Miracle-dz/UniFAR-FTUs huggingface dataset:
Additive Manufacturing of Energetic Materials and Its Uses in Various Applications

Fleck, Trevor J. M.S.M.E. , Purdue University, December 2017. Additive Manufacturing of Energetic Materials and Its Uses in Various Applications. Major Professor: Jeffrey F. Rhoads, School of Mechanical Engineering. The work discussed in this document seeks to utilize traditional additive manufacturing techniques to selectively deposit energetic materials. The goal was to gain a fundamental understanding of how to use commonplace 2D inkjet printing and 3D fused deposition technology to selectively deposit reactive materials. Doing so provides the ability to manipulate the geometry, as well as composition, of the energetic material during the manufacturing process. Achieving this level manipulation and control has shown to be nontrivial, if not impossible, using traditional manufacturing methods. The ability to change the geometry of the energetic material at will greatly increases the ability of these energetic materials to be integrated with a wide range of systems, such as transient electronics. To create a transient electronic device, a destruction mechanism and an initiation system need to be integrated with electronic components. Experiments in this document investigate nanothermites for their ability to serve as this destruction mechanism. Nanothermites were prepared at various equivalence ratios and syringe deposited onto silicon substrates. The resultant destruction was shown to vary with the equivalence ratio of the material. A wide range of substrate destruction was demonstrated, varying from disintegration to only charring the wafer. Materials prepared near stoichiometric conditions were shown to disintegrate the silicon substrates completely. As the equivalence ratio was raised, less severe destruction was observed. The ability inkjet print these nanothermites provides the geometric control necessary to incorporate them into electronic components. An ink formulation process was explored in an attempt to create a fuel and an oxidizer ink, which could be inkjet
Towards the application of Al2O3/ZnO nanolaminates in immunosensors: total internal reflection spectroscopic ellipsometry based evaluation of BSA immobilization

In this research Al2O3/ZnO nanolaminates were evaluated for possible application in the design of optical immunosensors. Total internal reflection ellipsometry (TIRE) was utilized to study the optical response during the formation of a bovine serum albumin (BSA) based monolayer on the surface of Al2O3/ZnO nanolaminates, which were pre-modified with a N-(3-aminopropyl)triethoxysilane (APTES) layer. The influence of the thicknesses of Al2O3 and ZnO layers on the performance of Al2O3/ZnO nanolaminate based structures has been assessed. This research has shown the noticeable contribution of multiple reflections from the interfaces between Al2O3 and ZnO for the enhancement of the optical response in a total internal reflection configuration. Al2O3/ZnO nanolaminates of 200 nm total thickness based on four 50 nm thick alternating Al2O3 and ZnO layers have shown better sensitivity than the nanolaminate based on two 100 nm oxide-layers. Real-time monitoring of the ellipsometric parameters Ψ(λ) and Δ(λ) has shown that BSA was successfully covalently attached to the nanolaminate/APTES surface. The surface concentration of immobilized BSA was evaluated from the real-time data of the ellipsometric parameters Ψ(λ) and Δ(λ). The expected advantages and disadvantages of Al2O3/ZnO nanolaminates during expected application in optical immunosensors are discussed.
Evaluation of protein and mineral nutrients in cattle hide scraps used for treating children with kwashiorkor in Bukavu

More than half of the world’s diseases can be attributed to unbalanced energy intake and to deficiencies in vitamins and mineral nutrients [1-3]. Developing countries pay the major tribute of it as they face serious health problems due to undernourishment of children and pregnant women [3]. Protein-energy malnutrition (PEM), also known as proteincalorie malnutrition, is currently the most serious nutritional problem faced by Africa and other developing regions. The two severe clinical forms of PEM are kwashiorkor and marasmus [4]. Kwashorkor is mainly a protein deficiency and marasmus an energy deficiency (calories). Beside those two forms, there are many intermediate forms, such stunting which is a short linear growth not translated in direct clinical disease [3,4]. Children who develop kwashiorkor may not grow or develop properly and may remain stunted for the rest of their lives.
An Evaluation of Layout Features for Information Extraction from Calls for Papers

We describe a feature-rich conditional random field model for the extraction of conference and workshop information (e.g. name, date, location, deadline) from calls for papers (CFPs). This has applications in the automatic construction of a conference knowledge base from a collection of CFPs. Relevant information in CFPs is often contained in regions that do not contain complete, grammatical sentences, but can be distinguished visually from other parts of the text by their formatting. We show that in this situation layout features, i.e. features that measure physical layout properties of a text, improve extraction accuracy considerably. On a corpus of CFPs we observe a 30% gain in F1 through the use of layout features.
Systems innovation: Managing manufacturing systems redesign

The purpose of this paper is to investigate innovation and present a perspective on the various approaches to manufacturing innovation. It seeks to examine how the current array of change approaches have contributed to the body of knowledge on manufacturing innovation, and it hypothesises as to why, despite this wealth of knowledge, organisations still have difficulties in developing effective innovation processes. This paper proposes the need for a system-based approach to process-related innovation within manufacturing organisations. A number of key innovation levers to support the management of the innovation process are defined and a brief introduction is provided to the 'total effective integration' achieved by the Systems Innovation Manager software.
Annotating Characters' Emotions in Drama

In this paper, we describe a methodology for annotating characters’ emotions in narrative media. Given the semantic annotation of the story, the emotional state of its characters is inferred through SWRL rules that encode the emotion appraisal process. In order to exemplify the model and test the emotional states generated by the SWRL module, we resort to a well-known dramatic situation and we compare the generated emotional states with the hand-coded annotation by drama experts.
Epigenetic Landscaping During hESC Differentiation to Neural Cells

The molecular mechanisms underlying pluripotency and lineage specification from embryonic stem cells (ESCs) are still largely unclear. To address the role of chromatin structure in maintenance of pluripotency in human ESCs (hESCs) and establishment of lineage commitment, we analyzed a panel of histone modifications at promoter sequences of genes involved in maintenance of pluripotency, self‐renewal, and in early stages of differentiation. To understand the changes occurring at lineage‐specific gene regulatory sequences, we have established an efficient purification system that permits the examination of two distinct populations of lineage committed cells; fluorescence activated cell sorted CD133+ CD45−CD34− neural stem cells and β‐III‐tubulin+ putative neurons. Here we report the importance of other permissive marks supporting trimethylation of Lysine 4 H3 at the active stem cell promoters as well as poised bivalent and nonbivalent lineage‐specific gene promoters in hESCs. Methylation of lysine 9 H3 was found to play a role in repression of pluripotency‐associated and lineage‐specific genes on differentiation. Moreover, presence of newly formed bivalent domains was observed at the neural progenitor stage. However, they differ significantly from the bivalent domains observed in hESCs, with a possible role of dimethylation of lysine 9 H3 in repressing the poised genes. STEM CELLS 2009;27:1298–1308
Fuzzy c-Means Classification of Multispectral Data Incorporating Spatial Contextual Information by using Markov Random Field

Disclaimer This document describes work undertaken as part of a programme of study at the Indian Institute of Remote Sensing and International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute. Abstract Remote sensing technologies provide a unique opportunity to map the real world phenomena in a much faster and economic way in comparison to the traditional ground survey methods. The continuous nature of geographical phenomena throws a typical challenge to prepare landuse/landcover maps from remotely sensed data. Very often landcover class changes gradually from one to another, therefore in such condition it is difficult to define sharp boundaries between two landcover classes and fuzzy classification techniques can be used to represent such conditions. However the Fuzzy c-Means classifier (FCM), the most common fuzzy classification technique, does not incorporate the spatial contextual information, which can be useful for further improvement in fuzzy classification results. Markov Random field (MRF) is a mathematical toolbox which characterizes the spatial contextual information in terms of smoothness prior assumption and incorporation of contextual information helped to improve the classification result for hard classifiers. In the present study an algorithm called contextual FCM classifier was developed by using the MRF model and its performance was justified in comparison to the standard FCM algorithm in the context of wetland mapping. The contextual FCM and FCM classifiers were used on AWiFS and LISS-III data with different spatial resolutions i.e. 60m and 20m respectively. For the purpose of validation soft reference data was generated from fine resolution LISS-IV data (5m) using Support Vector Machine (SVM) classifier. The applicability of Euclidean and Mahalanobis norm in contextual FCM classifier were also judged in the context of wetland mapping. The results of different classification techniques were validated using seven different accuracy assessment tools, namely, Root Mean Square Error (RMSE), Pearson's Product Correlation Coefficient (r), Fuzzy Error Matrix (FERM), Sub-Pixel Confusion Uncertainty Matrix (SCM), MIN-PROD, MIN-MIN and MIN-LEAST composite operators with respect to the soft reference data. The results suggest that proposed contextual FCM classifier can improve the fuzzy classification results by incorporating spatial contextual information for remotely sensed data. Therefore it was also found that for contextual FCM classifier Euclidean norm performs better in comparison to the Mahalanobis norm. Several experiments were performed to establish the suitable Simulated Annealing (SA) technique for …
Reconstructing wafer surfaces with model based shape from shading

A process and apparatus for selectively removing a plastic layer laminated to a metal substrate. A high intensity laser beam is precisely directed to open areas of a thin metallic mask overlying the plastic layer. During removal of the plastic layer from the metal substrate, the system also removes the adhesive resin which attaches the layers. A movable table transfers the sample to be skived across the laser beam and the beam is caused to sweep the sample in a rotating pattern by means of a rotating wobble-plate mirror system. The plastic layer and the adhesive resin which attaches it to the metal substrate are removed by a sequential combination of vaporization and explosion processes.
Effect of processing parameters of the continuous wet spinning system on the crystal phase of PVDF fibers

Poly(vinylidene difluoride) (PVDF) has been widely used in piezoelectric applications as films and nanofiber mats, but there are limited publications on piezoelectric wet-spun fibers. In this work, PVDF fibers were prepared using the wet spinning method, and the processing parameters, including the drawing ratio and heat setting temperature, were controlled in the continuous wet spinning system to increase the β-phase crystallinity of the fibers. In addition, the wet-spun PVDF fibers were compressed by a rolling press to eliminate voids in the fibers. Then, the compressed PVDF fibers were poled to align the molecular dipoles. The crystal structures of the PVDF fibers were investigated using X-ray diffraction and Fourier-transform infrared spectroscopy. Single filament tensile tests were performed to measure the tensile strength of the fibers. The morphologies of the PVDF fibers with respect to the processing parameters were observed by scanning electron microscope (SEM) and polarization optical microscopy. The piezoelectric constant of the prepared PVDF fibers was then measured using a d33 meter. The wet-spun PVDF fibers showed the highest β-phase and piezoelectric constants when the drawing ratio and heat setting temperature were 6 and 150 °C, respectively. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45712.
Contextual Latent Semantic Networks used for Document Classification

Widely used document classifiers are developed over a bag-of-words representation of documents. Latent semantic analysis based on singular value decomposition is often employed to reduce the dimensionality of such representation. This approach overlooks word order in a text that can improve the quality of classifier. We propose language independent method that records the context of particular word into a context network utilizing products of latent semantic analysis. Words' contexts networks are combined to one network that represents a document. A new document is classified based on a similarity between its network and training documents networks. The experiments show that proposed classifier achieves better performance than common classifiers especially when a foregoing reduction of dimensionality is significant.
Stochastic local search - methods, models, applications

A shift mechanism for a transmission of the type used in riding lawn mowers and the like is provided. The shift pattern is essentially of the lineal type and yet the transmission can be shifted from one gear to another without being shifted through all intermediate gears. For example, the transmission can be shifted from the third forward speed to reverse without progressing through the second and first forward speeds. The shift lever is moved lineally in a path transverse to its longitudinal extent to align the lever with a desired shift position. The lever is then pivoted in a vertical path to effect the desired shifting. The new shift also provides a higher mechanical advantage to make shifting easier for the operator.
Ectopic beats in approximate entropy and sample entropy-based HRV assessment

Approximate entropy (ApEn) and sample entropy (SampEn) are the promising techniques for extracting complex characteristics of cardiovascular variability. Ectopic beats, originating from other than the normal site, are the artefacts contributing a serious limitation to heart rate variability (HRV) analysis. The approaches like deletion and interpolation are currently in use to eliminate the bias produced by ectopic beats. In this study, normal R–R interval time series of 10 healthy and 10 acute myocardial infarction (AMI) patients were analysed by inserting artificial ectopic beats. Then the effects of ectopic beats editing by deletion, degree-zero and degree-one interpolation on ApEn and SampEn have been assessed. Ectopic beats addition (even 2%) led to reduced complexity, resulting in decreased ApEn and SampEn of both healthy and AMI patient data. This reduction has been found to be dependent on level of ectopic beats. Editing of ectopic beats by interpolation degree-one method is found to be superior to other methods.
Head-Lexicalized Bidirectional Tree LSTMs

Sequential LSTMs have been extended to model tree structures, giving competitive results for a number of tasks. Existing methods model constituent trees by bottom-up combinations of constituent nodes, making direct use of input word information only for leaf nodes. This is different from sequential LSTMs, which contain references to input words for each node. In this paper, we propose a method for automatic head-lexicalization for tree-structure LSTMs, propagating head words from leaf nodes to every constituent node. In addition, enabled by head lexicalization, we build a tree LSTM in the top-down direction, which corresponds to bidirectional sequential LSTMs in structure. Experiments show that both extensions give better representations of tree structures. Our final model gives the best results on the Stanford Sentiment Treebank and highly competitive results on the TREC question type classification task.
Processing Problems Associated with Gas Phase Solid Freeform Fabrication Using Pyrolytic Selective Area Laser Deposition

Concerns critical to selective area laser deposition are discussed. Variables affecting deposition rate, dimensional control, and surface uniformity are analyzed. Localized growth results in undesirable morphology, and contributing factors are cited. Catalytic powders increase nucleation rate and provide superior temperature profiles resulting in good surface uniformity. Specific process control devices are investigated.
p300/CBP-Associated Factor Selectively Regulates the Extinction of Conditioned Fear

It is well established that the activity of chromatin-modifying enzymes is crucial for regulating gene expression associated with hippocampal-dependent memories. However, very little is known about how these epigenetic mechanisms influence the formation of cortically dependent memory, particularly when there is competition between opposing memory traces, such as that which occurs during the acquisition and extinction of conditioned fear. Here we demonstrate, in C57BL/6 mice, that the activity of p300/CBP-associated factor (PCAF) within the infralimbic prefrontal cortex is required for long-term potentiation and is necessary for the formation of memory associated with fear extinction, but not for fear acquisition. Further, systemic administration of the PCAF activator SPV106 enhances memory for fear extinction and prevents fear renewal. The selective influence of PCAF on fear extinction is mediated, in part, by a transient recruitment of the repressive transcription factor ATF4 to the promoter of the immediate early gene zif268, which competitively inhibits its expression. Thus, within the context of fear extinction, PCAF functions as a transcriptional coactivator, which may facilitate the formation of memory for fear extinction by interfering with reconsolidation of the original memory trace.
Kr-85m activity as burnup measurement indicator in a pebble bed reactor based on ORIGEN2.1 Computer Simulation

Pebble Bed Reactor (PBR) is a high temperature gas-cooled reactor which employs graphite as a moderator and helium as a coolant. In a multi-pass PBR, burnup of the fuel pebble must be measured in each cycle by online measurement in order to determine whether the fuel pebble should be reloaded into the core for another cycle or moved out of the core into spent fuel storage. One of the well-known methods for measuring burnup is based on the activity of radionuclide decay inside the fuel pebble. In this work, the activity and gamma emission of Kr-85m were studied in order to investigate the feasibility of Kr-85m as burnup measurement indicator in a PBR. The activity and gamma emission of Kr-85 were estimated using ORIGEN2.1 computer code. The parameters of HTR-10 were taken as a case study in performing ORIGEN2.1 simulation. The results show that the activity revolution of Kr-85m has a good relationship with the burnup of the pebble fuel in each cycle. The Kr-85m activity reduction in each burnup step,in the range of 12% to 4%, is considered sufficient to show the burnup level in each cycle. The gamma emission of Kr-85m is also sufficiently high which is in the order of 1010 photon/second. From these results, it can be concluded that Kr-85m is suitable to be used as burnup measurement indicator in a pebble bed reactor.
Fully Alloying AuAg Nanorods in a Photothermal Nano-Oven: Superior Plasmonic Property and Enhanced Chemical Stability

Fully alloyed metallic nanomaterials have attracted much attention because of the superior chemical and physical properties to their single components. However, it has been difficult to make fully alloyed anisotropic nanostructures due to the required high energy input. Here we present the preparation of fully alloyed AuAg nanorods by using a photothermal nano-oven, in which Au@Ag nanorods can convert absorbed light to thermal energy while the silica shell can act as both the thermal insulator and protective layer to facilitate the alloying process. The as-prepared AuAg nanorods showed enhanced plasmonic property stemming from silver and much higher chemical stability in a corrosive environment derived from gold. This method opens up a new approach for the preparation of plasmonic nanostructures with desired properties.
Statistical Lexical Semantics in Computer Assisted Language Learning

In this project the relevance of meaning in second language learning is emphasized. The role of meaning has been overlooked in both second language learning and computer assisted language learning. Knowledge building processes rely heavily on how to mean in language, and thus the learning of words involves more than remembering one correct meaning of a word. Instead, it is how a word is used in different contexts that shall be learned in order to achieve a target language-like use of the new language. The concept of meaning is grounded in Vygotsky’s ideas of meaning which stressed a dialectical tension between a more stable meaning of a word shared by many language users and a more individualistic meaning based on how the word is used in different contexts by individuals. Thus the meaning of a word is both individualistic and contextualized. This project takes a practical stance in different methods developed within the fields of corpus linguistics and information retrieval such as different techniques for genre analysis, text comparison and vector-based models of lexical semantics. Some of these techniques have been used in educational contexts and have gained some important success especially in the field of evaluating students’ essays. However, the role of vector-based models of semantics and other methods for statistical text analysis have not been very well explored in second language reading and writing. This project aims to achieve both an understanding of these methods in this context and how they can be practically applied in computer assisted language learning.
A novel method for stock forecasting based on Fuzzy Time Series combined with Longest Common/Repeat Sub-sequence

Stock price forecasting is an important issue for investors since extreme accuracy in forecasting can bring about high profits. Fuzzy Time Series (FTS) and Longest Common/Repeated Sub-sequence (LCS/LRS) are two important issues for forecasting prices. However, to the best of our knowledge, there are no significant studies using LCS/LRS to predict stock prices. It is impossible that prices stay exactly the same as historic prices. Therefore, this paper proposes a state-of-the-art method which combines FTS and LCS/LRS to predict stock prices. This method is based on the principle that history will repeat itself. It uses different interval lengths in FTS to fuzzify the prices, and LCS/LRS to look for the same pattern in the historical prices to predict future stock prics. In the experiment, we examine various intervals of fuzzy time sets in order to achieve high prediction accuracy. The proposed method outperforms traditional methods in terms of prediction accuracy and, furthermore, it is easy to implement.