Miracle-dz/UniFAR-FTUs huggingface dataset:
Thymic Epithelium Phase of Postselection Expansion Driven by Identification of a Developmentally Regulated

To investigate events following the initiation of positive selection, we have used reaggregate organ cultures to follow the maturation of puriﬁed CD4 1 8 1 69 1 thymocytes; these thymocytes represent a subpopulation of thymocytes which have already received positive selection signals. Using a dilution analysis of an FITC-based membrane-binding dye, 5-(and -6)-carboxyﬂuorescein diacetate succinimidyl ester, to allow a quantitative measure of proliferation, we show that while newly selected CD4 1 and CD8 1 cells are nondividing, both subsets subsequently undergo a wave of postpositive selection proliferation involving multiple cell divisions. Moreover, in the presence of fetal stromal cells, postselection expansion is more extensive in newborn thymocytes compared with adult thymocytes, suggesting that this phase of expansion is developmentally regulated. We also show that proliferation of CD4 1 and CD8 1 cells is seen in reaggregates of puriﬁed MHC class II 1 thymic epithelial cells, while CD4 1 and CD8 1 cells generated from bcl-2 transgenic CD4 1 8 1 69 1 thymocytes in the absence of stromal cell support survive but do not proliferate; this observation indicates that MHC class II 1 thymic epithelial cells are both necessary and sufﬁcient to mediate this wave of cell division. Finally, the maturation of CD4 1 8 1 69 1 thymocytes and the subsequent proliferation of CD4 1 and CD8 1 cells occur in the presence of MHC-mismatched thymic stromal cells, suggesting that the later stages of positive selection and the associated postselection events do not depend on interactions with the same peptide/MHC complexes responsible for initiation. The Journal of Immunology, 1998, 160: 3666–3672.
DC Capacitor Voltage Balancing Control for Delta-Connected Cascaded H-Bridge STATCOM Considering Unbalanced Grid and Load Conditions

In this paper, a comprehensive control scheme for a delta-connected cascaded H-bridge (CHB) converter-based static synchronous compensator (STATCOM) is presented, especially focusing on improving dynamic performance by novel feedforward control method. The method can conspicuously improve the dynamics of zero-sequence circulating current regulation of delta-connected CHB STATCOM especially under grid fault condition as well as load unbalance without excessive dc cell capacitor voltage fluctuation. The validity of the proposed method is demonstrated by mathematical proof. Finally, the full-scaled simulation results and the down-scaled experimental results verify that stable operation is guaranteed for both emulated grid and load unbalance conditions.
IMPACT OF INTERNETADDICTION ON HEALTH ANXIETYIN MALAYSIAN YOUTH DURING COVID-19 PANDEMIC

The COVID-19 The pandemic affected the mental health of people. Individuals prefer to collect information from the Internet about the virus, disease, symptoms, reports on statistics, treatments, and any other information related to the pandemic. In this study, we aimed to explore the impact of Internet use in three different levels of users (Internet addicts, over-users, and average-users) on health anxiety during the pandemic among the young population in Malaysia. Four hundred and forty-eight young adult in Malaysia completed the online survey including demographic questionnaire, Short-form Health Anxiety Inventory, and Internet Addiction Test. Results showed that the Internet addict users have higher scores in the Total Health Anxiety and Health Anxiety factors (Illness Likelihood and Negative Consequences) compared to the average-users and over-users. It seems that high Internet usage is associated with high health anxiety. People may search more on the Internet for information related to the COVID-19 and the symptoms and it may increase their health anxiety. This study provides knowledge implications for mental health professionals about the association between Internet addiction and health anxiety. The findings may provide awareness to the public and professionals about one of the factors that are linked to individuals' health anxiety during the pandemic.
The Diphthong <ei> in Variationist Studies of Brazilian Portuguese: A Systematic Literature Review

: This study presents a systematic literature review of the monophthongization of the diphthong <ei> in Brazilian Portuguese. Monophthongization is a sound change by which a diphthong becomes a single vowel. Thus, the output of, for example, the word beira (‘edge’) can be b [e ʊ̯ ] ra or b [e] ra . Our primary sources, 10 Master’s theses that analyzed this phenomenon using quantitative sociolinguistic methodologies, focus on individually describing a region’s variety of Portuguese. However, the results were never systematically related to each other. Consequently, these works do not present a comprehensive overview of the production of <ei> in Brazilian Portuguese. Thus, this systematic review gathers and uniﬁes information dispersed in these studies, aiming to offer an overview of this optional phenomenon. The overall results demonstrate that the following context was the relevant linguistic variable, while the speaker’s educational level and dialect variation are the relevant non-linguistic variables for the application of the monophthongization rule.
A model based on the fractional Brownian motion for the temperature fluctuation in the Campi Flegrei caldera

The aim of this research is to identify an efficient model to describe the fluctuations around the trend of the soil temperatures monitored in the volcanic caldera of the Campi Flegrei area in Naples (Italy). The study focuses on the data concerning the temperatures in the mentioned area through a seven-year period. The research is initially finalized to identify the deterministic component of the model, given by the seasonal trend of the temperatures, which is obtained through an adapted regression method on the time series. Subsequently, the stochastic component from the time series is tested to represent a fractional Brownian motion (fBm). An estimation based on the periodogram of the data is used to estabilish that the data series follows a fBm motion, rather then a fractional Gaussian noise. An estimation of the Hurst exponent $H$ of the process is also obtained. Finally, an inference test based on the detrended moving average of the data is adopted in order to assess the hypothesis that the time series follows a suitably estimated fBm.
The Impact of Control Information Prioritization on QoS Performance Metrics

— Real-time protocols such as Real-time Transport Protocol (RTP) and its companion protocol Real-time Transport Control Protocol (RTCP) are the solution for customers who have progressively come to desire real-time services. The characteristics of RTP and RTCP packets differ since they carry different content types, i.e., real-time data and control data, respectively. The standard approach assumes that RTP real-time data packets and RTCP control packets are classified into the same media-oriented service class. That might impact the Quality of Service (QoS) for real-time services with regard to network status. Therefore, this paper proposes an idea of QoS enhancement by efficiently transmitting user control information and establishing the proper transmission rate dynamically in dependence upon the status of a network. To accomplish this task, RTCP control packets are classified the Signaling service class, which should be given the absolute preferential treatment over all other User service classes. Results obtained with NCTUns simulator and emulator show a significant impact of control information prioritization on QoS performance metrics and indicate that this approach could be a new starting point for research activities in the future.
Approximation theory for LQG (Linear-Quadratic-Gaussian) optimal control of flexible structures

An approximation theory is presented for the LQG (Linear-Quadratic-Gaussian) optimal control problem for flexible structures whose distributed models have bounded input and output operators. The main purpose of the theory is to guide the design of finite dimensional compensators that approximate closely the optimal compensator. The optimal LQG problem separates into an optimal linear-quadratic regulator problem and an optimal state estimation problem. The solution of the former problem lies in the solution to an infinite dimensional Riccati operator equation. The approximation scheme approximates the infinite dimensional LQG problem with a sequence of finite dimensional LQG problems defined for a sequence of finite dimensional, usually finite element or modal, approximations of the distributed model of the structure. Two Riccati matrix equations determine the solution to each approximating problem. The finite dimensional equations for numerical approximation are developed, including formulas for converting matrix control and estimator gains to their functional representation to allow comparison of gains based on different orders of approximation. Convergence of the approximating control and estimator gains and of the corresponding finite dimensional compensators is studied. Also, convergence and stability of the closed-loop systems produced with the finite dimensional compensators are discussed. The convergence theory is based on the convergence of the solutions of the finite dimensional Riccati equations to the solutions of the infinite dimensional Riccati equations. A numerical example with a flexible beam, a rotating rigid body, and a lumped mass is given.
University of Birmingham Automated verification of care pathways using constraint programming

—Bad construction of modeled care pathways can lead to satisﬁability problems during the pathway execution. These problems can ultimately result in medical errors and need to be checked as formally as possible. Therefore, this study proposes a set of algorithms using a free open-source library dedicated to constraint programming allied with a DSL to encode and verify care pathways, checking four possible problems: states in deadlock, non-determinism, inaccessible steps and transitions with logically equivalent guard conditions. We then test our algorithms in 84 real care pathways used both in hospitals and surgeries. Using our algorithms, we were able to ﬁnd 200 problems taking less than 1 second to complete the veriﬁcation on most pathways.
Title: Characterization of Sugar Industry Waste (Filter Cake) and Agro-waste Crop Residue as Potential Source of Livestock Feed Raw Materials By:

The potential searching on sources of livestock feed material is the major constrain in the sustainability of livestock sector for the future. The utilization of cereal crop residues (CCRs) is limited because they contain a large proportion of lignocellulosic compounds and little nitrogen. The filter cake is a poorly studied potential as forage for ruminants. In addition, they show higher fiber content. The analysis on characterization of filter cake, Teff straw, barley straw and corn cob were carried out through proximate analysis such as moisture content (MC), dry mater (DM), organic dry matter (ODM), ash content (AC), crude protein (CP), crude fiber (CF), Elemental analyzer, UV-Visible spectroscopy and Fourier transform infrared spectroscopy were used for analyzing the elemental profiles and functional feed constitutions. The proximate analysis of raw material feed stocks (in %) contains in the range of DM (96. 54 ± 0.7-89.5±0.5), ODM (98.33±0.3-79.4±0.4), MC (3.45±0.7-10.5±0.5), CP (2.32±0.58-12.92±0.36), CF (15.5±0.50-85±0.5), AC (1.69±0.03-20.6±0), FT (1.44±0.1-10.5±0.82), Carbohydrates (45.27- 90.42), energy value (327.26- 386.56 kcal.) and Sucrose of (3.85±0.83). And its elemental compositions can be addressed by their mineral matter contain up 1.69±0.03-20.6±0 %. The moisture content and dry matter contents may vary depends on factors such as cultivator of crops and cane, location, climate, dry length, soil pest diseases, cultivation practices, the harvesting practice and the processing of cans and crops. Large quantities of fibrous crop residues are already used as animal feed in many areas across these countries. There are too many areas in developing countries where ruminant livestock starves due to lack of feed. So this study shows that the direction of searching and compensating the availability of such important croup residues and the sugar industry by product (filter cake) as the raw sources of livestock feeds. employed to determine the presence of functional group of feed components in filter cake and the prepared feedstock samples. FTIR spectra were recorded for both filter cake and the prepared feedstock. Here in this study, free biomass residue was removed by centrifuging the sample slurry at 110 x 100 rpm for 20 minutes by using mechanical centrifuge machine and the supernatant liquid was decanted. The obtained product was washed three times with distilled/deionized water. Then, the purified sample was dried and ground with KBr powder, subsequently made a pellet by employing pellet maker. Then, a perfect sample-KBr pellet of thin and transparent filmy sample was obtained. The FTIR spectrum of KBr-sample pellet was carried out and the spectra were scanned (operated) in the range of 4000-400 cm -1 wave number at a resolution of 4cm -1 .
PLC Installation on Inner Ring Bore SHG Grinding Machine

— To supply product of high quality in the market for maintaining the market requirements, machinery should be up to date. This project work is based on the upgradation of the inner ring track grinding machine on a production line. A grinding machine is a type of machine, which uses an abrasive wheel on cutting tool. Each grain of abrasive operation on the wheel's surface cuts a small chip from the work-piece via deformation. Grinding is used to finish work-pieces that must show high surface and high accuracy of shape and dimension. The main objective is:-1. The aim of our system is to reduce the down time of the grinding machine i.e. to increase the productivity of bearings. Apart from down time, our objective is to improve the quality of the bearings. Quality defines the speed, precision and efficiency of bearings. Reduction of the cost of maintenance of the machine is also our prime objective. We meet our objectives with the help of Mitsubishi PLC and Servo System.
Identi¢cation of novel HrpXo regulons preceded by two cis-acting elements, a plant-inducible promoter box and a (cid:1) 10 box-like sequence, from the genome database ofXanthomonasoryzaepv.

A regulatory protein HrpXo of Xanthomonas oryzae pv. oryzae , the causal agent of bacterial leaf blight of rice, is known to control the expression of hrp genes that encode components of a type III secretion system and of some effector protein genes. In this study, we screened novel HrpXo regulons from the genome database of X. oryzae pv. oryzae , searching for ORFs preceded by two predicted sequence motifs, a plant-inducible promoter box-like sequence and a (cid:1) 10 box-like sequence. Using a gus reporter system, nine of 15 ORF candidates were expressed HrpXo dependently. We also showed by base-substituted mutagenesis that both motifs are essential for the expression of the genes.
Semantic Embedding Guided Attention with Explicit Visual Feature Fusion for Video Captioning

Video captioning, which bridges vision and language, is a fundamental yet challenging task in computer vision. To generate accurate and comprehensive sentences, both visual and semantic information is quite important. However, most existing methods simply concatenate different types of features and ignore the interactions between them. In addition, there is a large semantic gap between visual feature space and semantic embedding space, making the task very challenging. To address these issues, we propose a framework named semantic embedding guided attention with Explicit visual Feature Fusion for vidEo CapTioning, EFFECT for short, in which we design an explicit visual-feature fusion (EVF) scheme to capture the pairwise interactions between multiple visual modalities and fuse multimodal visual features of videos in an explicit way. Furthermore, we propose a novel attention mechanism called semantic embedding guided attention (SEGA), which cooperates with the temporal attention to generate a joint attention map. Specifically, in SEGA, the semantic word embedding information is leveraged to guide the model to pay more attention to the most correlated visual features at each decoding stage. In this way, the semantic gap between visual and semantic space is alleviated to some extent. To evaluate the proposed model, we conduct extensive experiments on two widely used datasets, i.e., MSVD and MSR-VTT. The experimental results demonstrate that our approach achieves state-of-the-art results in terms of four evaluation metrics.
Coordinated Charging and Discharging of Electric Vehicles: A New Class of Switching Attacks

In this work, we investigate that the abundance of Electric Vehicles (EVs) can be exploited to target the stability of the power grid. Through a cyber attack that compromises a lot of available EVs and their charging infrastructure, we present a realistic coordinated switching attack that initiates inter-area oscillations between different areas of the power grid. The threat model as well as linearized state-space representation of the grid are formulated to illustrate possible consequences of the attack. Two variations of switching attack are considered, namely, switching of EV charging and discharging power into the grid. Moreover, two possible attack strategies are also considered (i) using an insider to reveal the accurate system parameters and (ii) using reconnaissance activities in the absence of the grid parameters. In the former strategy, the system equations are used to compute the required knowledge to launch the attack. However, a stealthy system identification technique, which is tailored based on Eigenvalue Realization Algorithm (ERA), is proposed in latter strategy to calculate the required data for attack execution. The two-area Kundur, 39-Bus New England, and the Australian 5-area power grids are used to demonstrate the attack strategies and their consequences. The collected results demonstrate that by manipulation of EV charging stations and launching a coordinated switching attack to those portions of load, inter-area oscillations can be initiated. Finally, to protect the grid from this anticipated attack, a Support Vector Machine (SVM) based framework is proposed to detect and eliminate this attack even before being executed.
Comparison of neural network paradigms for their usefulness in a real-world network intrusion detection deployment and a proposed optimised approach

Cybersecurity and security of IoT are vital to the proliferation of the technology. Yet, the number of cyberattacks is on the rise. One of the recent advancements in the fight against cyberthreats are machine-learning based network intrusion detection systems. This paper compares three state of the art methods with a proposed, optimised approach to showcase higher metrics and better execution times.
DIRE and its Data: Neural Decompiled Variable Renamings with Respect to Software Class

The decompiler is one of the most common tools for examining executable binaries without the corresponding source code. It transforms binaries into high-level code, reversing the compilation process. Unfortunately, decompiler output is far from readable because the decompilation process is often incomplete. State-of-the-art techniques use machine learning to predict missing information like variable names. While these approaches are often able to suggest good variable names in context, no existing work examines how the selection of training data influences these machine learning models. We investigate how data provenance and the quality of training data affect performance, and how well, if at all, trained models generalize across software domains. We focus on the variable renaming problem using one such machine learning model, DIRE. We first describe DIRE in detail and the accompanying technique used to generate training data from raw code. We also evaluate DIRE’s overall performance without respect to data quality. Next, we show how training on more popular, possibly higher quality code (measured using GitHub stars) leads to a more generalizable model because popular code tends to have more diverse variable names. Finally, we evaluate how well DIRE predicts domain-specific identifiers, propose a modification to incorporate domain information, and show that it can predict identifiers in domain-specific scenarios 23% more frequently than the original DIRE model.
DARK-KERNEL: Design and Implementation of a Kernel Level Active Darknet Sensor

Darknet traffic is defined as the network traffic arriving at a network with destination IP addresses that are allocated to the network but are not assigned to any device. Darknet monitoring is an unsubstantiated way to observe cyberspace activities through active or passive monitoring. In passive monitoring, passive observation of the packets and their analysis is done. While in active monitoring, the monitoring machines (darknet sensors) also respond to the traffic to actively engage the attackers to find if the source of the traffic is spoofed. The darknet network traffic also contains valuable forensic information about malware and attack patterns. However, passive darknet monitoring is ineffective in developing threat intelligence because it captures packets from various spoofed IP addresses and cannot distinguish them from regular traffic. Therefore, one needs to validate incoming packet addresses in active mode to collect real threat intelligence. This paper implements a darknet sensor developed to be active by altering the network stack of a Linux kernel to collect and store all darknet traffic arriving at it and further validate TCP packets to filter out source IP address spoofing. To achieve this, we undertake systematic tracing of kernel source code, identifying and modifying specific sections of kernel source code with the goals to be an effective active darknet sensor and minimize the latency at the darknet sensor.
A Massively Parallel Modularity-Maximizing Algorithm with Provable Guarantees

Graph clustering is one of the most basic and popular unsupervised learning problems. Among the different formulations of the problem, the modularity objective has been particularly successful in helping design impactful algorithms; Most notably, the Louvain algorithm has become one of the most used algorithm for clustering graphs. However, one major limitation of the Louvain algorithm is its sequential nature which makes it impractical in distributed environments and on massive datasets. In this paper, we provide a parallel version of Louvain which works in the massively parallel computation model (MPC). We show that it recovers the ground-truth clusters in the classic stochastic block model in only a constant number of parallel rounds, and so for a wider regime of parameters than the standard Louvain algorithm as shown recently in [Cohen-Addad et al. 2020].