Hidrokarbon dan Minyak Bumi dalam Prespektif Al Quran

ABSTRACTAl-Quran integration in chemistry, especially Hydrocarbons and Petroleum materials can be done. This research is a literature study, using a qualitative descriptive method using incident data that is explained argumentatively and narrative. Hydrocarbons are chemical compounds found in the body of living things, consisting of the elements H, C, O and several other elements, these are found in QS. Ar Rahman verse 14, QS. Al Hijr: verse 28, QS. Ash Shaffaat: verse 11, QS. Ali Imran: verse 59. The process of forming petroleum is contained in QS. Al-A'la verses 4-5. Integration can also be done by studying the values of life contained in hydrocarbon materials. The results of the study are as follows (1) To be grateful in the Al Quran there are signs for chemistry. (2) Reproducing the unique concept of carbon atoms in life.Keywords: hydrocarbons, petroleum, al quran, integration, and chemistry ABSTRAK Integrasi Al Quran dalam ilmu kimia terutama materi hidrokarbon dan minyak bumi dapat dilakukan. Penelitian ini merupakan studi kepustakaan, menggunakan metode deskriptif kualitatif dengan menggunakan data peristiwa yang dijelaskan secara argumentatif dan naratif. Hidrokarbon merupakan senyawa kimia yang terdapat di dalam tubuh mahluk hidup yang terditi dari unsur H, C, O dan beberapan unsur lain hal ini terdapat dalam QS. Ar Rahman ayat 14, QS. Al Hijr: ayat 28, QS. Ash Shaffaat: ayat 11, QS. Ali Imran: ayat 59. Proses pembentukan minyak bumi terdapat dalam QS. Al-A’la ayat 4-5. Integrasi juga dapat dilakukan dengan kajian nilai-nilai kehidupan yang terdapat dalam materi hidrokarbon. Hasil kajian yaitu sebagai berikut (1) Mensyukuri di dalam Al Quran terdapat tanda-tanda untuk ilmu kimia. (2) Merupakan konsep kekhasan atom karbon dalam kehidupan sehari-hari. Kata kunci: hidrokarbon, minyak bumi, al quran, integrasi, dan ilmu kimia

Research-Based Forensic Analysis of a Vehicle Crash When Limited Information is Available: A Case Study

Forensic human factors analyses of personal injuries, including collisions, can be challenging when there is either limited evidence available and/or conflicting accounts of the events leading up to an incident. Gathering objective data under conditions representative of the incident in question can assist human factors practitioners in assessing the plausibility of certain facts and events, particularly in the aforementioned scenarios. The present work is part of the preliminary investigation of a collision in which little information was available about the incident. Data were gathered from nine participants to assist in determining a reasonable timeline of events leading up to impact between a disabled vehicle and a tractor trailer. The importance of reliance on such objective data as part of forensic analyses is discussed.

Assessing Trustworthiness of Crowdsourced Flood Incident Reports Using Waze Data: A Norfolk, Virginia Case Study

Climate change and sea-level rise are increasingly leading to higher and prolonged high tides, which, in combination with the growing intensity of rainfall and storm surges, and insufficient drainage infrastructure, result in frequent recurrent flooding in coastal cities. There is a pressing need to understand the occurrence of roadway flooding incidents in order to enact appropriate mitigation measures. Agency data for roadway flooding events are scarce and resource-intensive to collect. Crowdsourced data can provide a low-cost alternative for mapping roadway flood incidents in real time; however, the reliability is questionable. This research demonstrates a framework for asserting trustworthiness on crowdsourced flood incident data in a case study of Norfolk, Virginia. Publicly available (but spatially limited) flood incident data from the city in combination with different environmental and topographical factors are used to create a logistic regression model to predict the probability of roadway flooding at any location on the roadway network. The prediction accuracy of the model was found to be 90.5%. When applying this model to crowdsourced Waze flood incident data, 71.7% of the reports were predicted to be trustworthy. This study demonstrates the potential for using Waze incident report data for roadway flooding detection, providing a framework for cities to identify trustworthy reports in real time to enable rapid situation assessment and mitigation to reduce incident impact.

Topic Modelling on Pharmaceutical Incident Data

Focus of the current study is to explore and analyse textual data in the form of incidents in pharmaceutical industry using topic modelling. Topic modelling applied in the current study is based on Latent Dirichlet Allocation. The proposed model is applied on a corpus containing 190 incidents to retrieve key words with highest probability of occurrence. It is used to form informative topics related to incidents.

Identifying Incident Causal Factors to Improve Aviation Transportation Safety: Proposing a Deep Learning Approach

Aviation is a complicated transportation system, and safety is of paramount importance because aircraft failure often involves casualties. Prevention is clearly the best strategy for aviation transportation safety. Learning from past incident data to prevent potential accidents from happening has proved to be a successful approach. To prevent potential safety hazards and make effective prevention plans, aviation safety experts identify primary and contributing factors from incident reports. However, safety experts’ review processes have become prohibitively expensive nowadays. The number of incident reports is increasing rapidly due to the acceleration of advances in information technologies and the growth of the commercial and private aviation transportation industries. Consequently, advanced text mining algorithms should be applied to help aviation safety experts facilitate the process of incident data extraction. This paper focuses on constructing deep-learning-based models to identify causal factors from incident reports. First, we prepare the data sets used for training, validation, and testing with approximately 200,000 qualified incident reports from the Aviation Safety Reporting System (ASRS). Then, we take an open-source natural language model, which is well trained with a large corpus of Wikipedia texts, as the baseline and fine-tune it with the texts in incident reports to make it more suited to our specific research task. Finally, we build and train an attention-based long short-term memory (LSTM) model to identify primary and contributing factors in each incident report. The solution we propose has multilabel capability and is automated and customizable, and it is more accurate and adaptable than traditional machine learning methods in extant research. This novel application of deep learning algorithms to the incident reporting system can efficiently improve aviation safety.

How does domestic violence escalate over time?

A key assumption in the domestic violence literature is that abuse escalates in severity and frequency over time. However, very little is known about how violence and abuse unfolds within intimate relationships and there is no consensus on how escalation should be defined or how prevalent it is. A narrative review of the literature identified two primary definitions of escalation: a pattern of increasingly frequent and/or severe violent incidents, or the occurrence of specific violent acts (ie outcomes). Escalation appears to be limited to serious or prolific offenders rather than characterising all abusive relationships. However, disparities in prevalence estimates between those provided by victim–survivors and recorded incident data highlight the difficulty of measuring this aspect of abusive relationships.

Statistical analysis of the oil and HNS spill incidents occurred in NOWPAP region from 1990 to 2017

and Introduction This analysis has been implemented firstly under the project of ‘Development of information Sharing Platform on oil and HNS spills in the NOWPAP region' which was propose at the 18th MERRAC Focal Points meeting (August 2015) and approved by the 20th NOWPAP Intergovernmental meeting (November 2015). The detailed information on scope of data collection used in this analysis is as follows; Data used in this analysis was scoped from the year of 1990 to 2017Incidents of oil spillage with over 10 tons were only collected from the member states on a regular basisMERRAC established the guidelines to clear the terms and meanings to analyzeFrom 1990 to 1997, incidents of oil spill with over 50 tons were collectedThe incident data provides incident dates, locations, vessel types, incident types, pollution types and pollution quantities

Assessment of the pollution incident performance of water and sewerage companies in England

Comparison of water and sewerage company pollution performance in relation to severity, frequency and self-reporting of pollution incidents is made difficult by differences in environmental and operational conditions. In England, the deterioration in pollution incident performance across the water industry, makes it important to investigate common trends that could be addressed at national and regional levels. Yet, to date there has been no external peer-reviewed analysis of national pollution incident data in England. This project aimed to analyse available pollution incident data to assess and compare the performance of water and sewerage companies in England. Results indicated that there was significant variation in pollution incident numbers and the severity of the impact on the water environment for different asset types (operational property of the water and sewerage company such as a sewage treatment works). Increasing numbers of pollution incidents from pumping stations and sewage treatment works, were largely responsible for overall increases in pollution incidents. The highest increase in pollution incidents in 2019, was observed from pumping stations. Variation was evident in company self-reporting percentages across asset types. There were significant positive relationships between the self-reporting percentages of pollution incidents and total numbers of reported pollution incidents per 10,000 km sewer length for pumping stations and sewage treatment works. These results indicate that in at least these asset types, an estimated 5% of pollution incidents could go unreported, if not self-reported by the company. Pollution events not reported quickly by companies, can lead to more severe impacts to the water environment so rapid and consistent reporting of incidents is crucial for limiting damage. The results have significance for the water industry internationally, because the issues presented here are not restricted to England. Whilst this research highlighted a number of key areas for more detailed analysis, in the short-term, research should focus on investigating best practice for reporting pollution incidents. It is important to get an accurate baseline of the number of pollution incidents and whether a proportion are currently going unreported. This research should seek to aid the standardisation of reporting practice across the water industry.