Miracle-dz/UniFAR-FTUs huggingface dataset:
Water resources of Hot Springs County, Wyoming

The wells and springs inventoried in Hot Springs County most commonly had been completed in or issued from the Quaternary alluvium, Quaternary terrace deposits, Fort Union and Mesaverde Formations, Cody Shale, and the Frontier and Chugwater Formations. The largest discharges measured were from the Quaternary terrace deposits (400 gallons per minute) and the Phosphoria Formation (1,000 gallons per minute). Discharges from all other geologic units varied, but most wells and springs yielded 50 gallons per minute or less. Water-quality samples collected from springs that issued from the Absaroka Volcanic Supergroup, the Bighorn Dolomite, and the Flathead Sandstone had the lowest dissolved-solids concentrations, which ranged from 58 to 265 milligrams per liter, and the least variable water types. Water from the volcanic rocks was a sodium bicarbonate type; whereas, water from the Flathead Sandstone was a calcium bicarbonate type. Water types for all the other aquifers varied from sampling site to sampling site; however, water samples from the Fort Union Formation and the Cody Shale were consistently of the sodium sulfate type. The effect of oiland gas-development at Hamilton Dome on thermal spring discharges at Hot Springs State Park near Thermopolis was studied. The estimated drawdown from 1918, when the Hamilton Dome oil field was discovered, to 1988 was made using drill-stem data from previous studies. Drawdown at Big Spring in the Park was estimated to be less than 3 feet on the basis of recent oiland water-production data, previous modeling studies, and the estimated water-level drawdown of 330 feet in wells at the Hamilton Dome oil field. Streams originating in the Plains region of the county, such as Middle Fork Owl Creek, are ephemeral or intermittent; whereas, streams originating in the mountains, such as Gooseberry Creek, are perennial. Average annual runoff across the county ranges from 0.26 inches at a representative streamflow-gaging station near Worland in the plains region to 5.4 inches in the Owl Creek Mountains and southeastern Absaroka Range.
Evaluation of an Intervention on Activity Planning in CS1

A key factor in online learning is an instructional design that ensures that students maintain an adequate and constant learning pace throughout the course. This is especially relevant when a fundamentally practical and progressive learning approach is required, such as in introductory programming courses. This article describes an intervention conducted in a first-year subject of the Computer Engineering degree called “Programming Fundamentals”. This subject poses many challenges related to the introduction of abstract concepts, the completion of programming exercises in a specific language, and the monitoring of the pace of proposed learning activities so that students can achieve adequate learning. Based on academic results from several semesters, it was decided to make an intervention that modified the planning of learning activities to maintain motivation and learning pace throughout the semester, while reducing the time between completing the activities and receiving feedback. An analysis of the results following the change shows that more students complete the core activities, with a decrease in dropouts from continuous assessment and an increase in the number of students passing the course. Data analysis has been validated using propensity score matching, a method for evaluating interventions with a quasi-experimental design.
DIVERSITY OF SITONA SPECIES (COLEOPTERA, CURCULIONIDAE) IN CULTIVATED PLANTS BELONGING TO THE FABACEAE FAMILY

Sitona species are significant because they cause damage by feeding on shoots and leaves of Fabaceae plants. In this study, dominant species and intensity of Sitona in alfalfa, sainfoin, vetch, fodder pea, red lentil, and fenugreek plants cultivated in the Eastern and Southeastern Anatolia regions between April-July 2020-2021 were investigated. Samples were collected with a sweep net from 209 fields in 10 provinces of both regions. As a result of surveys, a total of 5168 individuals belonging to Coleositona and Sitona genus (Coleoptera: Curculionidae) were collected from six species of crops grown in two regions. Among them, 11 species were recorded from alfalfa, seven from red lentil, six from vetch, six from sainfoin, four from fenugreek, and four from fodder pea. S. macularius was the most dominant and abundance species in red lentil, vetch, fodder pea, sainfoin and fenugreek were 92.6%, 67.2%, 73.3%, 97.3% and 44.4%, respectively. In alfalfa too, the most dominant and abundant species was S. humeralis with 51.3%. The values of the Margalef diversity index showed minor differences between plant varieties. The Shannon-Weaver diversity index varied between 0.12 and 1.41. The maximum species diversity was recorded in alfalfa, whereas the minimum was observed in fodder pea and fenugreek plants.
From small to large baseline multiview stereo : dealing with blur, clutter and occlusions

This thesis addresses the problem of reconstructing the three-dimensional (3D) digital model of a scene from a collection of two-dimensional (2D) images taken from it. To address this fundamental computer vision problem, we propose three algorithms. They are the main contributions of this thesis. First, we solve multiview stereo with the off-axis aperture camera. This system has a very small baseline as images are captured from viewpoints close to each other. The key idea is to change the size or the 3D location of the aperture of the camera so as to extract selected portions of the scene. Our imaging model takes both defocus and stereo information into account and allows to solve shape reconstruction and image restoration in one go. The off-axis aperture camera can be used in a small-scale space where the camera motion is constrained by the surrounding environment, such as in 3D endoscopy. Second, to solve multiview stereo with large baseline, we present a framework that poses the problem of recovering a 3D surface in the scene as a regularized minimal partition problem of a visibility function. The formulation is convex and hence guarantees that the solution converges to the global minimum. Our formulation is robust to view-varying extensive occlusions, clutter and image noise. At any stage during the estimation process the method does not rely on the visual hull, 2D silhouettes, approximate depth maps, or knowing which views are dependent(i.e., overlapping) and which are independent(i.e., non overlapping). Furthermore, the degenerate solution, the null surface, is not included as a global solution in this formulation. One limitation of this algorithm is that its computation complexity grows with the number of views that we combine simultaneously. To address this limitation, we propose a third formulation. In this formulation, the visibility functions are integrated within a narrow band around the estimated surface by setting weights to each point along optical rays. This thesis presents technical descriptions for each algorithm and detailed analyses to show how these algorithms improve existing reconstruction techniques.
Triple‑negative breast cancer cells that survive ionizing radiation exhibit an Axl‑dependent aggressive radioresistant phenotype

This study aimed to investigate the aggressive behavior of triple-negative breast cancer (TNBC) cells that had survived ionizing radiation and explore the potential targets of TNBC combination treatment. Consistent with the previous literature, Axl was highly expressed in TNBC and closely associated with the degree of malignancy based on immunohistochemical staining. Using a gradient irradiation method, the ionizing radiation-resistant mouse TNBC cell line 4T-1/IRR was established. It was found that Axl expression was upregulated in 4T-1/IRR cells. After irradiation by X-ray, the cell viability and colony formation ability of 4T-1/IRR cells were significantly increased when compared with the 4T-1 cells. Combined radiotherapy with Axl inhibition by treatment with R428 and small interfering RNA lentivirus targeting Axl infection significantly reduced cell viability, colony formation ability, DNA double-stranded break repair, and the invasive and migratory ability of 4T-1/IRR cells. In vivo, the small animal radiation research platform was applied to precisely administer radiotherapy of the tumor-bearing mice. R428 treatment combined with 6 Gy X-ray significantly inhibited the growth of 4T-1/IRR cells-derived xenograft tumors in the BALB/c mouse. The results of western blotting showed that the critical molecular mechanism involved in the radioresistance of TNBC cells was the PI3K/Akt/mTOR signaling pathway induced by Axl activation. Thus, it is hypothesized that targeted Axl therapy combined with radiotherapy may have significant potential for the treatment of TNBC.
Fault Diagnosis of UAV Based on Adaptive Siamese Network With Limited Data

The multichannel data of unmanned aerial vehicle (UAV) is coupled and limited. Traditional diagnostic methods relying on precise mathematical models and expert knowledge are challenging to complete the fault diagnosis of UAV with limited data. In addition, noise and distortion in multichannel data can lead to the degradation of data quality and has an influence on the fault diagnosis method based on deep learning. To overcome these deficiencies, an adaptive Siamese network is constructed by two paralleled feature extractors with shared weights. Compared with other networks, the proposed network takes advantage of channel attention mechanism to weigh the features of different channels adaptively. Therefore, the proposed network can adaptively enhance feature weights based on channel importance and reduce the coupling effect of multichannel data. In addition, the utilization rate of sample data can be improved by random and repeated extraction, and the problem of limited data can also be effectively solved. Finally, the proposed Siamese network is applied to fault diagnosis of UAV with limited data, and the effects of the channel number, training samples, and noise on diagnosis are discussed. The experiment results show that the proposed Siamese network has high accuracy, low data dependency, and strong antinoise. The obtained research results provide an effective method for solving the coupled multichannel data with limited data in the fault diagnosis of UAV.
Exploring the Role of Leader Member Exchange on Job Satisfaction and Employee Performance: an Empirical Study in Indonesian MSMEs

Purpose: The purpose of this study was to analyze the relationship between leader member exchange (LMX) and job satisfaction, analyze the relationship between leader member exchange (LMX) and employee performance, analyze the relationship between job satisfaction and significant employee performance. Method: This research uses quantitative research methods. Quantitative research methods are used because in this study, the relationship between the variables to be studied has a causal relationship, to examine certain populations and samples, and aims to test the established hypotheses. The measurement scale in this study uses a Likert scale of 1 to 5 with categories namely strongly disagree, disagree, disagree, agree and strongly agree. With data collection techniques using online questionnaires. The analysis technique uses SEM (Structural Equation Modeling) with SmartPLS 3.0 software tools. Respondents in this study were 678 MSMEs owners in Indonesia who were determined by simple random sampling. Result: The results of this study indicate that the leader member exchange (LMX) has a positive and significant effect on job satisfaction. The leader member exchange (LMX) has a positive and significant impact on the performance of MSMEs employees. Job satisfaction has a positive and significant effect on the performance of MSMEs employees. Based on the results of the research and discussion it is concluded that LMX has an effect on job satisfaction. Furthermore, LMX has a significant positive effect on job satisfaction and job satisfaction has a positive and significant effect on the performance of MSMEs employees. Implication: MSMEs management can provide flexibility in completing tasks responsibly without feeling burdened, superiors must give confidence to subordinates that subordinates are able to complete their tasks so that subordinates will find their work very meaningful. As for maintaining employee performance from the application of empowerment by superiors, they must have trust in subordinates by giving freedom to subordinates to dare to make decisions quickly and precisely. The company builds a stronger corporate commitment so that employees feel more satisfied with their jobs. In maintaining employee performance by providing periodic training in dealing with future job demands
Sequential Model-based Optimization Approach Deep Learning Model for Classification of Multi-class Traffic Sign Images

—Autonomous vehicles are currently gaining popularity in the future mobility ecosystem. The development of autonomous driving systems is still challenging in the research area of image processing and signal processing. Extensive research work was conducted on various traffic sign datasets. It achieved respectable results, but a robust network structure is still needed to develop to improve the traffic sign recognition (TSR) system. In this research work, there is an alternative approach to designing deep learning models, which are implemented in TSR systems. The proposed deep learning model was also tested with different datasets to obtain the generalized model. The proposed model was based on a convolutional neural network (CNN), and Bayesian Optimization optimizes the model’s hyperparameters to find the best hyperparameters grid. After that, the optimized CNN model was used to classify the traffic sign images from three different datasets, including the German traffic sign recognition benchmark (GTSRB), the Belgium traffic sign classification (BTSC) dataset, and the Chinese traffic sign database, achieving the average accuracy scores of 99.57%, 99.15%, and 99.35%, respectively.
Near Infrared Spectroscopy for Prediction of Yeast and Mould Counts in Black Soldier Fly Larvae, Feed and Frass: A Proof of Concept

The use of black soldier fly larvae (BSFL) grown on different organic waste streams as a source of feed ingredient is becoming very popular in several regions across the globe. However, information about the easy-to-use methods to monitor the safety of BSFL is a major step limiting the commercialization of this source of protein. This study investigated the ability of near infrared (NIR) spectroscopy combined with chemometrics to predict yeast and mould counts (YMC) in the feed, larvae, and the residual frass. Partial least squares (PLS) regression was employed to predict the YMC in the feed, frass, and BSFL samples analyzed using NIR spectroscopy. The coefficient of determination in cross validation (R2CV) and the standard error in cross validation (SECV) obtained for the prediction of YMC for feed were (R2cv: 0.98 and SECV: 0.20), frass (R2cv: 0.81 and SECV: 0.90), larvae (R2cv: 0.91 and SECV: 0.27), and the combined set (R2cv: 0.74 and SECV: 0.82). However, the standard error of prediction (SEP) was considered moderate (range from 0.45 to 1.03). This study suggested that NIR spectroscopy could be utilized in commercial BSFL production facilities to monitor YMC in the feed and assist in the selection of suitable processing methods and control systems for either feed or larvae quality control.
Learning-Based Query Scheduling and Resource Allocation for Low-Latency Mobile-Edge Video Analytics

Mobile-edge computing can help enable low-latency and accurate video analytics. However, it is difficult to make efficient utilization of limited edge resources because of the diverse requirements of video queries. In this article, we investigate edge coordination for resource-efficient video query processing, in order to accommodate real-time queries on end cameras, edge nodes, or the cloud, with accuracy guarantee. This problem is challenging because: 1) video queries are with unpredictable arrivals and different resource demands; 2) the decision space of both query scheduling and resource allocation varies over time; and 3) it is critical to maintain long-term accurate analytics for all arrived queries. This problem boils down to making scheduling and resource allocation decisions, which is formulated as a mixed-integer nonlinear programming with a long-term accuracy constraint. Observing that both the scheduling and resource allocation of each query have the Markovian property, the Markov decision process and Lyapunov optimization are adopted to decompose the problem into sequential subproblems. An adaptive reinforcement learning-based approach relying on edge coordination is proposed. Extensive experimental results show that our proposal outperforms other benchmarks on latency and accuracy at a higher level of resource utilization efficiency in real-world data sets.
Physics-based and Data-driven Production Forecasting in Eagle Ford Shale

We develop and successfully verify a reliable method that matches the fieldwide oil and gas production from all horizontal hydrofractured wells in the Eagle Ford Shale and calculate the play-wide Estimated Ultimate Recovery (EUR). Unlike purely empirical industry-standard forecasting methods, our approach relies on the physics of hydrocarbon flow in a hydrofractured shale geometry and captures the probabilistic uncertainty of shale geology and well productivity. For this study, the Eagle Ford play is divided into 24 spatiotemporal well cohorts based on shale geology, fluid composition, and completion date. For each well cohort, we fit the distribution of annual production via Generalized Extreme Value statistics. Expected values are then used to construct historical well prototypes. Next, we extrapolate these well prototypes for up to two more decades, using a physical scaling method that accounts for variations in fluid composition across the Eagle Ford Shale. The resulting well prototypes provide robust history matches and predictions of total field production. Finally, to estimate the play-wide EUR, we calculate the well infill potentials for each subregion of
MHCF-CECSO: A Novel High-Performance Clustering Framework for Industrial IoT

With the rapid development of the Industrial Internet of Things (IIoT), the industrial wireless sensor network (IWSN) with strong information perception capability and strict service quality requirements has been derived. The robust operation of the network plays a crucial role in the collection and transmission of important industrial information. Therefore, it is imperative to solve the problems of short network life and low Quality of Service (QoS). Considering these problems, this article comprehensively considers the energy consumption, remaining energy, packet loss rate, and delay, and designs a new multiobjective clustering model to accurately and reasonably elect cluster heads. Based on the advantages of the model, a novel multiobjective high-performance clustering framework (namely, MHCF-CECSO) is proposed to improve the overall performance of IWSN. Specifically, in MHCF-CECSO, a novel chaotic multilevel elite clone snake optimization method is designed to enhance the optimal clustering mechanism. To further improve the clustering efficiency of MHCF-CECSO, chaos optimization is designed to perform an initial search for the snake group to enhance the diversity of the snake group and the algorithm’s ability to escape from local optimum. In addition, a new multilevel elite cloning strategy is designed, which effectively improves the convergence speed of MHCF-CECSO. Comparing experiments with three state-of-the-art clustering schemes are conducted in four different scenarios, and the results show that the proposed MHCF-CECSO outperforms the other three comparison schemes in terms of network lifetime, energy consumption control, and reliability.
Improving Structured Grid-Based Sparse Matrix-Vector Multiplication and Gauss–Seidel Iteration on GPDSP

Structured grid-based sparse matrix-vector multiplication and Gauss–Seidel iterations are very important kernel functions in scientific and engineering computations, both of which are memory intensive and bandwidth-limited. GPDSP is a general purpose digital signal processor, which is a very significant embedded processor that has been introduced into high-performance computing. In this paper, we designed various optimization methods, which included a blocking method to improve data locality and increase memory access efficiency, a multicolor reordering method to develop Gauss–Seidel fine-grained parallelism, a data partitioning method designed for GPDSP memory structures, and a double buffering method to overlap computation and access memory on structured grid-based SpMV and Gauss–Seidel iterations for GPDSP. At last, we combined the above optimization methods to design a multicore vectorization algorithm. We tested the matrices generated with structured grids of different sizes on the GPDSP platform and obtained speedups of up to 41× and 47× compared to the unoptimized SpMV and Gauss–Seidel iterations, with maximum bandwidth efficiencies of 72% and 81%, respectively. The experiment results show that our algorithms could fully utilize the external memory bandwidth. We also implemented the commonly used mixed precision algorithm on the GPDSP and obtained speedups of 1.60× and 1.45× for the SpMV and Gauss–Seidel iterations, respectively.
Integrated Transcriptome Analysis Identified Key Expansin Genes Associated with Wheat Cell Wall, Grain Weight and Yield

This research elucidates the dynamic expression of expansin genes during the wheat grain (Triticum aestivum L.) development process using comprehensive meta-analysis and experimental validation. We leveraged RNA-seq data from multiple public databases, applying stringent criteria for selection, and identified 60,852 differentially expressed genes across developmental stages. From this pool, 28,558 DEGs were found to exhibit significant temporal regulation in at least two different datasets and were enriched for processes integral to grain development such as carbohydrate metabolism and cell wall organization. Notably, 30% of the 241 known expansin genes showed differential expression during grain growth. Hierarchical clustering and expression level analysis revealed temporal regulation and distinct contributions of expansin subfamilies during the early stages of grain development. Further analysis using co-expression networks underscored the significance of expansin genes, revealing their substantial co-expression with genes involved in cell wall modification. Finally, qPCR validation and grain morphological analysis under field conditions indicated a significant negative correlation between the expression of select expansin genes, and grain size and weight. This study illuminates the potential role of expansin genes in wheat grain development and provides new avenues for targeted genetic improvements in wheat.