Miracle-dz/UniFAR-FTUs huggingface dataset:
cvcrand: A Package for Covariate-constrained Randomization and the Clustered Permutation Test for Cluster Randomized Trials

The cluster randomized trial (CRT) is a randomized controlled trial in which randomization is conducted at the cluster level (e.g. school or hospital) and outcomes are measured for each individual within a cluster. Often, the number of clusters available to randomize is small (≤ 20), which increases the chance of baseline covariate imbalance between comparison arms. Such imbalance is particularly problematic when the covariates are predictive of the outcome because it can threaten the internal validity of the CRT. Pair-matching and stratification are two restricted randomization approaches that are frequently used to ensure balance at the design stage. An alternative, less commonly-used restricted randomization approach is covariate-constrained randomization. Covariateconstrained randomization quantifies baseline imbalance of cluster-level covariates using a balance metric and randomly selects a randomization scheme from those with acceptable balance by the balance metric. It is able to accommodate multiple covariates, both categorical and continuous. To facilitate implementation of covariate-constrained randomization for the design of two-arm parallel CRTs, we have developed the cvcrand R package. In addition, cvcrand also implements the clustered permutation test for analyzing continuous and binary outcomes collected from a CRT designed with covariate-constrained randomization. We used a real cluster randomized trial to illustrate the functions included in the package.
Molecular characterization of methicillin resistant Staphylococcus aureus isolated from hospitals environments and patients in Northern Palestine

BACKGROUND: Staphylococcus aureus (S. aureus) is considered one of the most common pathogen to humans. Infections caused by this mocroorganism can be acquired through both hospital and community settings. This study was carried out to investigate molecular characterization of MRSA strains isolated from the patients and their environment in two hospitals (Rafidia hospital and Thabet hospital) inNorthern Palestine, and to determine the clonal identity between these strains and their possible contribution to nosocomial infections. METHODS: Two hundred sixty five swabbed samples were collected from these hospitals, S. aureus was isolated, antibiotic resistant genes were Panton–Valentin leukocidin (PVL) gene were detected and SCCmec and spA were typed by PCR and/or sequencing. RESULTS: The prevalence of MRSA among S. aureus isolates was 29% and 8.2% in Rafidia hospital and Thabet hospital, respectively. All strains resistant to oxacilllin disk were carried mecA gene. Majority of strains (84.6%) carried SCCmec type II (n = 11), type IVa and non-typeable were also detected. In addition, PVL was detected in 2 (14.3%) clinical strains. ERIC PCR patterns revealed that 2 strains recovered from patient bed and nasal swab isolated from Thabet Hospital were nontypeable, spA typing showed that they belonged to type t386 and have identical DNA sequences. Other 2 clinical isolates were spa typed, one belonged to clone t044, while the other is new clone not exist in database. CONCLUSIONS: Results may give evidence that environmental contamination possibly contributing to nosocomial infections.
VTracer: When Online Vehicle Trajectory Compression Meets Mobile Edge Computing

Vehicles can be easily tracked due to the proliferation of vehicle-mounted global positioning system (GPS) devices. <inline-formula><tex-math notation="LaTeX">${\sf VTracer}$</tex-math></inline-formula> is a cost-effective mobile system for online trajectory compression and tracing vehicles, taking the streaming GPS data as inputs. Online trajectory compression, which seeks a <italic>concise and (near) spatial-lossless</italic> data representation before revealing the next vehicle’s GPS position, is gradually becoming a promising way to alleviate burdens such as communication bandwidth, storing, and cloud computing. In general, an accurate online map-matcher is a prerequisite. This two-phase approach is nontrivial because we need to overcome the essential contradiction caused by the resource-constrained GPS devices and the heavy computation tasks. <inline-formula><tex-math notation="LaTeX">${\sf VTracer}$</tex-math></inline-formula> meets the challenge by leveraging the idea of mobile edge computing. More specifically, we offload the heavy computation tasks to the <italic>nearby</italic> smartphones of drivers (i.e., smartphones play the role of cloudlets), which are almost idle during driving. More importantly, they have relatively more powerful computing capacity. We have implemented <inline-formula><tex-math notation="LaTeX">${\sf VTracer}$</tex-math></inline-formula> on the Android platform and evaluate it based on a real driving trace dataset generated in the city of Chongqing, China. Experimental results demonstrate that <inline-formula><tex-math notation="LaTeX">${\sf VTracer}$</tex-math></inline-formula> achieves the excellent performance in terms of matching accuracy, compression ratio, and it also costs the acceptable memory, energy, and app size.
BLAST-NET: Semantic Segmentation of Human Blastocyst Components via Cascaded Atrous Pyramid and Dense Progressive Upsampling

Components of a human blastocyst (day-5 embryo) and their morphological attributes highly correlate with the embryo’s potentials for a viable pregnancy. Automatic semantic segmentation of human blastocyst components is a crucial step toward achieving objective quality assessment of such blastocyst. In this paper, a semantic segmentation system is proposed for human blastocyst components in microscopic images. The proposed Blast-Net features two novel components: a Cascaded Atrous Pyramid Pooling (CAPP) module to incorporate multi-scale global contextual priors, and a Dense Progressive Sub-pixel Upsampling (DPSU) module to recover the high-resolution prediction map. Experimental results confirm that the proposed method achieves the best-reported segmentation performance to date with a mean Jaccard Index of 82.85 % for microscopic images of the human blastocyst.
Markov Random Fields for Collaborative Filtering

In this paper, we model the dependencies among the items that are recommended to a user in a collaborative-filtering problem via a Gaussian Markov Random Field (MRF). We build upon Besag's auto-normal parameterization and pseudo-likelihood, which not only enables computationally efficient learning, but also connects the areas of MRFs and sparse inverse covariance estimation with autoencoders and neighborhood models, two successful approaches in collaborative filtering. We propose a novel approximation for learning sparse MRFs, where the trade-off between recommendation-accuracy and training-time can be controlled. At only a small fraction of the training-time compared to various baselines, including deep nonlinear models, the proposed approach achieved competitive ranking-accuracy on all three well-known data-sets used in our experiments, and notably a 20% gain in accuracy on the data-set with the largest number of items.
Tetanus - three cases in calves.

Tetanus toxemia is caused by a specific neurotoxin produced by Clostridium tetani in necrotic tissue. There is a characteristic elevation of the ears and tail with generalized stiffness. The incubation period varies from 1 to several weeks, but usually averages 10 – 14 days. Bloat is often one of the first signs noted in ruminants. Usually, but not always, associated with visible infected wounds. Tetanus occurs in all parts of the world and is most common in closely settled areas under intensive cultivation. It occurs in all farm animals, mainly as individual, sporadic cases, although outbreaks are occasionally observed in young cattle, goats, and sheep following wounding management procedure. Cases of tetanus in cattle of Slovenia are rare. Reported are three cases of tetanus in calves. Photos and video film were taken.
Implementing formulary recommendations in primary care: effect on patient outcomes.

This research aimed to measure the effect on health outcomes of implementing selected recommendations of the Grampian Joint Drug Formulary in primary care. Antibiotics used in the treatment of uncomplicated lower urinary tract infections (UTIs), ulcer healing agents and peripheral vasodilators were selected for study, thereby reflecting both acute and chronic prescribing. For the UTI study, 12 randomly selected high and low prescribers of trimethoprim, the recommended agent, each agreed to distribute 20 patient questionnaires. Following a period of 18 months and despite repeated contact with the GPs, only 89/480 (19%) questionnaires had been distributed. Patient response was, however, very high with 80 (90%) questionnaires returned. Health outcome measures identified that trimethoprim resulted in no or mild symptoms in 40/45 (91 %) of patients. These findings must be interpreted with caution due to the low level of questionnaire distribution and thus cannot be extrapolated to the total population of patients. In addition, the poor questionnaire distribution did not permit comparison between trimethoprim and non-recommended therapy. One hundred and eighty four patients receiving repeat prescriptions for ulcer healing agents were identified from one general practice. Therapy in 95 patients did not adhere to formulary recommendations. Changes to therapy were considered inappropriate in 11 patients due to factors such as severe depression and a further 8 were deemed unsuitable for participation for non-clinical reasons. The remaining 76 patients were contacted with 19 (25%) refusing to participate. Fifty seven patients were interviewed using the Glasgow Dyspepsia Severity Score and Short Form 36 (SF-36). Changes in health outcomes were measured for 21 patients where a change in therapy had taken place. These results were difficult to interpret due to the diversity of changes recommended and the lack of data relating to those patients not participating. Work involving peripheral vasodilators aimed to determine the effect on health outcomes of cessation of therapy. Forty five patients receiving these agents in 2 practices were identified, although 8 had not requested a prescription in the previous year. Two further patients were excluded from the study due to cancer and old age. The remaining 35 agreed to be interviewed using the Walking Impairment Questionnaire and SF-36. All patients were subsequently instructed to stop therapy for 2 months, although 6 (17%) refused to follow this instruction, one patient was seriously ill thus was excluded and 3 refused to be reinterviewed. Of the remaining 25 patients, no significant differences were observed in the domains studied. Seventeen patients (68%) expressed no desire to restart therapy, generating considerable savings. These results must be interpreted with caution since those not stopping therapy or refusing re-interview are likely to have responded differently to those completing the study. The measurement of health outcomes following formulary implementation deserves further work.
General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme

The incremental aggregated gradient algorithm is popular in network optimization and machine learning research. However, the current convergence results require the objective function to be strongly convex. And the existing convergence rates are also limited to linear convergence. Due to the mathematical techniques, the stepsize in the algorithm is restricted by the strongly convex constant, which may make the stepsize be very small (the strongly convex constant may be small). In this paper, we propose a general proximal incremental aggregated gradient algorithm, which contains various existing algorithms including the basic incremental aggregated gradient method. Better and new convergence results are proved even with the general scheme. The novel results presented in this paper, which have not appeared in previous literature, include: a general scheme, nonconvex analysis, the sublinear convergence rates of the function values, much larger stepsizes that guarantee the convergence, the convergence when noise exists, the line search strategy of the proximal incremental aggregated gradient algorithm and its convergence.
Elucidation of ball bearing performance utilizing product functions of vibration signals and locality sensitive discriminant analysis

Dimensionality is a decent term utilized for the dimension-related issues of feature vectors for the condition assessment of bearings. It has been a challenging task while dealing with the characteristic information conservation of the sampled data. Feature vectors with higher dimensions provide an accurate description of the condition, while lower dimension vectors are easy to be classified. These conditions make the dimensionality of feature space a big challenge in the performance evaluation of critical machine parts like bearings. This paper presents a judicious and wise application of a linear approach, locality sensitive discriminant analysis along with the rational use of local mean decomposition in performance degradation assessment. The combination effectively solves the problems of insufficient training samples and large dimensionality of fault features, which imparts excessive noise and causes loss of competent hidden characteristics. In such cases, local structures of feature space are more crucial than the global one. These obstacles can be answered by imparting locality sensitive discriminant analysis as an initial operation. Locality sensitive discriminant analysis is a linear dimensionality reduction tool, which explores the precise projections that amplify the margin between data points and prepare a conservation aid to preserve faulty information of bearings. The steps are followed to achieve the same: decomposition of vibration signal into product functions; calculation of fault features; third, higher dimensionality of the features is reduced with the implementation of locality sensitive discriminant analysis and some prime features are selected with the proposed criterion; the reduced features are further clustered and a trained model of assessment is prepared. Finally, health indicator is calculated from the trained model and test features. The proposed technique is verified on two bearing datasets. The superiority of the technique has been envisaged by comparing the method with different similar assessment methods i.e. time domain features, linear discriminant analysis, and principal component analysis.
On dynamically monitoring aggregate warranty claims for early detection of reliability problems

Abstract Warranty databases managed by most world-leading manufacturers are constantly expanding in the big data era. An important application of warranty databases is to detect unobservable reliability problems that emerge at design and/or manufacturing stages, through modeling and analysis of warranty claims data. Usually, serious reliability problems will result in certain abnormal patterns in warranty claims, which can be captured by appropriate statistical methods. In this article, a dynamic control charting scheme is developed for early detection of reliability problems by monitoring warranty claims one period after another, over the product life cycle. Instead of specifying a constant control limit, we determine the control limits progressively by considering stochastic product sales and non-homogeneous failure processes, simultaneously. The false alarm rate at each time period is controlled at a desired level, based on which abrupt changes in field reliability, if any, will be detected in a timely manner. Furthermore, a maximum-likelihood-based post-signal diagnosis scheme is presented to aid in identifying the most probable time of problem occurrence (i.e., change point). It is shown, through in-depth simulation studies and a real case study, that the proposed scheme is able to detect an underlying reliability problem promptly and meanwhile estimate the change point with an acceptable accuracy. Finally, a moving window approach concerning only recent production periods is introduced to extend the original model so as to mitigate the “inertia” problem.
Bayesian Spatio-Temporal Modeling for Forecasting, Trend Assessment and Spatial Trend Filtering

This work develops Bayesian spatio-temporal modeling techniques specifically aimed at studying several aspects of our motivating applications; to include vector-borne disease incidence and air pollution levels. A key attribute of the proposed techniques are that they are scalable to extremely large data sets which consist of spatio-temporally oriented observations. Largely, the scalability of our modeling strategies is accomplished in two primary ways. First, through the introduction of carefully constructed latent random variables we are able to develop Markov chain Monte Carlo (MCMC) sampling algorithms that consist primarily of Gibbs steps. This leads to the fast and easy updating of the model parameters from common distributions. Second, for the spatio-temporal aspects of the models, a novel sampling strategy for Gaussian Markov random fields (GRMFs) that can be easily implemented (in parallel) within MCMC sampling algorithms is used. The performance of the proposed modeling strategies are demonstrated through extensive numerical studies and are further used to analyze vector-borne disease data measured on canines throughout the conterminous United States and PM2.5 levels measured at weather stations throughout the Eastern United States. In particular, we begin by developing a Poisson regression model that can be used to forecast the incidence of vector-borne disease throughout a large geographic area. The proposed model accounts for spatio-temporal dependence through a vector autoregression and is fit through a Metropolis-Hastings based Markov chain Monte Carlo (MCMC) sampling algorithm. The model is used to forecast the prevalnce of Lyme disease (Chapter 2) and Anaplasmosis (Chapter 3) in canines throughout the United States. As a part of these studies we also evaluate the significance of various climatic and socio-economic drivers of disease. We then present (Chapter 4) the development of the chromatic sampler for GMRFs. The chromatic sampler is an MCMC sampling technique that exploits the Markov property of GMRFs to sample large groups of parameters in parallel. A greedy algorithm for finding such groups of parameters is presented. The methodology is found
A Game-Theoretical Approach for User Allocation in Edge Computing Environment

Edge Computing provides mobile and Internet-of-Things (IoT) app vendors with a new distributed computing paradigm which allows an app vendor to deploy its app at hired edge servers distributed near app users at the edge of the cloud. This way, app users can be allocated to hired edge servers nearby to minimize network latency and energy consumption. A cost-effective edge user allocation (EUA) requires maximum app users to be served with minimum overall system cost. Finding a centralized optimal solution to this EUA problem is NP-hard. Thus, we propose EUAGame, a game-theoretic approach that formulates the EUA problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the EUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the EUA problem can be solved effectively and efficiently.
Polarization-Reconfigurable Antenna Array for Millimeter-Wave 5G

The design of 5G 4-element antenna array system with the capability of switching its operation between right hand circular polarization (RHCP) and left hand circular polarization (LHCP) is proposed. The CP-reconfigurability is achieved by electronically switching two PIN-diode pairs in the feeding circuit, which consists of a branch line coupler and two feeding arms. The proposed configuration reduces the number of tuning elements and allows the use of imperfect PIN diodes at millimeter-wave frequency. The fabricated prototype exhibits a 3-dB axial ratio bandwidth from 27.2 to 28.35 GHz. A maximum measured gain of about 6 dBic and a radiation efficiency of around 51% over the bandwidth are achieved for both CP states.
Application of Marketing Mix Elements to Improve Customer Satisfaction of Main Library, University of Colombo

This research study focuses on the application of marketing mix elements mainly Product, Price, Place and Promotion to improve user satisfaction of a library. Libraries need to adopt marketing mix strategies in order to retain existing users and to attract more users to the library. The main objective of this study was to identify which element among product, price, place and promotion mostly affect library user satisfaction. This study involved 364 library users who were selected using Stratified Random Sampling technique. The sample size was determined by using Krejcie & Morgan’s “table for determining sample size of a given population”. Self-Administrated Questionnaires were used for data collection from the sample. Data analysis and presentation were done using SPSS and Microsoft Excel 2013 software packages. The major objective of this study was tested using stepwise multiple regression analysis and the specific objectives were tested using Pearson Correlation Coefficient. The result depicts that, there is a positive linear correlation between these four variables and library user satisfaction. There is a strong positive relationship between Product & Promotion elements with the library user satisfaction, when compared with other two elements. The result indicates that Promotion element had a significant impact on library user satisfaction. Library can focus more on promotion element and consider more on effective promotion methods in order to improve library user satisfaction. Survey results indicate that, 26% of the users prefer receiving emails regarding library services mostly as a promotional method. 41% of the respondents have stated that, library does not use effective promotion methods. Library can interact with their users through social networking, especially via Facebook. And most importantly a good word of mouth regarding the library may increase the positive image of the library and it would result in gaining more users. This research study is significant since the library administration can identify and develop favourable marketing strategies to improve user satisfaction by considering the marketing mix elements; product, price, place and especially promotion.