Search results for: disaster training

Authors: Arthur C. Tsai, Chii-Shyang Kuo, Vincent S. C. Chien, Michelle Liou, Philip E. Cheng

Abstract:

Meditation enhances mental abilities and it is an antidote to anxiety. However, very little is known about brain mechanisms and cortico-subcortical interactions underlying meditation-induced anxiety relief. In this study, the changes of phase-amplitude coupling (PAC) in which the amplitude of the beta frequency band were modulated in phase with delta rhythm were investigated after eight-week of meditation training. The study hypothesized that through a concentrate but relaxed mental training the delta-beta coupling in the frontal regions is attenuated. The delta-beta coupling analysis was applied to within and between maximally-independent component sources returned from the extended infomax independent components analysis (ICA) algorithm on the continuous EEG data during mediation. A unique meditative concentration task through relaxing body and mind was used with a constant level of moderate mental effort, so as to approach an ‘emptiness’ meditative state. A pre-test/post-test control group design was used in this study. To evaluate cross-frequency phase-amplitude coupling of component sources, the modulation index (MI) with statistics to calculate circular phase statistics were estimated. Our findings reveal that a significant delta-beta decoupling was observed in a set of frontal regions bilaterally. In addition, beta frequency band of prefrontal component were amplitude modulated in phase with the delta rhythm of medial frontal component.

Keywords: phase-amplitude coupling, ICA, meditation, EEG

Procedia PDF Downloads 427

Authors: N. Uaphongsathorn, C. Worawong, S. Thaewpia

Abstract:

Background and significance: The population is aging in Thai society, the elderly dependent is at great risk of various functional, psychological, and socio-economic problems as well as less access to health care. They may require long term care at home to maximize their functional abilities and activities of daily living and to improve their quality of life during their own age. Therefore, there is a need to develop a home-based long term care to meet the long term care needs of elders dependent. Methods: The research purpose was to develop long term care model among the elderly dependent in Chaiyaphum province in Northeast region of Thailand. Action Research which is composing of planning, action, observation, and reflection phases was used. Research was carried out for 12 months in all sub-districts of 6 districts in Chaiyaphum province. Participants (N = 1,010) participating in the processes of model development were comprised of 3 groups: a) a total of 110 health care professionals, b) a total of 600 health volunteers and family caregivers and c) a total of 300 the elderly dependent with chronically medical illnesses or disabilities. Descriptive statistics and content analysis were used to analyze data. Findings: Results have shown that the most common health problems among elders dependent with physical disabilities to function independently were cardiovascular disease, dementia, and traffic injuries. The development of home-based long term care model among elders dependent in Chaiyaphum province was composed of six key steps. They are: a) initiating policies supporting formal and informal caregivers for the elder dependent in all sub-districts, b) building network and multidisciplinary team, c) developing 3-day care manager training program and 3-day care provider training program d) training case managers and care providers for the elderly dependent through team and action learning, e) assessing, planning and providing care based on care individual’s needs of the elderly dependent, and f) sharing experiences for good practice and innovation for long term care at homes in district urban and rural areas. Among all care managers and care providers, the satisfaction level for training programs was high with a mean score of 3.98 out of 5. The elders dependent and family caregivers addressed that long term care at home could contribute to improving life’s daily activities, family relationship, health status, and quality of life. Family caregivers and volunteers have feeling a sense of personal satisfaction and experiencing providing meaningful care and support for elders dependent. Conclusion: In conclusion, a home-based long term care is important to Thai elders dependent. Care managers and care providers play a large role and responsibility to provide appropriate care to meet the elders’ needs in both urban and rural areas in Thai society. Further research could be rigorously studied with a larger group of populations in similar socio-economic and cultural contexts.

Keywords: elderly people, care manager, care provider, long term care

Procedia PDF Downloads 302

Authors: Hana A. Jameel Alsaeed, Rayyan Ibrahim Khaleel, Hanaa Hussein Mukhlif

Abstract:

Background: Contamination in Iraq's hospitals is a localized problem with high rates of disease And deaths that mainly affect poor areas. Thus, this study aims to evaluate hospital infections in the city of Mosul and to identify the etiology. So to assess environmental infection prevention in pediatric wards and newborn critical care units in Mosul city. Methods: The present study is a cross-sectional hospital based in Mosul-Iraq between (10th February to 1st April 2022). Purposive sample of 60 nurses from neonatal intensive care units and pediatric wards in three pediatric teaching hospitals in Mosul city; Data was gathered using a questionnaire created by the researchers after reviewing previous studies. Results: The study showed that the majority of the study infection prevention and control policy isn't available in 46.7% of departments, and 45% of hospital workers in Iraq don't know if there is an Iraqi version of it. 70% of the study group had participated in an infection control training program. Conclusions: In the majority of samples 55% of respondents to the study claimed not to be aware of these rules. 60% of the study's participants had never attended a course on infection prevention and control, according to the study's findings on education and training programs. In the neonatal and critical care unit, nurses' skill levels, years of experience, and actual duties varied by wide statistically significant differences.

Keywords: pediatric, infection control, assessment, mosul city

Procedia PDF Downloads 82

Authors: Kasbparast Mehdi, Hassani Zainab

Abstract:

Aim: The purpose of this research was to investigate the effectiveness of therapeutical sensorimotor exercise. The statistical population of women who were diagnosed with patellofemoral pain syndrome by a doctor and were between the ages of 35 and 45 and registered for the first time in a sports club in the 4th district of Tehran, 30 people by random sampling and according to The include and exclude criteria were selected and divided into 2 equal control and experimental and homogeneous groups (in terms of height, weight and BMI).In both control and experimental groups, the pain was measured using a Visual Analog Scale(VAS) functionality was measured using the step-down test and quality of life was measured using a World Health Organization Quality of Life Scale (WHOQOL-BREF) (pre-test). Then, only the experimental group performed sensorimotor exercises for 12 weeks and 3 sessions each week, a total of 24 sessions and each session for 1 hour, and during this period, the control group only continued their daily activities. After the end of the training period, the desired factors were evaluated again (post-test) in the same way as the pre-test was done for them (experimental group and control group), with the same quality. Findings: The statistical results showed that in the experimental group, the amount of pain, function and quality of life had a statistical improvement (P≤0.05). Conclusion: In general conclusion, it can be stated that using sensorimotor exercises not only improved functionality and quality of life but also reduced the amount of pain in people with patellofemoral pain syndrome.

Keywords: pain, PFPS, sensori motor training, functionality

Procedia PDF Downloads 75

Authors: Jennifer Bradley

Abstract:

With opportunities to live and work abroad on the rise, effective preparation and support for international employees needs to be addressed within the work-site. International employees must build new habits, routines and social networks in an unfamiliar culture. Culture shock typically occurs within the first year and can affect both physical and psychological health. Employers have the opportunity to support staff through the adaptation process and foster healthy habits and routines. Cross-cultural training that includes a combination of instructional teaching, cultural experiences, and practice, is shown to increase the international employee adaptation process. However, little evidence demonstrates that organizations provide all of these aspects for international employees. The occupational therapy practitioner (OTP) offers a unique perspective focusing on the employee transactional relationship and engagement of meaningful occupations to enhance and enable participation in roles, habits and routines within new cultural contexts. This paper examines one such program developed and implemented by an OTP at the New England Center for Children, in Abu Dhabi, United Arab Emirates. The effectiveness of the program was assessed via participant feedback and concluded that an international employee support program that focuses on a variety of meaningful experiences and knowledge can empower employees to navigate healthy practices, develop habits and routines, and foster positive inter-cultural relationships in the organization and community.

Keywords: occupational therapy practitioner, cross cultural training, international employee health, international employee support

Procedia PDF Downloads 159

Authors: Kebede Mairie, Getahun Belete, Mitike Abeba

Abstract:

Background: Leishmania aethiopica is a peculiar parasite causing cutaneous leishmaniasis in Ethiopia and its mainstay treatment is Sodium Stibogluconate. However, its treatment outcome in Ethiopia is not well documented. Objectives: To determine the treatment outcome of admitted cutaneous leishmaniasis patients and its associated factors in Addis Ababa, Ethiopia. Methods: A retrospective study was conducted from 1st November 2021 to 30th March 2022. Medical records of all cutaneous leishmaniasis-diagnosed and admitted patients who received parenteral sodium stibogluconate at All Africa Leprosy Rehabilitation and Training Center (ALERT) hospital, the main Leishmania treatment center in Ethiopia from July 2011 to September 2021 were reviewed. Results: A total of 827 charts of admitted cases from July 2011 to September 2021 were retrieved, but 667 (80.65%) were reviewed. Improvement in the treatment outcome was recorded in 93.36 % in the first course of SSG treatment and 96.23%, 94.62%, and 96.97% subsequently in the second, third and fourth treatment courses, respectively. Female gender and diffuse cutaneous leishmaniasis were the two predictive determinants in the treatment of cutaneous leishmaniasis. Conclusion: The study shows that parenteral sodium stibogluconate therapy treats hospitalized cutaneous leishmaniasis patients well, with female gender and diffuse cutaneous leishmaniasis having poor outcomes suggesting the need for a different approach for diffuse cutaneous leishmaniasis patients.

Keywords: cutaneous leishmaniasis, leishmania aethiopica, sodium stibogluconate, diffuse cutaneous leishmaniasis, pentostam

Procedia PDF Downloads 77

Authors: Dedjo Yao Simon, Takahiro Saito, Norikazu Inuzuka, Ikuo Sugiyama

Abstract:

The megacity of Abidjan is a coastal urban area where the number of floods reported and the associated impacts are on a rapid increase due to climate change, an uncontrolled urbanization, a rapid population increase, a lack of flood disaster mitigation and citizens’ awareness. The objective of this research is to reduce in the short and long term period, the human and socio-economic impact of the flood. Hydrological simulation is applied on free of charge global spatial data (digital elevation model, satellite-based rainfall estimate, landuse) to identify the flood-prone area and to map the risk of flood. A direct interview to a sample residents is used to validate the simulation results. Then a mobile application (Flood Locator) is prototyped to disseminate the risk information to the citizen. In addition, a value capture strategy is proposed to mobilize financial resource for disaster risk reduction (DRRf) to reduce the impact of the flood. The town of Cocody in Abidjan is selected as a case study area to implement this research. The mapping of the flood risk reveals that population living in the study area is highly vulnerable. For a 5-year flood, more than 60% of the floodplain is affected by a water depth of at least 0.5 meters; and more than 1000 ha with at least 5000 buildings are directly exposed. The risk becomes higher for a 50 and 100-year floods. Also, the interview reveals that the majority of the citizen are not aware of the risk and severity of flooding in their community. This shortage of information is overcome by the Flood Locator and by an urban flood database we prototype for accumulate flood data. Flood Locator App allows the users to view floodplain and depth on a digital map; the user can activate the GPS sensor of the mobile to visualize his location on the map. Some more important additional features allow the citizen user to capture flood events and damage information that they can send remotely to the database. Also, the disclosure of the risk information could result to a decrement (-14%) of the value of properties locate inside floodplain and an increment (+19%) of the value of property in the suburb area. The tax increment due to the higher tax increment in the safer area should be captured to constitute the DRRf. The fund should be allocated to the reduction of flood risk for the benefit of people living in flood-prone areas. The flood prevention system discusses in this research will minimize in the short and long term the direct damages in the risky area due to effective awareness of citizen and the availability of DRRf. It will also contribute to the growth of the urban area in the safer zone and reduce human settlement in the risky area in the long term. Data accumulated in the urban flood database through the warning app will contribute to regenerate Abidjan towards the more resilient city by means of risk avoidable landuse in the master plan.

Keywords: abidjan, database, flood, geospatial techniques, risk communication, smartphone, value capture

Procedia PDF Downloads 290

Authors: Aleksandra Czubek

Abstract:

As AI systems increasingly shape content creation, existing copyright frameworks face significant challenges in determining liability for AI-generated outputs. Current legal discussions largely focus on who bears responsibility for infringing works, be it developers, users, or entities benefiting from AI outputs. This paper introduces a novel concept of algorithmic obligations, proposing that AI developers be subject to proactive duties that ensure their models prevent copyright infringement before it occurs. Building on principles of obligations law traditionally applied to human actors, the paper suggests a shift from reactive enforcement to proactive legal requirements. AI developers would be legally mandated to incorporate copyright-aware mechanisms within their systems, turning optional safeguards into enforceable standards. These obligations could vary in implementation across international, EU, UK, and U.S. legal frameworks, creating a multi-jurisdictional approach to copyright compliance. This paper explores how the EU’s existing copyright framework, exemplified by the Copyright Directive (2019/790), could evolve to impose a duty of foresight on AI developers, compelling them to embed mechanisms that prevent infringing outputs. By drawing parallels to GDPR’s “data protection by design,” a similar principle could be applied to copyright law, where AI models are designed to minimize copyright risks. In the UK, post-Brexit text and data mining exemptions are seen as pro-innovation but pose risks to copyright protections. This paper proposes a balanced approach, introducing algorithmic obligations to complement these exemptions. AI systems benefiting from text and data mining provisions should integrate safeguards that flag potential copyright violations in real time, ensuring both innovation and protection. In the U.S., where copyright law focuses on human-centric works, this paper suggests an evolution toward algorithmic due diligence. AI developers would have a duty similar to product liability, ensuring that their systems do not produce infringing outputs, even if the outputs themselves cannot be copyrighted. This framework introduces a shift from post-infringement remedies to preventive legal structures, where developers actively mitigate risks. The paper also breaks new ground by addressing obligations surrounding the training data of large language models (LLMs). Currently, training data is often treated under exceptions such as the EU’s text and data mining provisions or U.S. fair use. However, this paper proposes a proactive framework where developers are obligated to verify and document the legal status of their training data, ensuring it is licensed or otherwise cleared for use. In conclusion, this paper advocates for an obligations-centered model that shifts AI-related copyright law from reactive litigation to proactive design. By holding AI developers to a heightened standard of care, this approach aims to prevent infringement at its source, addressing both the outputs of AI systems and the training processes that underlie them.

Keywords: ip, technology, copyright, data, infringement, comparative analysis

Procedia PDF Downloads 18

Authors: Michael A. Kraus, Navid Pourmoghaddam, Martin Botz, Jens Schneider, Geralt Siebert

Abstract:

Fragmentation analysis of tempered glass gives insight into the quality of the tempering process and defines a certain degree of safety as well. Different standard such as the European EN 12150-1 or the American ASTM C 1048/CPSC 16 CFR 1201 define a minimum number of fragments required for soda-lime safety glass on the basis of fragmentation test results for classification. This work presents an approach for the glass breakage pattern prediction using a Voronoi Tesselation over Random Fields. The random Voronoi tessellation is trained with and validated against data from several breakage patterns. The fragments in observation areas of 50 mm x 50 mm were used for training and validation. All glass specimen used in this study were commercially available soda-lime glasses at three different thicknesses levels of 4 mm, 8 mm and 12 mm. The results of this work form a Bayesian framework for the training and prediction of breakage patterns of tempered soda-lime glass using a Voronoi Random Field Tesselation. Uncertainties occurring in this process can be well quantified, and several statistical measures of the pattern can be preservation with this method. Within this work it was found, that different Random Fields as basis for the Voronoi Tesselation lead to differently well fitted statistical properties of the glass breakage patterns. As the methodology is derived and kept general, the framework could be also applied to other random tesselations and crack pattern modelling purposes.

Keywords: glass breakage predicition, Voronoi Random Field Tessellation, fragmentation analysis, Bayesian parameter identification

Procedia PDF Downloads 160

Authors: Subir Gupta, Subhas Ganguly

Abstract:

In this paper, we demonstrate the new area of application of image processing in metallurgical images to develop the more opportunity for structure-property correlation based approaches of alloy design. The present exercise focuses on the development of image processing tools suitable for phrase segmentation, grain boundary detection and recognition of micro-constituents in SEM micrographs of ferrite and pearlite steels. A comprehensive data of micrographs have been experimentally developed encompassing the variation of ferrite and pearlite volume fractions and taking images at different magnification (500X, 1000X, 15000X, 2000X, 3000X and 5000X) under scanning electron microscope. The variation in the volume fraction has been achieved using four different plain carbon steel containing 0.1, 0.22, 0.35 and 0.48 wt% C heat treated under annealing and normalizing treatments. The obtained data pool of micrographs arbitrarily divided into two parts to developing training and testing sets of micrographs. The statistical recognition features for ferrite and pearlite constituents have been developed by learning from training set of micrographs. The obtained features for microstructure pattern recognition are applied to test set of micrographs. The analysis of the result shows that the developed strategy can successfully detect the micro constitutes across the wide range of magnification and variation of volume fractions of the constituents in the structure with an accuracy of about +/- 5%.

Keywords: SEM micrograph, metallurgical image processing, ferrite pearlite steel, microstructure

Procedia PDF Downloads 199

Authors: Sajee A. Gamage, Champa J. Wijesinghe, Patricia Burtner, Ananda R. Wickremasinghe

Abstract:

Background: Teachers working in the context of special education have an enormous responsibility of enhancing performance skills of children in their classroom settings. Fine Motor Skills (FMS) are essential functional skills for children to gain independence in Activities of Daily Living. Children with Down Syndrome (DS) are predisposed to specific challenges due to deficits in FMS. This study is aimed to determine the teachers’ experience on improving FMS of children with DS in the context of special education of Southern Province, Sri Lanka. Methodology: A cross-sectional study was conducted among all consenting eligible teachers (n=147) working in the context of special education in government schools of Southern Province of Sri Lanka. A self-administered questionnaire was developed based on literature and expert opinion to assess teachers’ experience regarding deficits of FMS, limitations of classroom activity performance and barriers to improve FMS of children with DS. Results: Approximately 93% of the teachers were females with a mean age ( ± SD) of 43.1 ( ± 10.1) years. Thirty percent of the teachers had training in special educationand 83% had children with DS in their classrooms. Major deficits of FMS reported were deficits in grasping (n=116; 79%), in-hand manipulation (n=103; 70%) and bilateral hand use (n=99; 67.3%). Paperwork (n=70; 47.6%), painting (n=58; 39.5%), scissor work (n=50; 34.0%), pencil use for writing (n=45; 30.6%) and use of tools in the classroom (n=41; 27.9%) were identified as major classroom performance limitations of children with DS. Parental factors (n=67; 45.6%), disease specific characteristics (n=58; 39.5%) and classroom factors (n=36; 24.5%), were identified as major barriers to improve FMS in the classroom setting. Lack of resources and standard tools, social stigma and late school admission were also identified as barriers to FMS training. Eighty nine percent of the teachers informed that training fine motor activities in a special education classroom was more successful than work with normal classroom setting. Conclusion: Major areas of FMS deficits were grasping, in-hand manipulation and bilateral hand use; classroom performance limitations included paperwork, painting and scissor work of children with DS. Teachers recommended regular practice of fine motor activities according to individual need. Further research is required to design a culturally specific FMS assessment tool and intervention methods to improve FMS of children with DS in Sri Lanka.

Keywords: classroom activities, Down syndrome, experience, fine motor skills, special education, teachers

Procedia PDF Downloads 153

Authors: Mohammed Alateeq, Abdulaziz Alrshoud

Abstract:

Objectives: To examine the attitude and practice of family physicians in giving smoking cessation advice at King Abdul-Aziz Medical City for National Guard, Riyadh. Methods: Cross sectional study using validated self-reported questionnaire that distributed to all family physicians and primary health care doctors at the four main family medicine and primary health care centers, KAMC, Riyadh. Results: 73 physicians are contributed in this study. 28 (38.4%) physicians were from (KASHM ALAN) clinic, 26 (35.6%) physicians were from (UM ALHAMAM) Clinic. 13 (17.8%) physicians were from (ISKAN) clinic. 6 (8.2%) physicians were from the Employee Health Clinic. 73 (100%) of the target population agreed that giving brief smoking cessation advice is part of their duties. 67 (91.7%) agreed that Presence of hospital guidelines and special clinics for smoking cessation will encourage them to provide advice. Only 5 (6.84%) received training courses (1-4 weeks) in smoking cessation interventions. Conclusion: Most of the target population agreed that brief smoking cessation advice is part of their duties. Also, they agreed that Presence of hospital guidelines and special clinics for smoking cessation will encourage them to provide advice although most of them did not received a formal training in smoking cessation advice.

Keywords: advice, attitude, cessation, family physicians, smoking

Procedia PDF Downloads 291

Authors: Chia-Yi Liu, Bo-Bin Xiao, Wen-Bin Lin, Hsiang-Ning Wu, Liang-Hsun Huang

Abstract:

In order to reduce the risk of radiation damage that personnel may suffer during operations in the radiation environment, the use of automated guided vehicles to assist or replace on-site personnel in the radiation environment has become a key technology and has become an important trend. In this paper, we demonstrate our proof of concept for autonomous self-learning radiation source searcher in an unknown environment without a map. The research uses GPU version of Asynchronous Advantage Actor-Critic network (GA3C) of deep reinforcement learning to search for radiation sources. The searcher network, based on GA3C architecture, has self-directed learned and improved how search the anomalous radiation source by training 1 million episodes under three simulation environments. In each episode of training, the radiation source position, the radiation source intensity, starting position, are all set randomly in one simulation environment. The input for searcher network is the fused data from a 2D laser scanner and a RGB-D camera as well as the value of the radiation detector. The output actions are the linear and angular velocities. The searcher network is trained in a simulation environment to accelerate the learning process. The well-performance searcher network is deployed to the real unmanned vehicle, Dashgo E2, which mounts LIDAR of YDLIDAR G4, RGB-D camera of Intel D455, and radiation detector made by Institute of Nuclear Energy Research. In the field experiment, the unmanned vehicle is enable to search out the radiation source of the 18.5MBq Na-22 by itself and avoid obstacles simultaneously without human interference.

Keywords: deep reinforcement learning, GA3C, source searching, source detection

Procedia PDF Downloads 114

Authors: Balimu Mwiya, Mundia Muya, Chabota Kaliba, Peter Mukalula

Abstract:

Factors affecting construction unit cost vary depending on a country’s political, economic, social and technological inclinations. Factors affecting construction costs have been studied from various perspectives. Analysis of cost factors requires an appreciation of a country’s practices. Identified cost factors provide an indication of a country’s construction economic strata. The purpose of this paper is to identify the essential factors that affect unit cost estimation and their breakdown using artificial neural networks. Twenty-five (25) identified cost factors in road construction were subjected to a questionnaire survey and employing SPSS factor analysis the factors were reduced to eight. The 8 factors were analysed using the neural network (NN) to determine the proportionate breakdown of the cost factors in a given construction unit rate. NN predicted that political environment accounted 44% of the unit rate followed by contractor capacity at 22% and financial delays, project feasibility, overhead and profit each at 11%. Project location, material availability and corruption perception index had minimal impact on the unit cost from the training data provided. Quantified cost factors can be incorporated in unit cost estimation models (UCEM) to produce more accurate estimates. This can create improvements in the cost estimation of infrastructure projects and establish a benchmark standard to assist the process of alignment of work practises and training of new staff, permitting the on-going development of best practises in cost estimation to become more effective.

Keywords: construction cost factors, neural networks, roadworks, Zambian construction industry

Procedia PDF Downloads 364

Authors: Bara Yousef

Abstract:

The development of independency and functional participation is an important therapeutic goal for many children with cerebral palsy,They was many therapeutic approach have been used for treatment those children like neurodevelopment treatment, balance training strengthening and stretching exercise. More recently, therapy for children with cerebral palsy has focused on achieving functional goals using task-oriented interventions and summer camping model, which focus on activities that relevant and meaningful to the child, to learn more efficient and effective motor skills. We explore the effectiveness of using functional rehabilitation comparing with regular rehabilitation among 40 Saudi children with cerebral palsy in pediatric unit at Sultan Bin Abdul Aziz Humanitarian City-Ksa ,where 20 children randomly assign in control group who received rehabilitation based on regular therapy approach and other 20 children assign on experiment group who received rehabilitation based on functional therapy approach with an average of 45min OT treatment and 45 min PT treatment- daily within a period of 6 week. Our finding reported that children in experiment group has improved in gross motor function with an average from 49.4 to 57.6 based on GMFM 66 as primary outcome measure and improved in WeeFIM with an average from 52 to 62 while children in control group has improved with an average from 48.4 to 53.7 in GMFM and from 53 to and 58 in WeeFIM. Consequently, there has been growing interest in determining the effects of functional training programs as promising approach for these children.

Keywords: Cerebral Palsy (CP), gross motor function measure (GMFM66), pediatric Functional Independent Measure (WeeFIM), rehabilitation, disability

Procedia PDF Downloads 381

Authors: Vinicius G. Goecks, Gregory M. Gremillion, William D. Nothwang

Abstract:

Modern approaches to train intelligent agents rely on prolonged training sessions, high amounts of input data, and multiple interactions with the environment. This restricts the application of these learning algorithms in robotics and real-world applications, in which there is low tolerance to inadequate actions, interactions are expensive, and real-time processing and action are required. This paper addresses this issue introducing CyberSteer, a novel approach to efficiently design intrinsic reward functions based on human intention to guide deep reinforcement learning agents with no environment-dependent rewards. CyberSteer uses non-expert human operators for initial demonstration of a given task or desired behavior. The trajectories collected are used to train a behavior cloning deep neural network that asynchronously runs in the background and suggests actions to the deep reinforcement learning module. An intrinsic reward is computed based on the similarity between actions suggested and taken by the deep reinforcement learning algorithm commanding the agent. This intrinsic reward can also be reshaped through additional human demonstration or critique. This approach removes the need for environment-dependent or hand-engineered rewards while still being able to safely shape the behavior of autonomous robotic agents, in this case, based on human intention. CyberSteer is tested in a high-fidelity unmanned aerial vehicle simulation environment, the Microsoft AirSim. The simulated aerial robot performs collision avoidance through a clustered forest environment using forward-looking depth sensing and roll, pitch, and yaw references angle commands to the flight controller. This approach shows that the behavior of robotic systems can be shaped in a reduced amount of time when guided by a non-expert human, who is only aware of the high-level goals of the task. Decreasing the amount of training time required and increasing safety during training maneuvers will allow for faster deployment of intelligent robotic agents in dynamic real-world applications.

Keywords: human-robot interaction, intelligent robots, robot learning, semisupervised learning, unmanned aerial vehicles

Procedia PDF Downloads 259

Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

Abstract:

This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: visual search, deep learning, convolutional neural network, machine learning

Procedia PDF Downloads 215

Authors: Yu-Chu Yeh, WeiChin Hsu, Chih-Yen Chang

Abstract:

Substitution, combination, modification, putting to other uses, elimination, and rearrangement (SCAMPER) has been regarded as an effective technique that provides a structured way to help people to produce creative ideas and solutions. Although some neuroscience studies regarding creativity training have been conducted, no study has focused on SCAMPER. This study therefore aimed at examining whether the learning of SCAMPER through video tutorials would result in alternations of neural activation. Thirty college students were randomly assigned to the experimental group or the control group. The experimental group was requested to watch SCAMPER videos, whereas the control group was asked to watch natural-scene videos which were regarded as neutral stimulating materials. Each participant was brain scanned in a Functional magnetic resonance imaging (fMRI) machine while undertaking a creativity test before and after watching the videos. Furthermore, a two-way ANOVA was used to analyze the interaction between groups (the experimental group; the control group) and tasks (C task; M task; X task). The results revealed that the left precuneus significantly activated in the interaction of groups and tasks, as well as in the main effect of group. Furthermore, compared with the control group, the experimental group had greater activation in the default mode network (left precuneus and left inferior parietal cortex) and the motor network (left postcentral gyrus and left supplementary area). The findings suggest that the SCAMPER training may facilitate creativity through the stimulation of the default mode network and the motor network.

Keywords: creativity, default mode network, neural activation, SCAMPER

Procedia PDF Downloads 100

Authors: Gikunda Aloise, Mjema Saida, Barasa Herbert, Wanyungu John, Kimani Maureen

Abstract:

Background: Community Health Workforce (CHW) is health care providers at the community level (Level 1) and serves as a bridge between the community and the formal healthcare system. This human resource has enormous potential to extend healthcare services and ensures that the vulnerable, marginalized, and hard-to-reach populations have access to quality healthcare services at the community and primary health facility levels. However, these cadres are neither recognized, remunerated, nor in most instances, registered in a master list. Management and supervision of CHWs is not easy if their individual demographics, training capacity and incentives is not well documented through a centralized registry. Description: In February 2022, Amref supported the Kenya Ministry of Health in developing a community health workforce database called Community Health Workers Master List Registry (CHWML), which is hosted in Kenya Health Information System (KHIS) tracker. CHW registration exercise was through a sensitization meeting conducted by the County Community Health Focal Person for the Sub-County Community Health Focal Person and Community Health Assistants who uploaded information on individual demographics, training undertaken and incentives received by CHVs. Care was taken to ensure compliance with Kenyan laws on the availability and use of personal data as prescribed by the Data Protection Act, 2019 (DPA). Results and lessons learnt: By June 2022, 80,825 CHWs had been registered in the system; 78,174 (96%) CHVs and 2,636 (4%) CHAs. 25,235 (31%) are male, 55,505 (68%) are female & 85 (1%) are transgender. 39,979. (49%) had secondary education and 2500 (3%) had no formal education. Only 27 641 (34%) received a monthly stipend. 68,436 CHVs (85%) had undergone basic training. However, there is a need to validate the data to align with the current situation in the counties. Conclusions/Next steps: The use of CHWML will unlock opportunities for building more resilient and sustainable health systems and inform financial planning, resource allocation, capacity development, and quality service delivery. The MOH will update the CHWML guidelines in adherence to the data protection act which will inform standard procedures for maintaining, updating the registry and integrate Community Health Workforce registry with the HRH system.

Keywords: community health registry, community health volunteers (CHVs), community health workers masters list (CHWML), data protection act

Procedia PDF Downloads 140

Authors: Ande Nesmith

Abstract:

There is an ongoing shortage of foster parents worldwide to take on a growing population of children in need of out-of-home care. Currently, resources are primarily aimed at recruitment rather than retention. Retention rates are extraordinarily low, especially in the first two years of fostering. Qualitative interviews with 19 foster parents averaging 20 years of service provided insight into the challenges they faced and how they overcame them. Thematic analysis of interview transcripts identified sources of stress and resiliency. Key stressors included lack of support and responsiveness from the children’s social workers, false maltreatment allegations, and secondary trauma from children’s destructive behaviors and emotional dysregulation. Resilient parents connected with other foster parents for support, engaged in creative problem-solving, recognized that positive feedback from children usually arrives years later, and through training, understood the neurobiological impact of trauma on child behavior. Recommendations include coordinating communication between the foster parent licensing agency social workers and the children’s social workers, creating foster parent support networks and mentoring, and continuous training on trauma including effective parenting strategies. Research is needed to determine whether these resilience indicators in fact lead to long-term retention. Policies should include a mechanism to develop a cohesive line of communication and connection between foster parents and the children’s social workers as well as their respective agencies.

Keywords: foster care stability, foster parent burnout, foster parent resiliency, foster parent retention, trauma-informed fostering

Procedia PDF Downloads 350

Authors: Keshav Raj Dhakal, Jit Bahadur Rokaya Chhetri

Abstract:

The Micro Hydro (MH) is one of the successful technology in Rural Nepal. Out of 75 district, 59 districts have installed 1287 MH projects with a total capacity of 24 Mega Watt (MW). Now, the challenge is how to sustain them. Dolakha is a prominent district for sustainable endues of power to sustain the MH projects. A total of 37 MH projects have been constructed with producing 886 Kilo Watt (KW) of energy in the district. This study traces out the impact of earthquake in MH sector in Dolakha district. It shows that 59 % of projects have been affected by devastating earthquake in April and May, 2015 where 29 % are completely damaged. Most of the damages are in civil structures like Penstock, forebay, power house, Canal, Intake. Transmission and distribution line have been partially damaged. This paper analysis failure of the civil structural component of MH projects and its financial consequence to the community. This study recommends that a disaster impact assessment is essential before construction of MH projects.

Keywords: micro hydro, earthquake, structural failure, financial consequence

Procedia PDF Downloads 205

Authors: Sonja Goegele, Petra Kletzenbauer

Abstract:

The FH JOANNEUM- a university of applied sciences based in Austria - cooperates in teaching and research with well-known international universities and thus aims to foster so-called strategic partnerships. The exchange of university lecturers and other faculty members is a way to achieve and secure strategic company goals, in which excellent research and teaching play a central role in order to improve both the development of academics and administration. Thanks to mobility not only the university but also the involved people truly benefit in their professional development which can be seen on several levels: increased foreign language proficiency, excellent networking possibilities within the scientific community as well as reinforced didactic competencies in the form of different teaching and learning methodologies. The paper discusses mobility in the light of the university’s strategic paper entitled ‘Hands on 2022’ by presenting results from an empirical research study among faculty members who participate in exchange programmes on a regular basis. In the form of an online questionnaire, mobility was discussed from different angles such as networking, collaborative research, professional training for academics and the overall impact of the exchange within and outside the organization. From the findings, it can be concluded that mobility is an asset for any university. However, keeping in constant dialogue with partner universities requires more than the purpose of the exchange itself. Building rapport and keeping a relationship of trust are challenges that need to be addressed more closely in order to run successful mobility programmes. Best Practice examples should highlight the importance of mobility as a vital initiative to transfer disciplines.

Keywords: higher education, internationalization, mobility, strategic partnerships

Procedia PDF Downloads 139

Authors: B. Lee, B. H. Choi

Abstract:

The loss of safety for reservoirs brought about by climate change and facility aging leads to reservoir failures, which results in the loss of lives and property damage in downstream areas. Therefore, it is necessary to provide a reservoir failure alert system for downstream residents to detect the early signs of failure (with sensors) in real-time and perform safety management to prevent and minimize possible damage. 10 case studies were carried out to verify the water level management criteria of four levels (attention, caution, alert, serious). Peak changes in water level data were analysed. The results showed that ‘Caution’ and ‘Alert’ were closed to 33% and 66% of difference in level between flood water level and full water level. Therefore, it is adequate to use initial water level management criteria of reservoir failure alert system for the first year. Acknowledgment: This research was supported by a grant (2017-MPSS31-002) from 'Supporting Technology Development Program for Disaster Management' funded by the Ministry of the Interior and Safety(MOIS)

Keywords: alert system, management criteria, reservoir failure, sensor

Procedia PDF Downloads 200

Authors: Rafat Alwazna

Abstract:

Translation teaching is a particular activity that includes translators and interpreters training either inside or outside institutionalised settings, such as universities. It can also serve as a means of teaching other fields, such as foreign languages. Translation teaching began in the twentieth century. Teachers of translation hold the responsibilities of educating students, developing their translation competence and training them to be professional translators. The activity of translation teaching involves various tasks, including curriculum design, course delivery, material writing as well as application and implementation. The present paper addresses translation teaching during COVID-19 outbreak, seeking to find out the challenges encountered by translation teachers in online translation teaching and the discoveries/solutions arrived at to resolve them. The paper makes use of a comprehensive questionnaire, containing closed-ended and open-ended questions to elicit both quantitative as well as qualitative data from about sixty translation teachers who have been teaching translation at BA and MA levels during COVID-19 outbreak. The data shows that about 40% of the participants evaluate their online translation teaching experience during COVID-19 outbreak as enjoyable and exhilarating. On the contrary, no participant has evaluated his/her online translation teaching experience as being not good, nor has any participant evaluated his/her online translation teaching experience as being terrible. The data also presents that about 23.33% of the participants evaluate their online translation teaching experience as very good, and the same percentage applies to those who evaluate their online translation teaching experience as good to some extent. Moreover, the data indicates that around 13.33% of the participants evaluate their online translation teaching experience as good. The data also demonstrates that the majority of the participants have encountered obstacles in online translation teaching and have concurrently proposed solutions to resolve them.

Keywords: online translation teaching, electronic learning platform, COVID-19 outbreak, challenges, solutions

Procedia PDF Downloads 223

Authors: Ru-Yin Yang, Chi Wu, Cheng-Yu Tsai, Yin-Tzu Lin, Wen-Te Liu

Abstract:

Background: Sleep breathing disorder (SDB) is a condition demonstrated by recurrent episodes of the airway obstruction leading to intermittent hypoxia and quality fragmentation during sleep time. However, the procedures for SDB severity examination remain complicated and costly. Objective: The objective of this study is to establish a simplified examination method for SDB by the respiratory impendence pattern sensor combining the signal processing and machine learning model. Methodologies: We records heart rate variability by the electrocardiogram and respiratory pattern by impendence. After the polysomnography (PSG) been done with the diagnosis of SDB by the apnea and hypopnea index (AHI), we calculate the episodes with the absence of flow and arousal index (AI) from device record. Subjects were divided into training and testing groups. Neuron network was used to establish a prediction model to classify the severity of the SDB by the AI, episodes, and body profiles. The performance was evaluated by classification in the testing group compared with PSG. Results: In this study, we enrolled 66 subjects (Male/Female: 37/29; Age:49.9±13.2) with the diagnosis of SDB in a sleep center in Taipei city, Taiwan, from 2015 to 2016. The accuracy from the confusion matrix on the test group by NN is 71.94 %. Conclusion: Based on the models, we established a prediction model for SDB by means of the wearable sensor. With more cases incoming and training, this system may be used to rapidly and automatically screen the risk of SDB in the future.

Keywords: sleep breathing disorder, apnea and hypopnea index, body parameters, neuron network

Procedia PDF Downloads 150

Authors: Yehjune Heo

Abstract:

Fingerprint Anti-Spoofing approaches have been actively developed and applied in real-world applications. One of the main problems for Fingerprint Anti-Spoofing is not robust to unseen samples, especially in real-world scenarios. A possible solution will be to generate artificial, but realistic fingerprint samples and use them for training in order to achieve good generalization. This paper contains experimental and comparative results with currently popular GAN based methods and uses realistic synthesis of fingerprints in training in order to increase the performance. Among various GAN models, the most popular StyleGAN is used for the experiments. The CNN models were first trained with the dataset that did not contain generated fake images and the accuracy along with the mean average error rate were recorded. Then, the fake generated images (fake images of live fingerprints and fake images of spoof fingerprints) were each combined with the original images (real images of live fingerprints and real images of spoof fingerprints), and various CNN models were trained. The best performances for each CNN model, trained with the dataset of generated fake images and each time the accuracy and the mean average error rate, were recorded. We observe that current GAN based approaches need significant improvements for the Anti-Spoofing performance, although the overall quality of the synthesized fingerprints seems to be reasonable. We include the analysis of this performance degradation, especially with a small number of samples. In addition, we suggest several approaches towards improved generalization with a small number of samples, by focusing on what GAN based approaches should learn and should not learn.

Keywords: anti-spoofing, CNN, fingerprint recognition, GAN

Procedia PDF Downloads 184

Authors: Tracey Bone

Abstract:

Deaf inmates, whose first or preferred language is a Signed Language, experience barriers to accessing the necessary two-way communication with correctional staff, and the educational and social programs that will enhance their eligibility for conditional release from the federal prison system in Canada. The development of visual content to enhance the knowledge and skill development of correctional staff is a contemporary strategy intended to significantly improve the correctional experience for deaf inmates. This presentation reports on the development of two distinct training videos created to enhance staff’s understanding of the needs of deaf inmates; one a two-part simulation of an interaction with a deaf inmate, the second an interview with a deaf academic. Part one of video one demonstrates the challenges and misunderstandings inherent in communicating across languages without a qualified sign language interpreter; the second part demonstrates the ease of communication when communication needs are met. Video two incorporates the experiences of a deaf academic to provide the cultural grounding necessary to educate staff in the unique experiences associated with being a visual language user. Lack of staff understanding or awareness of deaf culture and language must not be acceptable reasons for the inadequate treatment of deaf visual language users in federal prisons. This paper demonstrates a contemporary approach to meeting the human rights and needs of this unique and often ignored inmate subpopulation. The deaf community supports this visual approach to enhancing staff understanding of the unique needs of this population. A study of its effectiveness is currently underway.

Keywords: accommodations, American Sign Language (ASL), deaf inmates, sensory deprivation

Procedia PDF Downloads 149

Authors: Erva Akin

Abstract:

– The use of copyrighted material for machine learning purposes is a challenging issue in the field of artificial intelligence (AI). While machine learning algorithms require large amounts of data to train and improve their accuracy and creativity, the use of copyrighted material without permission from the authors may infringe on their intellectual property rights. In order to overcome copyright legal hurdle against the data sharing, access and re-use of data, the use of copyrighted material for machine learning purposes may be considered permissible under certain circumstances. For example, if the copyright holder has given permission to use the data through a licensing agreement, then the use for machine learning purposes may be lawful. It is also argued that copying for non-expressive purposes that do not involve conveying expressive elements to the public, such as automated data extraction, should not be seen as infringing. The focus of such ‘copy-reliant technologies’ is on understanding language rules, styles, and syntax and no creative ideas are being used. However, the non-expressive use defense is within the framework of the fair use doctrine, which allows the use of copyrighted material for research or educational purposes. The questions arise because the fair use doctrine is not available in EU law, instead, the InfoSoc Directive provides for a rigid system of exclusive rights with a list of exceptions and limitations. One could only argue that non-expressive uses of copyrighted material for machine learning purposes do not constitute a ‘reproduction’ in the first place. Nevertheless, the use of machine learning with copyrighted material is difficult because EU copyright law applies to the mere use of the works. Two solutions can be proposed to address the problem of copyright clearance for AI training data. The first is to introduce a broad exception for text and data mining, either mandatorily or for commercial and scientific purposes, or to permit the reproduction of works for non-expressive purposes. The second is that copyright laws should permit the reproduction of works for non-expressive purposes, which opens the door to discussions regarding the transposition of the fair use principle from the US into EU law. Both solutions aim to provide more space for AI developers to operate and encourage greater freedom, which could lead to more rapid innovation in the field. The Data Governance Act presents a significant opportunity to advance these debates. Finally, issues concerning the balance of general public interests and legitimate private interests in machine learning training data must be addressed. In my opinion, it is crucial that robot-creation output should fall into the public domain. Machines depend on human creativity, innovation, and expression. To encourage technological advancement and innovation, freedom of expression and business operation must be prioritised.

Keywords: artificial intelligence, copyright, data governance, machine learning

Procedia PDF Downloads 83

Authors: W. S. Hsu, Y. Chiang, Y. S. Tseng, J. R. Wang, C. Shih, S. W. Chen

Abstract:

Kuosheng nuclear power plant (NPP) is a BWR/6 plant in Taiwan. There is more concern for the safety of NPPs in Taiwan after Japan Fukushima NPP disaster occurred. Hence, in order to estimate the safety of Kuosheng NPP spent fuel pool (SFP), by using TRACE, MELCOR, and SNAP codes, the safety analysis of Kuosheng NPP SFP was performed. There were two main steps in this research. First, the Kuosheng NPP SFP models were established. Second, the transient analysis of Kuosheng SFP was done by TRACE and MELCOR under the cooling system failure condition (Fukushima-like condition). The results showed that the calculations of MELCOR and TRACE were very similar in this case, and the fuel uncover happened roughly at 4^th day after the failure of cooling system. The above results indicated that Kuosheng NPP SFP may be unsafe in the case of long-term SBO situation. In addition, future calculations were needed to be done by the other codes like FRAPTRAN for the cladding calculations.

Keywords: TRACE, MELCOR, SNAP, spent fuel pool

Procedia PDF Downloads 331

Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

Abstract:

The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.

Keywords: structural health monitoring, dynamic response, artificial neural network, radial basis function network, genetic algorithm

Procedia PDF Downloads 304