Miracle-dz/UniFAR-FTUs huggingface dataset:
The Relationship Between Work-Family Conflict (WFC) and Family-Work Conflict (FWC) Towards Job Burnout: A Case at Sarawak Economic Development Corporation (SEDC)

The purpose of this study is to examine the relationship between work-family conflict (WFC) and family-work conflict (FWC) and its influences on job burnout. Besides that, this study also aims to determine the significant differences in work-family conflict (WFC), family-work conflict (FWC) and job burnout according to selected demographic factors such as gender and age. In this study, work-family conflict (WFC) and family-work conflict (FWC) will be as independent variables and job burnout as a dependent variable. The Work-Family Conflict (WFC) and Family-Work Conflict (FWC) Questionnaire (Netemeyer et. al., 1996) is chosen to measure the work and family conflict while Maslach Burnout Inventory-Genera1 Survey (MBI-GS) (Schaufeli et. al., 1996) is used to measure job burnout among employees in an organization. A data form is used to collect personal information particularly on selected personnel characteristics. Samples of 124 from a total population of 240 employees were selected randomly using simple random sampling technique. The survey response was analyzed by using the Statistical Package for Social Sciences (SPSS) Version 14. Frequencies and percentage statistics were computed from selected personal characteristics of employees. 5 questionnaires each on reliability analysis were extracted on The Work-Family Conflict (WFC) and Family-Work Conflict (FWC) inclusive 16 questionnaires from Maslach Burnout Inventory-General Survey (MBI-GS), meanwhile an independent sample t-test and one way ANOVA were conducted to determine the significant differences in mean scores on work-family conflict (WFC), family-work conflict (FWC) and job burnout according to gender and age. Pearson correlation coefficient was carried out to examine the relationship between work-family conflict (WFC) and family-work conflict (FWC) towards job burnout and lastly multiple linear regressions was conducted to determine the influences on both independent variables on job burnout. The findings of this study indicate that there is a significant relationship between work-family conflict (WFC) and family-work conflict (FWC) towards job burnout. Work-family conflict (WFC) predicts job burnout more than family-work conflict (FWC). However, in determining the differences in selected demographic factors, the result shows that only job burnout was significantly related to age groups. Therefore, it is recommended that an organization should create new environment on promoting values on work and family culture. Further research in variables in the two different sectors such as service and technica1 sectors can be carried out as there is lots of research focusing in service sectors only.
Preparation and in vitro-in vivo evaluation of acyclovir floating tablets

In the current study, floating dosage form containing acyclovir was developed to increase its oral bioavailability. Effervescent floating tablets containing 200 mg acyclovir were prepared by direct compression method with three different rate controlling polymers including Hydroxypropyl methylcellulose K4M, Carbapol 934, and Polyvinylpyrrolidone. Optimized formulation showed good floating properties and in vitro drug release characteristics with mean dissolution time and dissolution efficacy of about 4.76 h and 54.33%, respectively. X-ray radiography exhibited that the tablet would reside in the stomach for about 5 ± 0.7 h. After oral administration of floating tablet containing 200 mg acyclovir, the Cmax, Tmax, and AUC0–∞ of optimized gastroretentive formulation were found to be 551 ± 141 ng/mL, 2.75 ± 0.25 h and 3761 ± 909.6 ng/mL/h, respectively.
Restricted patience sorting and barred pattern avoidance

Patience Sorting is a combinatorial algorithm that can be viewed as an iterated, non-recursive form of the Schensted Insertion Algorithm. In recent work the authors have shown that Patience Sorting provides an algorithmic description for permutations avoiding the barred (generalized) permutation pattern $3-\bar{1}-42$. Motivated by this and a recently formulated geometric form for Patience Sorting in terms of certain intersecting lattice paths, we study the related themes of restricted input and avoidance of similar barred permutation patterns. One such result is to characterize those permutations for which Patience Sorting is an invertible algorithm as the set of permutations simultaneously avoiding the barred patterns $3-\bar{1}-42$ and $3-\bar{1}-24$. We then enumerate this avoidance set, which involves convolved Fibonacci numbers.
Macrophages in renal inflammation

This review describes recent advances in macrophage biology in the context of renal inflammation. It highlights the importance of the activated macrophage for the progression and resolution of renal disease, and discusses recent and potential future approaches to modify macrophage function selectively within the kidney to activate them specifically to promote the healing of kidney disease.
Improving Accuracy and Coverage in an Internet-Deployed Reputation Mechanism

P2P systems can benefit from reputation mechanisms to promote cooperation and help peers to identify good service providers. However, in spite of a large number of proposed reputation mechanisms, few have been investigated in real situations. BarterCast is a distributed reputation mechanism used by our Internet-deployed Bittorent-based file-sharing client Tribler. In BarterCast, each peer uses messages received from other peers to build a weighted, directed subjective graph that represents the upload and download activity in the system. A peer calculates the reputations of other peers by applying the maxflow algorithm to its subjective graph. For efficiency reasons, only paths of at most two hops are considered in this calculation. In this paper, we identify and assess three potential modifications to BarterCast for improving its accuracy and coverage (fraction of peers for which a reputation value can be computed). First, a peer executes maxflow from the perspective of the node with the highest betweenness centrality in its subjective graph instead of itself. Second, we assume a gossiping protocol that gives each peer complete information about upload and download activities in the system, and third, we lift the path length restriction in the maxflow algorithm. To assess these modifications, we crawl the Tribler network and collect the upload and download actions of the peers for three months. We apply BarterCast with and without the modifications on the collected data and measure accuracy and coverage.
The Bankruptcy Prediction by Neural Networks and Logistic Regression

Today, the intensity of industry competition has led many companies going bankrupt and pulling out of race. The early warning against the possibility of bankruptcy enables the managers and investors to take pre-emptive actions when it is necessary. The bankruptcy prediction models reveal the latent problems in financial structures like a warning bell and provide timely feedback to managers and investors as well as other people who benefit from this. The bankruptcy of manufacturing companies in Tehran Stock Exchange Market has been predicted in this study using artificial neural network in this respect. It has been also used the logistic regression to do compare with neural network as well. All information which has been used here is related to time periods from 2001 to 2011 and the bankrupt groups have been selected on the basis of Article 141 of the Commercial Code of Iran. In the years before bankruptcy, the financial management has the chance to predict the probability of bankruptcy by using this model and take necessary actions in this regard since the results derived from the neural network predictions are very consistent with reality. Moreover, this model is more accurate than that of logistic regression in prediction process.
Database on the structure of large ribosomal subunit RNA

The latest release of the large ribosomal subunit RNA database contains 429 sequences. All these sequences are aligned, and incorporate secondary structure information. The rRNA WWW Server at URL http://rrna.uia. ac.be/ provides researchers with an easily accessible resource to obtain the data in this database in a number of computer-readable formats. A new query interface has been added to the server. If necessary, the data can also be obtained by anonymous ftp from the same site.
Interleukin-8 Induces the Endothelial Cell Migration through the Activation of Phosphoinositide 3-Kinase-Rac1/RhoA Pathway

Endothelial cell migration is essential for tumor angiogenesis, and interleukin-8 (IL-8) has been shown to play an important role in tumor growth, angiogenesis, and metastasis. This study aimed to investigate the molecular mechanism of IL-8 induced endothelial cell migration. Our results indicated that IL-8 induced a rapid rearrangement of the actin cytoskeleton in EA.Hy926 cells, generating extensions resembling membrane ruffling and stress fibers. These processes required parallel upregulation of the small GTPases Rac1 and RhoA. Moreover, we demonstrated that IL-8 activated PI3K following the same kinetics observed from IL-8 induction of cytoskeletal rearrangement, suggesting the participation of PI3K in these processes. Taken together, our study demonstrates that PI3K-Rac1/RhoA signaling pathway plays a vital role in IL-8 induced endothelial cell migration, and provides new insight into the molecular mechanisms by which IL-8 contributes to tumor angiogenesis and metastasis.
Convolutional Neural Network Based Multi-feature Fusion for Non-rigid 3D Model Retrieval

This paper presents a novel convolutional neural network based multi-feature fusion learning method for non-rigid 3D model retrieval, which can investigate the useful discriminative information of the heat kernel signature (HKS) descriptor and the wave kernel signature (WKS) descriptor. At first, we compute the 2D shape distributions of the two kinds of descriptors to represent the 3D model and use them as the input to the networks. Then we construct two convolutional neural networks for the HKS distribution and the WKS distribution separately, and use the multi-feature fusion layer to connect them. The fusion layer not only can exploit more discriminative characteristics of the two descriptors, but also can complement the correlated information between the two kinds of descriptors. Furthermore, to further improve the performance of the description ability, the cross-connected layer is built to combine the low-level features with high-level features. Extensive experiments have validated the effectiveness of the designed multi-feature fusion learning method.
Printed actuators made with electroactive polymers on flexible substrates

This article presents a manufacturing process and the electro-mechanical characterization of actuators made with electroactive polymers based on a PDVF terpolymer. Indeed, the PVDF-TrFE-CFE presents a high electrostrictive strain under electrical field. By introducing it in a metal-insulator-metal structure processed on a thin PEN foil, cantilevers were realized on flexible substrates. The cantilevers fabrication process is described, and their characterization is done by the measurement of the displacement bending and the consequent calculation of the induced strain under electric field. A theoretical model is proposed and validated by the experimental measurements.
Modeling, system identication, and control for dynamic locomotion of the LittleDog robot on rough terrain

In this thesis, I present a framework for achieving a stable bounding gait on the LittleDog robot over rough terrain. The framework relies on an accurate planar model of the dynamics, which I assembled from a model of the motors, a rigid body model, and a novel physically-inspired ground interaction model, and then identified using a series of physical measurements and experiments. I then used the RG-RRT algorithm on the model to generate bounding trajectories of LittleDog over a number of sets of rough terrain in simulation. Despite significant research in the field, there has been little success in combining motion planning and feedback control for a problem that is as kinematically and dynamically challenging as LittleDog. I have constructed a controller based on transverse linearization and used it to stabilize the planned LittleDog trajectories in simulation. The resulting controller reliably stabilized the planned bounding motions and was relatively robust to significant amounts of time delays in estimation, process and estimation noise, as well as small model errors. In order to estimate the state of the system in real time, I modified the EKF algorithm to compensate for varying delays between the sensors. The EKF-based filter works reasonably well, but when combined with feedback control, simulated delays, and the model it produces unstable behavior, which I was not able to correct. However, the close loop simulation closely resembles the behavior of the control and estimation on the real robot, including the failure modes, which suggests that improving the feedback loop might result in bounding on the real LittleDog. The control framework and many of the methods developed in this thesis are applicable to other walking systems, particularly when operating in the underactuated regime. Thesis Supervisor: Russ L. Tedrake Title: Associate Professor of Computer Science and Engineering
A Hybrid Data Mining Model to Improve Customer Response Modeling in Direct Marketing

Direct marketing is concerned with which customers are more likely to respond to a product offering or promotion. A response model predicts if a customer is going to respond to a product offering or not. Typically historical purchase data is used to model customer response. In response modeling customers are partitioned in to two groups of respondents and non-respondents. Generally, the distribution of records (respondents and nonrespondents) in marketing datasets is not balanced. The most common approach to solve class imbalance problem is by using sampling techniques. However; sampling techniques have their shortcomings. Therefore, in this research we integrated supervised and unsupervised learning techniques and presented a novel approach to address class imbalance problem. Recently hybrid data mining techniques have been proposed and they intend to improve the performance of the basic classifiers. Finally we compared the performance of the hybrid approach to that of the sampling approach. We could show that the hybrid ANN model achieved higher prediction accuracy and higher area under the curve value than the corresponding values of the bagging neural network which was trained based on the sampled training set.
Distributed Generation of Su x

An algorithm for the distributed computation of suux arrays for large texts is presented. The parallelism model is that of a set of sequential tasks which execute in parallel and exchange messages among them. The underlying architecture is that of a high bandwidth network of processors. Our algorithm builds the suux array by quickly assigning an independent sub-problem to each processor and completing the process with a nal local sorting. We demonstrate that the algorithm has time complexity of O(b log n) computation and O(b) communication in the average case, where b corresponds to the local text size on each processor (i.e., text size n divided by r, the number of processors). This is faster than the best known sequential algorithm and improves over previous parallel algorithms to build suux arrays, both in time complexity and scaling factor.
Systematic review of the neural basis of social cognition in patients with mood disorders.

BACKGROUND This review integrates neuroimaging studies of 2 domains of social cognition--emotion comprehension and theory of mind (ToM)--in patients with major depressive disorder and bipolar disorder. The influence of key clinical and method variables on patterns of neural activation during social cognitive processing is also examined. METHODS Studies were identified using PsycINFO and PubMed (January 1967 to May 2011). The search terms were "fMRI," "emotion comprehension," "emotion perception," "affect comprehension," "affect perception," "facial expression," "prosody," "theory of mind," "mentalizing" and "empathy" in combination with "major depressive disorder," "bipolar disorder," "major depression," "unipolar depression," "clinical depression" and "mania. " RESULTS Taken together, neuroimaging studies of social cognition in patients with mood disorders reveal enhanced activation in limbic and emotion-related structures and attenuated activity within frontal regions associated with emotion regulation and higher cognitive functions. These results reveal an overall lack of inhibition by higher-order cognitive structures on limbic and emotion-related structures during social cognitive processing in patients with mood disorders. Critically, key variables, including illness burden, symptom severity, comorbidity, medication status and cognitive load may moderate this pattern of neural activation. LIMITATIONS Studies that did not include control tasks or a comparator group were included in this review. CONCLUSION Further work is needed to examine the contribution of key moderator variables and to further elucidate the neural networks underlying altered social cognition in patients with mood disorders. The neural networks under lying higher-order social cognitive processes, including empathy, remain unexplored in patients with mood disorders.
Emergence of conjunctive visual features by quadratic independent component analysis

In previous studies, quadratic modelling of natural images has resulted in cell models that react strongly to edges and bars. Here we apply quadratic Independent Component Analysis to natural image patches, and show that up to a small approximation error, the estimated components are computing conjunctions of two linear features. These conjunctive features appear to represent not only edges and bars, but also inherently two-dimensional stimuli, such as corners. In addition, we show that for many of the components, the underlying linear features have essentially V1 simple cell receptive field characteristics. Our results indicate that the development of the V2 cells preferring angles and corners may be partly explainable by the principle of unsupervised sparse coding of natural images.
A PROOF OF ANDREWS’ CONJECTURE ON PARTITIONS WITH NO SHORT SEQUENCES

Our main result establishes Andrews’ conjecture for the asymptotic of the generating function for the number of integer partitions of $n$ without $k$ consecutive parts. The methods we develop are applicable in obtaining asymptotics for stochastic processes that avoid patterns; as a result they yield asymptotics for the number of partitions that avoid patterns. Holroyd, Liggett, and Romik, in connection with certain bootstrap percolation models, introduced the study of partitions without $k$ consecutive parts. Andrews showed that when $k=2$ , the generating function for these partitions is a mixed-mock modular form and, thus, has modularity properties which can be utilized in the study of this generating function. For $k>2$ , the asymptotic properties of the generating functions have proved more difficult to obtain. Using $q$ -series identities and the $k=2$ case as evidence, Andrews stated a conjecture for the asymptotic behavior. Extensive computational evidence for the conjecture in the case $k=3$ was given by Zagier. This paper improved upon early approaches to this problem by identifying and overcoming two sources of error. Since the writing of this paper, a more precise asymptotic result was established by Bringmann, Kane, Parry, and Rhoades. That approach uses very different methods.
Divergence and Redundancy of 16S rRNA Sequences in Genomes with Multiple rrn Operons

ABSTRACT The level of sequence heterogeneity among rrn operons within genomes determines the accuracy of diversity estimation by 16S rRNA-based methods. Furthermore, the occurrence of widespread horizontal gene transfer (HGT) between distantly related rrn operons casts doubt on reconstructions of phylogenetic relationships. For this study, patterns of distribution of rrn copy numbers, interoperonic divergence, and redundancy of 16S rRNA sequences were evaluated. Bacterial genomes display up to 15 operons and operon numbers up to 7 are commonly found, but ∼40% of the organisms analyzed have either one or two operons. Among the Archaea, a single operon appears to dominate and the highest number of operons is five. About 40% of sequences among 380 operons in 76 bacterial genomes with multiple operons were identical to at least one other 16S rRNA sequence in the same genome, and in 38% of the genomes all 16S rRNAs were invariant. For Archaea, the number of identical operons was only 25%, but only five genomes with 21 operons are currently available. These considerations suggest an upper bound of roughly threefold overestimation of bacterial diversity resulting from cloning and sequencing of 16S rRNA genes from the environment; however, the inclusion of genomes with a single rrn operon may lower this correction factor to ∼2.5. Divergence among operons appears to be small overall for both Bacteria and Archaea, with the vast majority of 16S rRNA sequences showing <1% nucleotide differences. Only five genomes with operons with a higher level of nucleotide divergence were detected, and Thermoanaerobacter tengcongensis exhibited the highest level of divergence (11.6%) noted to date. Overall, four of the five extreme cases of operon differences occurred among thermophilic bacteria, suggesting a much higher incidence of HGT in these bacteria than in other groups.
Coordinated multihop scheduling: a framework for end-to-end services

In multihop networks, packet schedulers at downstream nodes have an opportunity to make up for excessive latencies due to congestion at upstream nodes. Similarly, when packets incur low delays at upstream nodes, downstream nodes can reduce priority and schedule other packets first. The goal of this paper is to define a framework for design and analysis of coordinated multihop scheduling (CMS) which exploit such internode coordination. We first provide a general CMS definition which enables us to classify a number of schedulers from the literature, including G-EDF, FIFO+, CEDF, and work-conserving CJVC as examples of CMS schedulers. We then develop a distributed theory of traffic envelopes which enables us to derive end-to-end statistical admission control conditions for CMS schedulers. We show that CMS schedulers are able to limit traffic distortion to within a narrow range resulting in improved end-to-end performance and more efficient resource utilization. Consequently, our technique exploits statistical resource sharing among flows, classes, and nodes, and our results provide the first statistical multinode multiclass admission control algorithm for networks of work conserving servers.
Absolute Soft Set Approach for Mining Association Patterns

Molodtsov initiated the concept of soft set as a new mathematical tool for dealing with uncertainties. In 2003, Maji put forward several notions on Soft Set Theory. In this paper, absolute soft set approach has been developed for mining association patterns from a transactional data set. This approach is used for mining associations has been illustrated with the help of an example and experiment on a real world data set. In particular, the work demonstrates that absolute soft set theory can be applied to problems that contain uncertainties especially in decision making problems. The proposed approach gives better picture of association relationship, confidence levels and is helpful in addressing the absolute association patterns.