Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Horse Locomotion Modifications Due to Training with Inertial Measurement Units: A Proof-of-Concept

Detecting fatigue during training sessions would help riders and trainers to optimize their training. It has been shown that fatigue could affect movement patterns. Inertial measurement units (IMUs) are wearable sensors that measure linear accelerations and angular velocities, and can also provide orientation estimates. These sensors offer the possibility of a non-invasive and continuous monitoring of locomotion during training sessions. However, the indicators extracted from IMUs and their ability to show these locomotion changes are not known. The present study aims at defining which kinematic variables and indicators could highlight locomotion changes during a training session expected to be particularly demanding for the horses. Heart rate and lactatemia were measured to attest for the horse’s fatigue following the training session. Indicators derived from acceleration, angular velocities, and orientation estimates obtained from nine IMUs placed on 10 high-level dressage horses were compared before and after a training session using a non-parametric Wilcoxon paired test. These indicators were correlation coefficients (CC) and root mean square deviations (RMSD) comparing gait cycle kinematics measured before and after the training session and also movement smoothness estimates (SPARC, LDLJ). Heart rate and lactatemia measures did not attest to a significant physiological fatigue. However, the statistics show an effect of the training session (p < 0.05) on many CC and RMSD computed on the kinematic variables, indicating a change in the locomotion with the training session as well as on SPARCs indicators (p < 0.05), and revealing here a change in the movement smoothness both in canter and trot. IMUs seem then to be able to track locomotion pattern modifications due to training. Future research should be conducted to be able to fully attribute the modifications of these indicators to fatigue.
Tissue-Based Proteomic Profiling in Patients with Hyperplasia and Endometrial Cancer

Uterine cancers are among the most prevalent gynecological malignancies, and endometrial cancer (EC) is the most common in this group. This study used tissue-based proteomic profiling analysis in patients with endometrial cancer and hyperplasia, and control patients. Conventional 2D gel electrophoresis, followed by a mass spectrometry approach with bioinformatics, including a network pathway analysis pipeline, was used to identify differentially expressed proteins and associated metabolic pathways between the study groups. Thirty-six patients (twelve with endometrial cancer, twelve with hyperplasia, and twelve controls) were enrolled in this study. The mean age of the participants was 46–75 years. Eighty-seven proteins were significantly differentially expressed between the study groups, of which fifty-three were significantly differentially regulated (twenty-eight upregulated and twenty-five downregulated) in the tissue samples of EC patients compared to the control (Ctrl). Furthermore, 26 proteins were significantly dysregulated (8 upregulated and 18 downregulated) in tissue samples of hyperplasia (HY) patients compared to Ctrl. Thirty-two proteins (nineteen upregulated and thirteen downregulated) including desmin, peptidyl prolyl cis-trans isomerase A, and zinc finger protein 844 were downregulated in the EC group compared to the HY group. Additionally, fructose bisphosphate aldolase A, alpha enolase, and keratin type 1 cytoskeletal 10 were upregulated in the EC group compared to those in the HY group. The proteins identified in this study were known to regulate cellular processes (36%), followed by biological regulation (16%). Ingenuity pathway analysis found that proteins that are differentially expressed between EC and HY are linked to AKT, ACTA2, and other signaling pathways. The panels of protein markers identified in this study could be used as potential biomarkers for distinguishing between EC and HY and early diagnosis and progression of EC from hyperplasia and normal patients.
Assessment of Ammonium–N and Nitrate–N Contamination of Shallow Groundwater in a Complex Agricultural Region, Central Western Taiwan

The characteristics of nitrogen contamination of shallow groundwater were evaluated through current status analysis and trend detection of ammonium–N and nitrate–N concentrations under various cropping patterns to assess the effectiveness of rational fertilization in the Choushui River alluvial fan, central Western Taiwan. The influence of cropping patterns on both ammonium–N and nitrate–N contamination associated with redox conditions/dissolved oxygen (DO) in shallow groundwater was also discussed in this study. The analysis revealed that shallow groundwater beneath double rice cropping and rotational cropping regions is still characterized by high ammonium–N concentration despite rational fertilization promotion. However, very few monitoring wells showed an upward trend of ammonium–N/nitrate–N concentrations, indicating that shallow groundwater is not further deteriorated by nitrogen pollution in most parts of the study area. Therefore, the remediation of nitrogen contaminated groundwater will be a long-term process and more effort must be invested. Moreover, the strict redox conditions defined by a single DO threshold value may not account for groundwater nitrogen pollution in the study area. It is difficult to determine the redox conditions and predominant nitrogen pollution patterns of shallow groundwater purely from cropping patterns. Instead, contamination may have resulted from an integrated process governed by several other factors. Tracing the potential sources of nitrogen pollution and establishing a more integral monitoring network should be implemented to formulate a more comprehensive nitrogen pollution control strategy in this area.
The link between transitive reasoning and mathematics achievement in preadolescence: the role of relational processing and deductive reasoning

Abstract The link between logic and mathematics has been recognized by theorists from different fields, but empirical investigations on this link remain scarce, and even less is known about the mechanisms underlying such a link. The current study aimed to address this issue by investigating the relations between transitive reasoning and children’s mathematical competence, and exploring the potential mechanisms involved. A sample of 101 sixth graders were assessed on their transitive reasoning skills, their mathematics achievement, various potential mediators, as well as general cognitive factors (serving as control variables). The results showed that children’s transitive reasoning performance was significantly related to their mathematics achievement beyond the effects of general cognitive factors, and this relation was mediated by fraction knowledge and Relation to Operands understanding. The findings not only confirmed the relation between transitive reasoning and mathematical competence, but also shed light on the mechanisms underlying such a relation.
ABCs of Photon Emission Microscopy

Photon emission microscopy (PEM) has proven to be a powerful tool for fault isolation and has adapted well to ongoing changes in technology and emerging needs. In this tutorial, the authors describe the fundamentals of photon emission, the essential elements of a typical PEM system, and the procedures involved in diagnosing various types of failures. They also classify a wide range of photon-emitting defects and explain how PEM is used for backside analysis of flip-chip packaged devices and for timing diagnostics.
Using Named Entities to Discover Heterogeneous Events on Twitter

Social media sites such as Twitter 1 and Facebook 2 have emerged as powerful means of communication that allow people to exchange information about their daily activities, latest news or real-world events. Aside social interactions among users, social medias are expected to provide added value services in a variety of domains (e.g sentiment analysis, trend analysis and event detection). Detecting events on social medias poses new challenges due to the sparsity and the informal nature of social media posts. One of the main challenges in detecting events in social media is to differentiate event and non event messages. To face this challenge, we propose to take advantage from the knowledge that can be extracted from the Linked Opened Data (e.g. DBpedia) to enrich the short textual messages with contextual information brought by the presence of named entities. We evaluate our approach on two gold-standard datasets and the preliminary results show that exploiting the ontological categories of the named entities has a positive impact on the classification output.
Solving Security Constrained Unit Commitment Problem Using Inductive Learning

Security-Constrained Unit Commitment (SCUC) is a large-scale energy optimization problem solved multiple times a day by independent system operators (ISOs). After receiving offers and bids, ISOs have only few hours to clear the day-ahead electricity market. It requires a lot of computational effort and time to solve a large-scale SCUC problem. However, by exploiting the fact that a UC problem is solved several times a day with only minor changes in the system data, we can improve the computational time by identifying the patterns in the historical data using inductive models. In this research, a 2-stage data-driven based approach is proposed to solve a SCUC problem. The presented algorithm was validated through simulations on IEEE-39 bus system and satisfactory results were obtained. The results were compared with those obtained using CPLEX MIQP solver.
Exploitation experience in partial discharge diagnostics of power transformers

This paper presents exploitation experience in partial discharge (PD) diagnostics of power transformers, with particular consideration of practical aspects and potential limitation of the contemporary measuring techniques. The objective of this study is to indicate typical problems related to PD diagnostics of power transformers and to propose practical solutions based on simultaneous application of several PD measuring methods. Three case studies based on real life scenarios are used to illustrate practical aspects and potential limitation of the contemporary PD diagnostics. Each scenario addresses a different PD issue, and different measurement results achieved using electrical, UHF and acoustic emission (AE) methods. Presented results extend the state-of-art through the detailed analysis of presented exploitation case studies. Thus, it may be potentially used for optimization of the maintenance process and improvements of PD diagnostics regarding power transformers.
Controllable shape synthesis for digital fabrication

The main goal of this thesis is to propose methods to synthesize shapes in a controllable manner, with the purpose of being fabricated. As 3D printers grow more accessible than ever, modeling software must now take into account fabrication constraints posed by additive manufacturing technologies. Consequently, efficient algorithms need to be devised to model the complex shapes that can be created through 3D printing. We develop algorithms for by-example shape synthesis that consider the physical behavior of the structure to fabricate. All the contributions of this thesis focus on the problem of generating complex shapes that follow geometric constraints and structural objectives. In a first time, we focus on dealing with fabrication constraints, and propose a method for synthesizing efficient support structures that are well-suited for filament printers. In a second time, we take into account appearance control, and develop new by-example synthesis methods that mixes in a meaningful manner criteria on the appearance of the synthesized shapes, and constraints on their mechanical behavior. Finally, we present a highly scalable method to control the elastic properties of printed structures. We draw inspiration from procedural texture synthesis methods, and propose an efficient algorithm to synthesize printable microstructures with controlled elastic properties
Millimeter-Wave Radar Measurement and Ray-Tracing Simulation for Urban Street Environment

Autonomous driving technology greatly contributes to the evolution of intelligent transportation systems (ITSs). As an important sensor, the millimeter-wave (mmWave) radar plays an irreplaceable role in the overall ITSs. In this paper, we use a mmWave automotive radar system to perform measurement in an urban street environment. The radar cross section of key targets is estimated from the measurements, and it is used to calibrate the electromagnetic parameters for the ray-tracing (RT) simulation. The calibrated simulation results are compared with simulation results in distance, angle, and velocity to verify the accuracy and reliability of the RT simulator. The presented method, process, and analysis in this work can be used for the understanding of mmWave radar channel and the design of autonomous driving technology.
Timely-throughput Optimal Scheduling for Wireless Flows with Deep Reinforcement Learning

This paper addresses the problem of scheduling real-time wireless flows under dynamic network conditions and general traffic patterns. The objective is to maximize the fraction of packets of each flow to be delivered within their deadlines, referred to as timely-throughput. The scheduling problem under restrictive frame-based traffic models or greedy maximal scheduling schemes like LDF has been extensively studied so far, but scheduling algorithms to provide deadline guarantees on packet delivery for general traffic under dynamic network conditions are very limited. We propose two scheduling algorithms using deep reinforcement learning approach to optimize timely-throughput for general traffic in dynamic wireless networks: RL-Centralized scheduling algorithm and RL-Decentralized scheduling algo-rithm. Specifically, we formulate the centralized scheduling problem as a Markov Decision Process (MDP) and a multi-environments double deep Q-network (ME-DDQN) structure is proposed to adapt to the dynamic network conditions. The decentralized scheduling problem is formulated as a Partially Observable Markov Decision Process (POMDP) and an expert-apprentice centralized training and decentralized execution (EA-CTDE) structure is designed to accelerate the training speed and achieve the optimal timely-throughput. The extensive results show that the proposed scheduling algorithms converge fast and adapt well to network dynamics with superior performance compared to baseline policies. Finally, experimental tests confirm simulation results and also show that the proposed algorithms are feasible in practice on resource limited platforms.
Analysis on the accuracy of the Embedded Element Patterns for one station of SKA-Low by using a Radius of Influence approach

This paper provides a detailed analysis on the use of a radius of influence approach to reduce the computational time for analyzing the EM performance of the radio telescope SKA-Low. For one station of SKA-Low, composed by 256 log-periodic antennas, we evaluate the accuracy in the individual antenna patterns and the duration of the EM simulations applying different radii of influence with respect to the full station case.
The Efficacy of S-Adenosyl Methionine and Probiotic Supplementation on Depression: A Synergistic Approach

Depression is a common and serious health issue affecting around 280 million people around the world. Suicidal ideation more frequently occurs in people with moderate to severe depression. Psychotherapy and pharmacological drugs are the mainstay of available treatment options for depressive disorders. However, pharmacological options do not offer complete cure, especially in moderate to severe depression, and are often seen with a range of adverse events. S-adenosyl methionine (SAMe) supplementation has been widely studied, and an impressive collection of literature published over the last few decades suggests its antidepressant efficacy. Probiotics have gained significant attention due to their wide array of clinical uses, and multiple studies have explored the link between probiotic species and mood disorders. Gut dysbiosis is one of the risk factors in depression by inducing systemic inflammation accompanied by an imbalance in neurotransmitter production. Thus, concomitant administration of probiotics may be an effective treatment strategy in patients with depressed mood, particularly in resistant cases, as these can aid in dysbiosis, possibly resulting in the attenuation of systemic inflammatory processes and the improvement of the therapeutic efficacy of SAMe. The current review highlights the therapeutic roles of SAMe and probiotics in depression, their mechanistic targets, and their possible synergistic effects and may help in the development of food supplements consisting of a combination of SAMe and probiotics with new dosage forms that may improve their bioavailability.
Enter and Discuss Orders and Prescriptions (EPA 4): A Curriculum for Fourth-Year Medical Students

Introduction Order entry, entrustable professional activity (EPA) 4, is one of several EPAs that residency program directors identify as a weakness for PGY 1 residents. A multispecialty survey of program directors indicated that only 69% of interns could be trusted to enter and discuss orders and prescriptions without supervision. To address this gap, we developed a formative workshop for fourth-year medical students. Methods Prior to the start of their subinternships, 366 fourth-year medical students engaged in an order entry workshop. Students performed chart reviews on electronic standardized patients within an educational electronic health record (EHR), placed admission orders, customized order sets, responded to safety alerts, utilized decision support tools, and incorporated high-value care considerations. Students used expert-validated rubrics to assess the quality of their admission orders and participated in a facilitated group discussion on key learning points. Finally, students participated in order entry, with all orders requiring cosignature by a supervising physician, during their clinical rotations. Students reported their confidence with order entry before and after the workshop and after the clinical rotation. Results One hundred seventeen students completed the pre- and postworkshop surveys, and 99 went on to complete the postcourse evaluation. Students showed a statistically significant increase in their confidence level following the workshop. Discussion Order entry is a critical, complex skill that requires deliberate instruction. This curriculum, which leverages the features of an educational EHR, can facilitate instruction, practice, and confidence gains regarding order entry prior to further application of these skills in the clinical environment.
Energy Efficiency Optimization for Radar-Communication Coexistence with Statistical CSIT

In this paper, we investigate maximizing the energy efficiency (EE) of a single-cell massive multiple-input multiple-output (MIMO) communication system coexisting with a surveillance radar with statistical channel state in-formation at the transmitter (CSIT). EE is maximized under the constraint of the received signal-to-disturbance ratio (SDR) on the radar side. Since the problem involves coupled variables, alternating optimization is adopted to decompose the original problem into communication and radar optimization. The communication optimization problem can be simplified via the proposed power allocation algorithms. For the radar optimization, there exists a closed-form solution for the radar transmit power. Simulation results verify the performance of the proposed algorithm.
Impact of Antenna Element Directivity and Reflection-Interference on Line-of-Sight Multiple Input Multiple Output Terahertz Systems

Terahertz (THz) communications are envisioned as the future of wireless communications. In order to combat the high path loss experienced by THz signals, beamforming using multiple-input multiple-output (MIMO) systems has been suggested. In this paper, two practical challenges for THz line-of-sight MIMO systems are investigated for the first time: the impact of antenna element directivity and self-induced inter-symbol interference on performance. Additionally, experimental results are presented to verify the findings.
Learning Approach for Energy Consumption Forecasting in Residential Microgrid

Residential energy consumption plays an important role in the social and economic development of the country. Highly accurate forecasting can aid in decision making and forecast for future residential electricity demand for smooth management of power system operations. However, residential load characteristics are influenced by human behavior, seasonal variation, and other social factors. Thus the share of uncertainty in the load will be at a significant level. Therefore, obtaining highly accurate load forecasts is a challenging task for the power system operator. In this research article, the authors propose a recurrent neural network based LSTM, GRU, Bi-LSTM, and Bi-GRU based learning approach for short-term residential energy consumption forecasting. Simulation results on a real 30 minute time interval energy consumption data set for 9 months of a residential prosumer microgrid located in central-Norway. The numerical results are show that the Bi-GRU model is achieving higher performance than others on the given load data set.
Modulation of ethoxyresorufin-o-deethylase, benzoxyresorufin-o-dealkylase and pentoxyresorufin-o-dealkylase expression by African walnut seeds lipid extract in 3-methylcholanthrene induced breast cancer in Wistar rats

Breast cancer is prevalent among women worldwide. African walnut ( Tetracarpidium conophorum ) seeds extract have been shown to have medicinal properties. This study is to determine the effects of feeding T. conophorum seeds lipid extract on 3-methylcholanthrene (MC) induced breast carcinogenesis and the expression of ethoxyresorufin- O -deethylase (CYP1A1), benzoxyresorufin- O -dealkylase (CYP1B1) and pentoxyresorufin- O -dealkylase (CYP2B1). The lipid was extracted using Soxhlet apparatus with n -hexane. Forty-five female Wistar rats of 21 days old were used, which were randomly divided into three major groups of 15 animals each. Groups A and B were fed for 12 weeks with diet containing T. conophorum seeds lipid extract (10%), and group B animals were administered MC (200 mg/kg body weight) intraperitoneally after 4 weeks of feeding. Group C animals were fed for 12 weeks with diet containing no T. conophorum seeds lipid extract and administered MC (200 mg/kg body weight) intraperitoneally after 4 weeks of feeding. Results indicated that CYP1A1, CYP1B1 and CYP2B1 were significantly ( p < 0.05) reduced in Group B animals liver cells compared to group C with higher expression. Prolonged latency period, reduce tumor weight and size characterized group B animals compared to group C. Histopathology results showed normal morphology of the liver hepatocytes of animals in group B, while necrosis and steatosis were seen in group C. This study therefore showed that T. conophorum seeds lipid extract contains bioactive components that may oppose breast carcinogenesis induced by MC.
Multifunctional and Deployable Origami Antennas

Multifunctional, deployable, and packable antennas are very important for many applications, including unmanned aerial vehicles, satellite communications (e.g., CubeSats) and general airborne and spaceborne communication systems. Notably, such antennas provide new capabilities for the aforementioned applications. In this work, we present emerging research on foldable and physically reconfigurable antennas, which can morph their shape to adapt and reconfigure their EM performance (e.g., frequency of operation, bandwidth, polarization, beamwidth, etc.).