Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Chemical Compositions of Case Hardening Steels for Distribution of Carbon and Cementite during Vacuum Carburizing

In order to develop the model to predict the microstructures of case hardening steels during vacuum carburizing, the effects of chemical compositions such as Cr and Si on carburizing behaviors were investigated using three steels based on Fe–1.1Cr–0.8Mn–0.25Si–0.2C (in mass%). The distributions of carbon near the carburized surface and grain boundary cementite (θ) were analyzed by Glow Discharge Optical Emission Spectroscopy (GD-OES) and image analysis of microstructures respectively. It was revealed that the distributions of carbon and the volume fractions of θ were affected by the chemical compositions i.e. they increased with increasing amount of Cr and decreased with increasing amount of Si. The results by X-ray Diffraction (XRD) and field emission electron probe microanalyzer (FE-EPMA) showed the carbon content in the retained austenite(γ) near the surface was around 1.4–1.6 mass% which was higher than the equilibrium condition. By the comparing the experimental and calculation results obtained using the commercial multi-elemental diffusion solver, DICTRA, it was confirmed that the carbon in the γ was supersaturated during vacuum carburizing and the surface condition was estimated to be equal to the carbon activity of the meta-equilibrium border of γ/(γ+graphite). Additionally the result of θ distribution suggests that the volume fraction of θ cannot be predicted by equilibrium calculations; rather, a kinetic model for θ growth is required.
Typical solution time for a vertex-covering algorithm on finite-connectivity random graphs

We analytically describe the typical solution time needed by a backtracking algorithm to solve the vertex-cover problem on finite-connectivity random graphs. We find two different transitions: The first one is algorithm dependent and marks the dynamical transition from linear to exponential solution times. The second one gives the maximum computational complexity, and is found exactly at the threshold where the system undergoes an algorithm-independent phase transition in its solvability. Analytical results are corroborated by numerical simulations.
Training Balance and Strength in the Elderly to Improve Function

Short‐term exposure to altered sensory input or destabilizing platform movement results in significant improvement in sway control and inhibition of inappropriate motor responses, resulting in improved balance during repetitive testing. In addition, there is recent evidence that strength and function can be increased in both active and frail elderly who participate in strength training programs. Therefore, the hypotheses to be tested are that (1) balance training alone, or (2) strength training alone will each be capable of significantly improving balance, gait, and functional mobility, and that (3) a combined program of balance and strength training will be more effective than either approach alone. These hypotheses will be tested relative to a control group, using a 2 times 2 design (30 subjects per group), in a community‐dwelling elderly at least 75 years of age. Intervention sessions of at least 45 minutes will occur three times per week for 3 months, with 6 months of follow‐up, home‐based Tai Chi training. The primary outcome variable is a basic measure of functional balance, ie, the occurrence of loss of balance during tilts of the support and/or movement of the visual surround.
Representing Text Chunks

Dividing sentences in chunks of words is a useful preprocessing step for parsing, information extraction and information retrieval. (Ramshaw and Marcus, 1995) have introduced a "convenient" data representation for chunking by converting it to a tagging task. In this paper we will examine seven different data representations for the problem of recognizing noun phrase chunks. We will show that the the data representation choice has a minor influence on chunking performance. However, equipped with the most suitable data representation, our memory-based learning chunker was able to improve the best published chunking results for a standard data set.
Initial Evidence for Adaptive Selection on the NADH Subunit Two of Freshwater Dolphins by Analyses of Mitochondrial Genomes

A small number of cetaceans have adapted to an entirely freshwater environment, having colonized rivers in Asia and South America from an ancestral origin in the marine environment. This includes the ‘river dolphins’, early divergence from the odontocete lineage, and two species of true dolphins (Family Delphinidae). Successful adaptation to the freshwater environment may have required increased demands in energy involved in processes such as the mitochondrial osmotic balance. For this reason, riverine odontocetes provide a compelling natural experiment in adaptation of mammals from marine to freshwater habitats. Here we present initial evidence of positive selection in the NADH dehydrogenase subunit 2 of riverine odontocetes by analyses of full mitochondrial genomes, using tests of selection and protein structure modeling. The codon model with highest statistical support corresponds to three discrete categories for amino acid sites, those under positive, neutral, and purifying selection. With this model we found positive selection at site 297 of the NADH dehydrogenase subunit 2 (dN/dS>1.0,) leading to a substitution of an Ala or Val from the ancestral state of Thr. A phylogenetic reconstruction of 27 cetacean mitogenomes showed that an Ala substitution has evolved at least four times in cetaceans, once or more in the three ‘river dolphins’ (Families Pontoporidae, Lipotidae and Inidae), once in the riverine Sotalia fluviatilis (but not in its marine sister taxa), once in the riverine Orcaella brevirostris from the Mekong River (but not in its marine sister taxa) and once in two other related marine dolphins. We located the position of this amino acid substitution in an alpha-helix channel in the trans-membrane domain in both the E. coli structure and Sotalia fluviatilis model. In E. coli this position is located in a helix implicated in a proton translocation channel of respiratory complex 1 and may have a similar role in the NADH dehydrogenases of cetaceans.
Microwave methods for additive layer manufacturing

This thesis presents the novel application of microwave technology to the process of additive layer manufacturing (ALM). A particle size sensor, based on microwave cavity perturbation, is described and subsequently demonstrated by the measurement of the complex permeability of a series of Titanium powders. The results are compared with existing theory and finite element simulations of metallic powders. The ability to discern changing particle size distributions is important in ensuring the reliable operation of selective laser melting machines but, to remain industrially relevant, it is vital that the proposed system can be produced at low cost. By way of demonstration, the design and construction of an inexpensive scalar network analyser was completed. A systematic study of surface resistance of a number of metal surfaces, produced by Selective Laser Melting, was undertaken. Using a dielectric resonator with a “lift-off” calibration procedure, the losses of surfaces manufactured in orthogonal orientations and different surface finishes were compared. Surface roughness measurements showed that microwave losses were not monotonically dependent on root-mean-square surface roughness; this was attributed to differing roughness feature size distributions. For microwave characterization of materials over a wide temperature range, it is often desirable to perform cavity perturbation measurements at elevated temperatures. However, it is shown here that heat treatment can permanently modify the surface resistance of a metal surface and potentially lead to inaccurate perturbation results. X-Ray diffraction measurements confirm the source of modification is due to changes in surface stress and the appearance of solution precipitates. The sensitivity of microwave measurements to surface stress also demonstrates the potential for microwave assessment of surfaces produced by ALM. Finally, to stimulate further work in this area, the design of a single mode microwave heating system was discussed and a prototype developed.
Chemical ordering patterns and magnetism of NiAl nanoclusters

We investigated the chemical ordering patterns and magnetism of face-centered cubic NixAl1−x nanoclusters having sizes 79 and 201 atoms and Ni concentrations x = 0, 0.25, 0.5, 0.75, and 1 using a combined basin hopping global sampling technique and spin-polarized density functional theory. The optimized nanoclusters display surface patterns with partially alloyed facets and a tendency for mixing. We also found a nonmagnetic to ferromagnetic phase transition at x = 0.75. This behavior is explained in terms of the distribution of the Ni atoms on the surface/subsurface region and local Ni clustering.
Robot navigation algorithm to wall following using fuzzy Kalman filter

Robot navigation and obstacle avoidance are from the most important problems in mobile robots, especially in unknown environments. The autonomous navigation of mobile robots has attracted a number of researchers over the years. A wide variety of approaches and algorithms have been proposed to tackle this complex problem. There are many successful localization methods that can determine the robot's position relative to a map using sonar, laser and camera data; however, most localization methods fail under common environmental conditions. In this paper we present a novel method for navigation of autonomous mobile robots in several indoor environments that optimize our old navigation approach, using fuzzy Kalman filter based on extracted lines and fusing data from laser scanner sensors. In this approach, the sonar data and laser measurement are combined using the fuzzy Kalman filter. Finally, the simulation results show that the proposed algorithm offers advantages over previous methods and has improved performance.
INFLUENCE OF SELECTED ADDITIVES ON WARM ASPHALT MIXTURES PERFORMANCE

Warm Mix Asphalt (WMA) is a technology that allows significant lowering of the mixing and compaction temperature of conventional Hot Mix Asphalt (HMA). This research presents the results of laboratory tests conducted to evaluate the properties and performance of warm mix asphalt. TheWMA has been prepared from two asphalt grades and one aggregate source with twoadditives (Aspha-min® and Sasobit®). The crumb rubber is also added to WMA which is prepared withAspha-min® and Sasobit® to evaluate effect of crumb rubber on WMA properties. The results of the study indicated that the two additives to the warm asphalt affected the mixture properties differently. When the mixture properties were compared, it was observed that Aspha-min® reduced the indirect tensile strength and resilient modulus values of the mixes, the two WMA additives increased the rut depths of the mixes, and both the additives reduced the tensile strength ratio of the mixes.WMA contains crumb rubber appears
Author ' s personal copy Energy and water balance of two contrasting loblolly pine plantations on the lower coastal plain of North Carolina , USA

During 2005–2007, we used the eddy covariance and associated hydrometric methods to construct energy and water budgets along a chronosequence of loblolly pine (Pinus taeda) plantations that included a mid-rotation stand (LP) (i.e., 13–15 years old) and a recently established stand on a clearcut site (CC) (i.e., 4–6 years old) in Eastern North Carolina. Our central objective was to quantify the differences in both energy and water balances between the two contrasting stands and understand the underlining mechanisms of environmental controls. We found that the LP site received about 20% more net radiation (Rn) due to its lower averaged albedo (a) of 0.25, compared with that at the CC (a = 0.34). The mean monthly averaged Bowen ratios (b) at the LP site were 0.89 0.7, significantly (p = 0.02) lower than at the CC site (1.45 1.2). Higher net radiation resulted in a 28% higher (p = 0.02) latent heat flux (LE) for ecosystem evapotranspiration at the LP site, but there was no difference in sensible heat flux (H) between the two contrasting sites. The annual total evapotranspiration (ET) at the LP site and CC site was estimated as 1011–1226 and 755–855 mm year , respectively. The differences in ET rates between the two contrasting sites occurred mostly during the non-growing seasons and/or dry periods, and they were small during peak growing seasons or wet periods. Higher net radiation and biomass in LP were believed to be responsible to the higher ET. The monthly ET/Grass Reference ET ratios differed significantly across site and season. The annual ET/P ratio for the LP and CC were estimated as 0.70–1.13 and 0.60–0.88, respectively, indicating higher runoff production from the CC site than the LP site. This study implied that reforestation practices reduced surface albedos and thus increased available energy, but they did not necessarily increase energy for warming the atmosphere in the coastal plain region where soil water was generally not limited. This study showed the highly variable response of energy and water balances to forest management due to climatic variability. Published by Elsevier B.V. * Corresponding author. Tel. : +1 919 515 9498; fax: +1 919 5132978. E-mail addresses: Ge_Sun@ncsu.edu, gesun@fs.fed. us (G. Sun).
ParaSplit: A Scalable Architecture on FPGA for Terabit Packet Classification

Packet classification is a fundamental enabling function for various applications in switches, routers and firewalls. Due to their performance and scalability limitations, current packet classification solutions are insufficient in ad-dressing the challenges from the growing network bandwidth and the increasing number of new applications. This paper presents a scalable parallel architecture, named Para Split, for high-performance packet classification. We propose a rule set partitioning algorithm based on range-point conversion to reduce the overall memory requirement. We further optimize the partitioning by applying the Simulated Annealing technique. We implement the architecture on a Field Programmable Gate Array (FPGA) to achieve high throughput by exploiting the abundant parallelism in the hardware. Evaluation using real-life data sets including Open Flow-like 11-tuple rules shows that Para Split achieves significant reduction in memory requirement, compared with the-state-of-the-art algorithms such as Hyper Split [6] and EffiCuts [8]. Because of the memory efficiency of Para Split, our FPGA design can support in the on-chip memory multiple engines, each of which contains up to 10K complex rules. As a result, the architecture with multiple Para Split engines in parallel can achieve up to Terabit per second throughput for large and complex rule sets on a single FPGA device.
Evaluating Low-Level Speech Features Against Human Perceptual Data

We introduce a method for measuring the correspondence between low-level speech features and human perception, using a cognitive model of speech perception implemented directly on speech recordings. We evaluate two speaker normalization techniques using this method and find that in both cases, speech features that are normalized across speakers predict human data better than unnormalized speech features, consistent with previous research. Results further reveal differences across normalization methods in how well each predicts human data. This work provides a new framework for evaluating low-level representations of speech on their match to human perception, and lays the groundwork for creating more ecologically valid models of speech perception.
Effects of Bedding Material Composition in Deep Litter Systems on Bedding Characteristics and Growth Performance of Limousin Calves

The objective of this study was to evaluate the effects of different litter mixture compositions on bedding system temperature, pH and volatile fatty acid and ammonia-N (NH3-N) content, and the serum physico-chemical parameters and growth indices of calves. Thirty-two Limousin calves (280±20 kg) were randomly assigned to four groups (n = 8 for each group) according to the bedding system used: i) control with soil only (CTR); ii) mixture with 50% paddy hulls (PH), 30% saw dusts (SD), 10% peat moss (PM) and 10% corn cobs (CC) (TRT1); iii) mixture with 15% PH, 15% SD, 10% PM, 40% CC, and 20% corn stover (CS) (TRT2); iv) mixture with 30% PH, 10% PM, 40% CC, and 20% CS (TRT3). The litter material combinations of different treatments were based on the cost of bedding system materials in China. The cost of four treatments from low to high: Control<TRT1<TRT2<TRT3. The control was no-cost treatment. The diet consisted of 60.8% silage and 39.2% concentrate (dry matter [DM] basis). The NH3-N level (271.83 to 894.72 mg/kg) was lowest for TRT1 (p<0.0001) and highest for TRT2 (p<0.0001). The acetate, propionate and butyrate levels were highest for the control group (p<0.0001). In all the groups, the pH value (6.90 to 9.09) increased at the beginning and later remained stable at below 9.09. The temperature of deep litter increased at the first week and reached the maximum (42.1°C) on day 38. 3,5,3′-Triiodothyronine (T3) levels in the TRT1 group animals (p<0.0001) were lower than those in the control and TRT2 animals. 3,5,3′,5′-Tetraiodothyronine (T4) in the TRT1 group (p = 0.006) was lower than that in the other treatment groups. Cortisol (COR) in the control and TRT1 group was lower (p<0.0001) than that in the TRT2 and TRT3 groups. Corticosterone (CORt) in the control group was higher (p<0.0001) than that in the treatment groups. The findings indicate that the deep litter bedding systems provided better conditions for animal health and growth performance compared with the control system. Furthermore, the litter composition of TRT1 was found to be optimal among the three treatment groups.
Learning Markov Network Structure with Decision Trees

Traditional Markov network structure learning algorithms perform a search for globally useful features. However, these algorithms are often slow and prone to finding local optima due to the large space of possible structures. Ravikumar et al. recently proposed the alternative idea of applying L1 logistic regression to learn a set of pair wise features for each variable, which are then combined into a global model. This paper presents the DTSL algorithm, which uses probabilistic decision trees as the local model. Our approach has two significant advantages: it is more efficient, and it is able to discover features that capture more complex interactions among the variables. Our approach can also be seen as a method for converting a dependency network into a consistent probabilistic model. In an extensive empirical evaluation on 13 datasets, our algorithm obtains comparable accuracy to three standard structure learning algorithms while running 1-4 orders of magnitude faster.
A Rough Sets-based Agent Trust Management Framework

In a virtual society, which consists of several autonomous agents, trust helps agents to deal with the openness of the system by identifying the best agents capable of performing a specific task, or achieving a special goal. In this paper, we introduce ROSTAM, a new approach for agent trust management based on the theory of Rough Sets. ROSTAM is a generic trust management framework that can be applied to any types of multi agent systems. However, the features of the application domain must be provided to ROSTAM. These features form the trust attributes. By collecting the values for these attributes, ROSTAM is able to generate a set of trust rules by employing the theory of Rough Sets. ROSTAM then uses the trust rules to extract the set of the most trusted agents and forwards the user's request to those agents only. After getting the results, the user must rate the interaction with each trusted agent. The rating values are subsequently utilized for updating the trust rules. We applied ROSTAM to the domain of cross-language Web search. The resulting Web search system recommends to the user the set of the most trusted pairs of translator and search engine in terms of the pairs that return the results with the highest precision of retrieval.
Fast Joint Compression and Summarization via Graph Cuts

Extractive summarization typically uses sentences as summarization units. In contrast, joint compression and summarization can use smaller units such as words and phrases, resulting in summaries containing more information. The goal of compressive summarization is to find a subset of words that maximize the total score of concepts and cutting dependency arcs under the grammar constraints and summary length constraint. We propose an efficient decoding algorithm for fast compressive summarization using graph cuts. Our approachfirstrelaxesthelength constraint using Lagrangian relaxation. Then we propose to bound the relaxed objective function by the supermodular binary quadratic programming problem, which can be solved efficiently using graph max-flow/min-cut. Since finding the tightest lower bound suffers from local optimality, we use convex relaxation for initialization. Experimental results on TAC2008 dataset demonstrate our method achieves competitive ROUGE score and has good readability, while is much faster than the integer linear programming (ILP) method.
Free-form mesh tracking: A patch-based approach

In this paper, we consider the problem of tracking nonrigid surfaces and propose a generic data-driven mesh deformation framework. In contrast to methods using strong prior models, this framework assumes little on the observed surface and hence easily generalizes to most free-form surfaces while effectively handling large deformations. To this aim, the reference surface is divided into elementary surface cells or patches. This strategy ensures robustness by providing natural integration domains over the surface for noisy data, while enabling to express simple patch-level rigidity constraints. In addition, we associate to this scheme a robust numerical optimization that solves for physically plausible surface deformations given arbitrary constraints. In order to demonstrate the versatility of the proposed framework, we conducted experiments on open and closed surfaces, with possibly non-connected components, that undergo large deformations and fast motions. We also performed quantitative and qualitative evaluations in multicameras and monocular environments, and with different types of data including 2D correspondences and 3D point clouds.
Reversible Data Hiding Scheme Based On H.264/AVC without Distortion Drift

There is greater possibility that reversible video data hiding scheme causes more distortions than reversible image data hiding. In H.264/AVC video streams, intra-frame distortion drift is a big problem in data hiding. This paper proposes a novel readable reversible data hiding scheme in H.264/AVC which can avert the distortion drift. We embed data into the quantized discrete cosine transform (DCT) coefficients of I frames which meet the directions of intra-frame prediction, and the directions of intra-frame prediction are utilized to avert the distortion drift. It is proved analytically and shown experimentally that the proposed algorithm can achieve high embedding capacity and low visual distortion, and the original compressed video can be recovered exactly after the hidden data, in addition, the proposed system is fast and simple. Performance comparisons with other existing schemes are provided to demonstrate the superiority of the proposed scheme.