Universities and colleges have a constant focus on improving student retention and degree completion rates. Degree completion impacts the reputation of the institution, as it is a reflection of institutional ability t...
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Universities and colleges have a constant focus on improving student retention and degree completion rates. Degree completion impacts the reputation of the institution, as it is a reflection of institutional ability to prepare graduates with the specific skills that contribute to society through STEM majors. Colleges and universities collect considerable amounts of student data; however, efforts need to be made to utilize the data to increase student success. For instance, by determining the factors that influence student retention and completion rates, it is possible to improve advising through intentional student advising. To this end, this research presents the application of support vector machines (SVM) to predict degree completion within three years by STEM community college students. SVM enables the classification of the input variables into expected classes, completion and not completion, by maximizing the margin between the points from the different classes constraining the misclassification. The model was developed using data on 282 students with 9 variables. The variables included age, gender, degree, and college GPA, among others. The model results, which include prediction and variables ranking, offer an important understanding about how to develop a more efficient and responsive system to support students.
Our world is now connected and even entangled in unprecedented ways through networked technologies. Yet pockets of unequal connectivity persist, and technical infrastructures for connectivity remain difficult to desig...
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The healthy and sustainable development of irrigation area not only serves as the guarantee of national food production security, but also shoulders the important task of regional ecological security. However, the unr...
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The healthy and sustainable development of irrigation area not only serves as the guarantee of national food production security, but also shoulders the important task of regional ecological security. However, the unreasonable exploitation and utilization of water resources for a long time in the irrigation area has led to the coexistence of water shortage and waste as well as a series of ecological and environmental problems, restricting the benign development of the irrigation area. The groundwater level plays an important role in maintaining the regional ecological system. It is of great significance for the healthy development of the irrigation area to realize the rational utilization of the limited water resources by strictly implementing restriction of total water use on the basis of ensuring the reasonable groundwater level. Therefore, this study established a spatial-temporal optimal regulation method of water resources in the irrigation area by using the optimal allocation of water resources, coupling groundwater numerical technology and the characteristics of hydrological cycle with comprehensively consideration of the dual constraints between total water consumption and reasonable groundwater depth. Besides, taking Helan County, a typical area of Qingtongxia Irrigation district in Ningxia as an example, a case study on optimal allocation of irrigation water resources was carried out. The results show that the irrigation water demand in Helan County Irrigation District (HCID) decreases by considering the absorption of shallow groundwater by roots, and the corresponding water use spatial-temporal distribution is optimized under the combined action of total available water allocation and groundwater depths. Compared with the situation without considering the direct utilization of groundwater by roots, the irrigation water demand of HCID decreases by 5.625 million m 3 and 0.98%. Compared with the actual current water use situation, spatially, the variance of
Currently, deep learning algorithms are developing in machine learning and deep neural networks. However, deep learning algorithms still have pressing problems to solve. That is, the data set is insufficient and of lo...
Currently, deep learning algorithms are developing in machine learning and deep neural networks. However, deep learning algorithms still have pressing problems to solve. That is, the data set is insufficient and of low quality for machine learning. One of the ways to solve this problem is to data augmentation the dataset. There are two types of image data sets: labeled data sets and unlabeled data sets. These annotations are important for object recognition. Deep learning algorithms typically use an annotated dataset. Similarly, the You Only Look Once (YOLOv5) network model also requires an annotated dataset. In this article, we manually created annotations for each image using the labelImg software tool. Different countries have different Traffic signs. The dataset of traffic signs of Uzbekistan (UTSD) has not yet been developed. Therefore, in this work, we developed a UTSD dataset for use in a traffic sign detection and recognition system (TSDR). The UTSD dataset contains 3957 Traffic sign images belonging to 56 classes. We improved the UTSD dataset by data augmentation.
In the current study, thermal patterns and flow resistance in a heat exchange tube equipped with a curved-wing tape (CWT) vortex generator were experimentally analyzed to boost performance. A range of Reynolds numbers...
In the current study, thermal patterns and flow resistance in a heat exchange tube equipped with a curved-wing tape (CWT) vortex generator were experimentally analyzed to boost performance. A range of Reynolds numbers (Re) from 4130 to 25,370 was utilized in the experiment. The curved wings were arranged in forward array patterns for pitch length ratios (P/D = PR = 2.5, 2, and 1.5) and three wing attack angles (α = 10°, 20°, and 30°). The friction factor, f, and the Nusselt number, Nu, were utilized to quantify the pressure loss and heat transmission caused by the CWT insertion. The measured data reveal that utilizing the CWT resulted in a roughly 2.55-4.37 times greater increase in Nu than the smooth tube, whereas the rise in f is approximately 7.36-46.9 times. Raising the α value causes the Nu and f to trend upward, but increasing PR causes them to trend downward. The CWT’s maximal thermal performance is approximately 1.58 at α = 10° and PR = 1.5. Correlations for f and Nu in the functional form of the CWT parameters have also been established.
Due to domain shift, a large performance drop is usually observed when a trained crowd counting model is deployed in the wild. While existing domain-adaptive crowd counting methods achieve promising results, they typi...
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In the lighting conditions such as snowing, hazing, raining, and weak lighting condition, the accuracy of traffic sign recognition is not very high. It is important to develop an algorithms for real-time fast detectio...
In the lighting conditions such as snowing, hazing, raining, and weak lighting condition, the accuracy of traffic sign recognition is not very high. It is important to develop an algorithms for real-time fast detection of road signs from video images and use them in driver assistance systems. Firstly, we image preprocessing by using dark channel prior based image dehazing for remove noises from the input image. In this article, we develop a transposed image filtering method combined with other fast parallel filtering algorithms. We propose a high-speed and high-accuracy method by improving image dehazing algorithms and analyzing GPU architecture. In this, we quickly removing fog from HD video images using DCP with image quality and visual effects.
Manufacturing has been undergoing many changes, with the latest one being the paradigm shift to Industry 5.0. In this long procedure, training is required at any level, from operators to managers. Thus, interventions ...
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Manufacturing has been undergoing many changes, with the latest one being the paradigm shift to Industry 5.0. In this long procedure, training is required at any level, from operators to managers. Thus, interventions must be made so that Teaching and Learning Factories are upgraded towards integrating Industry 5.0. To this end, an evaluation system has to be made, assessing the feasibility of the three pillars’ integration. This procedure can concern a qualitative assessment (or a quantitative one) of the feasibility and the other implicated concepts, such as upskilling. At the same time, multilevel metrics are relevant, such as Key Performance Indicators (KPIs) related to company practices, manufacturing itself, jobs and trainees. Herein, a summative differential evaluation scheme, based on heuristic aspects, is explored, under the framework of the aforementioned TLF interventions. Examples of companies’ ex-ante characterization are given. Then, potential extensions are being discussed towards achieving formative evaluation and potentially towards KPIs.
Protein−carbohydrate binding plays an essential role in biological processes including cellular recognition and immune signaling. However, glycans are hydrophilic with limited hydrophobic surfaces, a challenge for sel...
Protein−carbohydrate binding plays an essential role in biological processes including cellular recognition and immune signaling. However, glycans are hydrophilic with limited hydrophobic surfaces, a challenge for selective recognition by proteins. CH–π stacking interactions are pervasive in protein-carbohydrate binding sites and have emerged as critical drivers of protein–carbohydrate recognition. They are highly favorable and have a broad orientational landscape. However, it is unknown how CH−π stacking interaction orientational dynamics are influenced by the protein environment and what their functional interplay is with hydrogen bonds in protein–carbohydrate binding. Here, we employ well-tempered metadynamics simulations to obtain binding free energy landscapes for a set of protein−β-D-galactoside complexes with CH–π stacking interactions. Our data show that the favored orientation of a CH−π stacking interaction is controlled by the location of hydrogen bonds in the protein binding site. Complexes with extended carbohydrate ligands that form additional hydrogen bonds have more specific orientational dependences, while protein variant complexes with fewer hydrogen bonds have broader free energy landscapes with glycan ligands adopting multiple CH−π stacking interaction orientations. We also show that forming multiple CH−π stacking interactions facilitates the dynamics necessary for the translocation of oligosaccharide ligands within a processive enzyme. Our findings underscore the cooperative nature of hydrogen bonds and CH−π stacking interactions, demonstrating that tuning the number and positions of these interactions through evolution or protein engineering can alter ligand recognition or support ligand movement in protein binding sites.
Synchronization is a widespread phenomenon observed across natural and artificial networked systems. It often manifests itself by clusters of units exhibiting coincident dynamics. These clusters are a direct consequen...
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Synchronization is a widespread phenomenon observed across natural and artificial networked systems. It often manifests itself by clusters of units exhibiting coincident dynamics. These clusters are a direct consequence of the organization of the Laplacian matrix eigenvalues into spectral localized blocks. We show how the concept of spectral blocks can be leveraged to design straightforward yet powerful controllers able to fully manipulate cluster synchronization of a generic network, thus shaping at will its parallel functioning. Specifically, we demonstrate how to induce the formation of spectral blocks in networks where such structures would not exist, and how to achieve precise mastering over the synchronizability of individual clusters by dictating the sequence in which each of them enters or exits the synchronization stability region as the coupling strength varies. Our results underscore the pivotal role of cluster synchronization control in shaping the parallel operation of networked systems, thereby enhancing their efficiency and adaptability across diverse applications.
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