Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Robotic System for Visual Inspection in Electrical Substations and Cable Galleries

This article presents a research project focused on improving the efficiency and safety of maintenance processes in hazardous and difficult-to-access environments, such as open-air electrical substations and cable galleries. A mobile robot integrates perception technologies to gather and process an inner representation of the environment allowing it to recognize known inspection targets. The developed prototype incorporates teleoperation and autonomous mission capabilities, as well as object recovery and incremental learning techniques to enhance detection accuracy. The article also details creation of both real and synthetic datasets for training and testing, as well as the evaluation of the robot’s performance in object detection and tracking process. Results suggest that the developed technology can improve maintenance processes, reduce downtime and costs associated with equipment failures, and minimize occupational hazards.
MIMO-MPC Based Coordinated Load Frequency Control Considering the SOC of Battery and EV

With the rising integration of renewable energy sources, modern microgrids face the issue of decreasing inertia, rendering them more vulnerable against active power imbalances. Although numerous studies have aimed to address this issue by combining various power sources such as turbine generator, battery and electric vehicle (EV) fleet, the development of a coordinated controller remains a challenging task. In this regard, this study proposes a multi-input multi-output model predictive control-based approach to satisfy the load frequency control requirements, fully exploit the flexibility of EV fleet, and meet the state-of-charge demands of both the battery and EV fleet. The effectiveness of the proposed controller has been demonstrated through simulation results.
Scaling of Erosion Rate in Subsonic Jet Experiments and Apollo Lunar Module Landings

Small scale jet-induced erosion experiments are useful for identifying the scaling of erosion with respect to the various physical parameters (gravity, grain size, gas velocity, gas density, grain density, etc.), and because they provide a data set for benchmarking numerical flow codes. We have performed experiments varying the physical parameters listed above (e.g., gravity was varied in reduced gravity aircraft flights). In all these experiments, a subsonic jet of gas impinges vertically on a bed of sand or lunar soil simulant forming a localized scour hole beneath the jet. Videography captures the erosion and scour hole formation processes, and analysis of these videos post-test identifies the scaling of these processes. This has produced important new insights into the physics of erosion. Based on these insights, we have developed an erosion rate model that can be applied to generalized situations, such as the erosion of soil beneath a horizontal gas flow on a planetary surface. This is important to lunar exploration because the rate of erosion beneath the rocket exhaust plume of a landing spacecraft will determine the amount of sand-blasting damage that can be inflicted upon surrounding hardware. Although the rocket exhaust plume at the exit of the nozzle is supersonic, the boundary layer on the lunar surface where erosion occurs is subsonic. The model has been benchmarked through comparison with the Apollo landing videos, which show the blowing lunar soil, and computational fluid dynamics simulations of those landings.
Geometry from D-branes in Nonrelativistic String Theory

Nonrelativistic (NR) string theory was discovered as a framework that underlies and unifies the various noncommutative open string (NCOS) theories, which were originally envisioned as surprising exceptions to the maxim that all string theories are gravitational in nature. In that view, the fact that NCOS has a gravitational dual was believed to be directly analogous to the AdS/CFT correspondence. When NCOS theories were understood to be simply the particular classes of states of the underlying NR theory that include longitudinal D-branes, it was suggested that the duality between NCOS and the corresponding gravitational theory is not an instance of gauge/gravity-type duality, but of open-string/closed-string duality between D-branes and black branes. The present paper provides direct evidence in support of this perspective, by starting from a stack of D-branes in NR string theory and deriving the long-distance profile of the curved geometry in the corresponding black brane.
Coherent pair injection as a route towards the enhancement of supersolid order in many-body bosonic models

Over the last couple of decades, quantum simulators have been probing quantum many-body physics with unprecedented levels of control. So far, the main focus has been on the access to novel observables and dynamical conditions related to condensed-matter models. However, the potential of quantum simulators goes beyond the traditional scope of condensed-matter physics: Being based on driven-dissipative quantum optical platforms, quantum simulators allow for processes that are typically not considered in condensed-matter physics. These processes can enrich in unexplored ways the phase diagram of well-established models. Taking the extended Bose-Hubbard model as the guiding example, in this work we examine the impact of coherent pair injection, a process readily available in, for example, superconducting circuit arrays. The interest behind this process is that, in contrast to the standard injection of single excitations, it can be configured to preserve the U(1) symmetry underlying the model. We prove that this process favors both superfluid and density-wave order, as opposed to insulation or homogeneous states, thereby providing a novel route towards the access of lattice supersolidity.
Was I thinking about thinking about the task? Measurement issues with self-reports of task-related and task-unrelated thoughts

In two studies we examined participants self-reports of their cognitive state after performing tasks. The reports of cognitive state included separate single item measures of task-related and task-unrelated thoughts framed as opposing sets (thoughts about X and –X). In study 1, we examined two data sets from previously published research. The correlation between task-related and task-unrelated thoughts was ~r = −. 25. In addition, the two items loaded onto separate factors when they were combined with other measures from the Short Task Relevant Evaluation of Stress State (STRESS). In study 2, 33 participants played a simulated natural disaster strategy game for five sessions and after each session they self-reported their cognitive state during the game. The correlation between task-related and task-unrelated thoughts at the individual level was also low, ~r = −. 24. Participants may have difficulties accurately rating their thought content post-engaging in a task.
Heart Disease Prediction Based on MWMOTE and Res-BiGRU Models

Predicting the incidence rate of heart disease is of inestimable significance in the field of clinical medicine and health management. In recent years, machine learning and artificial intelligence technologies have developed rapidly, offering new possibilities for the prediction and diagnosis of heart disease. However, there are challenges in collecting disease data, as the number of diseased samples is often much larger than the normal sample size. Moreover, traditional prediction methods struggle to fully extract the spatio-temporal characteristics of the data, resulting in low prediction accuracy and issues such as gradient dispersion in complex prediction systems. To address these challenges, this paper proposes a new heart disease prediction model, which is based on the Minority Weighted Majority Oversampling Technique (MWMOTE) and improved Residual Bidirectional Gated Recurrent Unit (Res-BIGRU). The model utilizes the MWMOTE oversampling method to balance the positive and negative samples of the dataset, and employs CNN and BiGRU to extract features, while using residual connections to avoid gradient dispersion and alleviate network degradation. The model's optimal parameters are determined through continuous experiments. Testing and evaluating the model reveals an accuracy of 0.98. In comparison to multiple traditional prediction models reproduced for the experiment, this model demonstrates higher prediction accuracy, enabling better assistance for doctors in diagnosing heart disease.
Utilisation Des Tic Et Capacites Concurrentielles : Proposition D'Un Modele De Recherche Empirique

Abstract The issue of the links or underlying mechanisms be-tween IT (Information technology) and the firm per-formance remains unclear. Empirical research has ob-tained mitigated results.The Resource-based View seems more particularly adapted for explaining the heterogeneity of firm results.Powell and Dent-Micaleff (1977) have shown that IT alone have not produced sustained competitive advan-tage in the american retail distribution industry, but that some firms have won this type of advantage by using IT for leveraging human and business complementary in-tangible resources. Thus our research question consists in knowing if IT, as- sociated with human, organizational and business com-plementary resources, contribute to create differences in competitive capacities among firms. And if yes, how, by what particular combinations of complementary resources ? The proposed model is inspired at its starting point by Powell and Dent-Micaleff's one, but changes it in some important points : capacities rather than resources, net-works taken into account, IT actualisation, reasoning in terms of use and combinations of resources, IS (Information System) rather than IT orientation, differenciation between human and organizational resources.A new list and categorization of the variables are equally proposed. A quantitative methodology is pro-posed to measure these phenomenas.This new model, we hope, should allow a better com-prehension of the underlying mechanisms explaining how IT and IS can influence differences of competitive capacities between firms.
FMCW Based Positioning Using Multiple SHF RFID Transponders

In this work, we present a method for simultaneous ranging of multiple transponders. The method was implemented on a software defined radio (SDR) and investigated by measurement. The ranging method investigated here is based on a frequency-modulated continuous-wave (FMCW) radar. The signal, modulated via backscattering at the transponder, is used by the interrogator to distinguish and separate the signal of one transponder from clutter as well as from the backscattered signals of other transponders. We demonstrate in a series of measurements that simultaneous ranging of multiple transponders is possible with this novel technique. Using a bandwidth of 10MHz at 5. 8GHz a mean absolute error of 5. 9cm was achieved. The influence of clutter and the signal of other transponders is removed from the signal of the investigated transponder.
Research Progress in Heart Rate Variability Applications in Exercise Rehabilitation for Cardiovascular Diseases

Heart rate variability (HRV) is an important marker for assessing the balance of the autonomic nervous system and clinical prognosis, because it can be non-invasively and easily measured, and the results are accurate and valuable. HRV is widely applied in cardiovascular disease fields. Exercise training is an important part of cardiac rehabilitation. Personalized sports rehabilitation therapy can effectively prevent the emergence of cardiovascular diseases, decrease the risk of recurrent cardiovascular events, and ameliorate dysfunction, as well as limitations in life, work, and social participation, caused by adverse cardiovascular events. However, sports rehabilitation can have drawbacks, in that improper training can cause injury, excessive fatigue, or even harmful cardiovascular events. To support future applications, this article reviews recent applications of HRV in sports rehabilitation for cardiovascular diseases.
Canada at the Crossroads: Improving International Performance by Establishing a New Canadian Soccer League

Canada’s national soccer team program has met with disappointing results in the qualifying rounds of play in the CONCACAF zone that lead to World Cup qualification. Canada has not appeared in the World Cup tournament since 1986 and more recent performances place Canada well down in the FIFA world rankings. Canada has not benefitted from a national soccer league since 1993 and Canadian players have difficulty finding training opportunities abroad. This paper develops an econometric model to explain a nations FIFA world points to determine the expected improvement in Canada’s FIFA world points, and subsequent FIFA world and CONCACAF rankings, by establishing a hypothetical ten-team Tier II soccer league. The model suggests that only a modest improvement could be expected since most FIFA member nations already feature at least one professional or semi-professional league of some sort.
Central limit theorem for the average closure coefficient

Many real-world networks exhibit the phenomenon of edge clustering, which is typically measured by the average clustering coefficient. Recently, an alternative measure, the average closure coefficient, is proposed to quantify local clustering. It is shown that the average closure coefficient possesses a number of useful properties and can capture complementary information missed by the classical average clustering coefficient. In this paper, we study the asymptotic distribution of the average closure coefficient of a heterogeneous Erd\"{o}s-R\'{e}nyi random graph. We prove that the standardized average closure coefficient converges in distribution to the standard normal distribution. In the Erd\"{o}s-R\'{e}nyi random graph, the variance of the average closure coefficient exhibits the same phase transition phenomenon as the average clustering coefficient.
Salud Mental en Estudiantes de Lenguas

La salud mental se ha convertido en un tema de relevancia no solamente clínica, sino social. Esto implica que dentro de los diversos sistemas sociales que permiten el funcionamiento del mundo como lo conocemos se le ha otorgado también una mayor importancia y con ello las investigaciones sobre el tema han aumentado considerablemente. En este artículo se presentan los resultados de un inventario de síntomas de afecciones mentales aplicado a estudiantes de la Licenciatura en Idiomas de la Universidad Juárez Autónoma de Tabasco. Los resultados indican que los aprendices de lenguas no se encuentran en un estado óptimo de disposición para aprender, ya que mostraron altos índices de ansiedad y depresión.
Supplementary: Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model

We further explore the impact of the learned deep features on the segmentation performance of the contrast-based variational model (CVM). Figure.1 shows the corresponding results on Camelyon-16, in which the blue bar represents the CVM model on the training sets, while the orange bar represents the deep segmentation model on the test set. Note that the CVM is only applied to the training set, and is not applicable to the testing set as there are no input annotated points during the inference period. The three groups (from left to right) represent the deep segmentation models trained without CVM as the complementary supervision, with CVM as the complementary supervision, and the fullysupervised, respectively. From the first two groups it is clear that if the learned features are with better quality that the deep segmentation model achieves higher performance on the test set when complementary supervision from CVM is applied, the performance of CVM on the training set can be accordingly improved (from 0.683 to 0.724 in Dice coeffi-
Supplementary: A Generalized & Robust Framework For TimeStamp Supervision in Temporal Action Segmentation

In Sec. S1, we show the ablation results including the impact of prior, impact of auxiliary losses, impact of timestamp annotation position, impact of segmentation architecture, and number of epochs required for each maximization step. In Sec. S2, we visualize the performance of our EM-TSS. Sec. S3 details the posterior and prior for EM-Gen. In Sec. S4, we provide some additional details of our user study.
Supplementary: A Theory of Topological Derivatives for Inverse Rendering of Geometry

We organize the supplementary according to the section numbers used in the main manuscript. In Section 4.2, the derivation for topological derivative (TD) with respect to phase nucleation is outlined. We show a comparison between area-sampling and edge-sampling in Figure 1. In Figure 3 we show additional results for 2D vector graphics recovery using score-distillation-sampling [7]. We test our proposed theory on real-world data for 3D reconstruction in Figure 2, with discussion in Section 5.2. We discuss the relevance of curvature in the same section. A more detailed derivation for secondary visibility is in Section 5.2. 1 with additional illustrations for the optimization results shown in the main paper. We provide implementation details for all the experiments in Section 7.
Supplementary: Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction

1. Preliminaries In this section, we will make a brief summary of the technical terms, and medical priories related to this work. 1.1. Technical Terms Generally, structured MRIs are typically composed of the following six modalities: • T1-WI: T1-weighted imaging T1. • T2-WI: T2-weighted imaging T2. • DWI: Defusion weighted imaging. • ADC: Apparent deffusion coefficient. • T1-IP/OP: T1 in phase / out of phase. • T1ce: Contrast-enhanced T1 at the arterial phase (T1ce-AP), portal phase (T1ce-PP), and lag phase (T1ce-LP). The surgery-related indicators and the abbreviations are as follows: • AFP: Alpha-fetoprotein. • TBil: Total bilirubin. • ALT: Alanine aminotransferase. • GGT: Gamma-Glutamyltransferase. • HBs-Ag: Hepatitis B virus surface antigen. • HBV-DNA: Hepatitis B DNA. • HCV-Ab: Hepatitis C virus antibody. • PT: Prothrombin time. • G-Score: A metric for evaluating the severity of inflammation. According to Metavir scoring system [1], it has five levels from G0 to G4. • S-Score: A metric for evaluating the severity of fibrosis. According to Metavir scoring system, it has five levels from S0 to S4. 1.2. Medical Priories Magnetic Resonance Imaging (MRI). MRI is an imaging method that radiates energy from substances in the body to the surrounding environment through high-frequency magnetic field in vitro [3]. Generally speaking, MRI is the most important reference for the early diagnosis of liver cancer. The appearance of the tumor will show different brightness or contrast against the whole liver on different modality of MRI, which provides the most significant indication for doctors to judge the priority of cancer at the stage of early diagnosis. Surgery-related indicators. In fact, the overall treatment cycle for primary liver cancer tend to extend from years up to several decades, in which tumor resection surgery is only one of those complex procedures. Taking the first surgery as the dividing line, we summarize all indicators into three categories: • Preoperative – collected before the surgery.
III.—The Miocene Insect-fauna of Œningen, Baden

Thanks solely to the labours of the late Oswald Heer, the fossil insect-fauna of Œningen is better known than that of any other locality or horizon in the world. But it is by no means so well known as it should be; for although Heer, in his latest enumeration of the specimens seen by him (Urwelt der Schweiz, 2e Aufl. 1879, p. 383), repeats precisely the same figures he has already given in 1861 (Recherches sur le climat du pays tert., p. 197), indicating in an interval of eighteen years no addition to his material (over 5000 specimens), his repeated additions to the number of species from that locality show that he had not fully worked over what he had. Indeed, thirty years ago, I arranged for exhibition in the Museum of Comparative Zoology at Cambridge a collection of nearly one hundred and fifty named species secured by Prof. Louis Agassiz from Heer, of which more than forty still remain undescribed; there are also a considerable number of merely nominal species enumerated by Heer in his Urwelt der Schweiz and elsewhere, duly catalogued by me in my Index to Fossil Insects (Bull. U. S. Geol. Surv. No. 71), but as yet neither described nor figured. In addition to this it may be noted that in the enumeration referred to above Heer mentions 543 species of beetles, while less than 270 nominal species have yet been published from Œningen, and only seven species from the Swiss Miocene, included in the enumeration.
Multi-Agent Deep Reinforcement Learning based Collaborative Computation Offloading in Vehicular Edge Networks

Recently, to cope with the long communication distance and unreliability of cloud-based computing architectures, mobile edge computing has emerged as a solution with great promise. This pattern extends cloud-based services towards the vehicular edge network and enables vehicular tasks to be offloaded to intermediate Roadside Units (RSUs) directly. However, as more and more tasks are offloaded to RSUs, the computation capacity of a single RSU becomes insufficient. Without edge cooperation, overall resource utilization and effectiveness are prone to being underutilized. To address this issue, this paper investigates a collaborative computation offloading scheme where adjacent RSUs can process offloaded tasks collaboratively rather than individually. First, we explore a vehicular edge network where the bilateral synergy between RSUs is leveraged. In particular, we incorporate a price-based incentive mechanism into the resource allocation process to promote overall resource utilization. Second, considering the time-varying system conditions and uncertain resource requirements, the optimization problem is approximated as a Markov Decision Process (MDP) and extended to a multi-agent system. Finally, we propose a Multi-agent Deep deterministic policy gradient-based computation Offloading and resource Allocation scheme (MDOA) to solve the corresponding problem. Simulation results show that the proposed MDOA can not only achieve a higher long-term utility of RSUs but also have better performance than other baselines in different scenarios.
Fused Filament Fabrication for Metallic Materials: A Brief Review

Fused filament fabrication (FFF) is an extrusion-based additive manufacturing (AM) technology mostly used to produce thermoplastic parts. However, producing metallic or ceramic parts by FFF is also a sintered-based AM process. FFF for metallic parts can be divided into five steps: (1) raw material selection and feedstock mixture (including palletization), (2) filament production (extrusion), (3) production of AM components using the filament extrusion process, (4) debinding, and (5) sintering. These steps are interrelated, where the parameters interact with the others and have a key role in the integrity and quality of the final metallic parts. FFF can produce high-accuracy and complex metallic parts, potentially revolutionizing the manufacturing industry and taking AM components to a new level. In the FFF technology for metallic materials, material compatibility, production quality, and cost-effectiveness are the challenges to overcome to make it more competitive compared to other AM technologies, like the laser processes. This review provides a comprehensive overview of the recent developments in FFF for metallic materials, including the metals and binders used, the challenges faced, potential applications, and the impact of FFF on the manufacturing (prototyping and end parts), design freedom, customization, sustainability, supply chain, among others.