Covid-19 Space-time Cluster Detection Using Retrospective Analysis

Background: As of the 31st of January 2021, there had been 102,399,513 confirmed cases of COVID-19 worldwide, with 2,217,005 deaths reported to WHOThe goal of this study is to uncover the spatiotemporal patterns of COVID 19 in Ethiopia, which will aid in the planning and implementation of essential preventative measures. Methods We obtained data on COVID 19 cases reported in Ethiopia from November 23 to December 29, 2021, from an Ethiopian health data website that is open to the public.Kulldorff's retrospective space-time scan statistics were utilized to detect the temporal, geographical, and spatiotemporal clusters of COVID 19 at the county level in Ethiopia, using the discrete Poisson probability model. Results: In Ethiopia, between November 23 and December 29, 2021, a total of 22,199 COVID 19 cases were reported.The COVID 19 cases in Ethiopia were strongly clustered in spatial, temporal, and spatiotemporal distribution, according to the results of Kulldorff's scan. statisticsThe most likely Spatio-temporal cluster (LLR = 70369.783209, RR = 412.48, P 0.001) was mostly concentrated in Addis Ababa and clustered between 2021/11/1 and 2021/11/30.Conclusion: From November 23 to December 29, 2021, this study found three large COVID 19 space-time clusters in Ethiopia, which could aid in future resource allocation in high-risk locations for COVID 19 management and prevention.

Research on quantum cognition in autonomous driving

Autonomous vehicles for the intention of human behavior of the estimated traffic participants and their interaction is the main problem in automatic driving system. Classical cognitive theory assumes that the behavior of human traffic participants is completely reasonable when studying estimation of intention and interaction. However, according to the quantum cognition and decision theory as well as practical traffic cases, human behavior including traffic behavior is often unreasonable, which violates classical cognition and decision theory. Based on the quantum cognitive theory, this paper studies the cognitive problem of pedestrian crossing. Through the case analysis, it is proved that the Quantum-like Bayesian (QLB) model can consider the reasonability of pedestrians when crossing the street compared with the classical probability model, being more consistent with the actual situation. The experiment of trajectory prediction proves that the QLB model can cover the edge events in interactive scenes compared with the data-driven Social-LSTM model, being more consistent with the real trajectory. This paper provides a new reference for the research on the cognitive problem of intention on bounded rational behavior of human traffic participants in autonomous driving.

Evaluation of an integrative Bayesian peptide detection approach on a combinatorial peptide library

Detection of peptides lies at the core of bottom-up proteomics analyses. We examined a Bayesian approach to peptide detection, integrating match-based models (fragments, retention time, isotopic distribution, and precursor mass) and peptide prior probability models under a unified probabilistic framework. To assess the relevance of these models and their various combinations, we employed a complete- and a tail-complete search of a low-precursor-mass synthetic peptide library based on oncogenic KRAS peptides. The fragment match was by far the most informative match-based model, while the retention time match was the only remaining such model with an appreciable impact––increasing correct detections by around 8 %. A peptide prior probability model built from a reference proteome greatly improved the detection over a uniform prior, essentially transforming de novo sequencing into a reference-guided search. The knowledge of a correct sequence tag in advance to peptide-spectrum matching had only a moderate impact on peptide detection unless the tag was long and of high certainty. The approach also derived more precise error rates on the analyzed combinatorial peptide library than those estimated using PeptideProphet and Percolator, showing its potential applicability for the detection of homologous peptides. Although the approach requires further computational developments for routine data analysis, it illustrates the value of peptide prior probabilities and presents a Bayesian approach for their incorporation into peptide detection.

Metastatic gastroesophageal cancer in older patients – is this patient cohort represented in clinical trials?

Background Older patients are underrepresented in the clinical trials that determine the standards of care for oncological treatment. We conducted a review to identify whether there have been age-restrictive inclusion criteria in clinical trials over the last twenty five years, focusing on patients with metastatic gastroesophageal cancer. Methods A search strategy was developed encompassing Embase, PubMed and The Cochrane Library databases. Completed phase III randomised controlled trials evaluating systemic anti-cancer therapies in metastatic gastroesophageal malignancies from 1st January 1995 to 18th November 2020 were identified. These were screened for eligibility using reference management software (Covidence; Veritas Health Innovation Ltd). Data including age inclusion/exclusion criteria and median age of participants were recorded. The percentage of patients ≥ 65 enrolled was collected where available. The change over time in the proportion of studies using an upper age exclusion was estimated using a linear probability model. Results Three hundred sixty-three phase III studies were identified and screened, with 66 trials remaining for final analysis. The majority of trials were Asian (48%; n = 32) and predominantly evaluated gastric malignancies, (86%; n = 56). The median age of participants was 62 (range 18–94). Thirty-two percent (n = 21) of studies specified an upper age limit for inclusion and over half of these were Asian studies. The median age of exclusion was 75 (range 65–80). All studies prior to 2003 used an upper age exclusion (n = 12); whereas only 9 that started in 2003 or later did (17%). Among later studies, there was a very modest downward yearly-trend in the proportion of studies using an upper age exclusion (-0.02 per year; 95%CI -0.05 to 0.01; p = 0.31). Fifty-two percent (n = 34) of studies specified the proportion of their study population who were ≥ 65 years. Older patients represented only 36% of the trial populations in these studies (range 7–60%). Conclusions Recent years have seen improvements in clinical trial protocols, with many no longer specifying restrictive age criteria. Reasons for poor representation of older patients are complex and ongoing efforts are needed to broaden eligibility criteria and prioritise the inclusion of older adults in clinical trials.

The risk assessment on the security of industrial internet infrastructure under intelligent convergence with the case of G.E.'s intellectual transformation

<abstract> <p>The industrial internet depends on the development of cloud computing, artificial intelligence, and big data analysis. Intelligent fusion is dependent on the architecture and security features of the industrial internet. Firstly, the paper studies the infrastructure mode that needs to be solved urgently in the industrial internet and provides a possible infrastructure mode and related security evaluation system. Secondly, it analyses the digital transformation process with the case of G.E.os industrial nternet development practice. It clarifies that G.E. is forming a new value closed-loop through digital and strategy mixed channels. Thirdly, industrial internet security research is described within multiple viewpoints based on industrial internet applications, the security service and security assurance defense systemos architecture, and the non-user entrance probability model. Finally, the paper illustrates the changes in knowledge workflow and social collaboration caused by the industrial internet under intelligent manufacture.</p> </abstract>

Education and training system post Covid-19: results from a probability model to measure students’ learning performance

The Covid-19 affected people regardless of nationality, level of education, income or gender. However, students from privileged backgrounds, supported by their parents could find their way past closed school doors to alternative learning opportunities. This crisis has exposed the many inadequacies and inequalities in our education systems. This article presents the GPU System as a tool for collecting, managing and monitoring. The PON 2014/2020 For the School has been conceived for achieving an intelligent, equal, sustainable, and inclusive growth. In order to measure the learnings performance of students, a probability model was implemented to measure performance improvement. The data refer to the grades attributed to students before and after the delivery of the educational activities. Results show that the probability of registering a training success triggered by the training course is greater for the foreign languages area, generating inclusion and social integration mechanisms, as well as mediation and intercultural understanding. Il sistema di istruzione e di formazione dopo il Covid-19: risultati da un modello per misurare gli apprendimenti degli studenti. Il Covid-19 ha colpito tutti gli individui indipendentemente dalla nazionalità, dal livello di istruzione, dal reddito o dal genere. Tuttavia, gli studenti provenienti da ambienti privilegiati, supportati dai loro genitori hanno potuto intravedere più agevolmente la loro strada oltre le porte chiuse della scuola verso opportunità di apprendimento alternative. Questa crisi ha messo in luce le molte inadeguatezze e disuguaglianze nei nostri sistemi educativi. In questo studio si presenta il Sistema GPU come strumento di raccolta, gestione e monitoraggio. In tale contesto si inserisce il PON 2014/2020 Per la Scuola, concepito per realizzare una crescita intelligente, equa, sostenibile e inclusiva. Al fine di misurare le performance degli apprendimenti degli studenti è stato implementato un modello di probabilità finalizzato a misurare il successo formativo. I dati si riferiscono alle votazioni attribuite agli studenti prima e dopo l’azione formativa. I risultati mostrano come la probabilità di registrare un successo formativo generato dal percorso formativo intrapreso sia maggiore per l’area relativa alle lingue straniere, generando meccanismi di inclusione ed integrazione sociale, nonché la mediazione e la comprensione interculturale.

Variability in rainfall dispersion in Madhya Pradesh

An attempt has been made to examine distribution and dispersion in rainfall variability in Madhya Pradesh by applying Gamma distribution probability model, The spatial and regional distribution of shape and scale parameters of the Gamma distribution have been examined, Periods of water surpluses and deficiencies have been identified by comparing the probability rainfall with the water requirement. Regression equations have been developed to find probabilitistic rainfall from the mean rainfall. Agronomic practices have been evaluated for efficient utilization of water resources for crop planning.