Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Rural Water Supply in the United States

Governments have been forced to formulate and implement demand-responsive approaches focused on curtailing the challenges faced by their denizens in terms of water access. This research evaluates the current stature of rural water supply in the United States putting emphasis on the most significant legal and institutional considerations that hinder water access. A PRISMA guideline was implemented in order conduct a systematic review from 1990 to 2019. Findings show that there is a need for the government to reorganize its existing structures and offer its municipalities the requisite flexibility required to meet their ameliorative mandates.
Attacking the Core Structure of Complex Network

The concept of <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-core in complex networks plays a key role in many applications, e.g., understanding the global structure or identifying central/critical nodes, of a network. A malicious attacker with a jamming ability can exploit the vulnerability of the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-core structure to attack the network and invalidate the network analysis methods, e.g., reducing the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-shell values of nodes can deceive graph algorithms, leading to the wrong decisions. In this article, we investigate the robustness of the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-core structure under adversarial attacks by deleting edges, for the first time. First, we give the general definition of the targeted <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-core attack, map it to the set cover problem, which is NP-hard, and further introduce a series of evaluation metrics to measure the performance of attack methods. Then, we propose the <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> index theoretically as the probability that the terminal node of an edge does not belong to the innermost core, which is further used to guide the design of our heuristic attack methods, namely, COREATTACK and GreedyCOREATTACK. The experiments on a variety of real-world networks demonstrate that our methods behave much better than a series of baselines, in terms of much smaller edge change rate (ECR) and false attack rate (FAR), achieving state-of-the-art attack performance. More impressively, for certain real-world networks, only deleting one edge from the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-core may lead to the collapse of the innermost core, even if this core contains dozens of nodes. Such a phenomenon indicates that the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-core structure could be extremely vulnerable under adversarial attacks, and its robustness, thus, should be carefully addressed to ensure the security of many graph algorithms. An open-source implementation is available at <uri>https://github.com/Yocenly/COREATTACCK</uri>.
Bulk Field Effect Diode

The field effect diode (FED) is an attractive device to use in various digital and analog applications, but all previously proposed FEDs are based on the silicon-on-insulator (SOI) technology. In this article, a bulk version of FED (BFED) is proposed for the first time. Both SOI-FED and BFED are simulated using TCAD tools as a semiconductor drift-diffusion solver. The proposed bulk FED can operate as well as a regular SOI-FED. Most sensitive parameters in the design of the BFED are doping and thickness of <inline-formula> <tex-math notation="LaTeX">$\text{p}^{+}$ </tex-math></inline-formula>-drain and <inline-formula> <tex-math notation="LaTeX">$\text{n}^{+}$ </tex-math></inline-formula>-source. The effect of these parameters is investigated on the output characteristics of the BFED.
Trustworthy and Reliable Deep-Learning-Based Cyberattack Detection in Industrial IoT

A fundamental expectation of the stakeholders from the Industrial Internet of Things (IIoT) is its trustworthiness and sustainability to avoid the loss of human lives in performing a critical task. A trustworthy IIoT-enabled network encompasses fundamental security characteristics, such as trust, privacy, security, reliability, resilience, and safety. The traditional security mechanisms and procedures are insufficient to protect these networks owing to protocol differences, limited update options, and older adaptations of the security mechanisms. As a result, these networks require novel approaches to increase trust-level and enhance security and privacy mechanisms. Therefore, in this article, we propose a novel approach to improve the trustworthiness of IIoT-enabled networks. We propose an accurate and reliable supervisory control and data acquisition (SCADA) network-based cyberattack detection in these networks. The proposed scheme combines the deep-learning-based pyramidal recurrent units (PRU) and decision tree (DT) with SCADA-based IIoT networks. We also use an ensemble-learning method to detect cyberattacks in SCADA-based IIoT networks. The nonlinear learning ability of PRU and the ensemble DT address the sensitivity of irrelevant features, allowing high detection rates. The proposed scheme is evaluated on 15 datasets generated from SCADA-based networks. The experimental results show that the proposed scheme outperforms traditional methods and machine learning-based detection approaches. The proposed scheme improves the security and associated measure of trustworthiness in IIoT-enabled networks.
Part count and design of robust systems

Systems engineering frequently includes efforts to reduce part count with the goal of cutting costs, enhancing performance, or improving reliability. This paper examines the engineering practices related to part count, applying three different theories—Theory of Inventive Problem Solving, Axiomatic Design, and Highly Optimized Tolerance. Case studies from the jet engine industry are used to illustrate the complicated tradesoffs involved in real-world part count reduction efforts. The principal conclusions are that: (1) Part consolidation at the component level has generally been accomplished as technological advancements enable them which is consistent with the “law of ideality” in the Theory of Inventive Problem Solving; (2) part count reduction frequently increases coupling among functional requirements, design parameters, and processing variables while also delivering higher reliability which conflicts with the theory of Axiomatic Design; and (3) at the overall system level, jet engine part count has generally increased in response to escalating demands for system robustness as suggested by the theory of Highly Optimized Tolerance. © 2007 Wiley Periodicals, Inc. Syst Eng 10: 203–221, 2007 This paper was presented at the 16th Annual International Symposium of the International Council on Systems Engineering (INCOSE), July 9–14, 2006, Orlando, FL.
Visible-Light-Induced Recyclable g-C 3 N 4 Catalyzed C–H Hydroxylation of Quinoxalin-2(1 H )-ones

A visible-light-promoted hydroxylation of quinoxalin-2(1H)-ones using recyclable graphitic carbon nitride (g-C3N4) as a heterogeneous photocatalyst was developed. This protocol enabled the selective synthesis of various hydroxyl-containing quinoxalin-2(1H)-ones in good to excellent yields under an ambient air atmosphere. Importantly, this heterogeneous catalyst can be used at least 6 times without significant loss of activity.
SYMPOSIUM REVIEW: Phosphoinositides: lipid regulators of membrane proteins

Phosphoinositides are a family of minority acidic phospholipids in cell membranes. Their principal role is instructional: they interact with proteins. Each cellular membrane compartment uses a characteristic species of phosphoinositide. This signature phosphoinositide attracts a specific complement of functionally important, loosely attached peripheral proteins to that membrane. For example, the phosphatidylinositol 4,5‐bisphosphate (PIP2) of the plasma membrane attracts phospholipase C, protein kinase C, proteins involved in membrane budding and fusion, proteins regulating the actin cytoskeleton, and others. Phosphoinositides also regulate the activity level of the integral membrane proteins. Many ion channels of the plasma membrane need the plasma‐membrane‐specific PIP2 to function. Their activity decreases when the abundance of this lipid falls, as for example after activation of phospholipase C. This behaviour is illustrated by the suppression of KCNQ K+ channel current by activation of M1 muscarinic receptors; KCNQ channels require PIP2 for their activity. In summary, phosphoinositides contribute to the selection of peripheral proteins for each membrane and regulate the activity of the integral proteins.
Research status of sensor demodulation technology of fiber grating ultrasonic signal

Compared with ordinary gratings, phase-shifted fiber gratings have better ultrasonic response sensitivity and higher upper response frequency. The application of phase-shifted fiber gratings in ultrasonic signal detection has gradually become a research hotspot. This paper mainly introduces the demodulation principle of tunable laser power scanning and wavelength feedback control techniques such as wavelength stabilization, frequency stabilization and laser tracking. Most of the existing demodulation techniques are only suitable for single optical channel, and the demodulation frequency is low. The development direction of fiber Bragg grating demodulation technology is multi-channel synchronous stable high frequency demodulation.
Development on Deaf Support Application Based on Daily Sound Classification Using Image-based Deep Learning

According to statistics, the number of hearing-impaired persons among the disabled in Korea accounts for 27% of all persons with disabilities. However, there is insufficient support for the deaf and hard of hearing's protective devices and life aids compared to the large number. In particular, the hearing impaired misses much information obtained through sound and causes inconvenience in daily life. Therefore, in this paper, we propose a method to relieve the discomfort in the daily life of the hearing impaired. It analyzes sounds that can occur frequently and must be recognized in daily life and guide them to the hearing impaired through applications and vibration bracelets. Sound analysis was learned by using deep learning by converting sounds that often occur in daily life into the Mel-Spectrogram. The sound that actually occurs is recorded through the application, and then it is identified based on the learning result. According to the identification result, predefined alarms and vibrations are provided differently so that the hearing impaired can easily recognize it. As a result of the recognition of the four major sounds occurring in real life in the experiment, the performance showed an average of 85% and an average of 80% of the classification rate for mixed sounds. It was confirmed that the proposed method can be applied to real-life through experiments. Through the proposed method, the quality of life can be improved by allowing the hearing impaired to recognize and respond to sounds that are essential in daily life.
DBSCAN Clustering Algorithm Based on Big Data Is Applied in Network Information Security Detection

In order to improve the certainty and clarity of information security detection, an application method of big data clustering algorithm in information security detection is proposed. The experimental results show that when the amount of data is close to 6000, the efficiency of the improved algorithm is nearly 70% higher than that of DBSCAN, and it is still very close to the efficiency of the BIRCH algorithm. The algorithm has a high processing speed for large-scale data sets without increasing the time complexity and can also accurately cluster clusters of any shape. When the data set increases from 9000 rows to 58000 rows, in turn, the time-consuming of the traditional DBSCAN algorithm increases sharply, while the time-consuming of the improved DBSCAN algorithm is still stable, and the time-consuming gap between the two is getting bigger and bigger. At the same time, the algorithm adopts a heuristic adaptive algorithm to estimate some threshold parameters of the clustering algorithm, which can avoid the direct setting of the threshold parameters by the user and can effectively estimate the relevant threshold parameters, extract clusters of any shape, and the clustering effect is obvious.
Optimization of Spiral Inductors and LC Resonators Exploiting Space Mapping Technology

This thesis contributes to the computer-aided design (CAD) of spiral inductors and LC resonators with spiral inductors exploiting full-wave electromagnetic (EM) analysis. The spiral inductor is widely used in radio frequency integrated circuits (RF ICs), such as low noise amplifiers (LNA) and voltage controlled oscillators (VCO). The design of spiral inductors has a direct influence on the performance of these circuits. Recently proposed optimization methods for spiral inductors are usually based on circuit models, which are computationally efficient but inaccurate compared with full-wave electromagnetic (EM) simulations. For the first time, we develop an optimization technique for the design of spiral inductors and LC resonators exploiting both the computational efficiency of a (cheap) circuit model and the accuracy of a full-wave EM analysis, based on geometric programming (GP) and space mapping (SM). With the new technique, we can efficiently obtain EM-validated designs with considerable improvement over those obtained with traditional optimization methods. ABSTRACT iv v ACKNOWLEDGEMENT
Teachers’ Racial Bias: Can Teachers Rate an African American Boy Similarly to His Racial Counterparts on Problem Behaviors for a Broad Rating Scale?

Abstract After decades of advancement in education equality, a disproportional number of racially minoritized students are placed in special education. Addressing the disproportionality is a complicated issue due to various contributing factors. One possible cause is racial bias from teachers who rate problem behaviors of African American boys for special education evaluations. This study investigated the effect of a student’s race on teachers’ ratings of problem behavior for special education evaluations. In an online study, the race of an African American student, Asian American student, and European American student were manipulated in a vignette of a hypothetical child. Participants read one of three vignettes and completed the Achenbach System of Empirically Based Assessment Teacher Report Form and a 7-item questionnaire. Although results evinced teachers perceived the home life of the African American student as poorer quality, no racial bias in ratings of internalizing behaviors, externalizing behaviors, referrals for special education, likelihood of postsecondary education, and academic functioning were found. The strengths, limitations, and implications for experimental studies and clinical practice for examining racial bias are discussed. Impact Statement This experimental study seeks to determine when manipulating the race/ethnicity of a child, can teachers rate a vignette of a student similarly on a broad rating scale. No prior study has centered African American boys, used a norm-referenced broad rating scale for both externalizing disorders and internalizing disorders in a vignette, and included national sample in an online study on racial bias in the United States of America.
Privacy and Utility of X-Vector Based Speaker Anonymization

We study the scenario where individuals (speakers) contribute to the publication of an anonymized speech corpus. Data users leverage this public corpus for downstream tasks, e.g., training an automatic speech recognition (ASR) system, while attackers may attempt to de-anonymize it using auxiliary knowledge. Motivated by this scenario, speaker anonymization aims to conceal speaker identity while preserving the quality and usefulness of speech data. In this article, we study x-vector based speaker anonymization, the leading approach in the VoicePrivacy Challenge, which converts the speaker's voice into that of a random pseudo-speaker. We show that the strength of anonymization varies significantly depending on how the pseudo-speaker is chosen. We explore four design choices for this step: the distance metric between speakers, the region of speaker space where the pseudo-speaker is picked, its gender, and whether to assign it to one or all utterances of the original speaker. We assess the quality of anonymization from the perspective of the three actors involved in our threat model, namely the speaker, the user and the attacker. To measure privacy and utility, we use respectively the linkability score achieved by the attackers and the decoding word error rate achieved by an ASR model trained on the anonymized data. Experiments on LibriSpeech show that the best combination of design choices yields state-of-the-art performance in terms of both privacy and utility. Experiments on Mozilla Common Voice further show that it guarantees the same anonymization level against re-identification attacks among 50 speakers as original speech among 20,000 speakers.
D Meson Production in Deep Inelastic Scattering and Photoproduction with the H1 Detector at HERA

The charm quark production in ep scattering is dominated by the boson-gluon-fusion (BGF) process. For the presented analyses [1] the kinematic range is divided into two regions of the photon virtuality Q: The DIS regime covers Q > 5 GeV while Q ≈ 0 is called the photoproduction regime. Due to the presence of many hard scales (mc, Q , pT ) the predictive power of perturbative QCD (pQCD) is decreased since the choice of a hard scale is no longer unique. If one of the other scales is much larger than the mass, massless schemes where the mass of the produced quark is neglected can be applied otherwise massive schemes are applied. Because of the dominant BGF production process the charm cross section is directly sensitive to the gluon density in the proton.
Uncertainty Quantification for Numerical Solutions of the Nonlinear Partial Differential Equations by Using the Multi-Fidelity Monte Carlo Method

The Monte Carlo simulation is a popular statistical method to estimate the effect of uncertainties on the solutions of nonlinear partial differential equations, but it requires a huge computational cost of the deterministic model, and the convergence may become slow. For this reason, we developed the multi-fidelity Monte Carlo (MFMC) methods based on data-driven low-fidelity models for uncertainty analysis of nonlinear partial differential equations. Firstly, the nonlinear partial differential equations are transformed into ordinary differential equations (ODEs) by using finite difference discretization or Fourier transformation. Then, the reduced dimension model and discrete empirical interpolation method (DEIM) are coupled to construct effective nonlinear low-fidelity models in ODEs system. Finally, the MFMC method is used to combine the output information of the high-fidelity model and the low-fidelity models to give the optimal estimation of the statistics. Experimental results of the nonlinear Schrodinger equation and the Burgers’ equation show that, compared with the standard Monte Carlo method, the MFMC method based on the data-driven low-fidelity model in this paper can improve the calculation efficiency significantly.
Blurred Boundaries

The unprecedented social disruptions resulting from the COIVD-19 Pandemic have resulted in rapid change within the family and home. This paper uses semi-structured interviews with parents around the globe to examine the following reserach questions: 1. How have the spatial and temporal organizations of learning and working in the home been altered throughout the COVID-19 pandemic? 2. What are the alterations in the educational processes and the role of the family in response to the changes caused by the COVID-19 pandemic? We found that typical boundaries, those between the roles of family members, between work or school and home, and between leisure time and work time have been fundamentally blurred. While some of these boundaries are more porous than others, families report fundamental shifts, temporary and permanent in the way they organize their home and family, spatially and temporally, and the roles they take on within the family.
NMPC-based trajectory tracking control strategy for unmanned surface vehicle

To solve the unmanned surface vehicle trajectory tracking control problem under model uncertainty and external environmental disturbance, a trajectory tracking control strategy based on nonlinear model predictive control (NMPC) has been proposed. Firstly, a hydrodynamic model considering the uncertainty of model parameters and the influence of external environmental disturbance has been established; then, the fourth-order Lundgren-Kutta method has been used to discretize the nonlinear state space model, and the control quantity, control increment and output quantity of the system have been used as constraints to solve the optimization problem by numerical computation; finally, the reference circle has been used as the preset trajectory, and the trajectory tracking experiment has been carried out under the uncertainty of the model and external environmental disturbance. The experimental results show that the trajectory tracking strategy based on NMPC can better track the preset trajectory and has better anti-interference ability and control effect.
Genetic Diversity, Antimicrobial Resistance, and Virulence Factors of Enterococcus Faecalis Isolates Obtained from Stool Samples of Hospitalized Patients

Background: Enterococcus faecalis rapidly develops resistance to different antibiotics, thereby resulting in serious nosocomial infections associated with high mortality rates and different problems in the healthcare systems. Objectives: This study aimed to analyze the genetic diversity, antimicrobial resistance, and virulence factors of E. faecalis isolates obtained from the stool samples of patients in a hospital in the center of Iran. Methods: In this cross-sectional descriptive-analytical study, 108 stool samples were collected from September 2019 to February 2020 from 108 patients hospitalized in a hospital in the center of Iran. Enterococcus faecalis isolates were detected using the ddlE gene detection technique, and antimicrobial resistance testing was performed using the disc agar diffusion method. Moreover, polymerase chain reaction (PCR) was used to detect antimicrobial resistance genes and virulence factors. Genetic diversity was also analyzed by enterobacterial repetitive intergenic consensus using PCR (ERIC-PCR). The BioNumerics software was used to construct a dendrogram. Results: Of 108 isolates, 50 samples were E. faecalis (46.2%). The prevalence of multidrug resistance among E. faecalis isolates was 62%, and most isolates were resistant to antibiotics tetracycline (70%), erythromycin (68%), and rifampin (60%). Among the E. faecalis isolates, the most prevalent antimicrobial resistance genes were ermB (96%), aph (2′′) Ia (66%), aac(6′)-Ie (40%), and ermC (30%), and the most prevalent virulence genes were gelE (78%), asa1 (74%), and esp (74%). The genetic diversity analysis showed 25 ERIC types in two major clusters (ie, clusters H and J) and eight minor clusters (ie, clusters A-G and I). There was no significant difference between clusters H and J in terms of antimicrobial resistance and resistance genes (P > 0.05). In contrast, the prevalence of the asa1 gene was significantly higher in cluster J than in cluster H (P < 0.05). Conclusions: This study showed the high prevalence of multidrug resistance, and high heterogeneity among the E. faecalis isolates obtained from the stool samples of hospitalized patients.