Miracle-dz/UniFAR-FTUs huggingface dataset:
Node performance model of wireless sensor networks

The communication system of intelligence vehicle platform is consisted of a series of wireless communicators which is formed like wireless sensor network (WSN). Each of the vehicles behaves like a sensor in the WSN. The status data of the environment and the vehicle have been gathered and transmitted to a certain sink, another vehicle or the control center. So the quality-of-service (QoS) of the network should be guaranteed to ensure the reliability of the whole platform. The transmission path is consisted with nodes. The nodes' performance directly influence the performance of the network. This paper is established models of the nodes and packet behavior during the transmission. Data loss possibility and the delay can be reflected from the models. The result can provide a reference for the estimation of the reliability of the control system based on the WSN such as intelligence vehicle platform.
Associations of Four Community Factors With Longitudinal Change in Hemoglobin A1c Levels in Patients With Type 2 Diabetes

OBJECTIVE To evaluate associations of community factors with glycated hemoglobin (HbA1c). RESEARCH DESIGN AND METHODS We identified patients with type 2 diabetes who had an HbA1c ≥7.5% (58 mmol/mol) and subsequent HbA1c testing within 90–270 days. We used mixed-effect models to assess whether treatment intensification (TI) and community domains (community socioeconomic deprivation [CSD], food availability, fitness assets, and utilitarian physical activity favorability [quartiled]) were associated with HbA1c change over 6 and 24 months, controlling for demographics, HbA1c, BMI, and time with evidence of type 2 diabetes. We evaluated whether community domains modified associations of TI with HbA1c change using cross product terms. RESULTS There were 15,308 patients with 69,818 elevated HbA1c measures. The average reduction in HbA1c over 6 months was 0.07% less in townships with a high level of CSD (third quartile versus the first). Reductions were 0.10% greater for HbA1c in townships with the best food availability (versus worst). HbA1c reductions were 0.17–0.19% greater in census tracts in the second and third quartiles of utilitarian physical activity favorability versus the first. The association of TI with 6-month HbA1c change was weaker in townships and boroughs with the worst CSD (versus best) and in boroughs with the best fitness assets (versus worst). The association of TI with 24-month HbA1c change was weaker in census tracts with the worst CSD (versus third quartile) and strongest in census tracts most favorable for utilitarian physical activity (versus worst). CONCLUSIONS Community domains were associated with HbA1c change and blunted TI effectiveness.
Fatostatin blocks ER exit of SCAP but inhibits cell growth in a SCAP-independent manner

Sterol regulatory element-binding protein (SREBP) transcription factors are central regulators of cellular lipid homeostasis and activate expression of genes required for fatty acid, triglyceride, and cholesterol synthesis and uptake. SREBP cleavage activating protein (SCAP) plays an essential role in SREBP activation by mediating endoplasmic reticulum (ER)-to-Golgi transport of SREBP. In the Golgi, membrane-bound SREBPs are cleaved sequentially by the site-1 and site-2 proteases. Recent studies have shown a requirement for the SREBP pathway in the development of fatty liver disease and tumor growth, making SCAP a target for drug development. Fatostatin is a chemical inhibitor of the SREBP pathway that directly binds SCAP and blocks its ER-to-Golgi transport. In this study, we determined that fatostatin blocks ER exit of SCAP and showed that inhibition is independent of insulin-induced gene proteins, which function to retain the SCAP-SREBP complex in the ER. Fatostatin potently inhibited cell growth, but unexpectedly exogenous lipids failed to rescue proliferation of fatostatin-treated cells. Furthermore, fatostatin inhibited growth of cells lacking SCAP. Using a vesicular stomatitis virus glycoprotein (VSVG) trafficking assay, we demonstrated that fatostatin delays ER-to-Golgi transport of VSVG. In summary, fatostatin inhibited SREBP activation, but fatostatin additionally inhibited cell proliferation through both lipid-independent and SCAP-independent mechanisms, possibly by general inhibition of ER-to-Golgi transport.
Extracting Company-Specific Keyphrases from News Media

Recently, with rapid growth of news media in the Internet, it presents both challenges and opportunities. One challenge lies in how to automatically extract a small group of company-specific keyphrases from news media that can accurately describe a company. Company-specific keyphrase extraction is an ef cient way to mine information from the news article. There are mainly two kinds of approaches for keyphrase extraction: supervised and the unsupervised. In this paper, we propose entity-rank, a novel unsupervised model which is based PageRank and integrate it with the specific company entity information. The experiment result shows that our model has an improvement compared with several other baseline models.
Theory of metal cutting

Tribology of Metal CuttingMetal Cutting TheoryManufacturing Technology—Metal Cutting and Machine Tools, 4e (Volume II)Metal Cutting Theory and PracticeMetal CuttingMetal Cutting Theory and Cutting Tool DesignMetal CuttingMachine Tool StructuresApplication of Metal Cutting TheoryThe Theory of Orthogonal Cutting and Analysis of Parametric ReproducibilityMetal Cutting TechnologiesManufacturing AutomationTheory of Metal CuttingMetal Cutting Theory and Cutting Tool DesingFundamentals of Metal Machining and Machine ToolsCIRP Encyclopedia of Production EngineeringTheory of Friction and Its Part in the Metal Cutting ProcessGeometry of Single-point Turning Tools and DrillsTheory of Metal CuttingMechanics of Sheet Metal FormingManufacturing Technology IIFundamentals of Metal Cutting and Machine ToolsMetal Cutting and Design of Cutting Tools, Jigs & FixturesFundamentals of Modern ManufacturingMechanics and Theory of Metal CuttingPrinciples Of Metel CuttingMetal MachiningMetal Cutting PrinciplesMachiningOn the Art of Cutting MetalsMetal Cutting Theory and PracticeAdvanced Machining Processes of Metallic MaterialsMetal CuttingMetal Cutting Theories and ModelsMetal CuttingTheory of Metal CuttingMetal Cutting MechanicsMachining, Theory and PracticeASME 66-Prod-8Metal Cutting
Estimator of prediction error based on approximate message passing for penalized linear regression

We propose an estimator of prediction error using an approximate message passing (AMP) algorithm that can be applied to a broad range of sparse penalties. Following Stein’s lemma, the estimator of the generalized degrees of freedom, which is a key quantity for the construction of the estimator of the prediction error, is calculated at the AMP fixed point. The resulting form of the AMP-based estimator does not depend on the penalty function, and its value can be further improved by considering the correlation between predictors. The proposed estimator is asymptotically unbiased when the components of the predictors and response variables are independently generated according to a Gaussian distribution. We examine the behavior of the estimator for real data under nonconvex sparse penalties, where Akaike’s information criterion does not correspond to an unbiased estimator of the prediction error. The model selected by the proposed estimator is close to that which minimizes the true prediction error.
Tailored shear extrusion of carbon nanofibre/polyamide composites and its effect on electrical percolation threshold

Carbon nanofibre (CNF) thermoplastic composites can offer permanent conductivity at electrostatic discharge levels with very low filler levels, without affecting processing and other beneficial properties of the host polymer. This capacity makes these materials of particular interest for, for example, electrostatically painted automotive parts. However, ultimate properties depend largely on the processing history of the material. The present paper describes a methodology to optimise the production of nylon 6 composites reinforced with CNFs by balancing fibre dispersion and fibre degradation during melt compounding. The optimisation approach used combines a modelling routine of the compounding process with a multiobjective evolutionary algorithm able to deal with multiple objectives simultaneously. The electrical properties of the composites, compounded and subsequently injection moulded at several fibre loadings, revealed direct relationships with the modelling optimisation parameters. In this way, the potential of tailoring operating conditions and/or screw design during compounding to obtain desired final product properties was demonstrated.
Mega primer-mediated molecular cloning strategy for chimaeragenesis and long DNA fragment insertion

Molecular cloning methods based on primer and overlap-extension PCR are widely used due to their simplicity, reliability, low cost and high efficiency. In this article, an efficient mega primer-mediated (MP) cloning strategy for chimaeragenesis and long DNA fragment insertion is presented. MP cloning is a seamless, restriction/ligation-independent method that requires only three steps: (i) the first PCR for mega primer generation; (ii) the second PCR for exponential amplification mediated by the mega primers and (iii) DpnI digestion and transformation. Most importantly, for chimaeragenesis, genes can be assembled and constructed into the plasmid vector in a single PCR step. By employing this strategy, we successfully inserted four DNA fragments (approximately 500 bp each) into the same vector simultaneously. In conclusion, the strategy proved to be a simple and efficient tool for seamless cloning.
Extracellular vesicles from activated platelets: a semiquantitative cryo-electron microscopy and immuno-gold labeling study

ABSTRACT Cells release membrane vesicles in their surrounding medium either constitutively or in response to activating signals. Two main types of extracellular vesicles (EVs) are commonly distinguished based on their mechanism of formation, membrane composition and size. According to the current model, EVs shed from the plasma membrane, often called microvesicles, expose phosphatidylserine (PS) and range in size from 100 nm to 1 µm, while EVs originating from endosomal multi-vesicular bodies, called exosomes, contain tetraspanin proteins, including CD63, and range in size from 50 to 100 nm. Heijnen et al. [1] have shown that activated platelets release EVs corresponding to these two types of vesicles, using negative staining electron microscopy (EM) and immuno-gold labeling. Here, we apply cryo-EM and immuno-gold labeling to provide a quantitative analysis of EVs released by platelets activated by thrombin, TRAP and CRP-XL, as well as EVs from serum. We show that EVs activated by these three agonists present a similar size distribution, the majority of them forming a broad peak extending from 50 nm to 1 µm, about 50% of them ranging from 50 to 400 nm. We show also that 60% of the EVs from TRAP or CRP-XL activation expose CD41, a majority of them exposing also PS. To explain the presence of large EVs CD41-negative or PS-negative, several alternative mechanisms of EV formation are proposed. We find also that the majority of EVs in activated platelet samples expose CD63, and distinguish two populations of CD63-positive EVs, namely large EVs with low labeling density and small EVs with high labeling density.
Proportional fair scheduling schemes for highly transmit correlated channels

In this paper, we consider the downlink heterogeneous scenario with highly transmit correlated channels, and propose two kinds of the statistical channel state information (SCSI) aided proportional fair scheduling (PFS) schemes: 1) signal-to-noise ratio based PFS (SNR PFS) scheme and 2) SNR PFS scheme with threshold, which determine the preferred beamforming vector and proportional fair index by exploiting the SCSI and only send back the instantaneous channel quality indicator (CQI) at each timeslot. Although the SNR PFS scheme only requires little feedback overhead and low computational complexity, the performance of the SNR PFS scheme is similar to the conventional scheme (i.e., codebook-based PFS scheme), which achieves a tradeoff between system throughput and fairness. However, with the increasing number of users, a large number of CQI feedbacks will increase the signaling burden. To overcome this obstacle, the proposed SNR PFS scheme with threshold which limits the CQI feedbacks with poor channel quality would hardly affect the performance of the system. Finally, Our simulation results show that the average rate of the SNR PFS scheme with β = In (K/5) approaches to that of the SNR PFS scheme, while it has less limited feedback capacity than the SNR PFS scheme.
Form and function of the teleost lateral line revealed using three-dimensional imaging and computational fluid dynamics

Fishes sense weak water motion using the lateral line. Among the thousands of described fish species, this organ may differ in size, shape and distribution of individual mechanoreceptors or lateral line canals. The reasons for this diversity remain unclear, but are very likely related to habitat preferences. To better understand the performance of the organ in natural hydrodynamic surroundings, various three-dimensional imaging datasets of the cephalic lateral line were gathered using Leuciscus idus as representative freshwater teleost. These data are employed to simulate hydrodynamic phenomena around the head and within lateral line canals. The results show that changes in canal dimensions alter the absolute stimulation amplitudes, but have little effect on the relation between bulk water flow and higher frequency signals. By contrast, depressions in the skin known as epidermal pits reduce bulk flow stimulation and increase the ratio between higher-frequency signals and the background flow stimulus.
Fine-Grained Energy-Constrained Microprocessor Pipeline Design

Energy-constrained microprocessor design plays an important role in many emerging Internet of Things platforms operating on harvested or limited energy budget. For this purpose, operating at the supply voltage corresponding to the minimum energy point (MEP) can achieve significant energy savings. However, the MEP voltage is highly dependent on the threshold voltage, the structure, and the activity rate of the circuit. This is more pronounced in pipelined processors as different pipeline stages have different structure with huge intrinsic activity rate variations. Therefore, the energy-saving is limited when only a single MEP voltage is chosen for the entire microprocessor. To address this issue, we propose a fine-grained MEP tuning technique, in which the individual pipeline stages are designed to operate at their MEPs, through per pipeline stage supply and threshold voltage tuning, by considering their activity rates. The proposed optimization is applied to two processors, FabScalar and OpenSPARC, and simulation results show that the proposed technique can improve the energy efficiency of both the cores by almost 50%. The improvement in energy efficiency is obtained at the cost of 6% performance and < 2% area overhead.
flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry

Motivation Identification of cell populations in flow cytometry is a critical part of the analysis and lays the groundwork for many applications and research discovery. The current paradigm of manual analysis is time consuming and subjective. A common goal of users is to replace manual analysis with automated methods that replicate their results. Supervised tools provide the best performance in such a use case, however they require fine parameterization to obtain the best results. Hence, there is a strong need for methods that are fast to setup, accurate and interpretable. Results flowLearn is a semi‐supervised approach for the quality‐checked identification of cell populations. Using a very small number of manually gated samples, through density alignments it is able to predict gates on other samples with high accuracy and speed. On two state‐of‐the‐art datasets, our tool achieves Symbol‐measures exceeding 0.99 for 31%, and 0.90 for 80% of all analyzed populations. Furthermore, users can directly interpret and adjust automated gates on new sample files to iteratively improve the initial training. Symbol. No Caption available. Availability and implementation FlowLearn is available as an R package on https://github.com/mlux86/flowLearn. Evaluation data is publicly available online. Details can be found in the Supplementary Material.
Blacksheep: detecting compromised hosts in homogeneous crowds

The lucrative rewards of security penetrations into large organizations have motivated the development and use of many sophisticated rootkit techniques to maintain an attacker's presence on a compromised system. Due to the evasive nature of such infections, detecting these rootkit infestations is a problem facing modern organizations. While many approaches to this problem have been proposed, various drawbacks that range from signature generation issues, to coverage, to performance, prevent these approaches from being ideal solutions. In this paper, we present Blacksheep, a distributed system for detecting a rootkit infestation among groups of similar machines. This approach was motivated by the homogenous natures of many corporate networks. Taking advantage of the similarity amongst the machines that it analyses, Blacksheep is able to efficiently and effectively detect both existing and new infestations by comparing the memory dumps collected from each host. We evaluate Blacksheep on two sets of memory dumps. One set is taken from virtual machines using virtual machine introspection, mimicking the deployment of Blacksheep on a cloud computing provider's network. The other set is taken from Windows XP machines via a memory acquisition driver, demonstrating Blacksheep's usage under more challenging image acquisition conditions. The results of the evaluation show that by leveraging the homogeneous nature of groups of computers, it is possible to detect rootkit infestations.
Simulation of IBM Enterprise System/9000 Models 820 and 900

The discovery and removal of logic design errors early In the development cycle Is critical to timely availability of market-driven processor products. This paper describes the part played by simulation In the verification of the high-end models of the IBM Enterprise System/9000TM (ES/9000TM) processor family, and how that effort advanced the state of the art of logic design simulation. The Increased complexity of the ES/9000 design over that of the IBM Enterprise System/SOgO"" (ES/SOSO"") necessitated a larger simulation effort. New tools and methods were developed. Two simulation missions were established. Element simulation addressed ES/9000 functional elements (e.g., the storage controller) Individually using the Compiled Enhanced Functional Simulator (CEFS), a software tool. System simulation tested two or more functional elements together using the Engineering Verification Engine (EVE), a special-purpose hardware parallel processor, and an attached IBM 3092 Processor Controller (PCE). The results achieved by simulation are discussed, together with the methods used and the impact these results had on the overall verification of the ES/9000 Models 820 and 900. Introduction It has been documented [1-3] that the cost of uncovering hardware desip errors is high when working with custom VLSI circuits. Engineering changes are costly in terms of time and engineering effort. Hardware design errors often prevent complete testing of a function, which may delay the discovery of additional errors. All of these factors bear directly on the profitability of a product. The potential revenue and market advantage lost because of a long engineering test-floor effort is very large in today's increasingly competitive mainframe marketplace.
Elastic Instability of a Crystal Growing on a Curved Surface

Curving Crystals When a material with a different set of lattice parameters is grown on the surface of a crystal of a second material, the stresses at the interface can affect the growing crystal. Meng et al. (p. 634) studied the growth of colloidal crystals on top of a curved water droplet. Owing to the elastic stress caused by the bending of the crystal, strong distortions occurred in the growing crystal, but, nonetheless, large single-crystalline domains with no topological defects were formed. Constant-background Gaussian curvature alters crystal growth and favors the formation of anisotropic, ribbon-like domains. Although the effects of kinetics on crystal growth are well understood, the role of substrate curvature is not yet established. We studied rigid, two-dimensional colloidal crystals growing on spherical droplets to understand how the elastic stress induced by Gaussian curvature affects the growth pathway. In contrast to crystals grown on flat surfaces or compliant crystals on droplets, these crystals formed branched, ribbon-like domains with large voids and no topological defects. We show that this morphology minimizes the curvature-induced elastic energy. Our results illustrate the effects of curvature on the ubiquitous process of crystallization, with practical implications for nanoscale disorder-order transitions on curved manifolds, including the assembly of viral capsids, phase separation on vesicles, and crystallization of tetrahedra in three dimensions.
Online Unusual Behavior Detection for Temperature Sensor Networks

In modern smart building climate control systems, accurate detection of unusual behavior in temperature sensors (outliers) can help reduce or prevent waste of energy consumption in a Heating, Ventilation and Air Conditioning (HVAC) system. In this work, we propose online learning-distance based outlier detection method. In the new method, we train and tune a multilayer neural network to learn a nonlinear distance function from historical building operation data and detect outliers according to the calculated distance. The online detection method is less computational expensive than the offline version. By gradually including new and drop old building operation record, the new method is capable to adjust the underlying distance function on-the-fly. The converging speed of the learned distance function and tuning difficulty of network training are also discussed. The proposed online outlier detection method can work in an unsupervised manner except requiring only one data-specific parameter. In the experiments of two simulated buildings, the data-specific parameter can be chosen from a relatively wide range, which allows less tuning effort, to achieve good online detection precision and recall.
Influence of Sulfur on Heterogeneous Nucleus of Spheroidal Graphite

The nuclei of spheroidal graphite (abbreviated SG) were studied using an FE-SEM and an EDS. Many kinds of SG irons with different sulfur contents were prepared for the observation of the nucleus-like core materials. The Mg-treated SG iron was cast into a thin CO 2 sand mold and a chilled test piece that is usually used for chemical analysis. The core materials were observed using the chilled test piece. The number of SGs is maximum in the range from 0.010 to 0.025 mass%S of the base melt and the nucleus material is MgS. We propose that this is the most desirable S content for the production of the SG iron castings. This S level is nearly identical to that of the base melt chemical composition for commercial SG iron production. If the S content of the base melt is greater than 0.005 mass%S, the SG nucleus is a spherical MgS in the Mg-treated SG iron. On the other hand, when the S content is less than 0.0022 mass%, the nucleus is a hexagonal (Mg, Si, Al)N as reported by Skaland and Solberg et al.