Search results for: diagnostic feedback

Authors: Wen-Chao Tang, Tang-Yi Liu, Ming Gao, Gang Xu, Hua-Yuan Yang

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This study developed a method for teaching acupuncture manipulation in small groups optimized with the theory of motor learning. Sixty acupuncture students and their teacher participated in our research. Motion videos were recorded of their manipulations using the lifting-thrusting method. These videos were analyzed using Simi Motion software to acquire the movement parameters of the thumb tip. The parameter velocity curves along Y axis was used to generate small teaching groups clustered by a self-organized map (SOM) and K-means. Ten groups were generated. All the targeted instruction based on the comparative results groups as well as the videos of teacher and student was provided to the members of each group respectively. According to the theory and research of motor learning, the factors or technologies such as video instruction, observational learning, external focus and summary feedback were integrated into this teaching method. Such efforts were desired to improve and enhance the effectiveness of current acupuncture teaching methods in limited classroom teaching time and extracurricular training.

Keywords: acupuncture, group teaching, video instruction, observational learning, external focus, summary feedback

Procedia PDF Downloads 159

Authors: Alice Huffman, Joseph Hartland, Sam Gibbs

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Dermatology is often a neglected specialty in low-resource settings, despite the high morbidity associated with skin disease. This becomes even more significant when associated with HIV infection, as dermatological conditions are more common and aggressive in HIV positive patients. African countries have the highest HIV infection rates and skin conditions are frequently misdiagnosed and mismanaged, because of a lack of dermatological training and educational material. The frequent lack of diagnostic tests in the African setting renders basic clinical skills all the more vital. This project aimed to improve diagnosis and treatment of skin disease in the HIV population in a district hospital in Malawi. A basic dermatological clinical tool was developed and produced in collaboration with local staff and based on available literature and data collected from clinics. The aim was to improve diagnostic accuracy and provide guidance for the treatment of skin disease in HIV positive patients. A literature search within Embase, Medline and Google scholar was performed and supplemented through data obtained from attending 5 Antiretroviral clinics. From the literature, conditions were selected for inclusion in the resource if they were described as specific, more prevalent, or extensive in the HIV population or have more adverse outcomes if they develop in HIV patients. Resource-appropriate treatment options were decided using Malawian Ministry of Health guidelines and textbooks specific to African dermatology. After the collection of data and discussion with local clinical and pharmacy staff a list of 15 skin conditions was included and a booklet created using the simple layout of a picture, a diagnostic description of the disease and treatment options. Clinical photographs were collected from local clinics (with full consent of the patient) or from the book ‘Common Skin Diseases in Africa’ (permission granted if fully acknowledged and used in a not-for-profit capacity). This tool was evaluated by the local staff, alongside an educational teaching session on skin disease. This project aimed to reduce uncertainty in diagnosis and provide guidance for appropriate treatment in HIV patients by gathering information into one practical and manageable resource. To further this project, we hope to review the effectiveness of the tool in practice.

Keywords: dermatology, HIV, Malawi, skin disease

Procedia PDF Downloads 166

Authors: Joao Ferreira Do Rosario, Maria De Lurdes Calisto, Ana Teresa Machado, Nuno Gustavo, Rui Gonçalves

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The tourism sector is increasingly important to the economic performance of countries and a relevant theme to academic research, increasing the importance of understanding how and why tourists evaluate tourism locations. The city of Lisbon is currently a tourist destination of excellence in the European and world-wide panorama, registering a significant growth of the economic weight of its tourist activities in the Gross Added Value of the region. Although there is research on the feedback of those who visit tourist sites and different methodologies for studying tourist sites have been applied, this research seeks to be innovative in the objective of obtaining insights on the competitiveness in terms of attractiveness of the city of Lisbon as a tourist destination, based the feedback of tourists in the Facebook pages of the most visited museums and monuments of Lisbon, an interpretation that is relevant in the development of strategies of tourist attraction. The intangible dimension of the tourism offer, due to its unique condition of simultaneous production and consumption, makes eWOM particularly relevant. The testimony of consumers is thus a decisive factor in the decision-making and buying process in tourism. Online social networks are one of the most used platforms for tourists to evaluate the attractiveness's points of a tourism destination (e.g. cultural and historical heritage), with this user-generated feedback enabling relevant information about the customer-tourists. This information is related to the tourist experience representing the true voice of the customer. Furthermore, this voice perceived by others as genuine, opposite to marketing messages, may have a powerful word-of-mouth influence on other potential tourists. The relevance of online reviews sharing, however, becomes particularly complex, considering social media users’ different profiles or the possible and different sources of information available, as well as their associated reputation associated with each source. In the light of these trends, our research focuses on the tourists’ feedback on Facebook pages of the most visited museums and monuments of Lisbon that contribute to its attractiveness as a tourism destination. Sentiment Analysis is the methodology selected for this research, using public available information in the online context, which was deemed as an appropriate non-participatory observation method. Data will be collected from two museums (Museu dos Coches and Museu de Arte Antiga) and three monuments ((Mosteiro dos Jerónimos, Torre de Belém and Panteão Nacional) Facebook pages during a period of one year. The research results will help in the evaluation of the considered places by the tourists, their contribution to the city attractiveness and present insights helpful for the management decisions regarding this museums and monuments. The results of this study will also contribute to a better knowledge of the tourism sector, namely the identification of attributes in the evaluation and choice of the city of Lisbon as a tourist destination. Further research will evaluate the Lisbon attraction points for tourists in different categories beyond museums and monuments, will also evaluate the tourist feedback from other sources like TripAdvisor and apply the same methodology in other cities and country regions.

Keywords: Lisbon tourism, opinion mining, sentiment analysis, tourism location attractiveness evaluation

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Authors: Vikrum Malhotra, Manpreet Kaur, Ayesha Ghotto

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Background: Cardiac registries such as ACC Percutaneous Coronary Intervention Registry require high quality data to be abstracted, including data elements such as nuclear cardiology, diagnostic coronary angiography, and PCI. Introduction: The audit tool created is used by data abstractors to provide data audits and assess the accuracy and inter-rater reliability of abstraction performed by the abstractors for a health system. This audit tool solution has been developed across 13 registries, including ACC/NCDR registries, PCI, STS, Get with the Guidelines. Methodology: The data audit tool was used to audit internal registry abstraction for all data elements, including stress test performed, type of stress test, data of stress test, results of stress test, risk/extent of ischemia, diagnostic catheterization detail, and PCI data elements for ACC/NCDR PCI registries. This is being used across 20 hospital systems internally and providing abstraction and audit services for them. Results: The data audit tool had inter-rater reliability and accuracy greater than 95% data accuracy and IRR score for the PCI registry in 50 PCI registry cases in 2021. Conclusion: The tool is being used internally for surgical societies and across hospital systems. The audit tool enables the abstractor to be assessed by an external abstractor and includes all of the data dictionary fields for each registry.

Keywords: abstraction, cardiac registry, cardiovascular registry, registry, data

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Authors: Katerina H. Takova, Ivan N. Minkov, Gergana G. Zahmanova

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Molecular farming is the production of recombinant proteins in plants with the aim to use the protein as a purified product, crude extract or directly in the planta. Plants gain more attention as expression systems compared to other ones due to the cost effective production of pharmaceutically important proteins, appropriate post-translational modifications, assembly of complex proteins, absence of human pathogens to name a few. In addition, transient expression in plant leaves enables production of recombinant proteins within few weeks. Hepatitis E virus (HEV) is a causative agent of acute hepatitis. HEV causes epidemics in developing countries and is primarily transmitted through the fecal-oral route. Presently, all efforts for development of Hepatitis E vaccine are focused on the Open Read Frame 2 (ORF2) capsid protein as it contains epitopes that can induce neutralizing antibodies. For our purpose, we used the CMPV-based vector-pEAQ-HT for transient expression of HEV ORF2 in Nicotiana benthamina. Different molecular analysis (Western blot and ELISA) showed that HEV ORF2 capsid protein was expressed in plant tissue in high-yield up to 1g/kg of fresh leaf tissue. Electron microscopy showed that the capsid protein spontaneously assembled in low abundance virus-like particles (VLPs), which are highly immunogenic structures and suitable for vaccine development. The expressed protein was recognized by both human and swine HEV positive sera and can be used as a diagnostic reagent for the detection of HEV infection. Production of HEV capsid protein in plants is a promising technology for further HEV vaccine investigations. Here, we reported for a rapid high-yield transient expression of a recombinant protein in plants suitable for vaccine production as well as a diagnostic reagent. Acknowledgments -The authors’ research on HEV is supported with grants from the Project PlantaSYST under the Widening Program, H2020 as well as under the UK Biotechnological and Biological Sciences Research Council (BBSRC) Institute Strategic Programme Grant ‘Understanding and Exploiting Plant and Microbial Secondary Metabolism’ (BB/J004596/1). The authors want to thank Prof. George Lomonossoff (JIC, Norwich, UK) for his contribution.

Keywords: hepatitis E virus, plant molecular farming, transient expression, vaccines

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Authors: Jana Abu Ahmada, Zaineb Mohamed, Ilyasse Aksikas

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The linear quadratic control system of hyperbolic first order partial differential equations (PDEs) are presented. The aim of this research is to control chemical reactions. This is achieved by converting the PDEs system to ordinary differential equations (ODEs) using the method of characteristics to reduce the system to control it by using the integral reinforcement learning. The designed controller is applied to a catalytic cracking reactor. Background—Transport-Reaction systems cover a large chemical and bio-chemical processes. They are best described by nonlinear PDEs derived from mass and energy balances. As a main application to be considered in this work is the catalytic cracking reactor. Indeed, the cracking reactor is widely used to convert high-boiling, high-molecular weight hydrocarbon fractions of petroleum crude oils into more valuable gasoline, olefinic gases, and others. On the other hand, control of PDEs systems is an important and rich area of research. One of the main control techniques is feedback control. This type of control utilizes information coming from the system to correct its trajectories and drive it to a desired state. Moreover, feedback control rejects disturbances and reduces the variation effects on the plant parameters. Linear-quadratic control is a feedback control since the developed optimal input is expressed as feedback on the system state to exponentially stabilize and drive a linear plant to the steady-state while minimizing a cost criterion. The integral reinforcement learning policy iteration technique is a strong method that solves the linear quadratic regulator problem for continuous-time systems online in real time, using only partial information about the system dynamics (i.e. the drift dynamics A of the system need not be known), and without requiring measurements of the state derivative. This is, in effect, a direct (i.e. no system identification procedure is employed) adaptive control scheme for partially unknown linear systems that converges to the optimal control solution. Contribution—The goal of this research is to Develop a characteristics-based optimal controller for a class of hyperbolic PDEs and apply the developed controller to a catalytic cracking reactor model. In the first part, developing an algorithm to control a class of hyperbolic PDEs system will be investigated. The method of characteristics will be employed to convert the PDEs system into a system of ODEs. Then, the control problem will be solved along the characteristic curves. The reinforcement technique is implemented to find the state-feedback matrix. In the other half, applying the developed algorithm to the important application of a catalytic cracking reactor. The main objective is to use the inlet fraction of gas oil as a manipulated variable to drive the process state towards desired trajectories. The outcome of this challenging research would yield the potential to provide a significant technological innovation for the gas industries since the catalytic cracking reactor is one of the most important conversion processes in petroleum refineries.

Keywords: PDEs, reinforcement iteration, method of characteristics, riccati equation, cracking reactor

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Authors: Sugeng Rianto, P.W. Arinto Yudi, Soemarno Muhammad Nurhuda

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A computation of a 3D compressible fluid flow for virtual environment with haptic interaction can be a non-trivial issue. This is especially how to reach good performances and balancing between visualization, tactile feedback interaction, and computations. In this paper, we describe our approach of computation methods based on parallel programming on a GPU. The 3D fluid flow solvers have been developed for smoke dispersion simulation by using combinations of the cubic interpolated propagation (CIP) based fluid flow solvers and the advantages of the parallelism and programmability of the GPU. The fluid flow solver is generated in the GPU-CPU message passing scheme to get rapid development of haptic feedback modes for fluid dynamic data. A rapid solution in fluid flow solvers is developed by applying cubic interpolated propagation (CIP) fluid flow solvers. From this scheme, multiphase fluid flow equations can be solved simultaneously. To get more acceleration in the computation, the Navier-Stoke Equations (NSEs) is packed into channels of texel, where computation models are performed on pixels that can be considered to be a grid of cells. Therefore, despite of the complexity of the obstacle geometry, processing on multiple vertices and pixels can be done simultaneously in parallel. The data are also shared in global memory for CPU to control the haptic in providing kinaesthetic interaction and felling. The results show that GPU based parallel computation approaches provide effective simulation of compressible fluid flow model for real-time interaction in 3D computer graphic for PC platform. This report has shown the feasibility of a new approach of solving the compressible fluid flow equations on the GPU. The experimental tests proved that the compressible fluid flowing on various obstacles with haptic interactions on the few model obstacles can be effectively and efficiently simulated on the reasonable frame rate with a realistic visualization. These results confirm that good performances and balancing between visualization, tactile feedback interaction, and computations can be applied successfully.

Keywords: CIP, compressible fluid, GPU programming, parallel computation, real-time visualisation

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Authors: Michael Smalenberger

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Intelligent tutoring systems (ITS) are computer-based platforms which can incorporate artificial intelligence to provide step-by-step guidance as students practice problem-solving skills. ITS can replicate the benefits of one-on-one tutoring, foster transactivity in collaborative environments, and lead to substantial learning gains when used to supplement the instruction of a teacher or when used as the sole method of instruction. Developments improving the ease of ITS creation have recently increased their proliferation, leading many K-12 schools and institutions of higher education in the United States to regularly use ITS within classrooms. We investigated how students perceive their experience using an ITS. In this study, 111 undergraduate students used an ITS in a college-level introductory statistics course and were subsequently asked for feedback on their experience. Results show that their perceptions were generally favorable of the ITS, and most would seek to use an ITS both for STEM and non-STEM courses in the future. Along with detailed transaction-level data, this feedback also provides insights on the design of user-friendly interfaces, guidance on accessibility for students with impairments, the sequencing of exercises, students’ expectation of achievement, and comparisons to other tutoring experiences. We discuss how these findings are important for the creation, implementation, and evaluation of ITS as a mode and method of teaching and learning.

Keywords: college statistics course, intelligent tutoring systems, in vivo study, student perceptions of tutoring

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Authors: Chandra Prayaga, Lakshmi Prayaga, Aaron Wade, Kyle Rank, Gopi Shankar Mallu, Sri Satya, Harsha Pola

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In today's tech-driven world, where connectivity shapes our daily lives, maintaining physical and emotional health is crucial. Athletic trainers play a vital role in optimizing athletes' performance and preventing injuries. However, a shortage of trainers impacts the quality of care. This study introduces a vision-based exercise monitoring system leveraging Google's MediaPipe library for precise tracking of bicep curl exercises and simultaneous posture monitoring. We propose a three-stage methodology: landmark detection, side detection, and angle computation. Our system calculates angles at the elbow, wrist, neck, and torso to assess exercise form. Experimental results demonstrate the system's effectiveness in distinguishing between good and partial repetitions and evaluating body posture during exercises, providing real-time feedback for precise fitness monitoring.

Keywords: physical health, athletic trainers, fitness monitoring, technology driven solutions, Google’s MediaPipe, landmark detection, angle computation, real-time feedback

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Authors: Nastaran Hemmati, Farhad Nikkhahi, Amir Javadi, Sahar Eskandarion, Seyed Mahmuod Amin Marashi

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Neisseria meningitidis, Escherichia coli K, Streptococcus agalactiae, and Streptococcus pneumoniae cause 90% of bacterial meningitis. Almost all infected people die or have irreversible neurological complications. Therefore, it is essential to have a diagnostic kit with the ability to quickly detect these fatal infections. The project involved 212 patients from whom cerebrospinal fluid samples were obtained. After total genome extraction and performing multiplex quantitative polymerase chain reaction (qPCR), the presence or absence of each infectious factor was determined by comparing with standard strains. The specificity, sensitivity, positive predictive value, and negative predictive value calculated were 100%, 92.9%, 50%, and 100%, respectively. So, due to the high specificity and sensitivity of the designed primers, they can be used instead of bacterial culture that takes at least 24 to 48 hours. The remarkable benefit of this method is associated with the speed (up to 3 hours) at which the procedure could be completed. It is also worth noting that this method can reduce the personnel unintentional errors which may occur in the laboratory. On the other hand, as this method simultaneously identifies four common factors that cause bacterial meningitis, it could be used as an auxiliary method diagnostic technique in laboratories particularly in cases of emergency medicine.

Keywords: cerebrospinal fluid, meningitis, quantitative polymerase chain reaction, simultaneous detection, diagnosis testing

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Authors: Jaishree Ranganathan, Poonam Rajurkar, Angelina A. Tzacheva, Zbigniew W. Ras

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In today’s world, business often depends on the customer feedback and reviews. Sentiment analysis helps identify and extract information about the sentiment or emotion of the of the topic or document. Attribute selection is a challenging problem, especially with large datasets in actionable pattern mining algorithms. Action Rule Mining is one of the methods to discover actionable patterns from data. Action Rules are rules that help describe specific actions to be made in the form of conditions that help achieve the desired outcome. The rules help to change from any undesirable or negative state to a more desirable or positive state. In this paper, we present a Lexicon based weighted scheme approach to identify emotions from customer feedback data in the area of manufacturing business. Also, we use Rough sets and explore the attribute selection method for large scale datasets. Then we apply Actionable pattern mining to extract possible emotion change recommendations. This kind of recommendations help business analyst to improve their customer service which leads to customer satisfaction and increase sales revenue.

Keywords: actionable pattern discovery, attribute selection, business data, data mining, emotion

Procedia PDF Downloads 181

Authors: L. Beltran, S. Solano, T. Lacruz, M. Blanco, M. Rojo, M. Graell, A. R. Sepulveda

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Introduction and Objective: Given the difficulties of assessing Binge Eating Disorder during childhood, episodes of Loss of Control (LOC) eating can be a key symptom. The objective is to know the prevalence of food psychopathology depending on the type of evaluation and find out which psychological characteristics differentiate overweight or obese children who present LOC from those who do not. Material and Methods: 170 children from 8 to 12 years of age with overweight or obesity (P > 85) were evaluated through the Primary Care Centers of Madrid. Sociodemographic data and psychological measures were collected through the Kiddie-Schedule for Affective Disorders & Schizophrenia, Present & Lifetime Version (K-SADS-PL) diagnostic interview and self-applied questionnaires: Children's eating attitudes (ChEAT), depressive symptomatology (CDI), anxiety (STAIC), general self-esteem (LAWSEQ), body self-esteem (BES), perceived teasing (POTS) and perfectionism (CAPS). Results: 15.2% of the sample exceeded the ChEAT cut-off point, presenting a risk of pathological eating; 5.88% presented an Eating Disorder through the diagnostic interview (2.35% Binge Eating disorder), and 33.53% had LOC episodes. No relationship was found between the presence of LOC and clinical diagnosis of eating disorders according to DSM-V; however, the group with LOC presented a higher risk of eating psychopathology using the ChEAT (p < .02). Significant differences were found in the group with LOC (p < .02): higher z-BMI, lower body self-esteem, greater anxious symptomatology, greater frequency of teasing towards weight, and greater effect of teasing both towards weight and competitions; compared to their peers without LOC. Conclusion: According to previous studies in samples with overweight children, in this Spanish sample of children with obesity, we found a prevalence of moderate eating disorder and a high presence of LOC episodes, which is related to both eating and general psychopathology. These findings confirm that the exclusion of LOC episodes as a diagnostic criterion can underestimate the presence of eating psychopathology during this developmental stage. According to these results, it is highly recommended to promote school context programs that approach LOC episodes in order to reduce associated symptoms. This study is included in a Project funded by the Ministry of Innovation and Science (PSI2011-23127).

Keywords: childhood obesity, eating psychopathology, loss-of-control eating, psychological symptomatology

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Authors: Genady Grabarnik, Serge Yaskolko

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Generative Artificial Intelligence (AI) has significantly expanded its applicability with the incorporation of Large Language Models (LLMs) and become a technology with promise to automate some areas that were very difficult to automate before. The paper describes the introduction of generative Artificial Intelligence into Introductory Computer and Data Science courses and analysis of effect of such introduction. The generative Artificial Intelligence is incorporated in the educational process two-fold: For the instructors, we create templates of prompts for generation of tasks, and grading of the students work, including feedback on the submitted assignments. For the students, we introduce them to basic prompt engineering, which in turn will be used for generation of test cases based on description of the problems, generating code snippets for the single block complexity programming, and partitioning into such blocks of an average size complexity programming. The above-mentioned classes are run using Large Language Models, and feedback from instructors and students and courses’ outcomes are collected. The analysis shows statistically significant positive effect and preference of both stakeholders.

Keywords: introductory computer and data science education, generative AI, large language models, application of LLMS to computer and data science education

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Authors: Anshu Radha Aggarwal

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As per the Right to Education Act (RTE), 2009, every child in the age group of 6-14 years shall be admitted in a neighborhood school. All the Out of School Children identified have to be enrolled / mainstreamed in to age appropriate class and there-after be provided special training. This paper addresses issues emerging from provisions in the RTE Act that specifically refer to the enrolment of out-of school children into age appropriate classes and the requirement to provide special trainings that will enable this to take place. In the context of RTE Act, the Out-of-School Children are first enrolled in the formal school and then they are provided with Special Training through NRSTCs (Long Term / Short term basis). These centers are functioning in formal school campus itself. This paper specifies the role of special training centers (STC). It presents a re-envisioning of assessment that recognizes two principal functions of assessment, assessment for learning and assessment of learning, instead of the more familiar categories of formative, diagnostic, summative, and evaluative assessment. The use of these two functions of assessment highlights and emphasizes the role of special training centers (STC) to assess their level for giving them appropriate special training and to evaluate their improvement in learning level. Challenge of problem faced by teachers to do diagnostic assessment, including its place in the sequence of assessment procedures appropriate in identifying and addressing individual children’s learning difficulties are solved by special training centers (STC). It is important that assessment is used to identify children with learning difficulties at the earliest possible stage so that appropriate support and intervention can be put in place. So appropriate challenges with tools are presented here for their assessment at entry level and at completion level of primary children by special training centers (STC).

Keywords: right to education, assessment, challenges, out of school children

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Authors: Jae Young Song, In-Ohk Ouh, Seyeon Park, Byeong Sul Kang, Soo Dong Cho, In-Soo Cho

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Canine parvovirus (CPV) is a major pathogen of diarrhea disease in dogs. CPV type 2 has three of antigenic variants such as 2a, 2b, and 2c. CPV constructs a small non-enveloped, icosahedral capsid that contains single-stranded DNA. It has capsids that two largely overlapping virion proteins (VP), VP1 (82 kDa), and VP2 (65 kDa). Baculoviruses are insect pathogens that regulate insect populations in nature and are being successfully used to control insect pests. The proteins produced in the baculovirus-expression system are used for instance for functional studies, vaccine preparations, or diagnostics. The vaccines produced by baculovirus-expression system showed elicitation of antibodies. The recombinant baculovirus infected SF9 cells showed broken shape. The recombinant VP2 proteins from cell pellet or supernatant were confirmed by western blotting. The result showed that the recombinant VP2 protein bands were appeared at 65 kDa molecular weight in both cell pellet and supernatant of infected SF9 cell. These results indicated that the recombinant baculovirus infected SF9 cell express the recombinant VP2 protein successfully. In addition, the expressed recombinant VP2 protein is secreted from cell to supernatant. The baculovirus expression system can be used to produce the VP2 protein of CPV 2c. In addition, the secretion property of the expression of VP2 protein may decrease the cost of production, because it can be skipped the cell breaking step. The produced VP2 protein could be used for vaccine and the agent of diagnostic tests. This study provides the foundation of the production of CPV 2c vaccine and the diagnostic agent.

Keywords: baculovirus, canine parvovirus 2c, dog, Korea

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Authors: Taylor Kolody, Farkhund Iqbal, Rabia Batool, Benjamin Fung, Mohammed Hussaeni, Saiqa Aleem

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The rising threat of climate change has led to an increase in public awareness and care about our collective and individual environmental impact. A key component of this impact is our use of cars and other polluting forms of transportation, but it is often difficult for an individual to know how severe this impact is. While there are applications that offer this feedback, they require manual entry of what transportation mode was used for a given trip, which can be burdensome. In order to alleviate this shortcoming, a data from the 2016 TRIPlab datasets has been used to train a variety of machine learning models to automatically recognize the mode of transportation. The accuracy of 89.6% is achieved using single deep neural network model with Gated Recurrent Unit (GRU) architecture applied directly to trip data points over 4 primary classes, namely walking, public transit, car, and bike. These results are comparable in accuracy to results achieved by others using ensemble methods and require far less computation when classifying new trips. The lack of trip context data, e.g., bus routes, bike paths, etc., and the need for only a single set of weights make this an appropriate methodology for applications hoping to reach a broad demographic and have responsive feedback.

Keywords: classification, gated recurrent unit, recurrent neural network, transportation

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Authors: Zhdanova A. O., Volkov R. S., Kuznetsov G. V., Strizhak P. A.

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Fires in different types of facilities are a serious problem worldwide.It is still an unaccomplished science and technology objective to establish the minimum number and type of sensors in automatic systems of compartment fire suppression which would turn the fire-extinguishing agent spraying on and off in real time depending on the state of the fire, minimize the amount of agent applied, delay time in fire suppression and system response, as well as the time of combustion suppression. Based on the results of experimental studies, the conclusion was made that it is reasonable to use a gas analysis system and heat sensors (in the event of their prior activation) to determine the effectiveness of fire suppression (fire-extinguishing composition interacts with the fire). Thus, the concentration of CO in the interaction of the firefighting liquid with the fire increases to 0.7–1.2%, which indicates a slowdown in the flame combustion, and heat sensors stop responding at a gas medium temperature below 80 ºC, which shows a gradual decrease in the heat release from the fire. The evidence from this study suggests that the information received from the video recording equipment (video camera) should be used in real time as an additional parameter confirming fire suppression. Research was supported by Russian Science Foundation (project No 21-19-00009, https://rscf.ru/en/project/21-19-00009/).

Keywords: compartment fires, fire suppression, continuous control of fire behavior, feedback systems

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Authors: Uriel Martinez-Hernandez, Luke W. Boorman, Hamideh Kerdegari, Tony J. Prescott

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In this paper, we present architecture for the study of telepresence, immersion and human-robot interaction. The architecture is built around a wearable interface, developed here, that provides the human with visual, audio and tactile feedback from a remote location. We have chosen to interface the system with the iCub humanoid robot, as it mimics many human sensory modalities, such as vision, with gaze control and tactile feedback. This allows for a straightforward integration of multiple sensory modalities, but also offers a more complete immersion experience for the human. These systems are integrated, controlled and synchronised by an architecture developed for telepresence and human-robot interaction. Our wearable interface allows human participants to observe and explore a remote location, while also being able to communicate verbally with humans located in the remote environment. Our approach has been tested from local, domestic and business venues, using wired, wireless and Internet based connections. This has involved the implementation of data compression to maintain data quality to improve the immersion experience. Initial testing has shown the wearable interface to be robust. The system will endow humans with the ability to explore and interact with other humans at remote locations using multiple sensing modalities.

Keywords: telepresence, telerobotics, human-robot interaction, virtual reality

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Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: Afsha Shaikh, Mohammed Azizur Rahman, Ibrahim Hassan, Mayur Pal

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Student engagement in learning is instantly affected by the sources of learning methods available for them, such as videos showing the applications of the concept or showing a practical demonstration. Specific to the engineering discipline, there exist enormous challenging concepts that can be simplified when they are connected to real-world scenarios. For this study, the concept of heat exchangers was used as it is a part of multidisciplinary engineering fields. To make the learning experience enjoyable and impactful, 3-D printed heat exchanger models were created for students to use while working on in-class activities and assignments. Students were encouraged to use the 3-D printed heat exchanger models to enhance their understanding of theoretical concepts associated with its applications. To assess the effectiveness of the method, feedback was received by students pursuing undergraduate engineering via an anonymous electronic survey. To make the feedback more realistic, unbiased, and genuine, students spent nearly two to three weeks using the models in their in-class assignments. The impact of these tools on their learning was assessed through their performance in their ungraded assignments as well as their interactive discussions with peers. ‘Having to apply the theory learned in class whilst discussing with peers on a class assignment creates a relaxed and stress-free learning environment in classrooms’; this feedback was received by more than half the students who took the survey and found 3-D models of heat exchanger very easy to use. Amongst many ways to enhance learning and make students more engaged through interactive models, this study sheds light on the importance of physical tools that help create a lasting mental representation in the minds of students. Moreover, in this technologically enhanced era, the concept of augmented reality was considered in this research. E-drawings application was recommended to enhance the vision of engineering students so they can see multiple views of the detailed 3-D models and cut through its different sides and angles to visualize it properly. E-drawings could be the next tool to implement in classrooms to enhance students’ understanding of engineering concepts.

Keywords: student engagement, life-long-learning, visual demonstration, 3-D printed models, engineering education

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Authors: Silvio Ricardo Rodrigues Sanches, Bianca Cogo Barbosa, Beatriz Regina Brum, Cléber Gimenez Corrêa

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To evaluate the quality of a segmentation algorithm, the authors use subjective or objective metrics. Although subjective metrics are more accurate than objective ones, objective metrics do not require user feedback to test an algorithm. Objective metrics require subjective experiments only during their development. Subjective experiments typically display to users some videos (generated from frames with segmentation errors) that simulate the environment of an application domain. This user feedback is crucial information for metric definition. In the subjective experiments applied to develop some state-of-the-art metrics used to test segmentation algorithms, the videos displayed during the experiments did not contain audio. Audio is an essential component in applications such as videoconference and augmented reality. If the audio influences the user’s perception, using only videos without audio in subjective experiments can compromise the efficiency of an objective metric generated using data from these experiments. This work aims to identify if the audio influences the user’s perception of segmentation quality in background substitution applications with audio. The proposed approach used a subjective method based on formal video quality assessment methods. The results showed that audio influences the quality of segmentation perceived by a user.

Keywords: background substitution, influence of audio, segmentation evaluation, segmentation quality

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Authors: Kemka Ihemelandu, Chukwuemeka Ihemelandu

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Melanoma remains a public health crisis, with incidence rates increasing rapidly in the past decades. Improving diagnostic accuracy to decrease misdiagnosis using Artificial intelligence (AI) continues to be documented. Unfortunately, unintended racially biased outcomes, a product of lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone, have increasingly been recognized as a problem.Resulting in noted limitations of the accuracy of the Convolutional neural network (CNN)models. CNN models are prone to biased output due to biases in the dataset used to train them. Our aim in this study was the optimization of convolutional neural network algorithms to mitigate bias in the automated diagnosis of melanoma. We hypothesized that our proposed training algorithms based on a data augmentation method to optimize the diagnostic accuracy of a CNN classifier by generating new training samples from the original ones will reduce bias in the automated diagnosis of melanoma. We applied geometric transformation, including; rotations, translations, scale change, flipping, and shearing. Resulting in a CNN model that provided a modifiedinput data making for a model that could learn subtle racial features. Optimal selection of the momentum and batch hyperparameter increased our model accuracy. We show that our augmented model reduces bias while maintaining accuracy in the automated diagnosis of melanoma.

Keywords: bias, augmentation, melanoma, convolutional neural network

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Authors: Madeleine Cox

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Objective: discussion of diagnosis of vernix caseosa peritonitis, recovery and subsequent caesarean seciton Case: 30 year old G4P1 presented in labour at 40 weeks, planning a vaginal birth afterprevious caesarean section. She underwent an emergency caesarean section due to concerns for fetal wellbeing on CTG. She was found to have a thin lower segment with a very small area of dehiscence centrally. The operation was uncomplicated, and she recovered and went home 2 days later. She then represented to the emergency department day 6 post partum feeling very unwell, with significant abdominal pain, tachycardia as well as urinary retention. Raised white cell count of 13.7 with neutrophils of 11.64, CRP of 153. An abdominal ultrasound was poorly tolerated by the patient and did not aide in the diagnosis. Chest and abdominal xray were normal. She underwent a CT chest and abdomen, which found a small volume of free fluid with no apparent collection. Given no obvious cause of her symptoms were found and the patient did not improve, she had a repeat CT 2 days later, which showed progression of free fluid. A diagnostic laparoscopy was performed with general surgeons, which reveled turbid fluid, an inflamed appendix which was removed. The patient improved remarkably post operatively. The histology showed periappendicitis with acute appendicitis with marked serosal inflammatory reaction to vernix caseosa. Following this, the patient went on to recover well. 4 years later, the patient was booked for an elective caesarean section, on entry into the abdomen, there were very minimal adhesions, and the surgery and her subsequent recovery was uncomplicated. Discussion: this case represents the diagnostic dilemma of a patient who presents unwell without a clear cause. In this circumstance, multiple modes of imaging did not aide in her diagnosis, and so she underwent diagnostic surgery. It is important to evaluate if a patient is or is not responding to the typical causes of post operative pain and adjust management accordingly. A multiteam approach can help to provide a diagnosis for these patients. Conclusion: Vernix caseosa peritonitis is a rare cause of acute abdomen post partum. There are few reports in the literature of the initial presentation and no reports on the possible effects on future pregnancies. This patient did not have any complications in her following pregnancy or delivery secondary to her diagnosis of vernix caseosa peritonitis. This may assist in counselling other women who have had this uncommon diagnosis.

Keywords: peritonitis, obstetrics, caesarean section, pain

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Authors: Vita Kalnberzina, Lauren Miller Anderson

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The aim of the paper is to report on the results of the telecollaboration project results carried out between the students of the University of Latvia, National Louis University in the US, and Austral University in Chili during the Intercultural Communication course. The objectives of the study are 1) to compare different forms of student telecollaboration and virtual exchange, 2) to collect and analyse the student feedback on the telecollaboration project, 3) to evaluate the products (films) produced during the telecollaboration project. The methods of research used are as follows: Survey of the student feedback after the project, video text analysis of the films produced by the students, and interview of the students participating in the project. We would like to compare the results of a three-year collaboration project, where we tried out synchronous telecollaboration and asynchronous collaboration. The different variables that were observed were the impact of the different time zones, different language proficiency levels of students, and different curricula developed for collaboration. The main findings suggest that the effort spent by students to organize meetings in different time zones and to get to know each other diminishes the quality of the product developed and thus reduces the students' feeling of accomplishment. Therefore, we would like to propose that asynchronous collaboration where the national teams work on a film project specifically developed by the students of one university for the students of another university ends up with a better quality film, which in its turn appeals more to the students of the other university and creates a deeper intercultural bond between the collaborating students.

Keywords: telecollaboration, intercultural communication, synchronous collaboration, asynchronous collaboration

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Authors: Pieter A. Cornelissen, Joris J. Van Hoof

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The interest in improving occupational safety started almost simultaneously with the beginning of the Industrial Revolution. Yet, it was not until the late 1970’s before the role of communication was considered in scientific research regarding occupational safety. In recent years the importance of communication as a means to improve occupational safety has increased. Not only as communication might have a direct effect on safety performance and safety outcomes, but also as it can be viewed as a major component of other important safety-related elements (e.g., training, safety meetings, leadership). And while safety communication is an increasingly important topic in research, its operationalization is often vague and differs among studies. This is not only problematic when comparing results, but also in applying these results to practice and the work floor. By means of an in-depth analysis—building on an existing dataset—this review aims to overcome these problems. The initial database search yielded 25.527 articles, which was reduced to a research corpus of 176 articles. Focusing on the 37 articles of this corpus that addressed communication (related to safety outcomes and safety performance), the current study will provide a comprehensive overview of the role and effects of safety communication and outlines the conditions under which communication contributes to a safer work environment. The study shows that in literature a distinction is commonly made between safety communication (i.e., the exchange or dissemination of safety-related information) and feedback (i.e. a reactive form of communication). And although there is a consensus among researchers that both communication and feedback positively affect safety performance, there is a debate about the directness of this relationship. Whereas some researchers assume a direct relationship between safety communication and safety performance, others state that this relationship is mediated by safety climate. One of the key findings is that despite the strongly present view that safety communication is a formal and top-down safety management tool, researchers stress the importance of open communication that encourages and allows employees to express their worries, experiences, views, and share information. This raises questions with regard to other directions (e.g., bottom-up, horizontal) and forms of communication (e.g., informal). The current review proposes a framework to overcome the often vague and different operationalizations of safety communication. The proposed framework can be used to characterize safety communication in terms of stakeholders, direction, and characteristics of communication (e.g., medium usage).

Keywords: communication, feedback, occupational safety, review

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Authors: Monisha Edirisooriya, Wilma Jack, Dominique Twelves, Jennifer Royds, Fiona Scott, Nicola Mason, Arran Turnbull, J. Michael Dixon

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Introduction: Excision biopsy has been the investigation of choice for patients presenting with pathological axillary lymphadenopathy without a breast abnormality. Core biopsy of nodes can provide sufficient tissue for diagnosis and has advantages in terms of morbidity and speed of diagnosis. This study evaluates the diagnostic accuracy of core biopsy in patients presenting with axillary lymphadenopathy. Methods: Between 2009 and 2019, 165 patients referred to the Edinburgh Breast Unit had a total of 179 axillary lymph node core biopsies. Results: 152 (92%) of the 165 initial core biopsies were deemed to contain adequate nodal tissue. Core biopsy correctly established malignancy in 75 of the 78 patients with haematological malignancy (96%) and in all 28 patients with metastatic carcinoma (100%) and correctly diagnosed benign changes in 49 of 57 (86%) patients with benign conditions. There were no false positives and no false negatives. In 67 (85.9%) of the 78 patients with hematological malignancy, there was sufficient material in the first core biopsy to allow the pathologist to make an actionable diagnosis and not ask for more tissue sampling prior to treatment. There were no complications of core biopsy. On follow up, none of the patients with benign cores has been shown to have malignancy in the axilla and none with lymphoma had their initial disease incorrectly classified. Conclusions: This study shows that core biopsy is now the investigation of choice for patients presenting with axillary lymphadenopathy even in those suspected as having lymphoma.

Keywords: core biopsy, excision biopsy, axillary lymphadenopathy, non-breast malignancy

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Authors: Deniz Guven, Wil Ward, Jinming Duan, Li Bai

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Over the past two decades or so, Optical Coherence Tomography (OCT) has been used to diagnose retina and optic nerve diseases. The retinal nerve fibre layer, for example, is a powerful diagnostic marker for detecting and staging glaucoma. With the advances in optical imaging hardware, the adoption of OCT is now commonplace in clinics. More and more OCT images are being generated, and for these OCT images to have clinical applicability, accurate automated OCT image segmentation software is needed. Oct image segmentation is still an active research area, as OCT images are inherently noisy, with the multiplicative speckling noise. Simple edge detection algorithms are unsuitable for detecting retinal layer boundaries in OCT images. Intensity fluctuation, motion artefact, and the presence of blood vessels also decrease further OCT image quality. In this paper, we introduce a new method for segmenting three-dimensional (3D) OCT images. This involves the use of a Kalman filter, which is commonly used in computer vision for object tracking. The Kalman filter is applied to the 3D OCT image volume to track the retinal layer boundaries through the slices within the volume and thus segmenting the 3D image. Specifically, after some pre-processing of the OCT images, points on the retinal layer boundaries in the first image are identified, and curve fitting is applied to them such that the layer boundaries can be represented by the coefficients of the curve equations. These coefficients then form the state space for the Kalman Filter. The filter then produces an optimal estimate of the current state of the system by updating its previous state using the measurements available in the form of a feedback control loop. The results show that the algorithm can be used to segment the retinal layers in OCT images. One of the limitations of the current algorithm is that the curve representation of the retinal layer boundary does not work well when the layer boundary is split into two, e.g., at the optic nerve, the layer boundary split into two. This maybe resolved by using a different approach to representing the boundaries, such as b-splines or level sets. The use of a Kalman filter shows promise to developing accurate and effective 3D OCT segmentation methods.

Keywords: optical coherence tomography, image segmentation, Kalman filter, object tracking

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Authors: P. Nafisi Poor, P. Javid

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Over the past few years, the idea of adaptive buildings and specifically, adaptive building energy management systems (ABEMS) has become popular. Well-performed management in terms of energy is to create a balance between energy consumption and user comfort; therefore, in new energy management models, efficient energy consumption is not the sole factor and the user's comfortability is also considered in the calculations. One of the main ways of measuring this factor is by analyzing user feedback on the conditions to understand whether they are satisfied with conditions or not. This paper provides a comprehensive review of recent approaches towards energy management systems based on users' feedbacks and subsequently performs a comparison between them premised upon their efficiency and accuracy to understand which approaches were more accurate and which ones resulted in a more efficient way of minimizing energy consumption while maintaining users' comfortability. It was concluded that the highest accuracy rate among the presented works was 95% accuracy in determining satisfaction and up to 51.08% energy savings can be achieved without disturbing user’s comfort. Considering the growing interest in designing and developing adaptive buildings, these studies can support diverse inquiries about this subject and can be used as a resource to support studies and researches towards efficient energy consumption while maintaining the comfortability of users.

Keywords: adaptive buildings, energy efficiency, intelligent buildings, user comfortability

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Authors: Saloni Diwakar, Hansika Kapoor

Abstract:

The study undertook comparative analyses between 130 male and female managers in a power/electric company, relating to prevalent perceptions about gendered leadership, leadership efficacy, perceived organizational support, and diversity and inclusiveness. Results showed no significant difference in POS, leadership aspirations, expression, and self- and other leadership efficacy between male and female managers. However, within-groups analyses revealed that female managers reported a disparity between self and other leadership efficacy (value), to a far greater extent than male managers (value). Additionally, females reported a dip in POS during middle management, as compared to junior management, whereas men reported a steady increase in POS from junior, middle on to senior management. Descriptively, both men and women reported preferring gender neutral leadership traits, as compared to male or female centered traits, and both genders least preferred male centered leadership traits. Compared to women, male managers were found to significantly undervalue diversity and inclusion initiatives. Subjective feedback was elicited to corroborate quantitative output. Also, female participants provided subjective feedback regarding efficacy of existing D&I practices in the organization. Findings and implications are discussed relevant to existing gender inclusion agendas.

Keywords: gendered leadership, diversity, inclusivity, perceived organizational support

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Authors: H. Yousefnia, S. Zolghadri

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An early diagnosis of bone metastases is very important for providing a profound decision on a subsequent therapy. A prerequisite for the clinical application of new diagnostic radiopharmaceutical is the measurement of organ radiation exposure dose from biodistribution data in animals. In this study, the dosimetric studies of a novel agent for SPECT-imaging of bone methastases, 111In-(4-{[(bis(phosphonomethyl))carbamoyl]methyl}-7,10-bis(carboxymethyl)-1,4,7,10-tetraazacyclododec-1-yl) acetic acid (111In-BPAMD) complex, have been estimated in human organs based on mice data. The radiolabeled complex was prepared with high radiochemical purity at the optimal conditions. Biodistribution studies of the complex were investigated in male Syrian mice at selected times after injection (2, 4, 24 and 48 h). The human absorbed dose estimation of the complex was performed based on mice data by the radiation absorbed dose assessment resource (RADAR) method. 111In-BPAMD complex was prepared with high radiochemical purity >95% (ITLC) and specific activities of 2.85 TBq/mmol. Total body effective absorbed dose for 111In-BPAMD was 0.205 mSv/MBq. This value is comparable to the other 111In clinically used complexes. The results show that the dose to critical organs the complex is well within the acceptable considered range for diagnostic nuclear medicine procedures. Generally, 111In-BPAMD has interesting characteristics and can be considered as a viable agent for SPECT-imaging of the bone metastases in the near future.

Keywords: In-111, BPAMD, absorbed dose, RADAR

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