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In today’s information age, the ability of intelligent sharing and scheduling of college English translation corpus resources needs to be improved. Therefore, a method based on fuzzy autocorrelation statistical feature analysis is proposed. First of all, a model must be constructed to detect the semantically relevant dimensional features of college English translation corpus resources under the background of informatization, and to analyze the essential attributes of translation activities by using the hierarchical parameter detection method of translated texts in the narrative structure. Then, a quantitative difference coverage model of word clusters of different lengths is established, with lexical attribute extraction and statistical examination of these resources being performed via a similarity attribute extraction technique for high-frequency word clusters. Subsequently, a semantic dynamic attribute analysis model is developed to derive statistical attributes of college English translation corpus resources within the informatized context. Ultimately, based on the obtained attribute extraction results, a fuzzy autocorrelation statistical attribute analysis method is employed for clustering large datasets. Furthermore, an intelligent particle swarm optimization algorithm is implemented to extract and disseminate lexical attributes of college English translation corpus resources within the information-driven context, so that the college English translation corpus resources can be optimized under the information background. According to the simulation results, this method has excellent accuracy in extracting and sharing lexical features of translated texts, and its feature discrimination ability is also good. It can indeed improve the ability of extracting, sharing, and detecting lexical features of translated texts from college English translation corpus resources.

In this paper, we will take a further look at a generalized perceptron-like learning rule which uses dilation and translation parameters in order to enhance the recall performance of higher order Hopfield neural networks without significantly increasing their complexity. We will practically study the influence of these parameters on the perceptron learning and recall process, using a generalized version of the Hebbian learning rule for initialization. Our analysis will be based on a pattern recognition problem with random patterns. We will see that in case of a highly correlated set of patterns, there can be gained some improvements concerning the learning and recall performance. On the other hand, we will show that the dilation and translation parameters have to be chosen carefully for a positive result.

A software architecture design has many benefits including aiding comprehension, supporting early analysis, and providing guidance for subsequent development activities. An additional major benefit is if a partial prototype implementation can be automatically generated from a given software architecture design. However, in the past decade less progress was made on automatically realizing software architecture designs. In this paper, we present a translator for automatically generating an implementation from a software architectural description. The implementation not only captures the functionality of the given architecture description, but also contains additional monitoring code for ensuring desirable behavior properties through runtime verification. Our method takes a software description written in SAM, a software architecture model integrating dual formal methods Petri nets and temporal logic, and generates ArchJava/Java/AspectJ code. More specifically, the structure of a SAM architecture description produces ArchJava code, the behavior models of components/connectors represented in Petri nets lead to plain Java code, and the property specifications defined in temporal logic generate AspectJ code; the above code segments are then integrated into Java code. An experimental result is provided.

A swept volume is a nice tool for solving various types of interference problems. Previous investigations have concentrated on sweeping an object along an arbitrary path that results in complex algorithms. We study the most fundamental motion, which is translation along a linear path. After analyzing the structure of the swept volume, we present an incremental algorithm for constructing a swept volume. Our algorithm takes O(n²α(n)+T_c) time where n is the number of vertices in the original object and T_c is the time for handling face cycles. When the object is monotone with respect to the sweeping path, we show that its swept volume can be calculated in O(n) time after O(n log n+k) time preprocessing, where k is the number of regions in the arrangement of the projected image of the object.

A translation in an algebraic signature is a finite conjunction of equations in one variable. On a quasivariety K, a translation τ naturally induces a deductive system, called the τ-assertional logic of K. Two quasivarieties are τ-assertionally equivalent if they have the same τ-assertional logic. This paper is a study of assertional equivalence. It characterizes the quasivarieties equivalent to ones with various desirable properties, such as τ-regularity (a general form of point regularity). Special attention is paid to structural properties of quasivarieties that are assertionally equivalent to their varietal closures under an indicated translation.

We propose and examine a simple notion of translation in first order logics to give a basis to similarity-based fuzzy logic.

This study combines the concept of trigger points, events preceding bursts of growth, with a linguistic approach to show how firm growth unfolds through a process of translation. By marrying theories and methods rooted in the linguistic turn with firm growth theories, this study brings new insights on growth contributing to both the advancement of the trigger point concept and the wider understanding of entrepreneurial activities as complex and contextually bound processes dependent on human interaction. In doing so, the study also adheres to the current demand for advancing firm growth theory by relaxing the outcome-focussed approach and static life-cycle paradigm, and complementing it with alternative theoretical and methodological perspectives.

Distal radio-ulnar joint (DRUJ) instability is increasingly recognised and assessment can be subjective and difficult. Previous research has used cadaveric models or in-vivo with CT, with variable results. A test device was designed to establish normal values of in-vivo DRUJ dorso-palmar translation.

Twenty volunteers were recruited. Those with previous wrist/forearm injuries were excluded. The device held the elbow at 90° flexion and neutral forearm rotation, with the distal ulna secured. A dorso-palmar shear force was applied to the distal radius and displacement measured three times on each wrist alternately by the same operator.

The mean translation of the DRUJ is 5.5 mm. Same-sided mean measurements for two subjects taken days apart varied by 1 mm. The intra-class correlation coefficient was 0.93.

The device is reliable, reproducible and appears to be a simple valid test. Contralateral sides were comparable. It will primarily be a research device to guide clinical practice in DRUJ instability.

The English version of Hand20 questionnaire was translated into Greek and cultural adaptation was performed. The validity was assessed in 134 patients with a variety of upper limb disorders. A comparison of Hand20 and DASH was also performed. All patients completed EQ-5D, Hand20 and DASH questionnaire. Test–retest reliability was assessed in a subgroup of 37 patients. We assessed the convergent validity of Hand20 by correlating its scores to DASH and EQ-5D scores. We also compared the completeness of Hand20 and DASH. We found no statistically significant differences in Hand20 scores between the 1st and 2nd measurements as well as a strong correlation between Hand20 and the other two questionnaires. There were also better rates of response and fewer missing data even in elderly individuals.

Professor Roger D. Komberg — 2006 Nobel Prize in Chemistry.

This paper proposes a cross-language information retrieval (CLIR) system queried with technical compound keywords. Our system first translates queries into the document language. Instead of exhaustively enumerating new compound words in a bilingual dictionary, we produce a dictionary for base words, and compute the plausibility score for each combination of base word translations. Then, the most plausible combination is used for the subsequent retrieval process. Experimental results using the Internet documents showed that our system outperforms baseline CLIR systems. We also propose an interaction strategy to facilitate user feedback.

Translation and convolution associated with the discrete wavelet transform are investigated using properties of Calderón–Zygmund operator and Riesz fractional integral operator. Dual convolution is also studied. The wavelet convolution is applied to approximate functions belonging to certain L^p-spaces.

The paper provides a short description of the originally developed algorithm for searching of the conservative protein–RNA binding sites. The algorithm is applied to analyze chloroplast genes. The candidate protein–RNA binding sites were detected upstream of atpF, petB, clpP, psaA, psbA, and psbB genes in many chloroplasts of algae and plants. We suggest that some of these sites are involved in suppressing translation until splicing is completed.

One-way measurement based quantum computations (1WQC) may describe unitary transformations, via a composition of CPTP maps which are not all unitary themselves. This motivates the following decision problems. Is it possible to determine whether a "quantum-to-quantum" 1WQC procedure (having non-trivial input and output subsystems) performs a unitary transformation? Is it possible to describe precisely how such computations transform quantum states, by translation to a quantum circuit of comparable complexity? In this article, we present an efficient algorithm for transforming certain families of measurement-based computations into a reasonable unitary circuit model, in particular without employing the principle of deferred measurement.

DNA, RNA and proteins are among the most important macromolecules in a living cell. These molecules are polymerized by molecular machines. These natural nano-machines polymerize such macromolecules, adding one monomer at a time, using another linear polymer as the corresponding template. The machine utilizes input chemical energy to move along the template which also serves as a track for the movements of the machine. In the Alan Turing year 2012, it is worth pointing out that these machines are "tape-copying Turing machines". We review the operational mechanisms of the polymerizer machines and their collective behavior from the perspective of statistical physics, emphasizing their common features in spite of the crucial differences in their biological functions. We also draw the attention of the physics community to another class of modular machines that carry out a different type of template-directed polymerization. We hope this review will inspire new kinetic models for these modular machines.

The author presents theoretical mean-field analysis of the initial phase of the kinetics of intracellular replication of plus-stranded RNA viruses, which are abundant and include, e.g., the hepatitis C virus (HCV). The treatment is based on the conventional concept that the replication process of such viruses takes place at the membrane complexes formed with participation of viral proteins, e.g., NS5A in the HCV case. The key novel prediction supported by Monte Carlo calculations is that this scheme may be insufficient in order to describe the very initial phase of the process, because the initial intracellular viral RNA and protein populations may in this case go extinct rather than overcome the kinetic barrier for transition to the full-scale infection of a host cell. Practically, this means that in such situations, the conventional replication scenario should be complemented by another pathway, e.g., by replication outside the membrane without viral proteins, which operates in the very beginning.

Various algorithms have been devised to mathematically model the dynamic mechanism of the gene expression data. Gillespie’s stochastic simulation (GSSA) has been exceptionally primal for chemical reaction synthesis with future ameliorations. Several other mathematical techniques such as differential equations, thermodynamic models and Boolean models have been implemented to optimally and effectively represent the gene functioning. We present a novel mathematical framework of gene expression, undertaking the mathematical modeling of the transcription and translation phases, which is a detour from conventional modeling approaches. These subprocesses are inherent to every gene expression, which is implicitly an experimental outcome. As we foresee, there can be modeled a generality about some basal translation or transcription values that correspond to a particular assay.

The aim of this study is to examine the small-world properties of functional brain networks in Chinese to English simultaneous interpreting (SI) using functional near-infrared spectroscopy (fNIRS). In particular, the fNIRS neuroimaging combined with complex network analysis was performed to extract the features of functional brain networks underling three translation strategies associated with Chinese to English SI: “transcoding” that takes the “shortcut” linking translation equivalents between Chinese and the English, “code-mixing” that basically does not involve bilingual processing, and “transphrasing” that takes the “long route” involving a monolingual processing of meaning in Chinese and then another monolingual processing of meaning in English. Our results demonstrated that the small-world network topology was able to distinguish well between the transcoding, code-mixing and transphrasing strategies related to Chinese to English SI.

The mTOR signaling cascade controls cell growth and metabolism and its deregulation underlies a variety of metabolic disorders. The mTOR pathway is characterized by two unique mTOR complexes: mTORC1 and mTORC2. mTORC1 is activated in the presence of nutrients to promote cell growth and proliferation while mTORC2 responds to nutrient fluctuations in order to restore metabolic homeostasis and maintain cell survival. Due to the pivotal role of the mTORCs in cell growth and survival, mTOR signaling is often upregulated in many cancers. mTOR is involved in many aspects of anabolic metabolism but the function of mTORC1 in protein synthesis is perhaps the most well studied. In this review, we will discuss how both mTORC1 and mTORC2 control protein synthesis. We begin with an in-depth analysis of the two multiprotein complexes and the specific upstream and downstream proteins involved in their signaling. In particular, we elaborate on how mTORC1 regulates translation via its targets S6K1 and 4E-BP1. We then discuss how both mTORC1 and mTORC2 could be involved in the regulation of other translation regulators such as RNA binding proteins and RSK, and elaborate on the role of mTOR in ribosome biogenesis. Finally, we review how mTOR also controls the processes that provide building blocks for protein synthesis including amino acids, ribose, and nucleotides. We also consider the role of mTOR in controlling catabolic processes such as autophagy and the ubiquitin proteasome system which are critical for providing intracellular nutrient sources as well as maintaining cellular proteostasis. This review provides a thorough background on how mTOR can function as a molecular rheostat to control global protein translation in response to nutrient availability.

Duchenne muscular dystrophy (DMD) is an X-linked recessive lethal disorder affecting 3500 live born males. This disorder is caused by mutations in the gene that encodes dystrophin, a high molecular weight cytoskeletal protein. The large size and complexity of the gene pose limitations for detailed mutational analysis in patients, especially for non-deletion cases. In this report, we describe the characterization of a single nucleotide alteration in exon 37 from ectopic transcripts of immortalized lymphocytes from a DMD patient. This mutation is predicted to result in termination of translation of the dystrophin protein. In vitro translation of polypeptide using an amplified fragment of 2105 bases covering the nucleotide change was carried out from the extracted mRNA transcripts using the coupled reticulocyte lysate transcription/translation system. A truncated polypeptide of approximately 46 kDa was obtained, confirming premature chain termination had occurred. Demonstration of this mutational effect proves that the identified mutation is responsible for the pathogenic phenotype in the patient. This approach may be applied in future for direct identification of causative mutations resulting in truncated dystrophin in other DMD patients.