Miracle-dz/UniFAR-FTUs huggingface dataset:
Partial Purification and Characterization of Chromate Reductase of a Novel Ochrobactrum sp. Strain Cr-B4

Hexavalent chromium contamination is a serious problem due to its high toxicity and carcinogenic effects on the biological systems. The enzymatic reduction of toxic Cr(VI) to the less toxic Cr(III) is an efficient technology for detoxification of Cr(VI)-contaminated industrial effluents. In this regard, a chromate reductase enzyme from a novel Ochrobactrum sp. strain Cr-B4, having the ability to detoxify Cr(VI) contaminated sites, has been partially purified and characterized. The molecular mass of this chromate reductase was found to be 31.53 kD, with a specific activity 14.26 U/mg without any addition of electron donors. The temperature and pH optima for chromate reductase activity were 40°C and 8.0, respectively. The activation energy (Ea) for the chromate reductase was found to be 34.7 kJ/mol up to 40°C and the activation energy for its deactivation (Ed) was found to be 79.6 kJ/mol over a temperature range of 50–80°C. The frequency factor for activation of chromate reductase was found to be 566.79 s−1, and for deactivation of chromate reductase it was found to be 265.66 × 103 s−1. The reductase activity of this enzyme was affected by the presence of various heavy metals and complexing agents, some of which (ethylenediamine tetraacetic acid [EDTA], mercaptoethanol, NaN3, Pb2+, Ni2+, Zn2+, and Cd2+) inhibited the enzyme activity, while metals like Cu2+ and Fe3+ significantly enhanced the reductase activity. The enzyme followed Michaelis–Menten kinetics with Km of 104.29 µM and a Vmax of 4.64 µM/min/mg.
RISE-Based Adaptive Control of a Control Affine Uncertain Nonlinear System With Unknown State Delays

A continuous robust adaptive control method is designed for a class of uncertain nonlinear systems with unknown constant time-delays in the states. Specifically, a robust adaptive control method and a delay-free gradient-based desired compensation adaptation law (DCAL) are utilized to compensate for unknown time-delays, linearly parameterizable uncertainties, and additive bounded disturbances for a general nonlinear system. Despite these disturbances, a Lyapunov Krasovskii-based analysis is used to conclude that the system output asymptotically tracks a desired time varying bounded trajectory.
Shear Stress Investigation Across Mechanical Heart Valve

The particle image velocimetry technique was used to study the shear field across a transparent mechanical heart valve model in one cardiac cycle. Shear stress was continuously increased until peak systole and high turbulent stress was observed at the orifice of the central channel and also around the occluder trailing tips. The peak Reynolds shear stress was up to 500 N/m2 at peak systole, which was higher than the normal threshold for hemolysis.
Bidirectional Transmembrane Signaling by Cytoplasmic Domain Separation in Integrins

Although critical for development, immunity, wound healing, and metastasis, integrins represent one of the few classes of plasma membrane receptors for which the basic signaling mechanism remains a mystery. We investigated cytoplasmic conformational changes in the integrin LFA-1 (αLβ2) in living cells by measuring fluorescence resonance energy transfer between cyan fluorescent protein–fused and yellow fluorescent protein–fused αL and β2 cytoplasmic domains. In the resting state these domains were close to each other, but underwent significant spatial separation upon either intracellular activation of integrin adhesiveness (inside-out signaling) or ligand binding (outside-in signaling). Thus, bidirectional integrin signaling is accomplished by coupling extracellular conformational changes to an unclasping and separation of the α and β cytoplasmic domains, a distinctive mechanism for transmitting information across the plasma membrane.
Using data driven methods to predict stress hotspots in materials

This work focuses on integrating crystal plasticity based deformation models and machine learning techniques to gain data driven insights about the microstructural properties of polycrystalline metals. Inhomogeneous stress distribution in materials leads to the development of stress hotspots in polycrystalline metals under uniaxial tensile deformation. Synthetic 3D microstructures have been created to represent single phase equiaxed microstructures for alpha-Titanium and Copper alloys. A viscoplastic self consistent (VPSC) model is used to optimize the Voce hardening parameters to match the experimental stress-strain curve. Uniaxial tensile deformation is simulated using an image-based Fast Fourier Transform (FFT) technique, which provides full field solutions for local micro-mechanical fields (stress and strain rates). Stress hotspots are defined as the grains having stress values above the 90th percentile of the stress distribution. After identifying stress hotspots by thresholding stress values, we characterize their neighborhoods using metrics that reflect local crystallography, geometry, and connectivity. This data is used to create input feature vectors to train a random forest learning algorithm, which predicts the grains that will become stress hotspots. We are able to achieve an area under the receiving operating characteristic curve (ROC-AUC) of 0.82 for hexagonal close packed and 0.74 for face centered cubic materials. The results show the power and the limitations of the machine learning approach applied to the polycrystalline grain networks.
Coconut Shell Powder and Electrical Tree Inhibition of Solid Insulator

Electrical tree phenomenon commonly occurred in solid insulator material resulting from the inconsistency of stresses during the manufacturing process. The treeing phenomenon will degrade the characteristic of insulator and may lead to breakdown. The use of filler in the manufacturing process in order to enhancing the insulation property is a popular approach. This paper presents the use of coconut shell powder (CSP) filler in epoxy resin for inhibition of electrical tree growth. The particle sizes of CSP varied from 62, 75 and 125 µm. The CSP is filled with the ratio of 0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 2.0 and 3.0% by weight. We aim to observe the effects of varying the combination of CSP particle sizes and the ratio of epoxy resin for an inhibition of electrical tree growth. The electrical tree was tested by applying AC voltage of 15kV to the test sample and conducted for 360 minutes in this experiment. When the epoxy resin using CSP filler of 1% by weight is fixed for the particle size of 62, 75 and 125 µm, electrical tree length is at 1.24mm, 1.382 mm and 1.78 mm respectively. Whereas, when the particle size of CSP at 62 µm is fixed, a tree length is 1.98 mm and 1.24 mm for the ratio of 0.1 and 1% by weight respectively. Our study demonstrates that the epoxy resin using CSP filler at a ratio of 0.1% by weight can shorten length of electrical tree when compared with a ratio of 1.0% by weight. The electrical tree length was affected through the changes of permittivity by addition of CSP compound.
Robust pcps of proximity, shorter pcps and applications to coding

We continue the study of the trade-off between the length of PCP sand their query complexity, establishing the following main results(which refer to proofs of satisfiability of circuits of size <i>n</i>): 1 We present PCPs of length exp(Õ(log log <i>n</i>)<sup>2</sup>)•<i>n</i> that can be verified by making <i>o</i>(log log<i>n</i>) Boolean queries.For every ε>0, we present PCPs of length exp(log<sup>ε</sup> <i>n</i>)• <i>n</i> that can be verified by making a constant number of Boolean queries. In both cases, false assertions are rejected withconstant probability (which may be set to be arbitrarily close to 1). The multiplicative overhead on the length of the proof, introduced by transforming a proof into a probabilistically checkable one, is just quasi-polylogarithmic in the first case (ofquery complexity <i>o</i>(log log<i>n</i>)), and 2<sup>(log <i>n</i>)ε</sup>, for any ε>0, in the second case (of constant query complexity). In contrast, previous results required at least 2<sup> √log<i>n</i></sup> overhead in the length, even to get query complexity 2<sup> √log <i>n</i></sup>. Our techniques include the introduction of a new variant of PCPs that we call "Robust PCPs". These new PCPs facilitate proof composition, which is a central ingredient in construction of PCP systems. (A related notion and its composition properties were discovered independently by Dinur and Reingold. ) Our main technical contribution is a construction of a "length-efficient" Robust PCP. While the new construction uses many of the standard techniques in PCPs, it does differ from previous constructions in fundamental ways, and in particular does not use the "parallelization" step of Arora <i>et al. </i>. The alternative approach may be of independent interest. We also obtain analogous quantitative results for locally testable codes. In addition, we introduce a relaxed notion of locally decodable codes,and present such codes mapping <i>k</i> information bits to code words of length κ<sup>1+ε</sup>, for any ε>0.
The orphan receptor TR3 participates in angiotensin II-induced cardiac hypertrophy by controlling mTOR signalling

Angiotensin II (AngII) induces cardiac hypertrophy and increases the expression of TR3. To determine whether TR3 is involved in the regulation of the pathological cardiac hypertrophy induced by AngII, we established mouse and rat hypertrophy models using chronic AngII administration. Our results reveal that a deficiency of TR3 in mice or the knockdown of TR3 in the left ventricle of rats attenuated AngII‐induced cardiac hypertrophy compared with the respective controls. A mechanistic analysis demonstrates that the TR3‐mediated activation of mTORC1 is associated with AngII‐induced cardiac hypertrophy. TR3 was shown to form a trimer with the TSC1/TSC2 complex that specifically promoted TSC2 degradation via a proteasome/ubiquitination pathway. As a result, mTORC1, but not mTORC2, was activated; this was accompanied by increased protein synthesis, enhanced production of reactive oxygen species and enlarged cell size, thereby resulting in cardiac hypertrophy. This study demonstrates that TR3 positively regulates cardiac hypertrophy by influencing the effect of AngII on the mTOR pathway. The elimination or reduction of TR3 may reduce cardiac hypertrophy; therefore, TR3 is a potential target for clinical therapy.
Active Learning With Sampling by Uncertainty and Density for Data Annotations

To solve the knowledge bottleneck problem, active learning has been widely used for its ability to automatically select the most informative unlabeled examples for human annotation. One of the key enabling techniques of active learning is uncertainty sampling, which uses one classifier to identify unlabeled examples with the least confidence. Uncertainty sampling often presents problems when outliers are selected. To solve the outlier problem, this paper presents two techniques, sampling by uncertainty and density (SUD) and density-based re-ranking. Both techniques prefer not only the most informative example in terms of uncertainty criterion, but also the most representative example in terms of density criterion. Experimental results of active learning for word sense disambiguation and text classification tasks using six real-world evaluation data sets demonstrate the effectiveness of the proposed methods.
Activity Recognition with Finite State Machines

This paper shows how to learn general, Finite State Machine representations of activities that function as recognizers of previously unseen instances of activities. The central problem is to tell which differences between instances of activities are unimportant and may be safely ignored for the purpose of learning generalized representations of activities. We develop a novel way to find the "essential parts" of activities by a greedy kind of multiple sequence alignment, and a method to transform the resulting alignments into Finite State Machine that will accept novel instances of activities with high accuracy.
Term Weighting Schemes for Emerging Event Detection

As an event-based task, Emerging Event Detection (EED) faces the problems of multiple events on the same subject and the evolution of events. Current term weighting schemes for EED exploiting Named Entity, temporal information and Topic Modeling all have their limited utility. In this paper, a new term weighting scheme, which models the sparse aspect, global weight and local weight of each story, is proposed. Then, an unsupervised algorithm based on the new scheme is applied to EED. We evaluate our approach on two datasets from TDT5, and compare it with TFIDF and existing two schemes exploiting Topic Modeling. Experiments on Retrospective and On-line EED show that our scheme yields better results.
Toward software plug-and-play

The growing size and complexity of systems has revealed many shortcomings of existing software engineering practices, for example, lack of scalability. This in turn raised interest in component-based and architecture-driven software development. In all likelihood, component-based software will form the foundation on which future systems will be built. The shift toward developing systems from components has been more evolutionary than revolutionary. It has its roots in accepted architectural principles such as layering, modularization, and information hiding. But it also introduces its own principles and concepts and presents new challenges. This paper discusses research ideas and technologies that will facilitate the transition toward component-based software development by leveraging object-oriented middleware technologies such as CORBA and OLE. We also present an innovative component-based development environment to illustrate the ideas we introduce.
Augmented Lagrangian Coordination for Decomposed Design Problems

Designing large-scale systems frequently involves solving a complex mathematical program that requires, for various reasons, decomposition into a number of smaller systems. Practical studies have proved the effectiveness of multilevel hierarchical methods at early stages of design; these methods divide a large program into multiple levels and multiple systems at each level and the result is known as a multilevel or hierarchical mathematical program. Using insight provided by Lagrangian duality theory, the paper presents a new algorithm for bi-level programs that does not impose difficult-to-satisfy conditions of convexity and differentiability generally required for convergence. In addition, the algorithm renders each sub-problem independent so that they can be solved concurrently, which is a significant advantage in certain applications. This is done by combining classical Lagrangian duality (LD) and the augmented Lagrangian duality (ALD) in an algorithm to solve a bi-level problem. One of the traditional drawbacks of LD has been that it is only applicable to convex problems. ALD extends the theory to non-convex problems but at the expense of separability. Combining classical LD with ALD provides a simple method for decomposition without imposing restrictive conditions. The method is applied to two mathematical examples to illustrate its potential as well as associated numerical issues.
Effect of fat replacement by inulin or lupin-kernel fibre on sausage patty acceptability, post-meal perceptions of satiety and food intake in men†

The present study examined whether replacing fat with inulin or lupin-kernel fibre influenced palatability, perceptions of satiety, and food intake in thirty-three healthy men (mean age 52 years, BMI 27·4kg/m2), using a within-subject design. On separate occasions, after fasting overnight, the participants consumed a breakfast consisting primarily of either a full-fat sausage patty (FFP) or a reduced-fat patty containing inulin (INP) or lupin-kernel fibre (LKP). Breakfast variants were alike in mass, protein and carbohydrate content; however the INP and LKP breakfasts were 36 and 37% lower in fat and 15 and 17% lower in energy density respectively compared with the FFP breakfast. The participants rated their satiety before breakfast then evaluated patty acceptability. Satiety was rated immediately after consuming the breakfast, then over the subsequent 4·5h whilst fasting. Food consumed until the end of the following day was recorded. All patties were rated above ‘neither acceptable or unacceptable’, however the INP rated lower for general acceptability (P=0·039) and the LKP lower for flavour (P=0·023) than the FFP. The LKP breakfast rated more satiating than the INP (P=0·010) and FFP (P=0·016) breakfasts. Total fat intake was 18g lower on the day of the INP (P=0·035) and 26g lower on the day of the LKP breakfast (P=0·013) than the FFP breakfast day. Energy intake was lower (1521kJ) only on the day of the INP breakfast (P=0·039). Both inulin and lupin-kernel fibre appear to have potential as fat replacers in meat products and for reducing fat and energy intake in men.
Iron Oxide Doped Spray Dried Aluminum Oxide Granules for Selective Laser Sintering and Melting of Ceramic Parts

Powder bed density, flowability, and laser light absorption of the powder strongly influence the quality and performance of aluminum oxide parts processed by selective laser sintering and melting. By means of spray drying, highly dense granules with a good flowability are manufactured. An ultrasonic atomizer and a co‐current droplet air mixing are used to spray aqueous slurries consisting of either bimodal or trimodal distributions of aluminum oxide. Nano‐iron oxide improves the interaction with the green pulsed laser. The improved absorption of the green laser light is verified by means of an Ulbricht sphere. The appropriate amount of dispersant for consecutive water‐based spray granulation is determined by Zeta Potential measurements. Dibasic ammonium citrate is found to be the most suitable dispersant. A final uniform distribution of the iron oxide within the spray dried granules is confirmed by X‐ray μ‐beam fluorescence tomography. The powder quality is characterized by apparent density, tapped density, Hausner ratio, moisture content, and particle size distribution. Powders with a tapped density of almost 50% of the theoretical density and an absorbance of 69% (green laser light) lead to maximum densities of the laser processed parts of around 96%.
The New Large-Scale RNNLM System Based on Distributed Neuron

RNNLM (Recurrent Neural Network Language Model) can save the historical information of the training dataset by the last hidden layer and can also as input for training. It has become an interesting topic in the field of Natural Language Processing research. However, the immense training time overhead is a big problem. The large output layer, hidden layer, last hidden layer and the connections among them will generate enormous matrix in training. It is the main facts to influence the efficiency and scalability. At the same time, output layer class and small hidden layer should decrease the accuracy of RNNLM. In general, the lack of parallel for artificial neuron is main reason for these. We change the structure of RNNLM and design the new large-scale RNNLM by the center of distributed artificial neurons in hidden layer to stimulate the parallel characteristic of biological neuron system. Meanwhile, we change training method, and present the coordination strategy for distributed neuron. At last, the prototype of new large-scale RNNLM system is implemented based on Spark. The testing and analysis results show that the training time overhead is far less than the growth rate of the distributed neuron in hidden layer and size of training dataset. These results show our large-scale RNNLM system has efficiency and scalability advantage.
Iterative Multiuser Detection of Spectrally Efficient FDMA CPM

This paper presents two new iterative multiuser detection algorithms for spectrally efficient frequency division multiple access continuous phase modulation in additive white Gaussian noise. The detectors are derived from the sum-product algorithm and the factor graph framework, performing approximate maximum a posteriori (MAP) bit detection. A factor graph of the actual multiuser detection problem is considered, rather than only factor graphs of the individual single user detection problems combined with ad-hoc inter-user interference cancellation. The practical implementation of the detectors is based on the assumption that a suitable set of sum-product messages can be approximated by a Gaussian distribution. This methodology allows to considerably reduce the computational complexity and memory size requirements as compared to a straightforward application of the sum-product rules. The proposed algorithms further succeed in achieving a good error performance.
A novel small antenna for passive RFID transponder

This work discloses a novel electrically small planar antenna for microwave and UHF RFID passive transponders. The proposed antenna takes advantage of its unique topology in order to assure conjugate matching with essentially complex impedance of the electronic chip directly embedded into radiator. Very good performance is predicted theoretically and confirmed experimentally over an operating bandwidth of actual RFID system.
Heterotrophic Cultivation of Cyanobacteria: Study of Effect of Exogenous Sources of Organic Carbon, Absolute Amount of Nutrients, and Stirring Speed on Biomass and Lipid Productivity

The production of bioproducts from cyanobacteria with techno-economic feasibility is a challenge to these biotechnological processes. The choice of low-cost raw materials is of great importance for the overall economy of bioprocesses, as they represent a significant percentage in the final cost of the product. The objective of this work was to study the operational parameters of cultivation (exogenous sources of organic carbon and absolute amount of nutrients) to optimize productivity in bioproducts by Aphanothece microscopica Nägeli, for further evaluation of stirring speed. The experiments were performed in a bubble column bioreactor, operating at 30°C, pH of 7.6, C/N ratio of 20, 100 mg/L of inoculum, continuous aeration of 1 volume of air per volume of culture per minute (VVM), and absence of light. The results indicate that absolute amounts of 5,000/250 using cassava starch resulted in improved system performance, reaching biomass productivity of 36.66 mg/L/h in parallel with lipid productivity of 6.65 mg/L/h. Finally, experiments with variation in stirring speed indicate that 200 rpm resulted in better average rate of substrate consumption (44.01 mg/L/h), in parallel to biomass productivity of 39.27 mg/L/h. However, the increase of stirring speed had a negative effect on lipid productivity of the process. The technological route developed indicates potential to production of biomass and bulk oil, as a result of the capacity of cyanobacteria to adapt their metabolism in varying culture conditions, which provides opportunities to modify, control, and thereby maximize the formation of targeted compounds.
Predicting mechanical properties of polyvinylidene fluoride/carbon nanotube composites by molecular simulation

Pristine polyvinylidene fluoride (PVDF) and its CNT composites (PVDF/CNT) at three CNT weight fractions, 8 wt%, 16 wt% and 24 wt%, were used to investigate the influence of CNT fraction on the mechanical properties of PVDF. Molecular dynamics (MD) simulation was utilized to predict the mechanical properties of PVDF/CNT composites. The tensile stress–strain profiles indicate that the Young’s modulus and tensile strength of pristine PVDF can be significantly improved by the embedded CNTs. However, these CNTs induce stress concentration within the composites, resulting in increased brittleness and a fracture at lower strains. By taking advantage of this molecular simulation procedure, fast comparable predictive properties of PVDF/CNT composites could be performed, with the simulation results providing atomistic-level insights into this new pathway to reduce the cost and research time in related experiments.