Search results for: risk classification

Authors: Tio Novita Efriani

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Public sector organizations work in a complex and risky environment. Since the beginning of 2000s, the public sector has paid attention to the need for an effective risk management. The Indonesian public sector has also concerned about this issue and in 2008 it enacted the Government Regulation that gives mandate for the implementation of risk management in government organizations. This paper investigates risk management implementation in the Indonesian public sector organizations and the role of transformational leadership and internal audit activities. Data was collected via survey. A total of 202 effective responses (30% response rate) from employees in 34 government ministries were statistically analyzed by using Partial least square structural equation modelling (PLS-SEM) and the software was SmartPLS 3.0. All the constructs were lower order, except for the risk management implementation construct, which was treated as a second-order construct. A two-stage approach was employed in the analysis of the higher order component. The findings revealed that transformational leadership positively influence risk management implementation. The findings also found that the core and legitimate roles of internal audit in risk management positively affect the implementation of risk management. The final finding showed that internal auditing mediates a relationship between transformational leadership and risk management implementation. These results suggest that the implementation of risk management in the Indonesian public sector was significantly supported by internal auditors and leadership. The findings confirm the importance of transformational leadership and internal audit in the public sector risk management strategies.

Keywords: Indonesian public sector, internal audit, risk management, transformational leadership

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Authors: S. S. Patil, Sachidanand Kini

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Classification of land use patterns is compelling in complexity and variability of remote sensing imageries data. An imperative research in remote sensing application exploited to mine some of the significant spatially variable factors as land cover and land use from satellite images for remote arid areas in Karnataka State, India. The diverse classification techniques, unsupervised and supervised consisting of maximum likelihood, Mahalanobis distance, and minimum distance are applied in Bellary District in Karnataka State, India for the classification of the raw satellite images. The accuracy evaluations of results are compared visually with the standard maps with ground-truths. We initiated with the maximum likelihood technique that gave the finest results and both minimum distance and Mahalanobis distance methods over valued agriculture land areas. In meanness of mislaid few irrelevant features due to the low resolution of the satellite images, high-quality accord between parameters extracted automatically from the developed maps and field observations was found.

Keywords: Mahalanobis distance, minimum distance, supervised, unsupervised, user classification accuracy, producer's classification accuracy, maximum likelihood, kappa coefficient

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Authors: Guilhermina Torrao

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An extensive number of studies have been conducted to determine the factors which influence crash injury risk (CIR); however, uncertainties inherent to selected variables have been neglected. A review of existing literature is required to not only obtain an overview of the variables and measures but also ascertain the implications when comparing studies without a systematic view of variable taxonomy. Therefore, the aim of this literature review is to examine and report on peer-reviewed studies in the field of crash analysis and to understand the implications of broad variations in variable selection in CIR analysis. The objective of this study is to demonstrate the variance in variable selection and classification when modeling injury risk involving occupants of light vehicles by presenting an analytical review of the literature. Based on data collected from 64 journal publications reported over the past 21 years, the analytical review discusses the variables selected by each study across an organized list of predictors for CIR analysis and provides a better understanding of the contribution of accident and vehicle factors to injuries acquired by occupants of light vehicles. A cross-comparison analysis demonstrates that almost half the studies (48%) did not consider vehicle design specifications (e.g., vehicle weight), whereas, for those that did, the vehicle age/model year was the most selected explanatory variable used by 41% of the literature studies. For those studies that included speed risk factor in their analyses, the majority (64%) used the legal speed limit data as a ‘proxy’ of vehicle speed at the moment of a crash, imposing limitations for CIR analysis and modeling. Despite the proven efficiency of airbags in minimizing injury impact following a crash, only 22% of studies included airbag deployment data. A major contribution of this study is to highlight the uncertainty linked to explanatory variable selection and identify opportunities for improvements when performing future studies in the field of road injuries.

Keywords: crash, exploratory, injury, risk, variables, vehicle

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Authors: Nilamadhab Mohanty

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It is essential to use message formats that make communication understandable and correct. It is because; the information format can influence consumer decision on the purchase of a product. This study combines information from qualitative inquiry, media trend analysis, eye tracking experiment, and questionnaire data to examine the impact of specific message format and consumer perceived risk on attention to the information and risk retention. We investigated the influence of message framing (goal framing, attribute framing, and mix framing) on consumer memory, study time, and decisional uncertainty while deciding on the purchase of drugs. Furthermore, we explored the impact of consumer perceived risk (associated with the use of the drug, i.e., RISK-AB and perceived risk associated with the non-use of the drug, i.e., RISK-EB) on message format preference. The study used eye-tracking methods to understand the differences in message processing. Findings of the study suggest that the message format influences information processing, and participants' risk perception impacts message format preference. Eye tracking can be used to understand the format differences and design effective advertisements.

Keywords: message framing, consumer perceived risk, advertising, eye tracking

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: cancer classification, feature selection, deep learning, genetic algorithm

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Authors: Joshua Midha

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This paper provides an evaluation of an intervention designed to promote a risk-realistic environment for women in the workplace and regulate their risk-related decision-making. In past research, women -specifically women of color- are highly risk-averse, and this may prove to be an innate obstacle in gender progress in corporations. By helping women see the risks and the benefits and increasing potential benefits, we can increase the chances of success in the workplace. Our intervention was a success and significantly increased comfort, trust, and frequency in the use of decision-making skills in the workplace. In this paper, we explore the intervention, the methods, the results, and the implications.

Keywords: behavioral economics, decision support, risk, gender equality

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Authors: Majid Abdolrazzagh-Nezhad, Salwani Abdullah

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The job-shop scheduling problem (JSSP) is an important decision facing those involved in the fields of industry, economics and management. This problem is a class of combinational optimization problem known as the NP-hard problem. JSSPs deal with a set of machines and a set of jobs with various predetermined routes through the machines, where the objective is to assemble a schedule of jobs that minimizes certain criteria such as makespan, maximum lateness, and total weighted tardiness. Over the past several decades, interest in meta-heuristic approaches to address JSSPs has increased due to the ability of these approaches to generate solutions which are better than those generated from heuristics alone. This article provides the classification, constraints and objective functions imposed on JSSPs that are available in the literature.

Keywords: job-shop scheduling, classification, constraints, objective functions

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Authors: Yu-Hong Lin

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Background: An adverse drug reaction (ADR) reported is an injury which caused by taking medicines. Sometimes the severity of ADR reported may be minor, but sometimes it could be a life-threatening situation. In order to provide healthcare professionals as a better reference in clinical practice, we do data collection and analysis from our hospital. Methods: This was a retrospective study of ADRs reported performed from 2014 to 2015 in our hospital in Taiwan. We collected assessment items of ADRs reported, which contain gender and age, occurring sources, Anatomical Therapeutic Chemical (ATC) classification of suspected drugs, types of adverse reactions, Naranjo score calculating by Naranjo Adverse Drug Reaction Probability Scale and so on. Results: The investigation included two hundred and seven ADRs reported. Most of ADRs reported were occurring in outpatient department (92%). The average age of ADRs reported was 65.3 years. Less than 65 years of age were in the majority in this study (54%). Majority of all ADRs reported were males (51%). According to ATC classification system, the major classification of suspected drugs was cardiovascular system (19%) and antiinfectives for systemic use (18%) respectively. Among the adverse reactions, Dermatologic Effects (35%) were the major type of ADRs. Also, the major Naranjo scores of all ADRs reported ranged from 1 to 4 points (91%), which represents a possible correlation between ADRs reported and suspected drugs. Conclusions: Definitely, ADRs reported is still an extremely important information for healthcare professionals. For that reason, we put all information of ADRs reported into our hospital's computer system, and it will improve the safety of medication use. By hospital's computer system, it can remind prescribers to think of information about patient's ADRs reported. No drugs are administered without risk. Therefore, all healthcare professionals should have a responsibility to their patients, who themselves are becoming more aware of problems associated with drug therapy.

Keywords: adverse drug reaction, Taiwan, healthcare professionals, safe use of medicines

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Authors: Yung-Feng Yen, Yun-Ju Lai

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Background: Metabolic syndrome (MetS) is reversible; however, the effect of changes in MetS status on the risk of incident cancer has not been extensively studied. We aimed to investigate the effects of changes in MetS status on incident cancer risk. Methods: This prospective, longitudinal study used data from Taiwan’s MJ cohort of 157,915 adults recruited from 2002–2016 who had repeated MetS measurements 5.2 (±3.5) years apart and were followed up for the new onset of cancer over 8.2 (±4.5) years. A new diagnosis of incident cancer in study individuals was confirmed by their pathohistological reports. The participants’ MetS status included MetS-free (n=119,331), MetS-developed (n=14,272), MetS-recovered (n=7,914), and MetS-persistent (n=16,398). We used the Fine-Gray sub-distribution method, with death as the competing risk, to determine the association between MetS changes and the risk of incident cancer. Results: During the follow-up period, 7,486 individuals had new development of cancer. Compared with the MetS-free group, MetS-persistent individuals had a significantly higher risk of incident cancer (adjusted hazard ratio [aHR], 1.10; 95% confidence interval [CI], 1.03-1.18). Considering the effect of dynamic changes in MetS status on the risk of specific cancer types, MetS persistence was significantly associated with a higher risk of incident colon and rectum, kidney, pancreas, uterus, and thyroid cancer. The risk of kidney, uterus, and thyroid cancer in MetS-recovered individuals was higher than in those who remained MetS but lower than MetS-persistent individuals. Conclusions: Persistent MetS is associated with a higher risk of incident cancer, and recovery from MetS may reduce the risk. The findings of our study suggest that it is imperative for individuals with pre-existing MetS to seek treatment for this condition to reduce the cancer risk.

Keywords: metabolic syndrome change, cancer, risk factor, cohort study

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Authors: Pırıl Tekin, Rızvan Erol

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Risk management has become very important and popular in developing countries in recent years. Especially making patient and employee health and safety issues compulsory in the hospitals, raised the number of studies in Turkey. Also risk management become more important for hospital senior management from clinics to the laboratories. Because quality is really important to be chosen for both patients to consult and employees to prefer to work. And also risk management studies can lead to hospital management team about future works and methods. By this point of view, this study is the risk assessment carried out in the biggest dental hospital in the south part of Turkey. This study was conducted as a research case study, covering two different health care place; A Clinic and A Laboratory. It shows that the problems in this dental hospital and how it can solve all.

Keywords: risk management, healthcare, dental hospital, quality management

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Authors: Oguzhan Cepni, Doruk Kucuksarac, M. Hasan Yilmaz

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Changes in the global risk appetite cause co-movement in emerging market risk premiums. However, the sensitivity of the changes in risk premium to the global risk appetite may vary across emerging markets. In this study, how the global risk appetite affects Credit Default Swap (CDS) premiums in emerging markets are analyzed using Principal Component Analysis (PCA) and rolling regressions. The PCA results indicate that the first common component derived by the PCA accounts for almost 76 percent of the common variation in CDS premiums. Additionally, the explanatory power of the first factor seems to be high over the sample period. However, the sensitivity to the global risk factor tends to change over time and across countries. In this regard, fixed effects panel regressions are used to identify the macroeconomic factors driving the heterogeneity across emerging markets. The panel regression results point to the significance of government debt to GDP and international reserves to GDP in explaining sensitivity. Accordingly, countries with lower government debt and higher reserves tend to be less subject to the variations in the global risk appetite.

Keywords: credit default swaps, emerging markets, principal components analysis, sovereign risk

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Authors: Ravi Vishwanath, Saumya Kumaar, S. N. Omkar

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Brain-computer interfacing (BCI) is a technology that is almost four decades old, and it was developed solely for the purpose of developing and enhancing the impact of neuroprosthetics. However, in the recent times, with the commercialization of non-invasive electroencephalogram (EEG) headsets, the technology has seen a wide variety of applications like home automation, wheelchair control, vehicle steering, etc. One of the latest developed applications is the mind-controlled quadrotor unmanned aerial vehicle. These applications, however, do not require a very high-speed response and give satisfactory results when standard classification methods like Support Vector Machine (SVM) and Multi-Layer Perceptron (MLPC). Issues are faced when there is a requirement for high-speed control in the case of fixed-wing unmanned aerial vehicles where such methods are rendered unreliable due to the low speed of classification. Such an application requires the system to classify data at high speeds in order to retain the controllability of the vehicle. This paper proposes a novel method of classification which uses a combination of Common Spatial Paradigm and Linear Discriminant Analysis that provides an improved classification accuracy in real time. A non-linear SVM based classification technique has also been discussed. Further, this paper discusses the implementation of the proposed method on a fixed-wing and VTOL unmanned aerial vehicles.

Keywords: brain-computer interface, classification, machine learning, unmanned aerial vehicles

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Authors: T. C. Hsu, H. L. Lin

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Taiwan is mountainous country, 70% of land is covered with mountains. Because of extreme climate, the mountain villages with sensitive and fragile environment often get easily affected by inundation and debris flow from typhoon which brings huge rainfall. Due to inappropriate development, overuse and fewer access roads, occurrence of disaster becomes more frequent through downpour and rescue actions are postponed. However, risk map is generally established through administrative boundaries, the difference of urban and rural area is ignored. The neglect of mountain village characteristics eventually underestimates the importance of factors related to vulnerability and reduces the effectiveness. In disaster management, there are different strategies and actions at each stage. According to different tasks, there will be different risk indices and weights to analyze disaster risk for each stage and then it will contribute to confront threat and reduce impact appropriately on right time. Risk map is important in mitigation, but also in response stage because some factors such as road network will be changed by disaster. This study will use risk assessment to establish risk map of Shanlin District which is mountain village in Kaohsiung as a case study in mitigation and response stage through Analytic Hierarchy Process (AHP). AHP helps to recognize the composition and weights of risk factors in mountain village by experts’ opinions through survey design and is combined with present potential hazard map to produce risk map.

Keywords: risk assessment, mountain village, risk map, analytic hierarchy process

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Authors: Kolade Olawande Owoeye, George Weir

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Terrorism and extremism activities on the internet are becoming the most significant threats to national security because of their potential dangers. In response to this challenge, law enforcement and security authorities are actively implementing comprehensive measures by countering the use of the internet for terrorism. To achieve the measures, there is need for intelligence gathering via the internet. This includes real-time monitoring of potential websites that are used for recruitment and information dissemination among other operations by extremist groups. However, with billions of active webpages, real-time monitoring of all webpages become almost impossible. To narrow down the search domain, there is a need for efficient webpage classification techniques. This research proposed a new approach tagged: SentiPosit-based method. SentiPosit-based method combines features of the Posit-based method and the Sentistrenght-based method for classification of terrorism and extremism webpages. The experiment was carried out on 7500 webpages obtained through TENE-webcrawler by International Cyber Crime Research Centre (ICCRC). The webpages were manually grouped into three classes which include the ‘pro-extremist’, ‘anti-extremist’ and ‘neutral’ with 2500 webpages in each category. A supervised learning algorithm is then applied on the classified dataset in order to build the model. Results obtained was compared with existing classification method using the prediction accuracy and runtime. It was observed that our proposed hybrid approach produced a better classification accuracy compared to existing approaches within a reasonable runtime.

Keywords: sentiposit, classification, extremism, terrorism

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Authors: Geetika Barman, B. S. Daya Sagar

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In this article, we proposed a pixel-wise classification of hyperspectral images using a mathematical morphology operator-based distance metric called “dilation distance” and “erosion distance”. This method involves measuring the spatial distance between the spectral features of a hyperspectral image across the bands. The key concept of the proposed approach is that the “dilation distance” is the maximum distance a pixel can be moved without changing its classification, whereas the “erosion distance” is the maximum distance that a pixel can be moved before changing its classification. The spectral signature of the hyperspectral image carries unique class information and shape for each class. This article demonstrates how easily the dilation and erosion distance can measure spatial distance compared to other approaches. This property is used to calculate the spatial distance between hyperspectral image feature vectors across the bands. The dissimilarity matrix is then constructed using both measures extracted from the feature spaces. The measured distance metric is used to distinguish between the spectral features of various classes and precisely distinguish between each class. This is illustrated using both toy data and real datasets. Furthermore, we investigated the role of flat vs. non-flat structuring elements in capturing the spatial features of each class in the hyperspectral image. In order to validate, we compared the proposed approach to other existing methods and demonstrated empirically that mathematical operator-based distance metric classification provided competitive results and outperformed some of them.

Keywords: dilation distance, erosion distance, hyperspectral image classification, mathematical morphology

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Authors: Ahmad Dahlan Salleh, Nik Abdul Rahim Nik Abdul Ghani, Muhamad Firdaus M. Hatta

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Risk management has implications on pricing, governance arrangements, business practices and strategy. Nowadays, home financing contract offers more in the risk transfer form to increase bank profit. This is parallel with Islamic jurisprudence method al-Kharaj bi al-thaman (gain accompanies liability for loss) and al-ghurm bil ghunm (gain is justified with risk) that determine the matching between risk transfer and returns. Malaysian financing trend is to buy house. Besides, exists transparency lacking risk transfer issues to the clients because of not been informed clearly. Terms and conditions of each financing also do not reflect clearly that the risk has been transferred to the client, justifying a determination price been made. The assumption on risk occurrence is also inaccurate as each risk is different with the type of financing contract. This makes the Islamic Financial Services Act 2013 in providing standards that transparent and consistent can be used by Islamic financial institution less effective. This study examines how far the level of the risk and obligation incurred by bank and client under various Islamic home financing contract. This research is qualitative by using two methods, document analysis, and semi-structured interviews. Document analysis from literature review to identify profile, themes and risk transfer element in home financing from Islamic jurisprudence perspective. This study finds that need to create a risk transfer parameter by banks which are consistent with risk transfer theory according to Islamic jurisprudence. This study has potential to assist the authority in Islamic finance such as The Central Bank of Malaysia (Bank Negara Malaysia) in regulating Islamic banking industry so that the risk transfer valuation in home financing contract based on home financing good practice and determined risk limits.

Keywords: risk transfer, home financing contract, Sharia compliant, Malaysia

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Authors: Kemal Polat

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In this study, it has been estimated whether there is skin by using RBG values obtained from the camera and k-nearest neighbor (k-NN) classifier. The dataset used in this study has an unbalanced distribution and a linearly non-separable structure. This problem can also be called a big data problem. The Skin dataset was taken from UCI machine learning repository. As the classifier, we have used the k-NN method to handle this big data problem. For k value of k-NN classifier, we have used as 1. To train and test the k-NN classifier, 50-50% training-testing partition has been used. As the performance metrics, TP rate, FP Rate, Precision, recall, f-measure and AUC values have been used to evaluate the performance of k-NN classifier. These obtained results are as follows: 0.999, 0.001, 0.999, 0.999, 0.999, and 1,00. As can be seen from the obtained results, this proposed method could be used to predict whether the image is skin or not.

Keywords: k-NN classifier, skin or non-skin classification, RGB values, classification

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Authors: Amna Khan, Zareena Kausar, Saad Malik

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Bio Mechatronics has extended applications in the field of rehabilitation. It has been contributing since World War II in improving the applicability of prosthesis and assistive devices in real life scenarios. In this paper, classification accuracies have been compared for two classifiers against five positions of elbow. Electromyography (EMG) signals analysis have been acquired directly from skeletal muscles of human forearm for each of the three defined positions and at modified extreme positions of elbow flexion and extension using 8 electrode Myo armband sensor. Features were extracted from filtered EMG signals for each position. Performance of two classifiers, support vector machine (SVM) and linear discriminant analysis (LDA) has been compared by analyzing the classification accuracies. SVM illustrated classification accuracies between 90-96%, in contrast to 84-87% depicted by LDA for five defined positions of elbow keeping the number of samples and selected feature the same for both SVM and LDA.

Keywords: classification accuracies, electromyography, linear discriminant analysis (LDA), Myo armband sensor, support vector machine (SVM)

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Authors: P. S. Jagadeesh Kumar, Tracy Lin Huan, S. Meenakshi Sundaram

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Alzheimer’s disease is one of the prevalent kind of ailment, expected for impudent reconciliation or an effectual therapy is to be accredited hitherto. Probable detonation of patients in the upcoming years, and consequently an enormous deal of apprehension in early discovery of the disorder, this will conceivably chaperon to enhanced healing outcomes. Complex impetuosity of the brain is an observant symbolic of the disease and a unique recognition of genetic sign of the disease. Machine learning alongside deep learning and decision tree reinforces the aptitude to absorb characteristics from multi-dimensional data’s and thus simplifies automatic classification of Alzheimer’s disease. Susceptible testing was prophesied and realized in training the prospect of Alzheimer’s disease classification built on machine learning advances. It was shrewd that the decision trees trained with deep neural network fashioned the excellent results parallel to related pattern classification.

Keywords: Alzheimer's diagnosis, decision trees, deep neural network, machine learning, pattern classification

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Authors: Wanhyun Cho, Soonja Kang, Sanggoon Kim, Soonyoung Park

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We present probabilistic multinomial Dirichlet classification model for multidimensional data and Gaussian process priors. Here, we have considered an efficient computational method that can be used to obtain the approximate posteriors for latent variables and parameters needed to define the multiclass Gaussian process classification model. We first investigated the process of inducing a posterior distribution for various parameters and latent function by using the variational Bayesian approximations and important sampling method, and next we derived a predictive distribution of latent function needed to classify new samples. The proposed model is applied to classify the synthetic multivariate dataset in order to verify the performance of our model. Experiment result shows that our model is more accurate than the other approximation methods.

Keywords: multinomial dirichlet classification model, Gaussian process priors, variational Bayesian approximation, importance sampling, approximate posterior distribution, marginal likelihood evidence

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Authors: Yangze Lan, Ruihua Xv, Feng Zhou, Yijia Shan, Longhao Zhang, Qinghui Xv

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Receiving and dispatching trains is an important part of railroad organization, and the risky evaluation of operating personnel is still reflected by scores, lacking further excavation of wrong answers and operating accidents. With natural language processing (NLP) technology, this study extracts the keywords and key phrases of 40 relevant risk events about receiving and dispatching trains and reclassifies the risk events into 8 categories, such as train approach and signal risks, dispatching command risks, and so on. Based on the historical risk data of personnel, the K-Means clustering method is used to classify the risk level of personnel. The result indicates that the high-risk operating personnel need to strengthen the training of train receiving and dispatching operations towards essential trains and abnormal situations.

Keywords: receiving and dispatching trains, natural language processing, risk evaluation, K-means clustering

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Authors: Mohammadreza Ashouria, Majid Motamedzadeb

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Rapid Entire Body Assessment (REBA) and Quick Exposure Check (QEC) are two general methods to assess the risk factors of work-related musculoskeletal disorders (WMSDs). This study aimed to compare ergonomic risk assessment outputs from QEC and REBA in terms of agreement in distribution of postural loading scores based on analysis of working postures. This cross-sectional study was conducted in an engine oil company in which 40 jobs were studied. A trained occupational health practitioner observed all jobs. Job information was collected to ensure the completion of ergonomic risk assessment tools, including QEC, and REBA. The result revealed that there was a significant correlation between final scores (r=0.731) and the action levels (r =0.893) of two applied methods. Comparison between the action levels and final scores of two methods showed that there was no significant difference among working departments. Most of the studied postures acquired low and moderate risk level in QEC assessment (low risk=20%, moderate risk=50% and High risk=30%) and in REBA assessment (low risk=15%, moderate risk=60% and high risk=25%).There is a significant correlation between two methods. They have a strong correlation in identifying risky jobs and determining the potential risk for incidence of WMSDs. Therefore, there is a possibility for researchers to apply interchangeably both methods, for postural risk assessment in appropriate working environments.

Keywords: observational method, QEC, REBA, musculoskeletal disorders

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Authors: Joseph Uche Azubike, Evelyn Ngozi Agbasi, M. I. Ogbonna

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The audit committee of the board of directors could no longer handle the enterprise's risks. Therefore, a risk management committee was created to control them. Thus, this study examined how enterprise risk management committee characteristics affected Nigerian exchange-listed banks' financial performance from 2013 to 2022. The study's hypotheses and three objectives were to determine how enterprise risk management committee size, composition, and gender diversity affect Nigerian banks' performance. An ex-post facto study design collected secondary data from bank annual reports. We used descriptive statistics, correlation analysis, and Ordinary least square regression to analyze panel data. Enterprise risk management committee size and composition had both negative and no significant effect on bank financial performance in Nigeria, whereas enterprise risk committee gender diversity has a 10% favorable effect. The report advises that adding more women with relevant knowledge to the risk committee to boost performance and allowing women to be at the lead of such risk management could improve bank performance in Nigeria since they are noted to be thorough in their tasks.

Keywords: bank, committee, enterprise, management, performance, risk

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Authors: Mohammed E. Mansour, Tamador M. A. Elhassan, Nahid A. Ibrahim, Awatif A. Ahmed, Manal A. Abdalla

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Rift valley fever (RVF) is mosquito-borne disease. RVF is transboundary zoonotic disease. It has socioeconomic and public health importance. This paper describes qualitative risk of the RVF vaccine production. RVF is endemic in the Sudan. It has been reported in Sudan due to abundance of Ades Eqytie. Thus, there is huge effort to control it. Vaccination practices had significant role to control and manage RVF. The risk assessment explains the likelihood of a risk as likely. Thus, insecticides and repellents synergize the effort of the vaccination.

Keywords: qualitative analysis, risk assessment, rift valley fever vaccine, quality control

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Authors: Garg Ramneesh, Uppal Sanjeev, Mittal Rajinder, Shah Sheerin, Jain Vikas, Singla Bhupinder

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Introduction: Despite advances in trauma care, a classification system for soft tissue injuries of the face still needs to be objectively defined. Aim: To develop a classification system for soft tissue injuries of the face; that is objective, easy to remember, reproducible, universally applicable, aids in surgical management and helps to develop a structured data that can be used for future use. Material and Methods: This classification system includes those patients that need surgical management of facial injuries. Associated underlying bony fractures have been intentionally excluded. Depending upon the severity of soft tissue injury, these can be graded from 0 to IV (O-Abrasions, I-lacerations, II-Avulsion injuries with no skin loss, III-Avulsion injuries with skin loss that would need graft or flap cover, and IV-complex injuries). Anatomically, the face has been divided into three zones (Zone 1/2/3), as per aesthetic subunits. Zone 1e stands for injury of eyebrows; Zones 2 a/b/c stand for nose, upper eyelid and lower eyelid respectively; Zones 3 a/b/c stand for upper lip, lower lip and cheek respectively. Suffices R and L stand for right or left involved side, B for presence of foreign body like glass or pellets, C for extensive contamination and D for depth which can be graded as D 1/2/3 if depth is still fat, muscle or bone respectively. I is for damage to facial nerve or parotid duct. Results and conclusions: This classification system is easy to remember, clinically applicable and would help in standardization of surgical management of soft tissue injuries of face. Certain inherent limitations of this classification system are inability to classify sutured wounds, hematomas and injuries along or against Langer’s lines.

Keywords: soft tissue injuries, face, avulsion, classification

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Authors: E. Channol, O. Collet, N. Kostyuchyk, T. Mesbah, Quoc Hoang Long Nguyen

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In this paper, a pricing model is proposed for co-integrated commodities extending Larsson model. The futures formulae have been derived and tests have been performed with non-constant volatility. The model has been applied to energy commodities (gas, CO2, energy) and soft commodities (corn, wheat). Results show that non-constant volatility leads to more accurate short term prices, which provides better evaluation of value-at-risk and more generally improve the risk management.

Keywords: co-integration, soft commodities, risk management, value-at-risk

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Authors: Kwounghee Choi

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Technological convergence between the various sectors is expected to have a very large impact on future industrial and economy. This study attempts to do empirical approach between specific technologies’ classification. For technological convergence classification, it is necessary to set the target technology to be analyzed. This study selected target technology from national research and development plan. At first we found a source technology for analysis. Depending on the weight of source technology, NT-based, BT-based, IT-based, ET-based, CS-based convergence types were classified. This study aims to empirically show the concept of convergence technology and convergence types. If we use the source technology to classify convergence type, it will be useful to make practical strategies of convergence technology.

Keywords: technology convergence, source technology, convergence type, R&D strategy, technology classification

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Authors: Ashley Wei-Ting Wang, Wen-Yau Hsu

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Objectives: The current study applies and expands the Tripartite Model to elaborate the link between non-suicidal self-injury (NSSI) and suicidal behavior. We propose a structural model of NSSI and suicidal risk, in which negative affect (NA) predicts both anxiety and depression, positive affect (PA) predicts depression only, anxiety is linked to NSSI, and depression is linked to suicidal risk. Method: Four hundreds and eighty seven undergraduates participated. Data were collected by administering self-report questionnaires. We performed hierarchical regression and structural equation modeling to test the proposed structural model. Results: The results largely support the proposed structural model, with one exception: anxiety was strongly associated with NSSI and to a lesser extent with suicidal risk. Conclusions: We conclude that the co-occurrence of NSSI and suicidal risk is due to NA and anxiety, and suicidal risk can be differentiated by depression. Further theoretical and practical implications are discussed.

Keywords: non-suicidal self-injury, suicidal risk, anxiety, depression, the tripartite model, hierarchical relationship

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Authors: R. Berglund, T. Backström, M. Bellgran

Abstract:

This study explores how management addresses psychosocial risks in seven teams of engineers and technicians in the midst of the fourth industrial revolution. The sample is from an ongoing quasi-experiment about psychosocial risk management in a manufacturing company in Sweden. Each of the seven teams belongs to one of two clusters: a positive cluster or a negative cluster. The positive cluster reports a significantly positive change in psychosocial risk levels between two time-points and the negative cluster reports a significantly negative change. The data are collected using semi-structured interviews. The results of the computer aided thematic analysis show that there are more differences than similarities when comparing the risk treatment actions taken between the two clusters. Findings show that the managers in the positive cluster use more enabling actions that foster and support formal and informal relationship building. In contrast, managers that use less enabling actions hinder the development of positive group processes and contribute negative changes in psychosocial risk levels. This exploratory study sheds some light on how management can influence significant positive and negative changes in psychosocial risk levels during a risk management process.

Keywords: group process model, risk treatment, risk management, psychosocial

Procedia PDF Downloads 130

Authors: Hilalah Alturkistani, Alaa AlFaadhel, Nora AlJahani, Fatiha Djebbar

Abstract:

Cloud computing is one of the most widely used technology which provides opportunities and services to government entities, large companies, and standard users. However, cybersecurity risk management studies of cloud computing and resiliency approaches are lacking. This paper proposes resilient cloud cybersecurity risk assessment and management tailored specifically, to Dropbox with two approaches:1) technical-based solution motivated by a cybersecurity risk assessment of cloud services, and 2)a target personnel-based solution guided by cybersecurity-related survey among employees to identify their knowledge that qualifies them withstand to any cyberattack. The proposed work attempts to identify cloud vulnerabilities, assess threats and detect high risk components, to finally propose appropriate safeguards such as failure predicting and removing, redundancy or load balancing techniques for quick recovery and return to pre-attack state if failure happens.

Keywords: cybersecurity risk management plan, resilient cloud computing, cyberattacks, cybersecurity risk assessment

Procedia PDF Downloads 109