Weight Coefficients Research Articles

Protective effect of honokiol on cadmium-induced liver injury in chickens

Cadmium (Cd), a highly toxic heavy metal in the environment, poses a significant threat to livestock and poultry farming. Honokiol (HNK), a Chinese herbal extract with potent antioxidant activity, acts through oxidative damage and inflammation. Cd induces oxidative stress and causes liver damage in animals. However, whether HNK can alleviate Cd-induced liver injury in chickens and its mechanism remains unclear. In this study, the 48 chickens were randomly allocated into 4 groups, control group, Cd group (70 mg/kg Cd), HNK group (200 mg/kg HNK) and Cd + HNK group (70 mg/kg Cd+200 mg/kg HNK). Results showed that HNK improved the Cd induced reduction in chicken body weight, liver weight, and liver coefficient. HNK recovered the Cd induced liver damaged through increased serum liver biochemical indexes, impaired liver oxidase activity and the disordered the expression level of antioxidant genes. HNK alleviated Cd induced pathological and ultrastructure damage of liver tissue and liver cell that leads apoptosis. HNK decreased Cd contents in the liver, Cd induced disturbances in the levels of trace elements such as iron, copper, zinc, manganese, and selenium. HNK attenuated the damage to the gap junction structure of chicken liver cells caused by Cd and reduced the impairment of oxidase activity and the expression level of antioxidant genes induced by Cd. In conclusion, HNK presents essential preventive measures and a novel pharmacological potential therapy against Cd induced liver injury. Our experiments show that HNK can be used as a new green feed additive in the poultry industry, which provides a theoretical basis for HNK to deal with the pollution caused by Cd in the poultry industry.

Proposal of a novel multi-generation system based on dual-loop absorption power and compression refrigeration cycle

One of the promising approaches to compensate for imminent depletion of fossil energy resources and their adverse global impacts is enhancing the efficiency of systems utilizing low-temperature heat sources. The initial target of the research is to present a promising system for simultaneous production of power, refrigeration, heat, and hydrogen utilizing a combination of the absorption power cycle, the vapor compression refrigeration cycle, and a proton exchange membrane electrolyzer. On this subject, a comprehensive modeling of the energy and exergy of the proposed set-up is presented, and its thermodynamic performance is scrutinized. Furthermore, a comprehensive study of various parameters is performed to evaluate their impacts on system performance. The research aims to enhance and optimize the use of energy and various resources, contributing to the development of more sustainable efficient energy systems. Thermodynamic analysis of the multiple generation system shows that under baseline conditions and initial design, the system has the capability to produce net electrical power of approximately 17.12 kW, cooling power of about 201.5 kW, heating power of around 697.1 kW, and produce pure hydrogen at a rate of 0.153 kilograms per hour. The system exhibits an energy efficiency ratio of 1.364 and an exergy efficiency of 36.58 %. Moreover, optimization with single and multiple objectives with different weighting coefficients reveals that higher values of these parameters can be obtained. In other words, in the MOOM optimization mode, exergy efficiency increases to 7.16 %, and the energy performance ratio rises to 85.73 % compared to the BM mode. Additionally, through a parametric assessment to define the effect of input parameters on system performance, it demonstrates that increasing the temperature of the heat source may simultaneously increase the exergy efficiency and energy performance ratio of the system. The Grassmann diagram also indicates that the total exergy of the input fuel is about 196.9 kW. From this amount, approximately 121.5 kW are destroyed through the components of the system. Furthermore, about 3.39 kW are lost through the absorber coolant and waste from the hydrogen production unit. About 72.4 kW are attributed to products.

DBHF: A double fusion backbone and bidirectional feature hybrid fusion detector for high-precision inspection of avionics solder joint defects

The quality of avionics thermistor wire solder joints has a critical impact on the performance of in-orbit spacecraft. However, it is still hard to simultaneously realize a high-precision inspection of the multi-scale internal and external defects of avionics solder joints. Herein, in combination with active infrared thermography technique, an innovative DBHF detector with a double fusion backbone and a learnable weighted multi-scale bidirectional feature hybrid fusion pyramid network (BdFPN) is demonstrated based on RT-DETR framework. Specifically, the double fusion backbone framework comprises with two lightweight convolutional neural network-based backbones. Compared with conventional single backbone, the double-backbone design forms a multi-level Resnet-like structure, helping to enrich the extracted multi-dimension feature information and retain rich detail information of small-scale defects. The complex learnable weighted multi-scale BdFPN is designed for cross fusion of deep features. One or more additional multi-scale inputs are added to each node involved in feature fusion to aggregate more features from multiple scales and strengthen the interaction between low-level details and high-level semantic information, enabling the detector to make full use of multi-level feature information. Meanwhile, a learnable weight coefficient is set on each node involved in feature fusion and the importance of the input to each node is estimated by training, so that the effective information can be deeply aggregated at different levels. Our DBHF detector achieves a 92.7 % mAP@0.5 on a self-made avionics solder joint dataset with a body size of only 27.2 MB and FPS of 149, outperforming all the typical advanced real-time detectors.

Development and Experiment of Semi-Physical Simulation Platform for Space Manipulator.

To address the extended development cycle, high costs, and maintenance difficulties associated with existing microgravity simulation methods, this study has developed a semi-physical simulation platform for robotic arms tailored to different gravity environments and loading conditions. The platform represents difficult-to-model joints as physical objects, while the easily modeled components are simulated based on principles of similarity. In response to the strong coupling, nonlinearity, and excess force disturbance issues in the electric variable load loading system, a fractional-order linear active disturbance rejection control algorithm was employed. The controller parameters were tuned using an improved particle swarm algorithm with modified weight coefficients, and experimental results demonstrate that a fractional-order linear active disturbance rejection control improves response speed and disturbance rejection performance compared to linear sliding mode control. The study investigated the differences in the drive force of joint motors in space robotic arms under varying gravity environments and loading conditions. Experimental results indicate that load torque is the primary influencing factor on joint motor drive force, while radial force serves as a secondary influencing factor. Additionally, when the axis of the joint motor is perpendicular to the ground, it can, to some extent, simulate microgravity conditions on the ground.

Super-resolution deep learning reconstruction approach for enhanced visualization in lumbar spine MR bone imaging

ObjectivesThis study aims to assess the effectiveness of super-resolution deep-learning-based reconstruction (SR-DLR), which leverages k-space data, on the image quality of lumbar spine magnetic resonance (MR) bone imaging using a 3D multi-echo in-phase sequence. Materials and methodsIn this retrospective study, 29 patients who underwent lumbar spine MRI, including an MR bone imaging sequence between January and April 2023, were analyzed. Images were reconstructed with and without SR-DLR (Matrix sizes: 960 × 960 and 320 × 320, respectively). The signal-to-noise ratio (SNR) of the vertebral body and spinal canal and the contrast and contrast-to-noise ratio (CNR) between the vertebral body and spinal canal were quantitatively evaluated. Furthermore, the slope at half-peak points of the profile curve drawn across the posterior border of the vertebral body was calculated. Two radiologists independently assessed image noise, contrast, artifacts, sharpness, and overall image quality of both image types using a 4-point scale. Interobserver agreement was evaluated using weighted kappa coefficients, and quantitative and qualitative scores were compared via the Wilcoxon signed-rank test. ResultsSNRs of the vertebral body and spinal canal were notably improved in images with SR-DLR (p < 0.001). Contrast and CNR were significantly enhanced with SR-DLR compared to those without SR-DLR (p = 0.023 and p = 0.022, respectively). The slope of the profile curve at half-peak points across the posterior border of the vertebral body and spinal canal was markedly higher with SR-DLR (p < 0.001). Qualitative scores (noise: p < 0.001, contrast: p < 0.001, artifact p = 0.042, sharpness: p < 0.001, overall image quality: p < 0.001) were superior in images with SR-DLR compared to those without. Kappa analysis indicated moderate to good agreement (noise: κ = 0.56, contrast: κ = 0.51, artifact: κ = 0.46, sharpness: κ = 0.76, overall image quality: κ = 0.44). ConclusionSR-DLR, which is based on k-space data, has the potential to enhance the image quality of lumbar spine MR bone imaging utilizing a 3D gradient echo in-phase sequence.Clinical relevance statement: The application of SR-DLR can lead to improvements in lumbar spine MR bone imaging quality.

Quality evaluation of surrounding rocks in a mine shaft based on the game gray target model

Accurate evaluation of surrounding rock quality grade can help guarantee the safety of tunnel design and construction; hence, it has great significance in the construction of mine shafts. Accordingly, the uniaxial saturated compressive strength of the rock block Rc, rock quality index RQD, rock softening coefficient kR, integrity coefficient of the rock mass kv, depth H, unit weight of the rock mass γ, coefficient of quantification of the angle between the principal structural plane and shaft axis k2, and weight of the groundwater k1 are first selected as the indexes of assessment to introduce the game gray target model. Then, the gray target model of the surrounding rocks in a mine shaft project is established. The weight coefficient of each index is next calculated using the combination weighting method based on game theory, and the synthetic target center distance of each sample is determined using the gray target model. Finally, the quality level of the asphalt pavement is determined. The suggested model can be used to mine a small data sample to the maximum extent possible, thereby minimizing the information shortage caused by the small sample to a certain extent, to evaluate the final quality grade of the surrounding rocks quantitatively. Thus, the proposed approach provides a new scheme for the future quality assessments of surrounding rocks.

Late Fusion Multiview Clustering via Min-Max Optimization.

Multiview clustering (MVC) sufficiently exploits the diverse and complementary information among different views to improve the clustering performance. As a representative algorithm of MVC, the newly proposed simple multiple kernel k -means (SimpleMKKM) algorithm takes a min-max formulation and applies a gradient descent algorithm to decrease the resultant objective function. It is empirically observed that its superiority is attributed to the novel min-max formulation and the new optimization. In this article, we propose to integrate the min-max learning paradigm adopted by SimpleMKKM into late fusion MVC (LF-MVC). This leads to a tri-level max-min-max optimization problem with respect to the perturbation matrices, weight coefficient, and clustering partition matrix. To solve this intractable max-min-max optimization problem, we design an efficient two-step alternative optimization strategy. Furthermore, we analyze the generalization clustering performance of the proposed algorithm from the theoretical perspective. Comprehensive experiments have been conducted to evaluate the proposed algorithm in terms of clustering accuracy (ACC), computation time, convergence, as well as the evolution of the learned consensus clustering matrix, clustering with different numbers of samples, and analysis of the learned kernel weight. The experimental results show that the proposed algorithm is able to significantly reduce the computation time and improve the clustering ACC when compared to several state-of-the-art LF-MVC algorithms. The code of this work is publicly released at: https://xinwangliu.github.io/Under-Review.

Kraje Unii Europejskiej a ubóstwo, przed i po kryzysie COVID-19

The purpose of the paper is to evaluate and present the position of the European Union countries according to the poverty level before and after the COVID-19 crisis, combining five individual indicators. The GRA (Grey Relational Analysis) method was used to calculate the composite poverty indicator. To assess poverty, the study uses criteria from the official database of the European Commission and calculates the aggregate index for 2019 and 2022. All criteria in the model have the same relative importance because the method of equal weight coefficients is applied. The paper proved that the Czech Republic and Slovenia had the most favourable indicators of poverty in both periods, while Greece, Romania, Bulgaria, and Latvia occupied a critical position. In general, the countries of Southern Europe and the Baltic countries have poor poverty indicators. Compared to 2019, according to the country rankings, the poverty level is significantly higher in Finland, Germany, and Hungary, while after the pandemic, the following countries made considerable progress in reducing poverty: Poland, Belgium, Luxembourg, and Cyprus. The presented results can be useful to decision-makers at the macro level in the field of economic, social, and sustainable development policy.

Trade show performance evaluation based on stakeholder theory: A case study of trade shows in Changsha, China

AbstractA performance evaluation index system is built in this paper with four dimensions (service performance, sales performance, behavioral performance, and social performance) and 14 criteria considering the requirements of key stakeholders. The BWM‐CRITIC‐TOPSIS model is then developed for a comprehensive trade show performance evaluation. An illustrative application is carried out based on four trade shows held in Changsha, China, to demonstrate the validity and acceptability of the proposed evaluation index system and model. Findings indicate that social performance is assigned the highest weight coefficient: it is considered to be the essential criterion that constrains the performance level. The evaluation results show that the overall performance levels of the sampled trade shows increased from 2022 to 2023 and have gradually recovered from the COVID‐19 crisis. Their adaptability and flexibility in response to changing surroundings could be enhanced by meeting various stakeholders' expectations, which is also essential for improving their own resilience.

Optimization of rural environment through drainage system modernization: analysis of investment feasibility indicator in the modernization of drainage ditches on former agricultural lands

The benefits of installing pipes and drainage manholes, as well as covering open drainage ditches in built-up areas, have been listed in the report. A cost-effectiveness indicator for the investment, taking into account proprietary weighting coefficients, has been presented, which can be used as a formal argument determining the need for modernization of existing solutions in built-up areas. The indicator serves as an objective tool in the decision-making process at the municipal level. Modernization involving the installation of pipes and covering ditches, which previously served agricultural purposes, leads to an improvement in spatial order in a given area and also contributes to the protection of the natural environment through proper water management in former agricultural areas

Brain Connectivity Dynamics and Mittag–Leffler Synchronization in Asymmetric Complex Networks for a Class of Coupled Nonlinear Fractional-Order Memristive Neural Network System with Coupling Boundary Conditions

This paper investigates the long-time behavior of fractional-order complex memristive neural networks in order to analyze the synchronization of both anatomical and functional brain networks, for predicting therapy response, and ensuring safe diagnostic and treatments of neurological disorder (such as epilepsy, Alzheimer’s disease, or Parkinson’s disease). A new mathematical brain connectivity model, taking into account the memory characteristics of neurons and their past history, the heterogeneity of brain tissue, and the local anisotropy of cell diffusion, is proposed. This developed model, which depends on topology, interactions, and local dynamics, is a set of coupled nonlinear Caputo fractional reaction–diffusion equations, in the shape of a fractional-order ODE coupled with a set of time fractional-order PDEs, interacting via an asymmetric complex network. In order to introduce into the model the connection structure between neurons (or brain regions), the graph theory, in which the discrete Laplacian matrix of the communication graph plays a fundamental role, is considered. The existence of an absorbing set in state spaces for system is discussed, and then the dissipative dynamics result, with absorbing sets, is proved. Finally, some Mittag–Leffler synchronization results are established for this complex memristive neural network under certain threshold values of coupling forces, memristive weight coefficients, and diffusion coefficients.

Small Sample Time Series Classification Based on Data Augmentation and Semi-supervised Learning

Realistic scenarios produce labeled data and unlabeled data, however, there are significant challenges in labeling time series data. It is imperative to effectively integrate the relationship between labeled and unlabeled data within semi-supervised classification model. This paper presents a novel semi-supervised classification method, namely Data Augmentation-Fast Shapelet Semi-Supervised Classification, which employs a data augmentation module to enhance the diversity of data and improve the generalization ability of the model, as well as a feature fusion module to enhance the semi-supervised network. A conditional generative adversarial network is used to synthesize excellent labeled time series samples to enhance the homogeneous data in the sample space, the fast shapelets method is used to quickly extract the important shape feature vectors in the time series, self-supervised and supervised learning are combined to fully learn the unlabeled and labeled data of the time series dataset. Thejoint loss function combines the loss functions of the two networks to optimize multiple objectives. Reinforcement learning is used to determine the weight coefficients of the joint loss function, at the same time, the reward function is modified to bias the supervisory loss, which improves the classification performance of the model under limited labeled data, and the model can also better achieve the semi-supervised classification task. The proposed method is validated on the UCR benchmark dataset, Electrocardiogram dataset, and Electroencephalogram dataset, the results show that the semi-supervised classification method can perform a more accurate semi-supervised classification of the time series, with an accuracy better than the comparison methods. Meanwhile, we use the plant electrical signal dataset obtained from actual measurements for testing, the visualizationanalysis can clearly show the model role in the semi-supervised classification task, and the experimental results fully demonstrate the effectiveness and applicability of the proposed method.

Simulation of Dynamic Path Planning of Symmetrical Trajectory of Mobile Robots Based on Improved A* and Artificial Potential Field Fusion for Natural Resource Exploration

With the rapid development of new-generation artificial intelligence and Internet of Things technology, mobile robot technology has been widely used in various fields. Among them, the autonomous path-planning technology of mobile robots is one of the cores for realizing their autonomous driving and obstacle avoidance. This study conducts an in-depth discussion on the real-time and dynamic obstacle avoidance capabilities of mobile robot path planning. First, we proposed a preprocessing method for obstacles in the grid map, focusing on the closed processing of the internal space of concave obstacles to ensure the feasibility of the path while effectively reducing the number of grid nodes searched by the A* algorithm, thereby improving path search efficiency. Secondly, in order to achieve static global path planning, this study adopts the A algorithm. However, in practice, algorithm A has problems such as a large number of node traversals, low search efficiency, redundant path nodes, and uneven turning angles. To solve these problems, we optimized the A* algorithm, focusing on optimizing the heuristic function and weight coefficient to reduce the number of node traversals and improve search efficiency. In addition, we use the Bezier curve method to smooth the path and remove redundant nodes, thereby reducing the turning angle. Then, in order to achieve dynamic local path planning, this study adopts the artificial potential field method. However, the artificial potential field method has the problems of unreachable target points and local minima. In order to solve these problems, we optimized the repulsion field so that the target point is at the lowest point of the global energy of the gravitational field and the repulsive field and eliminated the local optimal point. Finally, for the path-planning problem of mobile robots in dynamic environments, this study proposes a hybrid path-planning method based on a combination of the improved A* algorithm and the artificial potential field method. In this study, we not only focus on the efficiency of mobile robot path planning and real-time dynamic obstacle avoidance capabilities but also pay special attention to the symmetry of the final path. By introducing symmetry, we can more intuitively judge whether the path is close to the optimal state. Symmetry is an important criterion for us to evaluate the performance of the final path.

Active filter parameter tuning method for harmonic voltage mitigation in wind power plants

It is challenging to determine the active filter control factors in wind power plants (WPP) to obtain effective harmonic voltage mitigation and avoid over-modulation or system instability problems caused by overlarge feedforward or feedback gains. To address this issue, an active filter tuning (AFT) method is proposed in this paper to offer a common parameter tuning and stability assessment strategy for both current-controlled and voltage-controlled harmonic impedance reshaping (HIR) methods by introducing a coordination factor including different weight coefficients for system stability margin, wind turbine (WT) harmonic suppression, and grid harmonic mitigation. The superiority of the proposed method is verified in a 20 MW WPP with four cases considering both WT harmonic voltage amplification and grid voltage amplification. Compared to previous methods, the AFT-based HIR method achieves a more flexible balance between system stability and harmonic voltage mitigation, obtaining better performance in WT harmonic suppression, grid harmonic suppression, system robustness, and transient response.

A Wind Power Fluctuation Smoothing Control Strategy for Energy Storage Systems Considering the State of Charge

With the significant increase in the scale of energy storage configuration in wind farms, improving the smoothing capability and utilization of energy storage has become a key focus. Therefore, a wind power fluctuation smoothing control strategy is proposed for battery energy storage systems (BESSs), considering the state of charge (SOC). First, a BESS smoothing wind power fluctuation system model based on model predictive control (MPC) is constructed. The objective function aims to minimize the deviation of grid-connected power from the target power and the deviation of the BESS’s remaining capacity from the ideal value by comprehensively considering the smoothing effect and the SOC. Second, when the wind power’s grid-connected power exceeds the allowable fluctuation value, the weight coefficients in the objective function are adjusted in real time using the first layer of fuzzy control rules combined with SOC partitioning. This approach smooths wind power fluctuations while preventing overcharging and overdischarging of the BESS. When the grid-connected power is within the allowable fluctuation range, the charging and discharging power of the BESS is further refined using a second layer of fuzzy control rules. This enhances the BESS’s capability and utilization for smoothing future wind power fluctuations by preemptively charging and discharging. Finally, the proposed control strategy is simulated using MATLAB R2021b with actual operational data from a wind farm as a case study. Compared to the traditional MPC control method, the simulation results demonstrate that the proposed method effectively controls the SOC within a reasonable range, prevents the SOC from entering the dead zone, and enhances the BESS’s ability to smooth wind power fluctuations.

Fast forward modeling of grounded electrical-source transient electromagnetic based on inverse Laplace transform adaptive hybrid algorithm

Frequency–time conversion is a crucial step in grounded electrical-source transient electromagnetic response calculation, and the performance of the algorithm is directly related to the overall accuracy and speed of forward modeling. In mainstream algorithms, algorithms with high accuracy often have slow computation speed while algorithms with high efficiency have unsatisfactory accuracy, especially when facing inversion problems that are difficult to meet requirements. This paper introduces three inverse Laplace transform algorithms for this problem: the Gaver–Stehfest algorithm, the Euler algorithm, and the Talbot algorithm. The performance of each algorithm in forward modeling was analyzed using half-space and layered models, and the optimal selection schemes for algorithm weight coefficients were provided. The numerical calculation results show that the Gaver–Stehfest algorithm has a unique advantage in computational efficiency, while the Talbot algorithm and Euler algorithm meet the accuracy requirements. After considering both accuracy and efficiency, the Talbot algorithm is selected to replace conventional algorithms for calculation of grounded electrical-source transient electromagnetic forward modeling. In addition, this paper combines the characteristics of the Gaver–Stehfest algorithm and the Talbot algorithm to implement an adaptive hybrid algorithm. This algorithm uses the Gaver–Stehfest algorithm for forward modeling in the early times and the Talbot algorithm to compensate for the decrease in accuracy in the later times. Through the comparison of forward modeling calculations, it can be seen that the hybrid algorithm proposed in this paper fully utilizes the advantages of both algorithms. The hybrid algorithm greatly improves computational speed while meeting accuracy requirements, and has significant advantages over conventional algorithms.

Research on image recognition of tomato leaf diseases based on improved AlexNet model

Aiming at the problems that the traditional image recognition technology is challenging to extract useful features and the recognition time is extended; the AlexNet model is improved to improve the effect of image classification and recognition. This study focuses on 8 types of tomato leaf diseases and healthy leaves. By using HOG and LBP weighted fusion to extract image features, a tomato leaf disease recognition model based on the AlexNet model is proposed, and transfer learning is used to train the AlexNet model. Transfer the knowledge learned by the AlexNet model on the PlantVillage image dataset to this model while reducing the number of fully connected layers. Keras deep learning framework and programming language Python were used. The model was implemented, and the classification and identification of tomato leaf diseases were carried out. The recognition rate of feature-weighted fusion classification is higher than that of serial and parallel methods, and the recognition time is the shortest. When the weight coefficient ratio of HOG and LBP is 3:7, the image recognition rate is the highest, and its value is 97.2 %. From the model performance curve See, when the number of iterations is more than 150 times, the training set and test accuracy rate both exceed 97 %, the loss rate shows a gradient decline, and the change is relatively stable; compared with the traditional AlexNet model, HOG + LBP + SVM model, and VGG model, improved AlexNet model has the highest recognition rate, and it has high recall value, accuracy, and F1 value; Compared with the latest convolutional neural network disease recognition models, improved AlexNet model recognition accuracy was 98.83 %, and the F1 value was 0.994. It shows that the model has good convergence performance, fast prediction speed, and low loss rate and can effectively identify 8 types of tomato leaf images, which provides a reference for the research on crop disease identification.

Research on Path Planning Algorithm of Driverless Ferry Vehicles Combining Improved A* and DWA.

In view of the fact that the global planning algorithm cannot avoid unknown dynamic and static obstacles and the local planning algorithm easily falls into local optimization in large-scale environments, an improved path planning algorithm based on the integration of A* and DWA is proposed and applied to driverless ferry vehicles. Aiming at the traditional A* algorithm, the vector angle cosine value is introduced to improve the heuristic function to enhance the search direction; the search neighborhood is expanded and optimized to improve the search efficiency; aiming at the problem that there are many turning points in the A* algorithm, a cubic quasi-uniform B-spline curve is used to smooth the path. At the same time, fuzzy control theory is introduced to improve the traditional DWA so that the weight coefficient of the evaluation function can be dynamically adjusted in different environments, effectively avoiding the problem of a local optimal solution. Through the fusion of the improved DWA and the improved A* algorithm, the key nodes in global planning are used as sub-target punctuation to guide the DWA for local planning, so as to ensure that the ferry vehicle avoids obstacles in real time. Simulation results show that the fusion algorithm can avoid unknown dynamic and static obstacles efficiently and in real time on the basis of obtaining the global optimal path. In different environment maps, the effectiveness and adaptability of the fusion algorithm are verified.

PENGARUH KOMITMEN CSR TERHADAP LOYALITAS KONSUMEN MELALUI KEPERRCAYAAN SEBAGAI VARIABEL INTERVENING (Studi Kasus Nasabah Bank Rakyat Indonesia)

Abstract In the era of advanced and modern globalization, people are required to be more selective in choosing banking products. On the other hand, banks must be able to maintain their marketshare. One way to seize the market is with CSR programs. BRI as one of the largest banks in Indonesia has a CSR program with the hope of increasing customer loyalty. The purpose of this study is todetermine the influence between CSR commitment to consumer loyalty through trust intervening variables.This study used quantitative methods with the research population of BRI customers. This study has several variables, namely the influence of CSR commitment (X) as an independent variable, consumer loyalty (Y) as a dependent variable, and trust as a mediating or intervening variable.Based on the results of hypothesis testing, it can beconclude dthat: 1) CSR commitment has a direct effect on consumer loyalty with an estimate regression weight coefficient value of 0.164 and a C.R. value of 2.117. The significance test between variable (X) andvariable (Y) shows a probability valueof 0.034 (p &lt; 0.05). 2) the effect ofthe mediation variable was tested usingthe Sobel test with t calculated resultsof 2.031 &gt; 1.96 with a signification level of 0.000 &lt; 0.05. These results show that variable (X) has a positive influence on variable (Y) and mediating variable has a positive and significant influence on variable (X) and variable (Y). So, CSR commitment can affect consumer loyalty through intervening variables, namely BRI customertrust. Keyword : Li festyle, consumer buying interest, trend, product knowledge.

A risk decision support model for hydrogen refueling stations based on cloud-analytic hierarchy process

With the rapid development of the hydrogen energy industry and the vigorous promotion of the application of hydrogen fuel cell vehicles, hydrogen refueling station (HRS) has gradually penetrated into cities, mostly built in densely populated areas. Once leakage, fire, and/or explosion accident occurs, it is highly likely to cause casualties, property damage, and environmental pollution. In order to avoid HRS risks, this paper establishes a risk decision support model based on Cloud-Analytic Hierarchy Process for HRS. Firstly, the static and dynamic features of HRS emergency plan selection are analyzed, and the weight coefficient of each feature is determined by using the cloud analysis hierarchy process. Then, the case-based reasoning theory and the weighted Euclidean distance based on the cloud model are used to establish a decision support model based on emergency plan optimization. Finally, taking the HRS hydrogen storage tank fire as an example, the risk decision support model based on the cloud analysis hierarchical process is applied to form the optimal scheme of the HRS hydrogen storage tank fire emergency plan. The results show that the weighted Euclidean distance scheme selection method based on cloud model can be effectively combined with the weight acquisition method based on cloud analysis hierarchy process to provide decision support for HRS emergency risk management and control.