Search results for: automated diagnoses

Authors: Amir Nazemi, Milad Ramezankhani, Marian Kӧrber, Abbas S. Milani

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The excellent strength to weight ratio of woven fabric composites, along with their high formability, is one of the primary design parameters defining their increased use in modern manufacturing processes, including those in aerospace and automotive. However, for emerging automated preform processes under the smart manufacturing paradigm, complex geometries of finished components continue to bring several challenges to the designers to cope with manufacturing defects on site. Wrinklinge. g. is a common defectoccurring during the forming process and handling of semi-finished textile composites. One of the main reasons for this defect is the weak bending stiffness of fibers in unconsolidated state, causing excessive relative motion between them. Further challenges are represented by the automated handling of large-area fiber blanks with specialized gripper systems. For fabric composites forming simulations, the finite element (FE)method is a longstanding tool usedfor prediction and mitigation of manufacturing defects. Such simulations are predominately meant, not only to predict the onset, growth, and shape of wrinkles but also to determine the best processing condition that can yield optimized positioning of the fibers upon forming (or robot handling in the automated processes case). However, the need for use of small-time steps via explicit FE codes, facing numerical instabilities, as well as large computational time, are among notable drawbacks of the current FEtools, hindering their extensive use as fast and yet efficient digital twins in industry. This paper presents a novel woven fabric simulation technique through the application of the material point method (MPM), which enables the use of much larger time steps, facing less numerical instabilities, hence the ability to run significantly faster and efficient simulationsfor fabric materials handling and forming processes. Therefore, this method has the ability to enhance the development of automated fiber handling and preform processes by calculating the physical interactions with the MPM fiber models and rigid tool components. This enables the designers to virtually develop, test, and optimize their processes based on either algorithmicor Machine Learning applications. As a preliminary case study, forming of a hemispherical plain weave is shown, and the results are compared to theFE simulations, as well as experiments.

Keywords: material point method, woven fabric composites, forming, material handling

Procedia PDF Downloads 150

Authors: Hatib Shabbir

Abstract:

Student support services play an important role in providing technical and motivational support to distance learner. ICT based systems have improved the efficiency and effectiveness of support services. In distance education, students being at distant require quick responses from their institution. In the manual system, it is practically hard to give prompt response to each and every student, so as a result student has to suffer a lot. The best way to minimize inefficiencies is to use automated systems. This project involves the development of centralized automated software that would not only replace the manual degree issuance system of 1.3 million students studying at AIOU but also provide online tracking to all the students applying for Degrees. DTS is also the first step towards the paperless culture which is adopted by the major organizations of the world. DTS would not only save university cost but also save students cost and time too by conveying all the information/objection through email and SMS. Moreover, DTS also monitors the performance of each and every individual working in the exam department AIOU and generates daily, monthly and yearly reports of every individual which helps a lot in continuous performance monitoring of the employees.

Keywords: aiou dts, dts aiou, dts, degree tracking aiou

Procedia PDF Downloads 200

Authors: Lee Rui, Rajeev Ramachandran

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The approach to recurrent fevers in the paediatric or adolescent age group is not a straightforward one. Causes range from infectious diseases to rheumatological conditions to endocrinopathies, and are usually accompanied by weight loss rather than weight gain. We present an interesting case of a 16-year-old girl brought by her mother to the General Pediatrics Clinic for concerns of recurrent fever paired with significant weight gain over 1.5 years, with no identifiable cause found despite extensive work-up by specialists ranging from Rheumatologists to Oncologists. This case provides a learning opportunity on the approach to weight gain paired with persistent fevers in a paediatric population, one which is not commonly encountered and prompts further evaluation and consideration of less common diagnoses. In a span of 2 years, the girl’s weight had increased from 55 kg at 13 years old (75th centile) to 73.9 kg at 16 years old (>97th centile). About 1 year into her rapid weight gain, she started developing recurrent fevers of documented temperatures > 37.5 – 38.6 every 2-3 days, resulting in school absenteeism when she was sent home after temperature-taking in school found her to be febrile. The rapid onset of weight gain paired with unexplained fevers prompted the treating physician to consider the diagnosis of ROHHAD syndrome. Rapid onset obesity with hypoventilation, hypothalamic dysfunction and autonomic dysregulation (ROHHAD) syndrome is a rare disorder first described in 2007. It is characterized by dysfunction of the autonomic and endocrine system, characterized by hyperphagia and rapid-onset weight gain. This rapid weight gain is classically followed by hypothalamic manifestations with neuroendocrine deficiencies, hypo-ventilatory breathing abnormalities, and autonomic dysregulation. ROHHAD is challenging to diagnose with and diagnosis is made based mostly on clinical judgement. However if truly diagnosed, the condition is characterized by high morbidity and mortality rates. Early recognition of sleep disorders breathing and targeted therapeutic interventions helps limit morbidity and mortality associated with ROHHAD syndrome. This case poses an interesting diagnostic challenge and a diagnosis of ROHHAD has to be considered, given the serious complications that can come with disease progression while conditions such as Munchausen’s or drug fever remain as diagnoses of exclusion until we have exhausted all other possible conditions.

Keywords: pediatrics, endocrine, weight gain, recurrent fever, adolescent

Procedia PDF Downloads 74

Authors: K. M. Alsager, N. O. Alshehri

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In this paper, we proposed the notion of single valued neutrosophic hesitant fuzzy rough set, by combining single valued neutrosophic hesitant fuzzy set and rough set. The combination of single valued neutrosophic hesitant fuzzy set and rough set is a powerful tool for dealing with uncertainty, granularity and incompleteness of knowledge in information systems. We presented both definition and some basic properties of the proposed model. Finally, we gave a general approach which is applied to a decision making problem in disease diagnoses, and demonstrated the effectiveness of the approach by a numerical example.

Keywords: single valued neutrosophic fuzzy set, single valued neutrosophic fuzzy hesitant set, rough set, single valued neutrosophic hesitant fuzzy rough set

Procedia PDF Downloads 246

Authors: Mounir Kehal

Abstract:

The post-globalization epoch has placed businesses everywhere in new and different competitive situations where knowledgeable, effective and efficient behavior has come to provide the competitive and comparative edge. Enterprises have turned to explicit - and even conceptualizing on tacit - knowledge management to elaborate a systematic approach to develop and sustain the intellectual capital needed to succeed. To be able to do that, you have to be able to visualize your organization as consisting of nothing but knowledge and knowledge flows, whilst being presented in a graphical and visual framework, referred to as automated organizational cartography. Hence, creating the ability of further actively classifying existing organizational content evolving from and within data feeds, in an algorithmic manner, potentially giving insightful schemes and dynamics by which organizational know-how is visualized. It is discussed and elaborated on most recent and applicable definitions and classifications of knowledge management, representing a wide range of views from mechanistic (systematic, data driven) to a more socially (psychologically, cognitive/metadata driven) orientated. More elaborate continuum models, for knowledge acquisition and reasoning purposes, are being used for effectively representing the domain of information that an end user may contain in their decision making process for utilization of available organizational intellectual resources (i.e. Autocart). In this paper, we present an empirical research study conducted previously to try and explore knowledge diffusion in a specialist knowledge domain.

Keywords: knowledge management, knowledge maps, knowledge diffusion, organizational cartography

Procedia PDF Downloads 280

Authors: Justin Tang

Abstract:

Balance tests are frequently used to diagnose concussions on the sidelines of sporting events. Manual scoring, however, is labor intensive and subjective, and many concussions go undetected. This study institutes a novel approach to conducting the Balance Error Scoring System (BESS) more quantitatively using Microsoft’s gaming system Kinect, which uses a contactless sensor and several cameras to receive data and estimate body limb positions. Using a machine learning approach, Visual Gesture Builder, and a deterministic approach, MATLAB, we tested whether the Kinect can differentiate between “correct” and erroneous stances of the BESS. We created the two separate solutions by recording test videos to teach the Kinect correct stances and by developing a code using Java. Twenty-two subjects were asked to perform a series of BESS tests while the Kinect was collecting data. The Kinect recorded the subjects and mapped key joints onto their bodies to obtain angles and measurements that are interpreted by the software. Through VGB and MATLAB, the videos are analyzed to enumerate the number of errors committed during testing. The resulting statistics demonstrate a high correlation between manual scoring and the Kinect approaches, indicating the viability of the use of remote tracking devices in conducting concussion tests.

Keywords: automated, concussion detection, contactless sensors, microsoft kinect

Procedia PDF Downloads 296

Authors: Georgii Emelianov, Mikhail Polikarpov, Fabian Hübner, Jochen Deuse, Jochen Schiemann

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Recycling refrigerating appliances plays a major role in protecting the Earth's atmosphere from ozone depletion and emissions of greenhouse gases. The performance of refrigerator recycling plants in terms of material retention is the subject of strict environmental certifications and is reviewed periodically through specialized audits. The continuous collection of Refrigerator data required for the input-output analysis is still mostly manual, error-prone, and not digitalized. In this paper, we propose an automated data collection system for recycling plants in order to deduce expected material contents in individual end-of-life refrigerating appliances. The system utilizes laser scanner measurements and optical data to extract attributes of individual refrigerators by applying transfer learning with pre-trained vision models and optical character recognition. Based on Recognized features, the system automatically provides material categories and target values of contained material masses, especially foaming and cooling agents. The presented data collection system paves the way for continuous performance monitoring and efficient control of refrigerator recycling plants.

Keywords: automation, data collection, performance monitoring, recycling, refrigerators

Procedia PDF Downloads 133

Authors: Thomai Voulgari, Vasilis Vasilopoulos, Antonis Skamnakis

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The aim of this study is to investigate the effect of the European election campaigns (May 23-26, 2019) on Twitter achieving with artificial intelligence tools such as troll factories and automated inauthentic accounts. Our research focuses on the last European Parliamentary elections that took place between 23 and 26 May 2019 specifically in Italy, Greece, Germany and France. It is difficult to estimate how many Twitter users are actually bots (Echeverría, 2017). Detection for fake accounts is becoming even more complicated as AI bots are made more advanced. A political bot can be programmed to post comments on a Twitter account for a political candidate, target journalists with manipulated content or engage with politicians and artificially increase their impact and popularity. We analyze variables related to 1) the scope of activity of automated bots accounts and 2) degree of coherence and 3) degree of interaction taking into account different factors, such as the type of content of Twitter messages and their intentions, as well as the spreading to the general public. For this purpose, we collected large volumes of Twitter accounts of party leaders and MEP candidates between 10th of May and 26th of July based on content analysis of tweets based on hashtags while using an innovative network analysis tool known as MediaWatch.io (https://mediawatch.io/). According to our findings, one of the highest percentage (64.6%) of automated “bot” accounts during 2019 European election campaigns was in Greece. In general terms, political bots aim to proliferation of misinformation on social media. Targeting voters is a way that it can be achieved contribute to social media manipulation. We found that political parties and individual politicians create and promote purposeful content on Twitter using algorithmic tools. Based on this analysis, online political advertising play an important role to the process of spreading misinformation during elections campaigns. Overall, inauthentic accounts and social media algorithms are being used to manipulate political behavior and public opinion.

Keywords: artificial intelligence tools, human-bot interactions, political manipulation, social networking, troll factories

Procedia PDF Downloads 115

Authors: Michael Zhang, Nicholas Marriage, Valerie Astle, Marie-Louise Ratican, Jonathan Ash, Haddijatou Hughes

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Objective: Young infants are often brought to the Emergency Department (ED) with a variety of complaints, some of them are non-specific and present as a diagnostic challenge to the attending clinician. Whilst invasive investigations such as blood tests and lumbar puncture are necessary in some cases to exclude serious infections, some basic clinical tools in additional to thorough clinical history can be useful to assess the risks of serious conditions in these young infants. This study aimed to examine the utilization of one of clinical tools in this regard. Methods: This retrospective observational study examined the medical records of infants under 6 months presenting to a mixed urban ED between January 2013 and December 2014. The infants deemed to have non-specific complaints or diagnoses by the emergency clinicians were selected for analysis. The ones with clear systemic diagnoses were excluded. Among all relevant clinical information and investigation results, utilization of Standard Paediatric Observation Chart (SPOC) was particularly scrutinized in these medical records. This specific chart was developed by the expert clinicians in local health department. It categorizes important clinical signs into some color-coded zones as a visual cue for serious implication of some abnormalities. An infant is regarded as SPOC positive when fulfills 1 red zone or 2 yellow zones criteria, and the attending clinician would be prompted to investigate and treat for potential serious conditions accordingly. Results: Eight hundred and thirty-five infants met the inclusion criteria for this project. The ones admitted to the hospital for further management were more likely to have SPOC positive criteria than the discharged infants (Odds ratio: 12.26, 95% CI: 8.04 – 18.69). Similarly, Sepsis alert criteria on SPOC were positive in a higher percentage of patients with serious infections (56.52%) in comparison to those with mild conditions (15.89%) (p < 0.001). The SPOC sepsis criteria had a sensitivity of 56.5% (95% CI: 47.0% - 65.7%) and a moderate specificity of 84.1% (95% CI: 80.8% - 87.0%) to identify serious infections. Applying to this infant population, with a 17.4% prevalence of serious infection, the positive predictive value was only 42.8% (95% CI: 36.9% - 49.0%). However, the negative predictive value was high at 90.2% (95% CI: 88.1% - 91.9%). Conclusions: Standard Paediatric Observation Chart has been applied as a useful clinical tool in the clinical practice to help identify and manage young sick infants in ED effectively.

Keywords: clinical tool, infants, non-specific complaints, Standard Paediatric Observation Chart

Procedia PDF Downloads 227

Authors: Ali Montazar

Abstract:

California and the Colorado River Basin are facing increasing uncertainty concerning water supplies. The Colorado River is the main source of irrigation water in the low desert of California. Currently, due to an increasing water-use competition and long-term drought at the Colorado River Basin, efficient use of irrigation water is one of the highest conservation priorities in the region. This study aims to present some of current irrigation technologies and management approaches in the low desert and assess the viability and potential of these water management practices. The results of several field experiments are used to assess five water conservation practices of sub-surface drip irrigation, automated surface irrigation, sprinkler irrigation, tail-water recovery system, and deficit irrigation strategy. The preliminary results of several ongoing studies at commercial fields are presented, particularly researches in alfalfa, sugar beets, kliengrass, sunflower, and spinach fields. The findings indicate that all these practices have significant potential to conserve water (an average of 1 ac-ft/ac) and enhance the efficiency of water use (15-25%). Further work is needed to better understand the feasibility of each of these applications and to help maintain profitable and sustainable agricultural production system in the low desert as water and labor costs, and environmental issues increase.

Keywords: automated surface irrigation, deficit irrigation, low desert of California, sprinkler irrigation, sub-surface drip irrigation, tail-water recovery system

Procedia PDF Downloads 127

Authors: Kemka C. Ihemelandu, Chukwuemeka U. Ihemelandu

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The incidence of melanoma has been increasing rapidly over the past two decades, making melanoma a current public health crisis. Unfortunately, even as screening efforts continue to expand in an effort to ameliorate the death rate from melanoma, there is a need to improve diagnostic accuracy to decrease misdiagnosis. Artificial intelligence (AI) a new frontier in patient care has the ability to improve the accuracy of melanoma diagnosis. Convolutional neural network (CNN) a form of deep neural network, most commonly applied to analyze visual imagery, has been shown to outperform the human brain in pattern recognition. However, there are noted limitations with the accuracy of the CNN models. Our aim in this study was the optimization of convolutional neural network algorithms for the automated diagnosis of melanoma. We hypothesized that Optimal selection of the momentum and batch hyperparameter increases model accuracy. Our most successful model developed during this study, showed that optimal selection of momentum of 0.25, batch size of 2, led to a superior performance and a faster model training time, with an accuracy of ~ 83% after nine hours of training. We did notice a lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone. Training set image transformations did not result in a superior model performance in our study.

Keywords: melanoma, convolutional neural network, momentum, batch hyperparameter

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Authors: Melissa K. Hord, Stephen P. Whiteside

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Impairment in functioning is a requirement for diagnosing psychopathology, identifying individuals in need of treatment, and documenting improvement with treatment. Further, identifying different types of functional impairment can guide educators and treatment providers. However, most assessment tools focus on symptom severity and few measures assess impairment associated with childhood anxiety disorders. The child- and parent-report versions of the Child Sheehan Disability Scale (CSDS) are measures that may provide useful information regarding impairment. The purpose of the present study is to examine whether children diagnosed with different anxiety disorders have greater impairment in school or home functioning based on self or parent report. The sample consisted of 844 children ages 5 to 19 years of age (mean 13.43, 61% female, 90.9% Caucasian), including 281 children diagnosed with obsessive compulsive disorder (OCD), 200 with generalized anxiety disorder (GAD), 176 with social phobia, 83 with separation anxiety, 61 with anxiety not otherwise specified (NOS), 30 with panic disorder, and 13 with panic with agoraphobia. To assess whether children and parents reported greater impairment in school or home functioning, a multivariate analysis of variance was conducted. (The assumptions of independence and homogeneity of variance were checked and met). A significant difference was found, Pillai's trace = .143, F (4, 28) = 4.19, p < .001, partial eta squared = .04. Post hoc comparisons using the Tukey HSD test indicated that children report significantly greater impairment in school with panic disorder (M=5.18, SD=3.28), social phobia (M=4.95, SD=3.20), and OCD (M=4.62, SD=3.32) compared to other diagnoses; whereas parents endorse significantly greater school impairment when their child has a social phobia (M=5.70, SD=3.39) diagnosis. Interestingly, both children and parents reported greater impairment in family functioning for an OCD (child report M=5.37, SD=3.20; parent report M=5.59, SD=3.38) diagnosis compared to other anxiety diagnoses. (Additional findings for the anxiety disorders associated with less impairment will also be presented). The results of the current study have important implications for educators and treatment providers who are working with anxious children. First, understanding that differences exist in how children and parents view impairment related to childhood anxiety can help those working with these families to be more sensitive during interactions. Second, evidence suggests that difficulties in one environment do not necessarily translate to another environment, thus caregivers may benefit from careful explanation of observations obtained by educators. Third, results support the use of the CSDS measure by treatment providers to identify impairment across environments in order to more effectively target interventions.

Keywords: anxiety, childhood, impairment, school functioning

Procedia PDF Downloads 252

Authors: Amir AlAmeeri, Mohamed Ibrahim

Abstract:

Various data points are constantly being measured in the production system, and due to the nature of the wells, these data points, such as pressure, temperature, water cut, etc.., fluctuations are constant, which requires high frequency monitoring and collection. It is a very difficult task to analyze these parameters manually using spreadsheets and email. An automated system greatly enhances efficiency, reduce errors, the need for constant emails which take up disk space, and frees up time for the operator to perform other critical tasks. Various production data is being recorded in an oil field, and this huge volume of data can be seen as irrelevant to some, especially when viewed on its own with no context. In order to fully utilize all this information, it needs to be properly collected, verified and stored in one common place and analyzed for surveillance and monitoring purposes. This paper describes how data is recorded by different parties and departments in the field, and verified numerous times as it is being loaded into a repository. Once it is loaded, a final check is done before being entered into a production monitoring system. Once all this is collected, various calculations are performed to report allocated production. Calculated production data is used to report field production automatically. It is also used to monitor well and surface facility performance. Engineers can use this for their studies and analyses to ensure field is performing as it should be, predict and forecast production, and monitor any changes in wells that could affect field performance.

Keywords: automation, oil production, Cheleken, exploration and production (E&P), Caspian Sea, allocation, forecast

Procedia PDF Downloads 130

Authors: Latika Choudhary

Abstract:

“The potential benefits of Artificial Intelligence are huge, So are the dangers.” - Dave Water. Artificial intelligence is one of the facet of Information technology domain which despite several attempts does not have a clear definition or ambit. However it can be understood as technology to solve problems via automated decisions and predictions. Artificial intelligence is essentially an algorithm based technology which analyses the large amounts of data and then solves problems by detecting useful patterns. Owing to its automated feature it will not be wrong to say that humans & AI have more utility than humans alone or computers alone.1 For many decades AI experienced enthusiasm as well as setbacks, yet it has today become part and parcel of our everyday life, making it convenient or at times problematic. AI and related technology encompass Intellectual Property in multiple ways, the most important being AI technology for management of Intellectual Property, IP for protecting AI and IP as a hindrance to the transparency of AI systems. Thus the relationship between the two is of reciprocity as IP influences AI and vice versa. While AI is a recent concept, the IP laws for protection or even dealing with its challenges are relatively older, raising the need for revision to keep up with the pace of technological advancements. This paper will analyze the relationship between AI and IP to determine how beneficial or conflictual the same is, address how the old concepts of IP are being stretched to its maximum limits so as to accommodate the unwanted consequences of the Artificial Intelligence and propose ways to mitigate the situation so that AI becomes the friend it is and not turn into a potential foe it appears to be.

Keywords: intellectual property rights, information technology, algorithm, artificial intelligence

Procedia PDF Downloads 61

Authors: Mounir Kehal, Adel Al Araifi

Abstract:

The post-globalisation epoch has placed businesses everywhere in new and different competitive situations where knowledgeable, effective and efficient behaviour has come to provide the competitive and comparative edge. Enterprises have turned to explicit- and even conceptualising on tacit- Knowledge Management to elaborate a systematic approach to develop and sustain the Intellectual Capital needed to succeed. To be able to do that, you have to be able to visualize your organization as consisting of nothing but knowledge and knowledge flows, whilst being presented in a graphical and visual framework, referred to as automated organizational cartography. Hence, creating the ability of further actively classifying existing organizational content evolving from and within data feeds, in an algorithmic manner, potentially giving insightful schemes and dynamics by which organizational know-how is visualised. It is discussed and elaborated on most recent and applicable definitions and classifications of knowledge management, representing a wide range of views from mechanistic (systematic, data driven) to a more socially (psychologically, cognitive/metadata driven) orientated. More elaborate continuum models, for knowledge acquisition and reasoning purposes, are being used for effectively representing the domain of information that an end user may contain in their decision making process for utilization of available organizational intellectual resources (i.e. Autocart). In this paper we present likewise an empirical research study conducted previously to try and explore knowledge diffusion in a specialist knowledge domain.

Keywords: knowledge management, knowledge maps, knowledge diffusion, organizational cartography

Procedia PDF Downloads 395

Authors: Nacer Eddine Chelbi, Ayet Bagane, Annie Saleh, Claude Sauvageau, Denis Gingras

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As recently stated by National Highway Traffic Safety Administration (NHTSA), to demonstrate the expected performance of a highly automated vehicles system, test approaches should include a combination of simulation, test track, and on-road testing. In this paper, we propose a new validation method for autonomous vehicles involving on-road tests (Field Operational Tests), test track (Test Matrix) and simulation (Worst Case Scenarios). We concentrate our discussion on the simulation aspects, in particular, we extend recent work based on Importance Sampling by using a Metropolis-Hasting algorithm (MHS) to sample collected data from the Safety Pilot Model Deployment (SPMD) in lane-change scenarios. Our proposed MH sampling method will be compared to the Importance Sampling method, which does not perform well in high-dimensional problems. The importance of this study is to obtain a sampler that could be applied to high dimensional simulation problems in order to reduce and optimize the number of test scenarios that are necessary for validation and certification of autonomous vehicles.

Keywords: automated driving, autonomous emergency braking (AEB), autonomous vehicles, certification, evaluation, importance sampling, metropolis-hastings sampling, tests

Procedia PDF Downloads 257

Authors: Balamurali Gunji, B. B. V. L. Deepak, B. B. Biswal, Amrutha Rout, Golak Bihari Mohanta

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Robots play an important role in the operations like pick and place, assembly, spot welding and much more in manufacturing industries. Out of those, assembly is a very important process in manufacturing, where 20% of manufacturing cost is wholly occupied by the assembly process. To do the assembly task effectively, Assembly Sequences Planning (ASP) is required. ASP is one of the multi-objective non-deterministic optimization problems, achieving the optimal assembly sequence involves huge search space and highly complex in nature. Many researchers have followed different algorithms to solve ASP problem, which they have several limitations like the local optimal solution, huge search space, and execution time is more, complexity in applying the algorithm, etc. By keeping the above limitations in mind, in this paper, a new automated optimal robotic assembly sequence planning using Artificial Bee Colony (ABC) Algorithm is proposed. In this algorithm, automatic extraction of assembly predicates is done using Computer Aided Design (CAD) interface instead of extracting the assembly predicates manually. Due to this, the time of extraction of assembly predicates to obtain the feasible assembly sequence is reduced. The fitness evaluation of the obtained feasible sequence is carried out using ABC algorithm to generate the optimal assembly sequence. The proposed methodology is applied to different industrial products and compared the results with past literature.

Keywords: assembly sequence planning, CAD, artificial Bee colony algorithm, assembly predicates

Procedia PDF Downloads 214

Authors: Hamilton V. Niculescu

Abstract:

This paper discusses findings relating to people's engagement with mobile communication technologies and remote automated systems. This interdisciplinary study employs a constructivist grounded theory methodology, with qualitative data that was generated following in-depth semi-structured interviews with 18 people living in Ireland being corroborated with participants' observations and quantitative data. Additional data was collected following participants' remote interaction with six custom-built automated enclosures, located at six different sites around Dublin, Republic of Ireland. This paper argues that ownership and education play a vital role in people engaging with and adoption of new technologies. Analysis of participants' behavior and attitude towards Information Communication Technologies (ICT) suggests that innovations do not always improve peoples' social inclusion. Technological innovations are sometimes perceived as destroying communities and create a dysfunctional society. Moreover, the findings indicate that a lack of public information and support from Irish governmental institutions, as well as limited off-the-shelves availability, has led to low trust and adoption of renewable technologies. A limited variation in participants' behavior and interaction patterns with technologies was observed during the study. This suggests that people will eventually adopt new technologies according to their needs and experience, even though they initially rejected the idea of changing their lifestyle.

Keywords: automation, communication, ICT, renewables

Procedia PDF Downloads 86

Authors: Jessica Gladden

Abstract:

This study explored the perceived benefits of trauma-sensitive yoga programs. Participants attended one of two six-week trauma-sensitive yoga programs utilizing the G.R.A.C.E model, a format developed based on Emerson’s trauma-sensitive yoga guidelines and modified by the instructors. Participants in this study completed surveys on their experiences. The results of the surveys indicated that participants perceived improvements in self-care, embodiment, and mood. These results show that trauma-sensitive yoga may have benefits beyond the treatment of specific diagnoses that could be applied to a variety of populations. Reflections from one of the researchers who teaches in this program, as well as qualitative statements from the participants, will be shared to support the continued use of this method.

Keywords: yoga, trauma-sensitive, yoga therapy, trauma

Procedia PDF Downloads 126

Authors: George N. Zaimes, Valasia Iakovoglou, Paschalis Koutalakis, Konstantinos Ioannou, Ioannis Kosmadakis, Panagiotis Tsardaklis, Theodoros Laopoulos

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The advancements in technology allow the development of a new system that can continuously measure surface soil erosion. Continuous soil erosion measurements are required in order to comprehend the erosional processes and propose effective and efficient conservation measures to mitigate surface erosion. Mitigating soil erosion, especially in Mediterranean countries such as Greece, is essential in order to maintain environmental and agricultural sustainability. In this paper, we present the Automated Soil Erosion Monitoring System (ASEMS) that measures surface soil erosion along with other factors that impact erosional process. Specifically, this system measures ground level changes (surface soil erosion), rainfall, air temperature, soil temperature and soil moisture. Another important innovation is that the data will be collected by remote communication. In addition, stakeholder’s awareness is a key factor to help reduce any environmental problem. The different dissemination activities that were utilized are described. The overall outcomes were the development of an innovative system that can measure erosion very accurately. These data from the system help study the process of erosion and find the best possible methods to reduce erosion. The dissemination activities enhance the stakeholder's and public's awareness on surface soil erosion problems and will lead to the adoption of more effective soil erosion conservation practices in Greece.

Keywords: soil management, climate change, new technologies, conservation practices

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Authors: Ian Walsh, Terry Nguyen-Khuong, Christopher H. Taron, Pauline M. Rudd

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Glycoproteins and their covalently bonded glycans play critical roles in the immune system, cell communication, disease and disease prognosis. Ultra performance liquid chromatography (UPLC) coupled with mass spectrometry is conventionally used to qualitatively and quantitatively characterise glycan structures in a given sample. Exoglycosidases are enzymes that catalyze sequential removal of monosaccharides from the non-reducing end of glycans. They naturally have specificity for a particular type of sugar, its stereochemistry (α or β anomer) and its position of attachment to an adjacent sugar on the glycan. Thus, monitoring the peak movements (both in the UPLC and MS1) after application of exoglycosidases provides a unique and effective way to annotate sugars with high detail - i.e. differentiating positional and linkage isomers. Manual annotation of an exoglycosidase experiment is difficult and time consuming. As such, with increasing sample complexity and the number of exoglycosidases, the analysis could result in manually interpreting hundreds of peak movements. Recently, we have implemented pattern recognition software for automated interpretation of UPLC-MS1 exoglycosidase digestions. In this work, we explain the software, indicate how much time it will save and provide example usage showing the annotation of positional and linkage isomers in Immunoglobulin G, apolipoprotein J, and simple glycan standards.

Keywords: bioinformatics, automated glycan assignment, liquid chromatography, mass spectrometry

Procedia PDF Downloads 172

Authors: Hanan M. Alghamdi, Kholud E. Alsalami, Manal I. Alshaikhi, Nouf M. Alsalami, Sara A. Awad, Ruqaya A. Alrabei

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Tourism in Saudi Arabia is one of the emerging sectors with rapid growth. The Kingdom of Saudi Arabia is characterized by its wonderful and historical areas, which constitute important cultural and tourist landmarks. These landmarks attract the attention of the government of Saudi Arabia; hence the improvement of the tourism sector becomes one of the important axes of Saudi Arabia's vision 2030. There is a need to enhance the tourist experience by facilitating the tourism process for visitors to the Kingdom of Saudi Arabia. This project aims to design an application to serve domestic tourists and visitors from outside the Kingdom of Saudi Arabia. This application will contain an automated tourist generate plan service by sentiment analysis of comments in Google Map using Lexicon for method Rule-based approach. There are thirteen regions in the kingdom of Saudi Arabia. The regions supported in this application will be Makkah and Asir regions. According to the output of the sentiment analysis, the application will recommend restaurants and cafes, activities (parks, museums) and shopping (shopping centers) in the generated plan. After that, the system will show the user a drop-down list of “Mega-events in Saudi Arabia” containing a link to the site of events in the Kingdom of Saudi Arabia. and “important information for you” public decency regulations.

Keywords: tourist automated plan, sentiment analysis, comments in google map, tourism in Saudi Arabia

Procedia PDF Downloads 111

Authors: Parsa Mahdavi, M. Amin Hariri-Ardebili

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The development of accurate machine learning and deep learning models traditionally demands hands-on expertise and a solid background to fine-tune hyperparameters. With the continuous expansion of datasets in various scientific and engineering domains, researchers increasingly turn to machine learning methods to unveil hidden insights that may elude classic regression techniques. This surge in adoption raises concerns about the adequacy of the resultant meta-models and, consequently, the interpretation of the findings. In response to these challenges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. AutoML encompasses crucial stages such as data preparation, feature engineering, hyperparameter optimization, and neural architecture search. This paper provides a comprehensive overview of the principles underpinning AutoML, surveying several widely-used AutoML platforms. Additionally, the paper offers a glimpse into the application of AutoML on various engineering datasets. By comparing these results with those obtained through classical machine learning methods, the paper quantifies the uncertainties inherent in the application of a single ML model versus the holistic approach provided by AutoML. These examples showcase the efficacy of AutoML in extracting meaningful patterns and insights, emphasizing its potential to revolutionize the way we approach and analyze complex datasets.

Keywords: automated machine learning, uncertainty, engineering dataset, regression

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Authors: Cassey Younghans

Abstract:

Background: Current health care trends demonstrate that there is an increasing number of patients being hospitalized with complex comorbidities. These complex needs require advanced therapies, and treatment goals often focus on doing everything possible to prolong life rather than focusing on the individual patient’s quality of life which is the goal of palliative care efforts. Patients benefit from palliative care in the early stages of the illness rather than after the disease progressed or the state of acuity has advanced. The clinical problem identified was that palliative care was not being implemented early enough in the disease process with patients who had complex medical conditions and who would benefit from the philosophy and skills of palliative care professionals. Purpose: The purpose of this quality improvement study was to increase the number of palliative care screenings and consults completed on adults after being admitted to one Non-ICU and Non-COVID hospital unit. Methods: A retrospective chart review assessing for possible missed opportunities to introduce palliation was performed for patients with six primary diagnoses, including heart failure, liver failure, end stage renal disease, chronic obstructive pulmonary disease, cerebrovascular accident, and cancer in a population of adults over the ago of 19 on one medical-surgical unit over a three-month period prior to the intervention. An educational session with the nurses on the benefits of palliative care was conducted by the researcher, and a screening tool was implemented. The expected outcome was to have an increase in early palliative care consultation with patients with complex comorbid conditions and a decrease in missed opportunities for the implementation of palliative care. Another retrospective chart review was completed following completion of the three month piloting of the tool. Results: During the retrospective chart review, 46 patients were admitted to the medical-surgical floor with the primary diagnoses identified in the inclusion criteria. Six patients had palliative care consults completed during that time. Twenty-two palliative care screening tools were completed during the intervention period. Of those, 15 of the patients scored a 7 or higher, suggesting that a palliative care consultation was warranted. The final retrospective chart review identified that 4 palliative consults were implemented during that time of the 31 patients who were admitted over the three month time frame. Conclusion: Educating nurses and implementing a palliative care screening upon admission can be of great value in providing early identification of patients who might benefit from palliative care. Recommendations – It is recommended that this screening tool should be used to help identify the patents of whom would benefit from a palliative care consult, and nurses would be able to initiated a palliative care consultation themselves.

Keywords: palliative care, screening, early, palliative care consult

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Authors: Umm-E-Hani, Faiza Babar, Hanif Durad

Abstract:

Malware-detecting approaches suffer many limitations, due to which all anti-malware solutions have failed to be reliable enough for detecting zero-day malware. Signature-based solutions depend upon the signatures that can be generated only when malware surfaces at least once in the cyber world. Another approach that works by detecting the anomalies caused in the environment can easily be defeated by diligently and intelligently written malware. Solutions that have been trained to observe the behavior for detecting malicious files have failed to cater to the malware capable of detecting the sandboxed or protected environment. Machine learning and deep learning-based approaches greatly suffer in training their models with either an imbalanced dataset or an inadequate number of samples. AI-based anti-malware solutions that have been trained with enough samples targeted a selected feature vector, thus ignoring the input of leftover features in the maliciousness of malware just to cope with the lack of underlying hardware processing power. Our research focuses on producing an anti-malware solution for detecting malicious PE files by circumventing the earlier-mentioned shortcomings. Our proposed framework, which is based on automated feature engineering through autoencoders, trains the model over a fairly large dataset. It focuses on the visual patterns of malware samples to automatically extract the meaningful part of the visual pattern. Our experiment has successfully produced a state-of-the-art accuracy of 99.54 % over test data.

Keywords: malware, auto encoders, automated feature engineering, classification

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Authors: Chia-Yen Lee, Hao-Jen Wang, Jhih-Hao Lai

Abstract:

In recent years, infrared (IR) imaging has been considered as a potential tool to assess the efficacy of chemotherapy and early detection of breast cancer. Regions of tumor growth with high metabolic rate and angiogenesis phenomenon lead to the high temperatures. Observation of differences between the heat maps in long term is useful to help assess the growth of breast cancer cells and detect breast cancer earlier, wherein the multi-time infrared image alignment technology is a necessary step. Representative feature points detection and matching are essential steps toward the good performance of image registration and quantitative analysis. However, there is no clear boundary on the infrared images and the subject's posture are different for each shot. It cannot adhesive markers on a body surface for a very long period, and it is hard to find anatomic fiducial markers on a body surface. In other words, it’s difficult to detect and match features in an IR sequence images. In this study, automated feature detection and matching algorithms with two type of automatic feature points (i.e., vascular branch points and modified Harris corner) are developed respectively. The preliminary results show that the proposed method could identify the representative feature points on the IR breast images successfully of 98% accuracy and the matching results of 93% accuracy.

Keywords: Harris corner, infrared image, feature detection, registration, matching

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Authors: Marek Dosbaba

Abstract:

Within the mining sector, SEM-based Automated Mineralogy (AM) has been the standard application for quickly and efficiently handling mineral processing tasks. Over the last decade, the trend has been to analyze larger numbers of samples, often with a higher level of detail. This has necessitated a shift from interactive sample analysis performed by an operator using a SEM, to an increased reliance on offline processing to analyze and report the data. In response to this trend, TESCAN TIMA Mineral Analyzer is designed to quickly create a virtual copy of the studied samples, thereby preserving all the necessary information. Depending on the selected data acquisition mode, TESCAN TIMA can perform hyperspectral mapping and save an X-ray spectrum for each pixel or segment, respectively. This approach allows the user to browse through elemental distribution maps of all elements detectable by means of energy dispersive spectroscopy. Re-evaluation of the existing data for the presence of previously unconsidered elements is possible without the need to repeat the analysis. Additional tiers of data such as a secondary electron or cathodoluminescence images can also be recorded. To take full advantage of these information-rich datasets, TIMA utilizes a new archiving tool introduced by TESCAN. The dataset size can be reduced for long-term storage and all information can be recovered on-demand in case of renewed interest. TESCAN TIMA is optimized for network storage of its datasets because of the larger data storage capacity of servers compared to local drives, which also allows multiple users to access the data remotely. This goes hand in hand with the support of remote control for the entire data acquisition process. TESCAN also brings a newly extended open-source data format that allows other applications to extract, process and report AM data. This offers the ability to link TIMA data to large databases feeding plant performance dashboards or geometallurgical models. The traditional tabular particle-by-particle or grain-by-grain export process is preserved and can be customized with scripts to include user-defined particle/grain properties.

Keywords: Tescan, electron microscopy, mineralogy, SEM, automated mineralogy, database, TESCAN TIMA, open format, archiving, big data

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Authors: V. Shapaval, N. K. Afseth, D. Tzimorotas, A. Kohler

Abstract:

Globally there is a dramatic increase in the demand for food, energy, materials and clean water since natural resources are limited. As a result, industries are looking for ways to reduce rest materials and to improve resource efficiency. Microorganisms have a high potential to be used as bio factories for the production of primary and secondary metabolites that represent high-value bio-products (enzymes, polyunsaturated fatty acids, bio-plastics, glucans, etc.). In order to find good microbial producers, to design suitable substrates from food rest materials and to optimize fermentation conditions, rapid analytical techniques for quantifying target bio products in microbial cells are needed. In the EU project FUST (R4SME, Fp7), we have developed a fully automated high-throughput FUST system based on micro-cultivation and FTIR spectroscopy that facilitates the screening of microorganisms, substrates and fermentation conditions for the optimization of the production of different high-value metabolites (single cell oils, bio plastics). The automated system allows the preparation of 100 samples per hour. Currently, The FUST system is in use for screening of filamentous fungi in order to find oleaginous strains with the ability to produce polyunsaturated fatty acids, and the optimization of cheap substrates, derived from food rest materials, and the optimization of fermentation conditions for the high yield of single cell oil.

Keywords: FTIR spectroscopy, FUST system, screening, biotechnology

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Authors: Tae-Uk Yun, Pullip Chung, Kee-Young Kwahk, Hyunchul Ahn

Abstract:

Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection using machine learning techniques over the past years. But, there have been no prior studies proposed an automated fake news detection method for Korean news to our best knowledge. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (topic modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as logistic regression, backpropagation network, support vector machine, and deep neural network can be applied. To validate the effectiveness of the proposed method, we collected about 200 short Korean news from Seoul National University’s FactCheck. which provides with detailed analysis reports from 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

Keywords: fake news detection, Korean news, machine learning, text mining

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Authors: Eileen Y. Wong, Katherine Jin, Anat Talmon

Abstract:

Background: Help seeking behaviors are highly contingent on socio-cultural factors such as ethnicity. Both Latino and Asian Americans underutilize mental health services compared to their White American counterparts. This difference may be related to the composite of one’s social support system, which includes family cohesion and social networks. Previous studies have found that Latino families are characterized by higher levels of family cohesion and social support, and Asian American families with greater family cohesion exhibit lower levels of help seeking behaviors. While both are broadly considered collectivist communities, within-culture variability is also significant. Therefore, this study aims to investigate the relationship between help seeking behaviors in the two cultures with levels of family cohesion and strength of social network. We also consider such relationships in light of previous traumatic events and diagnoses, particularly post-traumatic stress disorder (PTSD), to understand whether clinically diagnosed individuals differ in their strength of network and help seeking behaviors. Method: An adult sample (N = 2,990) from the National Latino and Asian American Study (NLAAS) provided data on participants’ social network, family cohesion, likelihood of seeking professional help, and DSM-IV diagnoses. T-tests compared Latino American (n = 1,576) and Asian American respondents (n = 1,414) in strength of social network, level of family cohesion, and likelihood of seeking professional help. Linear regression models were used to identify the probability of help-seeking behavior based on ethnicity, PTSD diagnosis, and strength of social network. Results: Help-seeking behavior was significantly associated with family cohesion and strength of social network. It was found that higher frequency of expressing one’s feelings with family significantly predicted lower levels of help-seeking behaviors (β = [-.072], p = .017), while higher frequency of spending free time with family significantly predicted higher levels of help-seeking behaviors (β = [.129], p = .002) in the Asian American sample. Subjective importance of family relations compared to that of one’s peers also significantly predict higher levels of help-seeking behaviors (β = [.095], p = .011) in the Asian American sample. Frequency of sharing one’s problems with relatives significantly predicted higher levels of help-seeking behaviors (β = [.113], p < .01) in the Latino American sample. A PTSD diagnosis did not have any significant moderating effect. Conclusion: Considering the underutilization of mental health services in Latino and Asian American minority groups, it is crucial to understand ways in which help seeking behavior can be encouraged. Our findings suggest that different dimensions within family cohesion and social networks have differential impacts on help-seeking behavior. Given the multifaceted nature of family cohesion and cultural relevance, the implications of our findings for theory and practice will be discussed.

Keywords: family cohesion, social networks, Asian American, Latino American, help-seeking behavior

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