nils-herrmann/clean_scirepeval huggingface dataset:
MMIC-Based Quadrature Hybrid Quasi-Circulators for Simultaneous Transmit and Receive

This paper presents a new type of monolithic microwave integrated circuit (MMIC)-based active quasi-circulator using phase cancellation and combination techniques for simultaneous transmit and receive (STAR) phased-array applications. The device consists of a passive core of three quadrature hybrids and active components to provide active quasi-circulation operation. The core of three quadrature hybrids can be implemented using Lange couplers. The device is capable of high isolation performance, high-frequency operation, broadband performance, and improvement of the noise figure (NF) at the receive port by suppressing transmit noise. For passive quasi-circulation operation, the device can achieve 35-dB isolation between the transmit and receive port with 2.6-GHz bandwidth (BW) and insertion loss of 4.5 dB at X-band. For active quasi-operation, the device is shown to have 2.3-GHz BW of 30-dB isolation with 1.5-dB transmit-to-antenna gain and 4.7-dB antenna-to-receive insertion loss, while the NF at the receive port is approximately 5.5 dB. The device is capable of a power stress test up to 34 dBm at the output ports at 10.5 GHz. For operation with typical 25-dB isolation, the device is capable of operation up to 5.6-GHz BW at X-band. The device is also shown to be operable up to W -band by simulation with ~15-GHz BW of 20-dB isolation. The proposed architecture is suitable for MMIC integration and system-on-chip applications.
The Poisson-Dirichlet Distribution And The Scale-Invariant Poisson Process

We show that the Poisson–Dirichlet distribution is the distribution of points in a scaleinvariant Poisson process, conditioned on the event that the sum T of the locations of the points in (0,1] is 1. This extends to a similar result, rescaling the locations by T , and conditioning on the event that T 6 1. Restricting both processes to (0, β] for 0 < β 6 1, we give an explicit formula for the total variation distance between their distributions. Connections between various representations of the Poisson–Dirichlet process are discussed.
Swing-Pay : A Digital Card Module using NFC and Biometric Authentication for Peer-to-Peer Payment

 Advancement in payment technologies have an important impact on the quality of life. The emerging payment technologies create both opportunities and challenges for future. Being a quick and convenient process, contactless payment gained its momentum, especially in merchants, where throughput is the main important parameter. However, it poses risk to issuers as no robust verification method of customer is available. Thus giving rise to quests to evolve and sustain a wellorganized, efficient, reliable and secure unified payment system, which may contribute to the smooth functioning of the market by eliminating scratch in business. This article presents an approach and module by which one card can communicate with the other using Near Field Communication (NFC) technology to transfer money from payer’s bank to payee’s bank by digital means. This approach eliminates the need of physical cash and also serves all types of payment and identity needs. Embodiments of this approach furnish a medium for cashless card-to-card transaction. The module, which is called Swing-Pay, communicates with its concerned bank via GSM. The security of this module is intensified using biometric authentication. The article also presents an app on Android platform, which works as a scanner of the proposed module to read the identity details of concerned person, the owner of the card. We have also presented the prototype of a digital card. This card can also be used as virtual identity card (ID), accumulating the information of all ID cards including electronic Passport, Voter ID, and Driving License.
Factors Influencing Strategic Decision-Making Processes

Decision-making is one of the most important functions of managers in any kind of organization. Among different manager's decisions strategic decision-making is a complex process that must be understood completely before it can be practiced effectively. Those responsible for strategic decision-making face a task of extreme complexity and ambiguity. For these reasons, over the past decades, numerous studies have been conducted to the construction of models to aid managers and executives in making better decisions concerning the complex and highly uncertain business environment. In spite of much work that has been conducted in the area of strategic decision-making especially during the 1990's, we still know little about strategic decision-making process and factors affecting it. This paper builds on previous theoretical and empirical studies to determine the extent to which contextual factors impact the strategic decision-making processes. Results showed that researches on contextual factors effecting strategic decision-making process are either limited or have produced contradictory results, especially studies relating decision’s familiarity, magnitude of impact, organizational size, firm’s performance, dynamism, hostility, heterogeneity, industry, cognitive diversity, cognitive conflict, and manager’s need for achievement to strategic decision-making processes. Thus, the study of strategic decision-making process remains very important and much more empirical research is required before any definitive conclusion can be reached.
Flow infusion electrospray ionisation mass spectrometry for high throughput, non-targeted metabolite fingerprinting: a review

Producing a comprehensive overview of the chemical content of biologically-derived material is a major challenge. Apart from ensuring adequate metabolome coverage and issues of instrument dynamic range, mass resolution and sensitivity, there are major technical difficulties associated with data pre-processing and signal identification when attempting large scale, high-throughput experimentation. To address these factors direct infusion or flow infusion electrospray mass spectrometry has been finding utility as a high throughput metabolite fingerprinting tool. With little sample pre-treatment, no chromatography and instrument cycle times of less than 5 min it is feasible to analyse more than 1,000 samples per week. Data pre-processing is limited to aligning extracted mass spectra and mass-intensity matrices are generally ready in a working day for a month’s worth of data mining and hypothesis generation. ESI-MS fingerprinting has remained rather qualitative by nature and as such ion suppression does not generally compromise data information content as originally suggested when the methodology was first introduced. This review will describe how the quality of data has improved through use of nano-flow infusion and mass-windowing approaches, particularly when using high resolution instruments. The increasingly wider availability of robust high accurate mass instruments actually promotes ESI-MS from a merely fingerprinting tool to the ranks of metabolite profiling and combined with MS/MS capabilities of hybrid instruments improved structural information is available concurrently. We summarise current applications in a wide range of fields where ESI-MS fingerprinting has proved to be an excellent tool for “first pass” metabolome analysis of complex biological samples. The final part of the review describes a typical workflow with reference to recently published data to emphasise key aspects of overall experimental design.
An Extensible Veried Validator For Simple Optimizations in LLVM

Recent advances in theorem proving technology have made it possible to write a completely bug-free compiler such as CompCert. However, this technology have not been fully applied to mainstream compilers such as GCC and LLVM due to the huge amount of work required. Nevertheless, we believe that the verified validation technique will reduce verification efforts significantly and thus make it feasible to provide a formal guarantee of correctness for the full LLVM compiler. As a first step towards the grand goal, we have developed an extensible verified validator for simple optimizations in LLVM, mainly targeted for all micro-optimizations (about 400) in the instruction combine pass. Our validator, based on a simple form of relational Hoare reasoning, can be used to validate various kinds of simple optimizations without losing completeness (i.e., it succeeds to validate all correct results at least by design). We achieve both generality and completeness, not by automation, but by extensibility, which allows to freely add inference rules that are proven to be correct.
A Fully Integrated Shark-Fin Antenna for MIMO-LTE, GPS, WLAN, and WAVE Applications

In this study, a three-dimensional compact antenna solution for the automotive industry is proposed. The antenna solution is designed to fit in a shark-fin case and is easily fabricated from a printed circuit board and a metal sheet with low-cost process and materials. The antenna solution covers Long Term Evolution (LTE), GPS, WLAN, and Wireless Access in the Vehicular Environment (WAVE) bands (850 MHz, 1575 MHz, 2.4 GHz, and 5.9 GHz, respectively). The planar inverted-F antennas are used as multiple-input–multiple-output antennas for the LTE band due to their low-profile structure. Modified planar monopoles are used to obtain omnidirectional radiation patterns for WLAN and WAVE bands. Antenna characteristics such as return loss, isolation, and radiation pattern have been simulated and measured to confirm the possibility for use in automotive applications.
Design and implementation of a robot for maze-solving with turning indicators using PID controller

Autonomous navigation is an important feature that allows a mobile robot to independently move from a point to another without an intervention from a human operator. Autonomous navigation within an unknown area requires the robot to explore, localize and map its surroundings. By solving a maze, the pertaining algorithms and behaviour of the robot can be studied and improved upon. This paper describes an implementation of a maze-solving robot designed to solve a maze with turning indicators. The black turning indicators tell the robot which way to turn at the intersections to reach at the centre of the maze. Detection of intersection line and turning indicators in the maze was done by using LDR sensors. Algorithm for straight-line correction was based on PI(D) controller.
Communication with Speech and Gestures : Applications of Recurrent Neural Networks to Robot Language Learning

Recurrent neural networks have recently shown significant potential in different language applications, ranging from natural language processing to language modelling. This paper introduces a research effort to use such networks to develop and evaluate natural language acquisition on a humanoid robot. Here, the problem is twofold. First, the focus will be put on using the gesture-word combination stage observed in infants to transition from single to multi-word utterances. Secondly, research will be carried out in the domain of connecting action learning with language learning. In the former, the long-short term memory architecture will be implemented, whilst in the latter multiple time-scale recurrent neural networks will be used. This will allow for comparison between the two architectures, whilst highlighting the strengths and shortcomings of both with respect to the language learning problem. Here, the main research efforts, challenges and expected outcomes are described.
Camera space shadow maps for large virtual environments

This paper presents a new single-pass shadow mapping technique that achieves better quality than the approaches based on perspective warping, such as perspective, light-space, and trapezoidal shadow maps. The proposed technique is appropriate for real-time rendering of large virtual environments that include dynamic objects. By performing operations in camera space, this solution successfully handles the general and the dueling frustum cases and produces high-quality shadows even for extremely large scenes. This paper also presents a fast nonlinear projection technique for shadow map stretching that enables complete utilization of the shadow map by eliminating wastage. The application of stretching results in a significant reduction in unwanted perspective aliasing, commonly found in all shadow mapping techniques. Technique is compared with other shadow mapping techniques, and the benefits of the proposed method are presented. The proposed shadow mapping technique is simple and flexible enough to handle most of the special scenarios. An API for a generic shadow mapping solution is presented. This API simplifies the generation of fast and high-quality shadows.
FAM-FACE-SG: a score for risk stratification of frequent hospital admitters

An accurate risk stratification tool is critical in identifying patients who are at high risk of frequent hospital readmissions. While 30-day hospital readmissions have been widely studied, there is increasing interest in identifying potential high-cost users or frequent hospital admitters. In this study, we aimed to derive and validate a risk stratification tool to predict frequent hospital admitters.



We conducted a retrospective cohort study using the readily available clinical and administrative data from the electronic health records of a tertiary hospital in Singapore. The primary outcome was chosen as three or more inpatient readmissions within 12 months of index discharge. We used univariable and multivariable logistic regression models to build a frequent hospital admission risk score (FAM-FACE-SG) by incorporating demographics, indicators of socioeconomic status, prior healthcare utilization, markers of acute illness burden and markers of chronic illness burden. We further validated the risk score on a separate dataset and compared its performance with the LACE index using the receiver operating characteristic analysis.



Our study included 25,244 patients, with 70% randomly selected patients for risk score derivation and the remaining 30% for validation. Overall, 4,322 patients (17.1%) met the outcome. The final FAM-FACE-SG score consisted of nine components: Furosemide (Intravenous 40 mg and above during index admission); Admissions in past one year; Medifund (Required financial assistance); Frequent emergency department (ED) use (≥3 ED visits in 6 month before index admission); Anti-depressants in past one year; Charlson comorbidity index; End Stage Renal Failure on Dialysis; Subsidized ward stay; and Geriatric patient or not. In the experiments, the FAM-FACE-SG score had good discriminative ability with an area under the curve (AUC) of 0.839 (95% confidence interval [CI]: 0.825-0.853) for risk prediction of frequent hospital admission. In comparison, the LACE index only achieved an AUC of 0.761 (0.745-0.777).



The FAM-FACE-SG score shows strong potential for implementation to provide near real-time prediction of frequent admissions. It may serve as the first step to identify high risk patients to receive resource intensive interventions.
Flow injection conductometric system with gas diffusion separation for the determination of Kjeldahl nitrogen in milk and chicken meat.

A simple flow injection (FI) conductometric system with gas diffusion separation was developed for the determination of Kjeldahl nitrogen (or proteins) in milk and chicken meat. The sample was digested according to the Kjeldahl standard method and the digest was diluted and directly injected into the donor stream consisting of 4M NaOH. In alkaline medium, ammonium was converted to ammonia, which diffused through the PTFE membrane to dissolve in an acceptor stream (water). Dissociation of ammonia caused a change in conductance of the acceptor solution, which was linearly proportional to the concentration of ammonium originally present in the injected solution. A conductometric flow through cell and an amplifier circuit was fabricated, which helped improve sensitivity of the conductometric detection system. With using a plumbing Teflon tape as a gas diffusion membrane and without thermostating control of the system, a linear calibration graph in range of 10-100mgL(-1) N-NH(4) was obtained, with detection limit of 1mgL(-1) and good precision (relative standard deviation of 0.3% for 11 replicate injections of 50mgL(-1) N-NH(4)). The developed method was validated by the standard Kjeldahl distillation/titration method for the analysis of milk and chicken meat samples. The proposed system had sample throughput of 35h(-1) and consumed much smaller amounts of chemical than the standard method (275mg vs 17.5g of NaOH per analysis, respectively).
A Classification Algorithm for Electroencephalography Signals by Self-Induced Emotional Stimuli

The aim of this paper is to propose a real-time classification algorithm for the low-amplitude electroencephalography (EEG) signals, such as those produced by remembering an unpleasant odor, to drive a brain-computer interface. The peculiarity of these EEG signals is that they require ad hoc signals preprocessing by wavelet decomposition, and the definition of a set of features able to characterize the signals and to discriminate among different conditions. The proposed method is completely parameterized, aiming at a multiclass classification and it might be considered in the framework of machine learning. It is a two stages algorithm. The first stage is offline and it is devoted to the determination of a suitable set of features and to the training of a classifier. The second stage, the real-time one, is to test the proposed method on new data. In order to avoid redundancy in the set of features, the principal components analysis is adapted to the specific EEG signal characteristics and it is applied; the classification is performed through the support vector machine. Experimental tests on ten subjects, demonstrating the good performance of the algorithm in terms of both accuracy and efficiency, are also reported and discussed.
Electrical energy storage for the grid: a battery of choices.

The increasing interest in energy storage for the grid can be attributed to multiple factors, including the capital costs of managing peak demands, the investments needed for grid reliability, and the integration of renewable energy sources. Although existing energy storage is dominated by pumped hydroelectric, there is the recognition that battery systems can offer a number of high-value opportunities, provided that lower costs can be obtained. The battery systems reviewed here include sodium-sulfur batteries that are commercially available for grid applications, redox-flow batteries that offer low cost, and lithium-ion batteries whose development for commercial electronics and electric vehicles is being applied to grid storage.
The role of regulatory T cells in multiple sclerosis

The dysregulation of inflammatory responses and of immune self-tolerance is considered to be a key element in the autoreactive immune response in multiple sclerosis (MS). Regulatory T (TREG) cells have emerged as crucial players in the pathogenetic scenario of CNS autoimmune inflammation. Targeted deletion of TREG cells causes spontaneous autoimmune disease in mice, whereas augmentation of TREG-cell function can prevent the development of or alleviate variants of experimental autoimmune encephalomyelitis, the animal model of MS. Recent findings indicate that MS itself is also accompanied by dysfunction or impaired maturation of TREG cells. The development and function of TREG cells is closely linked to dendritic cells (DCs), which have a central role in the activation and reactivation of encephalitogenic cells in the CNS. DCs and TREG cells have an intimate bidirectional relationship, and, in combination with other factors and cell types, certain types of DCs are capable of inducing TREG cells. Consequently, TREG cells and DCs have been recognized as potential therapeutic targets in MS. This Review compiles the current knowledge on the role and function of various subsets of TREG cells in MS and experimental autoimmune encephalomyelitis. We also highlight the role of tolerogenic DCs and their bidirectional interaction with TREG cells during CNS autoimmunity.
Network Performance Aware MPI Collective Communication Operations in the Cloud

This paper examines the performance of collective communication operations in message passing interfaces (MPI) in the cloud computing environment. The awareness of network topology has been a key factor in performance optimizations for existing MPI implementations. However, virtualization in the cloud environment not only hides the network topology information from the users, but also causes traffic interference and dynamics to network performance. Existing topology-aware optimizations are no longer feasible in the cloud environment. Therefore, we develop novel network performance aware algorithms for a series of collective communication operations including broadcast, reduce, gather and scatter. We further implement two common applications, N-body and conjugate gradient (CG). We have conducted our experiments with two complementary methods (on Amazon EC2 and simulations). Our experimental results show that the network performance awareness results in 25.4 and 28.3 percent performance improvement over MPICH2 on Amazon EC2 and on simulations, respectively. Evaluations on N-body and CG show 41.6 and 14.3 percent respectively on application performance improvement.
Association Between Early Lactate Levels and 30-Day Mortality in Clinically Suspected Sepsis in Children.

Improving emergency care of pediatric sepsis is a public health priority, but optimal early diagnostic approaches are unclear. Measurement of lactate levels is associated with improved outcomes in adult septic shock, but pediatric guidelines do not endorse its use, in part because the association between early lactate levels and mortality is unknown in pediatric sepsis.



To determine whether the initial serum lactate level is associated with 30-day mortality in children with suspected sepsis.



This observational cohort study of a clinical registry of pediatric patients with suspected sepsis in the emergency department of a tertiary children's hospital from April 1, 2012, to December 31, 2015, tested the hypothesis that a serum lactate level of greater than 36 mg/dL is associated with increased mortality compared with a serum lactate level of 36 mg/dL or less. Consecutive patients with sepsis were identified and included in the registry following consensus guidelines for clinical recognition (infection and decreased mental status or perfusion). Among 2520 registry visits, 1221 were excluded for transfer from another medical center, no measurement of lactate levels, and patients younger than 61 days or 18 years or older, leaving 1299 visits available for analysis. Lactate testing is prepopulated in the institutional sepsis order set but may be canceled at clinical discretion.



Venous lactate level of greater than 36 mg/dL on the first measurement within the first 8 hours after arrival.



Thirty-day in-hospital mortality was the primary outcome. Odds ratios were calculated using logistic regression to account for potential confounders.



Of the 1299 patients included in the analysis (753 boys [58.0%] and 546 girls [42.0%]; mean [SD] age, 7.3 [5.3] years), 899 (69.2%) had chronic medical conditions and 367 (28.3%) had acute organ dysfunction. Thirty-day mortality occurred in 5 of 103 patients (4.8%) with lactate levels greater than 36 mg/dL and 20 of 1196 patients (1.7%) with lactate levels of 36 mg/dL or less. Initial lactate levels of greater than 36 mg/dL were significantly associated with 30-day mortality in unadjusted (odds ratio, 3.00; 95% CI, 1.10-8.17) and adjusted (odds ratio, 3.26; 95% CI, 1.16- 9.16) analyses. The sensitivity of lactate levels greater than 36 mg/dL for 30-day mortality was 20.0% (95% CI, 8.9%-39.1%), and specificity was 92.3% (90.7%-93.7%).



In children treated for sepsis in the emergency department, lactate levels greater than 36 mg/dL were associated with mortality but had a low sensitivity. Measurement of lactate levels may have utility in early risk stratification of pediatric sepsis.