nils-herrmann/clean_scirepeval huggingface dataset:
Epigenetic mechanisms in diabetic complications and metabolic memory

The incidence of diabetes and its associated micro- and macrovascular complications is greatly increasing worldwide. The most prevalent vascular complications of both type 1 and type 2 diabetes include nephropathy, retinopathy, neuropathy and cardiovascular diseases. Evidence suggests that both genetic and environmental factors are involved in these pathologies. Clinical trials have underscored the beneficial effects of intensive glycaemic control for preventing the progression of complications. Accumulating evidence suggests a key role for epigenetic mechanisms such as DNA methylation, histone post-translational modifications in chromatin, and non-coding RNAs in the complex interplay between genes and the environment. Factors associated with the pathology of diabetic complications, including hyperglycaemia, growth factors, oxidant stress and inflammatory factors can lead to dysregulation of these epigenetic mechanisms to alter the expression of pathological genes in target cells such as endothelial, vascular smooth muscle, retinal and cardiac cells, without changes in the underlying DNA sequence. Furthermore, long-term persistence of these alterations to the epigenome may be a key mechanism underlying the phenomenon of ‘metabolic memory’ and sustained vascular dysfunction despite attainment of glycaemic control. Current therapies for most diabetic complications have not been fully efficacious, and hence a study of epigenetic mechanisms that may be involved is clearly warranted as they can not only shed novel new insights into the pathology of diabetic complications, but also lead to the identification of much needed new drug targets. In this review, we highlight the emerging role of epigenetics and epigenomics in the vascular complications of diabetes and metabolic memory.
Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics

The purpose of this study is to find a theoretically grounded, practically applicable and useful granularity level of an algorithmically constructed publication-level classification of research publications (ACPLC). The level addressed is the level of research topics. The methodology we propose uses synthesis papers and their reference articles to construct a baseline classification. A dataset of about 31 million publications, and their mutual citations relations, is used to obtain several ACPLCs of different granularity. Each ACPLC is compared to the baseline classification and the best performing ACPLC is identified. The results of two case studies show that the topics of the cases are closely associated with different classes of the identified ACPLC, and that these classes tend to treat only one topic. Further, the class size variation is moderate, and only a small proportion of the publications belong to very small classes. For these reasons, we conclude that the proposed methodology is suitable to determine the topic granularity level of an ACPLC and that the ACPLC identified by this methodology is useful for bibliometric analyses.
LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments

A new scan that matches an aided Inertial Navigation System (INS) with a low-cost LiDAR is proposed as an alternative to GNSS-based navigation systems in GNSS-degraded or -denied environments such as indoor areas, dense forests, or urban canyons. In these areas, INS-based Dead Reckoning (DR) and Simultaneous Localization and Mapping (SLAM) technologies are normally used to estimate positions as separate tools. However, there are critical implementation problems with each standalone system. The drift errors of velocity, position, and heading angles in an INS will accumulate over time, and on-line calibration is a must for sustaining positioning accuracy. SLAM performance is poor in featureless environments where the matching errors can significantly increase. Each standalone positioning method cannot offer a sustainable navigation solution with acceptable accuracy. This paper integrates two complementary technologies-INS and LiDAR SLAM-into one navigation frame with a loosely coupled Extended Kalman Filter (EKF) to use the advantages and overcome the drawbacks of each system to establish a stable long-term navigation process. Static and dynamic field tests were carried out with a self-developed Unmanned Ground Vehicle (UGV) platform-NAVIS. The results prove that the proposed approach can provide positioning accuracy at the centimetre level for long-term operations, even in a featureless indoor environment.
The Care Management Institute's Integrated Diabetes Care Program: a Collaborative Approach to Improving Patient Care and Outcomes

Diabetes represents one of the most common and debilitating conditions seen among Kaiser Permanente (KP) members. Because care often involves multiple providers and because follow-up requires persistence by patients and clinicians alike, ideal outcomes are often difficult to achieve. Management of diabetes therefore offers an excellent opportunity to practice population management–-a systems approach designed to ensure excellent care. Accordingly, through a broad KP collaboration, the Care Management Institute (CMI) developed a comprehensive approach to adult diabetes care: the Integrated Diabetes Care (IDC) Program. The IDC Program has three elements: an internally published report, Clinical Practice Guidelines for Adult Diabetes Care; a set of tools for applying population management and patient empowerment concepts; and an outcomes measurement component, ie, instruments for evaluating IDC Program impact and gathering feedback. In this article, we describe the IDC Program and the process by which it was developed. Included are specific examples of the tools and how they can be used at the population level and by individual clinicians in caring for patients. (top right) NEIL A. SOLOMON, MD, is the Clinical Strategies Consultant in the Care Management Institute at the Program Offices of Kaiser Permanente. His work focuses on improving quality and efficiency through the development of disease management strategies and other population-based health care innovations. He also works with Permanente physician leaders to review information on practice variation and to disseminate successful practices for internal clinical management improvement.
Combined Optimal Sizing and Control of Li-Ion Battery/Supercapacitor Embedded Power Supply Using Hybrid Particle Swarm–Nelder–Mead Algorithm

This paper examines and optimizes parameters that affect the sizing and control of a hybrid embedded power supply composed of Li-ion batteries and supercapacitors in electric vehicle applications. High demands including power and energy density, low charge/discharge power stress on the battery (long lifetime), lightweight design, and relatively modest cost at the same time cannot be provided solely by batteries or supercapacitors. For this reason, we propose the use of a Li-ion battery/supercapacitor hybrid embedded power supply for an urban electric vehicle. The sizing process of this system including the optimization of the power sharing is done thanks to a developed hybrid Particle Swarm-Nelder-Mead algorithm involving multi-objective optimization. This approach also allows us to optimize the proposed energy management strategies based on frequency rule-based control and different ways of supercapacitors energy regulation. Obtained results show that the hybrid embedded power supply with the proposed control strategies is able to offer the best performances for the chosen electric vehicle in terms of weight, initial cost, and battery lifetime.
Text summarization using rough sets

Text Summarization aims to generate concise and compressed form of original documents. The techniques used for text summarization may be categorized as extractive summarization and abstractive summarization. We consider extractive techniques which are based on selection of important sentences within a document. A major issue in extractive summarization is how to select important sentences, i.e., what criteria should be defined for selection of sentences which are eventually part of the summary. We examine this issue using rough sets notion of reducts. A reduct is an attribute subset which essentially contains the same information as the original attribute set. In particular, we defined and examined three types of matrices based on an information table, namely, discernibility matrix, indiscernibility matrix and equal to one matrix. Each of these matrices represents a certain type of relationship between the objects of an information table. Three types of reducts are determined based on these matrices. The reducts are used to select sentences and consequently generate text summaries. Experimental results and comparisons with existing approaches advocates for the use of the proposed approach in generating text summaries.
Ac ce pt ed M an us cr ip t Towards Energy-Efficient Scheduling for Real-Time Tasks under Uncertain Cloud Computing Environment

Green cloud computing has become a major concern in both industry and academia, and efficient scheduling approaches show promising ways to reduce the energy consumption of cloud computing platforms while guaranteeing QoS requirements of tasks. Existing scheduling approaches are inadequate for realtime tasks running in uncertain cloud environments, because those approaches assume that cloud computing environments are deterministic and pre-computed schedule decisions will be statically followed during schedule execution. In this paper, we address this issue. We introduce an interval number theory to describe the uncertainty of the computing environment and a scheduling architecture to mitigate the impact of uncertainty on the task scheduling quality for a cloud data center. Based on this architecture, we present a novel scheduling algorithm (PRS) that dynamically exploits proactive and reactive scheduling methods, for scheduling real-time, aperiodic, independent tasks. To improve energy efficiency, we propose three strategies to scale up and down the system’s computing resources according to workload to improve resource utilization and to reduce energy consumption for the cloud data center. We conduct extensive experiments to compare PRS with four typical baseline scheduling algorithms. The experimental results show that PRS performs better than those algorithms, and can effectively improve the performance of a cloud data center.
Automated intelligent system for sound signalling device quality assurance

This paper presents a novel approach to the detection and recognition of faulty audio signalling devices as part of an automated industrial manufacturing quality assurance process. The proposed system outperforms other well-established automated systems based on mel-frequency cepstrum coefficients (MFCC) and multi-layer perceptron (MLP). It uses both unlabelled sound data and labelled historical data acquired from human experts in detecting faulty signalling devices. The unlabelled data is used to train a deep neural network generative model to create multiple levels of hierarchical feature extractors which are used to train an MLP classifier, with the intent to model the human reasoning and judging processes in respect to sound classification. This paper presents the results of real world experiments based on data pertaining to the audio signalling quality assurance process for car instrument cluster manufacturing. These results show that the proposed system is able to successfully classify speakers into two groups: ‘‘Good’’ and ‘‘No good’’ depending on the part quality. The proposed system proves to be capable enough to eliminate the need for a manual inspection within the manufacturing process and is shown to be able to diagnose a fault with a high degree of accuracy. This work can be extended to other areas of automotive inspection where there is a need for a robust solution to sound detection and where an output signal is represented by a complex and changing frequency spectrum even with significant environmental noise.
AutoFuzz: Automated Network Protocol Fuzzing Framework

Assessing software security involves steps such as code review, risk analysis, penetration testing and fuzzing. During the fuzzing phase, the tester " s goal is to find flaws in software by sending unexpected input to the target application and monitoring its behavior. In this paper we introduce the AutoFuzz [1]-extendable, open source framework used for testing network protocol implementations. AutoFuzz is a " smart " , man-in-the-middle, semi-deterministic network protocol fuzzing framework. AutoFuzz learns a protocol implementation by constructing a Finite State Automaton (FSA) which captures the observed communications between a client and a server . In addition, AutoFuzz learns individual message syntax, including fields and probable types, by applying the bioinformatics techniques of . Finally, AutoFuzz can fuzz client or server protocol implementations by intelligently modifying the communication sessions between them using the FSA as a guide. AutoFuzz was applied to a variety of File Transfer Protocol (FTP) server implementations, confirming old and discovering new vulnerabilities.
User feedback in the appstore: An empirical study

Application distribution platforms - or app stores - such as Google Play or Apple AppStore allow users to submit feedback in form of ratings and reviews to downloaded applications. In the last few years, these platforms have become very popular to both application developers and users. However, their real potential for and impact on requirements engineering processes are not yet well understood. This paper reports on an exploratory study, which analyzes over one million reviews from the Apple AppStore. We investigated how and when users provide feedback, inspected the feedback content, and analyzed its impact on the user community. We found that most of the feedback is provided shortly after new releases, with a quickly decreasing frequency over time. Reviews typically contain multiple topics, such as user experience, bug reports, and feature requests. The quality and constructiveness vary widely, from helpful advices and innovative ideas to insulting offenses. Feedback content has an impact on download numbers: positive messages usually lead to better ratings and vice versa. Negative feedback such as shortcomings is typically destructive and misses context details and user experience. We discuss our findings and their impact on software and requirements engineering teams.
X-band all-waveguide radial combiner for high power applications

The development of a 12-way X-band all-waveguide radial divider/combiner is presented. The radial combiner is comprised of three parts: a center feed, a radial line, and peripheral waveguide ports. The center feed is comprised of two sections: a rectangular waveguide section and a mode transducer section. The latter is a circular waveguide fed by four-way in-phase combiner to convert the rectangular waveguide TE10 mode to a TE10 circular waveguide mode for in-phase feeding of all peripheral ports. For design evaluation, the 12-way combiner was built and tested but also two back-to-back test fixtures, one for the mode transducer and the second for the radial combiner were fabricated and tested as well. The measured insertion loss and phase imbalance of the combiner over a 10% operating bandwidth are less than 0.35 dB and ±5°, respectively. The structure is suitable for high power and should handle few kilowatts.
A comparison of volumetric information gain metrics for active 3D object reconstruction

In this paper, we investigate the following question:whenperformingnext best viewselection for volumetric 3D reconstruction of an object by a mobile robot equipped with a dense (camera-based) depth sensor, what formulation of information gain is best? To address this question, we propose several new ways to quantify the volumetric information (VI) contained in the voxels of a probabilistic volumetric map, and compare them to the state of the art with extensive simulated experiments. Our proposed formulations incorporate factors such as visibility likelihood and the likelihood of seeing new parts of the object. The results of our experiments allow us to draw some clear conclusions about the VI formulations that are most effective in different mobile-robot reconstruction scenarios. To the best of our knowledge, this is the first comparative survey of VI formulation performance for active 3D object reconstruction. Additionally, our modular software framework is adaptable to other robotic platforms and general reconstruction problems, and we release it open source for autonomous reconstruction tasks.
The cutting of cocaine and heroin: A critical review.

The illicit drug cutting represents a complex problem that requires the sharing of knowledge from addiction studies, toxicology, criminology and criminalistics. Therefore, cutting is not well known by the forensic community. Thus, this review aims at deciphering the different aspects of cutting, by gathering information mainly from criminology and criminalistics. It tackles essentially specificities of cocaine and heroin cutting. The article presents the detected cutting agents (adulterants and diluents), their evolution in time and space and the analytical methodology implemented by forensic laboratories. Furthermore, it discusses when, in the history of the illicit drug, cutting may take place. Moreover, researches studying how much cutting occurs in the country of destination are analysed. Lastly, the reasons for cutting are addressed. According to the literature, adulterants are added during production of the illicit drug or at a relatively high level of its distribution chain (e.g. before the product arrives in the country of destination or just after its importation in the latter). Their addition seems hardly justified by the only desire to increase profits or to harm consumers' health. Instead, adulteration would be performed to enhance or to mimic the illicit drug effects or to facilitate administration of the drug. Nowadays, caffeine, diltiazem, hydroxyzine, levamisole, lidocaïne and phenacetin are frequently detected in cocaine specimens, while paracetamol and caffeine are almost exclusively identified in heroin specimens. This may reveal differences in the respective structures of production and/or distribution of cocaine and heroin. As the relevant information about cutting is spread across different scientific fields, a close collaboration should be set up to collect essential and unified data to improve knowledge and provide information for monitoring, control and harm reduction purposes. More research, on several areas of investigation, should be carried out to gather relevant information.
Development of the asthma control test: a survey for assessing asthma control.

Asthma guidelines indicate that the goal of treatment should be optimum asthma control. In a busy clinic practice with limited time and resources, there is need for a simple method for assessing asthma control with or without lung function testing.



The objective of this article was to describe the development of the Asthma Control Test (ACT), a patient-based tool for identifying patients with poorly controlled asthma.



A 22-item survey was administered to 471 patients with asthma in the offices of asthma specialists. The specialist's rating of asthma control after spirometry was also collected. Stepwise regression methods were used to select a subset of items that showed the greatest discriminant validity in relation to the specialist's rating of asthma control. Internal consistency reliability was computed, and discriminant validity tests were conducted for ACT scale scores. The performance of ACT was investigated by using logistic regression methods and receiver operating characteristic analyses.



Five items were selected from regression analyses. The internal consistency reliability of the 5-item ACT scale was 0.84. ACT scale scores discriminated between groups of patients differing in the specialist's rating of asthma control (F = 34.5, P <.00001), the need for change in patient's therapy (F = 40.3, P <.00001), and percent predicted FEV(1) (F = 4.3, P =.0052). As a screening tool, the overall agreement between ACT and the specialist's rating ranged from 71% to 78% depending on the cut points used, and the area under the receiver operating characteristic curve was 0.77.



Results reinforce the usefulness of a brief, easy to administer, patient-based index of asthma control.
Neuroinflammation and Alzheimer's Disease: Implications for Microglial Activation.

Microglial activation is a hallmark of neuroinflammation, seen in most acute and chronic neuropsychiatric conditions. With growing knowledge about microglia functions in surveying the brain for alterations, microglial activation is increasingly discussed in the context of disease progression and pathogenesis of Alzheimer's disease (AD). Underlying molecular mechanisms, however, remain largely unclear. While proper microglial function is essentially required for its scavenging duties, local activation of the brain's innate immune cells also brings about many less advantageous changes, such as reactive oxygen species (ROS) production, secretion of proinflammatory cytokines or degradation of neuroprotective retinoids, and may thus unnecessarily put surrounding healthy neurons in danger. In view of this dilemma, it is little surprising that both, AD vaccination trials, and also immunosuppressive strategies have consistently failed in AD patients. Nevertheless, epidemiological evidence has suggested a protective effect for anti-inflammatory agents, supporting the hypothesis that key processes involved in the pathogenesis of AD may take place rather early in the time course of the disorder, likely long before memory impairment becomes clinically evident. Activation of microglia results in a severely altered microenvironment. This is not only caused by the plethora of secreted cytokines, chemokines or ROS, but may also involve increased turnover of neuroprotective endogenous substances such as retinoic acid (RA), as recently shown in vitro.



We discuss findings linking microglial activation and AD and speculate that microglial malfunction, which brings about changes in local RA concentrations in vitro, may underlie AD pathogenesis and precede or facilitate the onset of AD. Thus, chronic, "innate neuroinflammation" may provide a valuable target for preventive and therapeutic strategies.
Voice activity detection in noisy environments

The subject of this paper is robust voice activity detection (VAD) in noisy environments, especially in car environments. We present a comparison between several frame based VAD feature extraction algorithms in combination with different classifiers. Experiments are carried out under equal test conditions using clean speech, clean speech with added car noise and speech recorded in car environments. The lowest error rate is achieved applying features based on a likelihood ratio test which assumes normal distribution of speech and noise and a perceptron classifier. We propose modifications of this algorithm which reduce the frame error rate by approximately 30% relative in our experiments compared to the original algorithm.