Miracle-dz/UniFAR-FTUs huggingface dataset:
Resilience Assessment of an Urban Metro Complex Network: A Case Study of the Zhengzhou Metro

An urban metro network is susceptible to becoming vulnerable and difficult to recover quickly in the face of an unexpected attack on account of the system’s complexity and the threat of various emergencies. Therefore, it is necessary to assess the resilience of urban metro networks. However, the research on resilience assessment of urban metro networks is still in the development stage, and it is better to conduct said research using a technique which combines many attributes, multiple methods, and several cases. Therefore, based on the complex network modeling and topological characteristics analysis of metro systems, a metro network’s robustness and vulnerability measurement method under node interruption and edge failure is proposed for the first time in this study. Then, considering the three cases of general station interruption, interchange station interruption, and traffic tunnel failure, a quantitative resilience assessment model of metro networks is put forward, and the corresponding recovery strategies are discussed. Finally, a case study of the Zhengzhou Metro Network (ZZMN) under an extreme rainstorm is conducted to demonstrate the viability of the proposed model. The results show that ZZMN possesses scale-free and small-world network properties, and it is robust to random interruptions but vulnerable to deliberate attacks. ZZMN still needs to improve its effectiveness in information transmission. The centrality distribution for each node in the ZZMN network differs significantly, and each node’s failure has a unique impact on the network. The larger the DC, BC, and PR of a node is, the lower the network’s robustness after its removal is, and the stronger the vulnerability is. Compared with the three cases of general station interruption, interchange station interruption, and traffic tunnel failure, the network loss caused by tunnel failure was the lowest, followed by general station interruption, and the interruption at interchange stations was the most costly. Given the failures under various cases, the metro management department should prioritize selecting the optimal recovery strategy to improve the resilience of the metro network system. This study’s findings can assist in making urban metro systems less vulnerable to emergencies and more resilient for a quick recovery, which can provide scientific theoretical guidance and decision support for the safety and resilient, sustainable development of urban metro systems.
Feature Selection by mRMR Method for Heart Disease Diagnosis

Heart disease has become a non-ignorable threat to human health in recent years. Once without timely diagnosis and treatment, patients often suffer disability or even death. However, the diagnosis accuracy directly relies on different doctors’ experiences and various factors associated with heart disease bring heavy tasks on them make the situation worse. Therefore, to improve heart disease treatment, introducing computer-aided techniques to assist doctors in diagnosis is a feasible approach. At present, researchers usually use the processed dataset (13 features) selected by doctors from the unprocessed dataset (74 features) (UCI Machine Learning Repository) and apply the feature selection method to the dataset, it’s inappropriate because the feature scale is so small. People neglect the unprocessed dataset’s value and don’t realize it could contain some latent information. A comprehensive comparison is needed to demonstrate the unprocessed dataset’s advantages. Besides, the incremental feature combination method should be verified. As the minimum Redundancy - Maximum Relevance (mRMR) gains great success in feature selection, applying it as a feature filter can enhance classification accuracy. Thus, in this research, we introduced the mRMR method as a filter for feature selection and made a comprehensive comparison within several methods like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kendall, Random Forest, and other research works in several metrics. By analyzing the results, in most cases, the unprocessed dataset can enhance algorithm’s performance. The incremental feature selection method is effective and the mRMR is superior to other methods. Not only does it own the highest accuracies, but also the least supportive features. It has 100% accuracy with 8 features on the Cleveland dataset, 98.3% accuracy with 14 features on Hungarian, and 99% accuracy with 9 features on Long-beach-VA, respectively. Furthermore, we find that some features, which doctors regard as useless, play a part in classification, that should attract some attention from doctors.
Assessment of morphological pharmacognostic characteristics of the content and label information of dried herbs marketed as food supplements in Bulgaria

According to the current food legislation in Bulgaria the dried herbs are classified under the large group of food supplements and their trade is allowed in pharmacies, drugstores and grocery stores. The aim of this study is to assess the morphological pharmacognostic characteristics of the content and the additional information on the labels of food supplements containing dried herbs in Bulgaria, in the light of key standard and regulatory documents related to the quality of herbal substances and food supplements. 91 herbal substances of 10 main groups were studied, which included 103 commercial products from 8 companies. The authenticity of the herbal substances was confirmed with macro-and microscopic tests. The macroscopic morphological indicators which were used as elements of trade-commodity analysis were changes in color, presence of other parts of the same or different plants, organic and mineral impurities, evidences of diseases and pest infestations. The phenological phase in which the herbal substances had been collected was determined also. We found that very small part of the studied products meet the standards for all of the selected criteria according to the considered documents which implies the need of strengthening control.
Efficient prediction uncertainty approximation in the calibration of environmental simulation models

This paper is aimed at improving the efficiency of model uncertainty analyses that are conditioned on measured calibration data. Specifically, the focus is on developing an alternative methodology to the generalized likelihood uncertainty estimation (GLUE) technique when pseudolikelihood functions are utilized instead of a traditional statistical likelihood function. We demonstrate for multiple calibration case studies that the most common sampling approach utilized in GLUE applications, uniform random sampling, is much too inefficient and can generate misleading estimates of prediction uncertainty. We present how the new dynamically dimensioned search (DDS) optimization algorithm can be used to independently identify multiple acceptable or behavioral model parameter sets in two ways. DDS could replace random sampling in typical applications of GLUE. More importantly, we present a new, practical, and efficient uncertainty analysis methodology called DDS–approximation of uncertainty (DDS‐AU) that quantifies prediction uncertainty using prediction bounds rather than prediction limits. Results for 13, 14, 26, and 30 parameter calibration problems show that DDS‐AU can be hundreds or thousands of times more efficient at finding behavioral parameter sets than GLUE with random sampling. Results for one example show that for the same limited computational effort, DDS‐AU prediction bounds can simultaneously be smaller and contain more of the measured data in comparison to GLUE prediction bounds. We also argue and then demonstrate that within the GLUE framework, when behavioral parameter sets are not sampled frequently enough, Latin hypercube sampling does not offer any improvements over simple random sampling.
THE ANALYSIS OF SYSTEM SERVICES ON CUSTOMER SATISFACTION OF OPENING ONLINE SAVING ACCOUNTS IN BANK BUKU IV

Perkembangan bisnis perbankan digital saat ini merupakan salah satu agenda penting dalam industri Perbankan Indonesia. Kebutuhan akan transformasi menjadi prioritas utama bagi perusahaan, agar lebih agile dalam pasar yang berubah dengan cepat. Penggunaan technology informasi pada Bank telah mengubah pola pikir masyarakat dalam bertransaksi. Hal ini berdampak pada digitalisasi pembukaan rekening. Masyarakat dapat dengan mudah membuka rekening dimana saja dan kapan saja. Dalam penelitian ini membahas Bank BUKU 4. Penelitian ini menganalisis layanan sistem terhadap kepuasan nasabah dalam pembukaan rekening tabungan online pada bank buku 4 dengan variabel yang dianalisis yaitu Information Quality, System Quality, Service Quality, Trust, Complaint and Suggestion System dan Customer Satisfaction. Data yang diperoleh diuji dengan menggunakan Uji Validitas, Reliabilitas, Uji T, Uji F, dan Metode Importance Performance Analysis. Hasil uji T menunjukkan bahwa variabel Information Quality terhadap Customer Satisfaction tidak memiliki pengaruh positif. Sedangkan variabel System Quality, Service Quality, Trust, Complaint and Suggestion System terhadap Customer Satisfaction memiliki pengaruh positif. Hasil metode Importance Performance Analysis fokus kepada kuadran 1 (importance tinggi dan performance) untuk dilakukannya perbaikan layanan sistem pembukaan rekening tabungan online pada Bank BCA, Mandiri Bank, BNI Bank, BRI Bank, CIMB Niaga Bank, Danamon Bank, dan Panin Bank.
A new regulation control method for underactuated 3D overhead cranes

Interconnection and damping assignment passivity-based control (IDA-PBC) has been successfully applied to many mechanical, electrical, and electromechanical systems. For the control of such systems, existing IDA-PBC methods have concentrated on systems with underactuation degree one, such as the ball and beam system, the vertical takeoff and landing aircraft, and the pendulum on a cart. In this paper, we focus on the application of the IDA-PBC methodology to the regulation control of a three-dimensional (3D) overhead crane system with underactuation degree two. The goal of this study is to design and analyze a regulation controller that can drive the cart to the desired position precisely and eliminate the payload swing effectively. To achieve this challenging objective, we first find an ingenious way to solve partial differential equations for constructing a Lyapunov function candidate. Then, we devise a nonlinear controller on the basis of the constructed Lyapunov function and analyze the stability of the closed-loop system by using Lyapunov techniques and LaSalle’s invariance principle. Finally, both simulation and experimental results demonstrate the proposed strategy can achieve excellent positioning accuracy and significant swing elimination, and a comparison study between the proposed method and the existing PBC method is included as well.
DIMENSION FOR MARTINGALES

THIS IS A MINOR UPDATE OF THE ARXIV VERSION (11/2012), OF SOME DEFINITIONS AND PROBLEMS. We define that a martingale R H dZ has Dimension k if the rank of the matrix process H equals k almost surely, for almost every t. The definition is shown to be well defined, and the value can be used as a geometric invariant to classify and study martingales. We also define general Brownian motions in higher dimensions.
Does Social Investment Influence Poverty and Economic Growth in South Africa: A Cointegration Analysis?

Despite having a middle-income status, many South African households are either already in or are about to fall into poverty. The income and wealth distribution in South Africa is among the most uneven in the world, and many households lack even the most basic access to healthcare, clean energy, and clean water. Although it has increased government spending, South Africa’s government has made significant steps to combat poverty and inequality and encourage economic growth. Understanding the connection between social investment, poverty, inequality, and economic growth is, therefore, necessary to comprehend the ambiguity that currently prevails. In order to analyze the effects of social investment on poverty and economic growth in South Africa between 1990 and 2020, this paper uses the cointegration technique. The cointegration estimates indicate that there is no correlation between social investment, poverty, inequality, and economic development. According to study findings, South Africa’s macroeconomic policies, which seem to be more urban-focused, need to be modified and redirected into inclusive policies with strict constraints to assure their implementation. The transformation of rural and township life will be aided by this plan.
Steering vector correction in MVDR beamformer for speech enhancement

How to correct inaccurate steering vectors (SV) is an important issue for many beamformers. A SV consists of two essential components: weights and phases. In this work, we pro-pose two novel methods to correct respectively a SV’s weights and phases, under anechoic or low reverberant conditions. In an anechoic condition, the SV’s weights are constant across frequency bins, and we derive an analytic solution to update weights. In a low reverberant condition, we use a constrained polynomial regression to ﬁt SV’s weights across frequency bins, which is further cast as a solvable convex optimization problem. To correct SV’s phases, we exploit the linear phase relation across frequency bins in a SV of a microphone channel, and solve the optimization problem mainly by Newton’s method. We have evaluated our proposed approach on simulated multi-channel noisy speech based on CHiME-3 and LibriSpeech, and obtained promising results in PESQ and STOI of MVDR enhanced speech.
External Text Based Data Augmentation for Low-Resource Speech Recognition in the Constrained Condition of OpenASR21 Challenge

This paper describes our USTC NELSLIP system submitted to the Open Automatic Speech Recognition (OpenASR21) Challenge for the Constrained condition, where only a 10-hour speech dataset is allowed for training while additional text data is unlimited. To improve the low-resource speech recognition performance, we collect external text data for language modeling and train a text-to-speech (TTS) model to generate speech-text paired data. Our system is then built based on the conventional hybrid structure, where various subsystems are developed using different acoustic neural network architectures and different data augmentation methods. Finally, system fusion is employed to obtain the final result. Experiments on the Ope-nASR21 challenge show that the proposed system achieves the best performance for all testing languages.
A Charcot-Marie-Tooth-Causing Mutation in HSPB1 Decreases Cell Adaptation to Repeated Stress by Disrupting Autophagic Clearance of Misfolded Proteins

Charcot-Marie-Tooth (CMT) disease is the most common inherited neurodegenerative disorder with selective degeneration of peripheral nerves. Despite advances in identifying CMT-causing genes, the underlying molecular mechanism, particularly of selective degeneration of peripheral neurons remains to be elucidated. Since peripheral neurons are sensitive to multiple stresses, we hypothesized that daily repeated stress might be an essential contributor to the selective degeneration of peripheral neurons induced by CMT-causing mutations. Here, we mainly focused on the biological effects of the dominant missense mutation (S135F) in the 27-kDa small heat-shock protein HSPB1 under repeated heat shock. HSPB1S135F presented hyperactive binding to both α-tubulin and acetylated α-tubulin during repeated heat shock when compared with the wild type. The aberrant interactions with tubulin prevented microtubule-based transport of heat shock-induced misfolded proteins for the formation of perinuclear aggresomes. Furthermore, the transport of autophagosomes along microtubules was also blocked. These results indicate that the autophagy pathway was disrupted, leading to an accumulation of ubiquitinated protein aggregates and a significant decrease in cell adaptation to repeated stress. Our findings provide novel insights into the molecular mechanisms of HSPB1S135F-induced selective degeneration of peripheral neurons and perspectives for targeting autophagy as a promising therapeutic strategy for CMT neuropathy.
Transformation of number head together method as adaptive way from COVID-19 effects.

Background: COVID-19 affects education systems and leading to the closure of schools, universities and colleges. As the consequence, the learning process is done from home in which needs some adjustment on the delivery method of teaching-learning. To be specific, the teaching-learning process is about how teacher and students interact each other based on a certain method of learning. Purpose: This study aims to investigate to what extent the NHT method can be used for an alternative method in promoting teaching-learning process in COVID-19 era. Design and methods: The study used library research method which investigates data from the documents, such as articles, news, books etc. related to NHT. The findings are validated by using triangulation data source technique. Results: the study reveals Transforming the method of teaching from offline class to online class is a way to solve in transferring the knowledge to the students. NHT is one of the many methods can use the meetings conference platform.
Consistent high-frequency approximation for periodically driven quantum systems

We present a transparent way of deriving a systematic highfrequency expansion for the effective Hamiltonian and the micromotion operator of periodically driven quantum systems. It is based on the block diagonalization of the quasienergy operator in the extended Floquet Hilbert space by means of degenerate perturbation theory. For this purpose, we generalize the formalism of degenerate perturbation theory to the block diagonalization of hermitian operators into more than two subspaces. Our results are found to be equivalent to those obtained within a different approach in references [Phys. Rev. A 68, 013820 (2003)] and [Phys. Rev. X 4, 031027 (2014)]. We relate the high-frequency expansion to the Floquet-Magnus expansion [J. Phys. A 34, 2001 (2000)] and explain why the latter leads to an artifactual dependence of the quasienergy spectrum on the driving phase. Finally, we illustrate the method using the example of a periodically driven tight-binding lattice and discuss its limitations for systems of many interacting particles. ar X iv :1 50 2. 06 47 7v 3 [ co nd -m at . q ua nt -g as ] 2 5 Fe b 20 15 Consistent high-frequency approximation for periodically driven quantum systems 2
Confidence Measure for Automatic Age Estimation From Speech

Age estimation from speech is a problem that has wide applications in call centers, virtual assistants and IoT devices. The estimated age is used for various system decisions related to personalization, parental control, and anomaly detection. Performance of speech based automatic age estimation systems is generally stated in the literature using the Mean Absolute Error (MAE) and Pearson Correlation Coefﬁcient (PC). In real-world applications of these systems, MAE and PC provide little insight into the conﬁdence of a point estimate. An MAE of 5 years on a test set provides only the average error across all estimates in the test set and hence does not provide any information about the conﬁdence of each individual estimate. A conﬁdence score of predicted age is essential to know the trustworthiness of the predictions made by the system. This paper formulates age estimation from speech as a label distribution learning problem to come up with a measure of the conﬁdence related to the point estimate being within a desired range from the ground-truth. It further uses it to analyze the age estimation system under conditions of varying speech quality. We show that the proposed measure of conﬁdence is better than a ﬁxed error-margin.
Electrical Tree Characteristics of Bisphenol A Epoxy Resin/Maleopimaric Anhydride Curing System

Epoxy resin insulation materials are mainly derived from petrochemical materials which have the disadvantages of resource consumption and environmental pollution. In order to cure bisphenol A epoxy resin, a maleopimaric anhydride (MPA) curing agent was prepared from rosin, a renewable resource, and blended with a petroleum-based curing agent (methylhexahy-drophthalic anhydride). The influence of maleopimaric anhydride content on the initiation and growth characteristics of electrical trees was studied and analyzed in this paper using molecular dynamics simulation (MD) and electrical tree tests at an 18-kilovolt power frequency voltage. When the MPA content used was ≤10%, the free volume percentage of the curing system increased with MPA content, and thus the initiation voltage became lower; when the MPA content was ≥20%, the hydrogenated phenanthrene ring structure content increased significantly with increasing MPA content, and the rigidity of the curing system increased significantly; thus, the initiation voltage gradually increased. MPA4 had an 11.11% higher initiation voltage than the petroleum-based control group. The effect of the polar rigid structure within the curing system significantly inhibited the growth rate and length of electrical trees as MPA content increased. Electrical trees developed into light-colored, thin, and narrow dendritic structures when the MPA content reached 40%. The results show that curing epoxy resin with the rosin-based curing agent maleopimaric anhydride (MPA), in place of a petroleum-based curing agent, can produce environmentally friendly resins with excellent electrical tree resistance and potential application prospects.
Polymicrobial Biofilm Organization of Staphylococcus aureus and Pseudomonas aeruginosa in a Chronic Wound Environment

Biofilm on the skin surface of chronic wounds is an important step that involves difficulties in wound healing. The polymicrobial nature inside this pathogenic biofilm is key to understanding the chronicity of the lesion. Few in vitro models have been developed to study bacterial interactions inside this chronic wound. We evaluated the biofilm formation and the evolution of bacteria released from this biofilm on the two main bacteria isolated in this condition, Staphylococcus aureus and Pseudomonas aeruginosa, using a dynamic system (BioFlux™ 200) and a chronic wound-like medium (CWM) that mimics the chronic wound environment. We observed that all species constituted a faster biofilm in the CWM compared to a traditional culture medium (p < 0.01). The percentages of biofilm formation were significantly higher in the mixed biofilm compared to those determined for the bacterial species alone (p < 0.01). Biofilm organization was a non-random structure where S. aureus aggregates were located close to the wound surface, whereas P. aeruginosa was located deeper in the wound bed. Planktonic biofilm-detached bacteria showed decreased growth, overexpression of genes encoding biofilm formation, and an increase in the mature biofilm biomass formed. Our data confirmed the impact of the chronic wound environment on biofilm formation and on bacterial lifecycle inside the biofilm.