Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and Verification of an Industry Application to Improve the Project Manager-to-Project (PM2P) Practice

This research is a follow-up from a previous study regarding the development of a mathematical model for allocating project managers-to-projects (PM2P). The aim of the current study is to develop an industry application for the PM2P practice, in the form of a decision support system (DSS), as part of operationalising the mathematical model. Both the depth and breadth of literature surrounding the PM2P practice were appraised and integrated in a creative manner, using concepts from different disciplines, to produce an application customized to the working environment of a specific case organization. Based on real-life input data from the case organization to verify the functionality of the application, the results reveal the robustness and usefulness of the DSS. This study is the first major attempt to propose a novel and integrated industry application that is superior to existing PM2P approaches. This attempt is a contribution to existing knowledge because it addresses the issue of user-friendliness to practitioners for the first time.
Prevalence of Helicobacter pylori infection among dyspeptic patients with and without type 2 diabetes mellitus in Nigeria.

BACKGROUND This multicenter study was undertaken to determine the prevalence of Helicobacter pylori (H. pylori) infection among dyspeptic patients with and without type 2 diabetes mellitus (T2DM). METHODS Patients with dyspepsia were recruited from tertiary teaching hospitals, three in the South-West and one in the South-South regions of Nigeria, between November 2016 and August 2017. The participants had breath samples analyzed for H. pylori by the Urea Breath Test (UBT) following manufacturer's instructions. Dyspeptic patients who were diagnosed previously with T2DM were recorded. Crosstab using chi-square and correlation analyses were used to test (hypothesis) variables. RESULTS The entire cohort included 471 dyspeptics, 19 (4%) of whom had T2DM. H. pylori infection was reported in 232/471 (49.3%) dyspeptics and 13/19 T2DM patients, without significant difference between diabetics and nondiabetics. The majority (84.6%) of those positive for UBT and T2DM were in the age group 52-71 years, while none was in the age group 72-91 years. There was no statistical significance (P>0.05) between the age group, UBT and T2DM positive. CONCLUSIONS Our study showed that, in Nigeria, there is no difference in prevalence of H. pylori in dyspeptic patients with and without T2DM.
Podnebni tipi v Sloveniji

Prispevek obravnava podnebne tipe v Sloveniji. Izhodisce za podnebno clenitev je Koppenov podnebni sistem, po katerem imamo v Sloveniji tri osnovne podnebne tipe: zmernotoplo vlažno podnebje z vrocim poletjem (Cfa), zmernotoplo vlažno podnebje s toplim poletjem (Cfb) in gorsko podnebje (H). V podrobnem so osnovni tipi, glede na padavinski režim, povprecno temperaturo najhladnejsega in najtoplejsega meseca ter razmerje med oktobrskimi in aprilskimi temperaturami, razclenjeni na devet podtipov.
Does anti-Müllerian hormone predict change in menopausal symptoms following risk-reducing salpingo-oophorectomy? A prospective observational study

Abstract Objectives: The aim of this study was to investigate whether serum anti-Müllerian hormone (AMH) predicts symptom burden after risk-reducing salpingo-oophorectomy (RRSO) in order to individualize counseling. Methods: Patient-reported menopausal symptoms, sexual functioning, and psychological distress (depression and anxiety) were assessed 1 day before (T0) and 6 weeks (T1) and 7 months (T2) after RRSO. AMH was assessed before RRSO. Multivariable regression analysis was used to investigate the association between AMH and short-term and long-term change in symptom burden following RRSO. Results: Ninety-one premenopausal women at high risk of ovarian cancer were included. Presurgical AMH was not related significantly to change in symptoms post RRSO. As a secondary outcome we found that regular menses before RRSO was associated specifically with long-term increase in hot flushes (sr = 0.40, p = 0.001; total R2 = 0.171) and depression (sr = 0.29, p = 0.012; total R2 = 0.132). Earlier receipt of chemotherapy was associated with long-term improvement in sexual functioning (sr = 0.24, p = 0.041; total R2 = 0.348). Conclusion: In this cohort, AMH was not a significant predictor of change in symptoms following RRSO. Regular menses prior to RRSO and earlier receipt of chemotherapy were significantly, but relatively weakly, associated with changes in outcomes 6 weeks and/or 7 months after RRSO.
Average PSNR Optimized Cross Layer User Grouping and Resource Allocation for Uplink MU-MIMO OFDMA Video Communications

Wang et al. proposed cross layer resource allocation according to the channel state information of the physical layer and the video rate distortion of the application layer for uplink orthogonal frequency division multiple access (OFDMA) video transmission systems. Its object function is minimizing sum of all users’ video distortion. It shows significant improvement in average peak-signal-to-noise-ratio (PSNR) over the schemes which consider either the physical layer or application layer. However, minimizing sum of all users’ video distortion does not necessarily maximize average of all users’ PSNR. In this paper, we change the objective function for optimization to maximize average PSNR, and derive new optimal condition for cross layer subcarrier assignment for infinitesimal bandwidth increment. Furthermore, we extend to a multi-user multiple-input and multiple-output (MU-MIMO) uplink transmission system to improve information rate and thus video quality. We then propose an average PSNR optimized physical/application cross-layer user grouping and resource allocation for uplink MU-MIMO OFDMA video communications. The simulation results show that the proposed scheme has 4 dB PSNR gain over the previous Wang et al. cross-layer scheme and 5 dB PSNR gain over the previous physical layer only scheme for four users and SNR 12–18 dB.
The periodontal stem/progenitor cell inflammatory‐regenerative cross talk: A new perspective

Adult multipotent stem/progenitor cells, with remarkable regenerative potential, have been isolated from various components of the human periodontium. These multipotent stem/progenitor cells include the periodontal ligament stem/progenitor cells (PDLSCs), stem cells from the apical papilla (SCAP), the gingival mesenchymal stem/progenitor cells (G-MSCs), and the alveolar bone proper stem/progenitor cells (AB-MSCs). Whereas inflammation is regarded as the reason for tissue damage, it also remains a fundamental step of any early healing process. In performing their periodontal tissue regenerative/reparative activity, periodontal stem/progenitor cells interact with their surrounding inflammatory micro-environmental, through their expressed receptors, which could influence their fate and the outcome of any periodontal stem/progenitor cell-mediated reparative/regenerative activity. The present review discusses the current understanding about the interaction of periodontal stem/progenitor cells with their surrounding inflammatory micro-environment, elaborates on the inflammatory factors influencing their stemness, proliferation, migration/homing, differentiation, and immunomodulatory attributes, the possible underlying intracellular mechanisms, as well as their proposed relationship to the canonical and noncanonical Wnt pathways.
Smartphone Application Usage Prediction Using Cellular Network Traffic

The exponential rise of cellular network traffic demand due to an increased use of hand-held devices requires optimized methods to plan and deliver the necessary network bandwidth. In this paper, we propose to use cellular network traffic generated by smartphone applications to predict which applications the user is likely to be using. We conducted two experiments to assert such feasibility. In one controlled experiment required the users to use applications according to heuristic application usage guidelines. In the other experiment, the subjects were encouraged to use their phone as they normally would. In all cases, we recorded the session time, the uplink and downlink traffic and which applications are running. We subsequently used machine learning algorithms to assess the feasibility of predicting the running applications. We achieved 99% accuracy in the controlled traffic experiment. However, the performance was much lower in the arbitrary traffic monitoring experiment. This preliminary analysis may suggest that it could be possible for cellular network providers to predict what application users are running based on their real-time network usage. This would be in turn used for cellular network optimization and planning.
Optimized Super Fast Support Vector Classifiers Using Python and Acceleration of RBF Computations

An optimized implementation for a parameter-less radial-basis neural paradigm called super fast vector support classifier (SFSVC) is presented. Training resides only in a process of selecting centers and consequently it is a very fast process, linear in the number of hidden neurons. Several improvements make the resulting implementation a highly effective, high speed. , machine learning engine convenient for various applications on a large variety of platforms. It is shown that among various programming environments. , Python with optimized math library support represents the best choice for its implementation. An important (up to 40 times) speed-up for the testing time is achieved by implementing the distance calculations as matrix multiplications. , thus accelerating RBF computations on platforms with optimized math and linear algebra libraries such as Intel's MKL. Another important aspect of SFSVC. , making it suitable for various embedded platforms (e.g. micro controllers., FPGA., etc.) is the lack of any tunable parameter except a unique radius. , leading to a very convenient structure. The overall test plus training speed is better than for support vector machines while SFSVC has the important advantage of accepting arbitrary RBF kernels.
Blow Your Nose, Shrimp! Unexpectedly Dense Bacterial Communities Occur on the Antennae and Antennules of Hydrothermal Vent Shrimp

In crustaceans, as in other animals, perception of environmental cues is of key importance for a wide range of interactions with the environment and congeners. Chemoreception involves mainly the antennae and antennules, which carry sensilla that detect water-borne chemicals. The functional importance of these as exchange surfaces in the shrimp's sensory perception requires them to remain free of any microorganism and deposit that could impair the fixation of odorant molecules on sensory neurons. We report here the occurrence of an unexpected dense bacterial colonization on surface of the antennae and antennules of four hydrothermal vent shrimp species. Microscopic observation, qPCR and 16S rRNA barcoding reveal the abundance, diversity and taxonomic composition of these bacterial communities, that are compared with those found on a related coastal shrimp. Bacterial abundances vary among species. Bacteria are almost absent in coastal shrimp, meanwhile they fully cover the antennal flagella in some hydrothermal vent species. Epsilon- and Gammaproteobacteria dominate the hydrothermal shrimp-associated communities, whereas Alphaproteobacteria and Bacteroidetes are dominant in the coastal ones. Bacteria associated with vent shrimp species are most similar to known chemoautotrophic sulfur-oxidizers. Potential roles of these bacteria on the hydrothermal shrimp antennae and antennules and on sensory functions are discussed.
Modeling the Structure and Dynamics of Semantic Processing

Abstract The contents and structure of semantic memory have been the focus of much recent research, with major advances in the development of distributional models, which use word co‐occurrence information as a window into the semantics of language. In parallel, connectionist modeling has extended our knowledge of the processes engaged in semantic activation. However, these two lines of investigation have rarely been brought together. Here, we describe a processing model based on distributional semantics in which activation spreads throughout a semantic network, as dictated by the patterns of semantic similarity between words. We show that the activation profile of the network, measured at various time points, can successfully account for response times in lexical and semantic decision tasks, as well as for subjective concreteness and imageability ratings. We also show that the dynamics of the network is predictive of performance in relational semantic tasks, such as similarity/relatedness rating. Our results indicate that bringing together distributional semantic networks and spreading of activation provides a good fit to both automatic lexical processing (as indexed by lexical and semantic decisions) as well as more deliberate processing (as indexed by ratings), above and beyond what has been reported for previous models that take into account only similarity resulting from network structure.
Meta-Analysis of the Effects of QoI and DMI Fungicide Combinations on Fusarium Head Blight and Deoxynivalenol in Wheat.

Field trials were conducted in 17 U.S. states to evaluate the effects of quinone outside inhibitor (QoI) and demethylation inhibitor (DMI) fungicide programs on Fusarium head blight index (IND) and deoxynivalenol (DON) toxin in wheat. Four DMI-only treatments applied at Feekes 10.5. 1, five QoI-only treatments applied between Feekes 9 or Feekes 10.5, three QoI+DMI mixtures applied at Feekes 10.5, and three treatments consisting of a QoI at Feekes 9 followed by a DMI at Feekes 10.5. 1 were evaluated. Network meta-analytical models were fitted to log-transformed mean IND and DON data and estimated contrasts of log means were used to obtain estimates of mean percent controls relative to the nontreated check as measures of efficacy. Results from the meta-analyses were also used to assess the risk of DON increase in future trials. DMI at Feekes 10.5. 1 were the most effective programs against IND and DON and the least likely to increase DON in future trials. QoI-only programs increased mean DON over the nontreated checks and were the most likely to do so in future trials, particularly when applied at Feekes 10.5. The effects of QoI+DMI combinations depended on the active ingredients and whether the two were applied as a mixture at heading or sequentially. Following a Feekes 9 QoI application with a Feekes 10.5. 1 application of a DMI reduced the negative effect of the QoI on DON but was not sufficient to achieve the efficacy of the Feekes 10.5. 1 DMI-only treatments. Our results suggest that one must be prudent when using QoI treatments under moderate to high risk of FHB, particularly where the QoI is used without an effective DMI applied in combination or in sequence.
Modeling Heterogeneity in Healthcare Utilization Using Massive Medical Claims Data

ABSTRACT We introduce a modeling approach for characterizing heterogeneity in healthcare utilization using massive medical claims data. We first translate the medical claims observed for a large study population and across five years into individual-level discrete events of care called utilization sequences. We model the utilization sequences using an exponential proportional hazards mixture model to capture heterogeneous behaviors in patients’ healthcare utilization. The objective is to cluster patients according to their longitudinal utilization behaviors and to determine the main drivers of variation in healthcare utilization while controlling for the demographic, geographic, and health characteristics of the patients. Due to the computational infeasibility of fitting a parametric proportional hazards model for high-dimensional, large-sample size data we use an iterative one-step procedure to estimate the model parameters and impute the cluster membership. The approach is used to draw inferences on utilization behaviors of children in the Medicaid system with persistent asthma across six states. We conclude with policy implications for targeted interventions to improve adherence to recommended care practices for pediatric asthma. Supplementary materials for this article are available online.
A Discrete Fireworks Algorithm for Solving Large-Scale Travel Salesman Problem

Fireworks algorithm (FWA) is a newly proposed swarm intelligence optimization method. It simulates the fireworks explosion process to search for the best location of sparks and has demonstrated good performance in many continuous optimization problems. In this paper, we apply FWA to the travel salesman problem (TSP), a classical discrete optimization problem. We propose a discrete fireworks algorithm for TSP by combining the general framework of FWA and current ideas for solving the TSP. We call it DFWA-TSP. In DFWA-TSP, 2-opt and 3-opt edge exchange heuristic are implemented as the basic explosion operation in FWA. An adaptive strategy is designed to decide the explosion amplitude. A particular mutation method based on insertion is also used to cover the shortage of edge exchange and a new selection method based on the quality of fireworks is adopted to pick up good fireworks efficiently. Various experiments on both TSPLIB and synthetic data have been made to compare the performance of our algorithm with current heuristic methods for TSP, such as genetic algorithm and ant colony system algorithm. We conclude that our algorithm out-performs these algorithms, especially on large-scale cases.
Multimedia psychoeducation for patients with cancer who are eligible for clinical trials: A randomized clinical trial

Supporting patients’ decision making about clinical trials may enhance trial participation. To date, few theory‐based interventions have been tested to address this issue. The objective of the current study was aimed to evaluate the effect of a multimedia psychoeducation (MP) intervention, relative to a print education (PE) intervention, on patients’ decision support needs and attitudes about clinical trials.
The Impact of Test Voltage Waveform in Determining the Repetitive Partial Discharge Inception Voltage of Type I Turn/Turn Insulation Used in Inverter-Fed Induction Motors

The reliability of induction motors stators realized using Type I insulation systems is ensured only when the partial discharge inception voltage (PDIV) is larger than the operating voltage. The concept of Repetitive PDIV (RPDIV) has been proposed as a replacement of PDIV for tests performed under surges or repetitive square waves. RPDIV estimates exceed the PDIV level. The difference might depend on the surge repetition frequency, possibly providing contradicting results using different high voltage sources. Thus, the industry is concerned about the use of RPDIV in place of PDIV and the possible implications for the quality of the stators. Also, little has been discussed about the randomness of measurements and how to treat data using appropriate statistical tools. In this paper, we shall show results of PDIV and RPDIV tests performed on twisted pairs subjected to AC sinusoidal voltages, surges from a commercial surge generator, and unipolar square waves from a high voltage MOSFET. Tests are performed on ten twisted pairs to derive the distribution of RPDIV estimates and compare the differences using statistical tools. Different types of wire are considered to carry out a comparative analysis.
Parasitic Nematodes Exert Antimicrobial Activity and Benefit From Microbiota-Driven Support for Host Immune Regulation

Intestinal parasitic nematodes live in intimate contact with the host microbiota. Changes in the microbiome composition during nematode infection affect immune control of the parasites and shifts in the abundance of bacterial groups have been linked to the immunoregulatory potential of nematodes. Here we asked if the small intestinal parasite Heligmosomoides polygyrus produces factors with antimicrobial activity, senses its microbial environment and if the anti-nematode immune and regulatory responses are altered in mice devoid of gut microbes. We found that H. polygyrus excretory/secretory products exhibited antimicrobial activity against gram+/− bacteria. Parasites from germ-free mice displayed alterations in gene expression, comprising factors with putative antimicrobial functions such as chitinase and lysozyme. Infected germ-free mice developed increased small intestinal Th2 responses coinciding with a reduction in local Foxp3+RORγt+ regulatory T cells and decreased parasite fecundity. Our data suggest that nematodes sense their microbial surrounding and have evolved factors that limit the outgrowth of certain microbes. Moreover, the parasites benefit from microbiota-driven immune regulatory circuits, as an increased ratio of intestinal Th2 effector to regulatory T cells coincides with reduced parasite fitness in germ-free mice.
Reducing diabetic limb amputations in developing countries

Among all the diabetes complications, diabetic foot complications are associated with the highest morbidity and mortality. Across the globe, 40–60% of all lower extremity non-traumatic amputations are performed in patients with diabetes. The most important intervention in reducing diabetic limb amputation in developing countries is the education of patients about proper limb care. Cost-effective education should be targeted for both healthcare workers and patients. One of these programs is the Step by Step Foot Project, which was piloted and carried out in Tanzania and India. In this review, the author explores the feasible ways of reducing diabetic limb amputation which can be achieved through a trained diabetes workforce working in an effective system of care that focuses on the education of both the healthcare provider and the patient.
Predicting Contextual Influences on App Usage from a Rational Model of Time Allocation

Mobile devices have proven to be transformative tools that help users perform a variety of everyday tasks. However, they also have tremendous potential to disrupt productive and desired time allocation, facilitating time-squandering through self interruptions of workflow and undesired task switching through distracting apps. Existing research has identified a variety of context variables which help predict the next app selected, but seldom give treatment to the pattern of app usage durations essential to understanding time allocation. Here we take a psychological computing approach to identify the key environmental factors that increase risk of early termination through unwanted switching. Using a task foraging model for time allocation, we construct an integrated measure of the background factors increasing switching temptation, and show that these can be converted into a computable measure of decision context that strongly impacts app duration. The foraging model gives new insight into the structural factors that promote task persistence and predict switch temptations, and suggests new ways to design productive environments.
Dual-Band Microstrip Filtering Power Divider Based on One Single Multimode Resonator

A new design of dual-band (DB) microstrip filtering power divider (FPD) is presented and investigated in this letter. It is mainly constructed by integrating only a single multimode resonator and an isolation resistor, achieving the power division and DB filtering response, simultaneously. Compared with other reported ones, the presented FPD stands out by its simple layout and compact size with the reduced number of resonators. To validate the design concept, a prototype centering at 2.4 and 3.6 GHz is designed, fabricated, and tested. The measured results of the fabricated circuit well agree with the simulation ones, exhibiting a favorable frequency selectivity and good port-to-port isolation.