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Taking a grain supply chain (GASC) with a producer and a retailer as the research object, to encourage supply chain members’ efforts on reducing grain quality and quantity losses, government will offer the subsidy. The paper constructed and analyzed four subsidy models. Findings: (1) Under the four modes, the wholesale price is negatively correlated with the producers’ efforts of loss reduction (EOLR), positively correlated with the retailers’ EOLR, and negatively correlated with the retail price and the producer’ and retailer’ EOLR. (2) The GASC members’ EOLR can have a “spillover effect”, meaning that their own EOLR can benefit the other party. (3) When the subsidy coefficient meets a certain value, the government subsidies for the EOLR of supply chain members can increase the profits of the producer and the retailer, and can stimulate the members to take the efforts to reduce losses, achieving the goal of “food loss and waste”. [ABSTRACT FROM AUTHOR]

This paper presents an improved time-optimal trajectory planning algorithm leveraging the White Shark Optimizer (WSO), specifically designed for 6-degree-of-freedom (DOF) Supernumerary Robotic Limbs (SRL). This approach effectively addresses prevalent issues in trajectory planning, including low efficiency, premature convergence, and unstable operation. Beginning with an examination of the SRL’s structural peculiarities, an auxiliary joint is incorporated to rectify the coordinate offset, facilitating subsequent inverse kinematics analysis. By utilizing the WSO algorithm, we develop a timeoptimal trajectory optimization method within the joint space of the SRL. Moreover, to address the continuity challenges in the terminal movements of the SRL, we introduce a 3-5-3 piecewise polynomial trajectory planning method that integrates both 3- degree and 5-degree polynomials. Building on this foundational work, we present an optimization algorithm rooted in WSO to identify the most efficient time trajectory optimization strategy, considering multiple constraints. The effectiveness of the proposed optimization strategy is demonstrated through comprehensive co-simulation. A comparative analysis with current optimization algorithms highlights the advantages of the proposed time-optimal trajectory planning algorithm. [ABSTRACT FROM AUTHOR]

Accurate short-time passenger volume prediction can guarantee the efficient scheduling command of urban rail transit. However, the short-time passenger flow of rail transit has the characteristics of nonlinearity and high randomness. In order to improve the prediction accuracy of short-time passenger volume, the Seasonal-Trend decomposition using Loess (STL) method and Human Evolutionary Optimization Algorithm (HEOA) is employed to optimize the bi-directional long short-term memory neural network (BiLSTM). Thus, a combined STL-HEOA-BiLSTM prediction model is proposed. Firstly, the inbound passenger volume along the urban rail transit is classified according to the Pearson correlation number. Secondly, the STL algorithm decomposes different types of short-time passenger flow data into Trend component (Tt), Seasonal component (St) and Residual component (Rt). Thirdly, the HEOA optimizes the various types of hyper-parameters of the BiLSTM model. Finally, the optimized BiLSTM model predicts Tt, St and Rt individually, and the final prediction value is obtained based on the combination of the three predictions. Three evaluation metrics are used to assess the results and quantify the effectiveness of the combined model. The example analysis shows that the prediction accuracy of the combined STL-HEOA-BiLSTM model surpasses that of the other six common and combined models in forecasting short-duration passenger flow. This experimental result shows the effectiveness, accuracy and applicability of the STL-HEOA-BiLSTM model proposed in this paper. It is demonstrated that the proposed model has a reference value for urban rail transit operators. [ABSTRACT FROM AUTHOR]

Lung cancer with the highest mortality rate has attracted public attention. For the difficulty of treating lung cancer increases sharply over time, detecting lung cancer symptoms early on chest computed tomography (CT) is crucial for the subsequent treatment. The number of slices can affect the accuracy of lung cancer examination, so a deep multiple instance learning algorithm was designed and proposed to classify lung cancer effectively. First, feature information is extracted in the patient 3D CT image using the high and low frequency high dimensional features (HLFHD) to balance local detail and global overall information of images. Secondly, to find the decisive features, a sliding recurrent neural network (MSRNN) module is used to take into account the feature variations between slices. The experimental studies in this paper were constructed on two public datasets, namely, CIA and CC-CCII data. Finally, the experimental results show that the proposed algorithm can achieve an ACC of 0.97 and an AUC of 0.99 on the datasets. These results suggest that the proposed algorithm is well suited for lung cancer classification of any number of CT slices, and it can be effectively employed in computer-aided systems to achieve state-of-the-art performance. [ABSTRACT FROM AUTHOR]

This paper used full-scale experimental tests to study fine water mist system to extinguish fires in the electrical compartment of utility tunnel after cable fires. It used a 250 kW propane burner to ignite the cable. A high-pressure water mist system was implemented for cable fire extinguishing tests. The volume flow rate of fine water spray varied between 7 L/min and 10 L/min. The effect of different water mist volume flow rates on fire extinguishing in the electrical compartment of utility tunnel was investigated. In four cases, the ventilation speed of utility tunnels varied from 0 m/s to 1.2 m/s. The temperature above the ignition source and the lateral temperature distribution at different positions of the ignition source were measured. The effect of different longitudinal ventilation speeds on the fire extinguishing efficiency in the electrical compartment of utility tunnel were studied. The results showed that high-pressure water mist could reduce the internal temperature of the electrical compartment in utility tunnel to below 100 ℃ within 300 seconds. The high-pressure water mist system had good effects in reducing temperature and preventing re-ignition. Fine water mist system has an important practical significance in the electrical compartment of utility tunnel. [ABSTRACT FROM AUTHOR]

Plant diseases and pests represent significant threats to agricultural yields, underscoring the need for precise and prompt identification of pathogens and pests. In this paper, we propose a lightweight convolutional neural network model called Gated Asymmetrical GhostNet (GA-GhostNet), specifically designed for the automatic identification of plant diseases and pests. The model incorporates a Gated Multi-scale Coordinate Attention (GM-CA) module to filter out noise and irrelevant information in the image, while capturing location information of diseases and pests at different scales. The model also utilizes an improved feature extraction module called Asymmetrical Ghost (AG) module to enhance robustness to image flipping, as well as improve feature extraction capabilities. Additionally, the CutMix data augmentation method is employed to improve generalization ability. In extensive experimental evaluations on the IP102 pest dataset, GA-GhostNet achieved impressive results with 63.73% MRec, 67.37% MPre, 65.12% MF1, and 71.90% Acc. Moreover, through transfer learning on the Jute pest, Embrapa disease, and Apple disease datasets, the model outperforms numerous lightweight models, attaining accuracies of 99.89%, 96.97%, and 95.17%, respectively, while having only 3.73 million parameters. GA-GhostNet demonstrates high accuracy and efficiency in identifying plant diseases and pests. [ABSTRACT FROM AUTHOR]

nth order Fibonacci word fn is defined recursively by fixing the first two initial letters, namely f0 and f1. W.H. Chaun (1993) proposed a few ways of indexing the Fibonacci words with binary sequences in order to generate the Fibonacci words. Two-dimensional Fibonacci words (or Fibonacci arrays) were first defined in the year 2000 as an infinite sequence of arrays obtained recursively by fixing the four initial letters, namely f0,0, f0,1, f1,0 and f1,1. In 2021, few results on indexed Fibonacci and indexed involutive Fibonacci words have been studied. In this paper, we extend their work to Fibonacci arrays by defining a new way of indexing and proving results based on the indexing. [ABSTRACT FROM AUTHOR]

Abstract-In recent years, electric vehicles have garnered significant attention, with lithium-ion batteries (LIBs) being central to their operation. Researchers and scholars have prioritized the accurate estimation of the state of charge (SOC) within the battery management system (BMS) as a key area of study. In this paper, by analyzing different equivalent circuit models, we choose to use the second-order RC model, elaborate the Kalman filter (KF) principle, and propose the adaptive extended Kalman filter (AEKF) to construct the estimation model of SOC. MATLAB validates the AEKF estimation model under two different operating conditions, UDDS and LA92, and the results show that the designed model can efficiently and accurately estimate the battery charge state with high competitiveness and accurately predict the real SOC direction regardless of the initial state, AEKF is more competitive than KF in terms of SOC prediction accuracy, Despite the different initial values of SOC, the roof-mean-square error of prediction was able to be controlled around one percent. [ABSTRACT FROM AUTHOR]

In the wake of snowfall, it is crucial to restore safe road passage promptly and efficiently. Considering the timeliness of melting operations, comprehensiveness of melting areas, safety of operating personnel, and rationality of the amount of snow-melting agent, this paper proposes a dynamic spraying approach of snow-melting agent based on snow-melting autonomous vehicle. Firstly, a dynamic calculation model is designed based on real-time recognition of ice or snow covered on the road, which uses the first law of thermodynamics to calculate the required amount of snow-melting agent for specific road segments, especially considering the surrounding thermal supply effect and snow-covering depth predicted by micro-regional weather forecasts within the expected operating period to adjust the amount. Secondly, it coordinates the spraying amount with the operating speed, achieving quantitative and uniform spraying of snow-melting agents on specific road segments. In addition, a novel form of snow removal based on autonomous driving is designed, which can move smoothly on particular road segments in multiple scenarios at a speed that matches the amount of snow-melting agent sprayed. Numerical experiments were conducted on the overall approach to verify its feasibility, adaptability, and economic and environmental sustainability in complex and extensive areas. The proposed approach is beneficial for exploring new fields of uncrewed operations. It promotes safe, efficient, and environmental clearance of ice-covered or snow-covered roads. [ABSTRACT FROM AUTHOR]

Smart Cities rely on the Internet of Things (IoT) which is characterised by its diverse, distributed and complex infrastructure. These complexities present a range of cybersecurity challenges, making IoT security an area of great importance. The existing vulnerabilities in IoT leaves Smart Cities susceptible to various types of malicious attacks. These vulnerabilities can be exploited, ranging from opportunistic monetary gains to acts of terrorism that disrupt the systems of rival nations. The fact that conventional cybersecurity protection systems cannot be applied to IoT environments in Smart Cities is an additional challenge. The relationship between cybersecurity and IoT security of Smart Cities has become a crucial gap that must be thoroughly investigated. Hence, this paper extensively reviewing the existing literature concerning IoT applications in Smart Cities, with emphasis on cybersecurity and IoT security concerns. This study concludes that real-time threat detection and mitigation by leveraging fog computing and artificial intelligence models represent a significant advancement in securing IoT infrastructure against cyberattacks. The research outcomes contribute to the critical understanding of the challenges regarding IoT security and paves the way for advancing the research of cybersecurity within the Smart Cities ecosystem. [ABSTRACT FROM AUTHOR]

It is a difficult problem to locate the snake detecting robot in the closed complex environment of underground coal mine. The traditional localization methods usually depend on the estimation of complex ground parameters in coal mine tunnel, which leads to the complexity of establishing the localization model. In response to this compelling problem, we propose the new method to estimate the trajectory curvature and path angle of the robot, and a simple localization model of snake robot based on turning is established. On this basis, the dead reckoning localization method based on the combination of a high-precision 6-axis inertial navigation module MPU6050 and magnetic Hall encoders is proposed, which reduces the difficulty of traditional localization methods. In order to improve the localization accuracy of the robot, an algorithm combines Kalman filter and Deep learning is proposed. Firstly, the Kalman filter algorithm is used to eliminate the white Gaussian noise in the path angle signal of robot. Then, aiming at various drifts from low-frequency stage in the path angle signal, a prediction model of path angle output value based on LSTM (Long Short-Term Memory) deep neural network is established, which can predict the path angle output value for a period in the future, thereby achieving the robot's dead reckoning. The simulation and experiment results prove that the relative localization accuracy of the robot can be improved, in the simulated coal mine tunnel, the maximum relative error of the robot's location is 1.872×10-9 cm, and the maximum error of the path angle is 0.0028 rad, the determination coefficient R² is 1.000, which reflect that the established LSTM model has higher prediction accuracy, better performance, and stronger generalization ability than RBF (Radial Basis Function) neural network. The method proposed in this paper can provide a new idea for the localization problem of snake robots in the underground unstructured complex environment after the disaster. [ABSTRACT FROM AUTHOR]

The current deep learning-based multi-view stereo point cloud reconstruction method has been found to have low reconstruction accuracy for target boundary contours. This paper proposes a high-precision and high-completeness multi-view stereo reconstruction network (DL-PatchMatchNet) based on an improved PatchMatchNet. Firstly, to increase the robustness of the model's feature extraction, a deformable convolution-based feature extraction network is proposed. Secondly, to improve model reconstruction of target contours and boundaries, the Laplace pyramid residuals are introduced to guide the decoding process of the model. Lastly, a fused loss function (GSS) is proposed to enhance the accuracy of point cloud reconstruction by simultaneously considering geometric consistency loss, structural similarity metric and smoothing loss. The results of the experimental analysis on the DTU dataset demonstrate that the DL-PatchMatchNet model exhibits a lower mean absolute error (MAE) and error rate (ER) than other competing networks. This performance is reflected in the high accuracy and completeness of reconstruction achieved by the DL-PatchMatchNet model. [ABSTRACT FROM AUTHOR]

Recently, the internet has created a global community by connecting millions of individuals, devices, and organizations. Unfortunately, internet connectivity is still not available to many people around the world. To provide everyone with internet access, network infrastructures must be expanded, and internet connection costs must be reduced, particularly in developing regions. Therefore, Wireless Community Networks (WCNs) have emerged as a crucial alternative for individuals who lack direct access to internet services provided by traditional internet service providers. This research paper aims to investigate the routing protocols that have been widely employed in WCNs over the past decade. Specifically, it focuses on evaluating the strengths and limitations of these protocols to assess their effectiveness and reliability in establishing optimal routing paths for end-to-end message delivery across the network. A set of recommendations and future directions are proposed to facilitate further research into unresolved challenges related to WCN routing protocols. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) is an advanced branch of Artificial Intelligence (AI) focused on creating algorithms and statistical models that empower computer systems to learn from data and autonomously make informed decisions or accurate predictions, all without requiring explicit programming for every individual task. It enables computers to recognize patterns, relationships, and insights within the data and improve their performance through experience. Machine learning has had a significant impact on medical imaging in recent years, revolutionizing the field and enhancing healthcare practices. Machine Learning provides improved diagnostic accuracy, faster image analysis, reduced errors and variabilities and detection of anomalies and lesions. In this paper we applied various ML algorithms like Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naïve Bayes (NB) on HAM (Human Against Machine) 10000 skin cancer data set. Since the data set is huge, we used dimensionality reduction and feature extracting techniques like Principal Component Analysis (PCA) and Gabor filters. The data set has 10015 images of seven classes of skin cancer. Our findings reveal that Random Forest when used with PCA produced an accuracy of 89% and when it is used with Gabor feature extraction it produced an accuracy of 84%. The SVM classifier with PCA produced an accuracy of 82% and when used with the Gabor feature extraction SVM produced an accuracy of 84%. RF produced an increased accuracy of 92% when the data samples for each class is increased. [ABSTRACT FROM AUTHOR]

Wind field forecasting is crucial for human activities, but numerical weather prediction still has room to improve accuracy. In this paper, we formalize wind field forecast correction as a spatiotemporal sequence prediction task and propose MotionUVRNN, a recurrent neural network model suitable for wind field correction. The model consists of three elements: a feature decoupling module, an encoder, and a corrector. The feature decoupling module decouples the wind field into U-Wind (Wind component in x/longitude-direction) feature, V-Wind (Wind component in y/latitude direction) feature, and spatiotemporal feature. The encoder unifiedly models the motion trend and transient variation of the wind field. A novel long short-term memory dynamic capture unit (RS-LSTM) is proposed as the core part of the encoder to capture the different dynamic patterns of U-Wind and V-Wind. The corrector extracts and integrates multi-scale wind field information to capture regional features. By correcting the 3- 12 hour wind field forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF; EC for short), the proposed model reduced the wind speed prediction error by 53.37% to 46.52%, and lowered the wind direction error by 21.99% to 13.26%. Moreover, this model surpasses the existing leading spatiotemporal sequence prediction models in wind field correction. [ABSTRACT FROM AUTHOR]

Topological indices are numerical descriptors obtained using a molecular graph in order to study the medications thoroughly. They are used in the analysis and forecasting of various pharmaceuticals' physicochemical features. In this paper, a few degree-based topological indices of drugs - Apixaban, Betrixaban, Rivaroxaban, Edoxaban Warfarin, Clopidogrel, Rosuvastatin, Simvastatin, and Atorvastatin which are used in the treatment of stroke have been computed. Also, we have shown the correlation and standard error of the estimate with the physico-chemical properties of medications used in the treatment of stroke. This will help the pharmaceutical people to understand the properties of drugs without the process of experimentation. [ABSTRACT FROM AUTHOR]

Pesticides are the most common method used to eliminate pests, including aphids. Pesticides are the most common method used to eliminate pests, including aphids. Nonetheless, numerous farmers incorporate ladybugs into their pest management strategies as they serve as natural predators of aphids. By integrating these methods, farmers aim to achieve optimal outcomes in mitigating the detrimental effects of aphids on the agricultural sector. In this paper, the dynamics of interactions between aphids and ladybugs, including the impact of pesticides on aphid mortality, are represented using a system of nonlinear differential equations. This study treats the parameter representing aphid mortality caused by pesticides as a fuzzy number to account for variations in resistance levels. Additionally, the model incorporates four parameters that depict the interaction between aphids and ladybugs beyond considering the effect of pesticides. The parameters include the proportion of aphids consumed by ladybugs, the proportion of aphids capable of evading ladybugs, and the growth rates of both aphids and ladybugs. The triangular form is chosen to depict the fuzzy membership function because it reflects the resistance of aphids when pesticides are applied excessively. The dynamic model, incorporating a fuzzy parameter, is transformed into discrete-time models using the Non-Standard Finite Difference (NSFD) method for simulation purposes. The simulation outcomes align with the analysis findings, indicating a potential equilibrium between the populations of aphids and ladybugs. Various examinations on the impact of fuzzy pesticide parameters on the growth of aphids and ladybugs are provided. The findings demonstrate that pesticide application can substantially decrease the aphid population and can be tailored based on the interplay between aphids and ladybugs. Moreover, pesticide usage can be diminished with heightened ladybug growth and predation rates, thereby minimizing the occurrence of resistant aphids and enhancing the effectiveness of pesticide application. [ABSTRACT FROM AUTHOR]

This paper presents a decision-making approach grounded in rough set theory and evidential reasoning to address the demand for expert decision-making in greenhouse environmental control systems. Furthermore, a decision-making model is developed by integrating the D-S evidence theory with an expert knowledge table for greenhouse environmental control systems. The model's reasoning process encompasses continuous attribute discretization, expert decision table formation, attribute reduction, and evidence combination reasoning. Firstly, the fuzzy C-means clustering algorithm is employed to discretize the original environmental data and cluster it. Subsequently, an attribute reduction algorithm based on information entropy is utilized to optimize the decision table by eliminating unnecessary conditional attributes in expert knowledge. The reduced indicators are then combined using evidential theory. Finally, suitable greenhouse control methods are determined by the confidence decision proposed by the D-S evidence theory. To assess the efficacy of this intelligent decision-making algorithm based on rough set and D-S evidence theory, its performance is compared with traditional SVM algorithms and small-shot learning algorithms. The results indicate that this proposed method significantly enhances the credibility of control decision-making processes, with an average running time of 0.002378s for the fusion decision algorithm and 0.017939s for the support vector machine (SVM) algorithm, respectively. The SVM accuracy rate after testing and training stands at 90.34%. Moreover, retraining based on information entropy attribute reduction leads to a correct decision rate increase of up to 100%. This method notably improves confidence levels in decision-making processes while reducing uncertainty and demonstrates reliability when applied in making decisions regarding greenhouse environments. [ABSTRACT FROM AUTHOR]

In this paper, we establish inequalities concerning the growth of polynomials involving the polar derivative of the polynomials. [ABSTRACT FROM AUTHOR]

This paper studies the single-index varyingcoefficient quantile model with missing covariates at random. Firstly, some estimators of index parameters and their corresponding linkage function are given by using the inverse probability weighting method for missing data in two cases including parameter estimation and non-parametric estimation for the single-index varying-coefficient quantile regression model. In particular, the latter case focuses on the study of both known and unknown probability functions. Secondly, the established estimators are proved to be asymptotic normal under some suitable regularity conditions. Finally, the simulation studies are conducted to demonstrate the finite sample performance of the proposed method. [ABSTRACT FROM AUTHOR]

The progressive iterative approximation (PIA) is a versatile method for interpolating or fitting a given data set. The convergence behavior of PIA plays a pivotal role in determining the computational efficiency of data interpolation or fitting. This paper introduces an accelerated iterative approach, GS-PIA, derived from the Gauss-Seidel splitting, specifically designed for interpolating data points. The convergence and computational cost of the proposed iterative method are discussed. Numerical results underscore the superiority of GS-PIA in data interpolation when compared to other existing methods. [ABSTRACT FROM AUTHOR]

This paper deals with the estimation of multi element stress strength system reliability R when the stress and strength follow Burr type X distributions. The maximum likelihood and Bayesian methods are used to obtain the point estimates of R, and the confidence intervals of R are obtained via the delta and MCMC methods. The performances of the different point estimation methods and their corresponding confidence intervals are compared via the extensive simulation studies. To illustrate the effectiveness of the developed model and methods, a real data set is utilized. [ABSTRACT FROM AUTHOR]

Depending on demand, the selling price is the price at which the goods are offered for sale in response to their demand level. It is a pricing strategy that considers the relationship between supply and demand. The two-warehouse inventory model in the proposed model deals with perishable goods. The demands for the goods are time- and pricedependent, taking inflationary impacts into account. To improve operational effectiveness and customer happiness, the model allows for partial backlogs of shortages. The proposed model uses a positive definite Hessian matrix condition to get the best quantity and cycle length while minimising the nonlinear average total cost. This research paper conducts a numerical evaluation of the highly nonlinear average total cost using MATLAB software. Additionally, the study includes an analysis of the robustness of the results through sensitivity analysis. The conclusion drawn from the study helps summarise the essential findings and highlights the efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

In this work, we give a thorough examination of Newton's technique. We show that certain places outperform others in terms of where a good initial approximation may be made to assure convergence. Furthermore, to assure quicker and better convergence, certain criteria must be imposed on the function, such as dealing with additional terms from the Taylor series to achieve a technique comparable to Newton's method, but with a degree of convergence greater than two. We compare the use of Newton's technique for solving equations with a single variable to the solution of equations with many variables. While we widen our discussion to include the solution to complex-valued functions, our primary focus is on locating the roots of unity. Some new theories have been proven which is an addition to this topic, and their results are shown in the examples at the end of the paper. We investigate the incorrect choice of the starting approximation for the nth root of unity in the complex plane. When utilizing Newton's technique on a complex plane, we employ various stunning fractal graphs to explain the features and behavior of the roots of interest. [ABSTRACT FROM AUTHOR]

This paper concentrates on addressing fractional inequalities for exponential type convex functions. By means of exponential-type convexity, we firstly establish Hermite-Hadamard (HH) type inequalities for fractional integrals with exponential kernels. Secondly, based on the discovered fractional identity by separating [a; b] to n equal subintervals, and the fact that the twice derivative in absolute value is exponential type convex, we present multipoint-based HH inequalities, which cover the trapezoid- and Bullen-type inequalities for n = 1 and 2, correspondingly. During the period, some numerical examples with graphs are provided to show the validity of the deduced inequalities. [ABSTRACT FROM AUTHOR]

This research paper presents the results of two studies investigating human mobility patterns in the 15 largest Metropolitan Statistical Areas (MSAs) in the United States. It studied 14 daily mobility parameters aggregated at the MSA level, derived from four primary mobility parameters: Number of Visited Locations (N_LOC), Number of Unique Visited Locations (N_ULOC), Radius of Gyration (R_GYR), and Distance Traveled (D_TRAV) over a 30-day period. The first study was conducted on data from two large MSAs, one coastal and one inland (Boston and Atlanta, respectively). The aim was to examine associations between daily values of mobility parameters aggregated at the MSA level and identify those carrying similar or identical information. Results of factor analysis showed that these could be adequately described by two independent factors, pointing to one or two of the mobility parameters as sufficient to represent the whole set in analyses based on associations. These could either be D_TRAV, as it had high loadings on both factors, or N_LOC and R_GYR due to their high loadings on the two extracted factors. The second study was conducted on daily mobility datasets from the 15 MSAs. The aim was to compare daily mobility patterns of these MSAs and group them based on their mobility pattern similarities. Factor analysis of the aggregated mean daily distances (D_TRAV) across different MSAs over the studied period classified them into two distinct groups: one predominantly composed of inland MSAs and the other primarily of coastal MSAs. Strong weekly cycle trends emerged in these groups. Specifically, individuals from the inland MSA group tended to travel the furthest on Fridays and the least on Sundays, whereas those from the coastal MSA group traveled the most on Saturdays and the least on Mondays. This weekly pattern was robust, with 7-day lag autocorrelations of mean daily parameter values ranging between 0.81 to 0.99, excluding the mean daily N_LOC. These findings offer a foundational understanding of MSA mobility patterns, paving the way for more detailed studies on the nuances of these patterns. [ABSTRACT FROM AUTHOR]

The determination of transport schemes represents a critical facet of operational management in express freight logistics and constitutes a significant scientific challenge within the domain of freight transportation. Globally, there is an escalating focus on adopting low-carbon transit strategies, and it has been established that multimodal transport methods can significantly diminish carbon emissions associated with freight transportation. Moreover, the scheduled arrival and departure times for railway and aviation are pivotal in shaping the selection of transport schemes. Regrettably, these critical considerations are frequently neglected in the prevailing body of research. This paper endeavors to bridge this research gap by advocating for a lowcarbon transport scheme for express freight through the utilization of an integrated transportation system. It undertakes a collaborative optimization study concerning transportation modes and routes. This document proposes an optimization framework for the selection of express freight transport schemes that incorporates considerations for carbon emissions and fixed timetables. A three-layer service network is developed, integrating the fixed schedules of railway and aviation to formulate a space-time shortest path problem aimed at minimizing the generalized transportation cost. Three models are formulated to reduce the transportation costs and carbon emissions of logistics enterprises, taking into account diverse carbon emission policies. Subsequently, a two-stage heuristic algorithm is introduced to address this complex problem. The findings reveal that by accounting for customers' transportation time constraints and judiciously selecting transportation modes and routes, it is feasible to effectively minimize transportation costs and carbon emissions. [ABSTRACT FROM AUTHOR]

This paper presents a study on a Lotka-Volterra ammensalism model that incorporates the fear effect, which can potentially decrease the birth rate and raise the mortality rate of the species. For the autonomous case, equilibrium points' local and global stability are discussed. For the nonautonomous case, sufficient conditions which ensure the persistence and extinction, and global asymptotic stability of the positive solutions are obtained, respectively. The study has shown that with the increase of the fear effect, the final density of the affected population will decrease, and when the fear effect is large enough, it will cause population's extinction. [ABSTRACT FROM AUTHOR]

This paper proposes a novel cloud computingbased parallel deep reinforcement learning (DRL) energy management strategy (EMS) for connected plug-in hybrid vehicles. First, a proximal policy optimization (PPO) algorithm is developed. Since the cloud computing can reduce the computational burden of the connected vehicles, the PPO is deployed in the proposed cloud computing-based EMS. In order to improve the strategy adaptation, a parallel mechanism is proposed to achieve the information interaction with multiple vehicles. Considering the real-time control requirements, thread pool is proposed and applied in the cloud computing based parallel EMS. The thread pool-based strategy provides an efficient real-time control ability and strategy improvement solution. To verify the PPO based EMS, the dynamic programming, deep Q-network and double deep Q-network strategies are developed for comparison. It is found that the PPO can achieve similar fuel efficiency improvement with the DP strategy among the three DRL algorithms. For parallel training of multiple connected vehicles, the cloud computing-based parallel EMS improves fuel economy by approximately 7.7%. Threadpool based parallel real-time EMS reduces average time for computational interactions by 20% and further improves the fuel efficiency. The proposed strategy has the advantages of realtime control, adaptability and continuous learning for improved fuel efficiency. [ABSTRACT FROM AUTHOR]

The advancement of pneumonia detection technology is crucial for the automation and the enhancement of diagnostic accuracy in pneumonia. Recent developments have seen the application of automatic detection methods utilizing deep learning in medical imaging. However, the diagnosis of pneumonia is often hampered by the small size and indistinct boundaries of lesions. To enhance diagnostic efficiency and mitigate errors due to human factors, this paper introduces an improved pneumonia detection algorithm, YOLOv5-TAF. First, the incorporation of the Triplet Attention module into the backbone network establishes inter-dimension dependencies, augmenting model accuracy. Second, Adaptive Spatial Feature Fusion (ASFF) is employed to dynamically learn and apply spatial weights to each scale feature map, effectively filtering out conflicting information and enhancing the network's capability to integrate features of varying scales. Additionally, the substitution of the original activation function with FReLU increases the sensitivity of the activation space and markedly improves image quality. Comprehensive experimental analyses on the Chest X-Ray Images dataset have demonstrated that the YOLOv5-TAF algorithm achieves a mean Average Precision (mAP) of 91.71%, which is 3.57% higher than the YOLOv5s algorithm, with a detection speed of 58.72 frames/s. When compared with faster-RCNN, SSD, YOLOv8s, and other object detection algorithms, YOLOv5-TAF shows superior practicability in pneumonia detection. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) and ambient backscatter communication (AmBC) are promising technologies to enable spectrum efficiency in wireless communication systems. In this paper, we propose a downlink NOMA multiplexing-based Symbiotic radio (SR) AmBC system over Rayleigh fading channels. The system consists of one source node S, one backscatter device (BD), one nearby cellular user (User 1), and one far-away cellular user (User 2). In light of a viable non-linear energy harvesting (EH) model, we implement the most effective dynamic reflection strategy to enhance the backscattered signal's power, adhering to the BD's energycausality limitations. The analytical expressions for the outage probability of the proposed system are derived. Moreover, we compared the performance of the NOMA-based system against the OMA-based system. Simulation results verified our derived expressions and showed that the consumed power by the BD had a remarkable influence on the system outage performance. [ABSTRACT FROM AUTHOR]

This paper introduces an multi-compartment open vehicle routing problem with time window (MCOVRPTW) arising in fresh products distribution, which doesn't require the finished service vehicles return to the distribution center. There are three types of fresh products to be delivered by the multi-compartment vehicles, and the objective aims to find the vehicles scheduling scheme with the lowest cost. For this problem, we established a mathematical model in which the capacity constraint of each compartment and time window constraint must be satisfied. Then, we proposed a hybrid genetic algorithm with some local search-based operators, called GA-LS, to solve it. Each individual in initial population was generated by the nearest neighborhood method to obtain highquality initial population. When a child population was built, the local search procedure was executed on each individual in the child population to enhance its quality. Finally, we carried out some experiments to evaluate the effectiveness of the proposed algorithm. According to the results, the proposed algorithm can yield better solutions compared with existing approaches. Meanwhile, the proposed algorithm reveals good stability and convergence. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-scale information fusion based remote sensing small target detection method that aims to address the issue of low target detection accuracy in optical remote sensing images due to complex backgrounds, diversified scales, small targets, and different directions. Firstly, the architecture of the RepConv module significantly increases the detection accuracy of small targets without adding more inference time. Secondly, by introducing the ECA attention mechanism, a C3ECA module is constructed to effectively reduce the interference of complex background areas and achieve accurate positioning of the target area. The PANet structure in YOLOv5 is replaced by the BiFPN structure to balance the feature information of different scales and improve the detection performance of multi-scale objects. In addition, in order to solve the uncertainty of target direction and reduce the boundary discontinuity caused by angle regression, a circular smooth label method is used to provide an effective solution for target detection. The preprocessing method of image slices is employed to successfully achieve the target detection of high-resolution images. This approach greatly minimizes the issue of missed detection and erroneous detection of small objects in large images. The experimental findings indicate that the proposed approach markedly enhances the accuracy of remote sensing image detection and offers notable benefits in the recognition of small-scale targets. [ABSTRACT FROM AUTHOR]

In Currently, research in the field of infrared road object detection is primarily focused on enhancing model performance and robustness to address the challenges posed by complex real-world driving scenarios. In response to these challenges, this paper proposes an infrared road object detection algorithm based on an attention mechanism. By incorporating the CPCA module, which utilizes attention mechanisms, into the YOLOv8s model, the algorithm enhances the model's focus on unobstructed areas and highly illuminated sections, extracting crucial feature information to improve both accuracy and robustness. Additionally, the original model's downsampling layer is replaced with the Context Grided Network Block Downsampling (CGBD) module, which not only preserves feature edge information but also effectively handles local and contextual features, thereby enhancing the overall feature capturing capabilities of the model. To address the issue of equal aspect ratios in the model's original loss function, the proposed algorithm adopts the superior Weighted Intersection over Union (WIoU). This not only addresses the shortcomings of the original loss function (CIoU) but also demonstrates increased sensitivity in classification tasks. Experimental results show that the improved algorithm, compared to YOLOv8s, achieves a 1.4% increase in mean average precision (mAP), along with notable improvements in precision and recall. Furthermore, when compared to mainstream model algorithms, the enhanced model significantly outperforms in infrared road object detection tasks, providing validation of its effectiveness. [ABSTRACT FROM AUTHOR]

The way products are placed in a supermarket can be effective in increasing sales and profit. A reasonable approach is to group together items that are likely to be purchased together. Thus, managers with the support of mining methods and techniques, can assist customers locating the products they want to buy in an easy and quick way. Many product placement strategies have been proposed over the years to leverage an effective and efficient way to achieve this goal. In this paper, association rules for product arrangement in supermarkets are studied and an algorithm based on such rules is proposed. The algorithm considers several factors, such as the number of units sold of each product, a hierarchical structure for product classification developed by the United Nations Department of Economic and Social Affairs, a set of association rules generated from sales, and a set of constraints that restrict some products to be placed physically close to each other in a supermarket, even if they are usually purchase together. Real public sales data of a supermarket were used for the experiments, where the proposed algorithm is applied for the generation of supermarket layouts. The results show that some supermarket departments may share the same products or product categories. [ABSTRACT FROM AUTHOR]

The extended belief rule base (EBRB) system has been widely used in decision-making problems for its accuracy and efficiency. However, EBRB system needs to traverse all the rules in the rule base and has the problems of inefficiency and inconsistency. In view of this, an extended belief rule base system inference method based on a k-dimensional (KD) tree is implemented in this paper. First, the KD tree is introduced in the construction of rule base. Then, the K-Nearest Neighbor (KNN) query optimization algorithm, based on the space indexing technique of the KD tree, is used to search for key rules. Next, the obtained key rules are activated to participate in the inference process. In addition, several experiments are conducted on function fitting, oil pipeline leakage simulation and classification datasets from UCI to verify the inference performance of the proposed method. The experimental results illustrate that the extended belief rule base system based on the KD tree can effectively improve the accuracy and stability of EBRB reasoning. [ABSTRACT FROM AUTHOR]

Images captured under severe weather conditions, such as haze and fog, suffer from image quality degradation caused by atmospheric particle diffusion. This degradation manifests as color fading, reduced contrast, and adversely affects the performance of various computer vision tasks. To address this, this paper presents an end-to-end feature fusion attention network (FFA-Net) designed to directly restore haze-free images. By incorporating the SSIM loss into the original loss function, the proposed method effectively captures the visual disparities between the estimated defogged image and the authentic haze-free image. Additionally, it mitigates the color distortion problem inherent in the original algorithm. To address the challenge of low brightness in input images, a low illumination enhancement module is introduced, seamlessly integrated with the FFA-Net defogging method. Subsequently, a comparative analysis of different defogging algorithms is conducted using two distinct foggy datasets. Multiple evaluation metrics are employed to assess the performance of these algorithms. The findings indicate that our algorithm significantly outperforms others in terms of objective indicators such as PSNR and SSIM, as well as visual effects. [ABSTRACT FROM AUTHOR]

The Traveling Salesman Problem (TSP) seeks the shortest closed tour that visits each city once and returns to the starting city. This problem is NP-hard, so it is not easy to solve using conventional methods. The grey wolf optimization (GWO) algorithm has shown outstanding performance in many practical applications. However, it is inclined towards premature convergence. This paper proposes an improved GWO (I-GWO) algorithm, which hybridizes GWO with genetic algorithms (GA) for the TSP. The main feature of the I-GWO algorithm is that it can make full use of the advantages of the GWO algorithm and the GA algorithm to make up for their respective shortcomings. Moreover, to make the GWO suitable for solving the TSP, both the 2-opt operator strategy and hamming distance h ave been designed to implement the discrete GWO directly. Additionally, to increase the diversity of solutions by expanding the search space, we present a new population update strategy with crossover and mutation operations in the next iteration. Meanwhile, the integration of the Simulated Annealing (SA) algorithm into the Improved Grey Wolf Optimizer (I-GWO) enhances its local search capabilities. Experimental results show that the I-GWO algorithm competes with established optimal methods for solving the TSP, suggesting its potential for different TSP variants and logistic transport domains. [ABSTRACT FROM AUTHOR]

Network function virtualization can increase the versatility of computing and network platforms, enabling them to provide more flexible services. In terms of a softwaredefined network and network function virtualization environment, this paper explores the joint virtual network function (VNF) deployment and traffic routing in multisource multicast for VNF service chain deployment to minimize the deployment overhead, transmission delay. Firstly, a multi-objective optimization model, which minimizes the deployment overhead and transmission delay, is established to tackle this challenge problem. Then, a highly efficient coding scheme, crossover, and mutation operators are designed. Based on these, an improved genetic algorithm in multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework (GA-MOEA/D) is proposed to solve this multi-objective problem. Finally, simulation experiments are conducted using two widely used network topologies in order to demonstrate the performance of the proposed algorithm. The simulation results demonstrate that the proposed algorithm can obtain the smaller deployment overhead, transmission delay, load degree and energy consumption 5.2%-15.61%, 11.2%-26.4%, 8.3%-17.6% and 7.8%-14.4% than that of the compared algorithm, respectively. [ABSTRACT FROM AUTHOR]

The intelligent tracing car is a complex system with high linearity, and the design of path tracking strategy is one of the most important technologies in intelligent tracing car field. This paper proposed a novel path tracking strategy based on control dynamic model of the intelligent tracing car. The vehicle dynamic models are established separately according to the vehicle parameters and kinetic equation. The traditional path tracking strategy used the distance between the desired and actual displacements to compute the error for position control. However, the paper utilized the past and current states of the desired displacement to figure out a motion vector. The error was acquired through calculating the difference between the desired and current displacements along this motion vector. Simulation results showed excellent performance in position control and speed control for the intelligent tracing car. [ABSTRACT FROM AUTHOR]

Identifying critical factors, objectives, criteria, or alternatives in operating research methods is a vital decision-making problem in academic society. Besides, fuzzy theorems, inventory models, and optimization approaches, the analytic hierarchy process is an important tool to decide relative weights for several factors, objectives, criteria, or alternatives. This paper will examine the relationship among several methods to derive the relative weight. Saaty and Vargas provided an example to prove that the relative weight is the same by the Eigenvalue Method (EM) and by the Logarithmic Least Square Method (LLSM). Saaty and Vargas did not know the relative weight by the Least Square Method (LSM). It is an open question proposed by Saaty and Vargas. Saaty is the founding father of the Analytic Hierarchy Process (AHP) and Vargas is one of the main contributors to AHP, respectively. Hence, in this paper, we present a detailed examination of the open question. We obtain that there are two (non-consistent) comparison matrices under the Saaty-Vargas construction which satisfied the property of the relative weights derived by three methods: EM, LLSM, and LSM are the same. We not only solve the open question proposed by Saaty and Vargas but also extend it to a more general setting. We consider all possible conditions for a ∈ {2,3,...,9} such that for a = 2, we provided an analytical proof; for a = 3, we show a hybrid method such that a discrete sensitivity analysis for the open question proposed by Saaty and Vargas is completely solved by us; for a ∈ {4,5,...,9}, we present numerical approaches. Our findings help researchers to decide which evaluation method will be adopted to derive the relative weights for their future research. Our results will help practitioners to decide the relative weights for factors, objectives, criteria, or alternatives in research methods. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low recognition accuracy and large calculation amount of age estimation, this paper proposes a staged single aggregation backbone network VoVNetv4. In order to improve the feature extraction ability of the network, the Regional One-Shot Aggregation (ROSA) module is proposed in the network for regional encapsulation of the feature module. The classifier adopts the COnsistent RAnk Logits (CORAL) framework, which can solve the problem of inconsistency of classifiers in ordered regression CNNs for multiple binary classification tasks. This paper conducts experimental verification on the Asian Face Age Dataset (AFAD) and compares it with other research methods. The experimental results show that VoVNetv4 can effectively improve the feature extraction and detection ability of the network and improve the detection efficiency of age estimation. [ABSTRACT FROM AUTHOR]

In this paper, a series of fire experiments were conducted in a small-scale cable tunnel to study the effect of ventilation on fire characteristics near walls. The distance between the oil pool and the bottom cable tray is 0.15 m. Five main influencing factors were considered, namely, the number of cables per layer, the amount of fuel, the type of oil pool, ventilation conditions and the type of cable. In the cable compartment of a utility tunnel, the dimensionless maximum temperature rise and the dimensionless longitudinal temperature distribution at a distance of 0.5 m from the fire source near the wall were studied. The results showed that under natural ventilation, the combustion of cables is relatively sufficient in some cases, and the total mass loss rate of cables is different in each case. This indicates that different factors have a significant impact on cable combustion under natural ventilation. The influence of natural ventilation on the longitudinal distribution of dimensionless temperature was analyzed. The contribution of this paper is to prevent fires in the cable compartments of the utility tunnels and reduce social and economic losses. [ABSTRACT FROM AUTHOR]

This paper, we have improved the YOLOv6 object detection network to better detect high-density, small target objects in UAV aerial images. First of all, we designed a mixed data augmentation method which adds background samples to the training, so that the model can better identify falsely detected and falsely detected targets without changing the computational complexity. In addition, to prevent the loss of feature information caused by small target features in the upsampling stage, we have strengthened the Feature Pyramid Networks (FPN) with Feature Alignment Module (FAM) and Feature Selection Module (FSM). Finally, we have formed the Transformer Prediction Head (TPH) by using the Transformer Encoder Block to replace the prediction head of the original model. This ensures accurately locate targets in high-density scenes. The experiment used the Visdrone2019-DET drone aerial photography dataset. Compared with the original YOLOv6 network, the average precision (AP50) of the improved network on the verification set when the IoU is 0.5 was 59.73%, which is 6.02% higher than before the improvement. Comparison results with other mainstream object detection algorithms show that the performance of our proposed algorithm is better than other detectors. Therefore, the algorithm proposed in this paper can effectively detect objects with high density and small targets in UAV aerial photography scenes. [ABSTRACT FROM AUTHOR]

This paper analysis an attack on crypto currency signatures generated by Elliptic Curve Digital Signature Algorithm (ECDSA). This attack is done on three different dimensions with leakage of nonce weak nonce and by using Lenstra-Lenstra-Lovasz (LLL) by considering various crypto currency curve parameters such as number of private keys, single nonce, multiple nonces, single signature, multiple signatures on varying bit sizes of length 64, 128, 256, 512 and 1024. This work demonstrates demonstrate leak-weak nonce method performs exponentially better over LLL to crack ECDSA. In addition to finding multiple vulnerabilities on ECDSA and to avoid leak-weak nonces we use T-of-N threshold signing algorithm based on GG20 where nonce k is distributed across N parties unlike having with one single party. This technique is implemented and evaluated considering group of N parties. This paper brings out the pitfalls of ECDSA, when it is used to sign crypto currency transactions. [ABSTRACT FROM AUTHOR]

Loop closure detection is a key component of visual simultaneous localization and mapping (VSLM), which can effectively reduce the cumulative error of the system and improve the accuracy of mapping. The current loop closure detection method using deep learning can obtain accurate scene descriptions, but it is difficult to cope with the challenge of scene changes. This paper obtains complementary feature maps by fusing the shallow and deep image features of the ResNet50 network, and we incorporate a multiscale channel attention mechanism and a spatial attention mechanism after each layer of the ResNet50 network. The model can effectively extract discriminative scene landmarks, suppress the effects of irrelevant local features on similarity and be more robust to the problem of scene change. The method in this paper has been tested on several publicly available datasets and compared with mainstream methods. The experimental results show that the proposed method significantly outperforms mainstream methods in terms of accuracy-recall performance. [ABSTRACT FROM AUTHOR]

Over the past century, bankruptcy prediction has dominated economics headlines and continues to do so. An efficient way to deal with bankruptcy is to develop models that integrate multiple econometric measures and predicts a company's financial status. Different methods have been proposed in this domain ranging from statistical algorithms to artificial intelligence models. A novel approach to bankruptcy prediction was proposed in this paper. The novel method incorporates an improved particle swarm optimization (PSO) approach for determining key features and optimizing the long short-term memory system for bankruptcy prediction. This paper employs self-learning and multi-learning strategies to develop the cognitive and social learning parts of the PSO algorithm. The PSO algorithm then determines the optimal LSTM parameters. Our final bankruptcy prediction approach combines optimal feature selection with the LSTM model. Therefore, an improved method of feature selection was developed for the LSTM model. The data for evaluation is available in the machine learning repository at the University of California Irvine. We compare our improved model with the ordinary LSTM model, and the results show better performances in all the metrics for the improved LSTM model compared to the other models. [ABSTRACT FROM AUTHOR]

The problem of programming with fuzzy coefficients and fuzzy variables is more general. This paper studies the quadratic programming problem whose parameters are all fuzzy numbers based on the A-PSO algorithm. Especially, the four operations of triangular fuzzy numbers are extended and an improved sorting method for triangular fuzzy numbers is proposed that takes into consideration the diverse membership of each point. Moreover, the exact solution steps of the fully fuzzy quadratic programming problem are given by the new method proposed in this paper. Numerical examples are submitted to compare and analyze the algorithm and results, which demonstrate the efficiency and effectiveness of the proposed method. Finally, the method is increased to solve all fuzzy nonlinear programming problems. [ABSTRACT FROM AUTHOR]

Stiff equation is known for its rapid and slow varying time component, for which the method dedicated for this system must be capable on changing the step size depending on the varying component of the interval. This is to make sure that the computational cost can be reduced while the accuracy is preserved. In this paper, the diagonal block method derived from the family of backward differentiation formula is proposed for the direct solution of stiff Van der Pol equation. The method is implemented by varying the step size in the fixed ratios of 1, 2 and 10/19 which corresponds to constant, by halving and increasing the step respectively. The method is derived in block forms to compute the approximate solutions at two points simultaneously. By controlling the constants in its linear difference operator, the consistency of the derived method is verified. The Newton iteration technique which is derived in the block matrix form is also presented in this paper. The robustness of the proposed method is validated by solving the stiff Van der Pol equation directly and compared with the ode15s from MATLAB. Numerical results demonstrate the capability of the proposed method in solving the stiff ODEs directly. [ABSTRACT FROM AUTHOR]

There are five major injurious accidents in China's construction industry, namely, falling from height, collapse, object strike, electric shock, and mechanical injury, among which falling from height is the the most common with great harm. On the basis of previous relevant literature and safety technical regulations, this paper firstly identified the risk factors for each of the five most common height fallings. Besides, according to the accident cause theory, 18 factors leading to height falling accidents were determined and screened out from four aspects of personnel, external objects, management and environment. Moreover, questionnaires were distributed to 350 front-line workers with 298 valid questionnaires collected. After reliability and validity tests of SPSS, confirmatory factor analysis (CFA) was conducted using structural equation model (SEM) to compare the influence of risk factors. The results showed that workers' unsafe behaviors are the most likely to lead to height falling accidents, followed by the influence of external objects, management factors and environmental effects. Besides, effective protective equipment, weather changes during construction, safety education and training are worth special attention. This paper aims to enrich the literature research on the cause factors of height falling, and tends to provide some constructive suggestions for construction site safety management. [ABSTRACT FROM AUTHOR]