Classification System Framework Research Articles

Human Factors in General Aviation Loss of Control: Survey of Pilot Experiences

Inflight loss of control (LOC-I) is a major cause of general aviation (GA) accidents. Previous studies have identified LOC-I causes from sources such as the National Transportation Safety Board (NTSB) database. NTSB GA accident reports include limited detail on human factors. Getting pilots’ perspectives from other sources, such as surveys, may reveal insights into pilot actions and conditions. This study investigates the role of human factors in LOC-I by analyzing demographics in NTSB accidents and conducting a survey of pilots’ LOC-I experiences based on the Human Factors Analysis and Classification System framework. Note that 80% of the LOC-I events shared by the pilots involved pilot error, most of which were skill-based and decision errors. About one-quarter of pilots mentioned that their instructor had not prepared them well or taught them methods to recover from LOC-I. Several pilots mentioned that, because of their lack of LOC-I recovery training, they could not recover from LOC-I. The study helped in identifying specific pilot errors and issues in training that were not mentioned explicitly in the NTSB reports. Findings from the study may help improve training methods and operating procedures for GA pilots to prevent loss of control in the future.

Applying Machine Learning and Statistical Forecasting Methods for Enhancing Pharmaceutical Sales Predictions

In today’s evolving global world, the pharmaceutical sector faces an emerging challenge, which is the rapid surge of the global population and the consequent growth in drug production demands. Recognizing this, our study explores the urgent need to strengthen pharmaceutical production capacities, ensuring drugs are allocated and stored strategically to meet diverse regional and demographic needs. Summarizing our key findings, our research focuses on the promising area of drug demand forecasting using artificial intelligence (AI) and machine learning (ML) techniques to enhance predictions in the pharmaceutical field. Supplied with a rich dataset from Kaggle spanning 600,000 sales records from a singular pharmacy, our study embarks on a thorough exploration of univariate time series analysis. Here, we pair conventional analytical tools such as ARIMA with advanced methodologies like LSTM neural networks, all with a singular vision: refining the precision of our sales. Venturing deeper, our data underwent categorisation and were segmented into eight clusters premised on the ATC Anatomical Therapeutic Chemical (ATC) Classification System framework. This segmentation unravels the evident influence of seasonality on drug sales. The analysis not only highlights the effectiveness of machine learning models but also illuminates the remarkable success of XGBoost. This algorithm outperformed traditional models, achieving the lowest MAPE values: 17.89% for M01AB (anti-inflammatory and antirheumatic products, non-steroids, acetic acid derivatives, and related substances), 16.92% for M01AE (anti-inflammatory and antirheumatic products, non-steroids, and propionic acid derivatives), 17.98% for N02BA (analgesics, antipyretics, and anilides), and 16.05% for N02BE (analgesics, antipyretics, pyrazolones, and anilides). XGBoost further demonstrated exceptional precision with the lowest MSE scores: 28.8 for M01AB, 1518.56 for N02BE, and 350.84 for N05C (hypnotics and sedatives). Additionally, the Seasonal Naïve model recorded an MSE of 49.19 for M01AE, while the Single Exponential Smoothing model showed an MSE of 7.19 for N05B. These findings underscore the strengths derived from employing a diverse range of approaches within the forecasting series. In summary, our research accentuates the significance of leveraging machine learning techniques to derive valuable insights for pharmaceutical companies. By applying the power of these methods, companies can optimize their production, storage, distribution, and marketing practices.

Aldehyde Dehydrogenase Diversity in Azospirillum Genomes

Aldehyde dehydrogenases (ALDHs) are indispensable enzymes that play a pivotal role in mitigating aldehyde toxicity by converting them into less reactive compounds. Despite the availability of fully sequenced Azospirillum genomes in public databases, a comprehensive analysis of the ALDH superfamily within these genomes has yet to be undertaken. This study presents the identification and classification of 17 families and 31 subfamilies of ALDHs in fully assembled Azospirillum genomes. This classification system framework provides a more comprehensive understanding of the diversity and redundancy of ALDHs across bacterial genomes, which can aid in elucidating the distinct characteristics and functions of each family. The study also proposes the adoption of the ALDH19 family as a powerful phylogenetic marker due to its remarkable conservation and non-redundancy across various Azospirillum species. The diversity of ALDHs among different strains of Azospirillum can influence their adaptation and survival under various environmental conditions. The findings of this study could potentially be used to improve agricultural production by enhancing the growth and productivity of crops. Azospirillum bacteria establish a mutualistic relationship with plants and can promote plant growth by producing phytohormones such as indole-3-acetic acid (IAA). The diversity of ALDHs in Azospirillum can affect their ability to produce IAA and other beneficial compounds that promote plant growth and can be used as biofertilizers to enhance agricultural productivity.

Human Factors, Analysis Tools and Methods for Development of Reliable Aircraft Design

This paper provides an overview of different tools and methods that are capable of addressing human factors issues during an aircraft design programme. The huge amount of literature available is classified into a comprehensive birds eye view of the field. This is described in the form of taxonomy. Methods to determine the root cause of an airline incident/accident are discussed with application of Human Factors Analysis Classification System framework to few commercial airline accidents. Tools for identifying probability of human error in a task scenario are examined and a promising method for human error prediction is illustrated for a typical landing scenario. Also, different workload assessment methodologies are described in brief along with their strengths and limitations. Finally, a novel aircraft/component design methodology is proposed with human performance modelling tools that shall reduce the design life cycle and the inherent cost involved. In a nutshell, this paper basically discusses on how to design an aircraft that can reduce the incidents attributable to human factors.

Risk Assessment of Substation Integrated Anti-Misoperation System Considering Human Reliability

The integrated substation automation system (ISAS) based on IEC-61850 is the development direction of the intelligent substation. Currently, the reliability research of the ISAS rarely involves the quantitative influence of human factors. Therefore, a risk assessment technique of ISAS considering human reliability is proposed. The technique is based on Human Factors Analysis and Classification System framework, Fuzzy Fault Tree Analysis methodology, and Functional Decomposition method. Then, it's applied to the integrated anti-misoperation system in ISAS. The system's reliability considering human error is quantitatively evaluated through a series of studies. The results show that a quantitative method is valuable in assessing the reliability of ISAS. The main contributions of this paper are: 1) to give explicit formulas for risk assessment of ISAS, which objectively describes the system risk and provides risk prompts for dispatch operators; 2) to intuitively reflect how the reliability attributes of the system's equipment and network interfere with the reliability of the system; 3) to prove that human errors will seriously affect the risk level of the system. As the rate of human error increases, the risk level of the system rises until it collapses.

LEARNING HOAX NEWS PADA LOCAL DAN CLOUD COMPUTING DEPLOYMENT MENGGUNAKAN GOOGLE APP ENGINE

Abstract. Hoax news is fake news that contains information that intentionally misleads people and has a specific political agenda. Along with the development of technological developments, the news is increasingly unclear whether the truth is only in accordance with facts or mere hoaxes. Based on this problem, hoax news is carried out, one of which uses a machine that can process news classification automatically. Machine learning is implemented using a framework called the Flask framework that can run on both on-premises servers and cloud computing. Local servers with computational limitations that are static in nature have problems running large computations on the hoax news classification system framework which is characterized by a predictable time schedule so that a distributed automatic-scaling computing system can handle cloud computing loads. Cloud computing offers compute load-sharing automatic scalability that can provide stable compute computing. So this research focuses on the application of the machine learning hoax news classification model into the framework of the machine learning deployment Platform as a Service (PaaS) model in Cloud computing called Google App Engine (GAE). The application of the hoax news classification system in the Google App Engine environment runs with an average prediction time of 11.53 seconds, better and more stable than the local server's average prediction of 17.50 seconds.

Improving accident analysis in construction – Development of a contributing factor classification framework and evaluation of its validity and reliability

Construction industry is still one of the most dangerous industries and its fatal work injuries is almost three times higher than the average across all sectors. Previous researchers have attempted to apply more systemic models and methods to improve accident analysis in construction, but few studies have fully encompassed upstream factors such as decisions and actions at the level of the government and regulator in accident analysis. More importantly, no previous study has evaluated the validity and reliability of systemic accident analysis methods in construction. The present study, therefore, has two main aims: to develop a contributing factor classification framework to support systemic accident investigation in construction and to carry out an assessment of its validity and reliability. The classification framework was developed and assessed in two phases. The phase one involved generating a list of contributing factors from the review of 26 articles and the analysis of 532 construction accident reports. Five federal inspectors with expertise in accident investigation were involved in refining the list into 61 contributing factors and categorizing them into six levels of the Accimap framework. The phase two involved in assessing the validity and reliability of the framework with five practitioners in construction sector using three real construction accidents. This study contributes to the development of a contributing factors classification system framework for construction with acceptable validity and reliability.

Response of Transitional Mixtures Retaining Memory of In-Situ Overburden Pressure Monitored Using Electromagnetic and Piezo Crystal Sensors.

The major and minor components in granular soil materials determine their properties and behavior. This study explores the transitional behavior within threshold fines fraction of soil mixtures based on the data from the literature and experiments. From the literature survey, the void ratio, shear wave velocity, compression index, and friction angle capture the transitional turning point between the low and data-adjusted high threshold fines fractions. In particular, there is a dramatic change in hydraulic conductivity below the low threshold fines fraction that highlights the critical role of small amounts of fines in the fluid flow (e.g., clogging). From an experimental study, the engineering properties of natural soil samples identified using deformation and elastic wave sensors show transitional trends within the Revised Soil Classification System framework. The evolution of compressibility and shear wave velocity indicate that either coarse, fine, or both particles are likely to contribute to large and small strain stiffnesses when the effective stress is below 400 kPa. Thereafter, both engineering properties indicate that the soil sample retains a memory of in-situ overburden pressure when the effective stress is around 400 kPa. There is a critical role of fines that are slightly higher than low threshold fines fraction on engineering properties that promote the application of Revised Soil Classification System RSCS to natural soils.

Exploring the Contributory Factors of Confined Space Accidents Using Accident Investigation Reports and Semistructured Interviews.

BackgroundThe oil and gas industry is one of the riskiest industries for confined space injuries. This study aimed to understand an overall picture of the causal factors of confined space accidents through analyzing accident reports and the use of a qualitative approach.MethodsTwenty-one fatal occupational accidents were analyzed according to the Human Factors Analysis and Classification System approach. Furthermore, thirty-three semistructured interviews were conducted with employees in different roles to capture their experiences regarding the contributory factors. The content analyses of the interview transcripts were conducted using MAXQDA software.ResultsBased on accident reports, the largest proportions of causal factors (77%) were attributed to the organizational and supervisory levels, with the predominant influence of the organizational process. We identified 25 contributory factors in confined space accidents that were causal factors outside of the original Human Factors Analysis and Classification System framework. Therefore, modifications were made to deal with factors outside the organization and newly explored causal factors at the organizational level. External Influences as the fifth level considered contributory factors beyond the organization including Laws, Regulations and Standards, Government Policies, Political Influences, and Economic Status categories. Moreover, Contracting/Contract Management and Emergency Management were two extra categories identified at the organizational level.ConclusionsPreventing confined space accidents requires addressing issues from the organizational to operator level and external influences beyond the organization. The recommended modifications provide a basis for accident investigation and risk analysis, which may be applicable across a broad range of industries and accident types.

Developing a web-based system for supervised classification of remote sensing images

Web-based image classification systems aim to provide users with an easy access to image classification function. The existing work mainly focuses on web-based unsupervised classification systems. This paper proposes a web-based supervised classification system framework which includes three modules: client, servlet and service. It comprehensively describes how to combine the procedures of supervised classification into the development of a web system. A series of methods are presented to realize the modules respectively. A prototype system of the framework is also implemented and a number of remote sensing (RS) images are tested on it. Experiment results show that the prototype is capable of accomplishing supervised classification of RS images on the Web. If appropriate algorithms and parameter values are used, the results of the web-based solution could be as accurate as the results of traditional desktop-based systems. This paper lays the foundation on both theoretical and practical aspects for the future development of operational web-based supervised classification systems.

English

This study was focused on the roles of human factors in construction accident and also dealt with the probabilities of fours levels of injury. We used an empirical Bayesian method and the human factors analysis and classification system framework to analyze the probability distributions of the severity of accidents of high risk operations in hydropower construction. Accident severity in four levels of injury was modeled: severe injury, one death, two deaths, and three deaths. The results show the behavior characteristics of workers and factors influencing their operation violations. The calculation of posterior distributions of the levels of injury enables us to rank the factors with respect to their risk of injury. The study revealed that lack of the ability to determine hazards is the direct reason in many accidents; resource management, inadequate supervision and supervisory violations also play important roles in the occurrence of accidents.   Key words: Empirical Bayesian analysis, human factors, work accidents, risk analysis and assessment.

Instantiation of a Classification System Framework that Facilitates Reuse

Frameworks are a key technology to reduce software development costs and shorten the time-to- market. However, framework complexity presents reuse problems that limit its success as an application development alternative. Therefore, it has been widely recognized that is necessary to effectively communicate frameworks and provide appropriate tool support. Although advancements have been made in these directions, difficulties still endure. We argue the problem should also be addressed at another level – the framework model. Moreover, both the framework documentation and the level of support that tools can provide are decisively dependent on the considered framework model. In this paper, we present our proposed framework model and the respective supporting notation. We discuss their application in the design and description of a Classification System Framework. Then we describe how the framework can be reused based on the proposed description technique. This case study illustrates how the proposed model and description help to alleviate difficulties of framework reuse.