Miracle-dz/UniFAR-FTUs huggingface dataset:
Pore-scale modelling of multiphase reactive flow: application to mineral dissolution with production of $\text{CO}_{2}$

A micro-continuum approach is proposed to simulate the dissolution of solid minerals at the pore scale in the presence of multiple fluid phases. The approach employs an extended Darcy–Brinkman–Stokes formulation that accounts for the interfacial tension between the two immiscible fluid phases and the moving contact line at the mineral surface. The simulation framework is validated using an experimental microfluidic device that provides time-lapse images of the dissolution dynamics. The set-up involves a single-calcite crystal and the subsequent generation of $\text{CO}_{2}$ bubbles in the domain. The dissolution of the calcite crystal and the production of gas during the acidizing process are analysed. We then show that the production of $\text{CO}_{2}$ bubbles during the injection of acid in a carbonate formation may limit the overall dissolution rate and prevent the emergence of wormholes.
Simulated annealing heuristic for the general share-a-ride problem

ABSTRACT This research introduces an extension of the share-a-ride problem (SARP), called the general share-a-ride problem (G-SARP). Similarly to SARP, a taxi in G-SARP can service passenger and package requests at the same time. However, G-SARP allows the taxi to transport more than one passenger at the same time, which is more beneficial in practical situations. In addition, G-SARP has no restrictions on the maximum riding time of a passenger, and the number of parcel requests that can be inserted between the pick-up and drop-off points of a passenger is limited only by vehicle capacity. A simulated annealing (SA) algorithm is proposed to solve G-SARP. The proposed SA algorithm is compared with basic SA and tabu search (TS) algorithms. The results show that the proposed SA algorithm outperforms basic SA and TS algorithms. Moreover, further analysis shows that G-SARP solutions are better than those of SARP in most cases.
Which Bladder Filling Method for Pelvic Ultrasound is best? A Comparison Study of Three Standard Non-Foley-Catheter-Based Bladder Filling Methods (Oral, Intravenous, Oral/IV) in Preparation for Pelvic (Transabdominal) Ultrasound

Copyright: Servin A, et al. © (2023). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. James Espinosa MD Department of Emergency Medicine, Jefferson Health NJ, 18 East Laurel Road, Stratford, NJ 08084 E-mail: Jim010@aol.com ABSTRACT
Superpixels based Manifold Structured Sparse RPCA for Moving Object Detection

Moving Object Detection (MOD) is a fundamental step in various computer vision and video surveillance systems. Methods based on Robust Principal Component Analysis (RPCA) have often been used for MOD. If the low-rank and sparse matrices are relatively coherent, e.g., if there are similarities between the moving objects and the background regions, and/or when the background is more complicated e.g., dynamic scenes, camera jitter, and lighting conditions, the accuracy of these methods deteriorates. It is because these methods assume that the elements in the sparse component are mutually independent, and thus ignore the spatiotemporal structure of the sparse component. To handle this problem, we propose spatiotemporal structured sparse RPCA algorithm for moving object detection. For this purpose, we incorporate two different manifold regularizations on the sparse component based on the local and global invariance assumption. A spatial and a temporal graph Laplacian regularization is encoded in the form of spectral graph structure. Both graphs are constructed using multiple features extracted from superpixels computed over the input data matrix. We propose a novel objective function to disentangle moving objects in the presence of complicated backgrounds. We evaluate our algorithm on challenging videos taken from six different datasets, including dynamic backgrounds, lighting condition, and camera jitter sequences. Our experiments have demonstrated excellent results compared to the current methods.
A Visual Decision-Guided Analytics Tool for Finding the Viable Shortest Path over Geospatiotemporal Data

— We propose a Visual Decision-Guided Analytics tool that combines quantitative analysis and qualitative methodologies to determine the viable shortest path over geospatiotemporal data for rescue and recovery missions. Specifically, we first extend the object-oriented spatial-temporal data model as a multidimensional OLAP cube that enables military operators to analyze unified geo-data objects from multiple dimensions, such as time, space, and location, to help them make a better decision on routes. Second, we enhance the capability of the PostGIS query which allows the operators to (1) simulate the occurrence of events, (2) visualize and report geo-data objects, and (3) solve decision optimization problems based upon their events of interest. Third, we integrate optimization programming into geo-data visualization to determine the viable shortest path for rescue and recovery missions. This integration enables military units to make a visual decision on routes based upon the time-distance optimality. Finally, we develop a visual display that enables the operators to analyze other crucial factors, e.g., vehicle types, weather severity, soldiers’ specialties, etc., which are required to be interpreted by human perception, cognition, and knowledge to select the best path among all the viable routes for rescue and recovery missions.
New Ways of Supporting Arts Entrepreneurship: A Case Study of Maniobra

ABSTRACT:This research analyses Maniobra, a cultural employment initiative that offers guaranteed income and additional support for three years to selected artists in Puerto Rico. Maniobra's program design phase and preliminary first year results are analyzed through the author's personal experience with the project as an arts entrepreneur and scholar. This reflection suggests how to address barriers facing artist initiatives with a lens of diversity, equity, and inclusion. A conceptual framework analyzes the preliminary impacts through a combination of artistic, personal, and economic well-being. The article also highlights the importance of institutional trust in artists and artistic work, expanding traditional philanthropy models.
FRACTIONAL ORDER DERIVATIVE AND RELATIONSHIP BETWEEN DERIVATIVE AND COMPLEX FUNCTIONS

The concept of fractional order derivative can be found in widerange of many different areas. Due to this case, there are many methods aboutfractional order derivative (FOD). The most of them are Euler, RiemannLiouville, Grunwald-Letnikov, Oldham-Spanier, Miller-Ross, Kolwankar-Gangal,and Caputo methods which are fractional order derivatives as mentioned in theliterature. However, they are not sound and complete for constant and identityfunctions. This case means that they are curve fitting or curve approximationmethods.FOD concept was defined in [10]. The deficiencies of Euler, RiemannLiouville, Grunwald-Letnikov, Oldham-Spanier, Miller-Ross, Kolwankar-Gangal,and Caputo methods were illustrated in this study. In this study, the conceptof FOD and its relationships with complex functions was handled
Limon: A Scalable and Stable Key-Value Engine for Fast NVMe Devices

Modern fast NVMe devices with high throughput and ultra-low latency have brought new opportunities for persistent key-value (KV) engines. In this paper, we propose Limon, a persistent KV engine to exploit the performance potentials of fast NVMe devices. Limon targets three practical design aspects that existing KV engines fail to consider simultaneously: functionality, scalability, and stability. Limon carefully redesigns the index structure, on-disk KV record layout, and I/O processing of a persistent KV engine for these aspects. Specifically, Limon (i) proposes a semi-shared global index to improve scalability and range queries. (ii) employs a fast slab-based record layout with light-weight defragmentation to enable stable performance, and (iii) uses efficient asynchronous per-core I/O processing with two optimizations: DMA-backed buffer pool and page deduplication, to further improve scalability. Our evaluations with the YCSB benchmark and one production workload show that Limon outperforms state-of-the-art persistent key-value engines (i.e., SpanDB, KVell, and uDepot) by up to 1.2x to 3.8x and has the best scalability. Moreover, Limon has stable and predictable performance due to its novel record layout strategy.
Rotational Rectification Network for Robust Pedestrian Detection

Pedestrian detection performance has steadily improved on a variety of benchmark datasets such as Caltech, KITTI, INRIA and ETH, since the resurgence of deep neural networks. Across a majority of pedestrian datasets, it is assumed that pedestrians will be standing upright with respect to the image coordinate system. This assumption however, is not always valid for many vision-equipped mobile platforms such as mobile phones, UAVs or construction vehicles on rugged terrain. In these situations, the motion of the camera can cause images of pedestrians to be captured at extreme angles and this can lead to very poor pedestrian detection performance when using standard pedestrian detectors. To address this issue, we propose a Rotational Rectification Network (R2N) that can be inserted into any CNN-based pedestrian (or object) detector to maintain performance despite significant rotational variance. The rotational rectification network uses a rotation estimation module that passes rotational information to a spatial transformer network [12] to undistort image features and passes them downstream. To enable robust rotation estimation, we propose a Global Polar Pooling (GP-Pooling) operator to capture rotational shifts in convolutional features. Through our experiments, we show how our rotational rectification network can be used to enhance the performance of a state-of-the-art pedestrian detector under heavy image rotation.
Prediction Based Trajectory Planning for Safe Interactions Between Autonomous Vehicles and Moving Pedestrians in Shared Spaces

It is an essential issue for autonomous vehicles to keep driving safety while sharing spaces with other road users, especially moving pedestrians. An ideal trajectory planning of autonomous vehicles should be collision-free and feasible for the vehicles to execute. Previous efforts on trajectory planning mainly focused on traditional roads, and the research into pedestrian-vehicle interaction in shared spaces were still insufficient. This study proposed a prediction-planning collaboration method for autonomous vehicles to avoid collisions with moving pedestrians by predicting their motions, and generate feasible trajectories based on Frenet coordinate in shared spaces. Pre-crash scenarios were designed based on reported crashes, and simulations were performed to evaluate the collision avoidance performances of the proposed method. Meanwhile, validations with real world dataset were conducted to verify the practicability of the proposed framework in real driving environment. The results indicated that the proposed prediction-planning collaboration method could effectively predict the motions of moving pedestrians and ensure the safe interactions between autonomous vehicles and pedestrians in shared spaces.
Candidate gene polymorphism effects on the fatty acid composition of Wagyu-Cross beef

Objective. To assess the fatty acids composition (FA) in Wagyu beef and its crosses with Angus, Beefmaster, Brangus and Hereford, and to analyze its relationship with genetic markers related to lipid metabolism. Materials and methods . 111 Longissimus dorsi samples were collected and grouped by genetic group. FA were extracted and quantified in a gas-liquid chromatography and DNA markers theoretically associated to FA were typed. Hardy-Weinberg equilibrium and linkage disequilibrium were examined, the effect of crosses and the effect of genotypes were estimated. Results. The crosses did not show substantial differences in FA composition. Nine SNPs showed association with FA composition, and a significant effect was found in the SLC2A4 marker ss62538460 which influenced SFA, MUFA and MUFA/SFA; PLTP ss77832104 and IGF2R ss77831885 markers, influenced C16:0, MYOZ1 ss77832104 on C17:1 and PPARGC1A c.1892+19 on C18:2. In addition, previously described effects of MEF2C ss38329156 and SCD c.878 were supported. Conclusions. These results are first evidence on FA deposition in Wagyu cattle and their crosses, and proposes some loci in candidate genes with the possibility of implementation in assisted selection strategies.
Computationally Efficient Implementation of SLIM for Parameter Estimation in Few-Bit PMCW MIMO Radar Systems

Parameter estimation is an important task for few-bit phase-modulated continuous-wave (PMCW) multiple-input multiple-output (MIMO) radar systems. To estimate the targets’ parameters, sparse learning via iterative minimization (SLIM) has been adopted since it can achieve a finer resolution than conventional matched filtering. However, directly applying SLIM to estimate the targets’ time delays, Doppler shifts, directions, and amplitudes faces implementational challenges. The computational cost is high and a large memory space is required to store a dictionary matrix. To overcome this issue, in this work, we propose a computationally efficient scheme for parameter estimation using SLIM. In the proposed approach, initial estimates of the targets’ time delays and directions are obtained by using a small portion of the observations with SLIM. Then, the Doppler shifts are estimated and the estimates of time delays and directions are redetermined by SLIM with the entire observations. A debiasing process is also proposed to refine the estimates of the targets’ amplitudes. Simulation results and complexity analysis show that the proposed scheme can make SLIM practical to handle large scale parameter estimation problems.
Black hole microstates and holography in the D1D5 CFT

In this thesis we exploit the setup of AdS3/CFT2 holography, and in particular the D1D5 two-dimensional CFT, to describe states dual to geometries relevant for the “fuzzball” proposal for the description of six-dimensional black hole microstates. Precise holographic dualities between CFT and bulk geometric objects are established and checked, both for 2 and 3-charge states. In particular, VEVs of CFT operators of small conformal dimension are checked to encode deviations from AdS3 geometry near the spacetime boundary. 4-point functions of the “heavy-heavy-light-light” type are also considered and matching is found between CFT and bulk computations via the usual AdS/CFT prescription, with the heavy states being dual to (simple) microstate geometries. In this context, the issue of the presence of spurious singularities at leading order in the large N limit is assessed and cancellations are found even without considering sub-leading corrections, at the cost of considering the full detail of the D1D5 CFT (i.e. including the Virasoro blocks of operators of small dimension charged under the internal SU(2)L × SU(2)R R-symmetry group). Finally, more complicated 4-point functions, involving operators in the twisted sector of the CFT, are computed and the results are checked against known results in the literature with the aim of verifying the robustness of the (new) techniques used. Supersymmetric Ward identities are also derived, and checked for some cases, between correlators written in terms of bosons and in terms of fermions.
Contextualizing the current state of research on the use of machine learning for student performance prediction: A systematic literature review

Today, educational institutions produce large amounts of data with the deployment of learning management systems. These large datasets provide an untapped potential to support and enhance decision‐making and operations. In recent times, machine learning (ML) has been applied to develop models utilizing this “big” data to assist in decision‐making. This study presents a systematic literature review into the application of ML to predict student performance. A total of 162 research articles from January 2010 to October 2022 were critically reviewed and analyzed by applying Kitchenham's systematic literature review approach. Our analysis categorized the literature predicting students' academic performance into two categories: (i) predicting student performance in assessments, courses or programs, and identifying students at‐risk of failing their course/program (129 studies); and (ii) predicting student dropout or retention in a course or program (33 studies). Classification is the most commonly used approach for predicting student performance (138 studies), followed by regression (25 studies) and clustering (9 studies). Supervised learning methods are used more often than semi‐supervised learning. Five most popular ML algorithms include the Decision Tree, Random Forest, Naïve Bayes, Artificial Neural Network, and Support Vector Machine. Historical records of students' grades and class performance, academic related data from learning management systems, and students' demographics are the most common features used for predicting students' performance. The most common methods used for feature selection are Information Gain‐based selection algorithms, Correlation‐based feature selection, and Gain Ratio. The general platforms/tools/libraries used in the studies include WEKA, Python, R, Rapid Miner, and MATLAB. We also investigated possible actions considered in the literature to help at‐risk students. We only found very few studies that deployed remedial actions and evaluated their impact on students' performance. In conclusion, ML has shown great potential in the prediction of student performance, but also has many areas of further research.
Ultrasonographic evaluation of morphometric measurements of plantar metatarsal tendons and ligaments in Colombian Creole horses

Objective. Determine the morphometric values of the plantar metatarsal tendons and ligaments in clinically healthy animals. Materials and methods. Thirty animals were used throughout the study, the plantar metatarsal tendons and ligaments were evaluated starting from the plantaromedial aspect of the proximal region to the insertion of the suspensory branches in the sesamoids bones. The variables to be studied in each structure were cross-sectional area (cm2), lateral medial width (ALM) (cm) and dorsal palmar thickness (EDP) (cm). Results. It was found that the structure with the largest area in the proximal regions was the suspensory ligament (0.858 cm 2 ) followed by the lateral digital flexor (0.759 cm 2 ), in regions 1B and 2A the largest structure remained the suspensory ligament and in the region 2B, the deep digital flexor tendon was the largest structure (0.804 cm 2 ). Conclusions . The behavior of the variables in the Colombian Creole horse is similar to that reported in the literature and finally the first reference values of morphometric measurements of the tendons and ligaments of the plantar metatarsal in this breed are presented.
Identifying invertebrate indicators for streamflow duration assessments in forested headwater streams

Streamflow-duration assessment methods (SDAMs) are rapid, indicator-based tools for classifying streamflow duration (e.g., intermittent vs perennial flow) at the reach scale. Indicators are easily assessed stream properties used as surrogates of flow duration, which is too resource intensive to measure directly for many reaches. Invertebrates are commonly used as SDAM indicators because many are not highly mobile, and different species have life stages that require flow for different durations and times of the year. The objectives of this study were to 1) identify invertebrate taxa that can be used as SDAM indicators to distinguish between stream reaches having intermittent and perennial flow, 2) to compare indicator strength across different taxonomic and numeric resolutions, and 3) to assess the relative importance of season and habitat type on the ability of invertebrates to predict streamflow-duration class. We used 2 methods, random forest models and indicator species analysis, to analyze aquatic and terrestrial invertebrate data (presence/absence, density, and biomass) at the family and genus levels from 370 samples collected from both erosional and depositional habitats during both wet and dry seasons. In total, 36 intermittent and 53 perennial reaches were sampled along 31 forested headwater streams in 4 level II ecoregions across the United States. Random forest models for family- and genus-level datasets had stream classification accuracy ranging from 88.9 to 93.2%, with slightly higher accuracy for density than for presence/absence and biomass datasets. Season (wet/dry) tended to be a stronger predictor of streamflow-duration class than habitat (erosional/depositional). Many taxa at the family (58.8%) and genus level (61.6%) were collected from both intermittent and perennial reaches, and most taxa that were exclusive to 1 streamflow-duration class were rarely collected. However, 23 family-level or higher taxa (20 aquatic and 3 terrestrial) and 44 aquatic genera were identified as potential indicators of streamflow-duration class for forested headwater streams. The utility of the potential indicators varied across level II ecoregions in part because of representation of intermittent and perennial reaches in the dataset but also because of variable ecological responses to drying among species. Aquatic invertebrates have been an important field indicator of perennial reaches in existing SDAMs, but our findings highlight how including aquatic and terrestrial invertebrates as indicators of intermittent reaches can further maximize the data collected for streamflow-duration classifications.
THE EFFECT OF STRATEGIC ORGANIZATIONAL CHALLENGES ON MANAGING HUMAN RESOURCES THE JORDAN SME LOGISTIC COMPANIES

: Organizational development is a normal occurrence in administrative organizations because it is crucial to improving the functioning and efficiency of job performance, the human and organizational side is considered one of the most important elements of a successful renaissance in the life of administration in general and human resources management. This paper aims to investigate the effect of strategic organizational challenges on managing human resources the Jordan SME logistic companies. The survey was conducted focusing in the SME logistics sector, the data were collected from 114 responders which were selected from SME located in Amman, Jordan. Multi regression analyses were used to analyze the collected data, the findings showed a substantial impact of strategic organizational challenges on human resources planning in SME Companies