scholarly journals The human-elephant conflict: mapping the elephant area and the level of conflict vulnerability in the Mila Landscape, Pidie District, Aceh, Indonesia

2022 ◽  
Vol 956 (1) ◽  
pp. 012008
Author(s):  
R Rachmawaty ◽  
A Abdullah ◽  
K Khairil ◽  
D Syafrianti ◽  
A M Daud ◽  
...  
Keyword(s):  
High Risk ◽  
Sampling Technique ◽  
Low Risk ◽  
Risk Level ◽  
Medium Risk ◽  
In The Wild ◽  
Human Conflict ◽  
Conflict Risk ◽  
Observation Method ◽  
Level Of Risk

Abstract Elephants are designated as endangered animals because their population in the wild continues to decline. One of the causes of its extinction is the threat of conflict between elephants and humans. The conflict between Sumatran elephants and humans in Aceh continues to increase every year, but there is no resolution to this conflict. This study was aimed to analyse the level of risk of elephant-human conflict in the Mila area and map the conflict areas. The method used was the observation method with the purposive sampling technique. The data was analysed using the disaster risk formula. The results of the analysis of the risk level of elephant-human conflict in Mila District showed that the high-risk level was in Tuha Lala Village (35.24%), Babah Jurong Village (35.22%) and Kumbang Village (35.04%). The level of risk was moderate in Krueng Lala Village (27.64%), Andeue Mosque Village (30.38%) and Dayah Andeue Village (33.38%). Meanwhile, areas with a low-risk level were Kulu Village (21.65%) and Dayah Sinthop Village (20.32%). The mapping of conflict risk areas was coloured red for high risk, yellow for medium risk and green for low risk. The conclusion in this study is that Tula Lala Village, Babah Jurong Village and Kumbang Village are areas with high conflict marked in red. Krueng Lala Village, Andeu Mosque Village and Andeue Dayah Village are areas with moderate conflict which are marked in yellow. Meanwhile, Kulu Village and Dayah Sinthop Village are areas with low conflict marked in green.

scholarly journals Disaster Risk Analysis of Merapi Volcano Eruption in Cangkringan District Sleman Regency

2021 ◽  
Vol 884 (1) ◽  
pp. 012051
Author(s):  
M. Rani ◽  
N. Khotimah
Keyword(s):  
Risk Analysis ◽  
Disaster Risk ◽  
Low Risk ◽  
Risk Level ◽  
Merapi Volcano ◽  
Entire Area ◽  
Volcano Eruption ◽  
Disaster Risk Analysis ◽  
Medium Risk ◽  
Level Of Risk

Abstrak Cangkringan is located in the Merapi Volcano Disaster Prone Areas which has the potential to be affected by eruption. The eruption of Merapi Volcano is a consequence that must be faced by the local resident, so that the need for disaster risk analysis in the region through research is a must. This disaster risk analysis research aims to (1) Analyze the risk level of Merapi Volcano eruption in Cangkringan. (2) Analyze the risk distribution of Merapi Volcano eruption in Cangkringan.This research is a descriptive research with a quantitative approach conducted in Cangkringan District, Sleman Regency, Special Region of Yogyakarta. The population in this study is the entire village in Cangkringan. The entire area is the subject of this research. The variables of this reseach are hazard, vulnerability and capacity. This study used primary data and secondary data. Data collection techniques used are observation, interviews, and documentation. Data analysis techniques used are scoring, overlay and descriptive.The results of this study indicate: (1) The level of risk of Merapi Volcano Eruption in Cangkringan is divided into four levels which are high, medium, low and very low. The area of Cangkringan has a high level of risk covering an area of 19,00% of the total area, the medium-risk level is 38,38% of the total area, the low-risk level is 16,61% of the total area, the very low-risk level is 20,23% of the total area of Cangkringan District. The higher the level of disaster risk, the greater the potential loss due to the eruption of Merapi Volcano. (2) The distribution of disaster risk of Merapi Volcano Eruption in Cangkringan is in the entire village. The distribution of high-risk level is in part of Umbulharjo Village, part of Glagaharjo Village and part of Argomulyo Village. The distribution of medium-risk level is in part of Umbulharjo Village, part of Kepuharjo Village and part of Glagaharjo Village. The distribution of low-risk level is in part of Kepuharjo Village, part of Wukirsari Village and part of Argomulyo Village. The distribution of very low-risk level is in part of Wukirsari Village and part of Argomulyo Village.

scholarly journals RISK ASSESSMENT K3 PADA PROSES PENGOPERASIAN SCAFFOLDING PADA PROYEK APARTEMEN PT. X DI SURABAYA

2017 ◽  
Vol 4 (2) ◽  
pp. 199
Author(s):  
Yudha Bagus Persada
Keyword(s):  
Risk Assessment ◽  
High Risk ◽  
Hazard Identification ◽  
Low Risk ◽  
Risk Level ◽  
Cross Sectional ◽  
Total Potential ◽  
Space Objects ◽  
Medium Risk ◽  
Section Structure

ABSTRACTProblems that arised when employees works at height are worker did not wear full body harness, lanyard did not hanged perfectly to handrail, did not works according to the procedure, and using unsafe scaffolding. Hazard identification and risk assessment used as prevention for accident when operating scaffolding. This study was an observational study with cross sectional design and analyzed descriptively. The design used for the study carried out by observing cause and effect within a period of time and space. Objects of this study were frame scaffolding and scaffolding PCH, while subjects of this study were SHE Officer, SHE supervisor, workers section structure, finishing, plumbing hydrant, and mechanical engineering. Results of hazard identification using JSA method founds 43 potential hazards originating from 4 different types of work in this project. The results of the risk assessment of 43 potential hazards have been found that 1 hazard have low risk, 40 hazards have moderate risk , and high risk hazard are 2. The dominant risk from the operation of the scaffolding was 40 potential hazards or 93% of the total potential hazards and high-risk hazard eventhough only 5% of all potential hazards remains a top priority and should be controlled soon to reduce the high and medium risk becomes low risk. The likelihood-based control is more possible to reduce risk level than severity-based control.Keywords: risk assessment, scaffolding operation

scholarly journals Multidimensional dynamic healthcare personnel (HCP)-centric model from a low-income and middle-income country to support and protect COVID-19 warriors: a large prospective cohort study

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e043837
Author(s):  
Usha Dutta ◽  
Anurag Sachan ◽  
Madhumita Premkumar ◽  
Tulika Gupta ◽  
Swapnajeet Sahoo ◽  
...  
Keyword(s):  
High Risk ◽  
Low Income ◽  
Tertiary Care ◽  
Low Risk ◽  
Transmission Risk ◽  
Healthcare Personnel ◽  
Risk Zone ◽  
Increased Risk ◽  
Medium Risk ◽  
Risk Zones

ObjectivesHealthcare personnel (HCP) are at an increased risk of acquiring COVID-19 infection especially in resource-restricted healthcare settings, and return to homes unfit for self-isolation, making them apprehensive about COVID-19 duty and transmission risk to their families. We aimed at implementing a novel multidimensional HCP-centric evidence-based, dynamic policy with the objectives to reduce risk of HCP infection, ensure welfare and safety of the HCP and to improve willingness to accept and return to duty.SettingOur tertiary care university hospital, with 12 600 HCP, was divided into high-risk, medium-risk and low-risk zones. In the high-risk and medium-risk zones, we organised training, logistic support, postduty HCP welfare and collected feedback, and sent them home after they tested negative for COVID-19. We supervised use of appropriate personal protective equipment (PPE) and kept communication paperless.ParticipantsWe recruited willing low-risk HCP, aged <50 years, with no comorbidities to work in COVID-19 zones. Social distancing, hand hygiene and universal masking were advocated in the low-risk zone.ResultsBetween 31 March and 20 July 2020, we clinically screened 5553 outpatients, of whom 3012 (54.2%) were COVID-19 suspects managed in the medium-risk zone. Among them, 346 (11.4%) tested COVID-19 positive (57.2% male) and were managed in the high-risk zone with 19 (5.4%) deaths. One (0.08%) of the 1224 HCP in high-risk zone, 6 (0.62%) of 960 HCP in medium-risk zone and 23 (0.18%) of the 12 600 HCP in the low-risk zone tested positive at the end of shift. All the 30 COVID-19-positive HCP have since recovered. This HCP-centric policy resulted in low transmission rates (<1%), ensured satisfaction with training (92%), PPE (90.8%), medical and psychosocial support (79%) and improved acceptance of COVID-19 duty with 54.7% volunteering for re-deployment.ConclusionA multidimensional HCP-centric policy was effective in ensuring safety, satisfaction and welfare of HCP in a resource-poor setting and resulted in a willing workforce to fight the pandemic.

scholarly journals RISK ANALYSIS OF COCONUT PRODUCT MARKETING

2018 ◽  
Vol 6 (5) ◽  
pp. 138-148
Author(s):  
Ine Fausayana ◽  
Weka Gusmiarty Abdullah ◽  
La Ode Dawid
Keyword(s):  
High Risk ◽  
Risk Analysis ◽  
Risk Identification ◽  
Low Risk ◽  
Coconut Shell ◽  
Market Regulation ◽  
Shell Waste ◽  
Medium Risk ◽  
Product Marketing ◽  
Coconut Product

The aim of this study was to analysis the risks of coconut products marketing in Kendari City. The results of this study described risk identification in three stage of marketing of coconut product, namely (a) Five risks identified at the stage of storaging; broken coconut fruit, unsold product, fire market, theft of coconut fruits, and market regulation; (b) Three risks identified at the stage of processing; broken coconut, coconut shell waste, and damage to processing facilities; and (c) Four risks identified at the stage of selling; unsold product, non-strategic selling locations, substitute goods, and competitors. Overall, the risk on coconut products marketing was mapped at low risk. High risk was more prevalent in the stage of processing, which was caused by the risk of coconut shell waste. While medium risk was more prevalent in the stage of storaging.

Risk Taking Behaviour Associated with Alcohol use among College Student

2021 ◽  
pp. 317-320
Author(s):  
Puspanjali Mohapatro ◽  
Rashmimala Pradhan
Keyword(s):  
High Risk ◽  
Alcohol Use ◽  
Risk Taking ◽  
Rating Scale ◽  
Sampling Technique ◽  
Risk Behaviour ◽  
Low Risk ◽  
Living Standard ◽  
Risky Behaviour ◽  
High Risk Behaviour

Objective: This study is designed to examine the risk taking behaviours that are harmful to students at a selected university. In this case, high-risk behaviours have been studied, such as harmful behaviours, coercion, smoke, alcohol contain substance abuse, and drug addiction. Materials and methods: Current study which is a type of descriptive survey research. The sample of this study included 200 students from a selected university in Bhubaneswar, who were selected through a convenient sampling technique. The Self -structured questionnaire tool has been used for a to collect socio demographic variables. A Structured checklist developed to measure risk taking behaviour. For this section rating scale was adopted with score was low risk, medium risk and high risk. In this study, score range 14-28 divided in to 3 scales- Low risk (14-18), Medium (19-24), High (25-28). A behavioural rating scale was used to analyse the behaviour. Results: The results showed that the increase in risky behaviour among students was 87% and higher for boys than girls and 40% for campus students had a higher risk of alcohol use. About 69.5% of the age group 19-27 were involved in alcohol consumption due to level of high living standard, high sources of income and happiness. Conclusion: The results of the study on identification of risky behaviours to precedence among students, by accessing a high-risk behaviour profile will help policymakers accurately identify student behaviours to make plan for promoting health improvements activity, with to linking the group's real needs and challenges.

Algorithms for Individual Prediction of COVID-19 infection and Hospitalization among Symptomatic patients based on health data (Preprint)

2021 ◽  
Author(s):  
Rossella Murtas ◽  
Nuccia Morici ◽  
Chiara Cogliati ◽  
Massimo Puoti ◽  
Barbara Omazzi ◽  
...  
Keyword(s):  
High Risk ◽  
Prediction Models ◽  
Hospital Admissions ◽  
Health Data ◽  
Low Risk ◽  
Risk Prediction Models ◽  
Predictive Algorithms ◽  
Medium Risk ◽  
Risk Patients ◽  
At Home

BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic has generated a huge strain on the health care system worldwide. The metropolitan area of Milan, Italy was one of the most hit area in the world. OBJECTIVE Robust risk prediction models are needed to stratify individual patient risk for public health purposes METHODS Two predictive algorithms were implemented in order to foresee the probability of being a COVID-19 patient and the risk of being hospitalized. The predictive model for COVID-19 positivity was developed in 61.956 symptomatic patients, whereas the model for COVID-19 hospitalization was developed in 36.834 COVID-19 positive patients. Exposures considered were age, gender, comorbidities and symptoms associated with COVID-19 (vomiting, cough, fever, diarrhoea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnoea). RESULTS The predictive models showed a good fit for predicting COVID-19 disease [AUC 72.6% (95% CI 71.6%-73.5%)] and hospitalization [AUC 79.8% (95% CI 78.6%-81%)]. Using these results, 118,804 patients with COVID-19 from October 25 to December 11, 2020 were stratified into low, medium and high risk for COVID-19 severity. Among the overall population, 67.030 (56%) were classified as low-risk, 43.886 (37%) medium-risk, and 7.888 (7%) high-risk, with 89% of the overall population being assisted at home, 9% hospitalized, and 2% dead. Among those assisted at home, most people (60%) were classified as low risk, whereas only 4% were classified at high risk. According to ordinal logistic regression, the OR of being hospitalised or dead was 5.0 (95% CI 4.6-5.4) in high-risk patients and 2.7 (95% CI 2.6-2.9) in medium-risk patients, as compared to low-risk patients. CONCLUSIONS A simple monitoring system, based on primary care datasets with linkage to COVID-19 testing results, hospital admissions data and death records may assist in proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.

Artificial Intelligence for the Online Prediction of the Cool-down Time in a Subsea Pipeline After an Unplanned Shutdown

2021 ◽  
Author(s):  
Alberto Gerri ◽  
Ahmed Shokry ◽  
Enrico Zio ◽  
Marco Montini
Keyword(s):  
High Risk ◽  
Operating Conditions ◽  
Low Risk ◽  
Minimum Time ◽  
Computational Time ◽  
Risk Level ◽  
Support Vector ◽  
Svm Classifier ◽  
The Novel ◽  
Subsea Pipelines

Abstract Hydrates formation in subsea pipelines is one of the main reliability concerns for flow assurance engineers. A fast and reliable assessment of the Cool-Down Time (CDT), the period between a shut-down event and possible hydrates formation in the asset, is of key importance for the safety of operations. Existing methods for the CDT prediction are highly dependent on the use of very complex physics-based models that demand large computational time, which hinders their usage in an online environment. Therefore, this work presents a novel methodology for the development of surrogate models that predict, in a fast and accurate way, the CDT in subsea pipelines after unplanned shutdowns. The proposed methodology is, innovatively, tailored on the basis of reliability perspective, by treating the CDT as a risk index, where a critic CDT threshold (i.e. the minimum time needed by the operator to preserve the line from hydrates formation) is considered to distinguish the simulation outputs into high-risk and low-risk domains. The methodology relies on the development of a hybrid Machine Learning (ML) based model using datasets generated through complex physics-based model’ simulations. The hybrid ML-based model consists of a Support Vector Machine (SVM) classifier that assigns a risk level (high or low) to the measured operating condition of the asset, and two Artificial Neural Networks (ANNs) for predicting the CDT at the high-risk (low CDT) or the low-risk (high CDT) operating conditions previously assigned by the classifier. The effectiveness of the proposed methodology is validated by its application to a case study involving a pipeline in an offshore western African asset, modelled by a transient physics-based commercial software. The results show outperformance of the capabilities of the proposed hybrid ML-based model (i.e., SVM + 2 ANNs) compared to the classical approach (i.e. modelling the entire system with one global ANN) in terms of enhancing the prediction of the CDT during the high-risk conditions of the asset. This behaviour is confirmed applying the novel methodology to training datasets of different size. In fact, the high-risk Normalized Root Mean Square Error (NRMSE) is reduced on average of 15% compared to the NRMSE of a global ANN model. Moreover, it’s shown that high-risk CDT are better predicted by the hybrid model even if the critic CDT, which divides the simulation outputs in high-risk and low-risk values (i.e. the minimum time needed by the operator to preserve the line from hydrates formation), changes. The enhancement, in this case, is on average of 14.6%. Eventually, results show how the novel methodology cuts down by more than one hundred seventy-eight times the computational times for online CDT predictions compared to the physics-based model.

A practical composite risk score for the development of Haemolytic Uraemic Syndrome from Shiga toxin-producing Escherichia coli

2019 ◽  
Vol 29 (5) ◽  
pp. 861-868 ◽  
Author(s):  
Douglas Hamilton ◽  
John Cullinan
Keyword(s):  
Escherichia Coli ◽  
High Risk ◽  
Risk Score ◽  
Shiga Toxin ◽  
Haemolytic Uraemic Syndrome ◽  
Low Risk ◽  
Medium Risk ◽  
Composite Risk ◽  
Very High ◽  
Estimation Of Risk

Abstract Background Haemolytic Uraemic Syndrome (HUS) is a serious complication of Shiga toxin-producing Escherichia coli (STEC) infection and the key reason why intensive health protection against STEC is required. However, although many potential risk factors have been identified, accurate estimation of risk of HUS from STEC remains challenging. Therefore, we aimed to develop a practical composite score to promptly estimate the risk of developing HUS from STEC. Methods This was a retrospective cohort study where data for all confirmed STEC infections in Ireland during 2013–15 were subjected to statistical analysis with respect to predicting HUS. Multivariable logistic regression was used to develop a composite risk score, segregating risk of HUS into ‘very low risk’ (0–0.4%), ‘low risk’ (0.5–0.9%), ‘medium risk’ (1.0–4.4%), ‘high risk’ (4.5–9.9%) and ‘very high risk’ (10.0% and over). Results There were 1397 STEC notifications with complete information regarding HUS, of whom 5.1% developed HUS. Young age, vomiting, bloody diarrhoea, Shiga toxin 2, infection during April to November, and infection in Eastern and North-Eastern regions of Ireland, were all statistically significant independent predictors of HUS. Demonstration of a risk gradient provided internal validity to the risk score: 0.2% in the cohort with ‘very low risk’ (1/430), 1.1% with ‘low risk’ (2/182), 2.3% with ‘medium risk’ (8/345), 3.1% with ‘high risk’ (3/98) and 22.2% with ‘very high risk’ (43/194) scores, respectively, developed HUS. Conclusion We have developed a composite risk score which may be of practical value, once externally validated, in prompt estimation of risk of HUS from STEC infection.

scholarly journals Salivary Cytokine Biomarker Concentrations in Relation to Obesity and Periodontitis

2019 ◽  
Vol 8 (12) ◽  
pp. 2152 ◽  
Author(s):  
Sanna Syrjäläinen ◽  
Ulvi Kahraman Gursoy ◽  
Mervi Gursoy ◽  
Pirkko Pussinen ◽  
Milla Pietiäinen ◽  
...  
Keyword(s):  
High Risk ◽  
Cumulative Risk ◽  
Normal Weight ◽  
Low Risk ◽  
Control Group ◽  
Low Grade ◽  
Periodontal Status ◽  
Tnf Α ◽  
Medium Risk ◽  
And Control

Systemic low-grade inflammation is associated with obesity. Our aim was to examine the association between obesity and salivary biomarkers of periodontitis. Salivary interleukin (IL)-1-receptor antagonist (IL-1Ra), IL-6, IL-8, IL-10, and tumor necrosis factor (TNF)-α concentrations were measured from 287 non-diabetic obese (body mass index (BMI) of >35 kg/m2) individuals and 293 normal-weight (BMI of 18.5–25 kg/m2) controls. Periodontal status was defined according to a diagnostic cumulative risk score (CRS) to calculate the risk of having periodontitis (CRS I, low risk; CRS II, medium risk; CRS III, high risk). In the whole population, and especially in smokers, higher IL-8 and lower IL-10 concentrations were detected in the obese group compared to the control group, while in non-smoking participants, the obese and control groups did not differ. IL-1Ra and IL-8 concentrations were higher in those with medium or high risk (CRS II and CRS III, p < 0.001) of periodontitis, whereas IL-10 and TNF-α concentrations were lower when compared to those with low risk (CRS I). In multivariate models adjusted for periodontal status, obesity did not associate with any salivary cytokine concentration. In conclusion, salivary cytokine biomarkers are not independently associated with obesity and concentrations are dependent on periodontal status.

scholarly journals Improving vulnerability remediation through better exploit prediction

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Jay Jacobs ◽  
Sasha Romanosky ◽  
Idris Adjerid ◽  
Wade Baker
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Small Body ◽  
Low Risk ◽  
Machine Learning Techniques ◽  
Outcome Variable ◽  
Remediation Strategies ◽  
Learning Techniques ◽  
In The Wild ◽  
Cyber Risk

Abstract Despite significant innovations in IT security products and research over the past 20 years, the information security field is still immature and struggling. Practitioners lack the ability to properly assess cyber risk, and decision-makers continue to be paralyzed by vulnerability scanners that overload their staff with mountains of scan results. In order to cope, firms prioritize vulnerability remediation using crude heuristics and limited data, though they are still too often breached by known vulnerabilities for which patches have existed for months or years. And so, the key challenge firms face is trying to identify a remediation strategy that best balances two competing forces. On one hand, it could attempt to patch all vulnerabilities on its network. While this would provide the greatest ‘coverage’ of vulnerabilities patched, it would inefficiently consume resources by fixing low-risk vulnerabilities. On the other hand, patching a few high-risk vulnerabilities would be highly ‘efficient’, but may leave the firm exposed to many other high-risk vulnerabilities. Using a large collection of multiple datasets together with machine learning techniques, we construct a series of vulnerability remediation strategies and compare how each perform in regard to trading off coverage and efficiency. We expand and improve upon the small body of literature that uses predictions of ‘published exploits’, by instead using ‘exploits in the wild’ as our outcome variable. We implement the machine learning models by classifying vulnerabilities according to high- and low-risk, where we consider high-risk vulnerabilities to be those that have been exploited in actual firm networks.

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