Miracle-dz/UniFAR-FTUs huggingface dataset:
Epidemiological and clinical characteristics of the chikungunya outbreak in Ruili City, Yunnan Province, China

Chikungunya fever is an acute infectious disease caused by the chikungunya virus (CHIKV) that is characterized by fever, rash, and joint pain. CHIKV has infected millions of people in Africa, Asia, America, and Europe since it re‐emerged in the Indian Ocean region in 2004. Here, we report an outbreak of Chikungunya fever that occurred in Ruili of Yunnan Province, a city located on the border between China and Myanmar, in September 2019. The outbreak lasted for three months from September to December. Overall, 112 cases were confirmed by a real‐time reverse‐transcription polymerase chain reaction in the Ruili People's Hospital, and they showed apparent temporal, spatial, and population aggregation. Among them, 91 were local cases distributed in 19 communities of Ruili City, and 21 were imported cases. The number of female patients was higher than that of male patients, and most patients were between 20 and 60 years old. The main clinical manifestations included joint pain (91.96%), fever (86.61%), fatigue (58.04%), chills (57.14%), rash (48.21%), headache (39.29%), and so forth. Biochemical indexes revealed increased C‐reactive protein (63.39%), lymphopenia (57.17%), increased hemoglobin (33.04%), neutrophilia (28.57%), and thrombocytopenia (16.07%). Phylogenetic analysis of the complete sequences indicated that the CHIKV strains in this outbreak belonged to the Indian Ocean clade of the East/Central/South African genotype. We speculated that this chikungunya outbreak might be caused by CHIKV‐infected persons returning from Myanmar, and provided a reference for the formulation of effective treatment and prevention measures.
The effects of supported employment interventions in populations of people with conditions other than severe mental health: a systematic review

Abstract Aim: To assess the effectiveness of supported employment interventions for improving competitive employment in populations of people with conditions other than only severe mental illness. Background: Supported employment interventions have been extensively tested in severe mental illness populations. These approaches may be beneficial outside of these populations. Methods: We searched PubMed, Embase, CINAHL, PsycInfo, Web of Science, Scopus, JSTOR, PEDro, OTSeeker, and NIOSHTIC for trials including unemployed people with any condition and including severe mental illness if combined with other co-morbidities or other specific circumstances (e.g., homelessness). We excluded trials where inclusion was based on severe mental illness alone. Two reviewers independently assessed risk of bias (RoB v2.0) and four reviewers extracted data. We assessed rates of competitive employment as compared to traditional vocational rehabilitation or waiting list/services as usual. Findings: Ten randomised controlled trials (913 participants) were included. Supported employment was more effective than control interventions for improving competitive employment in seven trials: in people with affective disorders [risk ratio (RR) 10.61 (1.49, 75.38)]; mental disorders and justice involvement [RR 4.44 (1.36,14.46)]; veterans with posttraumatic stress disorder (PTSD) [RR 2.73 (1.64, 4.54)]; formerly incarcerated veterans [RR 2.17 (1.09, 4.33)]; people receiving methadone treatment [RR 11.5 (1.62, 81.8)]; veterans with spinal cord injury at 12 months [RR 2.46 (1.16, 5.22)] and at 24 months [RR 2.81 (1.98, 7.37)]; and young people not in employment, education, or training [RR 5.90 (1.91–18.19)]. Three trials did not show significant benefits from supported employment: populations of workers with musculoskeletal injuries [RR 1.38 (1.00, 1.89)]; substance abuse [RR 1.85 (0.65, 5.41)]; and formerly homeless people with mental illness [RR 1.55 (0.76, 3.15)]. Supported employment interventions may be beneficial to people from more diverse populations than those with severe mental illness alone. Defining competitive employment and increasing (and standardising) measurement of non-vocational outcomes may help to improve research in this area.
Next-Generation Sequencing and Proteomics of Cerebrospinal Fluid From COVID-19 Patients With Neurological Manifestations

The SARS-CoV-2 and its variants are still hitting the world. Ever since the outbreak, neurological involvements as headache, ageusia, and anosmia in COVID-19 patients have been emphasized and reported. But the pathogenesis of these new-onset neurological manifestations in COVID-19 patients is still obscure and controversial. As difficulty always lay in the diagnosis of neurological infection, current reports to validate the presence of SARS-CoV-2 in cerebrospinal fluid (CSF) almost relied on the basic methods and warranted improvement. Here we reported a case series of 8 patients with prominent new-onset neurological manifestations, who were screened out from a patch of 304 COVID-19 confirmed patients. Next-generation sequencing (NGS) and proteomics were conducted in the simultaneously obtained CSF and serum samples of the selected patients, with three non-COVID-19 patients with matched demographic features used as the controls for proteomic analysis. SARS-CoV-2 RNA was detected in the CSF of four COVID-19 patients and was suspicious in the rest four remaining patients by NGS, but was negative in all serum samples. Proteomic analysis revealed that 185 and 59 proteins were differentially expressed in CSF and serum samples, respectively, and that only 20 proteins were shared, indicating that the proteomic changes in CSF were highly specific. Further proteomic annotation highlighted the involvement of complement system, PI3K-Akt signaling pathway, enhanced cellular interaction, and macrophages in the CSF proteomic alterations. This study, equipped with NGS and proteomics, reported a high detection rate of SARS-CoV-2 in the CSF of COVID-19 patients and the proteomic alteration of CSF, which would provide insights into understanding the pathological mechanism of SARS-CoV-2 CNS infection.
Validation Methodologies and Their Impact in Construction Productivity Research

AbstractChoosing validation methods in construction research is challenging. This study explores trends in the use of validation methods and their impacts based on a statistical analysis of a sample set of 365 research articles on construction productivity. This sample set provides a balance of the breadth and cohesion required for significant comparisons and the temporal depth required for longitudinal analysis. The contributions of this research include (1) a critical review of the validation methodologies that can be used for research projects in construction engineering and management, (2) an investigation of the statistical relationship between validation methods and the impact in terms of citations of research over time in different source categories, and (3) statistically significant observations over the preceding thirty years of the increased use of functional demonstration as a validation methodology and the decreased existence of unclear validation. These results may be used in the future to in.
An algorithm with recovery guarantees for Expander Dictionary Learning

—In this paper we present a novel algorithm along with associated recovery guarantees for solving a particular dictionary learning problem. The dictionary in question is an m × n randomly generated, sparse, binary matrix with a ﬁxed number d of non-zeros per column, while the latent representation is random, real, k sparse and dissociated. The algorithm presented leverages properties of expander graphs to efﬁciently recover both the dictionary and latent representation simultaneously.
The relationship between sickness leave and job-interruption: evidence from severe health shocks

Increasing the economic activity of newly disabled individuals is one of the main challenges among most of the policymakers. The analysis of the EU-LFS data, shows that "own illnesses or disability" is reported as the main reason for leaving the last job for men blue-collars’ in the 5/6% of cases1. Unsurprisingly, the values reported by blue-collars are almost always double the percentages of white-collars; and this is potentially related to the type of jobs (often demanding) carried out by the former group of workers. In studying the link between health deterioration and labour market activity, many institutional factors come into play: in Italy, a country characterised by high levels of employment protection legislation2 (EPL), several national and European directives are in place to facilitate individuals with reduced working capabilities to retain their job. Unlawful dismissals and various supports for job maintenance like reallocation of tasks or changes in working-hours, are among the possibilities. Despite this, even in the case of permanent contracts, they do not work always appropriately.
Accurate closed-form expressions for the frequency-dependent line parameters of coupled on-chip interconnects on silicon substrate

Accurate closed-form expressions for the frequency-dependent [R], [L], [G], [C] line parameters of coupled on-chip interconnects on lossy silicon substrate are presented. The closed-form expressions for the frequency-dependent series impedance parameters are obtained using a complex image method. The frequency-dependent shunt admittance parameters are expressed using high- and low-frequency asymptotic static solutions. The proposed closed-from expressions are shown to be in good agreement with both quasi-static and full-wave electromagnetic solutions.
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory

Despite achieving remarkable efﬁciency, traditional network pruning techniques often follow manually-crafted heuristics to generate pruned sparse networks. Such heuristic pruning strategies are hard to guarantee that the pruned networks achieve test accuracy comparable to the original dense ones. Recent works have empirically identiﬁed and veriﬁed the Lottery Ticket Hypothesis (LTH): a randomly-initialized dense neural network contains an extremely sparse subnetwork, which can be trained to achieve similar accuracy to the former. Due to the lack of theoretical evidence, they often need to run multiple rounds of expensive training and pruning over the original large networks to discover the sparse subnetworks with low accuracy loss. By leveraging dynamical systems theory and inertial manifold theory, this work theoretically veriﬁes the validity of the LTH. We explore the possibility of theoretically lossless pruning as well as one-time pruning, compared with existing neural network pruning and LTH techniques. We reformulate the neural network optimization problem as a gradient dynamical system and reduce this high-dimensional system onto inertial manifolds to obtain a low-dimensional system regarding pruned subnetworks. We demonstrate the precondition and existence of pruned subnetworks and prune the original networks in
A proposal for wide range wavelength switching process using optical force

Optomechanical wavelength up-conversion based on optical force and core–shell scattering effects are used to control light coupling between two waveguides. This system consists of two parallel optical waveguides with 20 μm lengths suspended on a silica substrate embedded with Ag/Si/SiO2 core–shell nanoparticles. By mid-IR plane wave illumination with different intensities and different wavelengths on nanoparticles, scattering would increase and result in an improvement in attractive gradient optical force exerted on waveguides. Via bending waveguides toward each other, visible light propagating in the first waveguide would couple to another. PDMS as a polymer is used to reduce the required power for bending waveguides. Results reveal that when waveguides’ gap equilibrium is 400 nm and wavelengths of control and probe lights are 4.5 μm and 0.45 μm respectively, about 10.75 mW μm−2 power is needed to bend waveguides for total coupling of light between waveguides. The efficiency of the coupled waveguides system is %43.
Fast and Robust 3D Reconstruction Solution from Permissive Open-Source Code

With the growth of access to faster computers and more powerful cameras, the 3D reconstruction of objects has become one of the public's main topics of research and demand. This task is vigorously applied in creating virtual environments, creating object models, and other activities. One of the techniques for obtaining 3D features is photogrammetry, mapping objects and scenarios using only images. However, this process is very costly and can be pretty time-consuming for large datasets. This paper proposes a robust, efficient reconstruction pipeline with a low runtime in batch processing and permissive code. It is even possible to commercialize it without the need to keep the code open. We mix an improved structure from motion algorithm and a recurrent multi-view stereo reconstruction. We also use the Point Cloud Library for normal estimation, surface reconstruction, and texture mapping. We compare our results with state-of-the-art techniques using benchmarks and our datasets. The results showed a decrease of 69.4% in the average execution time, with high quality but a greater need for more images to achieve complete reconstruction.
Graph Differentiable Architecture Search with Structure Learning

Discovering ideal Graph Neural Networks (GNNs) architectures for different tasks is labor intensive and time consuming. To save human efforts, Neural Architecture Search (NAS) recently has been used to automatically discover adequate GNN architectures for certain tasks in order to achieve competitive or even better performance compared with manually designed architectures. However, existing works utilizing NAS to search GNN structures fail to answer the question How NAS is able to select the desired GNN architectures . In this paper, we investigate this question to solve the problem, for the ﬁrst time. We conduct theoretical analysis and measurement study with experiments to discover that gradient based NAS methods tend to select proper architectures based on the usefulness of different types of information with respect to the target task. Our explorations further show that gradient based NAS also suffers from noises hidden in the graph, resulting in searching suboptimal GNN architectures. Based on our ﬁndings, we propose a Graph differentiable Architecture Search model with Structure Optimization (GASSO), which allows differentiable search of the architecture with gradient descent and is able to discover graph neural architectures with better performance through employing graph structure learning as a denoising process in the search procedure. Extensive experiments on real-world graph datasets demonstrate that our proposed GASSO model is able to achieve the state-of-the-art performance compared with existing baselines.
Bayesian optimization of peripheral intraneural stimulation protocols to evoke distal limb movements

Objective. Motor neuroprostheses require the identification of stimulation protocols that effectively produce desired movements. Manual search for these protocols can be very time-consuming and often leads to suboptimal solutions, as several stimulation parameters must be personalized for each subject for a variety of target motor functions. Here, we present an algorithm that efficiently tunes peripheral intraneural stimulation protocols to elicit functionally relevant distal limb movements. Approach. We developed the algorithm using Bayesian optimization (BO) with multi-output Gaussian Processes (GPs) and defined objective functions based on coordinated muscle recruitment. We applied the algorithm offline to data acquired in rats for walking control and in monkeys for hand grasping control and compared different GP models for these two systems. We then performed a preliminary online test in a monkey to experimentally validate the functionality of our method. Main results. Offline, optimal intraneural stimulation protocols for various target motor functions were rapidly identified in both experimental scenarios. Using the model that performed best, the algorithm converged to stimuli that evoked functionally consistent movements with an average number of actions equal to 20% of the search space size in both the rat and monkey animal models. Online, the algorithm quickly guided the observations to stimuli that elicited functional hand gestures, although more selective motor outputs could have been achieved by refining the objective function used. Significance. These results demonstrate that BO can reliably and efficiently automate the tuning of peripheral neurostimulation protocols, establishing a translational framework to configure peripheral motor neuroprostheses in clinical applications. The proposed method can also potentially be applied to optimize motor functions using other stimulation modalities.
Distributed Nash Equilibrium Seeking Under Input Dead Zone

This article investigates the Nash equilibrium (NE) seeking problem for games in the presence of input dead-zone nonlinearity. The purpose of each player is to minimize its own cost function, which depends not only on its own decision variable, but also on decision variables of the other players. To obtain an NE, a novel two-time-scale distributed algorithm is designed. This new strategy consists of two parts: one is the fast compensating dynamics, which can rapidly compensate the effect of input dead zone; the other is the optimization dynamics that can distributively drive the players’ decision variables to achieve the NE. Since the proposed algorithm has a two-time-scale structure, singular perturbation techniques are used to prove that the players’ actions semiglobally practically asymptotically converge to the NE. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed strategy.
Deep Neural Network and Pseudo Relevance Feedback Based Query Expansion

: The neural network has attracted researchers immensely in the last couple of years due to its wide applications in various areas such as Data mining, Natural language processing, Image processing, and Information retrieval etc. Word embedding has been applied by many researchers for Information retrieval tasks. In this paper word embedding-based skip-gram model has been developed for the query expansion task. Vocabulary terms are obtained from the top “k” initially retrieved documents using the Pseudo relevance feedback model and then they are trained using the skip-gram model to find the expansion terms for the user query. The performance of the model based on mean average precision is 0.3176. The proposed model compares with other existing models. An improvement of 6.61%, 6.93%, and 9.07% on MAP value is observed compare to the Original query, BM25 model, and query expansion with the Chi-Square model respectively. The proposed model also retrieves 84, 25, and 81 additional relevant documents compare to the original query, query expansion with Chi-Square model, and BM25 model respectively and thus improves the recall value also. The per query analysis reveals that the proposed model performs well in 30, 36, and 30 queries compare to the original query, query expansion with Chi-square model, and BM25 model respectively.
Kruia rediscovered: phylogenetic implications, larval mor- phology, and biology of an enigmatic hydrophilid beetle from western Africa (Coleoptera: Hydrophilidae)

The enigmatic terrestrial water scavenger beetle (Hydrophilidae: Sphaeridiinae: Coelostomatini) of the genus Kruia Spangler & Perkins, 1981 was known by a single specimen from Liberia. We rediscovered it at two forested localities in Cameroon (Mt. Cameroon and Mt. Kupe, both of volcanic origin). Adults and larvae of a new species were collected by sifting forest leaf litter. To reveal the phylogenetic position of Kruia, we analyzed a dataset comprising 32 species (incl. 29 of the Coelostomatini) and seven DNA fragments. We recovered three strongly supported monophyla (Dactylosternum, Coelostoma and the Neotropical clade of Coelostomatini genera), with Kruia nested within Dactylosternum Wollaston, 1854, as sister of D. abdominale (Fabricius 1792). Despite of striking differences, Kruia adults share a unique reduction in the number of antennomeres from nine to eight with some species of the D. abdominale group. We, therefore, consider the genus-group name Kruia a junior synonym of Dactylosternum. We describe the Cameroonian species as Dactylosternum riberai sp. nov. and compare it with its sister Dactylosternum chrysopelma (Spangler & Perkins 1981), comb. nov. Mature larvae of the new species identified using DNA sequences are described and compared to other Dactylosternum larvae.
Meshless Generalized Finite Difference Method to Analyze Electromagnetic Performance of SPM Machines With Eccentric Rotor Shape

In this article, we propose an unconventional method, i.e., generalized finite difference method (GFDM), to analyze the electromagnetic performance of surface-mounted permanent magnet (SPM) machine with eccentric rotor shape. The remarkable characteristic of this method is that it is meshless. In this method, the solution region is discretized in the form of nodes, and the nodes can move with the moving structure. This is very helpful in modeling the SPM machine because there is no remeshing process during the rotation. However, mesh generation is required in the traditional finite-element analysis (FEA). First, the basic principle of the proposed GFDM is introduced. Second, the whole field domain is divided into five types of regions, viz., PMs, air gap, slot openings, slots, and stator core. Third, the differential equations satisfied by each region are discretized into algebraic equations by the GFDM. The interfaces need to be dealt with separately because of the discontinuity of the derivative of the vector potential. Then, the systems of equations are solved to analyze the electromagnetic performance of an SPM machine. Finally, this method is implemented on a 12-slot, 10-pole SPM machine. Meanwhile, the effectiveness of this method is verified by an FEA and experiment.