Search results for: squared prediction risk

Authors: Tayeb Aissaoui, Inas M. AlNashef

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In recent years, green solvents named deep eutectic solvents (DESs) have been found to possess significant properties and to be applicable in several technologies. Choline chloride (ChCl) mixed with urea at a ratio of 1:2 and 80 °C was the first discovered DES. In this article, chemical structure and combination mechanism of ChCl: urea based DES were investigated. Moreover, the implementation of this DES in water removal from natural gas was reported. Dehydration of natural gas by ChCl:urea shows significant absorption efficiency compared to triethylene glycol. All above operations were retrieved from COSMOthermX software. This article confirms the potential application of DESs in gas industry.

Keywords: COSMO-RS, deep eutectic solvents, dehydration, natural gas, structure, organic salt

Procedia PDF Downloads 288

Authors: Phong Le Ha

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In many cities the entire world, the relationship between cities and rivers is always considered as a fundament of urban history research because of their profound interactions. This kind of relationship makes the river zones become extremely sensitive in many aspects. One of the most important aspect is their roles in the drainage of cities. In this paper we will examine an extraordinary case of Hanoi, the capital of Vietnam and Red river zones. This river has contradictory impacts to this city: It is considered as a source of life of the inhabitants who live along its two banks, however, the risk of inundation caused by the complicated hydrology system of this river is always a real threat to the cities that it flows through. Morphologically, Red river was connected to the inner rivers system that made Hanoi a complete form of a river city. This structure combined with the topography of Hanoi helps this city to assure a stable drainage system in which the river zones in the north of Hanoi play some extreme important roles. Nevertheless, in the late 20 years, Hanoi's strong urbanization and the instability of Red river's complicated hydrology make the very remarkable transformations in the relationship river-city and in the river zones: The connection between the river and the city declines; the system of inner lakes are progressively replaced by habitat land; in the river zones, the infrastructure system can't adapt to the transformations of the new quarters which have the origin of the agricultural villages. These changes bring out many chances for the urban development, but also many risks and problems, particularly in the environment and technical sides. Among these, pluvial and used water evacuation is one of the most severe problems. The disappear of inner-city lakes, the high dike and the topographical changes of Hanoi blow up the risk of inundation of this city. In consequences, the riverine zones, particularly in the north of Hanoi, where the two most important water evacuation rivers of Hanoi meet each other, are burdened with the drainage pressure. The unique water treatment plant in this zone seems to be overcharged in receiving each day about 40000m3 of used water (not include pluvial water). This kind of problem leads also to another risk related to the environmental pollution (water pollution and air pollution). So, in order to better understand the situation and to propose the solutions to resolve the problems, an interdisciplinary research covering many different fields such urban planning, architecture, geography, and especially drainage and environment has been carried out. In general, this paper will analyze an important part of the research : the process of urban transformation of Hanoi (changes in urban morphology, infrastructure system, evolution of the dike system, ...) and the hydrological changes of Red river which cause the drainage and environmental problems. The conclusions of these analyses will be the solid base of the following researches focusing on the solutions of a sustainable development.

Keywords: drainage, environment, Hanoi, infrastructure, red rivers, urbanization

Procedia PDF Downloads 390

Authors: Yao Song

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Objectives: We aimed to explore the clinicodemographic characteristics and prognosis of grey zone squamous cell cancer (GZSCC) located in the overlapping or ambiguous area of the oral cavity and oropharynx and to identify valuable factors that would improve its differential diagnosis and prognosis. Methods: Information of GZSCC patients in the Surveillance, Epidemiology, and End Results (SEER) database was compared to patients with an oral cavity (OCSCC) and oropharyngeal (OPSCC) squamous cell carcinomas with corresponding HPV status, respectively. Kaplan-Meier method with log-rank test and multivariate Cox regression analysis were applied to assess associations between clinical characteristics and overall survival (OS). A predictive model integrating age, gender, marital status, HPV status, and staging variables was conducted to classify GZSCC patients into three risk groups and verified internally by 10-fold cross validation. Results: A total of 3318 GZSCC, 10792 OPSCC, and 6656 OCSCC patients were identified. HPV-positive GZSCC patients had the best 5-year OS as HPV-positive OPSCC (81% vs. 82%). However, the 5-year OS of HPV-negative/unknown GZSCC (43%/42%) was the worst among all groups, indicating that HPV status and the overlapping nature of tumors were valuable prognostic predictors in GZSCC patients. Compared with the strategy of dividing GZSCC into two groups by HPV status, the predictive model integrating more variables could additionally identify a unique high-risk GZSCC group with the lowest OS rate. Conclusions: GZSCC patients had distinct clinical characteristics and prognoses compared with OPSCC and OCSCC; integrating HPV status and other clinical factors could help distinguish GZSCC and predict their prognosis.

Keywords: GZSCC, OCSCC, OPSCC, HPV

Procedia PDF Downloads 73

Authors: Jiaqian Yin, Ruoling Chen, David Churchill, Huijuan Zou, Peipei Guo, Chunmei Liang, Xiaoqing Peng, Zhikang Zhang, Weiju Zhou, Yunxia Cao

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Purpose: This study aimed to explore the interaction of male and female age on implantation failure from in vitro fertilisation (IVF)/ intracytoplasmic sperm injection (ICSI) treatments in couples following their first cycles using the Anhui Maternal-Child Health Study (AMCHS). Methods: The AMCHS recruited 2042 infertile couples who were physically fit for in vitro fertilisation (IVF) or intracytoplasmic sperm injection (ICSI) treatment at the Reproductive Centre of the First Affiliated Hospital of Anhui Medical University between May 2017 to April 2021. This prospective cohort study analysed the data from 1910 cohort couples for the current paper data analysis. The multivariate logistic regression model was used to identify the effect of male and female age on implantation failure after controlling for confounding factors. Male age and female age were examined as continuous and categorical (male age: 20-<25, 25-<30, 30-<35, 35-<40, ≥40; female age: 20-<25, 25-<30, 30-<35, 35-<40, ≥40) predictors. Results: Logistic regression indicated that advanced maternal age was associated with increased implantation failure (P<0.001). There was evidence of an interaction between maternal age (30-<35 and ≥ 35) and paternal age (≥35) on implantation failure. (p<0.05). Only when the male was ≥35 years of increased maternal age was associated with the risk of implantation failure. Conclusion: In conclusion, there was an additive effect on implantation failure with advanced parental age. The impact of advanced maternal age was only seen in the older paternal age group. The delay of childbearing in both men and women will be a serious public issue that may contribute to a higher risk of implantation failure in patients needing assisted reproductive technology (ART).

Keywords: parental age, infertility, cohort study, IVF

Procedia PDF Downloads 145

Authors: Maryam Amiri, Leyli Mohammad-Khanli

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Cloud computing is becoming increasingly popular and more business applications are moving to cloud. In this regard, services that provide similar functional properties are increasing. So, the ability to select a service with the best non-functional properties, corresponding to the user preference, is necessary for the user. This paper presents an Evaluation Framework of Service Trustworthiness (EFST) that evaluates the trustworthiness of equivalent services without need to additional invocations of them. EFST extracts user preference automatically. Then, it assesses trustworthiness of services in two dimensions of qualitative and quantitative metrics based on the experiences of past usage of services. Finally, EFST determines the overall trustworthiness of services using Fuzzy Inference System (FIS). The results of experiments and simulations show that EFST is able to predict the missing values of Quality of Service (QoS) better than other competing approaches. Also, it propels users to select the most appropriate services.

Keywords: user preference, cloud service, trustworthiness, QoS metrics, prediction

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Authors: Tarek Litim, Ouahiba Taamallah

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The present paper is an investigation of the effect of burnishing on the surface integrity of a component made of 15NiCr6 steel. This work shows a statistical study based on regression, and Taguchi's design has allowed the development of mathematical models to predict the output responses as a function of the technological parameters studied. The response surface methodology (RSM) showed a simultaneous influence of the burnishing parameters and observe the optimal processing parameters. ANOVA analysis of the results resulted in the validation of the prediction model with a determination coefficient R=90.60% and 92.41% for roughness and hardness, respectively. Furthermore, a multi-objective optimization allowed to identify a regime characterized by P=10kgf, i=3passes, and f=0.074mm/rev, which favours minimum roughness and maximum hardness. The result was validated by the desirability of D= (0.99 and 0.95) for roughness and hardness, respectively.

Keywords: 15NiCr6 steel, burnishing, surface integrity, Taguchi, RSM, ANOVA

Procedia PDF Downloads 187

Authors: Tariq Mansoor, Lubos Hes, Vladimir Bajzik

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Socks comfort has great significance in our daily life. This significance even increased when we have undergone a work of low or high activity. It causes the sweating of our body with different rates. In this study, plain socks with differential fibre composition were wetted to saturated level. Then after successive intervals of conditioning, these socks are characterized by thermal resistance in dry and wet states. Theoretical thermal resistance is predicted by using combined filling coefficients and thermal conductivity of wet polymers instead of dry polymer (fibre) in different models. By this modification, different mathematical models could predict thermal resistance at different moisture levels. Furthermore, predicted thermal resistance by different models has reasonable correlation range between (0.84 -0.98) with experimental results in both dry (lab conditions moisture) and wet states. "This work is supported by Technical University of Liberec under SGC-2019. Project number is 21314".

Keywords: thermal resistance, mathematical model, plain socks, moisture loss rate

Procedia PDF Downloads 193

Authors: Boularas El Alia, H. Rezk-Allah, S. Chaoui, A. Chama, B. Rezk-Allah

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Aims: To assess the current exposure to the PHA among various workers in the sector of asphalt and road paving. Methods: The assessment of the exposure to PHA has been performed on workers (n=14) belonging to two companies, allocated into several activities such as road paving, manufacturing of coated bituminous warm, manufacturing of asphalt cut-back, manufacturing of emulsion of asphalt. A group of control subjects (n=18) was associated. The internal exposure to PHA was investigated by measurement of the urinary excretion of 2-naphtol, urine metabolite of naphtalene, one of the biomarkers of total PHA exposure. Urine samples were collected from the exposed workers, at the beginning of the week, at the beginning of the work shift (BWBS) and at the end of the work shift, at the end of the week (ESEW). In the control subjects, single samples of urine were collected after the end of the work shift.Every subject was invited to answer a questionnaire for the collection of technical and medical data as well as smoking habits and food intake. The concentration of 2-naphtol in the hydrolysate of urine was determined spectrophotometrically, after its reaction with the Fast Blue BB salt (diazotized 4-benzoylamino-2,5-diethoxyaniline). Results: For all the workers included in the study, the 2-urinary naphtol concentrations were higher than those in the control subjects (Median=9,55 µg/g creatinine) whether it is at (BWBS) (Md=16,2 µg/g creatinine) or at (ESEW) (n=18,Median=32,22 µg/g creatinine). Considerable differences are observed according to the category of job. The concentrations are also higher among smokers. Conclusion:The results show a significant exposure, mainly during manual laying, reveals an important risk particularly for the respiratory system.Considering the current criteria, carcinogenic risk due to the PHA seems not insignificant.

Keywords: PHA, asphalt, assessment, occupational, exposure

Procedia PDF Downloads 475

Authors: Richard Ren

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Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 122

Authors: Segen Asayehegn

Abstract:

Introduction: In Tigray, Ethiopia, next to cervical cancer, breast cancer is one of the most common cancer health problems for women. Objectives: This article is proposed to identify the prospective and potential risk factors affecting the time-to-first-recurrence of breast cancer patients in Tigray, Ethiopia. Methods: The data were taken from the patient’s medical record that registered from January 2010 to January 2020. The study considered a sample size of 1842 breast cancer patients. Powerful non-parametric and parametric shared frailty survival regression models (FSRM) were applied, and model comparisons were performed. Results: Out of 1842 breast cancer patients, about 1290 (70.02%) recovered/cured the disease. The median cure time from breast cancer is found at 12.8 months. The model comparison suggested that the lognormal parametric shared a frailty survival regression model predicted that treatment, stage of breast cancer, smoking habit, and marital status significantly affects the first recurrence of breast cancer. Conclusion: Factors like treatment, stages of cancer, and marital status were improved while smoking habits worsened the time to cure breast cancer. Recommendation: Thus, the authors recommend reducing breast cancer health problems, the regional health sector facilities need to be improved. More importantly, concerned bodies and medical doctors should emphasize the identified factors during treatment. Furthermore, general awareness programs should be given to the community on the identified factors.

Keywords: acceleration factor, breast cancer, Ethiopia, shared frailty survival models, Tigray

Procedia PDF Downloads 131

Authors: Pao-Peng Hsu, Ying-Hsiu Chen

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Historical data shows that there were high correlations among TAIEX Futures, Electronic Sector Index Futures, Finance Sector Index Futures and Taiwan Top 50 ETF. The performance under various futures is also discussed. We found that the worst portfolio is consisted of T50-ETF and T50-ETF futures and best portfolio is consisted of T50-ETF and TF. It implies that the annual return of a portfolio increases if a portfolio’s risk diversifies.

Keywords: arbitrage opportunities, ETF, futures, TAIEX

Procedia PDF Downloads 378

Authors: Damiaa Saad Khudor

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The paper describes an investigation for the thermal design of a fluidized bed cooler and prediction of heat transfer rate among the media categories. It is devoted to the thermal design of such equipment and their application in the industrial fields. It outlines the strategy for the fluidization heat transfer mode and its implementation in industry. The thermal design for fluidized bed cooler is used to furnish a complete design for a fluidized bed cooler of Sodium Bicarbonate. The total thermal load distribution between the air-solid and water-solid along the cooler is calculated according to the thermal equilibrium. The step by step technique was used to accomplish the thermal design of the fluidized bed cooler. It predicts the load, air, solid and water temperature along the trough. The thermal design for fluidized bed cooler revealed to the installation of a heat exchanger consists of (65) horizontal tubes with (33.4) mm diameter and (4) m length inside the bed trough.

Keywords: fluidization, powder technology, thermal design, heat exchangers

Procedia PDF Downloads 508

Authors: Ahmad Haidar

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The rapid adoption of Artificial Intelligence (AI) technology holds unforeseen risks like privacy violation, unemployment, and algorithmic bias, triggering research institutions, governments, and companies to develop principles of AI ethics. The extensive and diverse literature on AI lacks an analysis of the evolution of principles developed in recent years. There are two fundamental purposes of this paper. The first is to provide insights into how the principles of AI ethics have been changed recently, including concepts like risk management and public participation. In doing so, a NOISE (Needs, Opportunities, Improvements, Strengths, & Exceptions) analysis will be presented. Second, offering a framework for building Ethical AI linked to sustainability. This research adopts an explorative approach, more specifically, an inductive approach to address the theoretical gap. Consequently, this paper tracks the different efforts to have “trustworthy AI” and “ethical AI,” concluding a list of 12 documents released from 2017 to 2022. The analysis of this list unifies the different approaches toward trustworthy AI in two steps. First, splitting the principles into two categories, technical and net benefit, and second, testing the frequency of each principle, providing the different technical principles that may be useful for stakeholders considering the lifecycle of AI, or what is known as sustainable AI. Sustainable AI is the third wave of AI ethics and a movement to drive change throughout the entire lifecycle of AI products (i.e., idea generation, training, re-tuning, implementation, and governance) in the direction of greater ecological integrity and social fairness. In this vein, results suggest transparency, privacy, fairness, safety, autonomy, and accountability as recommended technical principles to include in the lifecycle of AI. Another contribution is to capture the different basis that aid the process of AI for sustainability (e.g., towards sustainable development goals). The results indicate data governance, do no harm, human well-being, and risk management as crucial AI for sustainability principles. This study’s last contribution clarifies how the principles evolved. To illustrate, in 2018, the Montreal declaration mentioned eight principles well-being, autonomy, privacy, solidarity, democratic participation, equity, and diversity. In 2021, notions emerged from the European Commission proposal, including public trust, public participation, scientific integrity, risk assessment, flexibility, benefit and cost, and interagency coordination. The study design will strengthen the validity of previous studies. Yet, we advance knowledge in trustworthy AI by considering recent documents, linking principles with sustainable AI and AI for sustainability, and shedding light on the evolution of guidelines over time.

Keywords: artificial intelligence, AI for sustainability, declarations, framework, regulations, risks, sustainable AI

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Authors: Zhi-Jun Lu, Qi Lu, Meng Wu, Qian Xiang, Jun Gu

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The analysis of perforated steel members is a 3D problem in nature, therefore the traditional analytical expressions for the ultimate load of thin-walled steel sections cannot be used for the perforated steel member design. In this study, finite element method (FEM) and artificial neural network (ANN) were used to simulate the process of stub column tests based on specific codes. Results show that compared with those of the FEM model, the ultimate load predictions obtained from ANN technique were much closer to those obtained from the physical experiments. The ANN model for the solving the hard problem of complex steel perforated sections is very promising.

Keywords: artificial neural network (ANN), finite element method (FEM), perforated sections, thin-walled Steel, ultimate load

Procedia PDF Downloads 347

Authors: Fangting Gao

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With the accelerated pace of industrialization and urbanization, the economic level has been significantly improved, and at the same time, the air quality situation has also become a focus of attention, which not only affects people's health but also has certain impacts on the economy and ecology. As the capital city of China, the air quality situation in Beijing has attracted much attention. In this paper, based on the day-by-day PM2.5, PM10, CO, NO₂, SO₂ and O₃ conditions in Beijing from 2018 to 2022, the characterization of pollutants is launched, and the seasonal decomposition and prediction of the main pollutants, PM2.5, PM10 and O3, are performed in STL. The results of the study show that (1) the overall air quality of Beijing has significantly improved from 2018 to 2022, and the main pollutants are PM2.5, PM10, and O₃; (2) the seasonal intensities of the main pollutants are higher, and they are influenced by seasonal factors; and (3) it is predicted that the O₃ concentration will have a trend of slowly increasing from 2023 to 2026, and the PM10 and PM2.5 pollution situation slowly improves.

Keywords: air pollutants, Beijing, characteristic analysis, STL

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Authors: Warda Khan, Remo Lachmann, Adarsh S. Garakahally

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The increasing complexity of software and its applicability in a wide range of industries, e.g., automotive, call for enhanced quality assurance techniques. Test automation is one option to tackle the prevailing challenges by supporting test engineers with fast, parallel, and repetitive test executions. A high degree of test automation allows for a shift from mundane (manual) testing tasks to a more analytical assessment of the software under test. However, a high initial investment of test resources is required to establish test automation, which is, in most cases, a limitation to the time constraints provided for quality assurance of complex software systems. Hence, a computer-aided creation of automated test cases is crucial to increase the benefit of test automation. This paper proposes the application of machine learning for the generation of automated test cases. It is based on supervised learning to analyze test specifications and existing test implementations. The analysis facilitates the identification of patterns between test steps and their implementation with test automation components. For the test case generation, this approach exploits historical data of test automation projects. The identified patterns are the foundation to predict the implementation of unknown test case specifications. Based on this support, a test engineer solely has to review and parameterize the test automation components instead of writing them manually, resulting in a significant time reduction for establishing test automation. Compared to other generation approaches, this ML-based solution can handle different writing styles, authors, application domains, and even languages. Furthermore, test automation tools require expert knowledge by means of programming skills, whereas this approach only requires historical data to generate test cases. The proposed solution is evaluated using various multi-label evaluation criteria (EC) and two small-sized real-world systems. The most prominent EC is ‘Subset Accuracy’. The promising results show an accuracy of at least 86% for test cases, where a 1:1 relationship (Multi-Class) between test step specification and test automation component exists. For complex multi-label problems, i.e., one test step can be implemented by several components, the prediction accuracy is still at 60%. It is better than the current state-of-the-art results. It is expected the prediction quality to increase for larger systems with respective historical data. Consequently, this technique facilitates the time reduction for establishing test automation and is thereby independent of the application domain and project. As a work in progress, the next steps are to investigate incremental and active learning as additions to increase the usability of this approach, e.g., in case labelled historical data is scarce.

Keywords: machine learning, multi-class, multi-label, supervised learning, test automation

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Authors: Pedro Batista, André Vinha, Filipe Castelo, Bárbara Costa, Ricardo Sousa, Raquel Ricardo, André Pinto

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Background: Septic arthritis is a diagnosis that must be considered in any patient presenting with acute joint swelling and fever. Among the several risk factors for septic arthritis, such as age, rheumatoid arthritis, recent surgery, or skin infection, diabetes mellitus can sometimes be the main risk factor. Staphylococcus aureus is the most common pathogen isolated in septic arthritis; however, it is uncommon in monomicrobial necrotizing fasciitis. Objectives: A case report of concomitant septic arthritis and necrotizing fasciitis in a patient with undiagnosed diabetes based on clinical history. Study Design & Methods: We report a case of a 58-year-old Portuguese previously healthy man who presented to the emergency department with fever and left knee swelling and pain for two days. The blood work revealed ketonemia of 6.7 mmol/L and glycemia of 496 mg/dL. The vital signs were significant for a temperature of 38.5 ºC and 123 bpm of heart rate. The left knee had edema and inflammatory signs. Computed tomography of the left knee showed diffuse edema of the subcutaneous cellular tissue and soft tissue air bubbles. A diagnosis of septic arthritis and necrotising fasciitis was made. He was taken to the operating room for surgical debridement. The samples collected intraoperatively were sent for microbiological analysis, revealing infection by multi-sensitive Staphylococcus aureus. Given this result, the empiric flucloxacillin (500 mg IV) and clindamycin (1000 mg IV) were maintained for 3 weeks. On the seventh day of hospitalization, there was a significant improvement in subcutaneous and musculoskeletal tissues. After two weeks of hospitalization, there was no purulent content and partial closure of the wounds was possible. After 3 weeks, he was switched to oral antibiotics (flucloxacillin 500 mg). A week later, a urinary infection by Pseudomonas aeruginosa was diagnosed and ciprofloxacin 500 mg was administered for 7 days without complications. After 30 days of hospital admission, the patient was discharged home and recovered. Results: The final diagnosis of concomitant septic arthritis and necrotizing fasciitis was made based on the imaging findings, surgical exploration and microbiological tests results. Conclusions: Early antibiotic administration and surgical debridement are key in the management of septic arthritis and necrotizing fasciitis. Furthermore, risk factors control (euglycemic blood glucose levels) must always be taken into account given the crucial role in the patient's recovery.

Keywords: septic arthritis, Necrotizing fasciitis, diabetes, Staphylococcus Aureus

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Authors: A. Hocine, A. Ghouaoula, S. M. Medjdoub, M. Cherifi

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The present paper proposed a meso–macro model describing the mechanical behaviour composite laminates of staking sequence [+θ/-θ]s under tensil loading. The behaviour of a layer is ex-pressed through elasticity coupled to damage. The elastic strain is due to the elasticity of the layer and can be modeled by using the classical laminate theory, and the laminate is considered as an orthotropic material. This means that no coupling effect between strain and curvature is considered. In the present work, the damage is associated to cracking of the matrix and parallel to the fibers and it being taken into account by the changes in the stiffness of the layers. The anisotropic damage is completely described by a single scalar variable and its evolution law is specified from the principle of maximum dissipation. The stress/strain relationship is investigated in plane stress loading.

Keywords: damage, behavior modeling, meso-macro model, composite laminate, membrane loading

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Authors: Adrien Marque, Daniel Delahaye, Pierre Maréchal, Isabelle Berry

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Wind direction and uncertainty are crucial in aircraft or unmanned aerial vehicle trajectories. By computing wind covariance matrices on each spatial grid point, these spatial grids can be defined as images with symmetric positive definite matrix elements. A data pre-processing step, a specific convolution, a specific max-pooling, and a specific flatten layers are implemented to process such images. Then, the neural network is applied to spatial grids, whose elements are wind covariance matrices, to solve classification problems related to the feasibility of unmanned aerial vehicles based on wind direction and wind uncertainty.

Keywords: wind direction, uncertainty level, unmanned aerial vehicle, convolution neural network, SPD matrices

Procedia PDF Downloads 40

Authors: Kotaro Sasai, Daijiro Mizutani, Kiyoyuki Kaito

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Expressways in Japan have been built in an accelerating manner since the 1960s with the aid of rapid economic growth. About 40 percent in length of expressways in Japan is now 30 years and older and has become superannuated. Time-related deterioration has therefore reached to a degree that administrators, from a standpoint of operation and maintenance, are forced to take prompt measures on a large scale aiming at repairing inner damage deep in pavements. These measures have already been performed for bridge management in Japan and are also expected to be embodied for pavement management. Thus, planning methods for the measures are increasingly demanded. Deterioration of layers around road surface such as surface course and binder course is brought about at the early stages of whole pavement deterioration process, around 10 to 30 years after construction. These layers have been repaired primarily because inner damage usually becomes significant after outer damage, and because surveys for measuring inner damage such as Falling Weight Deflectometer (FWD) survey and open-cut survey are costly and time-consuming process, which has made it difficult for administrators to focus on inner damage as much as they have been supposed to. As expressways today have serious time-related deterioration within them deriving from the long time span since they started to be used, it is obvious the idea of repairing layers deep in pavements such as base course and subgrade must be taken into consideration when planning maintenance on a large scale. This sort of maintenance requires precisely predicting degrees of deterioration as well as grasping the present situations of pavements. Methods for predicting deterioration are determined to be either mechanical or statistical. While few mechanical models have been presented, as far as the authors know of, previous studies have presented statistical methods for predicting deterioration in pavements. One describes deterioration process by estimating Markov deterioration hazard model, while another study illustrates it by estimating Proportional deterioration hazard model. Both of the studies analyze deflection data obtained from FWD surveys and present statistical methods for predicting deterioration process of layers around road surface. However, layers of base course and subgrade remain unanalyzed. In this study, data collected from FWD surveys are analyzed to predict deterioration process of layers deep in pavements in addition to surface layers by a means of estimating a deterioration hazard model using continuous indexes. This model can prevent the loss of information of data when setting rating categories in Markov deterioration hazard model when evaluating degrees of deterioration in roadbeds and subgrades. As a result of portraying continuous indexes, the model can predict deterioration in each layer of pavements and evaluate it quantitatively. Additionally, as the model can also depict probability distribution of the indexes at an arbitrary point and establish a risk control level arbitrarily, it is expected that this study will provide knowledge like life cycle cost and informative content during decision making process referring to where to do maintenance on as well as when.

Keywords: deterioration hazard model, falling weight deflectometer, inner damage, load bearing capacity, pavement

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Authors: Kais A. Kadhim

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This paper is a semantic analysis of the English modals in Obama’s speech. The main objective of this study is to analyze selected modal auxiliaries identified in selected speeches of Obama’s campaign based on Coates’ (1983) semantic clusters. A total of fifteen speeches of Obama’s campaign were selected as the primary data and the modal auxiliaries selected for analysis include will, would, can, could, should, must, ought, shall, may and might. All the modal auxiliaries taken from the speeches of Barack Obama were analyzed based on the framework of Coates’ semantic clusters. Such analytical framework was carried out to examine how modal auxiliaries are used in the context of persuading people in Obama’s campaign speeches. The findings reveal that modals of intention, prediction, futurity and modals of possibility, ability, permission are mostly used in Obama’s campaign speeches.

Keywords: modals, meaning, persuasion, speech

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Authors: Joy Winston James, Abdul Kadher Jilani

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This review article provides a comprehensive overview of social engineering attacks, specifically those executed through the Kali Linux operating system. It aims to present an in-depth analysis of the background and importance of social engineering in cybersecurity, the tools, and techniques used in these attacks, real-world case studies that demonstrate their effectiveness, and ethical considerations that need to be taken into account while using them. The article highlights the Kali Linux tools that are commonly used in social engineering attacks, including SET, Metasploit, and BeEF, and discusses techniques such as phishing, pretexting, and baiting that are crucial in conducting successful social engineering attacks. It further explores real-world case studies that demonstrate the effectiveness of these techniques, emphasizing the importance of implementing effective countermeasures to reduce the risk of successful social engineering attacks. Moreover, the article sheds light on ethical considerations that need to be taken into account while using social engineering tools, emphasizing the importance of using them ethically and legally. Finally, the article provides potential countermeasures such as two-factor authentication, strong password policies, and regular security audits to help individuals and organizations better protect themselves against this growing threat. By understanding the tools and techniques used in social engineering attacks and implementing appropriate countermeasures, individuals and organizations can minimize the risk of successful social engineering attacks and improve their cybersecurity posture. To illustrate the effectiveness of social engineering attacks, we present real-world case studies that demonstrate how easily individuals and organizations can fall prey to these attacks. We also discuss ethical considerations that must be taken into account while using social engineering tools, emphasizing the need for responsible and legal use of these tools.

Keywords: pen testing, hacking, Kali Linux, social engineering

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Authors: Mehmet Akif Aslan, Mehmet Simsek, Eyup Sensoy

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Big Data stands for today’s cutting-edge technology. As the technology becomes widespread, so does Data. Utilizing massive data sets enable companies to get competitive advantages over their adversaries. Out of many area of Big Data usage, logistics has significance role in both commercial sector and military. This paper lays out what big data is and how it is used in both military and commercial logistics.

Keywords: big data, logistics, operational efficiency, risk management

Procedia PDF Downloads 638

Authors: Mabrouka Morri, Amel Soualmia, Philippe Belleudy

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Vegetation affects the mean and turbulent flow structure. It may increase flood risks and sediment transport. Therefore, it is important to develop analytical approaches for the bed shear stress on vegetated bed, to predict resistance caused by vegetation. In the recent years, experimental and numerical models have both been developed to model the effects of submerged vegetation on open-channel flow. In this paper, different analytic models are compared and tested using the criteria of deviation, to explore their capacity for predicting the mean velocity and select the suitable one that will be applied in real case of rivers. The comparison between the measured data in vegetated flume and simulated mean velocities indicated, a good performance, in the case of rigid vegetation, whereas, Huthoff model shows the best agreement with a high coefficient of determination (R2=80%) and the smallest error in the prediction of the average velocities.

Keywords: analytic models, comparison, mean velocity, vegetation

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Authors: Gianna Zou

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Propensity score matching (PSM), introduced by Paul R. Rosenbaum and Donald Rubin in 1983, is a popular statistical matching technique which tries to estimate the treatment effects by taking into account covariates that could impact the efficacy of study medication in clinical trials. PSM can be used to reduce the bias due to confounding variables. However, PSM assumes that the response values are normally distributed. In some cases, this assumption may not be held. In this paper, a machine learning method - Bayesian Additive Regression Tree (BART), is used as a more robust method of matching. BART can work well when models are misspecified since it can be used to model heterogeneous treatment effects. Moreover, it has the capability to handle non-linear main effects and multiway interactions. In this research, a BART Matching Method (BMM) is proposed to provide a more reliable matching method over PSM. By comparing the analysis results from PSM and BMM, BMM can perform well and has better prediction capability when the response values are not normally distributed.

Keywords: BART, Bayesian, matching, regression

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Authors: Shervin Rashidinia, Sara Shahmoradi, Seyyed Shahin Eftekhari, Mohsen Talebizadeh, Mohammad Saleh Sadeghi

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Background: Beta-thalassemia Major (BTM) is a kind of hereditary hemolytic anemia which depended on regular monthly blood transfusion. However, iron deposition into the organs leads to multi-organ damage. The present study is the first study which aimed to evaluate the average of five-years serum ferritin level and compared by the prevalence of short stature and hypocalcemia. Materials/Methods: A cross-sectional retrospective study which a total of 140 patients with beta-thalassemia who were referred to Qom Thalassemia Clinic between February 2011 and July 2016 were enrolled to be reviewed. The exclusion criteria were consisting of incomplete medical records, diagnosis less than 2-years-ago and the blood transfusion less than every 4 weeks. The data including age, gender, weight, height, age of initial blood transfusion, age of initial chelation therapy, ferritin, and calcium were collected and analysis by SPSS version 24. Results: A total of 140 patients were enrolled. Of them, 75 (53.4%) were female. The mean age of the patients was 13.4±4.6 years.The mean age of initial diagnosis was 20.2±7.4 months. Hypocalcemia and short stature were occurred in 41 (29.3%) and 37 (26.4%) patients, respectively. The mean five-years serum ferritin level was significantly higher in the patients with short stature and hypocalcemia (P<0.0001). However, rise in serum ferritin level significantly increases the risk of short-stature and hypocalcemia (1.0004- and 1.0029 fold, respectively). Conclusion: We demonstrated that prevalence of short stature and hypocalcemia were significantly higher in the BTM.However, ferritin significantly increases the risk of short stature and hypocalcemia.

Keywords: beta-thalassemia, ferritin, growth retardation, hypocalcemia

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Authors: Iulian Clain

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In contemporary public administration, the demand for greater accountability, transparency, and efficient governance has intensified, particularly in the face of increasing public scrutiny and fiscal constraints. Internal auditing has emerged as a vital tool in strengthening governance structures, enhancing the effectiveness of public sector institutions, and ensuring compliance with regulatory frameworks. This paper examines the evolving role of internal auditing within public administration, with an emphasis on risk management, regulatory compliance, and fraud prevention. Building on institutional theory and risk management frameworks, this study explores how internal audits contribute to identifying operational inefficiencies, minimizing financial irregularities, and promoting ethical governance practices. Through case studies and comparative analysis of auditing practices across OECD nations, this research provides insights into how strategic internal audits can be harnessed to reinforce public sector governance, thereby improving the delivery of public services. The paper argues that the integration of internal audit findings into decision-making processes enhances not only financial accountability but also policy outcomes, fostering greater public trust in government institutions. Key recommendations are presented on how public institutions can further integrate internal auditing processes to enhance governance outcomes, focusing on best practices for institutionalizing audit functions within public sector governance frameworks. These findings are particularly relevant for policymakers, audit professionals, and public administration leaders striving to achieve better governance, operational efficiency, and integrity in the public sector.

Keywords: internal auditing role, public administration sciences, public administration audit, internal auditing in universities

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Authors: M. Keršič, M. Lužnik, J. Lužnik

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Cervical dilatation and membrane herniation before 26th week of gestation poses very high risk for extremely and very premature childbirth. Cerclage with mesh cap (mesh cerclage, MC) can greatly diminish this risk and provide additional positive effects. Between 2005 and 2014, MC has been performed in 9 patients with singleton pregnancies who had prolapsed membranes beyond external cervical/uterine os before 25th week of pregnancy (one in 29th). With patients in general anaesthesia, lithotomy and Trendelenburg position (about 25°) prolapsed membranes were repositioned in the uterine cavity, using tampon soaked in antiseptic solution (Skinsept mucosa). A circular, a type of purse-string suture (main band) with double string Ethilon 1 was applied at about 1 to 1.5 cm from the border of the external uterine os - 6 to 8 stitches were placed, so the whole external uterine os was encircled (modified McDonald). In the next step additional Ethilon 0 sutures were placed around all exposed parts of the main double circular suture and loosely tightened. On those sutures, round tailored (diameter around 6 cm) mesh (Prolene® or Gynemesh* PS) was attached. In all 9 cases, gestation was prolonged on average for 9 weeks and 4 days (67 days). In four cases maturity was achieved. Mesh was removed in 37th–38th week of pregnancy or if spontaneous labour began. In two cases, a caesarean section was performed because of breech presentation. In the first week after birth in 22nd week one new born died because of immaturity (premature birth was threatening in 18th week and then MC was placed). Ten years after first MC, 8 of 9 women with singleton pregnancy and MC performed have 8 healthy children from these pregnancies. Mesh cerclage successfully closed the opened cervical canal or uterine orifice and prevented further membrane herniation and membrane rupture. MC also provides a similar effect as with occluding the external os with suturing but without interrupting the way for excretion of abundant cervical mucus. The mesh also pulls the main circular band outwards and thus lowers the chance of suture cutting through the remaining cervix. MC prolonged gestation very successfully (mean for 9 weeks and 4 days) and thus increased possibility for survival and diminished the risk for complications in very early preterm delivered survivors in cases with cervical dilatation and membrane herniation before 26th week of gestation. Without action possibility to achieve at least 28th or 32nd week of gestation would be poor.

Keywords: cervical insufficiency, mesh cerclage, membrane protrusion, premature birth prevention, physical exam-indicated cerclage, rescue cerclage

Procedia PDF Downloads 186

Authors: Ya-Yi Yen

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The operating room is a medical environment full of high-risk, high-complexity and high-cost. In addition to assuring patient safety, the operating room should effort on the efficient and safe medical quality for the surgical patients of high risk, elders, and children. If the nursing staffs of operation room carry on the pre-operative visiting prior to surgery, the patient's anxiety and complications are expected to be alleviated, and the hospitalization days may also be shortened. Purpose: Applying the information system to enhance pre-operative visiting, case tracking, and effectiveness recording Method: (I) Application the information system to screen cases by integrating the operation scheduling, and linking the severe surgery codes, for to shorten the time to track cases of operative visiting. Through the improvement, the time required decreased to 1.5 minutes per day from 20 minutes per day, and nursing staffs’ satisfaction with satisfaction for tracking and visiting procedure of case increased to 86% from 54%. (II)The electronic establishment of the operative visiting record enhanced the integrity of the operative visiting record. The integrity rate was rise to 92% from 66%, while nursing staffs’ satisfaction with the visiting record increased to 82% from 61.3%. Since information technology continues evolving, the application of information technology is helpful to the integration of nursing information, simplification of processes, and saving of man-hours. This article introduces the application of information systems to simplify the processes and improve the effectiveness of operation visiting and tracking, including the saving of time, improving the integrity rate of record, and improving the satisfaction of nursing staffs.

Keywords: effectiveness, information system, perioperative nursing, pre-operative visiting

Procedia PDF Downloads 141

Authors: I. Goual, M. S. Goual, M. K. Gueddouda, Taïbi Saïd, Abou-Bekr Nabil, A. Ferhat

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This paper presents the study of hydro mechanical behavior of this optimal mixture. A first experimental phase was carried out in order to find the optimal mixture. This showed that the material composed of 80% tuff and 20% calcareous sand provides the maximum mechanical strength. The second experimental phase concerns the study of the drying- wetting behavior of the optimal mixture was carried out on slurry samples and compacted samples at the MPO. Experimental results let to deduce the parameters necessary for the prediction of the hydro-mechanical behavior of pavement formulated from tuff and calcareous sand mixtures, related to moisture. This optimal mixture satisfies the regulation rules and hence constitutes a good local eco-material, abundantly available, for the conception of pavements.

Keywords: tuff, sandy calcareous, road engineering, hydro mechanical behaviour, suction

Procedia PDF Downloads 502