Miracle-dz/UniFAR-FTUs huggingface dataset:
An Active Learning Method Based on Uncertainty and Complexity for Gearbox Fault Diagnosis

It is crucial to implement an effective and accurate fault diagnosis of a gearbox for mechanical systems. However, being composed of many mechanical parts, a gearbox has a variety of failure modes resulting in the difficulty of accurate fault diagnosis. Moreover, it is easy to obtain raw vibration signals from real gearbox applications, but it requires significant costs to label them, especially for multi-fault modes. These issues challenge the traditional supervised learning methods of fault diagnosis. To solve these problems, we develop an active learning strategy based on uncertainty and complexity. Therefore, a new diagnostic method for a gearbox is proposed based on the present active learning, empirical mode decomposition-singular value decomposition (EMD-SVD) and random forests (RF). First, the EMD-SVD is used to obtain feature vectors from raw signals. Second, the proposed active learning scheme selects the most valuable unlabeled samples, which are then labeled and added to the training data set. Finally, the RF, trained by the new training data, is employed to recognize the fault modes of a gearbox. Two cases are studied based on experimental gearbox fault diagnostic data, and a supervised learning method, as well as other active learning methods, are compared. The results show that the proposed method outperforms the two common types of methods, thus validating its effectiveness and superiority.
Improving insulation quality of ceramic insulated conductors by dip coating with boron oxide

In the past few years there has been a growing trend in the use of ceramic coated high temperature resistant wires for applications up to 500 °C. The main drawback in developing new materials has been the relatively poor dielectric characteristics at high temperature. One solution is to strengthen the ceramic layer by covering it with boron oxide and this additional layer improves the dielectric characteristics of the ceramic wire. Partial discharge inception voltage threshold is greatly increased compared to non-impregnated wires and this has been observed over a large temperature range.
Improve Energy Efficiency by Processor Overclocking and Memory Frequency Scaling

High power consumption has become one of most important issues for supercomputer systems. Although the performance of supercomputer systems has been greatly improved in the recent years, a large amount of energy consumption causes not very high energy efficiency of the supercomputer systems. Computer nodes are one of major components of supercomputer systems. Therefore, how to improve the energy efficiency of a computer node is our concern. It is very important to do the tradeoff between performance and energy consumption of a computer node for better energy efficiency. We take energy delay product (EDP) as our metric for energy efficiency. The lower EDP means the better energy efficiency. For a shared-memory multicore processor system, we utilize processor overclocking and memory frequency scaling to achieve better performance and lower power consumption, that brings about the better energy efficiency for our benchmarks. But, we can not ensure to achieve better energy efficiency at any time we use processor overclocking, and we also find that scaling down memory frequency combined with processor overclocking cannot make sure to reach higher energy efficiency and even make the energy efficiency worse. In order to achieve higher energy efficiency for a shared-memory multicore processor system, we propose a processor overclocking and memory frequency scaling based Holistic Energy-Efficient (HEE) algorithm, which determines when we should use processor overclocking to improve energy efficiency and when we could combine processor overclocking with memory frequency scaling for a better energy efficiency. We build a real experimental platform to evaluate the effectiveness of our HEE algorithm. From the detailed experimental results, our HEE algorithm can achieve an average 13% energy efficiency improvement and the maximal energy efficiency increase is up to 25.6%.
Review on Event-Driven Wake-Up Sensors for Ultra-Low Power Time-Domain Design

This paper introduces a review of event-driven wake-up sensors detection approaches and investigates time domain injection-locked oscillator design solutions to reduce power consumption. Threshold-based sensing, digital classification and analog feature extraction method are presented. To overcome the power consumption limits provided by state of the art threshold detection methods, a novel threshold detection method based on injection-locked oscillator time-domain comparator is proposed and designed in a FDSOI 22 nm process. Simulations show a power consumption of 790 pW for an input full scale signal of 320 mV with a frequency of 1 kHz.
ISFET Arrays in CMOS: A Head-to-Head Comparison Between Voltage and Current Mode

This paper demonstrates and compares three configurations for ISFET arrays fabricated in unmodified CMOS that facilitate chemical imaging with linear pH-to-output conversion. Specifically, the typical voltage-mode source-follower configuration is compared against our recently proposed current-mode approach using an ISFET in velocity saturation. These two topologies are also compared against an extension of the current-mode topology to include a programmable-gate capacitor inside the pixel stack for offset compensation. Various performance metrics, such as linearity, speed, power consumption, size, attenuation, and noise have been experimentally measured to quantify the performance of each approach and a figure-of-merit (FoM) has been defined to capture the overall performance. Using this FoM, the current-mode approach results in the best overall performance. Through this paper, however, the relative merits and limitations of each approach are identified to be used as a guide for future designs which are usually application-specific. Furthermore, we illustrate methods used to obtain standard ISFET performance metrics in order to standardize the comparison of ISFET arrays using the figure-of-merit.
Neo-Bedside Monitoring Device for Integrated Neonatal Intensive Care Unit (iNICU)

We report the design and implementation of an affordable bedside device, Neo is capable of acquiring vital data in real time by integrating with diverse devices connected to newborns in neonatal intensive care units (NICUs). NICUs are equipped with multiple vital sign monitoring devices that are connected to the premature newborn and acquire a few gigabytes of data every day. The continuous vital data from these devices are manually documented every hour. This introduces errors and loses a large amount of high-resolution data. The intermittent documentation of physiological data also makes it difficult for clinicians to visualize and detect the trends of diagnostic utility. Neo is built on an affordable Internet of Things platform that aggregates and sends the real-time data to a cloud-based big data platform called integrated NICU. Apart from minimizing documentation errors, the device enables data acquisition at sufficiently real-time rates as to indicate the current status of all the patients at the NICU. Neo automates immediate vital sign status and past trends as graphs or charts that the doctor and nurses can view from anywhere through the Internet. Physiological signal and clinical parameters from Neo is used to score different diseases like sepsis, respiratory distress syndrome, necrotizing enterocolitis, and retinopathy of prematurity. This score predicts the physiological health of the newborn and aid clinicians in decision-making ensuring timely intervention.
Methods and Approaches in Vocabulary Teaching and Their Influence on Students' Acquisition

Bakalařska prace sleduje dva nejvýznamnějsi trendy, ktere jsou v soucasnosti uplatňovany při výuce cizich jazyků. Srovnava jednotlive metodicke postupy a jejich užiti v zavislosti na dosažených znalostech studentů.
BACTERIOPHAGE THERAPY: REVIEW OF THE PRINCIPLES AND RESULTS OF THE USE OF BACTERIOPHAGE IN THE TREATMENT OF INFECTIONS

Bacteriophage Treatment in Other Suppurative Conditions. — The various accounts of the treatment of suppurative conditions with bacteriophage include cases in which the colon bacillus or staphylococcus, separately or together, were involved. In cases of mixed infections mixed bacteriophages, polyvalent for colon bacillus and staphylococcus, were used. There have been a number of reports of the successful use of bacteriophage in the treatment of infected wounds. McKinley74reported good results in the treatment of three staphylococcic wound infections but noted marked reactions even when the bacteriophage was applied in the form of a wet dressing. Rice57records excellent results in forty of forty-four wound infections, Dutton,56good results in forty cases. In these cases the bacteriophage was instilled into the wound or applied in the form of a wet dressing. Boyce, Lampert and McFetridge65treated forty-six wound cases with only two failures. Schultz53reports that eight
One-Class Learning Time-Series Shapelets

Shapelets are time-series segments effective for classifying time-series datasets. In recent years, the discovery of shapelets by classifier learning has been studied. Methods for shapelet discovery have attracted great interest because they provide not only interpretable results but also superior classifi-cation performance. However, they do not consider imbalanced classifications between majority and minority classes, which may occur in actual applications (e.g., anomaly detection). Our aim is to learn shapelets and classifiers using only training data for the majority class without the minority class. We propose a method called one-class learning time-series shapelets (OCLTS). OCLTS efficiently and simultaneously optimizes both the shapelets and a non-linear classifier based on a one-class support vector machine by a stochastic sub-gradient descent algorithm. Experimental results show the method’s effectiveness for interpretability and imbalanced binary classification.
Acceleration of Large Integer Multiplication with Intel AVX-512 Instructions

In this paper, we propose an implementation of large integer multiplication using Single Instruction Multiple Data (SIMD) instructions. We evaluated the implementation on an Intel Xeon Phi processor. The second generation Intel Xeon Phi processor, Knights Landing, has a set of Advanced Vector Extensions-512 (AVX-512) instructions. Using AVX-512, the processor can handle 512 bits at the same time and has the potential to multiply faster than a processor using Streaming SIMD Extensions (SSE) and AVX. Therefore, we applied AVX-512F (foundation) instructions to the program. In the multiplication of large integers, as the number of digits increases, various processing costs also become larger. One of these costs is carry processing. Therefore, we implemented a multiplication function using a reduced-radix representation and compared the execution time and the number of instructions against the GNU Multiple Precision Arithmetic Library (GMP). Furthermore, we used some optimization techniques for this kernel. We successfully achieved an execution time that was approximately 2.5x faster than GMP on the Knights Landing architecture.
Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation

Early recognition of risky trajectories during an Intensive Care Unit (ICU) stay is one of the key steps towards improving patient survival. Learning trajectories from physiological signals continuously measured during an ICU stay requires learning time-series features that are robust and discriminative across diverse patient populations. Patients within different ICU populations (referred here as domains) vary by age, conditions and interventions. Thus, mortality prediction models using patient data from a particular ICU population may perform suboptimally in other populations because the features used to train such models have different distributions across the groups. In this paper, we explore domain adaptation strategies in order to learn mortality prediction models that extract and transfer complex temporal features from multivariate time-series ICU data. Features are extracted in a way that the state of the patient in a certain time depends on the previous state. This enables dynamic predictions and creates a mortality risk space that describes the risk of a patient at a particular time. Experiments based on cross-ICU populations reveals that our model outperforms all considered baselines. Gains in terms of AUC range from 4% to 8% for early predictions when compared with a recent state-of-the-art representative for ICU mortality prediction. In particular, models for the Cardiac ICU population achieve AUC numbers as high as 0.88, showing excellent clinical utility for early mortality prediction. Finally, we present an explanation of factors contributing to the possible ICU outcomes, so that our models can be used to complement clinical reasoning.
False Data Injection Attacks Detection with Deep Belief Networks in Smart Grid

As a typical information physics system, the smart grid can realize the two-way transmission of information. The deep integration of the information physics system brings more risks to the system while improving the automation management level of the power system. The false data injection attack is a new type of attack method for power system state estimation. The attacker can bypass the traditional detection method to change the state estimation result, so that the control center makes wrong decisions and threatens the safe operation of the power grid. In this paper, we focus on the detection of false data injection attacks in smart grids. A DBN-based attack detection method is proposed. Unsupervised learning is performed from the bottom of the restricted Boltzmann machine to provide initial weight for the network. The backpropagation algorithm propagates the error from top to bottom and fine-tunes the model parameters. To evaluate the effectiveness of the proposed detection method, simulation experiment is performed in the IEEE standard test system. We set different contrast scenarios to verify the feasibility and effectiveness of the detection model. The results demonstrate that the proposed approach achieves better performance with comparison to the SVM based detection approach.
Spatio-Temporal Multiple Geo-Location Identification on Twitter

Twitter Geo-tags that indicate the exact location of messages have many applications from localized opinion mining during elections to efficient traffic management in critical situations. However, less than 6% of Tweets are Geo-tagged, which limits the implementation of those applications. There are two groups of solutions: content and network-based. The first group uses location indicative factors like URLs and topics, extracted from the content of tweets, to infer Geo-location for non geo-active users, whereas the second group benefits from friendship ties in the underlying social network graph. Friendship ties are better predictors compared to content information because they are less noisy and often follow the natural human spatial movement patterns. However, their prediction’s accuracy is still limited because they ignore the temporal aspects of human behavior and always assume a single location per user. This research aims to extend the current network-based approaches by taking users’ temporal dimension into account. We assume multiple locations per user during different time-slots and hypothesize that location predictability varies depending on the time and the properties of the social membership group. Thus, we propose a hierarchical solution to apply temporal categorizations on top of social network partitioning for multiple location prediction for users in Online Social Networks (OSNs) like Twitter. Given a large-scale Twitter dataset, we show that users’ location predictability exhibits different behavior in different time-slots and different social groups. We find that there are specific conditions where users are more predictable in terms of Geo-location. Our solution outperforms the state-of-the-art by improving the prediction accuracy by 16.6% in terms of Median Error Distance (MED) over the same recall.
Prevalence of overweight and obesity among school-going children of Satara district, Maharashtra

Background: Worldwide, obesity has been more than doubled since 1980 as per recent data. In the year 2014, it has been reported that more than 1.9 billion adults, 18 years and above, were overweight. Of these, over 600 million were obese which means that overall 39% of adults worldwide are either overweight or obese. Aims and Objectives: The aims and objectives of this study were to find the prevalence of overweight and obesity and factors associated with them among school children. Materials and Methods: The present study was community-based cross-sectional study. The study was conducted between January 1 and March 31, 2014 in four schools of Satara district, Maharashtra. List of all government and private schools in Satara district was obtained, and four schools were selected by simple random sampling. Thus, a total of 90 students from each school were enrolled in the study, so final sample size was 360. Results: Among 360 study participants, the prevalence of overweight was 9.16% and obesity was 2.5%. Of 185 boys and 175 girls, the prevalence of overweight and obesity was 12.97% in boys compared to 9.14% in girls. 14.04% of school students from private school was found overweight or obese compared to only 6.88% of students from government school. Family history of obesity, working status of mother, and less duration of sleeping have a direct relationship on childhood obesity. Conclusion: The findings of the present study showed that boys have more prevalence of overweight and obesity compared to girls. Private school-going children found more overweight and obese compared to government school-going children. Family history of obesity, working status of mother, and less duration of sleeping have a direct relationship on childhood obesity.
Prevalence of Skin Manifestations in Diabetes Mellitus at King Abdulaziz University Hospital

Background: The prevalence of diabetes in Saudi Arabia is now one of the highest in the world, with the rise in prevalence of Type II diabetes. Patients with diabetes mellitus commonly suffer from a wide variety of cutaneous disorders. Aims: This study estimates the prevalence of skin manifestations in patients with diabetes mellitus, at King Abdulaziz University Hospital. Materials and Methods: Five hundred and fifty-eight patients with the diagnosis of diabetes, attending the dermatologic and diabetic clinic were included in this study. Results: The common skin disorders were: xerosis (74.7%), pruritus (38.2%), diabetic dermopathy (30.1%), finger pebbles (25.6%), and thickened skin (22.2%). Conclusion: Skin manifestations in diabetics are common. High prevalence of xerosis in our diabetic population is significant and further studies are recommended.
Disrupting the Discourse of the Other: a Transformative Learning Study of African Art

The primary question of this study is: How does the disruption of African art discourse influence a group of university students’ perceptions of African aesthetics? This inquiry developed from previous studies on the exclusion of modern and contemporary African art in Western art museums. Through the theoretical lens of Postcolonial Theory and Critical Multiculturalism, this research conceptualizes the dominance of traditional African art in art museums, art history, and art education as a Western hegemonic discourse that normalizes perceptions of Africa and African aesthetics as the fixed primitive Other. Thus, this research applied Action Research (AR) methodology coupled with Transformative Learning Theory (TL) to disrupt the discourse of African art; with the purpose of affecting positive changes in perceptions of African aesthetics. The participants for this study were 10 students in a course (Art 1301 Honors Art Appreciation) I instructed at the University of North Texas in the fall (September–December) 2013 semester. Data was collected, analyzed, and interpreted from participants’ assignments and my research journal. This study comprised a dual enquiry on: 1. Discourse and Meaning-making; and 2. Disruption and Transformation. First, the study analyzed students’ perceptions of African aesthetics from their learning experience of traditional African art in an art museum. The findings affirmed traditional African art at the museum as a discourse of Africa as the Other of the West. Secondly, the study analyzed how students’ perceptions were influenced from their experience (in my classroom) of learning histories of modern and contemporary African art that disrupt the authenticity of traditional African art. The findings revealed that 80% of participants developed positive transformations. This research demonstrates how art education grounded in critical theory and transformative learning subverted African art as the discourse of the Other, developed students’ understandings of the multiple realities of Africa and African aesthetics, and encouraged positive transformations in students’ perceptions of African aesthetics.
THE ASSOCIATION OF INSULIN RESISTANCE WITH BODY MASS INDEX AND BODY FAT PERCENTAGES IN NON-DIABETIC OBESE WOMEN

Objective: Obesity is an important risk factor for cardiovascular diseases. The present study is aimed to evaluate the association of insulin resistance with body mass index (BMI) and body fat percentage (BFP) in obese female. Methods: Female participants aged 18 years or older, with a BMI ≥ 30 kg/m2, visited the obesity outpatient clinic between January – July 2015, were enrolled into the study. BMI and BFPs of all participants were assessed by bioimpedance analysis. Besides, fasting insulin (FI) and fasting plasma glucose (FPG) levels were measured and HOMA-IR was calculated using the formula; “FPG (mmol/L) x fasting insulin (uIU/mL) / 22.5”. Participants with a HOMA-IR level of 2.5 and above were considered to have insulin resistance. Results: One hundred and seventy females were included in the study and the mean age was40.53 ± 10.12 years. While there was a positive correlation between HOMA-IR and BMI, no significant relationship was observed with BFP (p=0.01 and p=0.523, respectively). There was a significant relationship between BMI and BFP (p<0.001). Conclusion: While there was a relationship between HOMA-IR which is the indicator of insulin resistance, and BMI, no relation was found with BFP.