Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Thermal Fatigue on Microstructure and Mechanical Properties of H13 Tool Steel Processed by Selective Laser Surface Melting

Selective laser surface melting, which brings together the bionic theory and the laser process, is an effective way to enhance the thermal fatigue behavior of materials. In this study, in order to examine the relationship between the mechanical properties and thermal fatigue behavior of materials processed by selective laser surface melting, the tensile properties at room temperature and elevated temperature of treated specimens and untreated specimens after different numbers of thermal fatigue cycles were investigated and compared. Moreover, the microstructure evolution and the microhardness of the laser-affected zone were investigated after different numbers of thermal fatigue cycles. The results show that microhardness of the laser-melted zone gradually decreases with an increasing number of thermal fatigue cycles; the number of thermal fatigue cycles has little effect on the grain size in the laser-melted zone, and the percentage of low-angle grain boundaries decreases with an increasing number of thermal fatigue cycles. The strength of specimens gradually decreases, whereas the fracture elongation gradually increases with an increasing number of thermal fatigue cycles at room temperature and elevated temperature. In addition, the stress distribution on the specimen surface during tensile test was investigated using the finite element method, and the results indicate that the stress transfer exists between the laser-affected zone and the untreated zone.
The enhancement of students’ mathematical representation in junior high school using cognitive apprenticeship instruction (CAI)

This study aims to get an in-depth understanding of the enhancement of students’ mathematical representation. This study is experimental research with pretest-posttest control group design. The subject of this study is the students’ of the eighth grade from junior high schools in Bandung: high-level and middle-level. In each school, two parallel groups were chosen as a control group and an experimental group. The experimental group was given cognitive apprenticeship instruction (CAI) treatment while the control group was given conventional learning. The results show that the enhancement of students’ mathematical representation who obtained CAI treatment was better than the conventional one, viewed which can be observed from the overall, mathematical prior knowledge (MPK), and school level. It can be concluded that CAI can be used as a good alternative learning model to enhance students’ mathematical representation.
Physical Human Activity Recognition Using Machine Learning Algorithms

With the rise in ubiquitous computing, the desire to make everyday lives smarter and easier with technology is on the increase. Human activity recognition (HAR) is the outcome of a similar motive. HAR enables a wide range of pervasive computing applications by recognizing the activity performed by a user. In order to contribute to the multi facet applications that HAR is capable to offer, predicting the right activity is of utmost importance. Simplest of the issues as the use of incorrect data manipulation or utilizing a wrong algorithm to perform prediction can hinder the performance of a HAR system. This study is designed to perform HAR by using two dimensionality reduction techniques followed by five different supervised machine learning algorithms as an aim to receive better predictive accuracy over the existing benchmark research. Correlation analysis (CA) and Principal component analysis (PCA) are used for feature reduction which resulted in 173 and 100 features respectively. Decision Tree, K Nearest Neighbor, Naive Bayes, Multinomial Logistic Regression and Artificial Neural Network algorithms were used to perform the classification task. The repeated random sub-sampling cross validation technique was used to perform the evaluation followed by a Wilcoxon signed rank test to evaluate the significance of the tests. The study resulted in ANN performing the best classification by achieving 97% of accuracy using the CA as feature reduction technique. The KNN and LR also provided satisfactory results and have received predictive results greater than the benchmark test. However, the decision tree and Naive bayes algorithms didn’t prove efficient.
Well-Prefacing in the Organization of Self-Initiated Repair

ABSTRACT This report describes the use of well as a preface to repair solutions in self-initiated repair segments. It extends our previous work on repair prefacing, which showed that repair prefaces cast a relationship between a projected repair solution and its trouble source, with each preface type casting this relationship in a distinctive way. Based on analysis of our collection of 135 recorded instances of the phenomenon, we find that well-prefacing is a practice speakers use to overtly cast a repair solution as a noteworthy revision of the inadequate (but not wholly wrong) formulation it is moving away from. We then compare well-prefacing to no-prefacing and introduce the practice of double prefacing by describing well+no-prefacing. We conclude by describing three kinds of repair common among the well-prefaced repairs in our collection. Data are in British and American English.
Evaluation Neural Networks on Selected Feature by Meta Heuristic Algorithms

Feature selection is one of the issues that have been raised in the discussion of machine learning and statistical identification model. We have provided definitions for feature selection and definitions needed to understand this issue, we check. Then, different methods for this problem were based on the type of product, as well as how to evaluate candidate subsets of features, we classify the following categories. As in previous studies may not have understood that different methods of assessment data into consideration, we propose a new approach for assessing similarity of data to understand the relationship between diversity and stability of the data is selected. After review and meta-heuristic algorithms to implement the algorithm found that the cluster algorithm has better performance compared with other algorithms for feature selection sustained.
Conductive collagen/polypyrrole-b-polycaprolactone hydrogel for bioprinting of neural tissue constructs

Abstract Bioprinting is increasingly being used for fabrication of engineered tissues for regenerative medicine, drug testing, and other biomedical applications. The success of this technology lies with the development of suitable bioinks and hydrogels that are specific to the intended tissue application. For applications such as neural tissue engineering, conductivity plays an important role in determining the neural differentiation and neural tissue regeneration. Although several conductive hydrogels based on metal nanoparticles (NPs) such as gold and silver, carbon-based materials such as graphene and carbon nanotubes and conducting polymers such as polypyrrole (PPy) and polyaniline were used, they possess several disadvantages. The long-term cytotoxicity of metal nanoparticles (NPs) and carbon-based materials restricts their use in regenerative medicine. The conductive polymers, on the other hand, are non-biodegradable and possess weak mechanical properties limiting their printability into three-dimensional constructs. The aim of this study is to develop a biodegradable, conductive, and printable hydrogel based on collagen and a block copolymer of PPy and polycaprolactone (PCL) (PPy-block-poly(caprolactone) [PPy-b-PCL]) for bioprinting of neural tissue constructs. The printability, including the influence of the printing speed and material flow rate on the printed fiber width; rheological properties; and cytotoxicity of these hydrogels were studied. The results prove that the collagen/PPy-b-PCL hydrogels possessed better printability and biocompatibility. Thus, the collagen/PPy-b-PCL hydrogels reported this study has the potential to be used in the bioprinting of neural tissue constructs for the repair of damaged neural tissues and drug testing or precision medicine applications.
Statistics for experimenters : an introduction to design, data analysis, and model building

Science and Statistics. COMPARING TWO TREATMENTS. Use of External Reference Distribution to Compare Two Means. Random Sampling and the Declaration of Independence. Randomization and Blocking with Paired Comparisons. Significance Tests and Confidence Intervals for Means, Variances, Proportions and Frequences. COMPARING MORE THAN TWO TREATMENTS. Experiments to Compare k Treatment Means. Randomized Block and Two--Way Factorial Designs. Designs with More Than One Blocking Variable. MEASURING THE EFFECTS OF VARIABLES. Empirical Modeling. Factorial Designs at Two Levels. More Applications of Factorial Designs. Fractional Factorial Designs at Two Levels. More Applications of Fractional Factorial Designs. BUILDING MODELS AND USING THEM. Simple Modeling with Least Squares (Regression Analysis). Response Surface Methods. Mechanistic Model Building. Study of Variation. Modeling Dependence: Times Series. Appendix Tables. Index.
FORMULATION AND CHARACTERIZATION OF HPMC AND HPMCAS BASED SOLID DISPERSIONS OF FENOFIBRATE: A COMPARATIVE STUDY

Objective: The aim of the present study was to investigate the effect of novel polymeric carriers and to develop solid dispersion formulation that could improve in vitro profile of Fenofibrate (FB). Methods: Spray drying technique was used to fabricate solid dispersions with hydrophilic carriers, mainly hydroxypropyl methylcellulose (HPMC) and hydroxypropyl methylcellulose acetate succinate (HPMCAS). Solid dispersions in the form of spray-dried powder were characterized with respect to the pure drug and the corresponding physical mixtures by optical microscopy, x-ray diffraction (XRD), fourier transform infrared (FT-IR) spectroscopy and differential scanning calorimetry (DSC). Size and morphology of optimized solid dispersion were performed by scanning electron microscopy (SEM). Furthermore, in vitro dissolution comparisons were carried out between the optimized solid dispersion against the pure drug and the physical mixtures. Results: Solubility studies demonstrated that the solubility of FB was not affected by pH change. The transformation of crystalline FB into an amorphous solid dispersion powder has been clearly demonstrated by optical microscopy. The molecular dispersion of drug in the dispersion matrix prepared by spray drying was confirmed in XRD and DSC studies. IR spectroscopy was observed with negligible incompatibility of the drug with polymers. Spherical morphology was observed in SEM with no evidence of FB crystals. The prepared solid dispersions exhibited dissolution improvement as compared to the pure drug and spray dried FB in 0.05 M SLS, with HPMCAS as the superior carrier over HPMC. Conclusion: The present study vouches better in vitro profile of FB from spray-dried HPMCAS based solid dispersions.
Studying the Seebeck coefficient of Fe2VAl compound in the high temperature region

The full-Heusler Fe2VAl compound is an emerging thermoelectric material with non-magnetic ground state. Here, the temperature dependence of Seebeck coefficient (S) of Fe2VAl is studied in the temperature region 320-620 K. The experimental value of S is found to be -119 µ V/K at T=320 K, then the absolute values of S decrease as the temperature increases. At T=620 K, the value of S is found to be -26 µ V/K. In order to understand the behavior of measured Seebeck coefficient, first-principles electronic structure calculations and transport properties calculations are carried out. The dispersion curve and density of states are computed with density functional theory (DFT) by using modified Becke-Johnson (mBJ) potential. The calculated band gap of the compound is ∼0.22 eV, which gives the good agreement with experimental value of band gap. The temperature dependence of S of Fe2VAl is obtained by using BoltzTraP package. The calculated and experimental values of S are compared and found to match reasonably goo.
Scalable highly flexible WDM switch for ONoC architectures

An Optical Network on Chip (ONoC) relies on a switch that is capable of routing information from one element of the chip to another one, presenting both high data rate and reduced latency during transmission. One efficient way to achieve such functionality, is to combine elementary 4x4 switches to form a general YxY switch. We propose here to enhance this elementary block into a reconfigurable non-blocking 4x4 switch that can take advantage of Wavelength Division Multiplexing to improve its global data rate. For added flexibility, we ensure wavelength granularity so that each connection adapts the number of wavelength taken to its needs. On the one hand, in a single wavelength configuration, we achieve the first fully non-blocking 4x4 switch with double-rings that is compared to state of the art achievements. On the other hand, in a n-wavelengths WDM configuration, we demonstrate the behavior of our reconfigurable nonblocking granular WDM switch with double or single rings. Each ring (or double ring) is thermally tuned during a setup step, to place the resonant peak exactly where expected, to ensure stability of the switch. This architecture is scalable at will, and can be used in a YxY n-wavelengths switch for reconfigurable architectures, that could then benefit from WDM’s increased data rate for applications in High Performance Computing.
Enhanced organic solar cell performance: Multiple surface plasmon resonance and incorporation of silver nanodisks into a grating-structure electrode

In this study, plasmonic nanostructures were examined to enhance the light harvesting of organic thin-film solar cells (OSCs) by multiple surface plasmon resonance (SPR) phenomena originating from the grating-coupled configuration with a Blu-ray Disc recordable (BD-R)-imprinted aluminum (Al) grating structure and the incorporation of a series of silver nanodisks (Ag NDs). The devices with such a configuration maximize the light utilization inside OSCs via light absorption, light scattering, and trapping via multiple surface plasmon resonances. Different types and sizes of metallic nanoparticles (NPs), i.e., gold nanoparticles (Au NPs), Ag nanospheres (Ag NSs), and Ag NDs, were used, which were blended separately in a PEDOT:PSS hole transport layer (HTL). The device structure comprised of grating-imprinted-Al/P3HT:PCBM/Ag ND:PEDOT:PSS/ITO. Results obtained from the J–V curves revealed that the power conversion efficiency (PCE) of grating-structured Al/P3HT:PCBM/PEDOT:PSS/ITO is 3.16%; this value is ~6% higher than that of a flat substrate. On the other hand, devices with flat Al and incorporated Au NPs, Ag NSs, or Ag NDs in the HTL exhibited PCEs ranging from 3.15% to 3.37%. Furthermore, OSCs with an Al grating substrate were developed by the incorporation of the Ag ND series into the PEDOT:PSS layer. Compared with that of a reference device, the PCEs of the devices increased to 3.32%–3.59% (11%–20% improvement), indicating that the light absorption enhancement at the active layer corresponds to the grating-coupled surface plasmon resonance and localized surface plasmon resonance excitations with strong near-field distributions penetrating into the active layer leading to higher efficiencies and subsequent better current generation.
Words Apart: A Study of Attitudes Toward Varieties of South African English Accents in a United States Employment Scenario

This study investigates Americans’ attitudes toward the four major accents of South African English (SAfE) and several of their subvarieties in a hypothetical U.S. employment scenario. Participants perceived that SAfE accents possess positive language personality traits in comparison with standard American English; however, respondents identify SAfE speakers as foreign and perceive specific SAfE-accented varieties to be superior and more dynamic (e.g., General/Cultivated White SAfE and Indian SAfE) in relation to other SAfE-accented speakers (e.g., Mesolect Black SAfE and Cape Flats SAfE), even when they do not correctly identify the speaker’s country of origin.
Nonlinguistic Pitch and Timing Patterns in Word Segmentation

Laura Dilley, Advisor How listeners locate word boundaries in fluent speech is a nontrivial problem. Traditionally, acoustic-phonetic information local to the point of segmentation has been considered most relevant to the question. Relatively little work has investigated acoustic cues distal (non-local) to the segmentation locus. Recently, however, Dilley and McAuley (2008) showed distal prosodic context effects on the perception of proximal (i.e., local) word boundaries using eight-syllable word strings (e.g. channel dizzy foot note book worm). The present work tested a variation of the former study by replacing the initial five syllables with five complex tones, which maintained the pitch and timing structures of original sequences; motivation for this experiment comes from a sizable literature concerned with the perceptual organization of nonlinguistic acoustic signals. It was hypothesized that the (nonlinguistic) acoustic structure of the distal context would influence the rate at which participants reported hearing disyllabic final words. Results generally showed null effects, suggesting that the perceptual system may only utilize distal pitch and timing information as it occurs within the structure of speech itself. Contrary to the assumptions of this thesis, the present results may support a view of speech perception in which the auditory system processes speech in a manner distinct from non-speech
Stream-Based Data Sampling Mechanism for Process Object

: Process object is the instance of process. Vertexes and edges are in the graph of process object. There are different types of the object itself and the associations between object. For the large-scale data, there are many changes reﬂected. Recently, how to ﬁnd appropriate real-time data for process object becomes a hot research topic. Data sampling is a kind of ﬁnding c hanges o f p rocess o bjects. T here i s r equirements f or s ampling to be adaptive to underlying distribution of data stream. In this paper, we have proposed a adaptive data sampling mechanism to ﬁnd a ppropriate d ata t o m odeling. F irst o f all, we use concept drift to make the partition of the life cycle of process object. Then, entity community detection is proposed to ﬁnd changes. Finally, we propose stream-based real-time optimization of data sampling. Contributions of this paper are concept drift, community detection, and stream-based real-time computing. Experiments show the effectiveness and feasibility of our proposed adaptive data sampling mechanism for process object.
Activated unsaturated sand filter as an alternative technology to remove copper, manganese, zinc and nickel from waters

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Making Sense of Qualitative Open-ended Reviews

While quantitative values can easily be compared, it is difficult to compare textual data. For an average end user it is often time-consuming and laborious to skim through thousands of open ended reviews put in place by other travellers/consumers. The algorithm proposed in this work is aimed to provide a quantitative summary of the thousands of reviews put forth by different individuals. The algorithm is aimed at determining the polarity (i.e. positivity, negativity and mixed/neutrality) of a review by searching for certain words and phrases that particularly refer to positive and negative emotions in an individual. The results obtained from the algorithm for traveller feedback is comparable to the cognitive ability of the average human mind. Use of the algorithm shall help towards saving time and work of an average human mind. The work presented is aimed at supporting an end user by providing him/her a quantitative positivity/negativity or neutrality summary across thousands of feedbacks/reviews put in place by other end users. Similar to comparing a value of 5 and 9 on a scale of 10, the algorithm outlined in this work shall help end users gauge and hence compare the degree of positivity or negativity associated with each user review.
Semi-supervised and unsupervised extensions to maximum-margin structured prediction

Structured prediction is the backbone of various computer vision and machine learning applications. Inspired by the success of maximum-margin classifiers in the recent years; in this thesis, we will present novel semi-supervised and unsupervised extensions to structured prediction via maximum-margin classifiers. For semi-supervised structured prediction, we have tackled the problem of recognizing actions from single images. Action recognition from a single image is an important task for applications such as image annotation, robotic navigation, video surveillance and several others. We propose approaching action recognition by first partitioning the entire image into “superpixels”, and then using their latent classes as attributes of the action. The action class is predicted based on a graphical model composed of measurements from each superpixel and a fully-connected graph of superpixel classes. The model is learned using a latent structural SVM approach, and an efficient, greedy algorithm is proposed to provide inference over the graph. Differently from most existing methods, the proposed approach does not require annotation of the actor (usually provided as a bounding box). For the unsupervised extension of structured prediction, we considered the case of labeling binary sequences. This case is important in a detection scenario, where one is interested in detecting an action or an event. In particular, we address the unsupervised SVM relaxation recently proposed in (Li et al. 2013) and extend it for structured prediction by merging it with structural SVM. The main contribution of the proposed extension (named Well-SSVM) is a re-organization of the feature map and loss function of structural SVM that permits finding the violating labelings required by the relaxation. Experiments on synthetic and real datasets in a
HCC-based interleaved boost converter with optimal switching frequency control of wind energy conversion system for DC microgrid application

This study exploited the hysteresis current control (HCC) with optimal switching frequency for interleaved boost converter to improve the power factor of wind energy conversion system (WECS) exclusively for DC microgrid applications. By fact that output power from WECS is variable due to unpredictable wind speed, where the HCC suffers with range of switching frequency, high switching loss, large input current ripple and poor power factor. To overcome the aforementioned problem, an automatic tuning procedure has been developed for proportional–integral–derivative (PID) voltage regulator to achieve optimal feedback gain of filter inductor current and thereby optimal switching frequency is maintained with good power factor, low harmonic distortion and improved efficiency. Additionally, the robust PID voltage regulator guarantees the stability and investigated through bode plots. Hardware prototype model is implemented in the laboratory standards with Spartan 3AN field programmable gate array control board. The experimental results obtained are confirming the theoretical background aspects of the operating regimes.