Miracle-dz/UniFAR-FTUs huggingface dataset:
Temperature dependence of the ac hopping conductivity

The temperature dependence of the ac conductivity is calculated on the basis of a Debye analysis for disordered materials with strong spatial fluctuations of the carrier mobility. Charge transport occurs at the percolation level in the region of a density-of-states tail falling exponentially into the band gap. A theoretical law derived for σ(ω, T) is in good agreement with the published experimental results. A parameter T 0 representing the fall of the density of states in the tail increases logarithmically with the ac field frequency
Segmentation of EIT images using fuzzy clustering. A preliminary study

We present a new application of a signal processing technique called fuzzy clustering for automatic identification of various structures seen in EIT images. This technique gives for each pixel a probability of belonging to a particular class. By assigning each pixel to the class for which it has the greatest probability of belonging, we obtain image segmentations. In EIT 19.5 nm images we distinguish the Quiet Sun, Coronal Holes, and the Active Regions, whereas in EIT 30.4 nm we extract the plages and part of the network boundaries. We also show how a multiwavelength approach leads to an improved segmen-tation of the different coronal structures.
Modafinil Administration to Preadolescent Rat Impairs Non-Selective Attention, Frontal Cortex D2 Expression and Mesolimbic GABA Levels

The misuse of psychostimulants is an increasing behavior among young people, highlighting in some countries the abuse of modafinil (MOD) as a neuropotentiator. However, several clinical trials are investigating MOD as an alternative pharmacological treatment for attentional deficit and hyperactivity disorder (ADHD) in children and adolescents. On the other hand, the early use of psychostimulants and the misdiagnosis rates in ADHD make it crucial to investigate the brain effects of this type of drug in young healthy individuals. The aim of this work was to evaluate the effects of chronic MOD treatment on neurochemicals (γ-aminobutyric acid and glutamate), dopamine receptor 2 (D2) expression and behavior (non-selective attention “NSA”) in the mesocorticolimbic system of young healthy Sprague–Dawley rats. Preadolescent male rats were injected with MOD (75 mg/kg, i.p.) or a vehicle for 14 days (from postnatal day 22 to 35). At postnatal day 36, we measured the GLU and GABA contents and their extracellular levels in the nucleus accumbens (NAc). In addition, the GLU and GABA contents were measured in the ventral tegmental area (VTA) and D2 protein levels in the prefrontal cortex (PFC). Chronic use of MOD during adolescence induces behavioral and neurochemical changes associated with the mesocorticolimbic system, such as a reduction in PFC D2 expression, VTA GABA levels and NSA. These results contribute to the understanding of the neurological effects of chronic MOD use on a young healthy brain.
Simulation and projection of climate extremes in China by multiple CMIP6 models

Funding information National Key Research and Development Program of China, Grant/Award Number: 2018YFE0196000; National Key Technology Development Project for Weather Forecasting of the China Meteorological Administration, Grant/ Award Number: YBGJXM (2020) 6A-06; National Natural Science Foundation of China, Grant/Award Numbers: 42077427, 41875111, 42175165; Chongqing Meteorological Department Technology Research Project, Grant/Award Number: YWJSGG-202206 Abstract This study evaluates the ability of 23 climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating extreme climate events over China. The multimodel ensemble (MME) performs better than most individual models in reproducing the climatological mean distribution of all extreme indices. The MME can reproduce well the climatological mean distributions of five extreme climate indices over China, including annual total precipitation (PTOT), maximum consecutive 5-day precipitation (RX5), simple daily intensity (SDII), maximum daily maximum temperature (TXX), and minimum daily minimum temperature (TNN), with Taylor skill scores exceeding 0.7. SDII and TXX are the most skilful precipitation and temperature extreme indices simulated by the MME, respectively. The MME has relatively lower skill in simulating the climatological mean distribution of warm days (TX90P) and cold nights (TN10P) over China. Future projections of these extreme climate indices by the end of the 21st century are explored with the MME under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. The PTOT and RX5 in northwestern China are all projected to increase by more than 30% under SSP5-8.5. R20 is projected to increase by 4–5 days over southeastern China under SSP5-8.5. There are fewer (more) consecutive dry days over north China (south China), with a change of 5 days under SSP5-8.5. The extreme temperature indices, including TX90P, TXX, and TNN, all increase with time and higher SSP scenarios. The three indices increase by 40–55%, 4–6 C, and 4– 7 C under SSP5-8.5 over east China, respectively. The TN10P decreases by more than 6% over east China. The changes in these extreme indices under SSP1-2.6 and SSP2-4.5 are similar to those under SSP5-8.5 but with a smaller magnitude. Large uncertainties still exist in the future projections, especially under the high SSP scenarios. Received: 19 January 2021 Revised: 30 May 2022 Accepted: 7 June 2022 Published on: 24 June 2022
EURAXLES – A global approach for design, production and maintenance of railway axles: WP1 – Advances in fatigue load analysis and reliability assessment of railway axles

The work presented in this paper was led within the collaborative project “Euraxles” of the FP7 program of the European Commission. It aimed at developing processes and methods that contribute to the minimization of the risk of fatigue failure of railway axles in service. This paper focuses on the development of a method to assess the reliability of axles according to fatigue damage. The proposed approach is mainly based on the stress strength interference analysis (SSIA) and the fatigue‐equivalent‐load (FEL) methods. It aims at calculating the axles’ probability of fatigue failure, by characterizing the variability of real in‐service loads and the scatter of the axles fatigue strength, and at evaluating more accurately the actual design margins. First of all, the main lines of the stress strength interference analysis method are recalled. This method aims at evaluating the in‐service reliability of components for their design or their homologation. It is used in many industries for various applications (mechanical components or systems, electronic elements, etc.). In the second part, the fatigue load analysis method that is proposed for railway axles is described. It starts with a post‐processing of an axle load measurement: from a time signal of forces applied to both wheels fitted on the axle, fatigue cycles of bending moment applied to the axle are identified and transformed into a cyclic equivalent load, the Meq, which is a measurement of the severity of the initial variable load. Then, virtual but realistic load spectra are generated, thanks to a classification operation followed by a random draw of elementary load data that considers the operation and maintenance conditions of the axle. All the spectra are then analysed thanks to the fatigue‐equivalent‐load method in order to build the distribution of in‐service load severities that gives a picture of the stress to which the axles are submitted. In the third and last part of the paper, the methods are applied to real data of “Société nationale des chemins de fer français” (SNCF), the French national railway operator. Sensitivity analyses are performed in order to quantify the effect on the Meq of variations of parameters and to verify the convergence and robustness of the process. Finally, results obtained for a passenger coach are given. The comparison between the distribution of load severities and the normative load, defined as according to european standards EN13103, shows that, for the studied axle, the normative load is very conservative. Using the axles fatigue limits identified on full‐scale tests, a stress strength interference analysis is performed to calculate the probability of failure of the axle.
Serial Change of Endotoxin Tolerance in a Polymicrobial Sepsis Model

Immune suppression is known to occur during sepsis. Endotoxin tolerance is considered a mechanism of immune suppression in sepsis. However, the timing and serial changes in endotoxin tolerance have not been fully investigated. In this study, we investigated serial changes in endotoxin tolerance in a polymicrobial sepsis model. Herein, we used a rat model of fecal slurry polymicrobial sepsis. After induction of sepsis, endotoxin tolerance of peripheral blood mononuclear cells (PBMCs) and splenocytes was measured at various time points (6 h, 12 h, 24 h, 48 h, 72 h, 5 days, and 7 days), through the measurement of TNF-α production after stimulation with lipopolysaccharide (LPS) in an ex vivo model. At each time point, we checked for plasma tumor necrosis factor (TNF)-α, interleukin (IL)-6, and IL-10 levels. Moreover, we analyzed reactive oxygen species (ROS) as measured by 2′,7′-dichlorodihydrofluorescein, plasma lactate, serum alanine aminotransferase (ALT), and creatinine levels. Nuclear factor (NF)-κB, IL-1 receptor-associated kinase (IRAK)-M, and cleaved caspase 3 levels were measured in the spleen. Endotoxin tolerance, measured by TNF-α production stimulated through LPS in PBMCs and splenocytes, was induced early in the sepsis model, starting from 6 h after sepsis. It reached a nadir at 24 to 48 h after sepsis, and then started to recover. Endotoxin tolerance was more prominent in the severe sepsis model. Plasma cytokines peaked at time points ranging from 6 to 12 h after sepsis. ROS levels peaked at 12 h and then decreased. Lactate, ALT, and serum creatinine levels increased up to 24 to 48 h, and then decreased. Phosphorylated p65 and IRAK-M levels of spleen increased up to 12 to 24 h and then decreased. Apoptosis was prominent 48 h after sepsis, and then recovered. In the rat model of polymicrobial sepsis, endotoxin tolerance occurred earlier and started to recover from 24 to 48 h after sepsis.
A Vision-Based System for Stage Classification of Parkinsonian Gait Using Machine Learning and Synthetic Data

Parkinson’s disease is characterized by abnormal gait, which worsens as the condition progresses. Although several methods have been able to classify this feature through pose-estimation algorithms and machine-learning classifiers, few studies have been able to analyze its progression to perform stage classification of the disease. Moreover, despite the increasing popularity of these systems for gait analysis, the amount of available gait-related data can often be limited, thereby, hindering the progress of the implementation of this technology in the medical field. As such, creating a quantitative prognosis method that can identify the severity levels of a Parkinsonian gait with little data could help facilitate the study of the Parkinsonian gait for rehabilitation. In this contribution, we propose a vision-based system to analyze the Parkinsonian gait at various stages using linear interpolation of Parkinsonian gait models. We present a comparison between the performance of a k-nearest neighbors algorithm (KNN), support-vector machine (SVM) and gradient boosting (GB) algorithms in classifying well-established gait features. Our results show that the proposed system achieved 96–99% accuracy in evaluating the prognosis of Parkinsonian gaits.
Point Spectra of Some Mutually Exciting Point Processes

IN contagious processes (e.g. measles, hijacking, etc.) the occurrence of events increases the probability of further events occurring in the near future. Also several series of events may interact with each other, for example one might consider notifications of some disease in a number of adjacent regions which would interact through infectives or carriers moving between the regions. In this paper we postulate a model for such processes and derive a general expression for the point spectral matrices. These theoretical spectra are useful for comparison with spectra estimated from data and thus provide a means of evaluating the fit of such a model in the manner of Bartlett (1963). The model studied was put forward in an earlier paper (Hawkes, 1971) but the solution was obtained only in special cases. In this paper an elegant solution is obtained for the. general case. Consider a stationary k-variate point process N(t), where Ni(t) represents the cumulative number of events in the ith process up to time t, with intensity vector X = of{dN(t)}/dt and covariance density matrix
Kinematic design of a novel Multi-legged robot with Rigid-flexible coupling grippers for asteroid exploration

Abstract Multi-legged robots with rigid-flexible coupling grippers have appealing applications to asteroid exploration with the microgravity. However, these robots usually have significantly complicated structures, which leads to a great challenge for the kinematic design.This paper proposes the kinematic design method for a novel multi-legged robot with the microspine gripper. First, the structure of the multi-legged asteroid exploration robot and the microspine gripper are demonstrated. Second, four performance evaluation indices, which are used to evaluate the stiffness, velocity, motion / force transfer efficiency and gripper attachment efficiency of the robot, are derived from the kinematic model. Non-dimensional design spaces of parameters to be optimized are drawn, and performance atlases are presented in design spaces. Third, the stiffness model of the microspine is derived. In addition, the constraint condition of the restoring spring is established, and the stiffness of restoring springs are optimized using the genetic algorithm. Several experiments are conducted to verify the stiffness model of the microspine. Finally, the prototype is developed and the experimental results validates the kinematic design method.
, Gene LRP 8 Disease and Myocardial Infarction in the A Novel Molecular Diagnostic Marker for Familial and Early-Onset Coronary Artery

Gong-Qing Shen, Domenico Girelli, Lin Li, Shaoqi Rao, Stephen Archacki, Oliviero Olivieri, Gene LRP8 Disease and Myocardial Infarction in the A Novel Molecular Diagnostic Marker for Familial and Early-Onset Coronary Artery Print ISSN: 1942-325X. Online ISSN: 1942-3268 Copyright © 2014 American Heart Association, Inc. All rights reserved. Dallas, TX 75231 is published by the American Heart Association, 7272 Greenville Avenue, Circulation: Cardiovascular Genetics doi: 10.1161/CIRCGENETICS.113. 000321 2014;7:514-520; originally published online May 27, 2014; Circ Cardiovasc Genet. http://circgenetics.ahajournals. org/content/7/4/514 World Wide Web at: The online version of this article, along with updated information and services, is located on the
Evaluation of Compliance Issues to Anti-glaucoma Medications Before and After a Structured Interventional Program

Background Glaucoma is one of the most common eye diseases in the elderly and the major cause of irreversible vision loss worldwide. Adherence to life-long therapies is crucial to prevent glaucoma progression. The current study aims to assess the educational element and its impact on glaucoma medication compliance over short and long periods. Methods This was a survey-based, prospective, interventional study, conducted via interviews of all glaucoma patients presented to the Ophthalmology Center at King Abdullah Medical City (KAMC), Makkah, Saudi Arabia. To achieve the study’s aim, a questionnaire with 31 items was utilized, followed by a structured program between September 2019 to June 2021. After that, a second questionnaire was used after a one month to one year to re-evaluate the intervention. Data was automatically collected in Microsoft Excel (Microsoft Corporation, Redmond, Washington, United States) and entered into IBM SPSS Statistics for Windows, Version 22.0 (Released 2013; IBM Corp., Armonk, New York, United States) for analysis. Results Non-compliance was detected in 15.7% of all recruited patients (n=134). However, the non-compliance percentage dropped to 10 (7.5%) after the structured program (P=0.028). Contributing factors were low educational level, bilateral eye disease, duration of treatment more than two years, and having more than two eye treatment bottles; however, the P-value was insignificant. Conclusions About one-sixth of our glaucoma patients were found to be non-compliant. However, the non-compliance reduced by more than half after the structured educational program. Treatment adherence can be improved by implementing awareness and correcting the beliefs about illness and medicines, thus potentially delaying disease development.
Enhancing the Performance of Multi-Agent Reinforcement Learning for Controlling HVAC Systems

Systems for heating, ventilation and air-conditioning (HVAC) of buildings are traditionally controlled by a rule-based approach. In order to reduce the energy consumption and the environmental impact of HVAC systems more advanced control methods such as reinforcement learning are promising. Reinforcement learning (RL) strategies offer a good alternative, as user feedback can be integrated more easily and presence can also be incorporated. Moreover, multi-agent RL approaches scale well and can be generalized. In this paper, we propose a multi-agent RL framework based on existing work that learns reducing on one hand energy consumption by optimizing HVAC control and on the other hand user feedback by occupants about uncomfortable room temperatures. Second, we show how to reduce training time required for proper RL-agent-training by using parameter sharing between the multiple agents and apply different pretraining techniques. Results show that our framework is capable of reducing the energy by around 6% when controlling a complete building or 8% for a single room zone. The occupants complaints are acceptable or even better compared to a rule-based baseline. Additionally, our performance analysis show that the training time can be drastically reduced by using parameter sharing.
Individual Inferences in Web-Based Information Environments: How Cognitive Processing Fluency, Information Access, Active Search Behaviors, and Task Competency Affect Metacognitive and Task Judgments

ABSTRACT Online information repositories increasingly serve as memory aids in people’s lives. Access to such information stores, however, can result in false perceived equivalencies between web-based information and personal knowledge, which can in turn influence judgments of oneself, of information search tasks, and of the Internet itself. Cognitive processing fluency, access to reliable web-based information, and actively searching for information are shown in a series of experiments to be associated with judgments related to metacognition and task performance. In the context of online information repositories accessed via web search activities, people are shown to (a) overemphasize the degree to which they find the web to be a ready source of relevant information, (b) overestimate their future task performance and the ease of tasks, and (c) inflate their own perceived cognitive and memory abilities. Results also show that those who are least competent in task completion overestimate their relative performance, whereas the most competent underestimate theirs, and that the availability of web-based information can inflate people’s estimated performance, particularly among the more competent. Collectively, three interrelated studies add considerable new insight regarding the impacts of near-ubiquitous access to contemporary information-saturated environments.
A LOW-PROFILE DUAL-BAND DUAL-POLARIZED DIPOLE ANTENNA FOR 5G COMMUNICATION APPLICATIONS

A dual-band dual-polarized dipole antenna with an artificial magnetic conductor (AMC) reflector is proposed, which can be applied in 5G base stations. The antenna consists of a pair of ±45◦ crossed dipoles and a wideband AMC reflector. By adopting arrow-shaped dipoles and introducing slots, dual-band characteristic is achieved. The AMC is designed to operate with 90◦ reflection-phase bandwidth of 2.1–3.9GHz (30%). Compared with using traditional reflector, the profile height can be reduced from 0. 25λ0 to 0. 11λ0 (where λ0 is the free-space wavelength at 2.6GHz). The measurement results show that the impedance bandwidth with |S11| < −14 dB is about 15.5% (2.44–2.85GHz) and 18.6% (3.17–3.82GHz), covering the Sub-6GHz bands. The average gain is 8.5 dBi in the lower band and 8.2 dBi in the upper band. At 2.6GHz and 3.45GHz, the half-power beamwidth of the antenna is 77◦ and 80◦, respectively. In the two bands, the port isolation of the antenna is more than 28 dB, and the cross-polarization level is less than −20 dB.
Supporting Flexible Assembly through Human Factors

There is a growing trend in many industries to expand the diversity of product offerings to meet widespread customer needs. This trend has increased the need for flexibility in manufacturing processes. Advances in flexible automation have far outpaced the research supporting flexibility in the manual operations that are required alongside automation. The objective of this study was to test a real-time computer support system that provided interactive assembly instructions through a graphical user interface combining text and illustration for workers on a flexible assembly line. The interface simplifies the expertise required of an assembly worker by replacing complex blueprints with simple directions and illustrations, thus supporting the rapid introduction of new products and the assembly of a wide variety of parts with minimal off-line training. The results of the study strongly support the use of this type of system. Assembly times were significantly reduced, in most cases by over 50%. Furthermore, assembly errors were virtually eliminated.
Characterizing the Spatiotemporal Patterns and Key Determinants of Homestay Industry Agglomeration in Rural China Using Multi Geospatial Datasets

Understanding the spatiotemporal patterns and key determinants of rural homestay industry agglomeration is crucial for the well-planning and well-management of rural tourism during the process of rural revitalization in China. By employing multi geospatial datasets, this study investigated the long-term spatiotemporal patterns and their key determinants of homestay inns during the period 2004–2019 in Moganshan, a well-known rural tourism destination in Zhejiang Province, China. The kernel density estimation and spatial autocorrelation were integrated to identify the hotspots of rural homestay inns at a fine scale. The key determinants were further uncovered using multiple stepwise regression and logistic regression models. The result shows that the overall growth of homestay inns was slow at the early stage and has progressed rapidly since 2014, with 94.2% of homestay inns newly opened during the period 2014–2019. The first hotspot was located in Moganshan National Park and then spread to the surrounding villages. Three hotspot zones have emerged, including the northern hotspot zone (Sihe-Xiantan), central hotspot zone (Houwu-Park-Liaoyuan), and southern hotspot zone (Ziling-Laoling-Lanshukeng) by 2019. The modeling indicates that government policy was an essential determinant for the increase in homestay inns, followed by entrepreneurship and investment. The new homestay inns were more likely to occur in settlements close to scenic spots, river networks, and cultivated land. Abundant scenic spots and heterogeneous landscapes were also preferred when selecting sites and executing landscape design for homestay inns. Our empirical study has provided practical insights for policy makers, entrepreneurs, and planners for future sustainable homestay industry development.