Search results for: multiple cranial nerve impairment

Authors: Husnil Khatimah, Fairol Halim

Abstract:

This research examines the moderating impact of security on intention to use e-money that adapted from some variables of the TAM (Technology Acceptance Model) and TPB (Theory of Planned Behavior). This study will use security as moderating variable and finds these relationship depends on customer intention to use e-money as payment tools. The conceptual framework of e-money transactions was reviewed to understand behavioral intention of consumers from perceived usefulness, perceived ease of use, perceived behavioral control and security. Quantitative method will be utilized as sources of data collection. A total of one thousand respondents will be selected using quota sampling method in Medan, Indonesia. Descriptive analysis and Multiple Regression analysis will be conducted to analyze the data. The article ended with suggestion for future studies.

Keywords: e-money transaction, TAM & TPB, moderating variable, behavioral intention, conceptual paper

Procedia PDF Downloads 455

Authors: Nana Kojo Ayimadu Baafi, Kwesi Amponsah-Tawiah

Abstract:

The study investigated the relationship between job-related anxiety and employee creativity. The study further explored the role of job resources in moderating the relationship between job-related anxiety and employee creativity within the oil and gas industries. The study utilized a cross-sectional survey design. A non-probability sampling technique, specifically convenience sampling, was used to sample 1200 participants from multiple companies within the oil and gas industries. The collected data were analyzed using Regression analysis and PROCESS macro for the moderation analysis. The study empirically demonstrated a negative significant relationship between job-related anxiety and employee creativity. It also exhibited that job resources moderated the relationship between job-related anxiety and creativity. This study addresses gaps in previous studies by highlighting the significance of job resources in how job-related anxiety affects employee creativity.

Keywords: employee creativity, job-related anxiety, job resource, human resources

Procedia PDF Downloads 50

Authors: Shuai Zhao, Shalendra D. Sharma, Jin Xu

Abstract:

This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.

Keywords: comparative politics, political executives, leaders’ characteristics, artificial intelligence

Procedia PDF Downloads 87

Authors: Henry Venter, Murali Thyloth, Alceu Casseb

Abstract:

Historically, mental and substance use disorders were not a global health priority. Since the 1993 World Development Report, the importance of the contribution of mental health and substance abuse on the relative global burden associated with disease morbidity has been recognized with 300 million people worldwide suffering from depression alone. This led to an international effort to improve the mental health of populations around the world. Despite these efforts some countries remain at the top of the list of countries with the highest rate of mental illness. Important research questions were asked: Would there be commonalities regarding mental health between these countries; would there be common factors leading to the high prevalence of mental illness; and how prepared are these countries with mental health delivery? Findings from this research can aid organizations and institutions preparing mental health service providers to focus training and preparation to address specific needs revealed by the study. Methods: Researchers decided to compare three distinctly different countries at the top of the list of countries with the highest rate of mental illness, the USA, India and Brazil, situated on three different continents with different economies and lifestyles. Data were collected using archival research methodology, reviewing records and findings of international and national health and mental health studies to subtract and compare data and findings. Results: The findings indicated that India is the most depressed country in the world, followed by the USA (and China) with Brazil in Latin America with the greatest number of depressed individuals. By 2020 roughly 20% of India, acountry of over one billion citizens, will suffer from some form of mental illnees, yet there are less than 4,000 experts available. In the USA 164.8 million people were substance abusers and an estimate of 47.6 million adults, 18 or older, had any mental illness in 2018. That means that about one in five adults in the USA experiences some form of mental illness each year, but only 41% of those affected received mental health care or services in the past year. Brazil has the greatest number of depressed individuals, in Latin America. Adults living in Sao Paulo megacity has prevalence of mental disorders at greater levels than similar surveys conducted in other areas of the world with more than one million adults with serious impairment levels. Discussion: The results show that, despite the vast socioeconomic differences between the three countries, there are correlations regarding mental health prevalence and difficulty to provide adequate services including a lack of awareness of how serious mental illness is, stigma for seeking mental illness, with comorbidity a common phenomenon, and a lack of partnership between different levels of service providers, which weakens mental health service delivery. The findings also indicate that mental health training institutions have a monumental task to prepare personnel to address the future mental health needs in each of the countries compared, which will constitute the next phase of the research.

Keywords: mental health epidemiology, mental health disorder, mental health prevalence, mental health treatment

Procedia PDF Downloads 114

Authors: Zhiyong Shan, Preethi Santhanam, Vinod Namboodiri, Rajiv Bagai

Abstract:

In recent years, the emerging network worms and attacks have distributive characteristics, which can spread globally in a very short time. Security management crossing networks to co-defense network-wide attacks and improve the efficiency of security administration is urgently needed. We propose a hierarchical distributed network security management system (HD-NSMS), which can integrate security management across multiple networks. First, we describe the system in macrostructure and microstructure; then discuss three key problems when building HD-NSMS: device model, alert mechanism, and emergency response mechanism; lastly, we describe the implementation of HD-NSMS. The paper is valuable for implementing NSMS in that it derives from a practical network security management system (NSMS).

Keywords: network security management, device organization, emergency response, cross-network

Procedia PDF Downloads 169

Authors: Adeniran Adetunji, Babalola M. Florence, Akande Ademola

Abstract:

In this paper, we made an overview on the concept of adaptive e-Learning system, enumerates the elements of adaptive learning concepts e.g. A pedagogical framework, multiple learning strategies and pathways, continuous monitoring and feedback on student performance, statistical inference to reach final learning strategy that works for an individual learner by “mass-customization”. Briefly highlights the motivation of this new system proposed for effective learning teaching. E-Review literature on the concept of adaptive e-learning system and emphasises on the Item Response Calibration, which is an important approach to developing an adaptive e-Learning system. This paper write-up is concluded on the justification of item response calibration/estimation towards designing a successful and effective adaptive e-Learning system.

Keywords: adaptive e-learning system, pedagogical framework, item response, computer applications

Procedia PDF Downloads 598

Authors: Kathryn McMillan

Abstract:

A recent study of 8500 adult Australians aged 16 and over revealed 62% had experienced childhood maltreatment. In response to multiple recommendations by bodies such as the Australian Law Reform Commission, parliamentary reports and stakeholder input, a number of key initiatives have been developed to grapple with the difficulties of a federal-state system and to screen and triage high-risk families navigating their way through the court system. The Lighthouse Project (LHP) is a world-first initiative of the Federal Circuit and Family Courts in Australia (FCFOCA) to screen family law litigants for major risk factors, including family violence, child abuse, alcohol or substance abuse and mental ill-health at the point of filing in all applications that seek parenting orders. It commenced on 7 December 2020 on a pilot basis but has now been expanded to 15 registries across the country. A specialist risk screen, Family DOORS, Triage has been developed – focused on improving the safety and wellbeing of families involved in the family law system safety planning and service referral, and ¬ differentiated case management based on risk level, with the Evatt List specifically designed to manage the highest risk cases. Early signs are that this approach is meeting the needs of families with multiple risks moving through the Court system. Before the LHP, there was no data available about the prevalence of risk factors experienced by litigants entering the family courts and it was often assumed that it was the litigation process that was fueling family violence and other risks such as suicidality. Data from the 2022 FCFCOA annual report indicated that in parenting proceedings, 70% alleged a child had been or was at risk of abuse, 80% alleged a party had experienced Family Violence, 74 % of children had been exposed to Family Violence, 53% alleged through substance misuse by party children had caused or was at risk of causing harm to children and 58% of matters allege mental health issues of a party had caused or placed a child at risk of harm. Those figures reveal the significant overlap between child protection and family violence, both of which are under the responsibility of state and territory governments. Since 2020, a further key initiative has been the co-location of child protection and police officials amongst a number of registries of the FCFOCA. The ability to access in a time-effective way details of family violence or child protection orders, weapons licenses, criminal convictions or proceedings is key to managing issues across the state and federal divide. It ensures a more cohesive and effective response to family law, family violence and child protection systems.

Keywords: child protection, family violence, parenting, risk screening, triage.

Procedia PDF Downloads 79

Authors: Anna Slupik, Anna Mosiolek, Sebastian Wojtowicz, Dariusz Bialoszewski

Abstract:

Introduction: Whole-body vibration (WBV) uses high frequency mechanical stimuli generated by a vibration plate and transmitted through bone, muscle and connective tissues to the whole body. Research has shown that long-term vibration-plate training improves neuromuscular facilitation, especially in afferent neural pathways, responsible for the conduction of vibration and proprioceptive stimuli, muscle function, balance and proprioception. Some researchers suggest that the vibration stimulus briefly inhibits the conduction of afferent signals from proprioceptors and can interfere with the maintenance of body balance. The aim of this study was to evaluate the influence of a single set of exercises associated with whole-body vibration on the joint position sense and body balance. Material and methods: The study enrolled 55 people aged 19-24 years. These individuals were randomly divided into a test group (30 persons) and a control group (25 persons). Both groups performed the same set of exercises on a vibration plate. The following vibration parameters: frequency of 20Hz and amplitude of 3mm, were used in the test group. The control group performed exercises on the vibration plate while it was off. All participants were instructed to perform six dynamic exercises lasting 30 seconds each with a 60-second period of rest between them. The exercises involved large muscle groups of the trunk, pelvis and lower limbs. Measurements were carried out before and immediately after exercise. Joint position sense (JPS) was measured in the knee joint for the starting position at 45° in an open kinematic chain. JPS error was measured using a digital inclinometer. Balance was assessed in a standing position with both feet on the ground with the eyes open and closed (each test lasting 30 sec). Balance was assessed using Matscan with FootMat 7.0 SAM software. The surface of the ellipse of confidence and front-back as well as right-left swing were measured to assess balance. Statistical analysis was performed using Statistica 10.0 PL software. Results: There were no significant differences between the groups, both before and after the exercise (p> 0.05). JPS did not change in both the test (10.7° vs. 8.4°) and control groups (9.0° vs. 8.4°). No significant differences were shown in any of the test parameters during balance tests with the eyes open or closed in both the test and control groups (p> 0.05). Conclusions. 1. Deterioration in proprioception or balance was not observed immediately after the vibration stimulus. This suggests that vibration-induced blockage of proprioceptive stimuli conduction can have only a short-lasting effect that occurs only as long as a vibration stimulus is present. 2. Short-term use of vibration in treatment does not impair proprioception and seems to be safe for patients with proprioceptive impairment. 3. These results need to be supplemented with an assessment of proprioception during the application of vibration stimuli. Additionally, the impact of vibration parameters used in the exercises should be evaluated.

Keywords: balance, joint position sense, proprioception, whole body vibration

Procedia PDF Downloads 329

Authors: Sadixa Sanjel, Ahmed Sadek, Naseef Mansoor, Zelalem Denekew

Abstract:

Collegiate ice hockey (NCAA) sports analytics is different from the national level hockey (NHL). We apply and compare multiple machine learning models such as Linear Regression, Random Forest, and Neural Networks to predict the win ratio for a team based on their statistics. Data exploration helps determine which statistics are most useful in increasing the win ratio, which would be beneficial to coaches and team managers. We ran experiments to select the best model and chose Random Forest as the best performing. We conclude with how to bridge the gap between the college and national levels of sports analytics and the use of machine learning to enhance team performance despite not having a lot of metrics or budget for automatic tracking.

Keywords: NCAA, NHL, sports analytics, random forest, regression, neural networks, game predictions

Procedia PDF Downloads 118

Authors: Joao Carlos Koch Junior, Risa Takashima

Abstract:

Learning disabilities have been extensively and increasingly studied in recent times. In spite of this, there is arguably a scarce number of studies addressing a key issue, which is the impact of foreign-language requirements on students with learning disabilities in higher education, and the lack of training or awareness of teachers regarding language learning disabilities. This study is an attempt to address this issue. An extensive review of the literature in multiple fields will be summarised. This, paired with a case-analysis of a university adopting a more inclusive approach towards special-needs students in its foreign-language programme, this presentation aims to establish a link between different studies and propose a number of suggestions to make language classrooms more inclusive.

Keywords: foreign language teaching, higher education, language teacher education, learning disabilities

Procedia PDF Downloads 451

Authors: Nada Souissi, Mourad Mroua

Abstract:

The main purpose of this study is to examine the interaction between financial asset volatility, economic factors and investor's behavioral indicators related to both the company's and the markets stocks for the period from January 2000 to January2020. Using multiple linear regression and Bayesian Networks modeling, results show a positive and negative relationship between investor's psychology index, economic factors and predicted stock market return. We reveal that the application of the Bayesian Discrete Network contributes to identify the different cause and effect relationships between all economic, financial variables and psychology index.

Keywords: Financial asset return predictability, Economic factors, Investor's psychology index, Bayesian approach, Probabilistic networks, Parametric learning

Procedia PDF Downloads 151

Authors: Amornchai Challcharoenwattana, Chanathip Pharino

Abstract:

Rising amount of MSW every day, maximizing material diversions from landfills via recycling is a prefer method to land dumping. Characteristic of Thai MSW is classified as 40 -60 per cent compostable wastes while potentially recyclable materials in waste streams are composed of plastics, papers, glasses, and metals. However, rate of material recovery from MSW, excluding composting or biogas generation, in Thailand is still low. Thailand’s recycling rate in 2010 was only 20.5 per cent. Central government as well as local governments in Thailand have tried to curb this problem by charging some of MSW management fees at the users. However, the fee is often too low to promote MSW minimization. The objective of this paper is to identify levels of willingness-to-pay (WTP) for MSW recycling in different social structures with expected outcome of sustainable MSW managements for different town settlements to maximize MSW recycling pertaining to each town’s potential. The method of eliciting WTP is a payment card. The questionnaire was deployed using online survey during December 2012. Responses were categorized into respondents living in Bangkok, living in other municipality areas, or outside municipality area. The responses were analysed using descriptive statistics, and multiple linear regression analysis to identify relationships and factors that could influence high or low WTP. During the survey period, there were 168 filled questionnaires from total 689 visits. However, only 96 questionnaires could be usable. Among respondents in the usable questionnaires, 36 respondents lived in within the boundary of Bangkok Metropolitan Administration while 45 respondents lived in the chartered areas that were classified as other municipality but not in BMA. Most of respondents were well-off as 75 respondents reported positive monthly cash flow (77.32%), 15 respondents reported neutral monthly cash flow (15.46%) while 7 respondent reported negative monthly cash flow (7.22%). For WTP data including WTP of 0 baht with valid responses, ranking from the highest means of WTP to the lowest WTP of respondents by geographical locations for good MSW management were Bangkok (196 baht/month), municipalities (154 baht/month), and non-urbanized towns (111 baht/month). In-depth analysis was conducted to analyse whether there are additional room for further increase of MSW management fees from the current payment that each correspondent is currently paying. The result from multiple-regression analysis suggested that the following factors could impacts the increase or decrease of WTP: incomes, age, and gender. Overall, the outcome of this study suggests that survey respondents are likely to support improvement of MSW treatments that are not solely relying on landfilling technique. Recommendations for further studies are to obtain larger sample sizes in order to improve statistical powers and to provide better accuracy of WTP study.

Keywords: MSW, willingness to pay, payment card, waste seperation

Procedia PDF Downloads 291

Authors: Spira A., Marabelle A., Kientop D., Moser E., Mumm J.

Abstract:

Background: Both interleukin-2 (IL-2) and interleukin-10 (IL-10) have been extensively studied for their stimulatory function on T cells and their potential to obtain sustainable tumor control in RCC, melanoma, lung, and pancreatic cancer as monotherapy, as well as combination with PD-1 blockers, radiation, and chemotherapy. While approved, IL-2 retains significant toxicity, preventing its widespread use. The significant efforts undertaken to uncouple IL-2 toxicity from its anti-tumor function have been unsuccessful, and early phase clinical safety observed with PEGylated IL-10 was not met in a blinded Phase 3 trial. Deka Biosciences has engineered a novel molecule coupling wild-type IL-2 to a high affinity variant of Epstein Barr Viral (EBV) IL-10 via a scaffold (scFv) that binds to epidermal growth factor receptors (EGFR). This patented molecule, termed DK210 (EGFR), is retained at high levels within the tumor microenvironment for days after dosing. In addition to overlapping and non-redundant anti-tumor function, IL-10 reduces IL-2 mediated cytokine release syndrome risks and inhibits IL-2 mediated T regulatory cell proliferation. Methods: DK210 (EGFR) is being evaluated in an open-label, dose-escalation (Phase 1) study with 5 (0.025-0.3 mg/kg) monotherapy dose levels and (expansion cohorts) in combination with PD-1 blockers, or radiation or chemotherapy in patients with advanced solid tumors overexpressing EGFR. Key eligibility criteria include 1) confirmed progressive disease on at least one line of systemic treatment, 2) EGFR overexpression or amplification documented in histology reports, 3) at least a 4 week or 5 half-lives window since last treatment, and 4) excluding subjects with long QT syndrome, multiple myeloma, multiple sclerosis, myasthenia gravis or uncontrolled infectious, psychiatric, neurologic, or cancer disease. Plasma and tissue samples will be investigated for pharmacodynamic and predictive biomarkers and genetic signatures associated with IFN-gamma secretion, aiming to select subjects for treatment in Phase 2. Conclusion: Through successful coupling of wild-type IL-2 with a high affinity IL-10 and targeting directly to the tumor microenvironment, DK210 (EGFR) has the potential to harness IL-2 and IL-10’s known anti-cancer promise while reducing immunogenicity and toxicity risks enabling safe concomitant cytokine treatment with other anti-cancer modalities.

Keywords: cytokine, EGFR over expression, interleukine-2, interleukine-10, clinical trial

Procedia PDF Downloads 87

Authors: Julio Jesus Salazar, Manuel Andres Jesus De Lama

Abstract:

There are very few models that serve to analyze the use of water in the socio-economic process. On the supply side, the joint use of groundwater has been considered in addition to the simple limits on the availability of surface water. In addition, we have worked on waterlogging and the effects on water quality (mainly salinity). In this paper, a 'complex' water economy is examined; one in which demands grow differentially not only within but also between sectors, and one in which there are limited opportunities to increase consumptive use. In particular, high-value growth, the growth of the production of irrigated crops of high value within the basins of the case study, together with the rapidly growing urban areas, provides a rich context to examine the general problem of water management at the basin level. At the same time, the long-term aridity of nature has made the eco-environment in the basins located on the coast of Peru very vulnerable, and the exploitation and immediate use of water resources have further deteriorated the situation. The presented methodology is the optimization with embedded simulation. The wide basin simulation of flow and water balances and crop growth are embedded with the optimization of water allocation, reservoir operation, and irrigation scheduling. The modeling framework is developed from a network of river basins that includes multiple nodes of origin (reservoirs, aquifers, water courses, etc.) and multiple demand sites along the river, including places of consumptive use for agricultural, municipal and industrial, and uses of running water on the coast of Peru. The economic benefits associated with water use are evaluated for different demand management instruments, including water rights, based on the production and benefit functions of water use in the urban agricultural and industrial sectors. This work represents a new effort to analyze the use of water at the regional level and to evaluate the modernization of the integrated management of water resources and socio-economic territorial development in Peru. It will also allow the establishment of policies to improve the process of implementation of the integrated management and development of water resources. The input-output analysis is essential to present a theory about the production process, which is based on a particular type of production function. Also, this work presents the Computable General Equilibrium (CGE) version of the economic model for water resource policy analysis, which was specifically designed for analyzing large-scale water management. As to the platform for CGE simulation, GEMPACK, a flexible system for solving CGE models, is used for formulating and solving CGE model through the percentage-change approach. GEMPACK automates the process of translating the model specification into a model solution program.

Keywords: water economy, simulation, modeling, integration

Procedia PDF Downloads 156

Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinie

Abstract:

Palynology is a field of interest in many disciplines due to its multiple applications: chronological dating, climatology, allergy treatment, and honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time consuming task that requires the intervention of experts in the field, which are becoming increasingly rare due to economic and social conditions. That is why the need for automation of this task is urgent. A lot of studies have investigated the subject using different standard image processing descriptors and sometimes hand-crafted ones.In this work, we make a comparative study between classical feature extraction methods (Shape, GLCM, LBP, and others) and Deep Learning (CNN, Autoencoders, Transfer Learning) to perform a recognition task over 80 regional pollen species. It has been found that the use of Transfer Learning seems to be more precise than the other approaches

Keywords: pollens identification, features extraction, pollens classification, automated palynology

Procedia PDF Downloads 138

Authors: Sujeet Kumar, Arun Prasad

Abstract:

The present work investigates the effect of multiple parameters on the unconfined compressive strength of the bauxite residue-lime mix. A number of unconfined compressive strength tests considering various curing time, lime content, dry density and moisture content were carried out. The results show that an empirical correlation may be successfully developed using volumetric lime content, porosity, moisture content, curing time unconfined compressive strength for the range of the bauxite residue-lime mix studied. The proposed empirical correlations efficiently predict the strength of bauxite residue-lime mix, and it can be used as a generalized empirical equation to estimate unconfined compressive strength.

Keywords: bauxite residue, curing time, porosity/volumetric lime ratio, unconfined compressive strength

Procedia PDF Downloads 237

Authors: Geetishree Mishra, S. Akhila

Abstract:

Embedded System Design, the most challenging course of electronics engineering has always been appreciated and well acclaimed by the students of electronics and its related branches of engineering. Embedded system, being a product of multiple application domains, necessitates skilled man power to be well designed and tested in every important aspect of both hardware and software. In the current industrial scenario, the requirements are even more rigorous and highly demanding and needs to be to be on par with the advanced technologies. Fresh engineers are expected to be thoroughly groomed by the academic system and the teaching community. Graduates with the ability to understand both complex technological processes and technical skills are increasingly sought after in today's embedded industry. So, the need of the day is to restructure the under-graduate course- both theory and lab practice along with the teaching methodologies to meet the industrial requirements. This paper focuses on the importance of such a need in the present education system.

Keywords: embedded system design, industry requirement, syllabus restructuring, project-based learning, teaching methodology

Procedia PDF Downloads 665

Authors: Petr Šimek

Abstract:

This work deals with the problem of synchronization of bus frames during transmission using USB (Universal Serial Bus). The principles for synchronization between USB and the non-deterministic CAN (Controller Area Network) bus will be described here. Furthermore, the work deals with ensuring the time sequence of communication frames when receiving from multiple communication bus channels. The structure of a general object for storing frames from different types of communication buses, such as CAN and LIN (Local Interconnect Network), will be described here. Finally, an evaluation of the communication throughput of bus frames for USB High speed will be performed. The creation of this architecture was based on the analysis of the communication of control units with a large number of communication buses. For the design of the architecture, a test HW with a USB-HS interface was used, which received previously known messages, which were compared with the received result. The result of this investigation is the block architecture of the control program for test HW ensuring correct data transmission via the USB bus.

Keywords: analysis, CAN, interface, LIN, synchronization, USB

Procedia PDF Downloads 66

Authors: Mohammed F. Almograbi

Abstract:

For reinforced concrete panels the risk of failure due to compression -tension state of stresses, results from pure shear or torsion, can be a major problem. The present calculation methods for such stresses from multiple influences are without taking into account the softening of cracked concrete remains conservative. The non-linear finite element method has become an important and increasingly used tool for the analysis and assessment of the structures by including cracking softening and tension-stiffening. The aim of this paper is to test a computer program refined recently and to simulate the compression response of cracked concrete element and to compare with the available experimental results.

Keywords: reinforced concrete panels, compression-tension, shear, torsion, compression softening, tension stiffening, non-linear finite element analysis

Procedia PDF Downloads 338

Authors: Secil Kaya Gulen

Abstract:

Face to face and virtual classrooms that came up with different conditions and environments, but similar purposes have different characteristics. Although virtual classrooms have some similar facilities with face-to-face classes such as program, students, and administrators, they have no walls and corridors. Therefore, students can attend the courses from a distance and can control their own learning spaces. Virtual classrooms defined as simultaneous online environments where students in different places come together at the same time with the guidance of a teacher. Distance education and virtual classes require different intellectual and managerial skills and models. Therefore, for effective use of virtual classrooms, the virtual property should be taken into consideration. One of the most important factors that affect the spread and effective use of the virtual classrooms is the perceptions and opinions of students -as one the main participants-. Student opinions and recommendations are important in terms of providing information about the fulfillment of expectation. This will help to improve the applications and contribute to the more efficient implementations. In this context, ideas and perceptions of the students related to the virtual classrooms, in general, were determined in this study. Advantages and disadvantages of virtual classrooms expected contributions to the educational system and expected characteristics of virtual classrooms have examined in this study. Students of an online distance education graduate program in which all the courses offered by virtual classrooms have asked for their opinions. Online Distance Education Graduate Program has totally 19 students. The questionnaire that consists of open-ended and multiple choice questions sent to these 19 students and finally 12 of them answered the questionnaire. Analysis of the data presented as frequencies and percentages for each item. SPSS for multiple-choice questions and Nvivo for open-ended questions were used for analyses. According to the results obtained by the analysis, participants stated that they did not get any training on virtual classes before the courses; but they emphasize that newly enrolled students should be educated about the virtual classrooms. In addition, all participants mentioned that virtual classroom contribute their personal development and they want to improve their skills by gaining more experience. The participants, who mainly emphasize the advantages of virtual classrooms, express that the dissemination of virtual classrooms will contribute to the Turkish Education System. Within the advantages of virtual classrooms, ‘recordable and repeatable lessons’ and ‘eliminating the access and transportation costs’ are most common advantages according to the participants. On the other hand, they mentioned ‘technological features and keyboard usage skills affect the attendance’ is the most common disadvantage. Participants' most obvious problem during virtual lectures is ‘lack of technical support’. Finally ‘easy to use’, ‘support possibilities’, ‘communication level’ and ‘flexibility’ come to the forefront in the scope of expected features of virtual classrooms. Last of all, students' opinions about the virtual classrooms seems to be generally positive. Designing and managing virtual classrooms according to the prioritized features will increase the students’ satisfaction and will contribute to improve applications that are more effective.

Keywords: distance education, virtual classrooms, higher education, e-learning

Procedia PDF Downloads 269

Authors: Aya Al Masri, Naima Oubenali, Safoin Aktaou, Thibault Julien, Malorie Martin, Fouad Maaloul

Abstract:

Introduction: The increased number of performed 'Computed Tomography (CT)' examinations raise public concerns regarding associated stochastic risk to patients. In its Publication 102, the ‘International Commission on Radiological Protection (ICRP)’ emphasized the importance of managing patient dose, particularly from repeated or multiple examinations. We developed a Dose Archiving and Communication System that gives multiple dose indexes (organ dose, effective dose, and skin-dose mapping) for patients undergoing radiological imaging exams. The aim of this study is to compare the organ dose values given by our software for patients undergoing CT exams with those of another software named "VirtualDose". Materials and methods: Our software uses Monte Carlo simulations to calculate organ doses for patients undergoing computed tomography examinations. The general calculation principle consists to simulate: (1) the scanner machine with all its technical specifications and associated irradiation cases (kVp, field collimation, mAs, pitch ...) (2) detailed geometric and compositional information of dozens of well identified organs of computational hybrid phantoms that contain the necessary anatomical data. The mass as well as the elemental composition of the tissues and organs that constitute our phantoms correspond to the recommendations of the international organizations (namely the ICRP and the ICRU). Their body dimensions correspond to reference data developed in the United States. Simulated data was verified by clinical measurement. To perform the comparison, 270 adult patients and 150 pediatric patients were used, whose data corresponds to exams carried out in France hospital centers. The comparison dataset of adult patients includes adult males and females for three different scanner machines and three different acquisition protocols (Head, Chest, and Chest-Abdomen-Pelvis). The comparison sample of pediatric patients includes the exams of thirty patients for each of the following age groups: new born, 1-2 years, 3-7 years, 8-12 years, and 13-16 years. The comparison for pediatric patients were performed on the “Head” protocol. The percentage of the dose difference were calculated for organs receiving a significant dose according to the acquisition protocol (80% of the maximal dose). Results: Adult patients: for organs that are completely covered by the scan range, the maximum percentage of dose difference between the two software is 27 %. However, there are three organs situated at the edges of the scan range that show a slightly higher dose difference. Pediatric patients: the percentage of dose difference between the two software does not exceed 30%. These dose differences may be due to the use of two different generations of hybrid phantoms by the two software. Conclusion: This study shows that our software provides a reliable dosimetric information for patients undergoing Computed Tomography exams.

Keywords: adult and pediatric patients, computed tomography, organ dose calculation, software comparison

Procedia PDF Downloads 165

Authors: Loujaina Abdelwahed

Abstract:

Debt relief granted to low-and middle-income countries effectively provides additional funds for governments that can be used to increase public investment on poverty-reducing services to alleviate poverty and boost economic growth. However, little is known about the extent to which the poor benefit from the increased public investment. This study aims to assess the impact of debt relief granted through multiple initiatives during the 1990s on poverty reduction. In particular, it assesses the impact on the level, depth and severity of poverty in 76 low-and middle income countries over the period 1990-2011. Debt relief is found to have a significant impact on reducing the level, the depth and the severity of poverty. Analysis of the different types of debt relief reveals that debt service relief reduces poverty, whereas debt principle relief does not have a significant impact.

Keywords: debt relief, developing countries, HIPC, poverty, system GMM estimator

Procedia PDF Downloads 400

Authors: Tallal Abdel Karim Bouzir, Nourdinne Zemmouri, Djihed Berkouk

Abstract:

In this research, a quantitative assessment of the urban sound environment of the city of Biskra, Algeria, was conducted. To determine the quality of the soundscape based on in-situ measurement, using a Landtek SL5868P sound level meter in 47 points, which have been identified to represent the whole city. The result shows that the urban noise level varies from 55.3 dB to 75.8 dB during the weekdays and from 51.7 dB to 74.3 dB during the weekend. On the other hand, we can also note that 70.20% of the results of the weekday measurements and 55.30% of the results of the weekend measurements have levels of sound intensity that exceed the levels allowed by Algerian law and the recommendations of the World Health Organization. These very high urban noise levels affect the quality of life, the acoustic comfort and may even pose multiple risks to people's health.

Keywords: road traffic, noise pollution, sound intensity, public health

Procedia PDF Downloads 268

Authors: Richard Ren

Abstract:

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 130

Authors: Jolanta Tamošaitienė

Abstract:

The article focuses on a developed comprehensive model to be used in an agile environment for the risk assessment and selection based on multi-attribute methods. The model is based on a multi-attribute evaluation of risk in construction, and the determination of their optimality criterion values are calculated using complex Multiple Criteria Decision-Making methods. The model may be further applied to risk assessment in an agile construction environment. The attributes of risk in a construction project are selected by applying the risk assessment condition to the construction sector, and the construction process efficiency in the construction industry accounts for the agile environment. The paper presents the comprehensive risk assessment model in an agile construction environment. It provides a background and a description of the proposed model and the developed analysis of the comprehensive risk assessment model in an agile construction environment with the criteria.

Keywords: assessment, environment, agile, model, risk

Procedia PDF Downloads 256

Authors: Davood Shishebori, Mohammad Saber Fallah Nezhad, Sina Seifi

Abstract:

In this paper, a variable multiple dependent state (MDS) sampling plan is developed based on the process capability index using Bayesian approach. The optimal parameters of the developed sampling plan with respect to constraints related to the risk of consumer and producer are presented. Two comparison studies have been done. First, the methods of double sampling model, sampling plan for resubmitted lots and repetitive group sampling (RGS) plan are elaborated and average sample numbers of the developed MDS plan and other classical methods are compared. A comparison study between the developed MDS plan based on Bayesian approach and the exact probability distribution is carried out.

Keywords: MDS sampling plan, RGS plan, sampling plan for resubmitted lots, process capability index (PCI), average sample number (ASN), Bayesian approach

Procedia PDF Downloads 301

Authors: Angelis P. Barlampas

Abstract:

Introduction: The term phantom lung mass describes the ovoid collection of fluid within the interlobular fissure, which initially creates the impression of a mass. The problem of correct differential diagnosis is great, especially in plain radiography. A case is presented with three nodular pulmonary foci, the shape, location, and density of which, as well as the presence of chronic loculated pleural effusions, suggest the presence of multiple phantom tumors of the lung. Purpose: The aim of this paper is to draw the attention of non-experienced and non-specialized physicians to the existence of benign findings that mimic pathological conditions and vice versa. The careful study of a radiological examination and the comparison with previous exams or further control protect against quick wrong conclusions. Methods: A hospitalized patient underwent a non-contrast CT scan of the chest as part of the general control of her situation. Results: Computed tomography revealed pleural effusions, some of them loculated, increased cardiothoracic index, as well as the presence of three nodular foci, one in the left lung and two in the right with a maximum density of up to 18 Hounsfield units and a mean diameter of approximately five centimeters. Two of them are located in the characteristical anatomical position of the major interlobular fissure. The third one is located in the area of the right lower lobe’s posterior basal part, and it presents the same characteristics as the previous ones and is likely to be a loculated fluid collection, within an auxiliary interlobular fissure or a cyst, in the context of the patient's more general pleural entrapments and loculations. The differential diagnosis of nodular foci based on their imaging characteristics includes the following: a) rare metastatic foci with low density (liposarcoma, mucous tumors of the digestive or genital system, necrotic metastatic foci, metastatic renal cancer, etc.), b) necrotic multiple primary lung tumor locations (squamous epithelial cancer, etc. ), c) hamartomas of the lung, d) fibrotic tumors of the interlobular fissures, e) lipoid pneumonia, f) fluid concentrations within the interlobular fissures, g) lipoma of the lung, h) myelolipomas of the lung. Conclusions: The collection of fluid within the interlobular fissure of the lung can give the false impression of a lung mass, particularly on plain chest radiography. In the case of computed tomography, the ability to measure the density of a lesion, combined with the provided high anatomical details of the location and characteristics of the lesion, can lead relatively easily to the correct diagnosis. In cases of doubt or image artifacts, comparison with previous or subsequent examinations can resolve any disagreements, while in rare cases, intravenous contrast may be necessary.

Keywords: phantom mass, chest CT, pleural effusion, cancer

Procedia PDF Downloads 56

Authors: Ghada A. Alfattni

Abstract:

Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

Procedia PDF Downloads 352

Authors: Nagmani Lnu

Abstract:

Often, Quality Engineering refers to testing the applications that either have a User Interface (UI) or an Application Programming Interface (API). We often find mature test practices, standards, and automation regarding UI or API testing. However, another kind is present in almost all types of industries that deal with data in bulk and often get handled through something called a Batch Application. This is primarily an offline application companies develop to process large data sets that often deal with multiple business rules. The challenge gets more prominent when we try to automate batch testing. This paper describes the approaches taken to test a Batch application from a Financial Industry to test the payment settlement process (a critical use case in all kinds of FinTech companies), resulting in 100% test automation in Test Creation and Test execution. One can follow this approach for any other batch use cases to achieve a higher efficiency in their testing process.

Keywords: batch testing, batch test automation, batch test strategy, payments testing, payments settlement testing

Procedia PDF Downloads 62

Authors: Soheila Zhaeentan, Anders Von Heijne, Elisabet Hagert, André Stark, Björn Salomonsson

Abstract:

Recommended treatment for traumatic rotator cuff tear (TRCT) is surgery within a few weeks after injury if the diagnosis is made early, especially if a functional impairment of the shoulder exists. This may lead to the assumption that a poor outcome then can be expected in delayed surgical treatment, when the patient is diagnosed at a later stage. The aim of this study was to investigate if a surgical repair later than three months after injury may result in successful outcomes and patient satisfaction. There is evidence in literature that good results of treatment can be expected up to three months after the injury, but little is known of later treatment with cuff repair. 73 patients (75 shoulders), 58 males/17 females, mean age 59 (range 34-­‐72), who had undergone surgical intervention for TRCT between January 1999 to December 2011 at our clinic, were included in this study. Patients were assessed by MRI investigation, clinical examination, Western Ontario Rotator Cuff index (WORC), Oxford Shoulder Score, Constant-­‐Murley Score, EQ-­‐5D and patient subjective satisfaction at follow-­‐up. The patients treated surgically within three months ( < 12 weeks) after injury (39 cases) were compared with patients treated more than three months ( ≥ 12 weeks) after injury (36 cases). WORC was used as the primary outcome measure and the other variables as secondary. A senior consultant radiologist, blinded to patient category and clinical outcome, evaluated all MRI-­‐images. Rotator cuff integrity, presence of arthritis, fatty degeneration and muscle atrophy was evaluated in all cases. The average follow-­‐up time was 56 months (range 14-­‐149) and the average time from injury to repair was 16 weeks (range 3-­‐104). No statistically significant differences were found for any of the assessed parameters or scores between the two groups. The mean WORC score was 77 (early group, range 25-­‐ 100 and late group, range 27-­‐100) for both groups (p= 0.86), Constant-­‐Murley Score (p= 0.91), Oxford Shoulder Score (p= 0.79), EQ-­‐5D index (p= 0.86). Re-­‐tear frequency was 24% for both groups, and the patients with re-­‐tear reported less satisfaction with outcome. Discussion and conclusion: This study shows that surgical repair of TRCT performed later than three months after injury may result in good functional outcomes and patient satisfaction. However, this does not motivate an intentional delay in surgery when there is an indication for surgical repair as that delay may adversely affect the possibility to perform a repair. Our results show that surgeons may safely consider surgical repair even if a delay in diagnosis has occurred. A retrospective cohort study on 75 shoulders shows good functional result after traumatic rotator cuff tear (TRCT) treated surgically up to one year after the injury.

Keywords: traumatic rotator cuff injury, time to surgery, surgical outcome, retrospective cohort study

Procedia PDF Downloads 223