ANALISIS DAN PERBANDINGAN QUALITY OF SERVICE VIDEO CONFERENCE JITSI DAN BIGBLUEBUTTON PADA VIRTUAL PRIVATE SERVER

The use of video conferencing technology is increasing due to the COVID-19 pandemic. Bigbluebutton and jitsi are examples of open source video conferencing platforms that can be installed on their own servers. The server is created using a cloud-based virtual machine. Analysis of quality of service which includes delay, packet loss, throughput, and jitter is needed to determine the quality of service and the comparison of the two platforms. Observations were also made on the use of CPU, memory / RAM, and disk usage for each server. There are 3 test scenarios carried out. Each scenario is carried out on each existing VM specification. From this test, it is known that in the delay parameter, the highest bigbluebutton is obtained, which is 35,35 ms. And then the highest jitsi delay is 17,66 ms. In packet loss parameters, jitsi obtained the highest yield, namely 0,29%, while for bigbluebutton only 0,16% of packet loss was the highest. Throughput, bigbluebutton and jitsi all got very bad results. However, bigbluebutton obtained better results, namely, the highest throughput was 5.6%. While Jitsi obtained the highest throughput, namely 2,8%. Whereas for the jitter parameter, jitsi obtained 0,00 ms results on all tests in each VM. Meanwhile, bigbluebutton, get 0,1 ms on test 3 on VM 1

TRACKING THE PILLING PIPELINE: LIMITATIONS, CHALLENGES AND A CALL FOR NEW METHODOLOGICAL FRAMEWORKS IN INCEL AND MANOSPHERE RESEARCH

The manosphere is an online network of disparate formations, which are united in their antipathy toward feminism, their reliance on evolutionary psychology and their belief that Western civilization is under threat. In recent years, a growing body of scholarship on the manosphere has emerged from a range of disciplinary perspectives. Much of this work sits within internet studies but there are also significant contributions from gender studies, social psychology and terrorism / cybersecurity studies. The purpose of this paper is to take stock of the current research, to identify methodological limitations, and to propose some new interdependent research frameworks and methods. To date, much of the work conducted on the manosphere and its various subgroups (e.g. incel) relies on gathering a dataset from one platform and subjecting it to either manual or machine analysis to identify key themes or characteristics. While this categorisation has been important, its frequent replication has led to a certain stagnation of knowledge, as we are missing the dynamic aspects of how and where ideas travel and interconnect. We call for a conceptual shift away from thinking of manosphere communities such as incel as isolated, homogenous identity groups, to conceiving of them instead as a multifaceted, ever-evolving online ecosystem. We map out a number of key pathways that need to be explored, outlining methodologies for each. Approaching the incel/manosphere as a dynamic ecosystem, we argue, will take knowledge of this phenomenon in important new directions, as well as opening up new space for inter-disciplinary collaboration.

Optimising The Reference Point Within A Journal Bearing Using Laser Alignment

This paper observes different alignment conditions in a journal-bearing-supported machine (i.e. propeller bearings within the stern tube of a marine vessel). A test rig is built so that the driven machine has fixed supports, roller bearings in an electrical motor. An alignment tolerance is met at the coupling of the journal, which is positioned within three journal bearings. This research can be applied to any industry with machines of the same characteristic. The journal bearings are designed to simulate the large tolerance seen in a vessel’s stern tube only for lubrication control oil is used for the bush bearings. Various sensors are placed on the test rig to record data for later machine analysis. Using proximity probes and accelerometers the vibration of the journals and ball bearings can be visualised using orbital plotting and Fast Fourier Transform (FFT) through both LabVIEW and MATLAB respectively. Four alignment procedures are applied to the coupling using a laser unit. The first two will be so that the offset and angular misalignment is produced in the FFT spectrums as per ISO10816 Mechanical vibration so that a reference point is formed for the test rig. The other two alignment procedures represent current industry practice. These procedures are then compared to see which is the preferred reference point that will optimise the reliability value of a whole system. Finding the procedure with minimal vibration will increase the design life of housing structures for such machines, which will increase the overall profit of the shipping industry. Our developments and procedures will be further utilized in practical classes to improve maritime engineering education.

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort

Background Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.

Simulation Tests of a Cow Milking Machine—Analysis of Design Parameters

The objective of this paper was to create a mathematical model of vacuum drops in a form that enables the testing of the impact of design parameters of a milking cluster on the values of vacuum drops in the claw. Simulation tests of the milking cluster were conducted, with the use of a simplified model of vacuum drops in the form of a fourth-degree polynomial. Sensitivity analysis and a simulation of a model with a simplified structure of vacuum drops in the claw were carried out. As a result, the impact of the milking machine’s design parameters on the milking process could be analysed. The results showed that a change in the local loss and linear drag coefficient in the long milk duct will have a lower impact on vacuum drops if a smaller flux of inlet air, a higher head of the air/liquid mix, and a higher diameter of the long milk tube are used.

COVID-19 Diagnosis from CT Imaging using Imaging and Machine Analysis

Coronavirus (COVID-19) is spreading rapidly around the world and, as of October 2020, more than 1,966,000 people have been infected in more than 200 countries. Early detection of COVID-19 is essential for the provision and protection of HIV-negative people in adequate health care for patients. To do this, we developed an automated diagnostic program for COVID-19 from pneumonia (CPA) obtained from chest tomography (CT). We propose, in particular, the Noise Resilient method of machine learning that focuses on regions of lung infection while making diagnostic decisions. Note that the sizes of the infection sites between COVID-19 and CAP are not well measured, in part due to the rapid progression of COVID-19 after the onset of symptoms. Large amounts of CVID-19 CT data from hospitals have been used to evaluate our frameworks.

Graphic novels through the lens of Goodreads reviews: Artistic, textual, or blend of both?

In this study, a textual analysis of the linguistic characteristics of Goodreads user-generated reviews associated with popular graphic novels revealed reviewers’ rich evaluations of both textual and visual characteristics of the novels as well as the embodied orientation of the reviewers’ narrations, wherein positive emotions associated with the reading experience dominated. Overall, the blend of users’ unique perceptions of textual and visual characteristics of graphic novels contributes to the genre’s vivid representation and discoverability. The machine analysis of user-generated reviews revealed a high rate of function words, pronouns, and auxiliary verbs, which may suggest reviewers’ social orientation. This high rate of function words and the overall positive tone of the reviews may also be interpreted as reviewers’ attempts to promote their reviews and influence others’ reading choices.