Miracle-dz/UniFAR-FTUs huggingface dataset:
On-line Monitoring System based on Vibration Signal of High Voltage Circuit Breaker

High voltage circuit breaker is one of the most important switch apparatus in electrical power system. It play a role in the switch control, which can be used to switch the operating mode during normal operation (the equipment run or quit). In order to ensure the normal operation of the grid, it can be removed quickly when a malfunction on the device or circuit. Real-time online monitoring of high voltage circuit breaker can understand the running status, master the operation characteristics and its tend of development. As early as possible find the potential faults then take preventive measures timely, so as to ensure the reliable operation of power system. Online monitoring of high voltage circuit breakers can reduce the premature or unnecessary maintenance and maintenance cost, improve pertinence of the maintenance firstly. Secondly, it can improve the life of the switching device and improve the reliability of electric power system significantly. In this paper, on-line monitoring system based on vibration signal of high voltage circuit breaker is presented
If started early in life, metformin treatment increases life span and postpones tumors in female SHR mice

Hyperglycemia and hyperinsulinemia accelerate both aging and cancer. Antidiabetic biguanides such as metformin decrease glucose, insulin and IGF-1 level. Metformin increases lifespan and prevents cancer in mice, although its effects vary, depending on mice strain and gender. Here we showed that chronic treatment of female outbred SHR mice with metformin started at the age of 3, 9 or 15 months decreased body temperature and postponed age-related switch-off of estrous function. Surprisingly, metformin did not affect levels of serum cholesterol, triglycerides, glucose and insulin. Treatment with metformin started at the age of 3 months increased mean life span by 14% and maximum life span by 1 month. The treatment started at the age of 9 months insignificantly increased mean life span by only 6%, whereas the treatment started at the age of 15 months failed to increase life span. The mean life span of tumor-free mice was increased by 21% in ‘the youngest group’, by 7% in ‘middle-aged group’ and in contrast was reduced by 13% in ‘the oldest group’. When started at the age of 3 and 9 months, metformin delayed the first tumor detection by 22% and 25%, correspondingly. Thus, in female SHR mice, metformin increased life span and postponed tumors when started at the young and middle but not at the old age. In contrast, metformin improves reproductive function when started at any age.
Lung Cancer Cells Inhibitors via Differential Transcriptional Reprogramming in Cancer-Selective Cytotoxicity by Histone Deacetylase Binding Domain Protein 3 Mediates − Methyl CpG

Histone deacetylase inhibitors (HDI) have been reported to inhibit the growth and survival of cancer cells while leaving normal cells untouched. However, the mechanisms underlying this selective cell death are poorly understood. Gene expression analysis revealed that HDI treatment induced up-regulation of p21 and down-regulation of ErbB2 in cancer cells but not normal cells. Overexpression of p21 and/or silencing of ErbB2 enhanced cancer cell growth inhibition, suggesting that HDI-induced up-regulation/ down-regulation of these genes play critical roles in HDIinduced growth inhibition of cancer cells. Most importantly, we found that the gene silencing factor methyl CpG–binding domain protein 3 (MBD3) was not only released from cancerselective promoter of the HDI up-regulated p21 gene but also recruited to that of the HDI-down-regulated ErbB2 gene. Furthermore, silencing of MBD3 by small interfering RNA abrogated the HDI-induced gene regulation and growth inhibition in lung cancer but not in normal cells. Together, our results support the critical potential of MBD3 in HDIinduced cancer-selective cell death via cancer differential gene expression. (Cancer Res 2005; 65(24): 11400-10)
Effects of golden hour thrombolysis: a Prehospital Acute Neurological Treatment and Optimization of Medical Care in Stroke (PHANTOM-S) substudy.

IMPORTANCE The effectiveness of intravenous thrombolysis in acute ischemic stroke is time dependent. The effects are likely to be highest if the time from symptom onset to treatment is within 60 minutes, termed the golden hour. OBJECTIVE To determine the achievable rate of golden hour thrombolysis in prehospital care and its effect on outcome. DESIGN, SETTING, AND PARTICIPANTS The prospective controlled Prehospital Acute Neurological Treatment and Optimization of Medical Care in Stroke study was conducted in Berlin, Germany, within an established infrastructure for stroke care. Weeks were randomized according to the availability of a specialized ambulance (stroke emergency mobile unit (STEMO) from May 1, 2011, through January 31, 2013. We included 6182 consecutive adult patients for whom a stroke dispatch (44.1% male; mean [SD] age, 73.9 [15.0] years) or regular care (45.0% male; mean [SD] age, 74.2 [14.9] years) were included. INTERVENTIONS The STEMO was deployed when the dispatchers suspected an acute stroke during emergency calls. If STEMO was not available (during control weeks, when the unit was already in operation, or during maintenance), patients received conventional care. The STEMO is equipped with a computed tomographic scanner plus a point-of-care laboratory and telemedicine connection. The unit is staffed with a neurologist trained in emergency medicine, a paramedic, and a technician. Thrombolysis was started in STEMO if a stroke was confirmed and no contraindication was found. MAIN OUTCOMES AND MEASURES Rates of golden hour thrombolysis, 7- and 90-day mortality, secondary intracerebral hemorrhage, and discharge home. RESULTS Thrombolysis rates in ischemic stroke were 200 of 614 patients (32.6%) when STEMO was deployed and 330 of 1497 patients (22.0%) when conventional care was administered (P < .001). Among all patients who received thrombolysis, the proportion of golden hour thrombolysis was 6-fold higher after STEMO deployment (62 of 200 patients [31.0%] vs 16 of 330 [4.9%]; P < .01). Compared with patients with a longer time from symptom onset to treatment, patients who received golden hour thrombolysis had no higher risks for 7- or 90-day mortality (adjusted odds ratios, 0.38 [95% CI, 0.09-1.70]; P = .21 and 0.69 [95% CI, 0.32-1.53]; P = .36) and were more likely to be discharged home (adjusted odds ratio, 1.93 [95% CI, 1.09-3.41]; P = .02). CONCLUSIONS AND RELEVANCE The use of STEMO increases the percentage of patients receiving thrombolysis within the golden hour. Golden hour thrombolysis entails no risk to the patients' safety and is associated with better short-term outcomes. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01382862.
Employee Satisfaction in Agencies with Merit-Based Pay: Differential Effects for Three Measures

ABSTRACT While many studies have sought to understand the association between merit-based pay and organizational performance, few have assessed the psychological well-being of employees in agencies with such incentive schemes. We show that employees in agencies with merit-based pay are less satisfied with their organization than are those working in agencies that do not implement the incentive systems. We also show that this negative effect is larger in the case of organizational satisfaction than for pay satisfaction or employee satisfaction with the job itself.
Judging Grammaticality: Experiments in Sentence Classification

A classifier which is capable of distinguishing a syntactically well formed sentence from a syntactically ill formed one has the potential to be useful in an L2 language-learning context. In this article, we describe a classifier which classifies English sentences as either well formed or ill formed using information gleaned from three different natural language processing techniques. We describe the issues involved in acquiring data to train such a classifier and present experimental results for this classifier on a variety of ill formed sentences. We demonstrate that (a) the combination of information from a variety of linguistic sources is helpful, (b) the trade-off between accuracy on well formed sentences and accuracy on ill formed sentences can be fine tuned by training multiple classifiers in a voting scheme, and (c) the performance of the classifier is varied, with better performance on transcribed spoken sentences produced by less advanced language learners.
Enhancing the Detection of Code Anomalies with Architecture-Sensitive Strategies

Research has shown that code anomalies may be related to problems in the architecture design. Without proper mechanisms to support the identification of architecturally-relevant code anomalies, software systems will degrade and might be discontinued as a consequence. Nowadays, metrics-based detection strategy is the most common mechanism to identify code anomalies. However, these strategies often fail to detect architecturally-relevant code anomalies. A key limitation is that they solely exploit measurable static properties of the source code. This paper proposes and evaluates a suite of architecture-sensitive detection strategies. These strategies exploit information related to how fully-modularized and widely-scoped architectural concerns are realized by the code elements. The accuracy of the proposed detection strategies is assessed in a sample of nearly 3500 architecturally-relevant code anomalies and 950 architectural problems distributed in 6 software systems. Our findings show that more than the 60% of code anomalies detected by the proposed strategies were related to architectural problems. Additionally, the proposed strategies identified on average 50% more architecturally-relevant code anomalies than those gathered with using conventional strategies.
Adaptive observer design for the cell temperature estimation in battery packs in electric vehicles

In this paper, an adaptive observer is applied to estimate the internal temperature of a lithium-ion cell in a battery pack. The cell's internal temperature is an important factor for its degradation, but it cannot be measured directly. Based on the measured surface temperature and a lumped thermal model for the battery pack, an adaptive observer is designed to estimate the internal temperature and internal resistance of a cell in battery packs at the same time. The capability of the adaptive observer could be beneficial for thermal management and battery health monitoring. Finally, simulation results show the good performance of the adaptive observer.
Calcium‐ATPase Activity of Red Blood Cell Ghosts from Preeclamptic Women, Antepartum and Postpartum

Objective: We determined the calcium activated adenosine triphosphatase (Ca‐ATPase) activity and level of lipid peroxidation thiobarbituric acid‐reactive substances (TBARS) of red blood cell ghosts in the antepartum and postpartum of normotensive and preeclamptic pregnant women. Methods: Samples of venous blood were obtained by venipuncture of nulliparous normotensive and preeclamptic pregnant women antepartum and two, four, six, and 20 weeks postpartum. The red blood cell ghosts were prepared and assayed for Ca‐ATPase activity and TBARS. Main Outcome Measure(s): We expected to find a return to normal values of both Ca‐ATPase activity and TBARS level of the red blood cell ghosts, modified in the preeclamptic pregnant women, during their puerperium. Results: The Ca‐ATPase activity of red cell ghosts from preeclamptic women, antepartum, is lower than that of normotensive pregnant women. The ATPase activity returns to normal values during the first six weeks of postpartum. The level of TBARS of red cell ghosts from preeclamptic women follows a pattern that is inversely proportional to the one of the Ca‐ATPase activity. Conclusions: Preeclampsia produces a significant diminution of the Ca‐ATPase activity and an increase in the levels of TBARS in the erythrocytes. As soon as all the symptoms of preeclampsia disappear in the postpartum, both Ca‐ATPase activity and TBARS return to normal values.
Vocal Separation using Singer-Vowel Priors Obtained from Polyphonic Audio

Single-channel methods for the separation of the lead vocal from mixed audio have traditionally included harmonicsinusoidal modeling and matrix decomposition methods, each with its own strengths and shortcomings. In this work we use a hybrid framework to incorporate prior knowledge about singer and phone identity to achieve the superior separation of the lead vocal from the instrumental background. Singer specific dictionaries learned from available polyphonic recordings provide the soft mask that effectively attenuates the bleeding-through of accompanying melodic instruments typical of purely harmonic-sinusoidal model based separation. The dictionary learning uses NMF optimization across a training set of mixed signal utterances while keeping the vocal signal bases constant across the utterances. A soft mask is determined for each test mixed utterance frame by imposing sparseness constraints in the NMF partial co-factorization. We demonstrate significant improvements in reconstructed signal quality arising from the more accurate estimation of singer-vowel spectral envelope.
Revisiting the Metabolism and Bioactivation of Ketoconazole in Human and Mouse Using Liquid Chromatography–Mass Spectrometry-Based Metabolomics

Although ketoconazole (KCZ) has been used worldwide for 30 years, its metabolic characteristics are poorly described. Moreover, the hepatotoxicity of KCZ limits its therapeutic use. In this study, we used liquid chromatography–mass spectrometry-based metabolomics to evaluate the metabolic profile of KCZ in mouse and human and identify the mechanisms underlying its hepatotoxicity. A total of 28 metabolites of KCZ, 11 of which were novel, were identified in this study. Newly identified metabolites were classified into three categories according to the metabolic positions of a piperazine ring, imidazole ring, and N-acetyl moiety. The metabolic characteristics of KCZ in human were comparable to those in mouse. Moreover, three cyanide adducts of KCZ were identified in mouse and human liver microsomal incubates as “flags” to trigger additional toxicity study. The oxidation of piperazine into iminium ion is suggested as a biotransformation responsible for bioactivation. In summary, the metabolic characteristics of KCZ, including reactive metabolites, were comprehensively understood using a metabolomics approach.
Radial basis function neural network in fault detection of automotive engines

Fault detection and isolation have become one of the most important aspects of automobile design. A fault detection (FD) scheme is developed for automotive engines in this paper. The method uses an independent Radial Basis Function (RBF) Neural Network model to model engine dynamics, and the modelling errors are used to form the basis for residual generation. A dependent RBFNN model is a model which uses output data of a plant as a target output then use it to train the neural network, while, The independent RBFNN model is a higher accuracy than the dependent model and the errors can be detected by this model, this is because this model does not dependent on the output of the plant and it will use its output as a target, so if any faults in the plant will be not effect in the model and this faults will be detected easily and clearly. The method is developed and the performance assessed using the engine benchmark, the Mean Value Engine Model (MVEM) with Matlab/Simulink. Five faults have been simulated on the MVEM, including three sensor faults, one component fault and one actuator fault. The three sensor faults considered are 10-20% change superimposed on the outputs of manifold pressure, temperature and crankshaft speed sensors; one component fault considered is air leakage in intake manifold and Exhaust Gas Recycle (EGR); the actuator fault considered is the malfunction of fuel injector. The simulation results showed that all the simulated faults can be clearly detected in the dynamic condition throughout the operating range. Keywords : Automotive engine, independent RBFNN model, RBF neural network, fault detection International Journal of Engineering, Science and Technology , Vol. 2, No. 10, 2010, pp. 1-8
Aspects of Social Accounting: Introducing the Process in an Australian Credit Union

This paper documents the introduction of triple-bottom-line reporting to an organisation within the banking sector. Particular focus is placed on the implementation and reporting of the social responsibility aspects of triple-bottom-line reporting. The case study examines the management of the change from a practical and strategic perspective. Impact of the introduction is examined internally to the organisation and external stakeholders are canvassed to examine the impact understanding and value of the strategic direction taken by the organisation. In addition the study delivers a descriptive snapshot of the state of play (limited to August 2002) in respect of Social Accounting and the disclosures by Australian Banks in Financial Statements and other published reports. Published Financial Reports and information published on the institutions web site are utilised to undertake an analysis of content and preferred method of reporting. There is a clear movement toward the reporting of social activities within the banking sector in Australia. The methods chosen range in type from casual statements to formal reports but no clear framework has been established as to the preferred mode or content. An analysis of the implementation and reporting process of a banking sector organisation will inform the development of the discipline of Social Accounting and in part assist in the development of a framework for reporting within the banking industry.
Thresholded Basis Pursuit: Quantizing Linear Programming Solutions for Optimal Support Recovery and Approximation in Compressed Sensing

We consider the Compressed Sensing problem. We have a large under-determined set of noisy measurements Y = GX + N, where X is a sparse signal and G is drawn from a random ensemble. In our previous work, we had shown that a signal-to-noise ratio, SNR = O(log n) is necessary and sufficient for support recovery from an information-theoretic perspective. In this paper we present a linear programming solution for support recovery. The solution of the problem amounts to solving min ‖Z‖1 s.t. Y = GZ, and quantizing/thresholding the resulting solution Z. We show that this scheme is guaranteed to perfectly reconstruct a discrete signal or control the element-wise reconstruction error for a continuous signal for specific values of sparsity. We show that in the linear regime when the sparsity, k, increases linearly with signal dimension, n, the sign pattern of X can be recovered with SNR = O(log n) and m = O(k) measurements. Our proof technique is based on perturbation of the noiseless l1 problem. Consequently, the achievable sparsity level in the noisy problem is comparable to that of the noiseless problem. Our result offers a sharp characterization in that neither the SNR nor the sparsity ratio can be significantly improved. In contrast previous results based on LASSO and MAX-Correlation techniques assume significantly larger SNR or sub-linear sparsity. We also show that our final result can be obtained from Dvoretsky theorem rather than the restricted isometry property (RIP). The advantage of this line of reasoning is that Dvoretsky’s theorem continues to hold for non-singular transformations while RIP property may not be satisfied for the latter case. We also consider approximation in terms of l2 and show that our bounds match existing bounds for LASSO in this case.
Sampling Theorems for Signals from the Union of Linear Subspaces. Sampling Theorems for Signals from the Union of Linear Subspaces

" This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. " Abstract Compressed sensing is an emerging signal acquisition technique that enables signals to be sampled well below the Nyquist rate, given that the signal has a sparse representation in an orthonormal basis. In fact, sparsity in an orthonormal basis is only one possible signal model that allows for sampling strategies below the Nyquist rate. In this paper we consider a more general signal model and assume signals that live on or close to the union of linear subspaces of low dimension. We present sampling theorems for this model that are in the same spirit as the Nyquist-Shannon sampling theorem in that they connect the number of required samples to certain model parameters. Contrary to the Nyquist-Shannon sampling theorem, which gives a necessary and sufficient condition for the number of required samples as well as a simple linear algorithm for signal reconstruction, the model studied here is more complex. We therefore concentrate on two aspects of the signal model, the existence of one to one maps to lower dimensional observation spaces and the smoothness of the inverse map. We show that almost all linear maps are one to one when the observation space is at least twice the dimension of the largest subspace in the union of subspaces. However, we also show that in order for the inverse map to have certain smoothness properties such as a given finite Lipschitz constant, the required observation dimension necessarily depends on the number of subspaces in the signal model. These results are then applied to two examples, the standard compressed sensing signal model in which the signal has a sparse representation in an orthonormal basis and to a sparse signal model with additional tree structure.
A meta-analysis of the mechanical properties of ice-templated ceramics and metals

Abstract Ice templating, also known as freeze casting, is a popular shaping route for macroporous materials. Over the past 15 years, it has been widely applied to various classes of materials, and in particular ceramics. Many formulation and process parameters, often interdependent, affect the outcome. It is thus difficult to understand the various relationships between these parameters from isolated studies where only a few of these parameters have been investigated. We report here the results of a meta analysis of the structural and mechanical properties of ice templated materials from an exhaustive collection of records. We use these results to identify which parameters are the most critical to control the structure and properties, and to derive guidelines for optimizing the mechanical response of ice templated materials. We hope these results will be a helpful guide to anyone interested in such materials.