Search results for: code error correction

Authors: Khaleel Alyahya, Faizah Alotaibi

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The coronavirus disease 19 (COVID-19) pandemic has caused an increase in general fear and anxiety around the globe. With the public health measures, including lockdown and travel restrictions, the COVID-19 period further resulted in a sudden increase in the vulnerability of people too ill mental health. This becomes greater among individuals who have a history of mental illness or are undergoing treatment and do not have easy access to medication and medical consultations. The study aims to measure the impact of COVID-19 and the degree of distress with the DASS scale on the mental health of residents living in Saudi Arabia. The study is a quantitative, observational, and cross-sectional conducted in Saudi Arabia to measure the impact of COVID-19 on the mental health of both citizens and residents of Saudi Arabia during pandemics. The study ran from February 2021 to June 2021, and a validated questionnaire was used. The targeted population of the study was Saudi citizens and non-Saudi residents. A sample size of 800 participants was calculated with a single proportion formula at 95% level of significance and 5% allowable error. The result revealed that participants who were always doing exercise experienced the lowest level of depression, anxiety, and stress. The highest prevalence of severe and extremely severe depression was among participants who sometimes do exercise at 53.2% for each. Similar results were obtained for anxiety and stress, where the extremely severe form was reported by those who sometimes did exercise at 54.8% and 72.2%, respectively. There was an inverse association between physical activity levels and levels of depression, anxiety, and stress during the COVID-19. Similarly, the levels of depression, anxiety, and stress differed significantly according to the exercise frequency during COVID-19.

Keywords: mental, COVID-19, pandemic, lockdown, depression, anxiety, stress

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Authors: Mounia El Jaouhari, Mira Goral, Samir Diouny

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Introduction: Previous research has indicated that language impairments are recognized as a feature of many neurodegenerative disorders, including non-language-led dementia subtypes such as Alzheimer´s disease (AD). In this preliminary study, the focal aim is to quantify the semantic content of naming and connected speech samples of Moroccan patients diagnosed with AD using two tasks taken from the culturally adapted and validated Moroccan version of the Boston Diagnostic Aphasia Examination. Methods: Five individuals with AD and five neurologically healthy individuals matched for age, gender, and education will participate in the study. Participants with AD will be diagnosed on the basis of the Moroccan version of the Diagnostic and Statistial Manual of Mental Disorders (DSM-4) screening test, the Moroccan version of the Mini Mental State Examination (MMSE) test scores, and neuroimaging analyses. The participants will engage in two tasks taken from the MDAE-SF: 1) Picture description and 2) Naming. Expected findings: Consistent with previous studies conducted on English speaking AD patients, we expect to find significant word production and retrieval impairments in AD patients in all measures. Moreover, we expect to find category fluency impairments that further endorse semantic breakdown accounts. In sum, not only will the findings of the current study shed more light on the locus of word retrieval impairments noted in AD, but also reflect the nature of Arabic morphology. In addition, the error patterns are expected to be similar to those found in previous AD studies in other languages.

Keywords: alzheimer's disease, anomia, connected speech, semantic impairments, moroccan arabic

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Authors: Induja R. Nimma, Anand Reddy Maligireddy, Artur Schneider, Melissa Lyle

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Background: Although heart failure is one of the most common causes of hospitalization in adult patients, there is limited knowledge on outcomes following initial hospitalization for COVID-19 with heart failure (HCF-19). We felt it pertinent to analyze 30-day readmission causes and outcomes among patients with HCF-19 using the United States using real-world big data via the National readmission database. Objective: The aim is to describe the rate and causes of readmissions and morbidity of heart failure with coinciding COVID-19 (HFC-19) in the United States, using the 2020 National Readmission Database (NRD). Methods: A descriptive, retrospective study was conducted on the 2020 NRD, a nationally representative sample of all US hospitalizations. Adult (>18 years) inpatient admissions with COVID-19 with HF and readmissions in 30 days were selected based on the International Classification of Diseases-Tenth Revision, Procedure Code. Results: In 2020, 2,60,372 adult patients were hospitalized with COVID-19 and HF. The median age was 74 (IQR: 64-83), and 47% were female. The median length of stay was 7(4-13) days, and the total cost of stay was 62,025 (31,956 – 130,670) United States dollars, respectively. Among the index hospital admissions, 61,527 (23.6%) died, and 22,794 (11.5%) were readmitted within 30 days. The median age of patients readmitted in 30 days was 73 (63-82), 45% were female, and 1,962 (16%) died. The most common principal diagnosis for readmission in these patients was COVID-19= 34.8%, Sepsis= 16.5%, HF = 7.1%, AKI = 2.2%, respiratory failure with hypoxia =1.7%, and Pneumonia = 1%. Conclusion: The rate of readmission in patients with heart failure exacerbations is increasing yearly. COVID-19 was observed to be the most common principal diagnosis in patients readmitted within 30 days. Complicated hypertension, chronic pulmonary disease, complicated diabetes, renal failure, alcohol use, drug use, and peripheral vascular disorders are risk factors associated with readmission. Familiarity with the most common causes and predictors for readmission helps guide the development of initiatives to minimize adverse outcomes and the cost of medical care.

Keywords: Covid-19, heart failure, national readmission database, readmission outcomes

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Authors: Filip Buruiana, Alexander Hall, Reimar Hofmann, Thomas Hofmann, Silviu Ganceanu, Alexandru Tudorica

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In-memory column-stores make interactive analysis feasible for many big data scenarios. PowerDrill is a system used internally at Google for exploration in logs data. Even though it is a highly parallelized column-store and uses in memory caching, interactive response times cannot be achieved for all datasets (note that it is common to analyze data with 50 billion records in PowerDrill). In this paper, we investigate two orthogonal approaches to optimize performance at the expense of an acceptable loss of accuracy. Both approaches can be implemented as outer wrappers around existing database engines and so they should be easily applicable to other systems. For the first optimization we show that memory is the limiting factor in executing queries at speed and therefore explore possibilities to improve memory efficiency. We adapt some of the theory behind data sketches to reduce the size of particularly expensive fields in our largest tables by a factor of 4.5 when compared to a standard compression algorithm. This saves 37% of the overall memory in PowerDrill and introduces a 0.4% relative error in the 90th percentile for results of queries with the expensive fields. We additionally evaluate the effects of using sampling on accuracy and propose a simple heuristic for annotating individual result-values as accurate (or not). Based on measurements of user behavior in our real production system, we show that these estimates are essential for interpreting intermediate results before final results are available. For a large set of queries this effectively brings down the 95th latency percentile from 30 to 4 seconds.

Keywords: big data, in-memory column-store, high-performance SQL queries, approximate SQL queries

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Authors: Shokouh Momeni, Mohammad Reza Salamat, Ali Asghar Rastegari

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Background: Osteoporosis is the most prevalent metabolic bone disease in postmenopausal women associated with reduced bone mass and increased bone fracture. Measuring bone density in the lumbar spine and hip is a reliable measure of bone mass and can therefore specify the risk of fracture. Dual-energy X-ray absorptiometry(DXA) is an accurate non-invasive system measuring the bone density, with low margin of error and no complications. The present study aimed to investigate the relationship between biochemical parameters with bone density in postmenopausal women. Materials and methods: This cross-sectional study was conducted on 87 postmenopausal women referred to osteoporosis centers in Isfahan. Bone density was measured in the spine and hip area using DXA system. Serum levels of calcium, phosphorus, alkaline phosphatase and magnesium were measured by autoanalyzer and serum levels of vitamin D were measured by high-performance liquid chromatography(HPLC). Results: The mean parameters of calcium, phosphorus, alkaline phosphatase, vitamin D and magnesium did not show a significant difference between the two groups(P-value>0.05). In the control group, the relationship between alkaline phosphatase and BMC and BA in the spine was significant with a correlation coefficient of -0.402 and 0.258, respectively(P-value<0.05) and BMD and T-score in the femoral neck area showed a direct and significant relationship with phosphorus(Correlation=0.368; P-value=0.038). There was a significant relationship between the Z-score with calcium(Correlation=0.358; P-value=0.044). Conclusion: There was no significant relationship between the values ​​of calcium, phosphorus, alkaline phosphatase, vitamin D and magnesium parameters and bone density (spine and hip) in postmenopaus

Keywords: osteoporosis, menopause, bone mineral density, vitamin d, calcium, magnesium, alkaline phosphatase, phosphorus

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Authors: Tomasz Kosicki

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The subject of the presentation will be the administrative and legal instruments of disciplining maintenance debtors adopted by the Polish legislator, the substantive legal bases of which were adopted in the Act of 7 September 2007 on assistance to persons entitled to maintenance (Journal of Laws of 2022, item 1205). These provisions are complemented by procedural regulations resulting from the Act of 14 June 1960 - Code of Administrative Procedure (Journal of Laws of 2021, item 735, as amended). The first part of the paper will focus on the administrative proceedings regarding the recognition of the debtor as evading maintenance obligations. The initiation of this procedure ex officio is preceded by a number of actions by public administration bodies, including Conducting a maintenance interview with the debtor, during which his health and professional situation and the reasons for non-payment of maintenance are determined, Professional activation in a situation where the lack of payment of maintenance results from the lack of employment. The reasons for initiating the above-mentioned administrative proceedings ex officio will be indicated, taking into account the current views of the judicial decisions. The second part of the paper will focus on the instrument of retaining the driving license of the debtor, who was previously found to be evading maintenance. The author points out that the detention of the driving license is one of the types of administrative sanctions of a very severe nature. Doubts of a constitutional nature will also be highlighted, as well as those concerning the effectiveness of this legal instrument and the protection of the debtor's rights. The thesis will be presented that the administrative procedure for the retention of a driving license does not fulfill its role and especially does not affect the collection of maintenance obligations from debtors. All the considerations will be based on the current and most representative views of the literature on the subject and the jurisprudence of Polish administrative courts.

Keywords: maintenance debtor, administrative proceedings, detention of driving license, administrative sanction, polish administrative law, public administration

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Authors: Rutebuka Evariste, Zhang Lixiao

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Mobile phone sales and stocks showed an exponential growth in the past years globally and the number of mobile phones produced each year was surpassing one billion in 2007, this soaring growth of related e-waste deserves sufficient attentions paid to it regionally and globally as long as 40% of its total weight is made from metallic which 12 elements are identified to be highly hazardous and 12 are less harmful. Different research and methods have been used to estimate the obsolete mobile phones but none has developed a dynamic model and handle the discrepancy resulting from improper approach and error in the input data. The study aim was to develop a comprehensive dynamic system model for simulating the dynamism of e-waste production from mobile phone regardless the country or region and prevail over the previous errors. The logistic model method combined with STELLA program has been used to carry out this study. Then the simulation for Rwanda has been conducted and compared with others countries’ results as model testing and validation. Rwanda is about 1.5 million obsoletes mobile phone with 125 tons of waste in 2014 with e-waste production peak in 2017. It is expected to be 4.17 million obsoletes with 351.97 tons by 2020 along with environmental impact intensity of 21times to 2005. Thus, it is concluded through the model testing and validation that the present dynamic model is competent and able deal with mobile phone e-waste production the fact that it has responded to the previous studies questions from Czech Republic, Iran, and China.

Keywords: carrying capacity, dematerialization, logistic model, mobile phone, obsolescence, similarity, Stella, system dynamics

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Authors: Felermino D. M. A. Ali, S. C. Ng

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E-learning is an evolutionary form of distance learning and has become better over time as new technologies emerged. Today, efforts are still being made to embrace E-learning systems with emerging technologies in order to make them better. Among these advancements, Educational Data Mining (EDM) is one that is gaining a huge and increasing popularity due to its wide application for improving the teaching-learning process in online practices. However, even though EDM promises to bring many benefits to educational industry in general and E-learning environments in particular, its principal drawback is the lack of easy to use tools. The current EDM tools usually require users to have some additional technical expertise to effectively perform EDM tasks. Thus, in response to these limitations, this study intends to design and implement an EDM application framework which aims at automating and simplify the development of EDM in E-learning environment. The application framework introduces a Service-Oriented Architecture (SOA) that hides the complexity of technical details and enables users to perform EDM in an automated fashion. The framework was designed based on abstraction, extensibility, and interoperability principles. The framework implementation was made up of three major modules. The first module provides an abstraction for data gathering, which was done by extending Moodle LMS (Learning Management System) source code. The second module provides data mining methods and techniques as services; it was done by converting Weka API into a set of Web services. The third module acts as an intermediary between the first two modules, it contains a user-friendly interface that allows dynamically locating data provider services, and running knowledge discovery tasks on data mining services. An experiment was conducted to evaluate the overhead of the proposed framework through a combination of simulation and implementation. The experiments have shown that the overhead introduced by the SOA mechanism is relatively small, therefore, it has been concluded that a service-oriented architecture can be effectively used to facilitate educational data mining in E-learning environments.

Keywords: educational data mining, e-learning, distributed data mining, moodle, service-oriented architecture, Weka

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Authors: Fabienne Fel, Eric Griette

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Reshoring, whether concerning back-reshoring or near-reshoring, is a quite recent phenomenon. Despite the economic and political interest of this topic, academic research questioning determinants of reshoring remains rare. Our paper aims at contributing to fill this gap. In order to better understand the reasons for reshoring, we conducted a study among 280 French firms during spring 2016, three-quarters of which sourced, or source, in China. 105 firms in the sample have reshored all or part of their Chinese production or supply in recent years, and we aimed to establish a typology of the motives that drove them to this decision. We asked our respondents about the history of their Chinese supplies, their current reshoring strategies, and their motivations. Statistical analysis was performed with SPSS 22 and SPAD 8. Our results show that change in commercial and financial terms with China is the first motive explaining the current reshoring movement from this country (it applies to 54% of our respondents). A change in corporate strategy is the second motive (30% of our respondents); the reshoring decision follows a change in companies’ strategies (upgrading, implementation of a CSR policy, or a 'lean management' strategy). The third motive (14% of our sample) is a mere correction of the initial offshoring decision, considered as a mistake (under-estimation of hidden costs, non-quality and non-responsiveness problems). Some authors emphasize that developing a short supply chain, involving geographic proximity between design and production, gives a competitive advantage to companies wishing to offer innovative products. Admittedly 40% of our respondents indicate that this motive could have played a part in their decision to reshore, but this reason was not enough for any of them and is not an intrinsic motive leading to leaving Chinese suppliers. Having questioned our respondents about the importance given to various problems leading them to reshore, we then performed a Principal Components Analysis (PCA), associated with an Ascending Hierarchical Classification (AHC), based on Ward criterion, so as to point out more specific motivations. Three main classes of companies should be distinguished: -The 'Cost Killers' (23% of the sample), which reshore their supplies from China only because of higher procurement costs and so as to find lower costs elsewhere. -The 'Realists' (50% of the sample), giving equal weight or importance to increasing procurement costs in China and to the quality of their supplies (to a large extend). Companies being part of this class tend to take advantage of this changing environment to change their procurement strategy, seeking suppliers offering better quality and responsiveness. - The 'Voluntarists' (26% of the sample), which choose to reshore their Chinese supplies regardless of higher Chinese costs, to obtain better quality and greater responsiveness. We emphasize that if the main driver for reshoring from China is indeed higher local costs, it is should not be regarded as an exclusive motivation; 77% of the companies in the sample, are also seeking, sometimes exclusively, more reactive suppliers, liable to quality, respect for the environment and intellectual property.

Keywords: China, procurement, reshoring, strategy, supplies

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Authors: Hamidreza Hoshyarmanesh, Hosein Madieh, Sanju Lama, Yaser Maddahi, Garnette R. Sutherland, Kourosh Zareinia

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This paper illustrates the concept of an algorithm to register specified markers on the neuroArm surgical manipulators, an image-guided MR-compatible tele-operated robot for microsurgery and stereotaxy. Two range-finding algorithms, namely time-of-flight and phase-shift, are evaluated for registration and supervisory control. The time-of-flight approach is implemented in a semi-field experiment to determine the precise position of a tiny retro-reflective moving object. The moving object simulates a surgical tool tip. The tool is a target that would be connected to the neuroArm end-effector during surgery inside the magnet bore of the MR imaging system. In order to apply flight approach, a 905-nm pulsed laser diode and an avalanche photodiode are utilized as the transmitter and receiver, respectively. For the experiment, a high frequency time to digital converter was designed using a field-programmable gate arrays. In the phase-shift approach, a continuous green laser beam with a wavelength of 530 nm was used as the transmitter. Results showed that a positioning error of 0.1 mm occurred when the scanner-target point distance was set in the range of 2.5 to 3 meters. The effectiveness of this non-contact approach exhibited that the method could be employed as an alternative for conventional mechanical registration arm. Furthermore, the approach is not limited by physical contact and extension of joint angles.

Keywords: 3D laser scanner, intraoperative MR imaging, neuroArm, real time registration, robot-assisted surgery, supervisory control

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Authors: Kevin Moustapha

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Psychopathy is perceived today as being «the most important concept in the criminal justice system» and as «the most important legal notion of the early 21 th century». The explosion of research related to psychopathy seems to perfectly illustrate this trend. Traditionally, many studies tend to focus on links between insanity defense and psychopathy. That is why our purpose in this article is to analyze psychopathic disorders in the scope of judges sentencing in Canada. Indeed, in every Canadian case related to dangerous offenders, judges must balance between fairness and protection of the individuals rights of the accused and protection of society from dangerous predators who may commit future acts of physical or sexual violence. Increasingly, psychopathic disorders are taking an important part in judge sentencing, especially in Canada. This phenomenon can be illustrated by the high proportion of psychopath offenders incarcerated in North American prisons. Many decisions in Canadians courtrooms seem to point out that psychopathy is often used as a strong argument by the judges to preserve public safety. The fact that psychopathy is often associated with violence, recklessness and recidivism, it could explain why many judges consider psychopathic disorders as an aggravating factor. Generally, the judge reasoning is based on article 753 of Canadian Criminal Code related to dangerous offenders, which is used for individuals who show a pattern of repetitive and persistent aggressive behaviour. However, with cognitive neurosciences, the psychopath’s situation in courtrooms would probably change. Cerebral imaging and news data provided by the neurosciences show that emotional and volitional functions in psychopath’s brains are impaired. Understanding these new issues could enable some judges to recognize psychopathic disorders as a mitigating factor. Two important questions ought to be raised in this article: can exploring psychopaths ‘brains really change the judge sentencing in Canadian courtrooms? If yes, can judges consider psychopathy more as a mitigating factor than an aggravating factor?

Keywords: criminal law, judges sentencing, neurosciences, psychopathy

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Authors: Madhav Khadilkar

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Construction of a large standby reservoir was required to provide secure water supply. The new reservoir was required to be constructed at the same location of an abandoned old open pond due to space constraints. Some investigations were carried out earlier to improvise and re-commission the existing pond. But due to a lack of quantified risk of settlement from voids in the underlying limestone, the shallow foundations were not found feasible. Since the reservoir was resting on hard strata for about three-quarter of plan area and one quarter was resting on soil underlying with limestone and considerably low subgrade modulus. Further investigations were carried out to ascertain the locations and extent of voids within the limestone. It was concluded that the risk due to lime dissolution was acceptably low, and the site was found geotechnically feasible. The hazard posed by limestone dissolution was addressed through the integrated structural and geotechnical analysis and design approach. Finite Element Analysis was carried out to quantify the stresses and differential settlement due to various probable loads and soil-structure interaction. Walls behaving as cantilever under operational loads were found undergoing in-plane bending and tensile forces due to soil-structure interaction. Sensitivity analysis for varying soil subgrade modulus was carried out to check the variation in the response of the structure and magnitude of stresses developed. The base slab was additionally checked for the loss of soil contact due to lime pocket formations at random locations. The expansion and contraction joints were planned to receive minimal additional forces due to differential settlement. The reservoir was designed to sustain the actions corresponding to allowable deformation limits per code, and geotechnical measures were proposed to achieve the soil parameters set in structural analysis.

Keywords: differential settlement, limestone dissolution, reservoir, soil structure interaction

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Authors: Ameer A. Jebur, William Atherton, Rafid M. Alkhaddar, Edward Loffill

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Superstructures like offshore platforms, tall buildings, transition towers, skyscrapers and bridges are normally designed to resist compression, uplift and lateral forces from wind waves, negative skin friction, ship impact and other applied loads. Better understanding and the precise simulation of the response of batter piles under the action of independent uplift loads is a vital topic and an area of active research in the field of geotechnical engineering. This paper investigates the use of finite element code (FEC) to examine the behaviour of model batter piles penetrated in dense sand, subjected to pull-out pressure by means of numerical modelling. The concept of the Winkler Model (beam on elastic foundation) has been used in which the interaction between the pile embedded depth and adjacent soil in the bearing zone is simulated by nonlinear p-y curves. The analysis was conducted on different pile slenderness ratios (lc⁄d) ranging from 7.5, 15.22 and 30 respectively. In addition, the optimum batter angle for a model steel pile penetrated in dense sand has been chosen to be 20° as this is the best angle for this simulation as demonstrated by other researcher published in literature. In this numerical analysis, the soil response is idealized as elasto-plastic and the model piles are described as elastic materials for the purpose of simulation. The results revealed that the applied loads affect the pullout pile capacity as well as the lateral pile response for dense sand together with varying shear strength parameters linked to the pile critical depth. Furthermore, the pile pull-out capacity increases with increasing the pile aspect ratios.

Keywords: slenderness ratio, soil-pile interaction, winkler model (beam on elastic foundation), pull-out capacity

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Authors: Mikiyas Yonas Fufa

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- Maternal and child health service was a critical service which may have many risks and many maternal and newborn mortality is there if not managed properly. In middle and low countries like Ethiopia accessibility and quality of MCH service is low. During this COVID-19 Pandemics even the pervious access of MCH will be decreased. So many pregnant mothers are not attending their ANC, Delivery and other services in the hospital because they think they are more vulnerable to COVID-19. This condition may make an increase of maternal and neonatal morbidity and mortality. The innovation is an idea (which is development of a mobile app prepared by Maternity Foundation organization that focuses on midwifery care. The app has detailed videos on danger signs in pregnancy and procedures during labor and delivery). By telling this to clients it is planned to explore the perception, attitude towards this innovation and barriers to accepting it. What is planned to study is to explore the perceptions and barriers towards using of new idea which is innovation of mHealth on the MCH services. It is planned to interview the pregnant mothers who come for ANC at health facility and mothers who are absent from their appointment of services. In this way it is planned to explore how the mothers accept this idea and what barriers make them from accepting this idea. This is a phenomenological qualitative study and application of diffusion of innovation theory on the MCH services. The participant will be selected by using quota sampling methods for the mother who are interviewed at hospitals and snowball/quota sampling methods for the mother who are absent from their appointment/visits. Sample size of the participant depends on the saturation of data/idea. Each participant will be interviewed based the open-ended questionnaires, and the interview will be recorded then transcribed then finally analyzed by the open code 4.03. Beneficiaries: The federal ministry of health prepares them to develop the apk of mhealth. Health professionals in the MCH will have a low overload and accessibility and the quality of care will be increased during COVID-19 Different collaborations will be participated and promote the mother to enjoy the new idea.

Keywords: COVID-19, m health, MCH, diffusion of innovation

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Authors: Mehrnoosh Abouzari, Shahrokh Shahraei

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With the development of artificial intelligence in the present age, intelligent machines and systems have proven their actual and potential capabilities and are mindful of increasing their presence in various fields of human life in the fields of industry, financial transactions, marketing, manufacturing, service affairs, politics, economics and various branches of the humanities .Therefore, despite the conservatism and prudence of law enforcement, the traces of artificial intelligence can be seen in various areas of law. Including judicial robotics capability estimation, intelligent judicial decision making system, intelligent defender and attorney strategy adjustment, dissemination and regulation of different and scattered laws in each case to achieve judicial coherence and reduce opinion, reduce prolonged hearing and discontent compared to the current legal system with designing rule-based systems, case-based, knowledge-based systems, etc. are efforts to apply AI in law. In this article, we will identify the ways in which AI is applied in its laws and regulations, identify the dominant concerns in this area and outline the relationship between these two areas in order to answer the question of how artificial intelligence can be used in different areas of law and what the implications of this application will be. The authors believe that the use of artificial intelligence in the three areas of legislative, judiciary and executive power can be very effective in governments' decisions and smart governance, and helping to reach smart communities across human and geographical boundaries that humanity's long-held dream of achieving is a global village free of violence and personalization and human error. Therefore, in this article, we are going to analyze the dimensions of how to use artificial intelligence in the three legislative, judicial and executive branches of government in order to realize its application.

Keywords: artificial intelligence, law, intelligent system, judge

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Authors: Taoues Hadour

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Twitter has become a popular means of communication used in a variety of fields, such as politics, journalism, and academia. This widely used online platform has an impact on the way people express themselves and is changing language usage worldwide at an unprecedented pace. The language used online reflects the linguistic battle that has been going on for several decades in French society. This study enables a deeper understanding of users' linguistic behavior online. The implications are important and allow for a rise in awareness of intercultural and cross-language exchanges. This project investigates the mixing of French-English language usage among French users of Twitter using a topic analysis approach. This analysis draws on Gumperz's theory of conversational switching. In order to collect tweets at a large scale, the data was collected in R using the rtweet package to access and retrieve French tweets data through Twitter’s REST and stream APIs (Application Program Interface) using the software RStudio, the integrated development environment for R. The dataset was filtered manually and certain repetitions of themes were observed. A total of nine topic categories were identified and analyzed in this study: entertainment, internet/social media, events/community, politics/news, sports, sex/pornography, innovation/technology, fashion/make up, and business. The study reveals that entertainment is the most frequent topic discussed on Twitter. Entertainment includes movies, music, games, and books. Anglicisms such as trailer, spoil, and live are identified in the data. Change in language usage is inevitable and is a natural result of linguistic interactions. The use of different languages online is just an example of what the real world would look like without linguistic regulations. Social media reveals a multicultural and multilinguistic richness which can deepen and expand our understanding of contemporary human attitudes.

Keywords: code-switching, French, sociolinguistics, Twitter

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Authors: Chakraborty Sudipta, A. R. Kambekar, Sarma Arnab

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Sea-level rise being one of the most important impacts of anthropogenic induced climate change resulting from global warming and melting of icebergs at Arctic and Antarctic, the investigations done by various researchers both on Indian Coast and elsewhere during the last decade has been reviewed in this paper. The paper aims to ascertain the propensity of consistency of different suggested methods to predict the near-accurate future sea level rise along the coast of Mumbai. Case studies at East Coast, Southern Tip and West and South West coast of India have been reviewed. Coastal Vulnerability Index of several important international places has been compared, which matched with Intergovernmental Panel on Climate Change forecasts. The application of Geographic Information System mapping, use of remote sensing technology, both Multi Spectral Scanner and Thematic Mapping data from Landsat classified through Iterative Self-Organizing Data Analysis Technique for arriving at high, moderate and low Coastal Vulnerability Index at various important coastal cities have been observed. Instead of data driven, hindcast based forecast for Significant Wave Height, additional impact of sea level rise has been suggested. Efficacy and limitations of numerical methods vis-à-vis Artificial Neural Network has been assessed, importance of Root Mean Square error on numerical results is mentioned. Comparing between various computerized methods on forecast results obtained from MIKE 21 has been opined to be more reliable than Delft 3D model.

Keywords: climate change, Coastal Vulnerability Index, global warming, sea level rise

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Authors: Dua K. S. Y. Klaas, Monzur A. Imteaz, Ika Sudiayem, Elkan M. E. Klaas, Eldav C. M. Klaas

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Developing tailored management strategies to ensure the sustainability of groundwater resource under climate and demographic changes is critical for tropical karst island, where relatively small watershed and highly porous soil nature make this natural resource highly susceptible and thus very sensitive to those changes. In this study, long-term impacts of climate variability on groundwater recharge and discharge at the Oemau spring, Rote Island, Indonesia were investigated. Following calibration and validation of groundwater model using MODFLOW code, groundwater flow was simulated for period of 2020-2090 under HadCM3 global model climate (GCM) scenarios, using input data of weather variables downscaled by Statistical Downscaling Model (SDSM). The reported analysis suggests that the sustainability of groundwater resources will be adversely affected by climate change during dry years. The area is projected to variably experience 2.53-22.80% decrease of spring discharge. A subsequent comprehensive set of management strategies as palliative and adaptive efforts was proposed to be implemented by relevant stakeholders to assist the community dealing with water deficit during the dry years. Three main adaptive strategies, namely socio-cultural, technical, and ecological measures, were proposed by incorporating physical and socio-economic characteristics of the area. This study presents a blueprint for assessing groundwater sustainability under climate change scenarios and developing tailored management strategies to cope with adverse impacts of climate change, which may become fundamental necessities across other tropical karst islands in the future.

Keywords: climate change, groundwater, management strategies, tropical karst island, Rote Island, Indonesia

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Authors: Jonghyuk Yoon, Hyoungwoon Song, Youngbae Kim, Eunju Kim

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Recently, with the rapid growth of semiconductor industry, lots of interests have been focused on after treatment system that remove the polluted gas produced from semiconductor manufacturing process, and a wet scrubber is the one of the widely used system. When it comes to mechanism of removing the gas, the polluted gas is removed firstly by chemical reaction in a reactor part. After that, the polluted gas stream is brought into contact with the scrubbing liquid, by spraying it with the liquid. Effective design of the reactor part inside the wet scrubber is highly important since removal performance of the polluted gas in the reactor plays an important role in overall performance and stability. In the present study, a CFD (Computational Fluid Dynamics) analysis was performed to figure out the thermal and flow characteristics inside unit a reactor of wet scrubber. In order to verify the numerical result, temperature distribution of the numerical result at various monitoring points was compared to the experimental result. The average error rates (12~15%) between them was shown and the numerical result of temperature distribution was in good agreement with the experimental data. By using validated numerical method, the effect of the reactor geometry on heat transfer rate was also taken into consideration. Uniformity of temperature distribution was improved about 15%. Overall, the result of present study could be useful information to identify the fluid behavior and thermal performance for various scrubber systems. This project is supported by the ‘R&D Center for the reduction of Non-CO₂ Greenhouse gases (RE201706054)’ funded by the Korea Ministry of Environment (MOE) as the Global Top Environment R&D Program.

Keywords: semiconductor, polluted gas, CFD (Computational Fluid Dynamics), wet scrubber, reactor

Procedia PDF Downloads 131

Authors: Maulita Sari Hani, Abraham B. Sianipar, Abdi Hasan, Erfa Canistya, Ismail Alaydrus, Asril Djunaidi

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Whale shark tourism offer potential benefits to support economic alternative livelihood. Since 2017, Conservation International Indonesia worked in Sumbawa to monitor whale shark distribution and identified species aggregation in Teluk Saleh. We conducted a survey on May 23th-27th, 2018 and involved 86 local community from five hamlets in Labuan Jambu village. Furthermore, forum group discussion (FGD) held with 20 village representative on July 30th, 2018. The result of frequency distribution demonstrated 95% of respondents show positive perceptions towards sustainable development of whale shark tourism with 40% willing to participate in boat rental services. The community also proposes to participate in providing other tourism services including the local guide (12%), food and beverage or F&B (8%), local transport (8%), and homestay (6%). 34% of respondents agreed to establish a new institution (under village officials) to coordinate tourism services provided by the local community. We also conducted participatory mapping with 15 key informants where the result confirmed 13 areas of whale shark aggregation with all-year-round sightings. The FGD results in 20 participants ready to start the pilot project of community-based whale shark tourism in August 2018, including 4 boat rental (3 speedboats and 1 floating cage boat), 6 homestays, 4 car rentals, 1 F&B, 1 gear rental, 2 guides, and 2 local products. In addition, we facilitate village official in establishing policy and regulations for whale shark conservation and sustainable community-based tourism through village regulation, code of conduct, best practices, and capacity building program.

Keywords: marine wildlife tourism, elasmobranch, conservation, sustainable tourism, co-management

Procedia PDF Downloads 159

Authors: Muhammad Minhaj Khan, Jaemin Lee, Suhong Lee, Jinyoung Chung, Johoo Whang

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The Republic of Korea has been facing strong storage crisis from nuclear waste generation as At Reactor (AR) temporary storage sites are about to reach saturation. Since the country is densely populated with a rate of 491.78 persons per square kilometer, Construction of High-level waste repository will not be a feasible option. In order to tackle the storage waste generation problem which is increasing at a rate of 350 tHM/Yr. and 380 tHM/Yr. in case of 20 PWRs and 4 PHWRs respectively, the study strongly focuses on the advancement of current nuclear power plants to GEN-IV sustainable and ecological nuclear systems by burning TRUs (Pu, MAs). First, Calculations has made to estimate the generation of SNF including Pu and MA from PWR and PHWR NPPS by using the IAEA code Nuclear Fuel Cycle Simulation System (NFCSS) for the period of 2016, 2030 (including the saturation period of each site from 2024~2028), 2089 and 2109 as the number of NPPS will increase due to high import cost of non-nuclear energy sources. 2ndly, in order to produce environmentally sustainable nuclear energy systems, 4 scenarios to burnout the Plutonium and MAs are analyzed with the concentration on burning of MA only, MA and Pu together by utilizing SFR, LFR and KALIMER-600 burner reactor after recycling the spent oxide fuel from PWR through pyro processing technology developed by Korea Atomic Energy Research Institute (KAERI) which shows promising and sustainable future benefits by minimizing the HLW generation with regard to waste amount, decay heat, and activity. Finally, With the concentration on front and back end fuel cycles for open and closed fuel cycles of PWR and Pyro-SFR respectively, an overall assessment has been made which evaluates the quantitative as well as economical combativeness of SFR metallic fuel against PWR once through nuclear fuel cycle.

Keywords: GEN IV nuclear fuel cycle, nuclear waste, waste sustainability, transmutation

Procedia PDF Downloads 346

Authors: Krupali Mukeshkumar, Jinesh Shah

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Background: Bilateral Sagittal Split Osteotomy (BSSO), used for the correction of mandibular deformities, is a common oral and maxillofacial surgical procedure. Inferior alveolar nerve dysfunction is commonly reported post-operatively by patients as paresthesia or anesthesia. The current literature lacks a consensus on the incidence of inferior alveolar nerve dysfunction as patients are not routinely assessed pre and post-operatively with an objective assessment. The range of incidence varies from 9% to 85% of patients, with some authors arguing that 100% of patients experience nerve dysfunction immediately post-surgery. Systematic reviews have shown a difference between incidence rates at different follow-up periods using objective and subjective methods. Aim: To identify the incidence of inferior alveolar nerve dysfunction following BSSO. Gold standard: Nerve dysfunction incidence rates similar or lower than current literature of 83% day one post-operatively and 18.4% at one year follow up. Setting: A retrospective cross-sectional audit of patients treated between 2017-2019 at the Royal Stoke University Hospital, Maxillofacial and Orthodontic departments. Sample: All patients who underwent a BSSO (with or without le fort one osteotomy) between 2017–2019 were identified from the database. Patients with pre-existing neurosensory disturbance, those who had a genioplasty at the same time and those with no follow-up were excluded. The sample consisted of 121 patients, 37 males and 84 females between the ages of 17-50 years at the time of surgery. Methods: Clinical records of 121 cases were reviewed to assess the age, sex, type of mandibular osteotomy, status of the nerve during the surgical procedure, type of bony split and incidence of nerve dysfunction at follow-up appointments. The surgical procedure was carried out by three Maxillo-facial surgeons and follow-up appointments were carried out in the Orthodontic and Oral and Maxillo-facial departments. Results: 120 patients were treated to correct the mandibular facial deformity and 1 patient was treated for sleep apnoea. Seventeen patients had a mandibular setback and 104 patients had mandibular advancement. 68 patients reported inferior alveolar nerve dysfunction at one week following their surgery. Seventy-six patients had temporary paresthesia present between 2 weeks and 12 months post-surgery. 13 patients had persistent nerve dysfunction at 12 months, of which 1 had a bad bony split during the BSSO. The incidence of nerve dysfunction postoperatively was 6.6% after 1 day, 56.1% at 1 week, 62.8% at 2 weeks, 59.5% between 3-6 weeks, 43.0% between 8-16 weeks and 10.7% at 1 year. Conclusions: The results of this audit show a similar incidence rate to the research gold standard at the one-year follow-up. Future Recommendations: No changes to surgical procedure or technique are indicated, but a need for improved documentation and a standardized approach for assessment of post-operative nerve dysfunction would be beneficial.

Keywords: bilateral sagittal split osteotomy, inferior alveolar nerve, mandible, nerve dysfunction

Procedia PDF Downloads 224

Authors: Çiğdem Avcı Karataş

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Mitigation of structural damage caused by earthquake and reduction of fatality is one of the main concerns of engineers in seismic prone zones of the world. To achieve this aim many technologies have been developed in the last decades and applied in construction and retrofit of structures. On the one hand Turkey is well-known a country of high level of seismicity; on the other hand steel-composite structures appear competitive today in this country by comparison with other types of structures, for example only-steel or concrete structures. Composite construction is the dominant form of construction for the multi-storey building sector. The reason why composite construction is often so good can be expressed in one simple way - concrete is good in compression and steel is good in tension. By joining the two materials together structurally these strengths can be exploited to result in a highly efficient design. The reduced self-weight of composite elements has a knock-on effect by reducing the forces in those elements supporting them, including the foundations. The floor depth reductions that can be achieved using composite construction can also provide significant benefits in terms of the costs of services and the building envelope. The scope of this paper covers analysis, materials take-off, cost analysis and economic comparisons of a multi-storey building with composite and steel frames. The aim of this work is to show that designing load carrying systems as composite is more economical than designing as steel. Design of the nine stories building which is under consideration is done according to the regulation of the 2007, Turkish Earthquake Code and by using static and dynamic analysis methods. For the analyses of the steel and composite systems, plastic analysis methods have been used and whereas steel system analyses have been checked in compliance with EC3 and composite system analyses have been checked in compliance with EC4. At the end of the comparisons, it is revealed that composite load carrying systems analysis is more economical than the steel load carrying systems analysis considering the materials to be used in the load carrying system and the workmanship to be spent for this job.

Keywords: composite analysis, earthquake, steel, multi-storey building

Procedia PDF Downloads 556

Authors: M. Govindarajan

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One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: data mining, ensemble, radial basis function, support vector machine, accuracy

Procedia PDF Downloads 239

Authors: Shveta Puri, Shiv Jyoti Pandey, G. M. Bhat, Neha Raina

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TheJammu and Kashmir region of the Northwest Himalaya had witnessed many devastating earthquakes in the recent past and has remained unexplored for any kind of seismic investigations except scanty records of the earthquakes that occurred in this region in the past. In this study, we have used local seismic data of year 2013 that was recorded by the network of Broadband Seismographs in J&K. During this period, our seismic stations recorded about 207 earthquakes including two moderate events of Mw 5.7 on 1st May, 2013 and Mw 5.1 of 2nd August, 2013.We analyzed the events of Mw 3-4.6 and the main events only (for minimizing the error) for source parameters, b value and sense of movement through waveform modeling for understanding seismotectonic and seismic hazard of the region. It has been observed that most of the events are bounded between 32.9° N – 33.3° N latitude and 75.4° E – 76.1° E longitudes, Moment Magnitude (Mw) ranges from Mw 3 to 5.7, Source radius (r), from 0.21 to 3.5 km, stress drop, from 1.90 bars to 71.1 bars and Corner frequency, from 0.39 – 6.06 Hz. The b-value for this region was found to be 0.83±0 from these events which are lower than the normal value (b=1), indicating the area is under high stress. The travel time inversion and waveform inversion method suggest focal depth up to 10 km probably above the detachment depth of the Himalayan region. Moment tensor solution of the (Mw 5.1, 02:32:47 UTC) main event of 2ndAugust suggested that the source fault is striking at 295° with dip of 33° and rake value of 85°. It was found that these events form intense clustering of small to moderate events within a narrow zone between Panjal Thrust and Kishtwar Window. Moment tensor solution of the main events and their aftershocks indicating thrust type of movement is occurring in this region.

Keywords: b-value, moment tensor, seismotectonics, source parameters

Procedia PDF Downloads 305

Authors: Malik Imtiaz Ahmad

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The prime sources of Islamic Law or Shariah are Quran and Sunnah and is applied to the personal and public affairs of Muslims. Islamic law is deemed to be divine and furnishes a complete code of conduct based upon universal values to build honesty, trust, righteousness, piety, charity, and social justice. The primary focus of this paper was to examine the development of Islamic jurisprudence (Fiqh) over time and its relevance to the field of Islamic finance. This encompassed a comprehensive analysis of the historical context, key legal principles, and their application in contemporary financial systems adhering to Islamic principles. This study aimed to elucidate the deep-rooted connection between Islamic law and finance, offering valuable insights for practitioners and policymakers in the Islamic finance sector. Understanding the historical context and legal underpinnings is crucial for ensuring the compliance and ethicality of modern financial systems adhering to Islamic principles. Fintech solutions are developing fields to accelerate the digitalization of Islamic finance products and services for the harmonization of global investors' mandate. Through this study, we focus on institutional governance that will improve Sharia compliance, efficiency, transparency in decision-making, and Islamic finance's contribution to humanity through the SDGs program. The research paper employed an extensive literature review, historical analysis, examination of legal principles, and case studies to trace the evolution of Islamic law and its contemporary application in Islamic finance, providing a concise yet comprehensive understanding of this intricate relationship. Through these research methodologies, the aim was to provide a comprehensive and insightful exploration of the historical evolution of Islamic law and its relevance to contemporary Islamic finance, thereby contributing to a deeper understanding of this unique and growing sector of the global financial industry.

Keywords: sharia, sequencing Islamic jurisprudence, Islamic congruent marketing, social development goals of Islamic finance

Procedia PDF Downloads 58

Authors: Bliss Singhal

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Machine learning (ML) is a branch of Artificial Intelligence (AI) where computers analyze data and find patterns in the data. The study focuses on the detection of metastatic cancer using ML. Metastatic cancer is the stage where cancer has spread to other parts of the body and is the cause of approximately 90% of cancer-related deaths. Normally, pathologists spend hours each day to manually classifying whether tumors are benign or malignant. This tedious task contributes to mislabeling metastasis being over 60% of the time and emphasizes the importance of being aware of human error and other inefficiencies. ML is a good candidate to improve the correct identification of metastatic cancer, saving thousands of lives and can also improve the speed and efficiency of the process, thereby taking fewer resources and time. So far, the deep learning methodology of AI has been used in research to detect cancer. This study is a novel approach to determining the potential of using preprocessing algorithms combined with classification algorithms in detecting metastatic cancer. The study used two preprocessing algorithms: principal component analysis (PCA) and the genetic algorithm, to reduce the dimensionality of the dataset and then used three classification algorithms: logistic regression, decision tree classifier, and k-nearest neighbors to detect metastatic cancer in the pathology scans. The highest accuracy of 71.14% was produced by the ML pipeline comprising of PCA, the genetic algorithm, and the k-nearest neighbor algorithm, suggesting that preprocessing and classification algorithms have great potential for detecting metastatic cancer.

Keywords: breast cancer, principal component analysis, genetic algorithm, k-nearest neighbors, decision tree classifier, logistic regression

Procedia PDF Downloads 73

Authors: Burak Oksuzoglu, Savas Yildirim, Ferhat Kutlu

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Opinion mining (OM) is one of the natural language processing (NLP) problems to determine the polarity of opinions, mostly represented on a positive-neutral-negative axis. The data for OM is usually collected from various social media platforms. In an era where social media has considerable control over companies’ futures, it’s worth understanding social media and taking actions accordingly. OM comes to the fore here as the scale of the discussion about companies increases, and it becomes unfeasible to gauge opinion on individual levels. Thus, the companies opt to automize this process by applying machine learning (ML) approaches to their data. For the last two decades, OM or sentiment analysis (SA) has been mainly performed by applying ML classification algorithms such as support vector machines (SVM) and Naïve Bayes to a bag of n-gram representations of textual data. With the advent of deep learning and its apparent success in NLP, traditional methods have become obsolete. Transfer learning paradigm that has been commonly used in computer vision (CV) problems started to shape NLP approaches and language models (LM) lately. This gave a sudden rise to the usage of the pretrained language model (PTM), which contains language representations that are obtained by training it on the large datasets using self-supervised learning objectives. The PTMs are further fine-tuned by a specialized downstream task dataset to produce efficient models for various NLP tasks such as OM, NER (Named-Entity Recognition), Question Answering (QA), and so forth. In this study, the traditional and modern NLP approaches have been evaluated for OM by using a sizable corpus belonging to a large private company containing about 76,000 comments in Turkish: SVM with a bag of n-grams, and two chosen pre-trained models, multilingual universal sentence encoder (MUSE) and bidirectional encoder representations from transformers (BERT). The MUSE model is a multilingual model that supports 16 languages, including Turkish, and it is based on convolutional neural networks. The BERT is a monolingual model in our case and transformers-based neural networks. It uses a masked language model and next sentence prediction tasks that allow the bidirectional training of the transformers. During the training phase of the architecture, pre-processing operations such as morphological parsing, stemming, and spelling correction was not used since the experiments showed that their contribution to the model performance was found insignificant even though Turkish is a highly agglutinative and inflective language. The results show that usage of deep learning methods with pre-trained models and fine-tuning achieve about 11% improvement over SVM for OM. The BERT model achieved around 94% prediction accuracy while the MUSE model achieved around 88% and SVM did around 83%. The MUSE multilingual model shows better results than SVM, but it still performs worse than the monolingual BERT model.

Keywords: BERT, MUSE, opinion mining, pretrained language model, SVM, Turkish

Procedia PDF Downloads 131

Authors: Gila Kurtz, Dan Kohen Vacs

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Social robots (SR) is an emerging field striving to deploy computers capable of resembling human shapes and mimicking human movements, gestures, and behaviors. The evolving capability of SR to interact with human offers groundbreaking ways for learning and training opportunities. Studies show that SR can offer instructional experiences for fostering creativity, entertainment, enjoyment, and curiosity. These added values are essential for empowering instructional opportunities as gamified learning experiences. We present our project focused on deploying an activity to be experienced in an escape room aimed at team-based learning scaffolded by an SR, NAO. An escape room is a well-known approach for gamified activities focused on a simulated scenario experienced by team-based participants. Usually, the simulation takes place in a physical environment where participants must complete a series of challenges in a limited amount of time. During this experience, players learn something about the assigned topic of the room. In the current learning simulation, students must "save the nation" by locating sensitive information stolen and stored in a vault of four locks. Team members have to look for hints and solve riddles mediated by NAO. Each solution provides a unique code for opening one of the four locks. NAO is also used to provide ongoing feedback on the team's performance. We captured the proceeding of our activity and used it to conduct an evaluation study among ten experts in related areas. The experts were interviewed on their overall assessment of the learning activity and their perception of the added value related to the robot. The results were very encouraging on the feasibility that NAO can serve as a motivational tutor in adults' collaborative game-based learning. We believe that this study marks the first step toward a template for developing innovative team-based training using escape rooms supported by a humanoid robot.

Keywords: social robot, NAO, learning, team based activity, escape room

Procedia PDF Downloads 62

Authors: Aid Abdelkrim

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A method of fatigue damage accumulation based upon application of energy parameters of the fatigue process is proposed in the paper. Using this model is simple, it has no parameter to be determined, it requires only the knowledge of the curve W–N (W: strain energy density N: number of cycles at failure) determined from the experimental Wöhler curve. To examine the performance of nonlinear models proposed in the estimation of fatigue damage and fatigue life of components under random loading, a batch of specimens made of 6082 T 6 aluminium alloy has been studied and some of the results are reported in the present paper. The paper describes an algorithm and suggests a fatigue cumulative damage model, especially when random loading is considered. This work contains the results of uni-axial random load fatigue tests with different mean and amplitude values performed on 6082T6 aluminium alloy specimens. The proposed model has been formulated to take into account the damage evolution at different load levels and it allows the effect of the loading sequence to be included by means of a recurrence formula derived for multilevel loading, considering complex load sequences. It is concluded that a ‘damaged stress interaction damage rule’ proposed here allows a better fatigue damage prediction than the widely used Palmgren–Miner rule, and a formula derived in random fatigue could be used to predict the fatigue damage and fatigue lifetime very easily. The results obtained by the model are compared with the experimental results and those calculated by the most fatigue damage model used in fatigue (Miner’s model). The comparison shows that the proposed model, presents a good estimation of the experimental results. Moreover, the error is minimized in comparison to the Miner’s model.

Keywords: damage accumulation, energy model, damage indicator, variable loading, random loading

Procedia PDF Downloads 385