Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dual Spring Modeling Approach for Static and Fatigue Failure Assessments of Carbon/Epoxy Composite Sub-Elements

A dual spring model is developed for the static and fatigue damage predictions of laminates interface in composite structures. Stress concentrations can be induced by the defects formed in the fabrication or service process. A conventional S-N based fatigue damage model may not be accurate to predict the fatigue life of a structure with high stress concentration. With the dual spring model, static delamination failure can be simulated using springs of cohesive type material model while fatigue delamination development can be predicted using linear springs, where the crack driving force is computed based on virtual crack closure technique (VCCT). A Paris law type fatigue growth law with its mode mixity is applied for fatigue crack growth prediction. After verified using benchmark examples, including Double Cantilever Beam (DCB), End-Notched Flexure (ENF) and Mix-Mode Bending (MMB), the proposed dual spring model is applied in the static and fatigue damage prediction of NASA/Boeing sub-elements and UTC sub-elements.
A dynamic load balancing strategy for parallel datacube computation

In recent years, OLAP technologies have become one of the important applications in the database industry. In particular, the datacube operation proposed in [5] receives strong attention among researchers as a fundamental research topic in the OLAP technologies. The datacube operation requires computation of aggregations on all possible combinations of each dimension attribute. As the number of dimensions increases, it becomes very expensive to compute datacubes, because the required computation cost grows exponentially with the increase of dimensions. Parallelization is very important factor for fast datacube computation. However, we cannot obtain sufficient performance gain in the presence of data skew even if the computation is parallelized. In this paper, we present a dynamic load balancing strategy, which enables us to extract the effectiveness of parallizing datacube computation sufficiently. We perform experiments based on simulations and show that our strategy performs well.
Control approach to computing the feedback capacity for stationary finite dimensional Gaussian channels

We firstly extend the interpretation of feedback communication over stationary finite dimensional Gaussian channels as feedback control systems by showing that, the problem of finding stabilizing feedback controllers with maximal reliable transmission rate over Youla parameters coincides with the problem of finding strictly causal filters to achieve feedback capacity recently derived in [1]. The aforementioned interpretation provides an approach to construct deterministic feedback coding schemes (with double exponential decaying error probability). We next propose an asymptotic capacity-achieving upper bounds, which can be numerically evaluated by solving finite dimensional dual optimizations. From the filters that achieve upper bounds, we derive feasible filters which lead to a sequence of lower bounds. Thus, from the lower bound filters we obtain communication systems that achieve the lower bound rate. Extensive examples show the sequence of lower bounds is asymptotic capacity-achieving as well.
The Andrews-Curtis Conjecture and Black Box Groups

The paper discusses the Andrews–Curtis graph Δk(G,N) of a normal subgroup N in a group G. The vertices of the graph are k-tuples of elements in N which generate N as a normal subgroup; two vertices are connected if one of them can be obtained from another by certain elementary transformations. This object appears naturally in the theory of black box finite groups and in the Andrews–Curtis conjecture in algebraic topology [3]. We suggest an approach to the Andrews–Curtis conjecture based on the study of Andrews–Curtis graphs of finite groups, discuss properties of Andrews–Curtis graphs of some classes of finite groups and results of computer experiments with generation of random elements of finite groups by random walks on their Andrews–Curtis graphs.
A Comparative Study of Face Recognition System Using PCA and LDA

This paper concern on Face recognition has a major impact in security measures which makes it one of the most appealing areas to explore. To perform face recognition, researchers adopt mathematical calculations to develop automatic recognition systems. As a face recognition system has to perform over wide range of database, dimension reduction techniques become a prime requirement to reduce time and increase accuracy. In this paper, face recognition is performed using Principal Component Analysis followed by Linear Discriminant Analysis based dimension reduction techniques. Sequencing of this paper is preprocessing, dimension reduction of training database set by PCA, extraction of features for class separability by LDA and finally testing by nearest mean classification techniques. The proposed method is tested over ORL face database. It is found that recognition rate on this database is 96.00% and hence showing efficiency of the proposed method than previously adopted methods of face recognition systems.
Fast motion estimation and mode decision with variable motion block sizes

The adaptive coding schemes in H.264 standard provide a significant coding efficiency and some additional features like error resilience and network friendliness. The variable block size motion compensation using multiple reference frames is one of the key H.264 coding elements to provide notable performance gain. However it is also the main culprit that increases the overall computational complexity. For this reason, this paper proposes a fast algorithm for variable block size motion estimation in H.264. In addition, we also propose a fast mode decision scheme by classifying modes based on rate-distortion cost. The experimental results show that the combined proposed methods provide significant improvement in processing speed without noticeable coding loss.
Lessons from early stages design of mobile applications

This paper presents a set of lessons that resulted from the design process of several mobile applications. It starts by addressing the difficulties that emerged through the data gathering, prototyping and evaluation stages also stressing the absence of adequate techniques and methods to support these activities. We explain how these problems and challenges were solved and how they can be applied in other domains and future projects. As a result, we provide a set of guidelines for designers to apply on the development and design of mobile applications and user interfaces. The paper also addresses three case studies in which these guidelines and procedures were validated, stressing their contributions and results.
Linear codes for sources and source networks: Error exponents, universal coding

For Slepian-Wolf source networks, the error exponents obtained by Korner,Marton, and the author are shown to be universally attainable by linear codes also. Improved exponents are derived for linear codes with "large rates. " Specializing the results to simple discrete memoryless sources reveals their relationship to the random coding and expurgated bounds for channels with additive noise. One corollary is that there are universal linear codes for this class of channels which attain the random coding error exponent for each channel in the class. The combinatorial approach of Csiszar-Korner-Marton is used. In particular, all results are derived from a lemma specifying good encoders in terms of purely combinatorial properties.
Mining progressive confident rules

Many real world objects have states that change over time. By tracking the state sequences of these objects, we can study their behavior and take preventive measures before they reach some undesirable states. In this paper, we propose a new kind of pattern called progressive confident rules to describe sequences of states with an increasing confidence that lead to a particular end state. We give a formal definition of progressive confident rules and their concise set. We devise pruning strategies to reduce the enormous search space. Experiment result shows that the proposed algorithm is efficient and scalable. We also demonstrate the application of progressive confident rules in classification.
Use of Amino Acids as Inducers for High-Level Protein Expression in the Single-Protein Production System

ABSTRACT By taking advantage of MazF, an ACA codon-specific mRNA interferase, Escherichia coli cells can be converted into a bioreactor producing only a single protein of interest by using an ACA-less mRNA for the protein. In this single-protein production (SPP) system, we engineered MazF by replacing two tryptophan residues in positions 14 and 83 with Phe (W14F) and Leu (W83L), respectively. Upon the addition of an inducer (IPTG [isopropyl-β-d-thiogalactopyranoside]), the mutated MazF [MazF(ΔW)] can still be produced even in the absence of tryptophan in the medium by using a Trp auxotroph, while a target protein having Trp residues cannot be produced. However, at 3 h after the addition of IPTG, the addition of tryptophan to the medium exclusively induces production of the target protein at a high level. A similar SPP system was also constructed with the use of a His-less protein [MazF(ΔH)] and a His auxotroph. Using these dual-induction systems, isotopic enrichments of 13C, 15N, and 2H were highly improved by almost complete suppression of the production of the unlabeled target protein. In both systems, isotopic incorporation reached more than 98% labeling efficiency, significantly reducing the background attributable to the unlabeled target protein.
High Conformational Stability of Secreted Eukaryotic Catalase-peroxidases

Background: Eukaryotic secreted KatGs are bifunctional enzymes extensively found in phytopathogenic fungi. Results: Structural peculiarities, mainly in the N-terminal domain, dominate stability and other properties of eukaryotic secreted KatGs. Conclusion: The distinctive requirements of secreted KatGs depend on very specific and fully conserved structural features. Significance: This might be exploited to control plant fungal diseases that are jeopardizing food security worldwide. Catalase-peroxidases (KatGs) are bifunctional heme enzymes widely spread in archaea, bacteria, and lower eukaryotes. Here we present the first crystal structure (1.55 Å resolution) of an eukaryotic KatG, the extracellular or secreted enzyme from the phytopathogenic fungus Magnaporthe grisea. The heme cavity of the homodimeric enzyme is similar to prokaryotic KatGs including the unique distal +Met-Tyr-Trp adduct (where the Trp is further modified by peroxidation) and its associated mobile arginine. The structure also revealed several conspicuous peculiarities that are fully conserved in all secreted eukaryotic KatGs. Peculiarities include the wrapping at the dimer interface of the N-terminal elongations from the two subunits and cysteine residues that cross-link the two subunits. Differential scanning calorimetry and temperature- and urea-mediated unfolding followed by UV-visible, circular dichroism, and fluorescence spectroscopy combined with site-directed mutagenesis demonstrated that secreted eukaryotic KatGs have a significantly higher conformational stability as well as a different unfolding pattern when compared with intracellular eukaryotic and prokaryotic catalase-peroxidases. We discuss these properties with respect to the structure as well as the postulated roles of this metalloenzyme in host-pathogen interactions.
An Empirical Framework Design to Examine the Improvement inSoftware Requirements through Negotiation

Negotiation is one promising effort during requirements elicitation process to improve the quality of software requirements. When negotiation is claimed beneficial theoretically, it is important that the deployment of negotiation is examined and the effectiveness of negotiation is evaluated through empirical study. This paper aims at providing an empirical framework design to examine the improvement in software requirements through negotiation. Besides, it elaborates the relevance of negotiation in requirements elicitation process and its effectiveness. An empirical study method is imposed to design the framework. The design is carefully established based the selection of population and participants, the experimental protocol, threats to validity and justification of measures.
MR Fluoroscopy Using Radial Multi-Gradient-Echo ( rMGE )

Methods We use a slightly modified version of the recently introduced rMGE technique. In order to speed up the coverage of all spatial orientations of the object the acquisition of one entire data set (one rotation) with angular increment AO is split up into the subsequent acquisition of n interleaved angularly under-sampled data sets (n interleaved rotations) as introduced in [4]. To minimize the gradient integral required for the rotation between successive views the views of a single echo-train are acquired with angular spacing of AO and the start orientation of successive echo trains within a single sweep differs by (n-1)A0. During continuous data-acquisition reconstruction was performed using the sliding window reconstruction technique [1] providing an image update at video rate with 25 frames per second using a dedicated reconstruction hardware [7].
Applications of DMD-based Inhomogeneous Illumination Photoluminescence Imaging for Silicon Wafers and Solar Cells

Under inhomogeneous illumination, a silicon wafer or solar cell can generate lateral current flow due to gradients in the quasi Fermi energy levels. This lateral current flow is impacted by a few of the key electrical parameters of the sample, such as emitter sheet resistance, series resistance and diffusion length. This lateral current flow can be directly correlated to the spatially resolved luminescence intensity from the photoluminescence image. Therefore, photoluminescence imaging with inhomogeneous illumination can provide additional information, which is usually difficult to extract from a conventional photoluminescence image at open circuit voltage. Index Terms – photoluminescence image, DMD, solar cells, silicon wafers, series resistance, diffusion length.
Learning to Achieve Goals

Temporal di(cid:11)erence methods solve the temporal credit assignment problem for reinforcement learning. An important subproblem of general reinforcement learning is learning to achieve dynamic goals. Although existing temporal di(cid:11)erence methods, such as Q learning, can be applied to this problem, they do not take advantage of its special structure. This paper presents the DG-learning algorithm, which learns e(cid:14)ciently to achieve dynamically changing goals and exhibits good knowledge transfer between goals. In addition, this paper shows how traditional relaxation techniques can be applied to the problem. Finally, experimental results are given that demonstrate the su-periority of DG learning over Q learning in a moderately large, synthetic, non-deterministic domain.
The Anytime Automaton

Approximate computing is an emerging paradigm enabling tradeoffs between accuracy and efficiency. However, a fundamental challenge persists: state-of-the-art techniques lack the ability to enforce runtime guarantees on accuracy. The convention is to 1) employ offline or online accuracy models, or 2) present experimental results that demonstrate empirically low error. Unfortunately, these approaches are still unable to guarantee acceptability of all application outputs at runtime. We offer a solution that revisits concepts from anytime algorithms. Originally explored for real-time decision problems, anytime algorithms have the property of producing results with increasing accuracy over time. We propose the Anytime Automaton, a new computation model that executes applications as a parallel pipeline of anytime approximations. An automaton produces approximate versions of the application output with increasing accuracy, guaranteeing that the final precise version is eventually reached. The automaton can be stopped whenever the output is deemed acceptable, otherwise, it is a simple matter of letting it run longer. We present an in-depth analysis of the model and demonstrate attractive runtime-accuracy profiles on various applications. Our anytime automaton is the first step towards systems where the acceptability of an application's output directly governs the amount of time and energy expended.
Serum levels of enclomiphene and zuclomiphene in men with hypogonadism on long‐term clomiphene citrate treatment

To determine the relative concentrations of enclomiphene (ENC) and zuclomiphene (ZUC) isomers in men with hypogonadism on long‐term clomiphene citrate (CC) therapy, and to determine whether patient age, body mass index (BMI) or duration of therapy were predictive of relative concentrations of ENC and ZUC.
A Sex Difference in the Specificity of Sexual Arousal

Sexual arousal is category-specific in men; heterosexual men are more aroused by female than by male sexual stimuli, whereas homosexual men show the opposite pattern. There is reason to believe that female sexual arousal is organized differently. We assessed genital and subjective sexual arousal to male and female sexual stimuli in women, men, and postoperative male-to-female transsexuals. In contrast to men, women showed little category specificity on either the genital or the subjective measure. Both heterosexual and homosexual women experienced strong genital arousal to both male and female sexual stimuli. Transsexuals showed a category-specific pattern, demonstrating that category specificity can be detected in the neovagina using a photoplethysmographic measure of female genital sexual arousal. In a second study, we showed that our results for females are unlikely to be explained by ascertainment biases. These findings suggest that sexual arousal patterns play fundamentally different roles in male and female sexuality.
Optimization of an Ammonia Synthesis Reactor using Simultaneous Approach

Ammonia is a valuable chemical produced in the industry and is wide used in the manufacture of fertilizers, plastics and pharmaceuticals. On the other hand, some important tools in chemical processes are modelling, simulation and optimization, and these types of studies have attracted considerable attention of many researchers. Several models of ammonia synthesis have been developed with the purpose of design and optimization. The typical design problem in this process requires obtained an optimal reactor length with maximum economic return and many works in literature have focused on the optimization of this reactor using different optimization techniques. In this work, an alternative approach to those presented in literature for solving this boundary value problem, and determine the optimal solution, is presented. The model of the reactor along with the kinetic form a non-linear differential-algebraic system. These differential-algebraic equations are discretized using orthogonal collocation on finite elements with continuous profiles approximated by Lagrange polynomials. In this manner, the resulting algebraic collocation equations are written as equality constraints in the optimization problem. Thus, the optimization problem is solved by implementing the IPOPT solver within the GAMS optimization-modeling platform. Although IPOPT solver here implemented is a local solver that does not guarantee the global optimum, multiple initial guesses were used to solve the problem and obtain an optimal solution of the problem. The obtained results are compared with other results available in literature and indicate that the used approach is a good alternative for dealing with this type of problems.
On the Security of Machine Learning in Malware C&C Detection

One of the main challenges in security today is defending against malware attacks. As trends and anecdotal evidence show, preventing these attacks, regardless of their indiscriminate or targeted nature, has proven difficult: intrusions happen and devices get compromised, even at security-conscious organizations. As a consequence, an alternative line of work has focused on detecting and disrupting the individual steps that follow an initial compromise and are essential for the successful progression of the attack. In particular, several approaches and techniques have been proposed to identify the command and control (C8C) channel that a compromised system establishes to communicate with its controller. A major oversight of many of these detection techniques is the design’s resilience to evasion attempts by the well-motivated attacker. C8C detection techniques make widespread use of a machine learning (ML) component. Therefore, to analyze the evasion resilience of these detection techniques, we first systematize works in the field of C8C detection and then, using existing models from the literature, go on to systematize attacks against the ML components used in these approaches.
Dunaliella tertiolecta (Chlorophyta) Avoids Cell Death Under Ultraviolet Radiation By Triggering Alternative Photoprotective Mechanisms

The effect of different ultraviolet radiation (UVR) treatments combining PAR (P), UVA (A) and UVB (B) on the molecular physiology of Dunaliella tertiolecta was studied during 6 days to assess the response to chronic UVR exposure. UVR reduced cell growth but did not cause cell death, as shown by the absence of SYTOX Green labeling and cellular morphology. However, caspase‐like enzymatic activities (CLs), (regarded as cell death proteases), were active even though the cells were not dying. Maximal quantum yield of fluorescence (Fv/Fm) and photosynthetic electron transport rate (ETR) dropped. Decreased nonphotochemical quenching (NPQ) paralleled a drop in xanthophyll cycle de‐epoxidation under UVB. Reactive oxygen species (ROS) and D1 protein accumulation were inversely correlated. PAB exhibited elevated ROS production at earlier times. Once ROS decayed, D1 protein recovered two‐fold compared with P and PA at later stages. Therefore, PsbA gene was still transcribed, suggesting ROS involvement in D1 recovery by its direct effect on mRNA‐translation. We add evidence of an UVB‐induced positive effect on the cells when P is present, providing photoprotection and resilience, by means of D1 repair. This allowed cells to survive. The photoprotective mechanisms described here (which are counterintuitive in principle) conform to an important ecophysiological response regarding light stress acclimation.
Adapting the Sniffin' Sticks to diagnose Parkinson's disease in Sri Lanka

Tests of odor identification might help differentiate Parkinson's disease (PD) from other causes of tremor, but they are culture and language specific and are not currently available for the population of Sri Lanka. We created a Sinhala adaptation of the 16‐item identification test from Sniffin' Sticks (SS‐16) and applied it to 89 nondemented Sri Lankan PD patients and 100 controls. Twelve of the SS‐16 items were correctly identified by at least 50% of the control subjects and were included in a battery, which we called as Colombo SS‐12. We used the diagnosis (PD or control) as outcome variable for a logistic regression using age, gender, smoking status and the SS‐12 as covariates, and found only the last two were significant covariates. The Colombo SS‐12 specificity was 93.0% with a sensitivity of 91.0%, indicating it could be a helpful tool in the diagnosis of PD in Sri Lanka. © 2009 Movement Disorder Society