did not affect the efficacy of the Jena Safety Anchor. Our findings demonstrate that post-hypnotic safety suggestions improve stress coping with long-lasting effects, which makes it a promising intervention to promote mental health and establish stress resilience in just one hypnosis session. [ABSTRACT FROM AUTHOR]

The ITER project is one of the largest international cooperative scientific projects in the world, aiming to verify the feasibility of magnetic confinement controlled nuclear fusion technology and provide a technical basis for the subsequent construction of fusion energy power stations. The success or failure of ITER will greatly affect the commercialization process of fusion energy. The probabilistic safety assessment (PSA) was a powerful means to evaluate the risk and reliability of nuclear facility and achieved great success in safety assessment of fission power plants. Based on this, the PSA progress for ITER was proposed in this paper. And the in-vessel leakage accident was investigated to verify the effectiveness of proposed method. The result shows the maximum possible radiological consequences of ITER in-vessel leakage accident of ITER is 1.6E−3 mSv, and the frequencies of this consequence is 1.63E−8/year. The reason of this consequence was also discussed in this paper. Those result could provide some valuable reference for radiation risk assessment and safety supervision of fusion commercial reactor in the nuclear future. [ABSTRACT FROM AUTHOR]

This paper demonstrates the application of the Johnson–Holmquist II (JH-2) model with correlated and validated parameters to simulate the behavior of a sandstone. The JH-2 model is used to simulate various tests, including single-element tests, structural quasi-static uniaxial and triaxial compression tests, and the split Hopkinson pressure bar test. Additionally, the model is used to simulate drop-weight impact test using a ball bearing and two loading scenarios involving small-scale blasting and projectile impacts. Quantitative and qualitative comparisons demonstrate that the JH-2 model agrees well with both experimental and analytical results. Limitations of the model are also highlighted, particularly for quasi-static problems, as the model was originally developed for high-strain-rate simulations. Ultimately, this study demonstrates that the JH-2 rock constitutive model can obtain reasonable results for a material other than the material for which the model was originally correlated and validated. This paper provides valuable guidance for modeling and simulating sandstone and other rock materials subjected to dynamic loadings. [ABSTRACT FROM AUTHOR]

The pharmaceutical industry is an important industry for the national economy and the people's livelihood, which is not only beneficial to the people's livelihood, but also has huge commercial value. How to promote the development of Chinese pharmaceutical industry is an urgent problem to be solved. In this study, 47 listed pharmaceutical companies are taken as cases, and Qualitative Comparative Analysis of Fuzzy Sets (fsQCA) is used to analyze the influence of five antecedent conditions on the total factor productivity of pharmaceutical enterprises from the perspective of corporate governance, and to explore the composition to Total Factor Productivity (TFP) improvement. The results are as follows. First, single corporate governance factor does not constitute the necessary condition to improve the TFP of pharmaceutical enterprises. Second, there are three configurations of high TFP of pharmaceutical enterprises, among these, two configurations belong to regulatory constraints type and one configuration belongs to the active board type. There is only one configurations to low TFP of pharmaceutical enterprises: the passive board. Based on the perspective of configuration, this paper discusses how corporate governance drives TFP improvement in pharmaceutical enterprises, which can provide systematic thinking and practical guidance for each company to promote its TFP improvement according to its own corporate structure. [ABSTRACT FROM AUTHOR]

The study of walkability is of great significance to the construction of healthy cities. In this paper, taking 1283 articles of walkability, which were included in Web of Science, as the research object. This paper adopts to analysis the research progress by using the method of scientometrics and knowledge networks analysis. Objectively and systematically analyze the research progress of walkability abroad from the aspects of publication overview, knowledge foundation, research direction and hot spots, etc. It is found that foreign researches on walkability mainly include three core directions: walkability and physical activity, walkability evaluation, walkability and urban design. Among them, walkability and physical activity orientation have been studied from various perspectives, such as various groups, various environmental types, different behavioral patterns and various chronic diseases. With the increasingly prominent urbanization problems and the rapid development of new technologies, multiple data, new methods and interdisciplinary cooperation will actively promote the vigorous development of walking suitability research. [ABSTRACT FROM AUTHOR]

This paper presents a data processing pipeline designed to extract information from the hyperspectral signature of unknown space objects. The methodology proposed in this paper determines the material composition of space objects from single pixel images. Two techniques are used for material identification and classification: one based on machine learning and the other based on a least square match with a library of known spectra. From this information, a supervised machine learning algorithm is used to classify the object into one of several categories based on the detection of materials on the object. The behaviour of the material classification methods is investigated under non-ideal circumstances, to determine the effect of weathered materials, and the behaviour when the training library is missing a material that is present in the object being observed. Finally the paper will present some preliminary results on the identification and classification of space objects. [ABSTRACT FROM AUTHOR]

The improvement of enterprise ESG performance is one of the key driving forces to achieve the goal of economic and social green development. There is a gap between knowledge and practice in the ESG performance of Chinese enterprises, and digital transformation (DT) provides new ideas for ESG development. The research purpose of this paper is to explore the impact mechanism of DT on ESG and the specific path of DT to drive ESG. It provides a reference for listed enterprises to rely on DT to empower their sustainable development capability. This paper takes the panel data of A-share listed enterprises from 2011 to 2021 as the sample and measures the core indicators using the text mining method, modified Jones model, and Roy-Chowdhury model. On this basis, using a combination of econometric models and qualitative comparative analysis, we empirically analyze the impact mechanisms of DT on ESG as well as the specific grouping paths that drive ESG performance. The main conclusions are shown as follows. First, DT can significantly reduce ESG, with an impact coefficient of − 0.013, which is significant at the 5% level. It reflects that the DT of enterprises at this stage has certain deficiencies. There is a matching lag in the enterprise's internal organizational resources. The entry of digital technology will have a certain impact on traditional operations, and the high uncertainty of DT adds some hidden costs to the enterprise. Secondly, there is an indirect suppression effect of accrued earnings management (AEM) in the transmission mechanism of DT affecting ESG. It is verified that DT can reduce information asymmetry and inhibit EM activities, thus reducing the impact on ESG. Finally, a total of six configurations achieved high ESG valuations. High technology practice-low performance manipulation; digital intelligence-low performance manipulation; digital intelligence-digital resources; digital resources-inadequate digital infrastructure.; high technology practice-bottom technology deficiency; digital intelligence-high performance manipulation. Through configuration analysis, the suppression effect of EM is further verified. The lack of AEM is usually the core condition of the high-valuation group. Meanwhile, digital intelligence, digital resources, and digital technology practice can drive the improvement of enterprise ESG. The instrumental variables approach and robustness tests support these findings. [ABSTRACT FROM AUTHOR]

Facing the shortage of special building materials packaging machinery with thermal insulation and low intelligence, this paper designs a set of mechanical and electrical integration packaging unit control system to reduce the risk of material transportation for different stakeholders. According to risk management tools, the system takes Mitsubishi PLC as the control core and combines with communication module, servo motor drive system and touch screen man–machine interface to realize the risk simulation and automatic control of the packaging unit. The simulation results of PID control model show that the parameters such as speed and torque can be stabilized in a relatively short period of time when the load is suddenly changed within 1.5 s. Theoretical verification of the system has small steady-state error, rapid response, and good control effect. The man–machine interface design was carried out and the actual corresponding test experiment was carried out. The experimental results showed that the overall operation rate of the packaging unit system reached 98.15%, the pass rate was 99.03%, and the production capacity was about 9600 packs/hour, which met the production requirements. The control system of the building material packaging unit designed in this paper realizes the equipment intelligence, has a high degree of automation, and shows good potential application value in the aspects of building information, reduction of construction risks and manufacturing intelligence. [ABSTRACT FROM AUTHOR]

Currently, collaborative distribution models have not reached the optimal state of carbon emissions. The cost of additional low-carbon expenditures and the problem of carbon data verification have led to the lack of motivation for reducing emissions among collaborative distribution enterprises. Therefore, how to incentivize them to adopt the low-carbon model is crucial for achieving low-carbon goal. By relying on a government-led digital platform, this paper designs a dual low-carbon incentive strategy to encourage enterprise-alliance to adopt a low-carbon distribution model. In this paper, we first construct an evolutionary game model of the government, enterprise-alliance and end-users; then we explore the conditions of the three-party equilibrium evolution strategy by solving the model and analyzing the stability; and finally, we conduct simulation validation and results analysis with the help of MATLAB. In summary, we found that government punishment is more effective at regulating enterprise-alliance than reward. End-users' behavior is affected by the costs they need to bear, and they no longer support enterprise-alliance to carry out collaborative low-carbon distribution above a certain threshold. [ABSTRACT FROM AUTHOR]

Dam construction has some qualitative and quantitative effects on groundwater resources. This effect may be in the form of an increasing groundwater table or a changing groundwater system. In this paper, the effect of the Rodbal dam construction on the Darab aquifer was investigated. For this paper water table levels from observation wells and precipitation data was used. The study aims to analyse the behaviour of the water table during precipitation by employing a combination of the standardisation process, Fuzzy Inference System (FIS), and Root Mean Square Error (RMSE) calculations. Fuzzy logic involves the fuzzification of input data, transforming precise values into fuzzy sets. The effectiveness of the FIS is highlighted, particularly in determining the number of membership functions for inputs. The performance of the results is assessed using indicators such as RMSE and Coefficient of Determination (R2). The FIS showed a high level of effectiveness in performance assessment, exhibiting a 70% similarity between the fuzzy and Mann–Kendall methods. Nonetheless, the Fuzzy Purpose Method corresponded more closely with the observed data, indicating a more accurate reflection of reality. The findings indicate that for P3, P7, and P8, the results from the Mann–Kendall method do not show a discernible trend. Conversely, the proposed Fuzzy method accounts for changes in the behaviour of these piezometers. [ABSTRACT FROM AUTHOR]

Rapid urbanization has resulted in the substantial population growth in metropolitan areas. However, existing research on population change of the cities predominantly draws on grid statistical data at the administrative level, overlooking the intra-urban variegation of population change. Particularly, there is a lack of attention given to the spatio-temporal change of population across different urban forms and functions. This paper therefore fills in the lacuna by clarifying the spatio-temporal characteristics of population growth in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2000 to 2020 through the methods of local climate zone (LCZ) scheme and urban–rural gradients. The results showed that: (1) High population density was observed in the compact high-rise (LCZ 1) areas, with a noticeable decline along urban–rural gradients. (2) The city centers of GBA experienced the most significant population growth, while certain urban fringes and rural areas witnessed significant population shrinkage. (3) The rate of growth tended to slow down after 2010, but the uneven development of population-based urbanization was also noticeable, as urbanization and industrialization varied across different LCZ types and cities in GBA. This paper therefore contributes to a deeper understanding of population change and urbanization by clarifying their spatio-temporal contingences at landscape level. [ABSTRACT FROM AUTHOR]

With the urge to secure and protect digital assets, there is a need to emphasize the immediacy of taking measures to ensure robust security due to the enhancement of cyber security. Different advanced methods, like encryption schemes, are vulnerable to putting constraints on attacks. To encode the digital data and utilize the unique properties of DNA, like stability and durability, synthetic DNA sequences are offered as a promising alternative by DNA encoding schemes. This study enlightens the exploration of DNA's potential for encoding in evolving cyber security. Based on the systematic literature review, this paper provides a discussion on the challenges, pros, and directions for future work. We analyzed the current trends and new innovations in methodology, security attacks, the implementation of tools, and different metrics to measure. Various tools, such as Mathematica, MATLAB, NIST test suite, and Coludsim, were employed to evaluate the performance of the proposed method and obtain results. By identifying the strengths and limitations of proposed methods, the study highlights research challenges and offers future scope for investigation. [ABSTRACT FROM AUTHOR]

Advances in computer image recognition have significantly impacted many industries, including healthcare, security and autonomous systems. This paper aims to explore the potential of improving image algorithms to enhance computer image recognition. Specifically, we will focus on regression methods as a means to improve the accuracy and efficiency of identifying images. In this study, we will analyze various regression techniques and their applications in computer image recognition, as well as the resulting performance improvements through detailed examples and data analysis. This paper deals with the problems related to visual image processing in outdoor unstructured environment. Finally, the heterogeneous patterns are converted into the same pattern, and the heterogeneous patterns are extracted from the fusion features of data modes. The simulation results show that the perception ability and recognition ability of outdoor image recognition in complex environment are improved. [ABSTRACT FROM AUTHOR]

In the process of human aging, significant age-related changes occur in brain tissue. To assist individuals in assessing the degree of brain aging, screening for disease risks, and further diagnosing age-related diseases, it is crucial to develop an accurate method for predicting brain age. This paper proposes a multi-scale feature fusion method called Tri-UNet based on the U-Net network structure, as well as a brain region information fusion method based on multi-channel input networks. These methods address the issue of insufficient image feature learning in brain neuroimaging data. They can effectively utilize features at different scales of MRI and fully leverage feature information from different regions of the brain. In the end, experiments were conducted on the Cam-CAN dataset, resulting in a minimum Mean Absolute Error (MAE) of 7.46. The results demonstrate that this method provides a new approach to feature learning at different scales in brain age prediction tasks, contributing to the advancement of the field and holding significance for practical applications in the context of elderly education. [ABSTRACT FROM AUTHOR]

The Friendship Paradox is a simple and powerful statement about node degrees in a graph. However, it only applies to undirected graphs with no edge weights, and the only node characteristic it concerns is degree. Since many social networks are more complex than that, it is useful to generalize this phenomenon, if possible, and a number of papers have proposed different generalizations. Here, we unify these generalizations in a common framework, retaining the focus on undirected graphs and allowing for weighted edges and for numeric node attributes other than degree to be considered, since this extension allows for a clean characterization and links to the original concepts most naturally. While the original Friendship Paradox and the Weighted Friendship Paradox hold for all graphs, considering non-degree attributes actually makes the extensions fail around 50% of the time, given random attribute assignment. We provide simple correlation-based rules to see whether an attribute-based version of the paradox holds. In addition to theory, our simulation and data results show how all the concepts can be applied to synthetic and real networks. Where applicable, we draw connections to prior work to make this an accessible and comprehensive paper that lets one understand the math behind the Friendship Paradox and its basic extensions. [ABSTRACT FROM AUTHOR]

In modern tunnel construction, TBM (Tunnel Boring Machine) plays a very important role. In response to the needs of tunnel wall reinforcement and TBM automated construction for tunnel construction, a shotcrete mechanism mounted on the TBM is designed. In order to evaluate the kinematic performance of the mechanism, this paper studies the forward and inverse kinematics and spatial architecture of the TBM shotcrete robot. Firstly, based on the D–H parameter method, the number of joints and links is determined and structural analysis is performed to obtain the robot's forward kinematics equation, achieving the mapping between joint space and pose space. Then, by determining the joint variables, the mapping of the end tool in Cartesian space is achieved. Finally, based on the Monte Carlo random sampling method, the workspace of the robot is constructed, and its reachability and flexibility within the robot workspace are evaluated. The performance of the device is verified by building a prototype, which meets the requirements well. Through the research in this paper, it can provide theoretical basis and guidance for the design and control of the shotcrete robot. [ABSTRACT FROM AUTHOR]