Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Adaptive Dynamic Load Balancing in Grid Environment using GRIDSIM

Grid computing has emerged as a new and important field and can be used to increase the performance of Distributed Computing. Grid computing has large and powerful applications of self-managing virtual computer out of a large collection of heterogeneous systems that sharing various resources which lead to the problem of load imbalance. The main goal of load balancing is to provide a distributed, low cost, scheme that balances the load across all the processors. In this paper an algorithm of load balancing for adaptive dynamic behaviour during the lifetime of a multistage parallel computation is proposed to analysing Load Balancing requirements in a grid environment. The comparison of load balancing algorithms is done on their qualitative parameters. A load balancing algorithm has been implemented and tested in a simulated Grid environment. The application has been developed using Java and SQL database server. The algorithm describes multiple aspects of load balancing algorithm and introduced number of concepts which explains its broad capabilities. Proposed algorithm is used to solve high demanding applications and all kinds of problems.It also fulfill the objectives of the grid environment to achieve high performance computing by optimal usage of geographically distributed and heterogeneous resources.
Dynamics of Viral Abundance and Diversity in a Sphagnum-Dominated Peatland: Temporal Fluctuations Prevail Over Habitat

Viruses impact microbial activity and carbon cycling in various environments, but their diversity and ecological importance in Sphagnum-peatlands are unknown. Abundances of viral particles and prokaryotes were monitored bi-monthly at a fen and a bog at two different layers of the peat surface. Viral particle abundance ranged from 1.7 x 106 to 5.6 x 108 particles mL-1, and did not differ between fen and bog but showed seasonal fluctuations. These fluctuations were positively correlated with prokaryote abundance and dissolved organic carbon, and negatively correlated with water-table height and dissolved oxygen. Using shotgun metagenomics we observed a shift in viral diversity between winter/spring and summer/autumn, indicating a seasonal succession of viral communities, mainly driven by weather-related environmental changes. Based on the seasonal asynchrony between viral and microbial diversity, we hypothesize a seasonal shift in the active microbial communities associated with a shift from lysogenic to lytic lifestyles. Our results suggest that temporal variations of environmental conditions rather than current habitat differences control the dynamics of virus-host interactions in Sphagnum-dominated peatlands.
What Can Boolean Networks Learn ?

We study the generalization abilities of networks that are composed of boolean nodes, i.e., nodes that implement only basic boolean functions: and, or and not. The majority of the network learning algorithms proposed so far generate networks where each node implements a threshold function and are inappropriate for the generation of boolean networks from training set data. We propose an algorithm that, given a training set, generates a boolean network of small complexity that is compatible with the training set. The algorithm, inspired in techniques used in the logic synthesis community for the design of VLSI circuits, generates both the connectivity pattern and the architecture of the network. Furthermore, the resulting network can be implemented in silicon in a straightforward way. Experimental results obtained in a set of problems from the machine learning literature show that the generalization performed by boolean networks synthesized with this algorithm compares favorably with the generalization obtained by alternative learning algorithms. Some of these results and examples of the layout of networks obtained using the algorithm are presented and discussed.
RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming

Motivation: Considerable attention has been focused on predicting RNA–RNA interaction since it is a key to identifying possible targets of non-coding small RNAs that regulate gene expression post-transcriptionally. A number of computational studies have so far been devoted to predicting joint secondary structures or binding sites under a specific class of interactions. In general, there is a trade-off between range of interaction type and efficiency of a prediction algorithm, and thus efficient computational methods for predicting comprehensive type of interaction are still awaited. Results: We present RactIP, a fast and accurate prediction method for RNA–RNA interaction of general type using integer programming. RactIP can integrate approximate information on an ensemble of equilibrium joint structures into the objective function of integer programming using posterior internal and external base-paring probabilities. Experimental results on real interaction data show that prediction accuracy of RactIP is at least comparable to that of several state-of-the-art methods for RNA–RNA interaction prediction. Moreover, we demonstrate that RactIP can run incomparably faster than competitive methods for predicting joint secondary structures. Availability: RactIP is implemented in C++, and the source code is available at http://www.ncrna. org/software/ractip/ Contact: ykato@kuicr.kyoto-u.ac. jp; satoken@k.u-tokyo.ac. jp Supplementary information: Supplementary data are available at Bioinformatics online.
Node selection for cooperative localization: Efficient energy vs. accuracy trade-off

In this paper, we present a cooperative localization algorithm for wireless sensor networks. We adopt an RSS-based localization algorithm due to its lower complexity with respect to other ranging techniques such as TOA or AOA. Since the energy consumption is a key aspect in sensor networks, we propose a node selection algorithm in order to avoid the use of all the nodes in the network for positioning purposes. Thus, we derive a practical solution tailored to the strict requirements of sensor networks in terms of complexity, size and cost. In particular, nodes in the network select the cooperating nodes by simply comparing their received RSS with a threshold. We present results based on both computer simulations and experimentation with the Crossbow IRIS motes showing that the proposed scheme offers a good trade-off in terms of position accuracy vs. energy consumption.
Memetic Collaborative Filtering Based Recommender System

Web based Decision Support systems like recommendation systems have become effective tools for decision making in the recent past. However the recommender systems employing conventional clustering techniques (KRS) like K-Means for collaborative filtering, suffer from the limitation of getting local optimum results. This paper presents Memetic Recommender System (MRS) based on the collaborative behavior of memes. Memetic Algorithms (MAs) are considered as one of the most successful approaches for combinatorial optimization. MAs are the genetic algorithms which incorporate local search in the evolutionary scheme. We propose a distinctive strategy to perform local search in memetic algorithms. MRS works in 2 phases-In the first phase a model is developed based on Memetic Clustering algorithm and in the second phase trained model is used to predict recommendations for the active user. Rigorous experiments were conducted to prove the decision support and statistical efficacy of MRS visa vis KRS. Results confirmed that the proposed approach yields much better performance as compared to the conventional collaborative filtering recommender system.
Detailed Multi‐dimensional Modeling of Direct Internal Reforming Solid Oxide Fuel Cells

Fuel flexibility is a significant advantage of solid oxide fuel cells (SOFCs) and can be attributed to their high operating temperature. Here we consider a direct internal reforming solid oxide fuel cell setup in which a separate fuel reformer is not required. We construct a multidimensional, detailed model of a planar solid oxide fuel cell, where mass transport in the fuel channel is modeled using the Stefan‐Maxwell model, whereas the mass transport within the porous electrodes is simulated using the Dusty‐Gas model. The resulting highly nonlinear model is built into COMSOL Multiphysics, a commercial computational fluid dynamics software, and is validated against experimental data from the literature. A number of parametric studies is performed to obtain insights on the direct internal reforming solid oxide fuel cell system behavior and efficiency, to aid the design procedure. It is shown that internal reforming results in temperature drop close to the inlet and that the direct internal reforming solid oxide fuel cell performance can be enhanced by increasing the operating temperature. It is also observed that decreases in the inlet temperature result in smoother temperature profiles and in the formation of reduced thermal gradients. Furthermore, the direct internal reforming solid oxide fuel cell performance was found to be affected by the thickness of the electrochemically‐active anode catalyst layer, although not always substantially, due to the counter‐balancing behavior of the activation and ohmic overpotentials.
Recent History of Stimulus Speeds Affects the Speed Tuning of Neurons in Area MT

Visual motion processing plays a key role in enabling primates' successful interaction with their dynamic environments. Although in natural environments the speed of visual stimuli continuously varies, speed tuning of neurons in the prototypical motion area MT has traditionally been assessed with stimuli that moved at constant speeds. We investigated whether the representation of speed in a continuously varying stimulus context differs from the representation of constant speeds. We recorded from individual MT neurons of fixating macaques while stimuli moved either at a constant speed or in a linearly accelerating or decelerating manner. We found clear speed tuning even when the stimulus consisted of visual motion with gradual speed changes. There were, however, important differences with the speed tuning as measured with constant stimuli: the stimulus context affected neuronal preferred speed as well as the associated tuning width of the speed tuning curves. These acceleration-dependent changes in response lead to an accurate representation of the acceleration of these stimuli in the MT cells. To elucidate the mechanistic basis of this signal, we constructed a stochastic firing rate model based on the constant speed response profiles. This model incorporated each cell's speed tuning and response adaptation dynamics and accurately predicted the response to constant speeds as well as accelerating and decelerating stimuli. Because the response of the model neurons had no explicit acceleration dependence, we conclude that speed-dependent adaptation creates a strong influence of temporal context on the MT response and thereby results in the representation of acceleration signals.
Glycogen synthase kinase-3β is required for epithelial-mesenchymal transition and barrier dysfunction in mouse podocytes under high glucose conditions

Epithelial-mesenchymal transition (EMT) is important for diabetic nephropathy (DN). Podocytes are specialized epithelial cells, which form a major component of the glomerular filtration barrier. Podocyte damage has been suggested to be the primary mechanism behind the albuminuria associated with DN. The present study aimed to determine the function of glycogen synthase kinase (GSK)-3β in EMT and barrier dysfunction of mouse podocytes exposed to high glucose (HG) conditions. Matured and differentiated podocytes were treated with normal glucose (NG), HG or NG + mannitol. Podocytes were also transfected with a small interfering RNA (siRNA) against GSK-3β or a scrambled siRNA, or were treated with lithium chloride (LiCl), a GSK-3β inhibitor, under NG or HG conditions. The expression levels of the epithelial cell markers, nephrin and podocin, and the myofibroblast cell markers, α-smooth muscle actin (SMA) and fibronectin, in podocytes by western blot analysis and immunofluorescence staining, respectively. The monolayer barrier function was assessed by albumin inflow. The phosphorylation and activity levels of GSK-3β were also quantified. It was observed that HG promotes EMT in podocytes, due to the increased levels of podocin and nephrin expression and the reduced α-SMA and fibronectin expression levels. HG also induced barrier dysfunction and increased the expression level of total GSK-3β, Try216-phosphorylated-GSK-3β and the GSK-3β activity in podocytes. Transfection of GSK-3β siRNA or treatment with LiCl reversed the HG-induced EMT and barrier dysfunction in podocytes. In conclusion, the present study determined that GSK-3β is required for EMT and barrier dysfunction in podocytes under HG conditions; therefore, GSK-3β may be a novel target for the treatment of DN.
Improving the Performance of Reliable Transport Protocols in Mobile Computing Environments

We explore the performance of reliable data communication in mobile computing environments. Motion across wireless cell boundaries causes increased delays and packet losses while the network learns how to route data to a host's new location. Reliable transport protocols like TCP interpret these delays and losses as signs of network congestion. They consequently throttle their transmissions, further degrading performance. We quantify this degradation through measurements of protocol behavior in a wireless networking testbed. We show how current TCP implementations introduce unacceptably long pauses in communication during cellular handoffs (800 ms and longer), and propose an end-to-end fast retransmission scheme that can reduce these pauses to levels more suitable for human interaction (200 ms). Our work makes clear the need for reliable transport protocols to differentiate between motion-related and congestion-related packet losses and suggests how to adapt these protocols to perform better in mobile computing environments. >
Population Size Predicts Lexical Diversity, but so Does the Mean Sea Level – Why It Is Important to Correctly Account for the Structure of Temporal Data

In order to demonstrate why it is important to correctly account for the (serial dependent) structure of temporal data, we document an apparently spectacular relationship between population size and lexical diversity: for five out of seven investigated languages, there is a strong relationship between population size and lexical diversity of the primary language in this country. We show that this relationship is the result of a misspecified model that does not consider the temporal aspect of the data by presenting a similar but nonsensical relationship between the global annual mean sea level and lexical diversity. Given the fact that in the recent past, several studies were published that present surprising links between different economic, cultural, political and (socio-)demographical variables on the one hand and cultural or linguistic characteristics on the other hand, but seem to suffer from exactly this problem, we explain the cause of the misspecification and show that it has profound consequences. We demonstrate how simple transformation of the time series can often solve problems of this type and argue that the evaluation of the plausibility of a relationship is important in this context. We hope that our paper will help both researchers and reviewers to understand why it is important to use special models for the analysis of data with a natural temporal ordering.
How Do Regulatory Reforms to Enhance Auditor Independence Work in Practice

We conducted a field study of the process by which a company and prospective auditors court each other and enter an engagement. The Request-for-Proposal (RFP) process we observed had been triggered by an audit partner rotation and governed by recent legislation empowering the audit committee to make the auditor selection decision. The RFP resulted in a change in audit firm, and the process was characterized by: (1) asymmetry of power with significant management control in selection of the external auditor; and (2): auditors’ repeated demonstrations of responsiveness and commitment to management, rather than the audit committee, to differentiate themselves from other audit firms. Our study raises concerns that reforms designed to promote independence may not operate in the manner intended by regulators, as auditors’ efforts to win the engagement were directed toward management, and the audit committee delegated the auditor appointment decision to management.
Multi‐Product Firms and Product Variety

The goal of this paper is to study the role of multi-product firms in the market provision of product variety. The analysis is conducted using the spokes model of non-localized competition proposed by Chen and Riordan (2007). Firstly, we show that multi-product firms are at a competitive disadvantage vis-a-vis single-product firms and can only emerge if economies of scope are sufficiently strong. Secondly, under duopoly product variety may be higher or lower with respect to both the first best and the monopolistically competitive equilibrium. However, within a relevant range of parameter values duopolists drastically restrict their product range in order to relax price competition, and as a result product variety is far below the efficient level.
Simian Virus 40 Large T Antigen Directed by Transcriptional Elements of the Human Surfactant Protein C Gene Produces Pulmonary Adenocarcinomas in Transgenic Mice 1

A model of pulmonary adenocarcinomas was produced in transgenic mice harboring a chimeric gene comprising the SV40 large T antigen under the control of a transcriptional region derived from the human surfactant protein C (SP-C) gene. Transgenic mice succumbed with pulmonary tumors within 4-5 months of age. By histology, the tumors were adenocarcinomas with lepidic, papillary, and solid growth patterns that were indistinguishable from adenocarcinomas occurring in humans. Immunocytochemistry demonstrated the lack of staining for neuroen docrine markers, consistent with the identification of the tumors as non-small cell rather than small cell carcinomas. The presence of SV40 large T inKNA in the lung and tumors was detected by in situ hybrid ization and Northern blot analysis. Exogenous SV40 large T mRNA and endogenous CC10 (a nonciliated respiratory epithelial cell marker) and SP-C (a Type II alveolar cell marker) mRNAs were expressed at variable levels in the lung tumors. SV40 large T mRNA and CC10 mRNA were detected in the majority of tumors, while SP-C mRNA was detected less frequently. The heterogeneity of bronchiolar and alveolar cell markers in the tumors from the transgenic mice supports the con cept that tumorigenesis was initiated in distinct subsets of epithelial cells that produce characteristic adenocarcinomas of the lung.
Participant Perspectives: Investigating the Experience of Low-Income Schizophrenics in Clinical Research Trials

The continued investigation into the experiences of individuals with schizophrenia who participate in biomedical research trials is necessary in order to understand participants’ perspectives, motivations, attitudes, values, and beliefs. As important stakeholders in the clinical research process, participant feedback is significant and can help shed light on, not only their experiences, but also deepen understandings when it comes to clinical trial participants’ perceptions of informed consent and personal autonomy. Conducting ethical research demands the exploration of these issues and specifically targeting this vulnerable group helped to address a gap in the literature. This study was conducted for InSite Clinical Research and gathered data in the form of in-depth semi-structured interviews and a short survey instrument with 20 low-income adults diagnosed with schizophrenia that participate in clinical research trials. Findings indicate overall positive research experiences, with motivations aligning with previous research when it comes to trial participation including: altruism, personal benefit, access to medications, financial incentives, and psychosocial treatment. Learning about their illness and themselves, autonomy, and debriefing were also particularly important within this group. Unique to this sample were findings of friendship. Trust in the research staff was identified as a major underlying value and shaping factor impacting informed consent decisions. These conclusions have implications for recruitment and informed consent practices at InSite Clinical Research.
Performance Implications of Resource Over-Allocation During the Live Migration

As the number of cloud users are increasing, it becomes essential for cloud service providers to allocate the right amount of resources to virtual machines, especially during live migration. In order to increase the resource utilization and reduce waste, the providers have started to think about the role of over-allocating the resources. However, the benefits of over-allocations are not without inherent risks. In this paper, we conducted an experiment using a large telecommunication application that runs inside virtual machines, here we have varied the number of vCPU resources allocated to these virtual machines in order to find the best choice which at the same time reduces the risk of underallocating resources after the migration and increases the performance during the live migration. During our measurements we have used VMware's vMotion to migrate virtual machines while they are running. The results of this study will help virtualized environment service providers to decide how much resources should be allocated for better performance during live migration as well as how much resource would be required for a given load.
PREAMPLIFYING CANTILEVERS FOR CONTACT RESONANCE MODE IMAGING

A novel preamplifying cantilever (PCL) design for scanning probe microscopes (SPM) that is capable of mechanically amplifying specimen movements is presented. The sample motions in both out-of-plane and in-plane directions are amplified by the PCL. The spatial resolution of the cantilever is in the nanometer scale and is comparable with existing SPM cantilevers. Some of the proposed applications are in the areas of ferroelectric domain imaging [1]-[5]. The amplification was demonstrated with measurements on ferroelectric materialswhich included periodically poled lithium niobate (PPLN). Feasibility tests show that a 100x mechanical preamplification is possible with the current cantilever design, providing a much better signal to noise ratio on both amplitude and phase response measurements than the previous art (e.g. [8]).
A Least-Squares Approach to Consistent Information Estimation in Semiparametric Models

A method is proposed for consistent information estimation in a class of semiparametric models. The method is based on the geometric interpretation of the efficient score function, that it is the residual of the orthogonal projection of the score function for the finite-dimensional parameter onto the tangent space for the infinitedimensional parameter. The empirical version of this projection is a least-squares nonparametric regression problem. Under appropriate conditions, the sum of squared residuals of this regression is shown to be a consistent estimator of the efficient Fisher information and is actually the observed information for a class of sieve maximum likelihood estimators. Simulations studies are conducted to evaluate finite sample performance of the estimator in two illustrating examples: Poisson proportional mean model for panel count data and Cox model for interval-censored data. Finally the method is applied to two real-life examples: bladder tumor study and breast cosmesis study.