Search results for: injury prediction

Authors: Yusuf S. Dambatta, Ahmed A. D. Sarhan

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Fused deposition modelling (FDM) is one of the most prominent rapid prototyping (RP) technologies which is being used to efficiently fabricate CAD 3D geometric models. However, the process is coupled with many drawbacks, of which the surface quality of the manufactured RP parts is among. Hence, studies relating to improving the surface roughness have been a key issue in the field of RP research. In this work, a technique of modelling the surface roughness in FDM is presented. Using experimentally measured surface roughness response of the FDM parts, an ANFIS prediction model was developed to obtain the surface roughness in the FDM parts using the main critical process parameters that affects the surface quality. The ANFIS model was validated and compared with experimental test results.

Keywords: surface roughness, fused deposition modelling (FDM), adaptive neuro fuzzy inference system (ANFIS), orientation

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Authors: Aneta Oblouková, Eva Vítková

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The article deals with the issue of water and sewerage charge rate prices in the Czech Republic. The research is specifically focused on the analysis of the development of the average prices of water and sewerage charge rate in the Czech Republic in the years 1994-2021 and on the validation of the chosen methodology relevant for the prediction of the development of the average prices of water and sewerage charge rate in the Czech Republic. The research is based on data collection. The data for this research was obtained from the Czech Statistical Office. The aim of the paper is to validate the relevance of the mathematical linear trend estimate technique for the calculation of the predicted average prices of water and sewerage charge rates. The real values of the average prices of water and sewerage charge rates in the Czech Republic in the years 1994-2018 were obtained from the Czech Statistical Office and were converted into a mathematical equation. The same type of real data was obtained from the Czech Statistical Office for the years 2019-2021. Prediction of the average prices of water and sewerage charge rates in the Czech Republic in the years 2019-2021 were also calculated using a chosen method -a linear trend estimation technique. The values obtained from the Czech Statistical Office and the values calculated using the chosen methodology were subsequently compared. The research result is a validation of the chosen mathematical technique to be a suitable technique for this research.

Keywords: Czech Republic, linear trend estimation, price prediction, water and sewerage charge rate

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Authors: Malo Pocheau-Lesteven, Olivier Le Maître

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Engineering and optimisation of complex systems is often achieved through multi-disciplinary analysis of the system, where each subsystem is modeled and interacts with other subsystems to model the complete system. The coherence of the output of the different sub-systems is achieved through the use of compatibility constraints, which enforce the coupling between the different subsystems. Due to the complexity of some sub-systems and the computational cost of evaluating their respective models, it is often necessary to build surrogate models of these subsystems to allow repeated evaluation these subsystems at a relatively low computational cost. In this paper, gaussian processes are used, as their probabilistic nature is leveraged to evaluate the likelihood of satisfying the compatibility constraints. This paper presents infilling strategies to build accurate surrogate models of the subsystems in areas where they are likely to meet the compatibility constraint. It is shown that these infilling strategies can reduce the computational cost of building surrogate models for a given level of accuracy. An application of these methods to velocity prediction programs used in offshore racing naval architecture further demonstrates these method's applicability in a real engineering context. Also, some examples of the application of uncertainty quantification to field of naval architecture are presented.

Keywords: infilling strategy, gaussian process, multi disciplinary analysis, velocity prediction program

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Authors: G. Alston Rush, Gus A. Rush III, M. F. Horstemeyer

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This study compares the shell-facemask responses of four commonly used American football helmets, under the National Operating Committee on Standards for Athletic Equipment (NOCSAE) drop impact test method, to show that the test standard would more accurately simulate in-use conditions by modification to include the facemask. In our study, the need for a more vigorous systematic approach to football helmet testing procedures is emphasized by comparing the Head Injury Criterion (HIC), the Gadd Severity Index (SI), and peak acceleration values for different helmets at different locations on the helmet under modified NOCSAE standard drop tower tests. Drop tests were performed on the Rawlings Quantum Plus, Riddell 360, Schutt Ion 4D, and Xenith X2 helmets at eight impact locations, impact velocities of 5.46 and 4.88 meters per second, and helmet configurations with and without facemasks. Analysis of NOCSAE drop test results reveal significant differences (p < 0.05) for when the facemasks were attached to helmets, as compared to the NOCSAE Standard, without facemask configuration. The boundary conditions of the facemask attachment can have up to a 50% decrease (p < 0.001) in helmet performance with respect to peak acceleration. While generally, all helmets with the facemasks gave greater HIC, SI, and acceleration values than helmets without the facemasks, significant helmet dependent variations were observed across impact locations and impact velocities. The variations between helmet responses could be attributed to the unique design features of each helmet tested, which include different liners, chin strap attachments, and faceguard attachment systems. In summary, these comparative drop test results revealed that the current NOCSAE standard test methods need improvement by attaching the facemasks to helmets during testing. The modified NOCSAE football helmet standard test gives a more accurate representation of a helmet’s performance and its ability to mitigate the on-field impact.

Keywords: football helmet testing, gadd severity index, head injury criterion, mild traumatic brain injury

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Authors: Aragorn Joaquin Pineda Dela Cruz

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Road traffic congestion is continually deteriorating in Hong Kong. The largest contributing factor is the increase in vehicle fleet size, resulting in higher competition over the utilisation of road space. This study proposes a project that can process closed-circuit television images and videos to provide real-time traffic detection and prediction capabilities. Specifically, a deep-learning model involving computer vision techniques for video and image-based vehicle counting, then a separate model to detect and predict traffic congestion levels based on said data. State-of-the-art object detection models such as You Only Look Once and Faster Region-based Convolutional Neural Networks are tested and compared on closed-circuit television data from various major roads in Hong Kong. It is then used for training in long short-term memory networks to be able to predict traffic conditions in the near future, in an effort to provide more precise and quicker overviews of current and future traffic conditions relative to current solutions such as navigation apps.

Keywords: intelligent transportation system, vehicle detection, traffic analysis, deep learning, machine learning, computer vision, traffic prediction

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Authors: Lavinia B. Dulla

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The exploration study of the fuzzy regression approach attempts to present that fuzzy regression can be used as a possible alternative to classical regression. It likewise seeks to assess the differences and characteristics of simple linear regression and fuzzy regression using the width of prediction interval, mean absolute deviation, and variance of residuals. Based on the simple linear regression model, the fuzzy regression approach is worth considering as an alternative to simple linear regression when the sample size is between 10 and 20. As the sample size increases, the fuzzy regression approach is not applicable to use since the assumption regarding large sample size is already operating within the framework of simple linear regression. Nonetheless, it can be suggested for a practical alternative when decisions often have to be made on the basis of small data.

Keywords: fuzzy regression approach, minimum fuzziness criterion, interval regression, prediction interval

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Authors: G. A. Khalid, M. D. Jones, R. Prabhu, A. Mason-Jones, W. Whittington, H. Bakhtiarydavijani, P. S. Theobald

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Head injury in childhood is a common cause of death or permanent disability from injury. However, despite its frequency and significance, there is little understanding of how a child’s head responds during injurious loading. Whilst Infant Post Mortem Human Subject (PMHS) experimentation is a logical approach to understand injury biomechanics, it is the authors’ opinion that a lack of subject availability is hindering potential progress. Computer modelling adds great value when considering adult populations; however, its potential remains largely untapped for infant surrogates. The complexities of child growth and development, which result in age dependent changes in anatomy, geometry and physical response characteristics, present new challenges for computational simulation. Further geometric challenges are presented by the intricate infant cranial bones, which are separated by sutures and fontanelles and demonstrate a visible fibre orientation. This study presents an FE model of a newborn infant’s head, developed from high-resolution computer tomography scans, informed by published tissue material properties. To mimic the fibre orientation of immature cranial bone, anisotropic properties were applied to the FE cranial bone model, with elastic moduli representing the bone response both parallel and perpendicular to the fibre orientation. Biofiedility of the computational model was confirmed by global validation against published PMHS data, by replicating experimental impact tests with a series of computational simulations, in terms of head kinematic responses. Numerical results confirm that the FE head model’s mechanical response is in favourable agreement with the PMHS drop test results.

Keywords: finite element analysis, impact simulation, infant head trauma, material properties, post mortem human subjects

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Authors: Amir Hossein Hejazi, Nima Amjady

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In recent years, due to environmental issues traditional energy sources had been replaced by renewable ones. Wind energy as the fastest growing renewable energy shares a considerable percent of energy in power electricity markets. With this fast growth of wind energy worldwide, owners and operators of wind farms, transmission system operators, and energy traders need reliable and secure forecasts of wind energy production. In this paper, a new forecasting strategy is proposed for short-term wind power prediction based on Echo State Networks (ESN). The forecast engine utilizes state-of-the-art training process including dynamical reservoir with high capability to learn complex dynamics of wind power or wind vector signals. The study becomes more interesting by incorporating prediction of wind direction into forecast strategy. The Big Bang-Big Crunch (BB-BC) evolutionary optimization algorithm is adopted for adjusting free parameters of ESN-based forecaster. The proposed method is tested by real-world hourly data to show the efficiency of the forecasting engine for prediction of both wind vector and wind power output of aggregated wind power production.

Keywords: wind power forecasting, echo state network, big bang-big crunch, evolutionary optimization algorithm

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Authors: Zainab Idrees Ahmad, Mahjabeen Qureshi, Zainab Hussain

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Standard precautions are a set of infection control practices used to prevent the transmission of diseases that can be acquired by contact with body fluids, non-intact skin, and mucous membranes. Lack of practice of SPs can result in a considerable increase in morbidity and mortality rates. Medical students (the future physicians) should have the highest knowledge of standard precautions to prevent the spread of nosocomial infections and ensure their safety as well. This study was designed. To assess the knowledge of medical students regarding standard precautions. And explore the attitude of medical students of MBBS in the third, fourth and final year towards standard precautions.: A descriptive cross-sectional study was conducted in the setting of Rawalpindi Medical University, Pakistan including the students of MBBS in their 3rd, 4th and final years. The study duration was from October 2022 to February 2023. The sample size calculated was 282 with a confidence interval of 95%. A questionnaire was structured utilizing the WHO guidelines on SPs assessing knowledge and attitude regarding hand hygiene, needle stick injury, use of gloves and mask, and sharp disposal. A total of 300 responses were received utilizing the technique of non-random convenience sampling. Data was analyzed using the latest version of SPSS.:Knowledge score regarding components of SPs, hand hygiene, and moments of hand hygiene was satisfactory. However, score regarding the use of PPE, needle stick injury, and sharp disposal was low. Almost all the students were compliant with the proper washing of hands but the observation of recommended time length was lacking. Compliance with the use of correct PPE and informing the supervisor upon getting a needle stick injury was low. This study signifies that medical students lack knowledge regarding standard precautions. This is alarming as this can be the vehicle for the spread of nosocomial infections. Proper training should be given to medical students to prevent the spread of hospital-acquired infections.

Keywords: attitude, knowledge, medical students, standard precautions

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Authors: Pintu Modak

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The core strength is essential not only for athletes but also for everyone to perform everyday's household chores with ease and efficiency. Core strength means to strengthen the muscles deep within the abdomen which connect to the spine and pelvis which control the position and movement of the central portion of the body. Strengthening of core muscles is important for injury prevention (lower back pain) and performance enhancement in sports. The purpose of the study was to review the literature and findings on the effects of Yoga exercise as a part of sports training method and fitness programs. Fifteen papers were found to be relevant for this review. There are five simple yoga poses: Ardha Phalakasana (Low plank), Vasisthasana (side plank), Purvottanasana (inclined plane), Sarvangasana (shoulder stand), and Virabhadrasana (Warrior) are found to be very effective for strengthening core muscles. They are the most effective poses to build core strength and flexibility to the core muscles. The study suggests that sports and fitness trainers should include these yoga exercises in their programs to strengthen core muscles.

Keywords: core strength, yoga, injuries, lower back

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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Authors: Hui Zhang, Sherif Beskhyroun

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Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. This includes abnormal changes caused by the strain field and abnormal symptoms of the structure, such as damage and deterioration. Currently, deploying sensors on a large scale in a building structure is a challenge. In this study, a highly stretchable strain sensors are used in this study to collect data sets of strain generated on the surface of full-size reinforced concrete (RC) frames under extreme cyclic load application. This sensing sheet can be switched freely between the test bending strain and the axial strain to achieve two different configurations. On this basis, the deep neural network prediction model of the frame beam and frame column is established. The training results show that the method can accurately predict the strain value and has good generalization ability. The two deep neural network prediction models will also be deployed in the SHM system in the future as part of the intelligent strain sensor system.

Keywords: strain sensing sheets, deep neural networks, strain measurement, SHM system, RC frames

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Authors: Seema Chopra, Amandeep Kaur, Vanita Suri, Shalini Gainder, Minakshi Rohilla

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Background: Acute kidney injury (AKI) associated with pregnancy is a serious medical complication which can lead to significant maternal as well as perinatal morbidity and mortality. Material and methods: This prospective observational study was carried out in the Obstetrics and Gynaecology department and dialysis unit of Nephrology department of PGIMER, Chandigarh from July 2013 to June 2014. Forty antenatal/postnatal/postabortal patients who fulfilled the AKIN criteria were enrolled in the study. All patients were followed up till 3 months postpartum. Results: Majority of the patients 23/40 (57.5%) with AKI presented in postpartum period, 14/40 (35%) developed AKI in antenatal period, and 3/40 (7.5%) were postabortal. AKI was attributable mostly to sepsis in 11/40 (27.5%) and PPH in 5/40 (12.5%). Hypertension and its complications causing AKI included eclampsia in 5/40 (12.5%) followed by 3/40 (7.5%) as HELLP syndrome and abruption placentae in 2/40(5%) patients. Three patients each (7.5%) had AFLP, TMA, and HEV as the cause of AKI. Renal replacement therapy in the form of hemodialysis was the treatment in majority of them (28 (70%)). After the acute event, 25 (62.5%) had complete recovery of their renal functions at 3 months follow up. Maternal mortality was seen in 25% (n=10) of the study patients. Conclusion: Timely initiation of RRT in patients with AKI associated with pregnancy has a good maternal outcome in the form of complete recovery of renal functions in 62.5% (25/40) of patients.

Keywords: AKI, dialysis, hypertension, sepsis, renal parameters

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Authors: Pedro Paulo Galindo Francisco, Eli Dhadad Junior

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This study addresses the Meese-Rogoff Puzzle by introducing the latest machine learning techniques as alternatives for predicting the exchange rates. Using RMSE as a comparison metric, Meese and Rogoff discovered that economic models are unable to outperform the random walk model as short-term exchange rate predictors. Decades after this study, no statistical prediction technique has proven effective in overcoming this obstacle; although there were positive results, they did not apply to all currencies and defined periods. Recent advancements in artificial intelligence technologies have paved the way for a new approach to exchange rate prediction. Leveraging this technology, we applied five machine learning techniques to attempt to overcome the Meese-Rogoff puzzle. We considered daily data for the real, yen, British pound, euro, and Chinese yuan against the US dollar over a time horizon from 2010 to 2023. Our results showed that none of the presented techniques were able to produce an RMSE lower than the Random Walk model. However, the performance of some models, particularly LSTM and N-BEATS were able to outperform the ARIMA model. The results also suggest that machine learning models have untapped potential and could represent an effective long-term possibility for overcoming the Meese-Rogoff puzzle.

Keywords: exchage rate, prediction, machine learning, deep learning

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Authors: Danilo López, Johana Hernández, Edwin Rivas

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The use of the Multilayer Perceptron Neural Networks (MLPNN) technique is presented to estimate the future state of use of a licensed channel by primary users (PUs); this will be useful at the spectral decision stage in cognitive radio networks (CRN) to determine approximately in which time instants of future may secondary users (SUs) opportunistically use the spectral bandwidth to send data through the primary wireless network. To validate the results, sequences of occupancy data of channel were generated by simulation. The results show that the prediction percentage is greater than 60% in some of the tests carried out.

Keywords: cognitive radio, neural network, prediction, primary user

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

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In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

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Authors: Ekta Yadav, Sukanta Bhattacharya, Brijesh Yadav, Ariel Hus, Jagjit Yadav

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Background and Significance: Respiratory exposure to toxicants (chemicals or particulates) causes disruption of lung homeostasis leading to lung toxicity/injury manifested as pulmonary inflammation, edema, and/or other effects depending on the type and extent of exposure. This emphasizes the need for investigating toxicant type-specific mechanisms to understand therapeutic targets. Aquaporins, aka water channels, are known to play a role in lung homeostasis. Particularly, the two major lung aquaporins AQP5 and AQP1 expressed in alveolar epithelial and vasculature endothelia respectively allow for movement of the fluid between the alveolar air space and the associated vasculature. In view of this, the current study is focused on understanding the regulation of lung aquaporins and other targets during inhalation exposure to toxic chemicals (Cigarette smoke chemicals) versus toxic particles (Carbon nanoparticles) or co-exposures to understand their relevance as markers of injury and intervention. Methodologies: C57BL/6 mice (5-7 weeks old) were used in this study following an approved protocol by the University of Cincinnati Institutional Animal Care and Use Committee (IACUC). The mice were exposed via oropharyngeal aspiration to multiwall carbon nanotube (MWCNT) particles suspension once (33 ugs/mouse) followed by housing for four weeks or to Cigarette smoke Extract (CSE) using a daily dose of 30µl/mouse for four weeks, or to co-exposure using the combined regime. Control groups received vehicles following the same dosing schedule. Lung toxicity/injury was assessed in terms of homeostasis changes in the lung tissue and lumen. Exposed lungs were analyzed for transcriptional expression of specific targets (AQPs, surfactant protein A, Mucin 5b) in relation to tissue homeostasis. Total RNA from lungs extracted using TRIreagent kit was analyzed using qRT-PCR based on gene-specific primers. Total protein in bronchoalveolar lavage (BAL) fluid was determined by the DC protein estimation kit (BioRad). GraphPad Prism 5.0 (La Jolla, CA, USA) was used for all analyses. Major findings: CNT exposure alone or as co-exposure with CSE increased the total protein content in the BAL fluid (lung lumen rinse), implying compromised membrane integrity and cellular infiltration in the lung alveoli. In contrast, CSE showed no significant effect. AQP1, required for water transport across membranes of endothelial cells in lungs, was significantly upregulated in CNT exposure but downregulated in CSE exposure and showed an intermediate level of expression for the co-exposure group. Both CNT and CSE exposures had significant downregulating effects on Muc5b, and SP-A expression and the co-exposure showed either no significant effect (Muc5b) or significant downregulating effect (SP-A), suggesting an increased propensity for infection in the exposed lungs. Conclusions: The current study based on the lung toxicity mouse model showed that both toxicant types, particles (CNT) versus chemicals (CSE), cause similar downregulation of lung innate defense targets (SP-A, Muc5b) and mostly a summative effect when presented as co-exposure. However, the two toxicant types show differential induction of aquaporin-1 coinciding with the corresponding differential damage to alveolar integrity (vascular permeability). Interestingly, this implies the potential of AQP1 as a differential marker of toxicant type-specific lung injury.

Keywords: aquaporin, gene expression, lung injury, toxicant exposure

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Authors: Eiji Nakamachi, Tsuyoshi Eguchi, Sayo Yamamoto, Yusuke Morita, H. Sakamoto

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In this study, we aim to reveal the relationship between the meniscal tearing and the articular cartilage injury of knee joint by using the dynamic explicit finite element (FE) method. Meniscal injuries reduce its functional ability and consequently increase the load on the articular cartilage of knee joint. In order to prevent the induction of osteoarthritis (OA) caused by meniscal injuries, many medical treatment techniques, such as artificial meniscus replacement and meniscal regeneration, have been developed. However, it is reported that these treatments are not the comprehensive methods. In order to reveal the fundamental mechanism of OA induction, the mechanical characterization of meniscus under the condition of normal and injured states is carried out by using FE analyses. At first, a FE model of the human knee joint in the case of normal state – ‘intact’ - was constructed by using the magnetron resonance (MR) tomography images and the image construction code, Materialize Mimics. Next, two types of meniscal injury models with the radial tears of medial and lateral menisci were constructed. In FE analyses, the linear elastic constitutive law was adopted for the femur and tibia bones, the visco-hyperelastic constitutive law for the articular cartilage, and the visco-anisotropic hyperelastic constitutive law for the meniscus, respectively. Material properties of articular cartilage and meniscus were identified using the stress-strain curves obtained by our compressive and the tensile tests. The numerical results under the normal walking condition revealed how and where the maximum compressive stress occurred on the articular cartilage. The maximum compressive stress and its occurrence point were varied in the intact and two meniscal tear models. These compressive stress values can be used to establish the threshold value to cause the pathological change for the diagnosis. In this study, FE analyses of knee joint were carried out to reveal the influence of meniscal injuries on the cartilage injury. The following conclusions are obtained. 1. 3D FE model, which consists femur, tibia, articular cartilage and meniscus was constructed based on MR images of human knee joint. The image processing code, Materialize Mimics was used by using the tetrahedral FE elements. 2. Visco-anisotropic hyperelastic constitutive equation was formulated by adopting the generalized Kelvin model. The material properties of meniscus and articular cartilage were determined by curve fitting with experimental results. 3. Stresses on the articular cartilage and menisci were obtained in cases of the intact and two radial tears of medial and lateral menisci. Through comparison with the case of intact knee joint, two tear models show almost same stress value and higher value than the intact one. It was shown that both meniscal tears induce the stress localization in both medial and lateral regions. It is confirmed that our newly developed FE analysis code has a potential to be a new diagnostic system to evaluate the meniscal damage on the articular cartilage through the mechanical functional assessment.

Keywords: finite element analysis, hyperelastic constitutive law, knee joint injury, meniscal tear, stress concentration

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Authors: Bronwyn Griffin, Caroline Acton, Tona Gillen, Roy Kimble

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Background: Bicycle riding is a common recreational activity enjoyed by many children throughout Australia that has been associated with the usual caveat of benefits related to exercise and recreation. Given Australia was the first country in the world to introduce cyclist helmet laws in 1991, very few publications have reviewed paediatric cycling injuries (fatal or non-fatal) since. Objectives: To identify trends in children (0-16 years) who required admission for greater than 24 hours following a bicycle-related injury (fatal and non-fatal) in Queensland. Further, to discuss changes that have occurred in paediatric cycling injury trends in Queensland since a prominent local study/publication in 1995. This paper aims to establish evidence to inform interventions promoting safer riding to parents, children and communities. Methods: Data on paediatric (0-16 years) cycling injuries in Queensland resulting in hospital admission more than 24 hours across three tertiary paediatric hospitals in Brisbane between November 2008-June 2015 was compiled by the Paediatric Trauma Data Registry for non-fatal injuries. The Child Death Review Team at the Queensland Families and Childhood Commission provided data on fatalities in children <17years from (June 2004 –June 2015). Comparing trends to a local study published in 1995 Results: Between 2008-2015 there were 197 patients admitted for greater than 24 hours following a cycling injury. The median age was 11 years, with males more frequently involved (n=139, 87%) compared to females. Mean length of stay was three days, with 47 (28%) children admitted to PICU, location of injury was most often the street (n=63, 37%). Between 2004 –2015 there were 15 fatalities (Incidence rate 0.25/100,000); all were male, 14/15 occurred on the street, with eight stated to have not been wearing a helmet, 11/15 children came from the least advantaged socio-economic group (SEIFA) compared to a local publication in 1995, finding of 94 fatalities between (1981-1992). Conclusions: There has been a notable decrease in incidence of fatalities between the two time periods with incidence rates dropping from 1.75-0.25/100,000. More statistics need to be run to ascertain if this is a true reduction or perhaps a decrease in children riding bicycles. Injuries that occur on the street that come in contact with a car remain of serious concern. The purpose of this paper is not to discourage bicycle riding among child and adolescent populations, rather, inform parents and the wider community about the risks associated with cycling in order to reduce injuries associated with this sport, whilst promoting safe cycling.

Keywords: paediatric, cycling, trauma, prevention, emergency

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Authors: Kuo-Wei Liao, Jessica Gitomarsono

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Bridge reliability against natural hazards such as floods or earthquakes is an interdisciplinary problem that involves a wide range of knowledge. Moreover, due to the global climate change, engineers have to design a structure against the multi-hazard threats. Currently, few of the practical design guideline has included such concept. The bridge foundation in Taiwan often does not have a uniform width. However, few of the researches have focused on safety evaluation of a bridge with a complex pier. Investigation of the scouring depth under such situation is very important. Thus, this study first focuses on investigating and improving the scour prediction formula for a bridge with complicated foundation via experiments and artificial intelligence. Secondly, a probabilistic design procedure is proposed using the established prediction formula for practical engineers under the multi-hazard attacks.

Keywords: bridge, reliability, multi-hazards, scour

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Authors: James Britton, Vijaya Krishna, Manus Biggs, Abhay Pandit

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Regeneration of the spinal cord after injury remains a great challenge due to the complexity of this organ. Inflammation and gliosis at the injury site hinder the outgrowth of axons and hence prevent synaptic reconnection and reinnervation. Hyaluronic acid (HA) is the main component of the spinal cord extracellular matrix and plays a vital role in cell proliferation and axonal guidance. In this study, we have synthesized and characterized a photo-cross-linkable HA-tyramine (tyr) hydrogel from a chemical, mechanical, electrical, biological and structural perspective. From our experimentation, we have found that HA-tyr can be synthesized with controllable degrees of tyramine substitution using click chemistry. The complex modulus (G*) of HA-tyr can be tuned to mimic the mechanical properties of the native spinal cord via optimization of the photo-initiator concentration and UV exposure. We have examined the degree of tyramine-tyramine covalent bonding (polymerization) as a function of UV exposure and photo-initiator use via Photo and Nuclear magnetic resonance spectroscopy. Both swelling and enzymatic degradation assays were conducted to examine the resilience of our 3D printed hydrogel constructs in-vitro. Using a femtosecond 780nm laser, the two-photon polymerization of HA-tyr hydrogel in the presence of riboflavin photoinitiator was optimized. A laser power of 50mW and scan speed of 30,000 μm/s produced high-resolution spatial patterning within the hydrogel with sustained mechanical integrity. Using dorsal root ganglion explants, the cytocompatibility of photo-crosslinked HA-tyr was assessed. Using potentiometry, the electrical conductivity of photo-crosslinked HA-tyr was assessed and compared to that of native spinal cord tissue as a function of frequency. In conclusion, we have developed a biocompatible hydrogel that can be used for photolithographic 3D printing to fabricate tissue engineered constructs for neural tissue regeneration applications.

Keywords: 3D printing, hyaluronic acid, photolithography, spinal cord injury

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Authors: B. K. Umesh Kumar, Waleed Al Shukaili

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Air travel is one of the safest modes of travel. Inflight injuries occur due to various factors such as air turbulence, spillage of hot liquids, and fall of improperly stowed overhead baggage. Injuries occur not only to passengers but also to the flight attendants who are handling the passengers throughout the flight. A retrospective study of all records of crew safety report by the captain of the aircraft for all the flights from 01 Mar 2015 to 31 Mar 2019 in a National Carrier of Middle Eastern country, were analyzed. There was one injury to Flight attendant every 1200 flights. Commonest aircraft involved was Boeing. Inflight phase had 82% of all injuries. 63% of accidents involved female Attendants. Commonest age group involved was from 25-30 years. Cart and container injuries were the commonest and accounted for nearly 62% of the total injuries followed by turbulence. Back injuries were the commonest injuries followed by ankle, shoulder, and burns. Mean days of absence from work seen in shoulder injuries 40 days followed by injuries to back, which accounted for 38 Days. Reduction in injuries to flight attendants can be brought about by proper selection of crew, reduction in cart load. Proper maintenance of cart and container plays a major role in prevention of occupational accidents.

Keywords: flight attendants, in-flight injuries, types of injuries, work related injury prevention

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Authors: Jan Stodt, Christoph Reich

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The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.

Keywords: audit, machine learning, assessment, metrics

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Authors: Peifang Hsieh

Abstract:

The prevention of child abuse has aroused serious concerns in Taiwan because of the disparity between the increasing amount of reported child abuse cases that doubled over the past decade and the scarcity of social workers. New Taipei city, with the most population in Taiwan and over 70% of its 4 million citizens are migrant families in which the needs of children can be easily neglected due to insufficient support from relatives and communities, sees urgency for a social support system, by preemptively identifying and outreaching high-risk families of child abuse, so as to offer timely assistance and preventive measure to safeguard the welfare of the children. Big data analysis is the inspiration. As it was clear that high-risk families of child abuse have certain characteristics in common, New Taipei city decides to consolidate detailed background information data from departments of social affairs, education, labor, and health (for example considering status of parents’ employment, health, and if they are imprisoned, fugitives or under substance abuse), to cross-reference for accurate and prompt identification of the high-risk families in need. 'The Service Center for High-Risk Families' (SCHF) was established to integrate data cross-departmentally. By utilizing the machine learning 'random forest method' to build a risk prediction model which can early detect families that may very likely to have child abuse occurrence, the SCHF marks high-risk families red, yellow, or green to indicate the urgency for intervention, so as to those families concerned can be provided timely services. The accuracy and recall rates of the above model were 80% and 65%. This prediction model can not only improve the child abuse prevention process by helping social workers differentiate the risk level of newly reported cases, which may further reduce their major workload significantly but also can be referenced for future policy-making.

Keywords: child abuse, high-risk families, big data analysis, risk prediction model

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Authors: Siti Nurhidayah Naqiah Abdull Rani, Herlina Abdul Rahim

Abstract:

Near infrared (NIR) spectroscopy has always been of great interest in the food and agriculture industries. The development of predictive models has facilitated the estimation process in recent years. In this research, 176 crude palm oil (CPO) samples acquired from Felda Johor Bulker Sdn Bhd were studied. A FOSS NIRSystem was used to tak e absorbance measurements from the sample. The wavelength range for the spectral measurement is taken at 1600nm to 1900nm. Partial Least Square Regression (PLSR) prediction model with 50 optimal number of principal components was implemented to study the relationship between the measured Free Fatty Acid (FFA) values and the measured spectral absorption. PLSR showed predictive ability of FFA values with correlative coefficient (R) of 0.9808 for the training set and 0.9684 for the testing set.

Keywords: palm oil, fatty acid, NIRS, PLSR

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Authors: Zane Turner, Kevin Labille, Susan Gauch

Abstract:

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We present a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

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Authors: Zane Turner, Kevin Labille, Susan Gauch

Abstract:

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexicon built from financial conference call logs. We introduce a method to generate a sentiment lexicon based upon an existing probabilistic approach. By using a domain-specific lexicon, we outperform traditional techniques and demonstrate that domain-specific sentiment lexicons provide higher accuracy than generic sentiment lexicons when predicting stock price change.

Keywords: computational finance, sentiment analysis, sentiment lexicon, stock movement prediction

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Authors: Bidya Dhar Sahu, Meghana Koneru, R. Shyam Sunder, Ramakrishna Sistla

Abstract:

Environmental and occupational exposure to hexavalent chromium [Cr(VI)] via textile manufacture, metallurgy, spray paints, stainless steel industries, drinking water containing chromium are often known to cause acute renal injury in humans and animals. Nephrotoxicity is the major effect of chromium poisoning. In the present study, we investigated the potential renoprotective effect and underlying mechanisms of carvedilol using rat model of potassium dichromate (K2Cr2O7)-induced nephrotoxicity. Exploration of the underlying mechanisms of carvedilol revealed that carvedilol attenuated nuclear translocation and DNA binding activity of NF-κB (p65), restored antioxidant and mitochondrial respiratory enzyme activities and attenuated apoptosis related protein expressions in kidney tissues. The serum levels of TNF-α, the renal iNOS and myeloperoxidase activity were significantly decreased in carvedilol pre-treated K2Cr2O7-induced nephrotoxic rats. These results were further supported and confirmed by histological findings. In conclusion, the findings of the present study demonstrated that carvedilol is an effective chemoprotectant against K2Cr2O7-induced nephrotoxicity in rats.

Keywords: apoptosis, carvedilol, inflammation, potassium dichromate-induced nephrotoxicity, applied pharmacology

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Authors: Divvela Vishnu Sai Kumar, Deepak Arora, Sheenu Rizvi

Abstract:

Forecasting a cyclone through satellite images consists of the estimation of the intensity of the cyclone and predicting it before a cyclone comes. This research work can help people to take safety measures before the cyclone comes. The prediction of the intensity of a cyclone is very important to save lives and minimize the damage caused by cyclones. These cyclones are very costliest natural disasters that cause a lot of damage globally due to a lot of hazards. Authors have proposed five different CNN (Convolutional Neural Network) models that estimate the intensity of cyclones through INSAT-3D IR images. There are a lot of techniques that are used to estimate the intensity; the best model proposed by authors estimates intensity with a root mean squared error (RMSE) of 10.02 kts.

Keywords: estimating cyclone intensity, deep learning, convolution neural network, prediction models

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Authors: Dorota Witkowska, Paweł Gosek, Lukasz Swiecicki, Wojciech Jernajczyk, Bruce J. West, Miroslaw Latka

Abstract:

In clinical practice, the selection of an antidepressant often degrades to lengthy trial-and-error. In this work we employ a normalized wavelet power of alpha waves as a biomarker of antidepressant treatment response. This novel EEG metric takes into account both non-stationarity and intersubject variability of alpha waves. We recorded resting, 19-channel EEG (closed eyes) in 22 inpatients suffering from unipolar (UD, n=10) or bipolar (BD, n=12) depression. The EEG measurement was done at the end of the short washout period which followed previously unsuccessful pharmacotherapy. The normalized alpha wavelet power of 11 responders was markedly different than that of 11 nonresponders at several, mostly temporoparietal sites. Using the prediction of treatment response based on the normalized alpha wavelet power, we achieved 81.8% sensitivity and 81.8% specificity for channel T4.

Keywords: alpha waves, antidepressant, treatment outcome, wavelet

Procedia PDF Downloads 316