nils-herrmann/clean_scirepeval huggingface dataset:
Speed Control of Induction Motor using PI and V/F

In this paper, Induction Motors (IM) are widely used in the industrial application due to a high power/weight ratio, high reliability and low cost. A Space Vector PWM (SVPWM) is utilized for PWM controlling scheme. The performance of both the speed and torque is promoted by a modified PI controller and V/F scalar control. A scalar control is a simple method and it's operated to control the magnitude of the control quantities in constant speed application. V/F scalar control has been implemented and compared with the PI controller. The simulation results showed that Indirect Field oriented control (IFOC) induction motor drive employ decoupling of the stator current components which produces torque and flux. The complete mathematical model of the system is described and simulated in MATLAB/SIMULINK. The simulation results provides a smooth speed response and high performance under various dynamic operations.
A Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT

The recent expansion of the Internet of Things (IoT) and the consequent explosion in the volume of data produced by smart devices have led to the outsourcing of data to designated data centers. However, to manage these huge data stores, centralized data centers, such as cloud storage cannot afford auspicious way. There are many challenges that must be addressed in the traditional network architecture due to the rapid growth in the diversity and number of devices connected to the internet, which is not designed to provide high availability, real-time data delivery, scalability, security, resilience, and low latency. To address these issues, this paper proposes a novel blockchain-based distributed cloud architecture with a software defined networking (SDN) enable controller fog nodes at the edge of the network to meet the required design principles. The proposed model is a distributed cloud architecture based on blockchain technology, which provides low-cost, secure, and on-demand access to the most competitive computing infrastructures in an IoT network. By creating a distributed cloud infrastructure, the proposed model enables cost-effective high-performance computing. Furthermore, to bring computing resources to the edge of the IoT network and allow low latency access to large amounts of data in a secure manner, we provide a secure distributed fog node architecture that uses SDN and blockchain techniques. Fog nodes are distributed fog computing entities that allow the deployment of fog services, and are formed by multiple computing resources at the edge of the IoT network. We evaluated the performance of our proposed architecture and compared it with the existing models using various performance measures. The results of our evaluation show that performance is improved by reducing the induced delay, reducing the response time, increasing throughput, and the ability to detect real-time attacks in the IoT network with low performance overheads.
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model

With the goal of making high-resolution forecasts of regional rainfall, precipitation nowcasting has become an important and fundamental technology underlying various public services ranging from rainstorm warnings to flight safety. Recently, the Convolutional LSTM (ConvLSTM) model has been shown to outperform traditional optical flow based methods for precipitation nowcasting, suggesting that deep learning models have a huge potential for solving the problem. However, the convolutional recurrence structure in ConvLSTM-based models is location-invariant while natural motion and transformation (e.g., rotation) are location-variant in general. Furthermore, since deep-learning-based precipitation nowcasting is a newly emerging area, clear evaluation protocols have not yet been established. To address these problems, we propose both a new model and a benchmark for precipitation nowcasting. Specifically, we go beyond ConvLSTM and propose the Trajectory GRU (TrajGRU) model that can actively learn the location-variant structure for recurrent connections. Besides, we provide a benchmark that includes a real-world large-scale dataset from the Hong Kong Observatory, a new training loss, and a comprehensive evaluation protocol to facilitate future research and gauge the state of the art.
QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.

In 2003, the QUADAS tool for systematic reviews of diagnostic accuracy studies was developed. Experience, anecdotal reports, and feedback suggested areas for improvement; therefore, QUADAS-2 was developed. This tool comprises 4 domains: patient selection, index test, reference standard, and flow and timing. Each domain is assessed in terms of risk of bias, and the first 3 domains are also assessed in terms of concerns regarding applicability. Signalling questions are included to help judge risk of bias. The QUADAS-2 tool is applied in 4 phases: summarize the review question, tailor the tool and produce review-specific guidance, construct a flow diagram for the primary study, and judge bias and applicability. This tool will allow for more transparent rating of bias and applicability of primary diagnostic accuracy studies.
Building a Generic SOAP Framework over Binary XML

The prevailing binding of SOAP to HTTP specifies that SOAP messages be encoded as an XML 1.0 document which is then sent between client and server. XML processing however can be slow and memory intensive, especially for scientific data, and consequently SOAP has been regarded as an inappropriate protocol for scientific data. Efficiency considerations thus lead to the prevailing practice of separating data from the SOAP control channel. Instead, it is stored in specialized binary formats and transmitted either via attachments or indirectly via a file sharing mechanism, such as GridFTP or HTTP. This separation invariably complicates development due to the multiple libraries and type systems to be handled; furthermore it suffers from performance issues, especially when handling small binary data. As an alternative solution, binary XML provides a highly efficient encoding scheme for binary data in the XML and SOAP messages, and with it we can gain high performance as well as unifying the development environment without unduly impacting the Web service protocol stack. In this paper we present our implementation of a generic SOAP engine that supports both textual XML and binary XML as the encoding scheme of the message. We also present our binary XML data model and encoding scheme. Our experiments show that for scientific applications binary XML together with the generic SOAP implementation not only ease development, but also provide better performance and are more widely applicable than the commonly used separated schemes
SmartParking: A Secure and Intelligent Parking System Using NOTICE

Parking is costly and limited in almost every major city in the world. Innovative parking systems for meeting near-term parking demand are needed. In this paper, we propose a novel, secure and intelligent parking system based on the concept and framework of NOTICE [WO07], a secure and privacy-aware architecture for the notification of traffic incidents. The proposed system, called SmartParking, is a service-oriented intelligent parking system through which drivers can view and reserve a parking spot on the fly. The parking process can then be a straightforward and non-stop process. More importantly, SmartParking is a secure and privacy-aware parking system. The proposed infrastructure prevents most security/privacy attacks. We address hardware/software architecture and implementations. The evaluation of this proposed system proves its efficiency.
Analyzing the Windkessel Model as a Potential Candidate for Correcting Oscillometric Blood-Pressure Measurements

Developing a good model for oscillometric blood-pressure measurements is a hard task. This is mainly due to the fact that the systolic and diastolic pressures cannot be directly measured by noninvasive automatic oscillometric blood-pressure meters (NIBP) but need to be computed based on some kind of algorithm. This is in strong contrast with the classical Korotkoff method, where the diastolic and systolic blood pressures can be directly measured by a sphygmomanometer. Although an NIBP returns results similar to the Korotkoff method for patients with normal blood pressures, a big discrepancy exist between both methods for severe hyper- and hypotension. For these severe cases, a statistical model is needed to compensate or calibrate the oscillometric blood-pressure meters. Although different statistical models have been already studied, no immediate calibration method has been proposed. The reason is that the step from a model, describing the measurements, to a calibration, correcting the blood-pressure meters, is a rather large leap. In this paper, we study a “databased” Fourier series approach to model the oscillometric waveform and use the Windkessel model for the blood flow to correct the oscillometric blood-pressure meters. The method is validated on a measurement campaign consisting of healthy patients and patients suffering from either hyper- or hypotension.
Modeling and Exploration of the Voltage-Controlled Magnetic Anisotropy Effect for the Next-Generation Low-Power and High-Speed MRAM Applications

Spin transfer torque magnetic random access memory (STT-MRAM) has been widely regarded as a potential nonvolatile memory candidate in the next-generation computer architectures. Nevertheless, the write energy consumption and delay are two significant concerns for STT-MRAM, blocking its applications for working memories. Recently, magnetic tunnel junction (MTJ) based on voltage-controlled magnetic anisotropy (VCMA) effect shows tremendous superiority in terms of dynamic write energy and delay over the STT-based one, attracting much attention for advanced low-power and high-speed MRAM designs. In this paper, we evaluate the prospects and challenges of the VCMA-MTJ devices for advanced MRAM applications. First, the magnetization dynamics of the free layer of VCMA-MTJ devices are studied by solving a modified Landau–Lifshitz–Gilbert equation. Afterward, a VCMA-MTJ electrical model is built by integrating the VCMA effect, Slonczewski STT model, Brinkman resistance model, and tunnel magnetoresistance model. Finally, three MTJ switching strategies, including precessional VCMA, STT-assisted precessional VCMA and STT-assisted thermally-activated VCMA, are studied for MRAM applications. Our results show that the STT-assisted precessional VCMA strategy is the most potential one for high-speed and low-power VCMA-MRAM design. This paper provides models, strategies, and guidelines for VCMA-MRAM design and application.
Towards Autonomous Micro UAV Swarms

Micro Unmanned Aerial Vehicles (UAVs) such as quadrocopters have gained great popularity over the last years, both as a research platform and in various application fields. However, some complex application scenarios call for the formation of swarms consisting of multiple drones. In this paper a platform for the creation of such swarms is presented. It is based on commercially available quadrocopters enhanced with on-board processing and communication units enabling full autonomy of individual drones. Furthermore, a generic ground control station is presented that serves as integration platform. It allows the seamless coordination of different kinds of sensor platforms.
A Predictive Model for Selective Targeting of the Warburg Effect through GAPDH Inhibition with a Natural Product.

Targeted cancer therapies that use genetics are successful, but principles for selectively targeting tumor metabolism that is also dependent on the environment remain unknown. We now show that differences in rate-controlling enzymes during the Warburg effect (WE), the most prominent hallmark of cancer cell metabolism, can be used to predict a response to targeting glucose metabolism. We establish a natural product, koningic acid (KA), to be a selective inhibitor of GAPDH, an enzyme we characterize to have differential control properties over metabolism during the WE. With machine learning and integrated pharmacogenomics and metabolomics, we demonstrate that KA efficacy is not determined by the status of individual genes, but by the quantitative extent of the WE, leading to a therapeutic window in vivo. Thus, the basis of targeting the WE can be encoded by molecular principles that extend beyond the status of individual genes.
A nurses ’ guide to the critical reading of research

A sound theoretical foundation to guide practice is enhanced by the ability of nurses to critique research. This article provides a structured route to questioning the methodology of nursing research. Primary Argument Nurses may find critiquing a research paper a particularly daunting experience when faced with their first paper. Knowing what questions the nurse should be asking is perhaps difficult to determine when there may be unfamiliar research terms to grasp. Nurses may benefit from a structured approach which helps them understand the sequence of the text and the subsequent value of a research paper.  A framework is provided within this article to assist in the analysis of a research paper in a systematic, logical order. The questions presented in the framework may lead the nurse to conclusions about the strengths and weaknesses of the research methods presented in a research article. The framework does not intend to separate quantitative or qualitative paradigms but to assist the nurse in making broad observations about the nature of the research.
Detection of seam carving and localization of seam insertions in digital images

"Seam carving" is a recently introduced content aware image resizing algorithm. This method can also be used for image tampering. In this paper, we explore techniques to detect seam carving (or seam insertion) without knowledge of the original image. We employ a machine learning based framework to distinguish between seam-carved (or seam-inserted) and normal images. It is seen that the 324-dimensional Markov feature, consisting of 2D difference histograms in the block-based Discrete Cosine Transform domain, is well-suited for the classification task. The feature yields a detection accuracy of 80% and 85% for seam carving and seam insertion, respectively. For seam insertion, each new pixel that is introduced is a linear combination of its neighboring pixels. We detect seam insertions based on this linear relation, with a high detection accuracy of 94% even for very low seam insertion rates. We show that the Markov feature is also useful for scaling and rotation detection.
How Important Is Deliberate Practice? Reply to Ericsson (2016).

Ericsson and colleagues' view that individual differences in expertise can largely be accounted for by accumulated deliberate practice is not supported by the available empirical evidence. Extending earlier work (Macnamara, Hambrick, & Oswald, 2014), we found that deliberate practice accounted for a sizeable amount of variance in sports performance (18%), but it left a much larger amount unexplained. Ericsson's (2016, this issue) evaluation of our research is undercut by contradictions, omissions, and errors. We agree with Ericsson that future longitudinal research will deepen understanding of expertise, but our goal was to evaluate the importance of deliberate practice based on existing evidence.
Meta-analytic evidence for common and distinct neural networks associated with directly experienced pain and empathy for pain

A growing body of evidence suggests that empathy for pain is underpinned by neural structures that are also involved in the direct experience of pain. In order to assess the consistency of this finding, an image-based meta-analysis of nine independent functional magnetic resonance imaging (fMRI) investigations and a coordinate-based meta-analysis of 32 studies that had investigated empathy for pain using fMRI were conducted. The results indicate that a core network consisting of bilateral anterior insular cortex and medial/anterior cingulate cortex is associated with empathy for pain. Activation in these areas overlaps with activation during directly experienced pain, and we link their involvement to representing global feeling states and the guidance of adaptive behavior for both self- and other-related experiences. Moreover, the image-based analysis demonstrates that depending on the type of experimental paradigm this core network was co-activated with distinct brain regions: While viewing pictures of body parts in painful situations recruited areas underpinning action understanding (inferior parietal/ventral premotor cortices) to a stronger extent, eliciting empathy by means of abstract visual information about the other's affective state more strongly engaged areas associated with inferring and representing mental states of self and other (precuneus, ventral medial prefrontal cortex, superior temporal cortex, and temporo-parietal junction). In addition, only the picture-based paradigms activated somatosensory areas, indicating that previous discrepancies concerning somatosensory activity during empathy for pain might have resulted from differences in experimental paradigms. We conclude that social neuroscience paradigms provide reliable and accurate insights into complex social phenomena such as empathy and that meta-analyses of previous studies are a valuable tool in this endeavor.
Surgical reconstruction of penile stump in a patient with Klingsor syndrome.

Self-mutilation of genitalia is an extremely rare entity, usually found in psychotic patients. Klingsor syndrome is a condition in which such an act is based upon religious delusions. The extent of genital mutilation can vary from superficial cuts to partial or total amputation of penis to total emasculation. The management of these patients is challenging. The aim of the treatment is restoration of the genital functionality. Microvascular reanastomosis of the phallus is ideal but it is often not possible due to the delay in seeking medical attention, non viability of the excised phallus or lack of surgical expertise. Hence, it is not unusual for these patients to end up with complete loss of the phallus and a perineal urethrostomy. We describe a patient with Klingsor syndrome who presented to us with near total penile amputation. The excised phallus was not viable and could not be used. The patient was managed with surgical reconstruction of the penile stump which was covered with loco-regional flaps. The case highlights that a functional penile reconstruction is possible in such patients even when microvascular reanastomosis is not feasible. This technique should be attempted before embarking upon perineal urethrostomy.
Human arm trajectory formation.

In order to investigate the strategies used to plan and control multijoint arm trajectories, two-degrees-of-freedom arm movements performed by normal adult humans were recorded. Only the shoulder and elbow joints were active. When a subject was told simply to move his hand from one visual target to another, the path of the hand was roughly straight, and the hand speed profile of their straight trajectories was bell-shaped. When the subject was required to produce curved hand trajectories, the path usually had a segmented appearance, as if the subject was trying to approximate a curve with low curvature elements. Hand speed profiles associated with curved trajectories contained speed valleys or inflections which were temporally associated with the local maxima in the trajectory curvature. The mean duration of curved movements was longer than the mean for straight movements. These results are discussed in terms of trajectory control theories which have originated in the fields of mechanical manipulator control and biological motor control. Three explanations for the results are offered.
NaLIX: an interactive natural language interface for querying XML

Database query languages can be intimidating to the non-expert, leading to the immense recent popularity for keyword based search in spite of its significant limitations. The holy grail has been the development of a natural language query interface. We present NaLIX, a generic interactive natural language query interface to an XML database. Our system can accept an arbitrary English language sentence as query input, which can include aggregation, nesting, and value joins, among other things. This query is translated, potentially after reformulation, into an XQuery expression that can be evaluated against an XML database. The translation is done through mapping grammatical proximity of natural language parsed tokens to proximity of corresponding elements in the result XML. In this demonstration, we show that NaLIX, while far from being able to pass the Turing test, is perfectly usable in practice, and able to handle even quite complex queries in a variety of application domains. In addition, we also demonstrate how carefully designed features in NaLIX facilitate the interactive query process and improve the usability of the interface.
Novel 4-thiazolidinone derivatives as potential antifungal and antibacterial drugs.

As part of ongoing studies in developing new antimicrobials, a class of structurally novel 4-thiazolidinone derivatives incorporating three known bioactive nuclei such as thiazole, thiazolidinone and adamantane was synthesized by the multi-step reaction protocol, already reported in the literature. NMR and Molecular Modeling techniques were employed for structure elucidation and Z/E potential isomerism configuration of the analogues. Evaluation of antibacterial and antifungal activity showed that almost all compounds exhibited better results than reference drugs thus they could be promising candidates for novel drugs.
Ketamine: Old Drug but New Use for Neuropathic Pain

Neuropathic pain is a debilitating form of chronic pain that affects 6.9-10% of the population. Health-related quality-of-life is impeded by neuropathic pain, which not only includes physical impairment, but the mental wellbeing of the patient is also hindered. A reduction in both physical and mental wellbeing bares economic costs that need to be accounted for. A variety of medications are in use for the treatment of neuropathic pain, such as calcium channel α2δ agonists, serotonin/noradrenaline reuptake inhibitors and tricyclic antidepressants. However, recent studies have indicated a lack of efficacy regarding the aforementioned medication. There is increasing clinical and pre-clinical evidence that can point to the use of ketamine, an “old” anaesthetic, in the management of neuropathic pain. Conversely, to see ketamine being used in neuropathic pain, there needs to be more conclusive evidence exploring the long-term effects of sub-anesthetic ketamine.
Multi-View Clustering via Joint Nonnegative Matrix Factorization

Many real-world datasets are comprised of different representations or views which often provide information complementary to each other. To integrate information from multiple views in the unsupervised setting, multiview clustering algorithms have been developed to cluster multiple views simultaneously to derive a solution which uncovers the common latent structure shared by multiple views. In this paper, we propose a novel NMFbased multi-view clustering algorithm by searching for a factorization that gives compatible clustering solutions across multiple views. The key idea is to formulate a joint matrix factorization process with the constraint that pushes clustering solution of each view towards a common consensus instead of fixing it directly. The main challenge is how to keep clustering solutions across different views meaningful and comparable. To tackle this challenge, we design a novel and effective normalization strategy inspired by the connection between NMF and PLSA. Experimental results on synthetic and several real datasets demonstrate the effectiveness of our approach.