Measuring and Modeling Group Dynamics in Open-Source Software Development: A Tensor Decomposition Approach

Many open-source software projects depend on a few core developers, who take over both the bulk of coordination and programming tasks. They are supported by peripheral developers, who contribute either via discussions or programming tasks, often for a limited time. It is unclear what role these peripheral developers play in the programming and communication efforts, as well as the temporary task-related sub-groups in the projects. We mine code-repository data and mailing-list discussions to model the relationships and contributions of developers in a social network and devise a method to analyze the temporal collaboration structures in communication and programming, learning about the strength and stability of social sub-groups in open-source software projects. Our method uses multi-modal social networks on a series of time windows. Previous work has reduced the network structure representing developer collaboration to networks with only one type of interaction, which impedes the simultaneous analysis of more than one type of interaction. We use both communication and version-control data of open-source software projects and model different types of interaction over time. To demonstrate the practicability of our measurement and analysis method, we investigate 10 substantial and popular open-source software projects and show that, if sub-groups evolve, modeling these sub-groups helps predict the future evolution of interaction levels of programmers and groups of developers. Our method allows maintainers and other stakeholders of open-source software projects to assess instabilities and organizational changes in developer interaction and can be applied to different use cases in organizational analysis, such as understanding the dynamics of a specific incident or discussion.

IMPACT OF THE MATHEMATICAL MODELING ON CONCEPTUAL UNDERSTANDING AMONG STUDENT-TEACHERS

The present study investigates the effect of mathematical modeling on conceptual understanding among student-teachers. Also, the study proposes relevant materials and activities in mathematical modeling. Two classes of 140 student-teachers participated in the study. Mathematical modeling instruction was used in the treatment group, while the comparison group was taught by a teacher-centered method. T-Test results showed a difference between the treatment and comparison groups in terms of conceptual understanding. Moreover, the mathematical modeling group showed improvement in the knowledge, comprehension, and application of geometry and measurement concepts. The results of this study are novel as they are introduced to student-teachers to facilitate teaching mathematics among children. The researchers suggested engaging student-teachers in rich modeling activities, which would deepen their students' mathematics learning.

Establishment and Verification On A Death Risk Model of Sepsis Patients Within 30 Days

Background/Objective: To establish and validate an individualized nomogram to predict the probability of death within 30 days in patients with sepsis would help clinical physicians to make correct decision. Methods We collected data of 1,205 patients with sepsis. These included 16 indexes like age and blood, randomly assigned to the modeling and verification groups. In the modeling group, the independent risk factors related to death within 30 days were analyzed. Besides, a nomogram was established to draw the receiver-operating characteristic (ROC) curve of the subjects. Subsequently, the discriminant ability of the model was evaluated by the area under the ROC curve (AUC). Then, a calibration chart and Hosmer-Lemeshow test were employed to evaluate the calibration degree of the model, and the Decline Curve Analysis (DCA) test was used to evaluate the clinical effect of the model. Results The different independent risk factors related to the death of sepsis patients within 30 days included pro-brain natriuretic peptide (pro.bnp), albumin, lactic acid (lac), oxygenation index, mean arterial pressure (map), and hematocrit (hct). The AUC of the modeling and verification groups were 0.815 and 0.806, respectively. Moreover, the P-values of the Hosmer-Lemeshow test in the two groups were 0.391 and 0.100, respectively, and the DCA curves of the two groups were both above the two extreme curves. Conclusion Our model presents good significance for predicting the death of sepsis patients within 30 days. Therefore, there is a need to implement this model in clinical practice, as prompt prediction could help tailor treatment regimens and enhance survival outcomes.

Intelligent and Adaptive Web Page Recommender System

In this manuscript, an Intelligent and Adaptive Web Page Recommender System is proposed that provides personalized, global and group mode of recommendations. The authors enhance the utility of a trie node for storing relevant web access statistics. The trie node enables dynamic clustering of users based on their evolving browsing patterns and allows a user to belong to multiple groups at each navigation step. The system takes cues from the field of crowd psychology to augment two parameters for modeling group behavior: Uniformity and Recommendation strength. The system continuously tracks the user’s responses in order to adaptively switch between different recommendation-criteria in the group and personalized modes. The experimental results illustrate that the system achieved the maximum F1 measure of 83.28% on CTI dataset which is a significant improvement over the 70% F1 measure reported by Automatic Clustering-based Genetic Algorithm, the prior web recommender system.

Modeling ocean-induced rapid Earth rotation variations: an update

We revisit the problem of modeling the ocean’s contribution to rapid, non-tidal Earth rotation variations at periods of 2–120 days. Estimates of oceanic angular momentum (OAM, 2007–2011) are drawn from a suite of established circulation models and new numerical simulations, whose finest configuration is on a "Image missing"$$^\circ $$ ∘ grid. We show that the OAM product by the Earth System Modeling Group at GeoForschungsZentrum Potsdam has spurious short period variance in its equatorial motion terms, rendering the series a poor choice for describing oceanic signals in polar motion on time scales of less than $$\sim $$ ∼ 2 weeks. Accounting for OAM in rotation budgets from other models typically reduces the variance of atmosphere-corrected geodetic excitation by $$\sim $$ ∼ 54% for deconvolved polar motion and by $$\sim $$ ∼ 60% for length-of-day. Use of OAM from the "Image missing"$$^\circ $$ ∘ model does provide for an additional reduction in residual variance such that the combined oceanic–atmospheric effect explains as much as 84% of the polar motion excitation at periods < 120 days. Employing statistical analysis and bottom pressure changes from daily Gravity Recovery and Climate Experiment solutions, we highlight the tendency of ocean models run at a 1$$^\circ $$ ∘ grid spacing to misrepresent topographically constrained dynamics in some deep basins of the Southern Ocean, which has adverse effects on OAM estimates taken along the 90$$^\circ $$ ∘ meridian. Higher model resolution thus emerges as a sensible target for improving the oceanic component in broader efforts of Earth system modeling for geodetic purposes.

ICAM-1-carrying targeted nano contrast agent for evaluating inflammatory injury in rabbits with atherosclerosis

To investigate the feasibility of using ICAM-1-targeted nano ultrasonic contrast to evaluate the degree of inflammatory injury at different stages in the abdominal aorta of rabbits with atherosclerosis (AS). Twenty-five experimental rabbits were assigned to five groups: the control group (A); the week-4 after modeling group (B); the week-8 after modeling group (C); the week-12 after modeling group (D); the week-16 after modeling group (E). All groups were given 2D ultrasonography, conventional ultrasonic contrast (SonoVue), and ICAM-1-targeted nano ultrasonic contrast, respectively. Signal intensity of vascular perfusion was evaluated. Signal intensity of ICAM-1-targeted nano ultrasonic contrast was substantially enhanced and prolonged in the vascular wall of the abdominal bubble aorta increased in B, C, D, and E groups (all P < 0.05). A positive linear correlation between intensity and the expression of ICAM-1 (r = 0.895, P < 0.001). The intensity of outer membrane was enhanced from week 4 to week 12, and both the intima-media membrane and outer membrane were enhanced with double-layer parallel echo at week 16, which was in line with the progression of atherosclerotic plaque inflammatory injury. ICAM-1-targeted nano contrast agent would be possibly a novel non-invasive molecular imaging method for plaque inflammatory injury and site high expression of specific adhesion molecules in early atherosclerotic lesions.

Nomogram Model to Predict the Probability of Ovarian Hyperstimulation Syndrome in the Treatment of Patients With Polycystic Ovary Syndrome

ObjectiveThe objective of this study was to explore the risk factors of ovarian hyperstimulation syndrome (OHSS) in patients with polycystic ovary syndrome (PCOS) undergoing in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) and to establish a nomogram model evaluate the probability of OHSS in PCOS patients.MethodsWe retrospectively analyzed clinical data from 4,351 patients with PCOS receiving IVF/ICSI in our reproductive medical center. The clinical cases were randomly divided into a modeling group (3,231 cases) and a verification group (1,120 cases) according to a ratio of about 3:1. The independent risk factors correlation with the occurrence of OHSS was identified by logistic regression analysis. Based on the selected independent risk factors and correlated regression coefficients, we established a nomogram model to predict the probability of OHSS in PCOS patients, and the predictive accuracy of the model was measured using the area under the receiver operating curve (AUC).ResultsUnivariate and multivariate logistic regression analyses showed that FSH (OR, 0.901; 95% CI, 0.847–0.958; P&lt;0.001), AMH (OR, 1.259; 95% CI, 1.206–1.315; P&lt;0.001), E2 value on the day of hCG injection (OR, 1.122; 95% CI, 1.021–1.253; P&lt;0.001), total dosage of Gn used (OR, 1.010; 95% CI, 1.002–1.016; P=0.041), and follicle number on the day of hCG injection (OR, 0.134; 95% CI, 1.020–1.261; P=0.020) are the independent risk factors for OHSS in PCOS patients. The AUC of the modeling group is 0.827 (95% CI, 0.795–0.859), and the AUC of the verification group is 0.757 (95% CI, 0.733–0.782).ConclusionThe newly established nomogram model has proven to be a novel tool that can effectively, easily, and intuitively predict the probability of OHSS in the patients with PCOS, by which the clinician can set up a better clinical management strategies for conducting a precise personal therapy.

The Effect of Modeling Methods on Mirror Neurons Activity and a Motor Skill Acquisition and Retention

Purpose: Mirror neurons have been suggested as a potential neural mechanism of observational learning. The purpose of the present study was to investigate the effect of self-modeling, skilled model, and learning model on mu rhythm suppression and golf putting acquisition and retention. Method: The study was conducted on 45 male volunteer students (age, 19.4 ± 0.37 years) in three experimental groups: self-modeling, skilled, and learning models with six sessions of physical and observational training in three periods of pre-test, acquisition, and retention. In the pre-test, after the initial familiarity with the skill, participants performed 10 golf putting actions while scores were recorded. Then, electrical brain waves in C3, C4 and Cz regions were recorded during the observation 10 golf putting actions by their group-related models. The acquisition period consisted of golf putting training during six sessions, each of which included six blocks of 10 trials. Before each training block, participants observed 10 times in the forms of video of golf putting related to their group. Acquisition and delayed retention tests were also performed by recording scores of 10 golf putting actions, as well as recording electrical brain waves while observing the skill performed by the related model. Results: Mixed analysis of variance (ANOVA) showed that the mu rhythm suppression the pre-test was more in the self-modeling group in contrast to skilled model and learning model groups, but this suppression in all three groups in the acquisition and retention tests was not significantly different. In putting task variables, all three groups that did not a significant difference in the pretest period made considerable progress in learning the desired skill from the pre-test to the acquisition test, and this progress was somewhat stable until the retention test. Also, both in the acquisition and the retention periods, the self-modeling group displayed better performance than the other two groups; however, there was no significant difference between these groups. Conclusion: These findings suggest that the model-observer similarity is an important factor in modeling interventions and can affect the rate of mu rhythm suppression.

Factors affecting in-stent restenosis after angioplasty with the Enterprise stent for intracranial atherosclerotic diseases

This study investigated factors affecting the safety and in-stent restenosis after intracranial stent angioplasty using the Enterprise stent for symptomatic intracranial atherosclerotic stenosis. Between January 2017 and March 2019, patients with intracranial atherosclerotic stenosis treated with Enterprise stent angioplasty were enrolled, including 400 patients in the modeling group and 89 patients in the validation group. The clinical factors affecting in-stent restenosis after Enterprise stent angioplasty in the modeling group were analyzed, and a logistic regression model of these factors was established and validated in the validation group. The receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) were analyzed. In the modeling group with 400 patients, there were 410 lesions, including 360 stenotic lesions and 50 occluded lesions, with 176 (42.9%) lesions in the anterior circulation and 234 (57.1%) in the posterior circulation. Successful stenting was performed in 398 patients (99.5%). Stenosis was significantly (P < 0.05) improved after stenting compared with before stenting (27.7% ± 2.9% vs. 77.9% ± 8.0%). Periprocedural complications included ischemic stroke (3.25%), hemorrhagic stroke (0.75%), and death (0.50%), with a total periprocedural complication rate of 4.0%. The first follow-up angiography was performed in 348 (87.0%) patients with 359 lesions 3.5–14 months (mean 5.7 months) after stenting. In-stent restenosis occurred in 62 (17.3%) lesions, while the other 295 (82.7%) had no restenosis. Lesion location, calcification degree, balloon expansion pressure, residual stenosis, intraprocedural dissection, and cerebral blood flow TICI grade were significant (P < 0.05) risk factors for in-stent restenosis. The in-stent restenosis prediction model was established as follows: P = 1/[1 + e−(−6.070–1.391 location + 2.745 calcification + 4.117 balloon inflation pressure + 2.195 intraprocedural dissection + 1.163 residual stenosis + 1.174 flow TC grade)]. In the validation group, the AUC in the ROC curve analysis was 0.902 (95% CI: 0.836–0.969), and when the cutoff value was 0.50, the sensitivity and specificity of this model were shown to be 76.92% and 80.26%, respectively, in predicting in-stent restenosis at angiographic follow-up, with a total coincidence rate of 79.78%. In conclusion, in-stent restenosis after intracranial Enterprise stenting is affected by stenosis location, calcification, balloon inflation pressure, intraprocedural arterial dissection, residual stenosis, and cerebral flow grade, and establishment of a logistic model with these factors can effectively predict in-stent restenosis.