nils-herrmann/clean_scirepeval huggingface dataset:
Predictive torque control of a permanent magnet synchronous motor fed by a matrix converter without weighting factor
Venue: 2016 7th Power Electronics and Drive Systems Technologies Conference (PEDSTC)

Finite control set model predictive control is a flexible control scheme with fast dynamic. The standard form of this controller is based on the minimization of a cost function. The cost function needs weighting factors that is dependent to the system parameters and operating point. The tuning of the weighting factors requires nontrivial process. This paper presents a predictive direct torque and stator flux control of a permanent magnet synchronous motor fed by a matrix converter without weighting factor. The proposed method uses a multiobjective optimization based on a ranking approach instead of a cost function. Therefore, the tuning of weighting factors is unnecessary. Simulation results are presented to confirm the good performance of the proposed method.
Condition monitoring of induction motors: A review
Venue: 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

The electrical motor condition monitoring is a growing technology to detect the fault of an induction motor. It detects the unexpected faults of a critical system. Definite harmonic signals of the line current are located by a popular method known as motor current signature analysis. Different faults of an induction motor such as rotor, stator, bearing, vibration, air gap eccentricity and their different diagnosis techniques are also explored. In fact, the actual fault detection by using the human involvement is widely replaced by the automated technology, namely fuzzy logic based systems, genetic algorithm, neural networks, wavelet technique, Vienna monitoring etc. It is truly evident that the scope of this area is vast. Hence, acknowledging the need for future research, this review paper presents a bird's eye view on different types of faults and their diagnostics' schemes.
Investigating the performance of QoS enabled LTE networks for IEC 61850 based smart grid applications
Venue: 2016 IEEE International Energy Conference (ENERGYCON)

With the proportion of de centralized energy resources (DERs) in power networks on the increase and active consumer participation in energy exchanges becoming common, the automation of power grids needs to become distributed, fast and reliable. Such advanced automation schemes require seamless communication between Intelligent Electronic Devices (IEDs) within a substation and the IEDs of remote Distributed Generation (DG) units. The IEC 61850 standard provides common data models for IEDs, defines standard communication services for information exchange and places requirements on the communication systems supporting automation of the power grid. This paper investigates the performance of Long Term Evolution (LTE) communications technology with enabled Quality of Service (QoS) features, for wide area measurement acquisition and control, using IEC 61850 standard communication services. This investigation performed real time testing using a test configuration based on the integration of an Ericsson LTE network infrastructure with an IED and a Supervisory Control and Data Acquisition (SCADA) system. Test results show that QoS enabled LTE satisfies the stringent transfer time requirements for the P4, P5, and P6 classes, defined in the IEC 61850 standard, even under heavy radio network congestion conditions.
Software defined networking for resilient communications in Smart Grid active distribution networks
Venue: 2016 IEEE International Conference on Communications (ICC)

Emerging Software Defined Networking (SDN) technology provides excellent flexibility to large scale networks in terms of control, management, security, and maintenance. In this paper, we propose an SDN based communication infrastructure for Smart Grid distribution networks among substations. A Smart Grid communication infrastructure consists of a large number of heterogenous devices that exchange real time information for monitoring the status of the grid. We then investigate how SDN enabled Smart Grid infrastructure can provide resilience to active distribution substations with self recovery. Specifically, by introducing redundant and wireless communication links that can be used during the emergencies, we show that SDN controllers can be effective for restoring the communication while providing a lot of flexibility. Furthermore, to be able to effectively evaluate the performance of the proposed work in terms of various fine grained network metrics, we developed a Mininet based testing framework and integrated it with ns 3 network simulator. Finally, we conducted experiments by using actual Smart Grid communication data to assess the recovery performance of the proposed SDN based system. The results show that SDN is a viable technology for the Smart Grid communications with almost negligible delays in switching to backup wireless links during the times of link failures in reliable fashion.
Fundamentals of aerosol therapy in critical care
Venue: Critical Care

Drug dosing in critically ill patients is challenging due to the altered drug pharmacokinetics pharmacodynamics associated with systemic therapies. For many drug therapies, there is potential to use the respiratory system as an alternative route for drug delivery. Aerosol drug delivery can provide many advantages over conventional therapy. Given that respiratory diseases are the commonest causes of critical illness, use of aerosol therapy to provide high local drug concentrations with minimal systemic side effects makes this route an attractive option. To date, limited evidence has restricted its wider application. The efficacy of aerosol drug therapy depends on drug related factors (particle size, molecular weight) device factors, patient related factors (airway anatomy, inhalation patterns) and mechanical ventilation related factors (humidification, airway) This review identifies the relevant factors which require attention for optimization of aerosol drug delivery that can achieve better drug concentrations at the target sites and potentially improve clinical outcomes.
Cynaropicrin: A Comprehensive Research Review and Therapeutic Potential As an Anti Hepatitis C Virus Agent
Venue: Frontiers in pharmacology

The different pharmacologic properties of plants containing cynaropicrin, especially artichokes, have been known for many centuries. More recently, cynaropicrin exhibited a potential activity against all genotypes of hepatitis C virus (HCV) Cynaropicrin has also shown a wide range of other pharmacologic properties such as anti hyperlipidemic, anti trypanosomal, anti malarial, antifeedant, antispasmodic, anti photoaging, and anti tumor action, as well as activation of bitter sensory receptors, and anti inflammatory properties (e.g. associated with the suppression of the key pro inflammatory NF kB pathway) These pharmacological effects are very supportive factors to its outstanding activity against HCV. Structurally, cynaropicrin might be considered as a potential drug candidate, since it has no violations for the rule of five and its water solubility could allow formulation as therapeutic injections. Moreover, cynaropicrin is a small molecule that can be easily synthesized and as the major constituent of the edible plant artichoke, which has a history of safe dietary use. In summary, cynaropicrin is a promising bioactive natural product that, with minor hit to lead optimization, might be developed as a drug for HCV.
The Hidden and External Costs of Pesticide Use

A fair evaluation of the net benefits provided by pesticides is essential to feed the current debate on their benefits and adverse consequences. Pesticides provide many benefits by killing agricultural and human pests. However, they also entail several types of costs, including internal costs due to the purchase and application of pesticides, and various other costs due to the impact of treatments on human health and the environment. Here, we provide a comprehensive review of these costs and their evaluation. We define four categories of costs: regulatory costs, human health costs, environmental costs and defensive expenditures. Those costs are either internal to the market, but hidden to the users, or external to the market and most often paid by a third party. We analysed 61 papers published between 1980 and 2014, and 30 independent dataset. Regulatory costs reached very large values, e.g. US$4 billion yearly in the United States in the 2000s. However, if all regulations were respected, these costs would have jumped to US$22 billion in this country. Health costs studies generally did not take into account fatal cases due to chronic exposure such as fatal outcomes of cancers. Doing so would have increased estimates of health costs by up to tenfold, e.g. from US$1.5 billion to US$15 billion in the United States in 2005.
Person Re Identification via Multiple Coarse to Fine Deep Metrics
Venue: ECAI

Person re identification, aiming to identify images of the same person from various cameras views in different places, has attracted a lot of research interests in the field of artificial intelligence and multimedia. As one of its popular research directions, the metric learning method plays an important role for seeking a proper metric space to generate accurate feature comparison. However, the existing metric learning methods mainly aim to learn an optimal distance metric function through a single metric, making them difficult to consider multiple similar relationships between the samples. To solve this problem, this paper proposes a coarse to fine deep metric learning method equipped with multiple different Stacked Auto Encoder (SAE) networks and classification networks. In the perspective of the human's visual mechanism, the multiple different levels of deep neural networks simulate the information processing of the brain's visual system, which employs different patterns to recognize the character of objects. In addition, a weighted assignment mechanism is presented to handle the different measure manners for final recognition accuracy. The experimental results conducted on two public datasets, i.e. VIPeR and CUHK have shown the prospective performance of the proposed method.
Context Based Mobile User Interface

Context awareness is increasingly gaining applicability in interactive ubiquitous mobile computing systems. Each context aware application has its own set of behaviors to react to context modifications. Hence, every software engineer needs to clearly understand the goal of the development and to categorize the context in the application. We incorporate context based modifications into the appearance or the behavior of the interface, either at the design time or at the run time. In this paper, we present application behavior adaption to the context modification via a context based user interface in a mobile application. We are interested in a context based user interface in a mobile device that is automatically adapted based on the context information. We use the adaption tree, named in our methodology, to represent the adaption of mobile device user interface to various context information. The context includes the user's domain information and dynamic environment changes. Each path in the adaption tree, from the root to the leaf, presents an adaption rule. An e commerce application is chosen to illustrate our approach. This mobile application was developed based on the adaption tree in the Android platform. The automatic adaption to the context information has enhanced human computer interactions.
A fast and robust local descriptor for 3D point cloud registration
Venue: Inf. Sci.

This paper proposes a novel local feature descriptor, called a local feature statistics histogram (LFSH) for efficient 3D point cloud registration. An LFSH forms a comprehensive description of local shape geometries by encoding their statistical properties on local depth, point density, and angles between normals. The sub features in the LFSH descriptor are low dimensional and quite efficient to compute. In addition, an optimized sample consensus (OSAC) algorithm is developed to iteratively estimate the optimum transformation from point correspondences. OSAC can handle the challenging cases of matching highly self similar models. Based on the proposed LFSH and OSAC, a coarse to fine algorithm can be formed for 3D point cloud registration. Experiments and comparisons with the state of the art descriptors demonstrate that LFSH is highly discriminative, robust, and significantly faster than other descriptors. Meanwhile, the proposed coarse to fine registration algorithm is demonstrated to be robust to common nuisances, including noise and varying point cloud resolutions, and can achieve high accuracy on both model data and scene data.
Small temporal pole encephalocele: A hidden cause of "normal" MRI temporal lobe epilepsy
Venue: Epilepsia

Small temporal pole encephalocele (STPE) can be the pathologic substrate of epilepsy in a subgroup of patients with noninformative magnetic resonance imaging (MRI) Herein, we analyzed the clinical, neurophysiologic, and radiologic features of the epilepsy found in 22 patients with STPE, and the frequency of STPE in patients with refractory focal epilepsy (RFE)
Epilepsy with temporal encephalocele: Characteristics of electrocorticography and surgical outcome
Venue: Epilepsia

Temporal lobe encephaloceles (TEs) are increasingly identified in patients with epilepsy due to advances in neuroimaging. Select patients become seizure free with lesionectomy. In practice, however, many of these patients will undergo standard anterior temporal lobectomy. Herein we report on the first series of patients with refractory temporal lobe epilepsy (TLE) with encephalocele to undergo chronic or intraoperative electrocorticography (ECoG) in order to characterize the putative epileptogenic nature of these lesions and help guide surgical planning. This retrospective study includes nine adult patients with magnetic resonance imaging/computed tomography (MRI/CT) defined temporal encephalocele treated between 2007 and 2014 at University of California San Francisco (UCSF) Clinical features, ECoG, imaging, and surgical outcomes are reviewed. Six patients underwent resective epilepsy surgery. Each case demonstrated abnormal epileptiform discharges around the cortical area of the encephalocele. Two underwent tailored lesionectomy and four underwent lesionectomy plus anterior medial temporal resection. Postoperatively, five patients, including both with lesionectomy only, had Engel class Ia surgical outcome, and one had a class IIb surgical outcome. The role of TE in the pathogenesis of epilepsy is uncertain. ECoG can confirm the presence of interictal epileptiform discharges and seizures arising from these lesions. Patients overall had a very good surgical prognosis, even with selective surgical approaches.
People and their smartphones mapping mobile interaction in the modern connected world

The purpose of this paper is to investigate how a balanced study group consisting of 152 participants interact with, operate, customize, and control their smartphone applications.  This work uses a qualitative research methodology involving an online user study questionnaire, supported by e mailed user screenshots and online conversations.  In terms of smartphone age, 72 per cent of the participants' smartphones were less than two years old. This high level of churn rate was anticipated and will please retailers and marketers alike. This study found that the majority of smartphone users regularly arrange their app icons and that their categorization principle was based primarily on application associated functionality and frequency of use. This group of users seemed less concerned about the risks of privacy and security, and even when they had lost or had their smartphone stolen, few (5.2 per cent) had suffered from fraud, in contrast to the general perception of risk.  This is one of the few studies to have investigated the area of smartphone use from the users' perspective, leading to important insights into application user behaviour and icon arrangement, and as well as alternative possible implications for launcher design.
Ceci n'est pas une pipe: A deep convolutional network for fine art paintings classification
Venue: 2016 IEEE International Conference on Image Processing (ICIP)

"Ceci n'est pas une pipe" French for "This is not a pipe" This is the description painted on the first painting in the figure above. But to most of us, how could this painting is not a pipe, at least not to the great Belgian surrealist artist Rene Magritte. He said that the painting is not a pipe, but rather an image of a pipe. In this paper, we present a study on large scale classification of fine art paintings using the Deep Convolutional Network. Our objectives are two folds. On one hand, we would like to train an end to end deep convolution model to investigate the capability of the deep model in fine art painting classification problem. On the other hand, we argue that classification of fine art collections is a more challenging problem in comparison to objects or face recognition. This is because some of the artworks are non representational nor figurative, and might requires imagination to recognize them. Hence, a question arose is that does a machine have or able to capture "imagination" in paintings? One way to find out is train a deep model and then visualize the low level to high level features learnt. In the experiment, we employed the recently publicly available large scale "Wikiart paintings" dataset that consists of more than 80,000 paintings and our solution achieved state of the art results (68% in overall performance.
Changing land use and its impact on the habitat suitability for wintering Anseriformes in China's Poyang Lake region.
Venue: The Science of the total environment

As an internationally important wetland for migratory waterbirds, China's Poyang Lake region has experienced substantial changes in land use during the past two decades owing to climate change and anthropogenic disturbances. Recent dam constructions on the Yangtze River and its tributaries for agriculture and hydroelectric power exert strong effects on the hydrological regimes of this lake. However, few studies have investigated how the land use changes through time affect the habitat suitability for wintering Anseriformes the largest community in this region. Thus, it is necessary to timely monitor changes in the habitat quality and understand the potential factors that alter it. In this study, three periods (1995, 2005 and 2014) of typical environmental indicators that have direct impacts on foraging and resting for the Anserformes, including proximity to water (density of lakes, rivers and ponds) human disturbances (density of residences and various road networks) preferred land cover types and food availability (NDVI) are integrated to develop a habitat suitability index model for habitat mapping. The results indicate that long term lake shrinkage in low water periods led to greatly expanded wetlands in these years, which provided more suitable habitat for migratory waterfowl. The amount of highly suitable habitat in 2014 was nearly twice as much as in 1995. Recent survey data from 1997 to 2013 also revealed an increase in the population size, and confirmed the improvement of habitat suitability in the Poyang Lake region. Spatial analysis revealed that land use changes contributed most to the improved habitat coverage between 1995 and 2014. However, the relative significances of these transformations for highly suitable and moderately suitable habitats are strikingly different. Increases in wetland and paddy field area are the main reasons for explaining these improvements, respectively. The framework model proposed in this study will help governments to evaluate habitat conservation and restoration for protecting waterbirds in a spatially explicit way.
The Negative Impact of Technology on Social Networking among Students at UTM Skudai 2016

Recently social media network such as Facebook, Instagram, Twitters and Youtube has led to the popularity. Social media, which once act as an electronic connection between users has gained wider acceptability and usability and is also becoming probably the most important communication tools and is addictive among students especially in the higher education. There's no denying the benefits we have gained from technological advancements, but as with all things in life moderation is key. Many students tend to use social medias against the ethics enshrines by Islamic laws therefore awareness about the dangers of excessive use of electronics will help in avoiding any undesirable issue. The negative impacts of addictive usage of social medias includes isolation, lack of social skills and bonds, obesity, depression, poor sleep habit, increase bullying, lack of privacy, lack of social and sexual boundaries, and mental and emotional disturbances. Therefore, this study examines the significant impact of social media on UTM students and to identify recommendations to overcome the negative impact. Quantitative method is applied in this research distributed to students in Universiti Teknologi Malaysia where the respondents is required to rate on a likert scale basis. Questionnaire is developed to explore the participating students' the level of social media's usage and its negative impact. At the end of this paper, some suggestions are included to overcome the negative impact for a better use to this social networking site. The last and not least, this study will be of great benefits to the university as it has shown the dangers of uncontrolled use of these social medias by students and therefore the need to put in place measures to prevent the negative effects.  Technology, Social Medias, Impact of Social Medias, Students, UTM
MiR 155 regulated molecular network orchestrates cell fate in the innate and adaptive immune response to Mycobacterium tuberculosis
Venue: Proceedings of the National Academy of Sciences

The mechanism by which Mycobacterium tuberculosis (Mtb) modulates the host immune response is not fully understood. We have used a systems biology approach to generate a microRNA regulatory network composed of 77 microRNAs that are associated with Mtb macrophage interactions. We have determined a unique and dual role for one of these regulators, miR 155, as a rheostat regulating the survival of both innate and adaptive immune cells. On the one hand, miR 155 maintains the survival of Mtb infected macrophages, providing a niche favoring bacterial replication. On the other hand, miR 155 maintains the survival of Mtb specific T cells, enabling an effective adaptive response. Our work underscores the value of systems based prediction of pathogen specific microRNA networks as a tool to define host pathogen interactions. The regulation of host pathogen interactions during Mycobacterium tuberculosis (Mtb) infection remains unresolved. MicroRNAs (miRNAs) are important regulators of the immune system, and so we used a systems biology approach to construct an miRNA regulatory network activated in macrophages during Mtb infection. Our network comprises 77 putative miRNAs that are associated with temporal gene expression signatures in macrophages early after Mtb infection. In this study, we demonstrate a dual role for one of these regulators, miR 155. On the one hand, miR 155 maintains the survival of Mtb infected macrophages, thereby providing a niche favoring bacterial replication; on the other hand, miR 155 promotes the survival and function of Mtb specific T cells, enabling an effective adaptive immune response. MiR 155 induced cell survival is mediated through the SH2 domain containing inositol 5 phosphatase 1 (SHIP1)/protein kinase B (Akt) pathway. Thus, dual regulation of the same cell survival pathway in innate and adaptive immune cells leads to vastly different outcomes with respect to bacterial containment.