Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Bioprocessing of ADEPT Fusion Proteins using Ultra Scale-Down Techniques

Today with the advancement of modern medicine we are increasingly facing an aging community with a variety of complicated disease like the various forms of Cancer. Although man made synthetic chemicals can treat simple diseases, scientists predict that a new range of therapeutics containing antibody fragments and fusions proteins, may be important because of their ability to target specific sites. The new family of complicated biopharmaceuticals, such as fusions of larger molecules are more likely to be fragile and delicate to the unforgiving large-scale manufacturing environments. So new research needs to be conducted into where the damage may be occurring and how to stop it from happening.
Corrective Exercises on the Balance Performance of Mentally-Retarded Students With Developmental Coordination Disorder

* Corresponding Author: Parisa Sedaghati, PhD. Address: Department of corrective exercises and sport injuries, Faculty of Physical Education and Sport Sciences, Guilan Universit, Rasht, Iran. Phone: +98 (912) 3483831 E-mail: Parisasedaghati@yahoo.com Research Paper: The Effect of Selected Balance/ Corrective Exercises on the Balance Performance of Mentally-Retarded Students With Developmental Coordination Disorder
Overexpression of long non-coding RNAs DUXAP9 and DUXAP10 is associated with prognosis in patients with hepatocellular carcinoma after hepatectomy.

AIM Hepatocellular carcinoma (HCC) is a common and aggressive malignant tumor with an especially high prevalence in Asian populations. This study aimed to identify differentially expressed lncRNAs using expression microarray and to explore the association between differential expression of lncRNAs and prognosis of HCC. METHODS We retrospectively reviewed 63 patients with primary HCC that underwent a curative liver resection at the Department of General Surgery, Jingmen First People's Hospital. The expression level of lncRNAs DUXAP9 and DUXAP10 were detected by real-time PCR. Prognostic factors were evaluated using Kaplan-Meier curves and Cox proportional hazards models. RESULTS By microarray profiling of lncRNAs, 256 were found to be differentially expressed, including 162 upregulated and 94 downregulated (P<0.05, fold change >2). Two candidate lncRNAs were determined as targets in this study, including DUXAP9 (upregulated by 6.35 fold) and DUXAP10 (upregulated by 4.53 fold). DUXAP9 and DUXAP10 were downregulated in the normal liver cell lines Chang liver, HL7702, THLE-2, THLE-3, FL62891, and AML12, which were significantly lower than HCC cell lines SMMC-7721, Hep3B, HuH7, MHCC-97H, HCC-LM, and SK-Hep-1 (P<0.05). Overexpression of lncRNAs DUXAP9 and DUXAP10 were associated with decreasing OS rates, respectively (P=0.0263 and P=0.0285). Meanwhile, Overexpression of DUXAP9 and DUXAP10 was associated with decreasing PFS rates, respectively (P=0.0174 and P=0.0041). After adjusting for competing risk factors, we identified microvascular invasion (P=0.014), tumor size (P=0.026), and lncRNAs DUXAP9 (P=0.001) and DUXAP10 (P=0.036) expression levels as independent prognostic factors associated with prognosis of patients with HCC. CONCLUSIONS We found that lncRNAs DUXAP9 and DUXAP10 are expressed significantly higher in HCC tissues compared with non-tumorous tissues. Overexpression of DUXAP9 and DUXAP10 were independent risk factors associated with prognosis of patients with HCC.
Forecasting Social Interactions from Dynamic Graphs : A Case Study of Twitter , GitHub , and YouTube

Most of the existing graph analytics focuses on learning from static rather than dynamic graphs using hand-crafted network features or recently emerged graph embeddings learned independently from a downstream predictive task, solving predictive rather than forecasting tasks directly. To address these limitations, we propose (1) a novel task – forecasting over dynamic graphs, and (2) a novel deep learning, multi-task, node-aware attention model that focuses on forecasting social interactions, going beyond recently emerged approaches for learning dynamic graph embeddings. Our model relies on graph convolutions and recurrent layers to forecast social interactions in large samples of real-world dynamic graphs1 – Twitter, GitHub, and YouTube up to seven days in advance. Our model can successfully forecast (a) retweets and mentions of a specific news source on Twitter (focusing on deceptive and credible news sources), (b) user-repository interactions on GitHub (focusing on cryptocurrency ecosystems), (c) comments to a specific video on YouTube within the next day with mean absolute error less than 2% and R2 exceeding 0.78. We demonstrate that learning from connectivity information over time in combination with node embeddings yields better forecasting results than when we use a two-step approach with Node2Vec and DeepWalk. Moreover, by evaluating model generalizability across three social platforms with different types of interactions we provide novel insights on how the size of the training and forecasting windows, and graph topological properties influence forecasting performance.
Open Chinese entity relation extraction method using dependency analysis

The invention discloses an open Chinese entity relation extraction method using dependency analysis. According to the method, firstly, sentences are subjected to dependency analysis; then, a Chinese grammar heuristic rule and the dependency analysis result are combined for extracting relation words; next, the named entity position is determined according to the distance; and finally, the triple output is carried out. The experiment is carried out on SogouCA and SogouCS language databases. The result shows that the method provided by the invention is applicable to large-scale language databases, and has good transportability. The method provided by the invention fundamentally overcomes the limitation of intrinsic properties of complicated Chinese grammar, diverse expression modes, rich semantics and the like.
Social role conceptions and CSR policy succes

Businesses are eager to present themselves as honest and reliable corporate citizens who care about the overall well-being of society. This article researches whether different role conceptions of businesses regarding social issues are related to their success in dealing with social demands. Do socially active companies have a better social reputation than inactive companies? This relationship is determined by first extracting the social role conceptions of the companies from their Corporate Social Responsibility reports and then comparing this data to their social reputations. The analysis shows that there is indeed a relationship between these two variables. Companies with a broad social role conception score significantly better on their social reputations than companies with a narrow role conception. Social role conceptions therefore matter when dealing with social
July 2017 , in Pattaya , Thailand ISBN : 9780998900018 59 AUTOMATIC LIBRARY SHELF READING FROM BOOK-SPINE IMAGE RECOGNITION

This paper has proposed the automatic library shelf reading from book spine image recognition. Program classified books into 10 categories by analyzing the average color of color tag on book’s spine. The system has three main steps. The first step is preprocessing. It consists of removing noise from the RGB image by Median filter and adjusting the contrast by gray-level transformation. The second step is the color tag edge detection using Difference of Gaussians technique. The third step is book categories considering from the color average of each color tag on book’s spine. The average accuracy is
Generalized Subdivision Surface Scheme Based on 2 D Lagrange Interpolating Polynomial and its Error Estimation Research Article

This work gives the idea for constructing subdivision rules for surface based on 2D Lagrange interpolating polynomial [13]. In this method, subdivision rules for quad mesh has been obtained directly from the Lagrange interpolating polynomial. We also see that the simple interpolatory subdivision scheme for quadrilateral nets with arbitrary topology is presented by L. Kobbelt [5], can be directly calculated from the proposed generalized formula for subdivision surface refinement rules. Furthermore, some characteristics, applications and error bounds of the proposed work are also discussed.
Real-Time Random Number Generation with Ring Oscillator Based Double Physically Unclonable Function

Integrated Circuit (IC) is a semiconductor wafer that is fabricated for millions of components. Although it is accepted that the same chemical properties are observed in the same type of ICs produced within the processes of wafer production, masking, etching, doping, atomic diffusion, ion implantation, metallization and packaging steps, these properties differ at microscopic levels. For example, the propagation delay is different in two logic elements in the same integrated circuit, and the silicon distribution in each LE is not equal. These differences have created the concept of Physically Unclonable Function (PUF). PUF is a function that creates values specific to the physical environment in which it operates. This article presents the implementation of a ring oscillator(RO) based PUF design in two different ICs to generate random numbers. Two different FPGA (Field Programmable Gate Array) cores were used in the proposed structure, and PUF structures were implemented on these hardware elements. The raw random numbers obtained by the generated system were post-processed to be used in applications (e.g. encryption, game programming). The study used Von Neumann and hash functions for post-processing. NIST and Autocorrelation tests were also administered to check the validity of the obtained random numbers.
2 TRAINING A NEURAL RANKERWITH MIMIC LEARNING

Deep neural networks have become a primary tool for solving problems in many elds. ey are also used for addressing information retrieval problems and show strong performance in several tasks. Training these models requires large, representative datasets and for most IR tasks, such data contains sensitive information from users. Privacy and condentiality concerns prevent many data owners from sharing the data, thus today the research community can only benet from research on large-scale datasets in a limited manner. In this paper, we discuss privacy preserving mimic learning, i.e., using predictions from a privacy preserving trained model instead of labels from the original sensitive training data as a supervision signal. We present the results of preliminary experiments in which we apply the idea of mimic learning and privacy preserving mimic learning for the task of document re-ranking as one of the core IR tasks. is research is a step toward laying the ground for enabling researchers from data-rich environments to share knowledge learned from actual users’ data, which should facilitate research collaborations.
Elastic wavefield tomography with probabilistic petrophysical clustering

Full waveform inversion (FWI) aims to use all information provided by seismic data to deliver high resolution models of subsurface parameters. However, multiparameter FWI suffers from an inherent trade-off between parameters and from ill-posedness due to the highly nonlinear nature of FWI. Also, the models recovered using elastic FWI are subject to local minima if the initial models are far from the optimal solution. In addition, an objective function purely based on the misfit between recorded and modeled data may honor the seismic data, but disregard the geological context. Hence, the inverted models may be geologically inconsistent, and not represent feasible lithological units. We propose that all the aforementioned difficulties can be alleviated by explicitly incorporating petrophysical information into the inversion through a penalty function based on multiple probability density functions (PDFs), where each PDF represents a different lithology with distinct properties. We treat lithological units as clusters and use unsupervised K-means clustering to separate the petrophysical information into different units of distinct lithologies are not easily distinguishable. Through several synthetic examples, we demonstrate that the proposed framework leads FWI to elastic models that are superior to models obtained either without incorporating petrophysical information, or with a probabilistic penalty function based on a single PDF.
Processing Natural Language Requirement to Extract Basic Elements of a Class

This paper presents the efficient way to obtain basic elements of a class diagram from natural language (NL) requirements. User provides the requirements in simple English in paragraph and the designed tool i.e. NLPC (Natural language Processing for Class) applies natural language processing (NLP) methods to analyze the given input. NL text is semantically analyzed to obtain classes, data members and member functions. NLPC helps to fill the gap between the informal natural language used to describe problems and the formal modeling languages used to specify software solutions. Input to this tool is clearly specified user requirement. With correct inputs, NLPC undergoes stages like Preprocessing, Part of Speech (POS) Tagging, Class Identification, Attribute and Function identification and then plotting the classes.
A Review on CPU Scheduling Algorithms in Cloud Environment

: Cloud computing is a new paradigm in which computing is delivered as a service rather than a product, whereby shared resources, software, and information are provided to consumers as a utility over networks. Cloud computing is capable to provide massive computing or storage resources without the need to invest money or face the trouble to build or maintain such huge resources. Consumers only need to pay for using the services just like they do in case of another day to day utility services such as water, gas, electricity, etc. Scheduling algorithms are used for dispatching user tasks or jobs to a particular resource or data. Scheduling is a challenging job in the cloud because the capability and availability of resources vary dynamically. In this paper, we provide a review of various scheduling techniques used in cloud computing environment.
Implementation of SDN Controller as Load Balancing Using Datacenter Topology

: The load balancing is the most important issue which helps to reduce traffic in datacenter topology. However balancing the traffic load and optimizing better path are very important . In this paper we used Dijkstra’s Algorithm to implement the load balancing in software define network (SDN) controller. Mininet and floodlight controller were used to setup the network environment. The result show that the load balancing algorithm is increased bandwith and reduced latency of the network, in order to achieve a better performance and better resource utilization of the network.
Functional evaluation of spinal canal surgical reconstruction by laminoplasty technique in patients with cervical spondylosis

* From Grupo de Coluna do Departamento de Ortopedia e Traumatologia da Irmandade da Santa Casa de Misericórdia de São Paulo/Faculdadade de Ciências Médicas da Santa Casa de São Paulo (Head, Professor Osmar Pedro Arbix de Camargo). 1. Adjunct Professor and Head, Grupo de Coluna do Departamento de Ortopedia e Traumatologia da Irmandade da Santa Casa de Misericórdia de São Paulo. 2. Assistant Orthopedic Surgeon, Grupo de Coluna do Departamento de Ortopedia e Traumatologia da Irmandade da Santa Casa de Misericórdia de São Paulo. 3. Assistant Surgeon and Instructor, Grupo de Coluna do Departamento de Ortopedia e Traumatologia da Irmandade da Santa Casa de Misericórdia de São Paulo. 4. Resident, Departamento de Ortopedia e Traumatologia da Irmandade da Santa Casa de Misericórdia de São Paulo. * Trabalho realizado no Grupo de Coluna do Departamento de Ortopedia e Traumatologia da Irmandade da Santa Casa de Misericórdia de São Paulo/ Faculdadade de Ciências Médicas da Santa Casa de São Paulo (Serviço do Prof. Dr. Osmar Pedro Arbix de Camargo). 1. Professor Adjunto e Chefe do Grupo de Coluna do Departamento de Ortopedia e Traumatologia da Irmandade da Santa Casa de Misericórdia de São Paulo. 2. Assistente de Coluna do Departamento de Ortopedia e Traumatologia da Irmandade da Santa Casa de Misericórdia de São Paulo. 3. Assistente e Professor Instrutor do Grupo de Coluna do Departamento de Ortopedia e Traumatologia da Irmandade da Santa Casa de Misericórdia de São Paulo. 4. Residente do Departamento de Ortopedia e Traumatologia da Irmandade da Santa Casa de Misericórdia de São Paulo.
ENABLING PERFORMANCE MANAGEMENT IN SMEs: A STUDY INTO WHAT SMEs NEED TO MEASURE AND HOW THEY SHOULD MANAGE PERFORMANCE

This paper proposes and validates a set of requirements specifications for a performance measurement system that would support the adoption of performance management best practices within Small and Medium sized Enterprises. The literature review concludes that characteristics of performance measurement practices currently in use, limit their adoption within an SME environment. In addition, an in-depth multiple case studies create an understanding of the actual state and desired future state of performance measurement practices within SMEs. Based on the literature review and case studies a conceptual model for an ICT enabled performance measurement system was proposed
7 Understanding Linkages Between Public Participation and Management of Protected Areas – Case Study of Serbia

The issue of participation is an important issue in protected area management. For instance, the IV IUCN World Congress on National Parks and Protected Areas convened in Caracas, Venezuela, called for increased community participation and human equity in decisionmaking for protected areas in order to improve their management (IUCN, 1993). The term participation can be interpreted in very different ways, and therefore it is essential to define it carefully.
High Luminosity LHC Optics and Layout HLLHCV1.4

The goal of the High Luminosity Project is the upgrade of the LHC to deliver an integrated luminosity of at least 250 fb-1 per year in each of the two high-luminosity, general-purpose detectors ATLAS and CMS. This article presents the latest layout design and the corresponding optics features, which comprise optimisation of the orbit corrector and crab cavity systems, and new estimates of the performance reach thanks to the new concept of fully remote alignment. In addition, the new optics version incorporates improvements required by beam instrumentation, dump system, and collimation system, as well as low-beta solutions for the LHCb experiment.
Proton Exchange Membrane Fuel Cell Stack Design Optimization Using an Improved Jaya Algorithm

Fuel cell stack configuration optimization is known to be a problem that, in addition to presenting engineering challenges, is computationally hard. This paper presents an improved computational heuristic for solving the problem. The problem addressed in this paper is one of constrained optimization, where the goal is to seek optimal (or near-optimal) values of (i) the number of proton exchange membrane fuel cells (PEMFCs) to be connected in series to form a group, (ii) the number of such groups to be connected in parallel, and (iii) the cell area, such that the PEMFC assembly delivers the rated voltage at the rated power while the cost of building the assembly is as low as possible. Simulation results show that the proposed method outperforms four of the best-known methods in the literature. The improvement in performance afforded by the proposed algorithm is validated with statistical tests of significance.
Student vs. faculty perspectives on quality instruction: Gender bias, “hotness,” and “easiness” in evaluating teaching

ABSTRACT This article systematically examines differences between student and faculty perspectives on quality instruction, using local data for a regional university's college of business matched to RateMyProfessors.com evaluations. Particular attention is focused on gender bias, “hotness,” and “easiness. ” In addition to documenting a student bias or preference in favor of male faculty, this article also shows how students and faculty have different views of “easiness” and “hotness. ” For this college, students value “easiness” while faculty penalize it; however “hotness” has no measurable effect on faculty in this study.
Articles, Adjectives and Age of Onset:

A comparison of the error profiles of monolingual (child L1) learners of Dutch, Moroccan children (child L2) and Moroccan adults (adult L2) learning Dutch as their L2 shows that participants in all groups massively overgeneralize [-neuter] articles to [+neuter] contexts. In all groups, the reverse gender mistake infrequently occurs. Gender expressed by Dutch attributive adjectives reveals an age-related asymmetry between the three groups, however. Whereas participants in the child groups overgeneralize one particular suffix (namely the schwa), adult participants use both adjectival forms, the schwa-adjective and the bare adjective, incorrectly. It is argued that the asymmetry observed in adjectives reflects that adult learners exploit an input-based, lexical learning route whereas children rely on grammarbased representations. The similarity in article selection between all groups follows from the assumption that adults, like children, make use of lexical frames. Crucially, lexical frames can successfully describe the distribution of gender-marked articles, but they cannot account for gender in adjectives.
Comparison of MPPT Strategies for Solar Modules

The paper studies the behavior of three different Maximum Power Point Tracking (MPPT) strategies applied to solar modules: three-point perturb and observe, fixed-step incremental conductance, and variable-step incremental conductance. It starts with a brief description of each method and then it compares them considering three criteria: simplicity of implementation, capability to follow irradiance variations and sensitivity to noise in the required measurements. Diverse simulations have been performed and the main results are presented.
Energy-Based Modelling for Dialogue State Tracking

The uncertainties of language and the complexity of dialogue contexts make accurate dialogue state tracking one of the more challenging aspects of dialogue processing. To improve state tracking quality, we argue that relationships between different aspects of dialogue state must be taken into account as they can often guide a more accurate interpretation process. To this end, we present an energy-based approach to dialogue state tracking as a structured classification task. The novelty of our approach lies in the use of an energy network on top of a deep learning architecture to explore more signal correlations between network variables including input features and output labels. We demonstrate that the energy-based approach improves the performance of a deep learning dialogue state tracker towards state-of-the-art results without the need for many of the other steps required by current state-of-the-art methods.