How social learning shapes the efficacy of preventative health behaviors in an outbreak

The global pandemic of COVID-19 revealed the dynamic heterogeneity in how individuals respond to infection risks, government orders, and community-specific social norms. Here we demonstrate how both individual observation and social learning are likely to shape behavioral, and therefore epidemiological, dynamics over time. Efforts to delay and reduce infections can compromise their own success, especially when disease risk and social learning interact within sub-populations, as when people observe others who are (a) infected and/or (b) socially distancing to protect themselves from infection. Simulating socially-learning agents who observe effects of a contagious virus, our modelling results are consistent with with 2020 data on mask-wearing in the U.S. and also concur with general observations of cohort induced differences in reactions to public health recommendations. We show how shifting reliance on types of learning affect the course of an outbreak, and could therefore factor into policy-based interventions incorporating age-based cohort differences in response behavior.

Application of Representation Learning based Chronological Modeling for Network Intrusion Detection

An autoencoder has the potential to overcome the limitations of current intrusion detection methods by recognizing benign user activity rather than differentiating between benign and malicious activity. However, the line separating them is quite blurry with a significant overlap. The first part of this study aims to investigate the rationale behind this overlap. The results suggest that although a subset of traffic cannot be separated without labels, timestamps have the potential to be leveraged for identification of activity that does not conform to the normal or expected behavior of the network. The second part aims to eliminate dependence on visual-inspections by exploring automation. The trend of errors for HTTP traffic was modeled chronologically using resampled data and moving averages. This model successfully identified attacks that had orchestrated over HTTP within their respective time slots. These results support the hypothesis that it is technically feasible to build an anomaly-based intrusion detection system where each individual observation need not be categorized.

Phenotypic Variability of The F2 Populations Derived from Crosses Between Local and Introduced Okra Cultivars

Okra (Abelmoschus esculenthus) is a vegetable plant consumed in immature and tender texture fruits. Crosses of local okra with introduced varieties are carried out to improve the character of the local cultivars. This study aimed to assess the variability of agronomic characters, both quantitative and qualitative characters of various families of the F2 populations and select superior families that can be used to produce new cultivars. The F2 population was derived from inbreeding and selection of the crosses between local okra cultivars with B291 and Ve022 as introduced cultivars. The study used an experimental method with an individual observation. Qualitative characters were described based on the descriptor by IBPGR, while quantitative characters were analyzed using descriptive statistics. Results showed variation in each F2 family in plant height, the number of flowers per plant and the number of fruits per plant, except for SOMB291-16. FOHVE022-8, FOHB291-15, FOHVE022-17, SOMB291-23 and SOMB291-24 families could be selected due to the extended picking time were in line with the increase of fruit quality. A broad variability of quantitative characters was found for all characters, indicating a high opportunity to obtain valuable traits and desirable segregants in F2 populations. Variations in qualitative characters were found in stem color, leaf shape, fruit color and fruit shape. The selection of plants with specific characters could be maintained with inbreeding or self-pollinated desirable segregants.

The ‘Icarus effect’ of preventative health behaviors

1AbstractOngoing efforts to combat the global pandemic of COVID-19 via public health policy have revealed the critical importance of understanding how individuals understand and react to infection risks. We here present a model to explore how both individual observation and social learning are likely to shape behavioral, and therefore epidemiological, dynamics over time. Efforts to delay and reduce infections can compromise their own success, especially in populations with age-structure in both disease risk and social learning —two critical features of the current COVID-19 crisis. Our results concur with anecdotal observations of age-based differences in reactions to public health recommendations. We show how shifting reliance on types of learning affect the course of an outbreak, and could therefore factor into policy-based interventions.

ASSESSMENT OF RISK FACTORS AND DETERMINING THE LEVEL OF PREVENTABLE FETAL DEATH IN WOMEN WITH TROUBLED PREGNANCY

Objective: To examine the risk factors of stillbirth in the Republic of Tajikistan. Methods: Maps of individual observation of the course of pregnancy and the history of births of women with antenatal and intranatal fetal death in institutions of III and II levels have been studied. Retrospectively has been conducted the clinical audit of 187 cases of stillbirth. Results: The main causes of stillbirths were intrauterine growth retardation syndrome and fetal malformations. The most common risk factors for stillbirth were factors associated with inadequate medical care and factors related to family and women. At the same time, most of the cases of antenatal fetal death (83%) and intranatal fetal death (74%) were preventable or conditionally preventable. Conclusions: Our research confirms the need for perinatal audit, which aims to find the causes and risk factors of stillbirth with the subsequent implementation of solutions to prevent such cases of stillbirths in the future. Keywords: Stillbirths, antenatal fetal death, intranatal fetal death, classification of the ReCoDe, risk factors, levels of, levels of preventable stillbirth

Issues of accounting for outliers in assessing of the nutrients runoff

The research work is devoted to the analysis of the highest values of nutrient concentrations in the runoff in the Velikaya River for the period 1969–2009. According to the results of the study, when assessing the numerical characteristics of river water pollution, it is necessary to exclude outliers from the observation series. The presence of outliers in the calculation together with the main observational data leads to a significant overestimation of the numerical characteristics of river pollution in the average annual and multi-year period. In this case, it is necessary to study the characteristics of the outliers themselves, regardless of the initial samples. The paper presents an attempt to define the probability of outliers by combining outliers information on individual observation series.

Logical Observation Identifiers Names and Codes for Laboratorians

Context.— The Logical Observation Identifiers Names and Codes (LOINC) system is supposed to facilitate interoperability, and it is the federally required code for exchanging laboratory data. Objective.— To provide an overview of LOINC, emerging issues related to its use, and areas relevant to the pathology laboratory, including the subtleties of test code selection and importance of mapping the correct codes to local test menus. Data Sources.— This review is based on peer-reviewed literature, federal regulations, working group reports, the LOINC database (version 2.65), experience using LOINC in the laboratory at several large health care systems, and insight from laboratory information system vendors. Conclusions.— The current LOINC database contains more than 55 000 numeric codes specific for laboratory tests. Each record in the LOINC database includes 6 major axes/parts for the unique specification of each individual observation or measurement. Assigning LOINC codes to a laboratory's test menu should be a defined process. In some cases, LOINC can aid in distinguishing laboratory data among different information systems, whereby such benefits are not achievable by relying on the laboratory test name alone. Criticisms of LOINC include the complexity and resource-intensive process of selecting the most correct code for each laboratory test, the real-world experience that these codes are not uniformly assigned across laboratories, and that 2 tests that may have the same appropriately assigned LOINC code may not necessarily have equivalency to permit interoperability of their result data. The coding system's limitations, which subsequently reduce the potential utility of LOINC, are poorly understood outside of the laboratory.

Bagan Kendali Robust Multivariat untuk Pengamatan Individual

The most widely used of control chart in multivariate control processing is control chart T2 Hotelling. There are 2 kinds of control chart T2 Hotelling, namely T2 Hotelling for group observation and T2 Hotelling for individual observation. In this paper, discuss the control chart T2 Hotelling for individual observation. This control chart is used for monitoring of mean vector and sample of covariance matrix. Mean vector and sample of covariance matrix are very sensitive with respect to extreme point (outliers). Therefore, it is needed an estimator of mean vector and has a stocky population covariance matrix to the outliers data. One method that can be used to detect data that contains outliers is Minimum Covariance Determinant (MCD). From the calculation results, obtained that control chart T2 Hotelling by using Fast-MCD algorithm is more sensitive to detect outliers data than T2 Hotelling classically.Keyword: T2 Hotelling, Minimum Covariance Determinant (MCD), robust, outlier AbstrakBagan kendali yang paling banyak digunakan dalam pengendalian proses secara multivariat adalah bagan kendali T2 Hotelling. Ada 2 jenis dari bagan kendali Hotelling yaitu bagan kendali Hotelling untuk pengamatan kelompok dan individual. Pada tulisan ini membahas bagan kendali Hotelling untuk pengamatan individual. Bagan kendali ini digunakan untuk memonitor vektor rata-rata dan matriks kovariansi sampel. Vektor rata-rata dan matriks kovariansi sampel sangat sensitif terhadap titik ekstrim (outliers). Oleh karena itu dibutuhkan estimator vektor rata-rata dan matriks kovariansi populasi yang kekar terhadap data outliers. Salah satu metode yang dapat digunakan untuk mendeteksi data yang mengandung outliers adalah Minimum Covariance Determinant (MCD). Dari hasil perhitungan diperoleh bahwa bagan kendali T2 Hotelling dengan algoritma Fast-MCD lebih sensitif mendeteksi data outliers daripada T2 Hotelling klasik.Kata Kunci: T2 Hotelling, Minimum Covariance Determinant (MCD), robust, outlier.

Impact analysis of observation coupling on reliability indices in a geodetic network

An optimally designed geodetic network is characterised by an appropriate level of precision and the lowest possible setup cost. Reliability, translating into the ability to detect blunders in the observations and higher certainty of the obtained point positions, is an important network characteristic. The principal way to provide appropriate network reliability is to acquire a suitably large number of redundant observations. This approach, however, faces limitations resulting from the extra cost. This paper analyses the possibility of providing appropriate reliability parameters for networks with moderate redundancy. A common problem in such cases are dependencies between observations preventing the acquisition of the required reliability index for each of the individual observation. The authors propose a methodology to analyse dependencies between observations aiming to determine the possibility of acquiring the optimal reliability indices for each individual observation or groups of observations. The suggested network structure analysis procedures were illustrated with numerical examples.