Miracle-dz/UniFAR-FTUs huggingface dataset:
An Automatic Survey System for Paved and Unpaved Road Classification and Road Anomaly Detection using Smartphone Sensor

For developing countries like Timor-Leste, regular road surface monitoring is a major challenge not only for maintaining road quality but also for national plan of road network construction. In Timor-Leste nearly 50% of roads are still unpaved. For this reason, an automated system is required to do a survey of paved and unpaved roads. In this study, we present a new approach for the use of smartphones sensor to classify paved and unpaved roads, and anomaly detection. Although, the most remarkable factor to differentiate paved and unpaved road is based on amplitude of the vertical acceleration, each vehicle has a different type of suspension system. Therefore, we used high-dimensional features and state-of-the-art machine learning techniques to make the system robust for differences of vehicle and also smartphone type. This study divided into two stages such as paved and unpaved road classification and road anomaly detection such as pothole and bump. For paved and unpaved road classification, we tried to use the SVM, HMM and ResNet and compared the performance of these models. Of all comparison, the ResNet was the best choice in this study, because it outperformed the SVM and HMM on the all performance evaluation criteria. Furthermore, the KNN and DTW are applied for anomaly detection on the paved road. The KNN-DTW are also compared to the other machine learning techniques like SVM and classical KNN using same criteria. As a result of the comparison, the KNN-DTW and SVM performed better than classical KNN.
Horizontal Plasmid Transfer by Transformation in Escherichia coli: Environmental Factors and Possible Mechanisms

Transformation is one mode of horizontal gene transfer (HGT) in bacteria, wherein extracellular naked DNA is taken up by cells that have developed genetic competence. Sensitivity to DNase, which degrades naked DNA, is the key to distinguishing transformation from the DNase-resistant HGT mechanisms. In general, Escherichia coli is not believed to be naturally transformable; it develops high competence only under artificial conditions, including exposure to high Ca2+ concentrations. However, E. coli can reportedly express modest competence under certain conditions that are feasible in natural environments outside laboratory. In addition, recent data suggest that environmental factors influence multiple routes of transformation. In this mini review, we (1) summarize our studies on transformation-based HGT using E. coli experimental systems and (2) discuss the possible occurrence of transformation via multiple mechanisms in the environment and its possible impact on the spread of antibiotic resistance genes.
Analytical Model of a Spoke-Type Permanent Magnet Synchronous In-Wheel Motor With Trapezoid Magnet Accounting for Tooth Saturation

This paper presents an analytical model for the prediction of the performance in a spoke-type permanent magnet synchronous in-wheel motor with a trapezoid magnet accounting for tooth saturation. The impact of magnet shape on modeling is considered. To match the standard boundary conditions, the trapezoid magnet is simplified to sector magnets. According to the invariance principle of the magnetic field created by the magnet, the dimension of the sector magnet is calculated with a lumped magnetic circuit model. Then, the motor is divided into six subdomains, i.e., shaft, inner sector magnet, outer sector magnet, rotor slot, air-gap, and stator slot. Based on the boundary conditions, the magnetic field, back electromotive force, and torque are obtained by solving Poisson's and Laplace's equations. The effect of tooth saturation is considered. The finite-element method and experiments are employed to validate the accuracy of the analytical model. In addition, the proposed method can also be used to evaluate the performance of the spoke-type motor with any irregular magnet.
A Bank of Kalman Filters for Current Sensors Faults Detection and Isolation of DFIG for Wind Turbine

This paper presents a model based Fault diagnosis approach. This approach is based on a bank of Kalman filters. These filters are structured according to the Dedicated Observer Scheme (DOS). , for detecting and isolating multiple and simultaneous current sensors faults of a doubly fed induction generator (DFIG) widely used in variable speed wind turbines. Then. , a linear varying parameter model of DFIG is established and the faults diagnosis procedure is implemented. The DFIG model design and the model-based Fault Detection and Isolation (FDI) approach are performed in the Matlab/Simulink environment.
Reducing the Energy Cost of Human Running Using an Unpowered Exoskeleton

In this paper, we present a new perspective to design an unpowered exoskeleton for metabolic rate reduction in running. According to our studies on human biomechanics, it was observed that having a torsional spring that applies torque as a linear function of the difference between two hips angles (<inline-formula> <tex-math notation="LaTeX">${d}$ </tex-math></inline-formula>-angle), compared with a local spring which applies torque as a function of hip angle (<inline-formula> <tex-math notation="LaTeX">${h}$ </tex-math></inline-formula>-angle), provides a better condition for hip moment compensation and, consequently, metabolic rate reduction. Accordingly, a new type of unpowered exoskeleton device for realization of this idea was designed, and a prototype of this exoskeleton was constructed. This exoskeleton was tested on 10 healthy active subjects for running at 2.5 m s<sup>−1</sup>. In this experiment, 8.0 ± 1.5% (mean ± s.e.m.) metabolic rate reduction (compared with the no-exoskeleton case) was achieved.
Exploration of AC-DC and DC-DC Converters

This paper gives a summary in regard to DC and DC-DC converters and includes the types of converters used in several applications such as Power Factor Correction (PFC), industrial, motor drives and renewable energy system. Nowadays Bridgeless Boost Power Factor Correction Rectifiers and resonant converters are widely proposed, simulations are done and prototypes are developed for these kinds of applications. According to that the converters are analyzed, compared and tabulated.
Letter: identifying coeliac disease among patients with bloating

EDITORS, We read with great interest the editorial entitled “Finding the coeliacs—should everyone be screened? ” and the accompanying original manuscript. The issues including who should be screened and false‐ positive coeliac serology testing necessitating an oesophagogastroduodenoscopies (OGD) are real concerns. We performed a prospective study on the prevalence of coeliac disease in patients presenting to secondary care (Mater Dei Hospital, Malta) in whom one of the symptoms was bloating. Bloating is associated with both functional gastrointestinal disorders with or without associated symptoms and organic diseases. Bloating within the general population varies worldwide from 15% to 30% while approximately 75% of patients with functional gastrointestinal disorders experience bloating. Coeliac disease with a prevalence of approximately 1% in Mediterranean countries, is one of the organic diseases where bloating may be one of the symptoms. In Malta, currently, patients diagnosed with coeliac disease account for 0.42% of the population. In our study, 520 of 1680 adult patients (31%), who presented to our out‐patient clinic complained of bloating. The mean age was 42.5 (SD ± 13.7) years and 73% were female. All these patients had coeliac serology testing done using anti‐tissue transglutaminase IgA (EuroImmun AG, Germany) and a total serum IgA level. Patients with positive serology then underwent OGD with duodenal biopsies. The patients had a multitude of other symptoms, which included: abdominal pain (72.5%), diarrhoea (32.7%), dyspepsia (30.6%), nausea (16%), vomiting (6.35%), bleeding (6.5%) and weight loss (7.9%). Within the patients with bloating, 3.9% had positive coeliac serology with 2.7% having duodenal histology consistent with coeliac disease. The raised levels of serum anti‐tissue transglutaminase varied widely. However, all the patients with false‐positive serology had levels that were less than twice the upper limit. Meanwhile only 20% of coeliacs had their pre‐ diagnosis serology levels which were less than this level. This confirms that all such patients require a duodenal biopsy regardless of serology. The majority (69%) of patients diagnosed with coeliac disease were female. The mean follow‐up was 69.4 months (range: 20.3‐ 92.1) with no changes in diagnosis, providing reassurance that the patients had an appropriate diagnosis. Other diagnosis which possibly contributed to the patients' symptoms were Helicobacter pylori infection (6.5%), inflammatory bowel disease (3.3%), hydronephrosis (2.1%), hypothyroidism (0.8%), intra‐abdominal malignancy (1.3%) and uterine fibroids (0.6%). Although all the above patients reported that their bloating resolved once they were treated for these conditions, one cannot assume that this was the cause of the symptoms as bloating could have been transient and/or functional. Thus, although bloating is a very common symptom in both primary and secondary care, its presence should always prompt screening for coeliac disease. Any positive coeliac serology should then always be followed by duodenal biopsies for histological confirmation.
Software Architecture Solution Based on SDN for an Industrial IoT Scenario

The Industry 4.0 (I4.0) adoption comprises the change of traditional factories intosmartusing the ICTs. The goal is to monitor processes, objects, machinery, and workers in order to have real-time knowledge about what is going on in the factory and for achieving an efficient data collection, management, and decision-making that help improve the businesses in terms of product quality, productivity, and efficiency. Internet of Things (IoT) will have an important role in the I4.0 adoption because future smart factories are expected to rely on IoT infrastructures composed of constellations of hundreds or thousands of sensor devices spread all over the industrial facilities. However, some problems could arise in the massive IoT deployment in a medium-high factory: thousands of IoT devices to cope from different technologies and vendors could mean dozens of vendor tools and user interfaces to manage them. Moreover, the heterogeneity of IoT devices could entail different communication protocols, languages, and data formats, which can result in lack of interoperability. On the other hand, conventional IT networks and industrial machinery are expected to be managed together with the IoT infrastructure, maybe using a tool or a set of tools, fororchestratingthe whole smart factory. This work meets these challenges presenting an open-source software architecture solution based on OpenDaylight (ODL), a Software Defined Network (SDN) controller, for orchestrating an industrial IoT scenario. This work is addressed by shedding light on critical aspects from the SDN controller architectural choices, to specific IoT interfaces and the difficulties for covering the wide range of communication protocols, popular in industrial contexts. Such a global view of the process gives light to practical difficulties appearing in introducing SDN in industrial contexts, providing an open-source architecture solution that guarantees devices and networks interoperability and scalability, breaking the vendor lock-in barriers and providing a vendor-agnostic solution for orchestrating all actor of an I4.0 smart factory.
Electronic Health Record-Based Screening for Substance Abuse

Abstract Existing methods of screening for substance abuse (standardized questionnaires or clinician's simply asking) have proven difficult to initiate and maintain in primary care settings. This article reports on how predictive modeling can be used to screen for substance abuse using extant data in electronic health records (EHRs). We relied on data available through Veterans Affairs Informatics and Computing Infrastructure (VINCI) for the years 2006 through 2016. We focused on 4,681,809 veterans who had at least two primary care visits; 829,827 of whom had a hospitalization. Data included 699 million outpatient and 17 million inpatient records. The dependent variable was substance abuse as identified from 89 diagnostic codes using the Agency for Healthcare Quality and Research classification of diseases. In addition, we included the diagnostic codes used for identification of prescription abuse. The independent variables were 10,292 inpatient and 13,512 outpatient diagnoses, plus 71 dummy variables measuring age at different years between 20 and 90 years. A modified naive Bayes model was used to aggregate the risk across predictors. The accuracy of the predictions was examined using area under the receiver operating characteristic (AROC) curve in 20% of data, randomly set aside for the evaluation. Many physical/mental illnesses were associated with substance abuse. These associations supported findings reported in the literature regarding the impact of substance abuse on various diseases and vice versa. In randomly set-aside validation data, the model accurately predicted substance abuse for inpatient (AROC = 0.884), outpatient (AROC = 0.825), and combined inpatient and outpatient (AROC = 0.840) data. If one excludes information available after substance abuse is known, the cross-validated AROC remained high, 0.822 for inpatient and 0.817 for outpatient data. Data within EHRs can be used to detect existing or predict potential future substance abuse.
Inter-organizational Knowledge Flow and R&D portfolio of Patent Citation Network - The Case of the Socket Wrench Industry

Manufacturers invest in research and development, absorb the technical knowledge of competing operators, to become the core competencies, and further construct a different portfolio of technical capacities. This study, from the perspective of patent statistics, examines retrieved patent information and formulates patent statistics to use patent backward citation, then group the information of the knowledge betweenness is obtained. The patent between the citation network and the relative citation propensity in order to measure the source of technology and direction of movement, explore the industrial technical competitive advantages of Socket Wrench Industry. This study analyzes the strengths and weaknesses of the research and development portfolio of knowledge betweenness of Socket Wrench Industry. Knowledge betweenness companies can effectively monitor their own technical capabilities, and technological advantages to adjust the direction of R&D.
Model Inversion Attacks for Prediction Systems: Without Knowledge of Non-Sensitive Attributes

While online services based on machine learning (ML) have been attracting considerable attention in both academic and business, privacy issues are becoming a threat that cannot be ignored. Recently, Fredrikson et al. [USENIX 2014] proposed a new paradigm of model inversion attacks, which allows an adversary to expose the sensitive information of users by using an ML system for an unintended purpose. In particular, the attack reveals the sensitive attribute values of the target user by using their non-sensitive attributes and the output of the ML model. Here, for the attack to succeed, the adversary needs to possess the non-sensitive attribute values of the target user prior to the attack. However, in reality, even if this information (i.e., non-sensitive attributes) is not necessarily information the user regards as sensitive, it may be difficult for the adversary to actually acquire it. In this paper, we propose a general model inversion (GMI) framework to capture the above scenario where knowledge of the non-sensitive attributes is not necessarily provided. Here, our framework also captures the scenario of Fredrikson et al. Notably, we generalize the paradigm of Fredrikson et al. by additionally modeling the amount of auxiliary information the adversary possesses at the time of the attack. Our proposed GMI framework enables a new type of model inversion attack for prediction systems, which can be carried out without knowledge of the non-sensitive attributes. At a high level, we use the paradigm of data poisoning in a novel way and inject malicious data into the set of training data to modify the ML model into a target ML model, which we can attack without having to have knowledge of the non-sensitive attributes. Our new attack enables the inference of sensitive attributes in the user input from only the output of the ML model, even when the non-sensitive attributes of the user are not available to the adversary. Finally, we provide a concrete algorithm of our model inversion attack on prediction systems based on linear regression models, and give a detailed description of how the data poisoning algorithm is constructed.We evaluate the performance of our new model inversion attack without the knowledge of non-sensitive attributes through experiments with actual data sets.
Evolutionary origins of vocal mimicry in songbirds

Vocal learning is an important behavior in oscines (songbirds). Some songbird species learn heterospecific sounds as well as conspecific vocalizations. The emergence of vocal mimicry is necessarily tied to the evolution of vocal learning, as mimicry requires the ability to acquire sounds through learning. As such, tracking the evolutionary origins of vocal mimicry may provide insights into the causes of variation in song learning programs among songbirds. We compiled a database of known vocal mimics that comprised 339 species from 43 families. We then traced the evolutionary history of vocal mimicry across the avian phylogeny using ancestral trait reconstruction on a dataset of oscine passerines for which vocalizations have been described. We found that the common ancestor to oscines was unlikely to mimic sounds, suggesting that song learning evolved with mechanisms to constrain learning to conspecific models. Mimicry then evolved repeatedly within the songbird clade, either through relaxation of constraints on conspecific learning or through selection for active vocal mimicry. Vocal mimicry is likely ancestral in only a handful of clades, and we detect many instances of independent origins of mimicry. Our analysis underscores the liability of vocal mimicry in songbirds, and highlights the evolutionary flexibility of song learning mechanisms.
Ethical Leadership and Follower Moral Actions: Investigating an Emotional Linkage

The effectiveness of ethical leadership has been extensively investigated. However, compared to the outcomes of ethical leadership, we still lack enough knowledge about the mechanisms underlying ethical leadership and its outcomes. Drawing from social information processing theory, this paper explores an emotional explanation for the effectiveness of ethical leadership. Adopting a time-lagged research design with responses from 64 leaders and 289 followers, the present research found that ethical leadership invokes followers’ other-praising emotions and eventually enhances their moral actions. Further, leader core self-evaluation contributes to the positive effects of ethical leadership on followers’ other-praising moral emotions and subsequent moral actions. Theoretical and practical implementations of these observations were discussed.
Clinical characteristics of dermatomyosits/polymyositis associated interstitial lung disease according to the autoantibody.

BACKGROUND Dermatomyositis (DM) and polymyositis (PM) often have association with interstitial lung disease (ILD) which have disease specific autoantibody. METHODOLOGY We reviewed medical records of DM/PM associated ILD from January 2000 to December 2017 according to the autoantibody. RESULT We identified 52 patients, of whom 30 were antibody negative, 18 had anti aminoacyl-tRNA synthetases (ARS) antibodies and 4 had anti melanoma differentiation-associated gene (MDA)-5 antibody. In high resolution computed tomography (HRCT) of the chest, area of ground glass opacity (GGO), consolidation, and lung tip consolidation were more extensive in anti MDA-5 antibody positive patients (p=0.051, p=0.026, and p=0.027, respectively). Among laboratory findings, GOT had strong correlations with CPK (r=0.889, p < 0.001), and LDH (r=0.910, p < 0.001). Among roentgenographic findings, there were moderate correlations between GGO and consolidation (r=0.668, p < 0.001), and between reticular shadow and traction bronchiectasis (p=0.633, p < 0.001). ILD patients with anti MDA-5 antibodies had decreased survival (1.00 vs 84.3, 22.9 months, p < 0.001). CONCLUSION ILD patients with anti ARS antibody had intense inflammation, but reversible fibrosis and good prognosis. On the other hand, anti MDA-5 antibody positive ILD patients had shorter survival. Extent of parenchymal shadow and serum GOT were useful indicator of disease activity of PM/DM associated ILD patients in our cohort. J. Med. Invest. 65:251-257, August, 2018.
Comparison of Detection Method on Malaria Cell Images

In the image analysis process, thresholding is one of the most important preprocessing steps. Thresholding is a sort of picture division that separates protest apportioning a picture into a closer view and foundation. This project will describe a few selected thresholding methods such as Fuzzy C-Mean Algorithm's method, Wolf's method, Bradley's method, Bernsen's method, Triangle's Method and Deghost's Method. Each method will experiment with the malaria image. The objective of thresholding method is to simplify an image into something that is easier to examine. By using MATLAB R2017b as its core programming software, the image will be separated by unused background with uncertainty. The thresholding method will undergo image quality analysis such as Sensitivity and Specificity. Based on numerical anaylsis the Fuzzy C-Mean Algorithm method is more effective and good performance compared to the other methods.
Causal Markov Elman Network for Load Forecasting in Multinetwork Systems

This paper proposes a novel causality analysis approach called the causal Markov Elman network (CMEN) to characterize the interdependence among heterogeneous time series in multinetwork systems. The CMEN performance, which comprises inputs filtered by Markov property, successfully characterizes various multivariate dependencies in an urban environment. This paper also proposes a novel hypothesis of characterizing joint information between interconnected systems such as electricity and transportation networks. The proposed methodology and the hypotheses are then validated by information theory distance-based metrics. For cross validation, the CMEN is applied to the electricity load forecasting problem using actual data from Tallahassee, Florida.
Integrated Test Structures for Reliability Investigation under Dynamic Stimuli

Reliability for advanced CMOS nodes is becoming very challenging. Static stress may not be sufficient to understand digital circuit reliability in case of transient and frequency effect. For NBTI mechanism, it is known that projection end-of-life degradation based on DC stress is pessimistic and thus poorly accurate. This paper presents dedicated test structures to evaluate different wear-out mechanisms under dynamic stimuli. Firstly, oxide breakdown is investigated with 1GHz AC signal generated by a ring oscillator. Post-breakdown devices characteristics are significantly different between static and dynamic stress. Then, with another structure, the effect of hot carrier is shown in data path with different duty cycles and frequencies. Finally, a built-in $\text{V}_{\mathbf {th}}$ measurement structure is developed to investigate the negative bias temperature instability. It was measured that dynamic stimuli results in a different degradation magnitude than DC or low frequency one.