110 research outputs found
Strategies for assessing and reducing inherent occupational health hazard and risk based on process information
Over the last few decades, the concept of inherent occupational health has gained increasing attention to reduce occupational hazards that may adversely impact workers' health. In order to assess occupational hazards in the chemical process, different inherent occupational health assessment methods have been developed at the early stages of process development and design. The methods in the order of process information availability - ranging from the detailed piping and instrumentation diagrams to a simple sketch of process concepts are the: occupational health index (OHI), health quotient index (HQI) and inherent occupational health index (IOHI). This paper proposes systematic heuristic frameworks to assist process designers and engineers in assessing and reducing inherent occupational health hazards or risks based on process information availability. Strategies for reducing health hazards or risks in the OHI, HQI and IOHI methods based on inherently safer design (ISD) keywords of minimization, substitution, moderation and simplification are included in this study. It is worth mentioning that the proposed frameworks act as guidelines for design engineers in systematically selecting the appropriate index and methodology to assess and reduce health hazards/risks based on the availability of the process information. A case study is solved to illustrate the proposed framework
Guidelines for process safety hazard assessment based on process information
In any new chemical process development and design, process safety is a critical aspect to be considered besides economic and technical feasibility of the manufacture of the product. A lack of proper hazard assessment during the design phase may later result in accidents with disastrous consequences to workers, the public as well as the environment. Many methods have been introduced to qualitatively and quantitatively assess the safety level of processes. Despite the availability of a large amount of methods, a systematic framework that details guidelines for hazard identification, risk assessment, safety measure design, and safe critical decision-making is still missing. To address this issue, the main objective of this study was to propose a systematic framework that outlines comprehensive guidelines for assessing the safety performance of processes based on information from the piping and instrumentation diagram (P&ID). Apart from proposing the framework, appropriate strategies for minimizing safety hazards and risks are also recommended. In addition, the user is assisted in selecting the most appropriate assessment method according to his or her needs and the scope and constraints of the assessment. A case study is presented to illustrate the application of the proposed framework
Guidelines for Process Safety Hazard Assessment Based on Process Information
In any new chemical process development and design, process safety is a critical aspect to be considered besides economic and technical feasibility of the manufacture of the product. A lack of proper hazard assessment during the design phase may later result in accidents with disastrous consequences to workers, the public as well as the environment. Many methods have been introduced to qualitatively and quantitatively assess the safety level of processes. Despite the availability of a large amount of methods, a systematic framework that details guidelines for hazard identification, risk assessment, safety measure design, and safe critical decision-making is still missing. To address this issue, the main objective of this study was to propose a systematic framework that outlines comprehensive guidelines for assessing the safety performance of processes based on information from the piping and instrumentation diagram (P&ID). Apart from proposing the framework, appropriate strategies for minimizing safety hazards and risks are also recommended. In addition, the user is assisted in selecting the most appropriate assessment method according to his or her needs and the scope and constraints of the assessment. A case study is presented to illustrate the application of the proposed framework
Guidelines for Process Safety Hazard Assessment Based on Process Information
In any new chemical process development and design, process safety is a critical aspect to be considered besides economic and technical feasibility of the manufacture of the product. A lack of proper hazard assessment during the design phase may later result in accidents with disastrous consequences to workers, the public as well as the environment. Many methods have been introduced to qualitatively and quantitatively assess the safety level of processes. Despite the availability of a large amount of methods, a systematic framework that details guidelines for hazard identification, risk assessment, safety measure design, and safe critical decision-making is still missing. To address this issue, the main objective of this study was to propose a systematic framework that outlines comprehensive guidelines for assessing the safety performance of processes based on information from the piping and instrumentation diagram (P&ID). Apart from proposing the framework, appropriate strategies for minimizing safety hazards and risks are also recommended. In addition, the user is assisted in selecting the most appropriate assessment method according to his or her needs and the scope and constraints of the assessment. A case study is presented to illustrate the application of the proposed framework
PLASER: Pronunciation Learning via Automatic Speech Recognition
PLASER is a multimedia tool with instant feedback designed to teach English pronunciation for high-school students of Hong Kong whose mother tongue is Cantonese Chinese. The objective is to teach correct pronunciation and not to assess a student's overall pronunciation quality. Major challenges related to speech recognition technology include: allowance for non-native accent, reliable and corrective feedbacks, and visualization of errors
Co-Clustering Multi-View Data Using the Latent Block Model
The Latent Block Model (LBM) is a prominent model-based co-clustering method,
returning parametric representations of each block cluster and allowing the use
of well-grounded model selection methods. The LBM, while adapted in literature
to handle different feature types, cannot be applied to datasets consisting of
multiple disjoint sets of features, termed views, for a common set of
observations. In this work, we introduce the multi-view LBM, extending the LBM
method to multi-view data, where each view marginally follows an LBM. In the
case of two views, the dependence between them is captured by a cluster
membership matrix, and we aim to learn the structure of this matrix. We develop
a likelihood-based approach in which parameter estimation uses a stochastic EM
algorithm integrating a Gibbs sampler, and an ICL criterion is derived to
determine the number of row and column clusters in each view. To motivate the
application of multi-view methods, we extend recent work developing hypothesis
tests for the null hypothesis that clusters of observations in each view are
independent of each other. The testing procedure is integrated into the model
estimation strategy. Furthermore, we introduce a penalty scheme to generate
sparse row clusterings. We verify the performance of the developed algorithm
using synthetic datasets, and provide guidance for optimal parameter selection.
Finally, the multi-view co-clustering method is applied to a complex genomics
dataset, and is shown to provide new insights for high-dimension multi-view
problems
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Analysis of Risk Factors and Diagnosis for Anxiety Disorder in Older People with the Aid of Artificial Intelligence: Observational Study
Anxiety disorders are the most common mental health problems particularly in older people who suffer from loneliness and social isolation, chronic health conditions, financial insecurity and other factors that can lead to anxiety disorders. The high prevalence and health risks of anxiety disorders, and the requirement for effective mental care, coupled with recent advances in artificial intelligence, has resulted in an increase exploration of how machine learning can aid the diagnosis and prediction of mental health problems. Data from the Trinity-Ulster-Department of Agriculture (TUDA) study will be utilized to identify risk factors for anxiety in community dwelling older adults using machine learning techniques. The TUDA study includes detailed information on biochemical, clinical, nutritional, lifestyle, and sociodemographic factors in 5186 older people recruited from the Republic of Ireland and Northern Ireland. These characteristics could foster the prediction of anxiety disorders using supervised machine learning methods. Biomarker risk factor analysis was conducted to facilitate feature engineering. In this observational study, several classical machine learning models have been trained to predict anxiety disorders. Comparing the accuracy results and determining the impact of features on the predictions of each method. The models' performance was assessed on a held-out test set and achieved an accuracy of 85.4% (sensitivity: 67.0%, specificity: 90.3%) and 83.4% (sensitivity: 81.5%, specificity: 83.9%) for two best performing methods i.e., random forest and support vector machine respectively, using the standard Synthetic Minority Oversampling Technique. Risk factors such as female sex, loneliness, separated/divorced conditions, lifestyle-related, socio-economic low status, chronic diseases, and family related diseases were identified. These results will aid in the early detection of anxiety disorder in future studies
Finishing the euchromatic sequence of the human genome
The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead
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