Nudel and FAK as Antagonizing Strength Modulators of Nascent Adhesions through Paxillin

Competition for binding to the cellular protein paxillin by the proteins Nudel and focal adhesion kinase is important for the proper regulation of cell adhesion and migration

The optimization model of the vendor selection for the joint procurement from a total cost of ownership perspective

Purpose: This paper is an attempt to establish the mathematical programming model of the vendor selection for the joint procurement from a total cost of ownership perspective. Design/methodology/approach: Fuzzy genetic algorithm is employed to solve the model, and the data set of the ball bearings purchasing problem is illustrated as a numerical analysis. Findings: According to the results, it can be seen that the performance of the optimization model is pretty good and can reduce the total costs of the procurement. Originality/value: The contribution of this paper is threefold. First, a literature review and classification of the published vendor selection models is shown in this paper. Second, a mathematical programming model of the vendor selection for the joint procurement from a total cost of ownership perspective is established. Third, an empirical study is displayed to illustrate the application of the proposed model to evaluate and identify the best vendors for ball bearing procurement, and the results show that it could reduce the total costs as much as twenty percent after the optimization

Cascade Failure-Based Identification and Resilience of Critical Nodes in Automotive Supply Chain Networks

In the case of cascade failure, due to the close connection of the automobile supply chain network, the chain reaction caused by it should not be ignored; therefore, to find out the important nodes in the automobile supply chain network, to reduce the damage of cascade failure on the supply chain network, and to improve the destruction resistance of the automobile supply chain network is a problem that we should focus on. This paper takes Tesla’s new energy automotive supply chain network as an example to study the impact of cascade failure on the destructive resistance of the automotive supply chain network. From the analysis of the identification results, it is found that the key nodes in the automobile supply chain network with strong influence on risk propagation are mostly charging pile enterprises, motor enterprises, and electronic control enterprises at the core, such as Hengdian Electromagnetics, Wanma Stocks, etc. Meanwhile, Changxin Science and Technology, as a central control panel manufacturer with a large number of indirect suppliers, is also in the top position. Through the proposed key node identification method, it has good practical application value for preventing risk transmission in the automotive supply chain

Optimizing Cold Chain Distribution Routes Considering Dynamic Demand: A Low-Emission Perspective

Cold chain logistics, with its high carbon emissions and energy consumption, contradicts the current advocacy for a “low-carbon economy”. Additionally, in the real delivery process, customers often generate dynamic demand, which has the characteristic of being sudden. Therefore, to help cold chain distribution companies achieve energy-saving and emission-reduction goals while also being able to respond quickly to customer needs, this article starts from a low-carbon perspective and constructs a two-stage vehicle distribution route optimization model that minimizes transportation costs and refrigeration costs, alongside carbon emissions costs. This research serves to minimize the above-mentioned costs while also ensuring a quick response to customer demands and achieving the goals of energy conservation and emission reduction. During the static stage, in order to determine the vehicle distribution scheme, an enhanced genetic algorithm is adopted. During the dynamic optimization stage, a strategy of updating key time points is employed to address the dynamic demand from customers. By comparing the dynamic optimization strategy with the strategy of dispatching additional vehicles, it is demonstrated that the presented model is capable of achieving an overall cost reduction of approximately 17.13%. Notably, carbon emission costs can be reduced by around 17.11%. This demonstrates that the dynamic optimization strategy effectively reduces the usage of distribution vehicles and lowers distribution costs

Using Constrained K-Means Clustering for Soil Texture Mapping with Limited Soil Samples

Soil texture is one of the most important physical properties of soil and plays a crucial role in determining its suitability for crop cultivation. Currently, supervised classification machine learning methods are most commonly used in digital soil mapping. However, these methods may not yield optimal predictive performance due to the limited number of soil samples. Therefore, we propose using Constrained K-Means Clustering to combine a small number of labeled samples with a large amount of unlabeled data, thereby achieving improved prediction in soil texture mapping. In this study, we focused on a typical hilly region in northern Jurong City, Jiangsu Province, China, and used Constrained K-Means Clustering as our mapping model. GF-2 remote sensing imagery and the ALOS digital elevation model (DEM), along with their derived variables, were employed as environmental variables. In Constrained K-Means Clustering, the choice of distance method is a key parameter. Here, we used four different distance methods (euclidean, maximum, manhattan, and canberra) and compared the results with those of the random forest (RF) and multilayer perceptron (MLP) models. Notably, the euclidean distance method within Constrained K-Means Clustering achieved the highest overall accuracy (OA), Kappa coefficient, and Macro F1 Score, with values of 0.77, 0.68, and 0.75, respectively. These methods were higher than those obtained by the RF and MLP models by 0.12, 0.18, and 0.12, and 0.18, 0.26, and 0.18, respectively. This indicates that Constrained K-Means Clustering demonstrates strong predictive performance in soil texture mapping. Moreover, land use (LU), multi-resolution of ridge top flatness index (MRRTF), topographic position index (TPI), and plan curvature (PlC) emerged as the key environmental variables for predicting soil texture. Overall, Constrained K-Means Clustering proves to be an effective digital soil mapping approach, offering a novel perspective for soil texture mapping with limited samples

Landscape Classification System Based on RKM Clustering for Soil Survey UAV Images–Case Study of the Small Hilly Areas in Jurong City

With the advantages of high accuracy, low cost, and flexibility, Unmanned Aerial Vehicle (UAV) images are now widely used in the fields of land survey, crop monitoring, and soil property prediction. Since the distribution of soil and landscape are closely related, this study makes use of the advantages of UAV images to classify the landscape to build a landscape classification system for soil investigation. Firstly, land use, object, and topographic factor were selected as landscape factors based on soil-forming factors. Then, based on multispectral images and Digital Elevation Models (DEM) acquired by UAV, object-oriented classification of different landscape factors was carried out. Additionally, we selected 432 sample data and validation data from the field survey. Finally, the landscape factor classification results were superimposed to obtain the landscape unit applicable to the system classification. The landscape classification system oriented to the soil survey was constructed by clustering 11,897 landscape units through the rough K-mean clustering algorithm. Compared to K-mean clustering, the rough K-mean clustering was better, with a Silhouette Coefficient of 0.26247 significantly higher than that of K-mean clustering. From the classification results, it can be found that the overall classification results are somewhat fragmented, but the landscape boundaries at the small area scale are consistent with the actual situation and the fragmented small spots are less. Comparing the small number of landscape boundaries obtained from the actual survey, we can find that the landscape boundaries in the landscape classification map are generally consistent with the actual landscape boundaries. In addition, through the analysis of two soil profile data within a landscape category, we found that the identified soil type of soil formation conditions and the landscape factor type of the landscape category is approximately the same. Therefore, this landscape classification system can be effectively used for soil surveys, and this landscape classification system is important for soil surveys to carry out the selection of survey routes, the setting of profile points, and the determination of soil boundaries

Soil Classification Mapping Using a Combination of Semi-Supervised Classification and Stacking Learning (SSC-SL)

In digital soil mapping, machine learning models have been widely applied. However, the accuracy of machine learning models can be limited by the use of a single model and a small number of soil samples. This study introduces a novel method, semi-supervised classification combined with stacking learning (SSC-SL), to enhance soil classification mapping in hilly and low-mountain areas of Northern Jurong City, Jiangsu Province, China. This study incorporated Gaofen-2 (GF-2) remote sensing imagery along with its associated remote sensing indices, the ALOS Digital Elevation Model (DEM) and their derived topographic factors, and soil parent material data in its modelling process. We first used three base learners, Ranger, Rpart, and XGBoost, to construct the SL model. In addition, we employed the fuzzy c-means clustering algorithm (FCM) to construct a clustering map. To fully leverage the information from a multitude of environmental variables, understand the distribution of data, and enhance the effectiveness of the classification, we selected unlabelled samples near the boundaries of the patches on the clustering map. The SSC-SL model demonstrated superior stability and performance, with optimal accuracy at a 0.9 confidence level, achieving an overall accuracy of 0.77 and a kappa coefficient of 0.73. These metrics exceeded those of the highest performing base learner (Ranger model) by 10.4% and 12.3%, respectively, and they outperformed the least effective base learner (Rpart model) by 27.3% and 32.9%. It notably improves the spatial distribution accuracy of soil types. Key environmental variables influencing soil type distribution include soil parent material (SPM), land use (LU), the multi-resolution valley bottom flatness index (MRVBF), and Elevation (Ele). In conclusion, the SSC-SL model offers a novel and effective approach for enhancing the predictive accuracy of soil classification mapping