Search results for: injury prediction

Authors: Hiroaki Hirose, Hiroshi Ikeda, Isao Takeda

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The Japanese version of the SF-36 has been employed to assess individuals’ health-related QOL (HRQOL). This study aimed to clarify the HRQOL of motorists with a spinal cord injury, in order to compare these individuals' SF-36 scores and national standard values. A total of 100 motorists with a spinal cord injury participated in this study. Participants’ HRQOL was evaluated using the Japanese version of the SF-36 (second edition). The score for each subscale was standardized based on data on the Japanese population. The average scores for NPF, NRP, NBP, NGH, NVT, NSF, NRE, and NMH were 10.9, 41.8, 45.9, 47.1, 46.1, 46.7, 46.0, and 47.4 points, respectively. Subjects showed significantly lower scores for NPF and NRP compared with national standard values, which were both ≤ 45.0 points, but relatively normal scores for the other items: NBP, NGH, NVT, NSF, NRE and NMH (> 45.0 points). The average scores for PCS, MCS and RCS were 21.9, 56.0, and 50.0 points, respectively. Subjects showed a significantly lower PCS score (≤ 20.0 points); however, the MCS score was higher (> 55.0 points) along with a relatively normal RCS score in these individuals (= 50.0 points).

Keywords: health-related QOL, HRQOL, SF-36, motorist, spinal cord injury, Japan

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Authors: P. S. Jagadeesh Kumar, S. Meenakshi Sundaram, Wenli Hu, Yang Yung

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In 3D video games, the dominance of production is unceasingly growing with a protruding level of affordability in terms of budget. Afterward, the automation of speech-lip synchronization technique is customarily onerous and has advanced a critical research subject in virtual reality based 3D video games. This paper presents one of these automatic tools, precisely riveted on the synchronization of the speech and the lip movement of the game characters. A robust and precise speech recognition segment that systematized with Algebraic Code Excited Linear Prediction method is developed which unconventionally delivers lip sync results. The Algebraic Code Excited Linear Prediction algorithm is constructed on that used in code-excited linear prediction, but Algebraic Code Excited Linear Prediction codebooks have an explicit algebraic structure levied upon them. This affords a quicker substitute to the software enactments of lip sync algorithms and thus advances the superiority of service factors abridged production cost.

Keywords: algebraic code excited linear prediction, speech-lip synchronization, video games, virtual reality

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Authors: Pradeep Singh, Shrish Verma

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Software fault prediction models are created by using the source code, processed metrics from the same or previous version of code and related fault data. Some company do not store and keep track of all artifacts which are required for software fault prediction. To construct fault prediction model for such company, the training data from the other projects can be one potential solution. The earlier we predict the fault the less cost it requires to correct. The training data consists of metrics data and related fault data at function/module level. This paper investigates fault predictions at early stage using the cross-project data focusing on the design metrics. In this study, empirical analysis is carried out to validate design metrics for cross project fault prediction. The machine learning techniques used for evaluation is Naïve Bayes. The design phase metrics of other projects can be used as initial guideline for the projects where no previous fault data is available. We analyze seven data sets from NASA Metrics Data Program which offer design as well as code metrics. Overall, the results of cross project is comparable to the within company data learning.

Keywords: software metrics, fault prediction, cross project, within project.

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Authors: Haozhe Xiang

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With the rise of the Internet of Things era, intelligent products are gradually integrating into people's lives. Pedestrian trajectory prediction has become a key issue, which is crucial for the motion path planning of intelligent agents such as autonomous vehicles, robots, and drones. In the current technological context, deep learning technology is becoming increasingly sophisticated and gradually replacing traditional models. The pedestrian trajectory prediction algorithm combining neural networks and attention mechanisms has significantly improved prediction accuracy. Based on in-depth research on deep learning and pedestrian trajectory prediction algorithms, this article focuses on physical environment modeling and learning of historical trajectory time dependence. At the same time, social interaction between pedestrians and scene interaction between pedestrians and the environment were handled. An improved pedestrian trajectory prediction algorithm is proposed by analyzing the existing model architecture. With the help of these improvements, acceptable predicted trajectories were successfully obtained. Experiments on public datasets have demonstrated the algorithm's effectiveness and achieved acceptable results.

Keywords: deep learning, graph convolutional network, attention mechanism, LSTM

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Authors: Sally Shepherd, Jessica Wong, David Read

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Traumatic brachiocephalic artery aneurysm is a rare injury that typically occurs as a result of a blunt chest injury. A 19-year-old female sustained a head-on, high speed motor vehicle crash into a tree. Upon release after 45 minutes of entrapment, she was tachycardic but normotensive, with a significant seatbelt sign across her chest and open deformed right thigh with weak pulses in bilateral lower limbs. A chest XR showed mild upper mediastinal widening. A CT trauma series plus gated CT chest revealed a grade 3a aortic arch transection with brachiocephalic pseudoaneurysm. Endovascular repair of the brachiocephalic artery was attempted post-presentation but was unsuccessful as the first stent migrated to the infrarenal abdominal aorta and the second stent across the brachiocephalic artery origin had a persistent leak at the base. She was transferred to Intensive Care for strict blood pressure control. She returned to theatre 5 hours later for a median sternotomy, aortic arch repair with an 8mm graft extraction, and excision of the innominate artery pseudoaneurysm. She had an uncomplicated post-operative recovery. This case highlights that brachiocephalic artery injury is a rare but potentially lethal injury as a result of blunt chest trauma. Safe management requires a combined Vascular and Cardiothoracic team approach, as stenting alone may be insufficient.

Keywords: blunt chest injury, Brachiocephalic aneurysm, innominate artery, trauma

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: R. Christopher, C. Brandt, N. Damons

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Background: The sequence of prevention model states that by constant assessment of injury, injury mechanisms and risk factors are identified, highlighting that collecting and recording of data is a core approach for preventing injuries. Several screening tools are available for use in the clinical setting. These screening techniques only recently received research attention, hence there is a dearth of inconsistent and controversial data regarding their applicability, validity, and reliability. Several systematic reviews related to common soccer injuries have been conducted; however, none of them addressed the screening tools for common soccer injuries. Objectives: The purpose of this study was to conduct a review of screening tools and their accuracy for common injuries in soccer. Methods: A systematic scoping review was performed based on the Joanna Briggs Institute procedure for conducting systematic reviews. Databases such as SPORT Discus, Cinahl, Medline, Science Direct, PubMed, and grey literature were used to access suitable studies. Some of the key search terms included: injury screening, screening, screening tool accuracy, injury prevalence, injury prediction, accuracy, validity, specificity, reliability, sensitivity. All types of English studies dating back to the year 2000 were included. Two blind independent reviewers selected and appraised articles on a 9-point scale for inclusion as well as for the risk of bias with the ACROBAT-NRSI tool. Data were extracted and summarized in tables. Plot data analysis was done, and sensitivity and specificity were analyzed with their respective 95% confidence intervals. I² statistic was used to determine the proportion of variation across studies. Results: The initial search yielded 95 studies, of which 21 were duplicates, and 54 excluded. A total of 10 observational studies were included for the analysis: 3 studies were analysed quantitatively while the remaining 7 were analysed qualitatively. Seven studies were graded low and three studies high risk of bias. Only high methodological studies (score > 9) were included for analysis. The pooled studies investigated tools such as the Functional Movement Screening (FMS™), the Landing Error Scoring System (LESS), the Tuck Jump Assessment, the Soccer Injury Movement Screening (SIMS), and the conventional hamstrings to quadriceps ratio. The accuracy of screening tools was of high reliability, sensitivity and specificity (calculated as ICC 0.68, 95% CI: 52-0.84; and 0.64, 95% CI: 0.61-0.66 respectively; I² = 13.2%, P=0.316). Conclusion: Based on the pooled results from the included studies, the FMS™ has a good inter-rater and intra-rater reliability. FMS™ is a screening tool capable of screening for common soccer injuries, and individual FMS™ scores are a better determinant of performance in comparison with the overall FMS™ score. Although meta-analysis could not be done for all the included screening tools, qualitative analysis also indicated good sensitivity and specificity of the individual tools. Higher levels of evidence are, however, needed for implication in evidence-based practice.

Keywords: accuracy, screening tools, sensitivity, soccer injuries, specificity

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Authors: Yong-Il Lee, Do-Yeon Hwang, Won-Seog Jeong, Duckshin Park

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Recently, because of the high-speed trains forced ventilation, it is important to control the ventilation. The ventilation is for controlling various contaminants, temperature, and humidity. The high-speed train route is straight to a destination having a high speed. And there are many mountainous areas in Korea. So, tunnel rate is higher then other country. KTX HVAC block off the outdoor air, when entering tunnel. So the high tunnel rate is an effect of ventilation in the KTX cabin. It is important to reduction rate in CO2 concentration prediction. To meet the air quality of the public transport vehicles recommend standards, the KTX cabin of CO2 concentration should be managed. In this study, the concentration change was predicted by CO2 prediction simulation in route to be opened.

Keywords: CO2 prediction, KTX, ventilation, infrastructure and transportation engineering

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Authors: Katawut Kaewbanjong

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We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably.

Keywords: prediction model, statistical analysis, software project, user satisfaction factor

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Authors: K. Petcharaporn, S. Kumchoo

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The acidity (citric acid) is one of the chemical contents that can refer to the internal quality and the maturity index of tomato. The titratable acidity (%TA) can be predicted by a non-destructive method prediction by using the transmittance short wavelength (SW-NIR). Spectroscopy in the wavelength range between 665-955 nm. The set of 167 tomato samples divided into groups of 117 tomatoes sample for training set and 50 tomatoes sample for test set were used to establish the calibration model to predict and measure %TA by partial least squares regression (PLSR) technique. The spectra were pretreated with MSC pretreatment and it gave the optimal result for calibration model as (R = 0.92, RMSEC = 0.03%) and this model obtained high accuracy result to use for %TA prediction in test set as (R = 0.81, RMSEP = 0.05%). From the result of prediction in test set shown that the transmittance SW-NIR spectroscopy technique can be used for a non-destructive method for %TA prediction of tomatoes.

Keywords: tomato, quality, prediction, transmittance, titratable acidity, citric acid

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Authors: Venkat S. Somayajula

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Ground surface temperature history prediction model plays a vital role in determining standards for international nuclear waste management. International standards for borehole based nuclear waste disposal require paleoclimate cycle predictions on scale of a million forward years for the place of waste disposal. This research focuses on developing a paleoclimate cycle prediction model using Bayesian long-short term memory (LSTM) neural architecture operated on accumulated borehole temperature history data. Bayesian models have been previously used for paleoclimate cycle prediction based on Monte-Carlo weight method, but due to limitations pertaining model coupling with certain other prediction networks, Bayesian models in past couldn’t accommodate prediction cycle’s over 1000 years. LSTM has provided frontier to couple developed models with other prediction networks with ease. Paleoclimate cycle developed using this process will be trained on existing borehole data and then will be coupled to surface temperature history prediction networks which give endpoints for backpropagation of LSTM network and optimize the cycle of prediction for larger prediction time scales. Trained LSTM will be tested on past data for validation and then propagated for forward prediction of temperatures at borehole locations. This research will be beneficial for study pertaining to nuclear waste management, anthropological cycle predictions and geophysical features

Keywords: Bayesian long-short term memory neural network, borehole temperature, ground surface temperature history, paleoclimate cycle

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Authors: Jiping Cai, Ningzhi Wangyang, Jun Shao

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Traumatic brain injury (TBI) is one of the major causes of morbidity and mortality that leads to structural and functional damage in several parts of the brain, such as cranial nerves, optic nerve tract or other circuitry involved in vision and occipital lobe, depending on its location and severity. As a result, the function associated with vision processing and perception are significantly affected and cause blurred vision, double vision, decreased peripheral vision and blindness. Here two cases complaining of monocular vision loss (actually temporal hemianopia) due to traumatic chiasmal syndrome after frontal head injury were reported, and were compared the findings with individual case reports published in the literature. Reported cases of traumatic chiasmal syndrome appear to share some common features, such as injury to the frontal bone and fracture of the anterior skull base. The degree of bitemporal hemianopia and visual loss acuity have a variable presentation and was not necessarily related to the severity of the craniocerebral trauma. Chiasmal injury may occur even in the absence bony chip impingement. Isolated bitemporal hemianopia is rare and clinical improvement usually may not occur. Mechanisms of damage to the optic chiasm after trauma include direct tearing, contusion haemorrhage and contusion necrosis, and secondary mechanisms such as cell death, inflammation, edema, neurogenesis impairment and axonal damage associated with TBI. Beside visual field test, MRI evaluation of optic pathways seems to the strong objective evidence to demonstrate the impairment of the integrity of visual systems following TBI. Therefore, traumatic chiasmal syndrome should be considered as a differential diagnosis by both neurosurgeons and ophthalmologists in patients presenting with visual impairment, especially bitemporal hemianopia after head injury causing frontal and anterior skull base fracture.

Keywords: bitemporal hemianopia, brain injury, optic chiasma, traumatic chiasmal syndrome.

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Authors: Soha A. Bahanshal, Byung G. Kim

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Identification of patients at high risk for hospital readmission is of crucial importance for quality health care and cost reduction. Predicting hospital readmissions among diabetic patients has been of great interest to many researchers and health decision makers. We build a prediction model to predict hospital readmission for diabetic patients within 30 days of discharge. The core of the prediction model is a modified k Nearest Neighbor called Hybrid Fuzzy Weighted k Nearest Neighbor algorithm. The prediction is performed on a patient dataset which consists of more than 70,000 patients with 50 attributes. We applied data preprocessing using different techniques in order to handle data imbalance and to fuzzify the data to suit the prediction algorithm. The model so far achieved classification accuracy of 80% compared to other models that only use k Nearest Neighbor.

Keywords: machine learning, prediction, classification, hybrid fuzzy weighted k-nearest neighbor, diabetic hospital readmission

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Authors: Elif Ates, Naile Bilgili

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Background: One of the most important medical complications that individuals with spinal cord injury (SCI) face are the neurogenic bladder. Objectives: To review methods used for management of neurogenic bladder and their effects. Methods: The study was conducted by searching CINAHL, Ebscohost, MEDLINE, Science Direct, Ovid, ProQuest, Web of Science, and ULAKBİM National Databases for studies published between 2005 and 2015. Key words used during the search included ‘spinal cord injury’, ‘bladder injury’, ‘nursing care’, ‘catheterization’ and ‘intermittent urinary catheter’. After examination of 551 studies, 21 studies which met inclusion criteria were included in the review. Results: Mean age of individuals in all study samples was 42 years. The most commonly used bladder management method was clean intermittent catheterization (CIC). Compliance with CIC was found to be significantly related to spasticity, maximum cystometric capacity, and the person performing catheterization (p < .05). The main reason for changing the existing bladder management method was urinary tract infections (UTI). Individuals who performed CIC by themselves and who voided spontaneously had better life quality. Patient age, occupation status and whether they performed CIC by themselves or not were found to be significantly associated with depression level (p ≤ .05). Conclusion: As the most commonly used method for bladder management, CIC is a reliable and effective method, and reduces the risk of UTI development. Individuals with neurogenic bladder have a higher prevalence of depression symptoms than the normal population.

Keywords: bladder management, catheterization, nursing, spinal cord injury

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Authors: Stepan Kuchar, Martin Golasowski, Radim Vavrik, Michal Podhoranyi, Boris Sir, Jan Martinovic

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The main goal of this article is to describe the online flood monitoring and prediction system Floreon+ primarily developed for the Moravian-Silesian region in the Czech Republic and the basic process it uses for running automatic rainfall-runoff and hydrodynamic simulations along with their calibration and uncertainty modeling. It takes a long time to execute such process sequentially, which is not acceptable in the online scenario, so the use of high-performance computing environment is proposed for all parts of the process to shorten their duration. Finally, a case study on the Ostravice river catchment is presented that shows actual durations and their gain from the parallel implementation.

Keywords: flood prediction process, high performance computing, online flood prediction system, parallelization

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Authors: Suriya

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Rapid socio-economic development and urbanization have led to an increasingly serious air pollution problem in Ulaanbaatar (UB), the capital of Mongolia. PM₂.₅ pollution has become the most pressing aspect of UB air pollution. Therefore, monitoring and predicting PM₂.₅ concentration in UB is of great significance for the health of the local people and environmental management. As of yet, very few studies have used models to predict PM₂.₅ concentrations in UB. Using data from 0:00 on June 1, 2018, to 23:00 on April 30, 2020, we proposed two deep learning models based on Bayesian-optimized LSTM (Bayes-LSTM) and CNN-LSTM. We utilized hourly observed data, including Himawari8 (H8) aerosol optical depth (AOD), meteorology, and PM₂.₅ concentration, as input for the prediction of PM₂.₅ concentrations. The correlation strengths between meteorology, AOD, and PM₂.₅ were analyzed using the gray correlation analysis method; the comparison of the performance improvement of the model by using the AOD input value was tested, and the performance of these models was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The prediction accuracies of Bayes-LSTM and CNN-LSTM deep learning models were both improved when AOD was included as an input parameter. Improvement of the prediction accuracy of the CNN-LSTM model was particularly enhanced in the non-heating season; in the heating season, the prediction accuracy of the Bayes-LSTM model slightly improved, while the prediction accuracy of the CNN-LSTM model slightly decreased. We propose two novel deep learning models for PM₂.₅ concentration prediction in UB, Bayes-LSTM, and CNN-LSTM deep learning models. Pioneering the use of AOD data from H8 and demonstrating the inclusion of AOD input data improves the performance of our two proposed deep learning models.

Keywords: deep learning, AOD, PM2.5, prediction, Ulaanbaatar

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Authors: Janki Shankar, Shu Ping Chen

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Background: New immigrants are one of most vulnerable sections of the Canadian society. Unable to gain entry into Canada’s strictly regulated professions and trades, several skilled and qualified new immigrants take up precarious jobs without adequate occupational health and safety training, thereby increasing their risk of sustaining occupational injury and illness compared to Canadian born workers. Access to timely and appropriate support is critical for injured new immigrant workers who face additional challenges compared to Canadian born workers in accessing information and support post-injury. The purpose of our study was to explore the post-injury experiences and support needs of new immigrant workers who have sustained work-related injuries. Methods: Using an interpretive research approach and semi structured face to face qualitative interviews, 27 new immigrant workers from a range of industries operating in two cities in a province in Canada were interviewed. All had sustained work-related injuries and reported these to their work supervisors. A constant comparative approach was used to identify key themes across the worker experiences. Results: Findings reveal several factors that can shape the experiences of new immigrant workers and influence their return-to-work outcomes. Conclusion: Based on the insights of study participants, policies, practices, and potential interventions informed by their needs and preferences are proposed that can improve return to work outcomes for these workers.

Keywords: new immigrant workers, post-injury experiences, return to work outcomes, qualified

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Authors: A. Majidian

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The life prediction of thermal power plant components is necessary to prevent the unexpected outages, optimize maintenance tasks in periodic overhauls and plan inspection tasks with their schedules. One of the main critical components in a power plant is condenser because its failure can affect many other components which are positioned in downstream of condenser. This paper deals with factors affecting life of condenser. Failure rates dependency vs. these factors has been investigated using Artificial Neural Network (ANN) and fuzzy logic algorithms. These algorithms have shown their capabilities as dynamic tools to evaluate life prediction of power plant equipments.

Keywords: life prediction, condenser tube, neural network, fuzzy logic

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Authors: Tarek Aboueldahab, Amin Mohamed Nassar

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Wind energy is a fluctuating energy source unlike conventional power plants, thus, it is necessary to accurately predict short term wind speed to integrate wind energy in the electricity supply structure. To do so, we present a hybrid artificial intelligence model of short term wind speed prediction based on passive aggregation of the particle swarm optimization and neural networks. As a result, improvement of the prediction accuracy is obviously obtained compared to the standard artificial intelligence method.

Keywords: artificial intelligence, neural networks, particle swarm optimization, passive aggregation, wind speed prediction

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Authors: Seema Dhariwal, Kiran Maan, Ruchi Baghel, Apoorva Sharma, Poonam Rana

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Introduction: Traumatic brain injury (TBI) is defined as the temporary or permanent alteration in brain function and pathology caused by an external mechanical force. It represents the leading cause of mortality and morbidity among children and youth individuals. Various models of TBI in rodents have been developed in the laboratory to mimic the scenario of injury. Blast overpressure injury is common among civilians and military personnel, followed by accidents or explosive devices. In addition to this, the lateral Controlled cortical impact (CCI) model mimics the blunt, penetrating injury. Method: In the present study, we have developed two different mild TBI models using blast and CCI injury. In the blast model, helium gas was used to create an overpressure of 130 kPa (±5) via a shock tube, and CCI injury was induced with an impact depth of 1.5mm to create diffusive and focal injury, respectively. C57BL/6J male mice (10-12 weeks) were divided into three groups: (1) control, (2) Blast treated, (3) CCI treated, and were exposed to different injury models. Serum was collected on Day1 and day7, followed by biphasic extraction using MTBE/Methanol/Water. Prepared samples were separated on Charged Surface Hybrid (CSH) C18 column and acquired on UHPLC-Q-TOF-MS using ESI probe with inhouse optimized parameters and method. MS peak list was generated using Markerview TM. Data were normalized, Pareto-scaled, and log-transformed, followed by multivariate and univariate analysis in metaboanalyst. Result and discussion: Untargeted profiling of lipids generated extensive data features, which were annotated through LIPID MAPS® based on their m/z and were further confirmed based on their fragment pattern by LipidBlast. There is the final annotation of 269 features in the positive and 182 features in the negative mode of ionization. PCA and PLS-DA score plots showed clear segregation of injury groups to controls. Among various lipids in mild blast and CCI, five lipids (Glycerophospholipids {PC 30:2, PE O-33:3, PG 28:3;O3 and PS 36:1 } and fatty acyl { FA 21:3;O2}) were significantly altered in both injury groups at Day 1 and Day 7, and also had VIP score >1. Pathway analysis by Biopan has also shown hampered synthesis of Glycerolipids and Glycerophospholipiods, which coincides with earlier reports. It could be a direct result of alteration in the Acetylcholine signaling pathway in response to TBI. Understanding the role of a specific class of lipid metabolism, regulation and transport could be beneficial to TBI research since it could provide new targets and determine the best therapeutic intervention. This study demonstrates the potential lipid biomarkers which can be used for injury severity diagnosis and identification irrespective of injury type (diffusive or focal).

Keywords: LipidBlast, lipidomic biomarker, LIPID MAPS®, TBI

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Authors: Sruthi Ramagiri, Rajeev T.

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Free radical damage has been entailed as the major culprit in the ischemic stroke contributing for oxidative damage. Recent investigations on Hydroxy Safflower Yellow A (HSYA) suggested its role in cerebral ischemia and various neurodegenerative disorders with unidentified molecular mechanisms. The current study was designed to investigate putative therapeutic role and possible molecular mechanisms of HSYA administration during the onset of reperfusion in cerebral ischemia-reperfusion (I/R) injury in cerebral stroke. Cerebral stroke was achieved by focal ischemic model. HSYA (10 mg/kg) was injected intravenously via the tail vein 5 minutes before reperfusion. Losses of sensorimotor abilities were evaluated by neurological scoring, spontaneous locomotor activity, and rotarod performance. Extent of oxidative stress was evaluated by biochemical parameters i.e., malondialdehyde (MDA), Glutathione (GSH), Super Oxide Dismutase (SOD) and catalase levels. The infarct volume of brain was assessed by 2,3,5-triphenyl tetrazolium chloride (TTC) staining technique. Increased cerebral injury (I/R) was evidenced by motor impairment, increased infarct volume and elevation of MDA levels along with significant reduction in antioxidant i.e.,MDA levels along with significant reduction in antioxidant i.e., GSH, SOD and catalase levels when compared to sham control. However, post conditioning with HSYA (10 mg/kg, i.v.) at the onset of reperfusion has significantly ameliorated sensorimotor abilities, attenuated MDA levels and reduced the infarct volume as compared with vehicle treated I/R injury group. Moreover, HSYA treatments improved antioxidant enzyme levels as compared with vehicle treated I/R-injury group. In conclusion, it may be suggested that HSYA post conditioning could be novel therapeutic approach against I/R injury in cerebral stroke possibly through its anti-oxidant mechanism.

Keywords: HSYA, Ischemia reperfusion injury, oxidative stress, stroke

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Authors: Muhandiram Rallage Ruvini Nisansala Yatigammana, Anuruddhika Kumudu Kumari Rajakaruna Jayathilaka

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Sports injuries are common among teenagers and children engaged in organized sports. While most sports injuries are typical, some rare occurrences involve conditions such as eye, dental, cervical, and rare internal organ injuries, such as pancreatic injuries. These injuries, especially traumatic pancreatitis, require prompt attention due to their potential for severe and sometimes fatal complications. This case revolves around a real accident involving a 12-year-old girl, Piyumi, who suffered a face-to-face collision during netball practice, resulting in severe abdominal pain. After a medical examination, she was diagnosed with a rare pancreatic injury, uncommon in children compared to adults. In Piyumi’s case, she had a grade 3 pancreatic injury and underwent non-surgical management, successfully healing her wound without surgery. The study attempts to fill empirical and population gaps, addressing a rarely discussed injury experienced by a 12-year-old female netball player. The paper will also provide an in-depth understanding of pancreatic injury, which is a rare sports injury. The study’s main objective was to investigate the incidence and characteristics of pancreatic injury, particularly focusing on pancreatic trauma, among children and adolescents engaged in high-impact sports, such as netball. This research adopted a case study strategy, employing interviews as the primary data collection method. Interviews were conducted with Piyumi, her parents, and the two specialist doctors directly involved in her treatment, providing firsthand accounts and insights. By examining the case, the paper arrives at three main conclusions. Firstly, pancreatic damage is uncommon, especially in the sports world, and proper diagnosis is essential to avoiding health concerns, particularly for minors. Secondly, CT (Computed Tomography) was useful in locating the injury, as injuries can be diagnosed very well with Computed Tomography (CT) images. Finally, and most importantly, pancreatic injuries are infrequent, but trauma can still occur, particularly in high-impact sports or accidents involving extreme force or falls. These injuries should be accurately diagnosed and treated promptly.

Keywords: child athlete, pancreatic injury, rare sports injuries, sportswoman

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Authors: Thinh Ngo, Paul Rad, Brian Kelley

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Noise estimation is essential in today wireless systems for power control, adaptive modulation, interference suppression and quality of service. Deep learning (DL) has already been applied in the physical layer for modulation and signal classifications. Unacceptably low accuracy of less than 50% is found to undermine traditional application of DL classification for SNR prediction. In this paper, we use divide-and-conquer algorithm and classifier fusion method to simplify SNR classification and therefore enhances DL learning and prediction. Specifically, multiple CNNs are used for classification rather than a single CNN. Each CNN performs a binary classification of a single SNR with two labels: less than, greater than or equal. Together, multiple CNNs are combined to effectively classify over a range of SNR values from −20 ≤ SNR ≤ 32 dB.We use pre-trained CNNs to predict SNR over a wide range of joint channel parameters including multiple Doppler shifts (0, 60, 120 Hz), power-delay profiles, and signal-modulation types (QPSK,16QAM,64-QAM). The approach achieves individual SNR prediction accuracy of 92%, composite accuracy of 70% and prediction convergence one order of magnitude faster than that of traditional estimation.

Keywords: classification, CNN, deep learning, prediction, SNR

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Authors: Pengwei Qiao, Sucai Yang, Wenxia Wei

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Soil pollution has become an important issue in China. Accurate spatial distribution prediction of pollutants with interpolation methods is the basis for soil remediation in the site. However, a relatively strong variability of pollutants would decrease the prediction accuracy. Theoretically, partition interpolation can result in accurate prediction results. In order to verify the applicability of partition interpolation for a site, benzo (b) fluoranthene (BbF) in four soil layers was adopted as the research object in this paper. IDW (inverse distance weighting)-, RBF (radial basis function)-and OK (ordinary kriging)-based partition interpolation accuracies were evaluated, and their influential factors were analyzed; then, the uncertainty and applicability of partition interpolation were determined. Three conclusions were drawn. (1) The prediction error of partitioned interpolation decreased by 70% compared to unpartitioned interpolation. (2) Partition interpolation reduced the impact of high CV (coefficient of variation) and high concentration value on the prediction accuracy. (3) The prediction accuracy of IDW-based partition interpolation was higher than that of RBF- and OK-based partition interpolation, and it was suitable for the identification of highly polluted areas at a contaminated site. These results provide a useful method to obtain relatively accurate spatial distribution information of pollutants and to identify highly polluted areas, which is important for soil pollution remediation in the site.

Keywords: accuracy, applicability, partition interpolation, site, soil pollution, uncertainty

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Authors: Amr A. Fouad, Waleed H. Albuali, Iyad Jresat

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The protective effect of thymoquinone (TQ) was investigated in rats exposed to testicular injury induced by sodium arsenite (10mg/kg/day, orally, for two days). TQ treatment (10mg/kg/day, intraperitoneal injection) was applied for five days, starting three day before arsenic administration. TQ significantly attenuated the arsenic-induced decreases of serum testosterone, and testicular reduced glutathione level, and significantly decreased the elevations of testicular malondialdehyde and nitric oxide levels resulted from arsenic administration. Also, TQ ameliorated the arsenic-induced testicular tissue injury observed by histopathological examination. In addition, TQ decreased the arsenic-induced expression of inducible nitric oxide synthase and caspase-3 in testicular tissue. It was concluded that TQ may represent a potential candidate to protect against arsenic-induced testicular injury.

Keywords: thymoquinone, arsenic, testes, rats

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Authors: Engin Eyceyurt, Josko Zec

Abstract:

The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application.

Keywords: drive test, LTE, machine learning, uplink throughput prediction

Procedia PDF Downloads 126

Authors: Pingshan Chen, Zhiliang Dong

Abstract:

In order to estimate the post-construction settlement more objectively, the power-polynomial model is proposed, which can reflect the trend of settlement development based on the observed settlement data. It was demonstrated by an actual case history of an embankment, and during the prediction. Compared with the other three prediction models, the power-polynomial model can estimate the post-construction settlement more accurately with more simple calculation.

Keywords: prediction, model, post-construction settlement, soft ground

Procedia PDF Downloads 399

Authors: Yang Zhang, Jian He

Abstract:

Heart disease is one of the leading causes of death in the world, and coronary heart disease (CHD) is one of the major heart diseases. Electrocardiogram (ECG) is widely used in the detection of heart diseases, but the traditional manual method for CHD prediction by analyzing ECG requires lots of professional knowledge for doctors. This paper introduces sliding window and continuous wavelet transform (CWT) to transform ECG signals into images, and then ResNet and Bi-LSTM are introduced to build the ECG feature extraction network (namely ECGNet). At last, an auxiliary system for coronary heart disease prediction was developed based on modified ResNet18 and Bi-LSTM, and the public ECG dataset of CHD from MIMIC-3 was used to train and test the system. The experimental results show that the accuracy of the method is 83%, and the F1-score is 83%. Compared with the available methods for CHD prediction based on ECG, such as kNN, decision tree, VGGNet, etc., this method not only improves the prediction accuracy but also could avoid the degradation phenomenon of the deep learning network.

Keywords: Bi-LSTM, CHD, ECG, ResNet, sliding window

Procedia PDF Downloads 55

Authors: Fanniyah Anis, Bram Kilapong

Abstract:

Myocardial reperfusion injury is defined as the cellular damage that results from a period of ischemia, followed by the reestablishment of the blood supply to the infarcted tissue. Ventricular tachycardia is one of the most commonly encountered reperfusion arrhythmia as one of the types of myocardial perfusion injury. Prompt and early treatment can reduce mortality, despite limited resources of the hospital in high risk patients with history of triple vessel disease. Case report, Male 53 years old has been diagnosed with NSTEMI with 3VD and comorbid disease of Hypertension and has undergone revascularization management with Percutaneous coronary intervention. Ventricular tachycardia leading to cardiac arrest occurred right after the stent was inserted. Resuscitation was performed for almost 2 hours until spontaneous circulation returned. Patient admitted in ICU with refractory cardiac shock despite using combination of ionotropic and vasopressor agents under standard non-invasive monitoring due to the limitation of the hospital. Angiography was performed again 5 hours later to exclude other possibilities of blockage of coronary arteries and conclude diagnosis of myocardial reperfusion injury. Patient continually managed with combination of antiplatelet agents and maintenance dose of anti-arrhythmia agents. The handling of the patient was to focus more on supportive and preventive from further deteriorating of the condition. Patient showed clinically improvement and regained consciousness within 24 hours. Patient was successfully discharged from ICU within 3 days without any neurological sequela and was discharge from hospital after 3 days observation in general ward. Limited Resource of hospital did not refrain the physician from attaining a good outcome for this myocardial reperfusion injury case and angiography alone can be used to confirm the diagnosis of myocardial reperfusion injury.

Keywords: limited resources hospital, myocardial reperfusion injury, prolonged resuscitation, refractory cardiogenic shock, reperfusion arrhythmia, revascularization, triple-vessel disease

Procedia PDF Downloads 274

Authors: Dina Muharib

Abstract:

Specific metabolic challenges are present following spinal cord injury. The acute stage is characterized by a reduction in metabolic activity, as well as a negative nitrogen balance that cannot be corrected, even with aggressive nutritional support. Metabolic demands need to be accurately monitored to avoid overfeeding. Enteral feeding is the optimal route following SCI. When oral feeding is not possible, nasogastric, followed by nasojejunal, then by percutaneous endoscopic gastrostomy, if necessary, is suggested.

Keywords: SCI, energy, protein, nutrition assessment, eneral feeding, nitrogen balance

Procedia PDF Downloads 429