Search results for: injury prediction

Authors: Husham Bayazed

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Immune responses following surgical trauma play a pivotal role in predicting postoperative outcomes from healing and recovery to postoperative complications. Postoperative complications, including infections and protracted recovery, occur in a significant number of about 300 million surgeries performed annually worldwide. Complications cause personal suffering along with a significant economic burden on the healthcare system in any community. The accurate prediction of postoperative complications and patient-targeted interventions for their prevention remain major clinical provocations. Recent Findings: Recent studies are focusing on immune dysregulation mechanisms that occur in response to surgical trauma as a key determinant of postoperative complications. Antecedent studies mainly were plunging into the detection of inflammatory plasma markers, which facilitate in providing important clues regarding their pathogenesis. However, recent Single-cell technologies, such as mass cytometry or single-cell RNA sequencing, have markedly enhanced our ability to understand the immunological basis of postoperative immunological trauma complications and to identify their prognostic biological signatures. Summary: The advent of proteomic technologies has significantly advanced our ability to predict the risk of postoperative complications. Multiomic modeling of patients' immune states holds promise for the discovery of preoperative predictive biomarkers and providing patients and surgeons with information to improve surgical outcomes. However, more studies are required to accurately predict the risk of postoperative complications in individual patients.

Keywords: immune dysregulation, postoperative complications, surgical trauma, flow cytometry

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Authors: Sakshi Chopra, Ashima Nehra, Sumit Sinha, Harsimarpreet Kaur, Ravindra Mohan Pandey

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Background: Cognitive rehabilitation aims to retrain brain injured individuals with cognitive deficits to restore or compensate lost functions. As illiterates or people with low literacy levels represent a significant proportion of the world, specific rehabilitation modules for such populations are indispensable. Literacy is significantly associated with all neuropsychological measures and retraining programs widely use written or spoken techniques which essentially require the patient to read or write. So, the aim of the study was to develop and standardize a literacy free neuropsychological rehabilitation program for improving cognitive functioning in patients with mild and moderate Traumatic Brain Injury (TBI). Several studies have pointed out to the impairments seen in memory, executive functioning, and attention and concentration post-TBI, so the rehabilitation program focussed on these domains. Visual item memorization, stick constructions, symbol cancellations, and colouring techniques were used to construct the retraining program. Methodology: The development of the program consisted of planning, preparing, analyzing, and revising the different modules. The construction focussed on areas of retraining immediate and delayed visual memory, planning ability, focused and divided attention, concentration, and response inhibition (to control irritability and aggression). A total of 98 home based retraining modules were prepared in the 4 domains (42 for memory, 42 for executive functioning, 7 for attention and concentration, and 7 for response inhibition). The standardization was done on 20 healthy controls to review, select and edit items. For each module, the time, errors made and errors per second were noted down, to establish the difficulty level of each module and were arranged in increasing level of difficulty over a period of 6 weeks. The retraining tasks were then administered on 11 brain injured individuals (5 after Mild TBI and 6 after Moderate TBI). These patients were referred from the Trauma Centre to Clinical Neuropsychology OPD, All India Institute of Medical Sciences, New Delhi, India. Results: The time was taken, errors made and errors per second were analysed for all domains. Education levels were divided into illiterates, up to 10 years, 10 years to graduation and graduation and above. Mean and standard deviations were calculated. Between group and within group analysis was done using the t-test. The performance of 20 healthy controls was analyzed and only a significant difference was observed on the time taken for the attention tasks and all other domains had non-significant differences in performance between different education levels. Comparing the errors, time taken between patient and control group, there was a significant difference in all the domains at the 0.01 level except the errors made on executive functioning, indicating that the tool can successfully differentiate between healthy controls and patient groups. Conclusions: Apart from the time taken for symbol cancellations, the entire cognitive rehabilitation program is literacy free. As it taps the major areas of impairment post-TBI, it could be a useful tool to rehabilitate the patient population with low literacy levels across the world. The next step is already underway to test its efficacy in improving cognitive functioning in a randomized clinical controlled trial.

Keywords: cognitive rehabilitation, illiterates, India, traumatic brain injury

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Authors: Geng Xiangren, Liu Lei, Gui Ye-Wei, Tang Wei, Wang An-ling

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The malfunction of thermal protection system (TPS) caused by aerodynamic heating is a latent trouble to aircraft structure safety. Accurately predicting the structure temperature field is quite important for the TPS design of hypersonic vehicle. Since Thornton’s work in 1988, the coupled method of aerodynamic heating and heat transfer has developed rapidly. However, little attention has been paid to the influence of structural deformation on aerodynamic heating and structural temperature field. In the flight, especially the long-endurance flight, the structural deformation, caused by the aerodynamic heating and temperature rise, has a direct impact on the aerodynamic heating and structural temperature field. Thus, the coupled interaction cannot be neglected. In this paper, based on the method of static aero-thermo-elasticity, considering the influence of aero-thermo-elasticity deformation, the aerodynamic heating and heat transfer coupled results of hypersonic vehicle wing model were calculated. The results show that, for the low-curvature region, such as fuselage or center-section wing, structure deformation has little effect on temperature field. However, for the stagnation region with high curvature, the coupled effect is not negligible. Thus, it is quite important for the structure temperature prediction to take into account the effect of elastic deformation. This work has laid a solid foundation for improving the prediction accuracy of the temperature distribution of aircraft structures and the evaluation capacity of structural performance.

Keywords: aerothermoelasticity, elastic deformation, structural temperature, multi-field coupling

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Authors: Brandon Knopp, Matthew Harris

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Purpose: Tip-apex distance (TAD) has long been discussed as a metric for determining risk of failure in the fixation of peritrochanteric fractures. TAD measurements over 25 millimeters (mm) have been associated with higher rates of screw cut out and other complications in the first several months after surgery. However, there is limited evidence for the efficacy of this measurement in predicting the long-term risk of negative outcomes following hip fixation surgery. The purpose of our study was to investigate risk factors including TAD for hospital readmission, loss of pre-injury ambulation and development of complications within 1 year after hip fixation surgery. Methods: A retrospective review of proximal hip fractures treated with single screw intramedullary devices between 2016 and 2020 was performed at a 327-bed regional medical center. Patients included had a postoperative follow-up of at least 12 months or surgery-related complications developing within that time. Results: 44 of the 67 patients in this study met the inclusion criteria with adequate follow-up post-surgery. There was a total of 10 males (22.7%) and 34 females (77.3%) meeting inclusion criteria with a mean age of 82.1 (± 12.3) at the time of surgery. The average TAD in our study population was 19.57mm and the average 1-year readmission rate was 15.9%. 3 out of 6 patients (50%) with a TAD > 25mm were readmitted within one year due to surgery-related complications. In contrast, 3 out of 38 patients (7.9%) with a TAD < 25mm were readmitted within one year due to surgery-related complications (p=0.0254). Individual TAD measurements, averaging 22.05mm in patients readmitted within 1 year of surgery and 19.18mm in patients not readmitted within 1 year of surgery, were not significantly different between the two groups (p=0.2113). Conclusions: Our data indicate a significant improvement in hospital readmission rates up to one year after hip fixation surgery in patients with a TAD < 25mm with a decrease in readmissions of over 40% (50% vs 7.9%). This result builds upon past investigations by extending the follow-up time to 1 year after surgery and utilizing hospital readmissions as a metric for surgical success. With the well-documented physical and financial costs of hospital readmission after hip surgery, our study highlights a reduction of TAD < 25mm as an effective method of improving patient outcomes and reducing financial costs to patients and medical institutions. No relationship was found between TAD measurements and secondary outcomes, including loss of pre-injury ambulation and development of complications.

Keywords: hip fractures, hip reductions, readmission rates, open reduction internal fixation

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Authors: Nadzeya Marozkina, Joe Zein, Benjamin Gaston

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Introduction: We have observed that many patients with Primary Ciliary Dyskinesia (PCD) benefit from asthma medications. In healthy airways, the ciliary function is normal. Antigens and irritants are rapidly cleared, and NO enters the gas phase normally to be exhaled. In the PCD airways, however, antigens, such as Dermatophagoides, are not as well cleared. This defect leads to oxidative stress, marked by increased DUOX1 expression and decreased superoxide dismutase [SOD] activity (manuscript under revision). H₂O₂, in high concentrations in the PCD airway, injures the airway. NO is oxidized rather than being exhaled, forming cytotoxic peroxynitrous acid. Thus, antigen stasis on PCD airway epithelium leads to airway injury and may predispose PCD patients to asthma. Indeed, recent population genetics suggest that PCD genes may be associated with asthma. We therefore hypothesized that PCD patients would be predisposed to having asthma. Methods. We analyzed our database of 18 million individual electronic medical records (EMRs) in the Indiana Network for Patient Care research database (INPCR). There is not an ICD10 code for PCD itself; code Q34.8 is most commonly used clinically. To validate analysis of this code, we queried patients who had an ICD10 code for both bronchiectasis and situs inversus totalis in INPCR. We also studied a validation cohort using the IBM Explorys® database (over 80 million individuals). Analyses were adjusted for age, sex and race using a 1 PCD: 3 controls matching method in INPCR and multivariable logistic regression in the IBM Explorys® database. Results. The prevalence of asthma ICD10 codes in subjects with a code Q34.8 was 67% vs 19% in controls (P < 0.0001) (Regenstrief Institute). Similarly, in IBM*Explorys, the OR [95% CI] for having asthma if a patient also had ICD10 code 34.8, relative to controls, was =4.04 [3.99; 4.09]. For situs inversus alone the OR [95% CI] was 4.42 [4.14; 4.71]; and bronchiectasis alone the OR [95% CI] =10.68 (10.56; 10.79). For both bronchiectasis and situs inversus together, the OR [95% CI] =28.80 (23.17; 35.81). Conclusions: PCD causes antigen stasis in the human airway (under review), likely predisposing to asthma in addition to oxidative and nitrosative stress and to airway injury. Here, we show that, by several different population-based metrics, and using two large databases, patients with PCD appear to have between a three- and 28-fold increased risk of having asthma. These data suggest that additional studies should be undertaken to understand the role of ciliary dysfunction in the pathogenesis and genetics of asthma. Decreased antigen clearance caused by ciliary dysfunction may be a risk factor for asthma development.

Keywords: antigen, PCD, asthma, nitric oxide

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Authors: Nadish Anand, Richard D. Gould

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A simplistic model is introduced for determining the thermal characteristics of a Low-rise Residential (LRR) building and then predicts the energy usage by its Heating Ventilation & Air Conditioning (HVAC) system according to changes in weather conditions which are reflected in the Ambient Temperature (Outside Air Temperature). The LRR buildings are treated as a simple lump for solving the heat transfer problem and the model is derived using the lumped capacitance model of transient conduction heat transfer from bodies. Since most contemporary HVAC systems have a thermostat control which will have an offset temperature and user defined set point temperatures which define when the HVAC system will switch on and off. The aim is to predict without any error the Body Temperature (i.e. the Inside Air Temperature) which will estimate the switching on and off of the HVAC system. To validate the mathematical model derived from lumped capacitance we have used EnergyPlus simulation engine, which simulates Buildings with considerable accuracy. We have predicted through the low order model the Inside Air Temperature of a single house kept in three different climate zones (Detroit, Raleigh & Austin) and different orientations for summer and winter seasons. The prediction error from the model for the same day as that of model parameter calculation has showed an error of < 10% in winter for almost all the orientations and climate zones. Whereas the prediction error is only <10% for all the orientations in the summer season for climate zone at higher latitudes (Raleigh & Detroit). Possible factors responsible for the large variations are also noted in the work, paving way for future research.

Keywords: building energy, energy consumption, energy+, HVAC, low order model, lumped capacitance

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Authors: Said Alshukri, Mazhar Hussain Malik

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Green hydrogen is hydrogen produced using renewable energy sources. In the last few years, Oman aimed to reduce its dependency on fossil fuels. Recently, the hydrogen economy has become a global trend, and many countries have started to investigate the feasibility of implementing this sector. Oman created an alliance to establish the policy and rules for this sector. With motivation coming from both global and local interest in green hydrogen, this paper investigates the potential of producing hydrogen from wind and solar energies in three different locations in Oman, namely Duqm, Salalah, and Sohar. By using machine learning-based software “WEKA” and local metrological data, the project was designed to figure out which location has the highest wind and solar energy potential. First, various supervised models were tested to obtain their prediction accuracy, and it was found that the Random Forest (RF) model has the best prediction performance. The RF model was applied to 2021 metrological data for each location, and the results indicated that Duqm has the highest wind and solar energy potential. The system of one wind turbine in Duqm can produce 8335 MWh/year, which could be utilized in the water electrolysis process to produce 88847 kg of hydrogen mass, while a solar system consisting of 2820 solar cells is estimated to produce 1666.223 MWh/ year which is capable of producing 177591 kg of hydrogen mass.

Keywords: green hydrogen, machine learning, wind and solar energies, WEKA, supervised models, random forest

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

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Background: Soccer is one of the most common sports in the world. It is associated with a significant chance of injury either during training or during the course of an actual match. Studies on the epidemiology of soccer injuries have been widely conducted, but methodological appraisal is lacking to make evidence-based decisions. Objectives: The purpose of this study was to conduct a systematic review of common injuries in soccer and their risk factors. Methods: A systematic 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 searched. The quality of selected studies was rated, and data extracted and tabulated. Plot data analysis was done, and incidence rates and odds ratios were calculated, with their respective 95% confidence intervals. I² statistic was used to determine the proportion of variation across studies. Results: The search yielded 62 studies, of which 21 were screened for inclusion. A total of 16 studies were included for the analysis, ten for qualitative and six for quantitative analysis. The included studies had, on average, a low risk of bias and good methodological quality. The heterogeneity amongst the pooled studies was, however, statistically significant (χ²-p value < 0.001). The pooled results indicated a high incidence of soccer injuries at an incidence rate of 6.83 per 1000 hours of play. The pooled results also showed significant evidence of risk factors and the likelihood of injury occurrence in relation to these risk factors (OR=1.12 95% CI 1.07; 1.17). Conclusion: Although multiple studies are available on the epidemiology of soccer injuries and risk factors, only a limited number of studies were of sound methodology to be included in a review. There was also significant heterogeneity amongst the studies. The incidence rate of common soccer injuries was found to be 6.83 per 1000 hours of play. This incidence rate is lower than the values reported by the majority of previous studies on the occurrence of common soccer injuries. The types of common soccer injuries found by this review support the soccer injuries pattern reported in existing literature as muscle strain and ligament sprain of varying severity, especially in the lower limbs. The risk factors that emerged from this systematic review are predominantly intrinsic risk factors. The risk factors increase the risk of traumatic and overuse injuries of the lower extremities such as hamstrings and groin strains, knee and ankle sprains, and contusion.

Keywords: incidence, prevalence, risk factors, soccer injuries

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Authors: R. O. Osaseri, A. R. Usiobaifo

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The power transformer which is responsible for the voltage transformation is of great relevance in the power system and oil-immerse transformer is widely used all over the world. A prompt and proper maintenance of the transformer is of utmost importance. The dissolved gasses content in power transformer, oil is of enormous importance in detecting incipient fault of the transformer. There is a need for accurate prediction of the incipient fault in transformer oil in order to facilitate the prompt maintenance and reducing the cost and error minimization. Study on fault prediction and diagnostic has been the center of many researchers and many previous works have been reported on the use of artificial intelligence to predict incipient failure of transformer faults. In this study machine learning technique was employed by using gradient decent algorithms and Support Vector Machine (SVM) in predicting incipient fault diagnosis of transformer. The method focuses on creating a system that improves its performance on previous result and historical data. The system design approach is basically in two phases; training and testing phase. The gradient decent algorithm is trained with a training dataset while the learned algorithm is applied to a set of new data. This two dataset is used to prove the accuracy of the proposed model. In this study a transformer fault diagnostic model based on Support Vector Machine (SVM) and gradient decent algorithms has been presented with a satisfactory diagnostic capability with high percentage in predicting incipient failure of transformer faults than existing diagnostic methods.

Keywords: diagnostic model, gradient decent, machine learning, support vector machine (SVM), transformer fault

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Authors: Mohammadjavad Sotoudeheian

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COVID-19, which began in December 2019, uses the angiotensin-converting enzyme 2 (ACE2) receptor to enter and spread through the cells. ACE2 mRNA is present in almost every organ, including nasopharynx, lung, as well as the brain. Ports of entry of SARS-CoV-2 into the central nervous system (CNS) may include arterial circulation, while viremia is remarkable. However, it is imperious to develop neurological symptoms evaluation CSF analysis in patients with COVID-19, but theoretically, ACE2 receptors are expressed in cerebellar cells and may be a target for SARS-CoV-2 infection in the brain. Recent evidence agrees that SARS-CoV-2 can impact the brain through direct and indirect injury. Two biomarkers for CNS injury, glial fibrillary acidic protein (GFAP) and neurofilament light chain (NFL) detected in the plasma of patients with COVID-19. NFL, an axonal protein expressed in neurons, is related to axonal neurodegeneration, and GFAP is over-expressed in CNS inflammation. GFAP cytoplasmic accumulation causes Schwan cells to misfunction, so affects myelin generation, reduces neuroskeletal support over NfLs during CNS inflammation, and leads to axonal degeneration. Interleukin-6 (IL-6), which extensively over-express due to interleukin storm during COVID-19 inflammation, regulates gene expression, as well as GFAP through STAT molecular pathway. IL-6 also impresses the phosphoinositide 3-kinase (PI3K)/STAT/smads pathway. The PI3K/ protein kinase B (Akt) pathway is the main modulator upstream of the mammalian target of rapamycin (mTOR), and alterations in this pathway are common in neurodegenerative diseases. Most neurodegenerative diseases show a disruption of autophagic function and display an abnormal increase in protein aggregation that promotes cellular death. Therefore, induction of autophagy has been recommended as a rational approach to help neurons clear abnormal protein aggregates and survive. The mTOR is a major regulator of the autophagic process and is regulated by cellular stressors. The mTORC1 pathway and mTORC2, as complementary and important elements in mTORC1 signaling, have become relevant in the regulation of the autophagic process and cellular survival through the extracellular signal-regulated kinase (ERK) pathway.

Keywords: mTORC1, COVID-19, PI3K, autophagy, neurodegeneration

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Authors: Saba Gachpaz, Hamid Reza Heidari

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The sharp increase in population growth leads to more pressure on agricultural areas to satisfy the food supply. To achieve this, more resources should be consumed and, besides other environmental concerns, highlight sustainable agricultural development. Land-use management is a crucial factor in obtaining optimum productivity. Machine learning is a widely used technique in the agricultural sector, from yield prediction to customer behavior. This method focuses on learning and provides patterns and correlations from our data set. In this study, nine physical control factors, namely, soil classification, electrical conductivity, normalized difference water index (NDWI), groundwater level, elevation, annual precipitation, pH of water, annual mean temperature, and slope in the alluvial plain in Khuzestan (an agricultural hotspot in Iran) are used to decide the best agricultural land use for both rainfed and irrigated agriculture for ten different crops. For this purpose, each variable was imported into Arc GIS, and a raster layer was obtained. In the next level, by using training samples, all layers were imported into the python environment. A random forest model was applied, and the weight of each variable was specified. In the final step, results were visualized using a digital elevation model, and the importance of all factors for each one of the crops was obtained. Our results show that despite 62% of the study area being allocated to agricultural purposes, only 42.9% of these areas can be defined as a suitable class for cultivation purposes.

Keywords: land suitability, machine learning, random forest, sustainable agriculture

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Authors: Ahmed Alghurabi, Mysara Mohyaldinn, Shiferaw Jufar, Obai Younis, Abdullah Abduljabbar

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Erosion modeling equations were typically acquired from regulated experimental trials for solid particles entrained in single-phase or multi-phase flows. Evidently, those equations were later employed to predict the erosion damage caused by the continuous impacts of solid particles entrained in streamflow. It is also well-known that the particle impact angle and velocity do not change drastically in gas-sand flow erosion prediction; hence an accurate prediction of erosion can be projected. On the contrary, high-density fluid flows, such as water flow, through complex geometries, such as sand screens, greatly affect the sand particles’ trajectories/tracks and consequently impact the erosion rate predictions. Particle tracking models and erosion equations are frequently applied simultaneously as a method to improve erosion visualization and estimation. In the present work, computational fluid dynamic (CFD)-based erosion modeling was performed using a commercially available software; ANSYS Fluent. The continuous phase (water flow) behavior was simulated using the realizable K-epsilon model, and the secondary phase (solid particles), having a 5% flow concentration, was tracked with the help of the discrete phase model (DPM). To accomplish a successful erosion modeling, three erosion equations from the literature were utilized and introduced to the ANSYS Fluent software to predict the screen wire-slot velocity surge and estimate the maximum erosion rates on the screen surface. Results of turbulent kinetic energy, turbulence intensity, dissipation rate, the total pressure on the screen, screen wall shear stress, and flow velocity vectors were presented and discussed. Moreover, the particle tracks and path-lines were also demonstrated based on their residence time, velocity magnitude, and flow turbulence. On one hand, results from the utilized erosion equations have shown similarities in screen erosion patterns, locations, and DPM concentrations. On the other hand, the model equations estimated slightly different values of maximum erosion rates of the wire-wrapped screen. This is solely based on the fact that the utilized erosion equations were developed with some assumptions that are controlled by the experimental lab conditions.

Keywords: CFD simulation, erosion rate prediction, material loss due to erosion, water-sand flow

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Authors: Silvia Arrate, Waldo Salud, Eloy París

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The wear of cutting tools is one of the most decisive elements when planning tunneling works, programming the maintenance stops and saving the optimum stock of spare parts during the evolution of the excavation. Being able to predict the behavior of cutting tools can give a very competitive advantage in terms of costs and excavation performance, optimized to the needs of the TBM itself. The incredible evolution of data science in recent years gives the option to implement it at the time of analyzing the key and most critical parameters related to machinery with the purpose of knowing how the cutting head is performing in front of the excavated ground. Taking this as a case study, Metro Line 3 of Guadalajara in Mexico will develop the feasibility of using Specific Energy versus data science applied over parameters of Torque, Penetration, and Contact Force, among others, to predict the behavior and status of cutting tools. The results obtained through both techniques are analyzed and verified in the function of the wear and the field situations observed in the excavation in order to determine its effectiveness regarding its predictive capacity. In conclusion, the possibilities and improvements offered by the application of digital tools and the programming of calculation algorithms for the analysis of wear of cutting head elements compared to purely empirical methods allow early detection of possible damage to cutting tools, which is reflected in optimization of excavation performance and a significant improvement in costs and deadlines.

Keywords: cutting tools, data science, prediction, TBM, wear

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Authors: Sophia Liang Zhou

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For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

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Authors: Bangun F., Crevits B., Bellet T., Banet A., Boy G. A., Katili I.

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This paper seeks the incoherencies in cognitive process during an accident of Powered Two Wheelers (PTW) by understanding the factual sequences of events and causal relations for each case of accident. The principle of this approach is undertaking in-depth investigations on case per case of PTW accidents based on elaborate data acquisitions on accident sites that officially stamped in Police Crash Report (PCRs) 2012 of Medan with criteria, involved at least one PTW and resulted in serious injury and fatalities. The analysis takes into account four modules: accident chronologies, perpetrator, and victims, injury surveillance, vehicles and road infrastructures, comprising of traffic facilities, road geometry, road alignments and weather. The proposal for improvement could have provided a favorable influence on the chain of functional processes and events leading to collision. Decision Aiding Processes (DAP) assists in structuring different entities at different decisional levels, as each of these entities has its own objectives and constraints. The entities (A) are classified into 6 groups of accidents: solo PTW accidents; PTW vs. PTW; PTW vs. pedestrian; PTW vs. motor-trishaw; and PTW vs. other vehicles and consecutive crashes. The entities are also distinguished into 4 decisional levels: level of road users and street systems; operational level (crash-attended police officers or CAPO and road engineers), tactical level (Regional Traffic Police, Department of Transportation, and Department of Public Work), and strategic level (Traffic Police Headquarters (TCPHI)), parliament, Ministry of Transportation and Ministry of Public Work). These classifications will lead to conceptualization of Problem Situations (P) and Problem Formulations (I) in DAP context. The DAP concerns the sequences process of the incidents until the time the accident occurs, which can be modelled in terms of five activities of procedural rationality: identification on initial human features (IHF), investigation on proponents attributes (PrAT), on Injury Surveillance (IS), on the interaction between IHF and PrAt and IS (intercorrelation), then unravel the sequences of incidents; filtering and disclosure, which include: what needs to activate, modify or change or remove, what is new and what is priority. These can relate to the activation or modification or new establishment of law. The PrAt encompasses the problems of environmental, road infrastructure, road and traffic facilities, and road geometry. The evaluation model (MP) is generated to bridge P and I since MP is produced by the intercorrelations among IHF, PrAT and IS extracted from the PCRs 2012 of Medan. There are 7 findings of incoherences: lack of knowledge and awareness on the traffic regulations and the risks of accidents, especially when riding between 0 < x < 10 km from house, riding between 22 p.m.–05.30 a.m.; lack of engagements on procurement of IHF Data by CAPO; lack of competency of CAPO on data procurement in accident-sites; no intercorrelation among IHF and PrAt and IS in the database systems of PCRs; lack of maintenance and supervision on the availabilities and the capacities of traffic facilities and road infrastructure; instrumental bias with wash-back impacts towards the TCPHI; technical robustness with wash-back impacts towards the CAPO and TCPHI.

Keywords: decision aiding processes, evaluation model, PTW accidents, police crash reports

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Authors: Ahmed Z. Mazen, Nejat Rahmanian, Iqbal Mujtaba, Ali Hassanpour

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In oil and gas well drilling, the drill bit is an important part of the Bottom Hole Assembly (BHA), which is installed and designed to drill and produce a hole by several mechanisms. The efficiency of the bit depends on many drilling parameters such as weight on bit, rotary speed, and mud properties. When the bit is pulled out of the hole, the evaluation of the bit damage must be recorded very carefully to guide engineers in order to select the bits for further planned wells. Having a worn bit for hole drilling may cause severe damage to bit leading to cutter or cone losses in the bottom of hole, where a fishing job will have to take place, and all of these will increase the operating cost. The main factor to reduce the cost of drilling operation is to maximize the rate of penetration by analyzing real-time data to predict the drill bit wear while drilling. There are numerous models in the literature for prediction of the rate of penetration based on drilling parameters, mostly based on empirical approaches. One of the most commonly used approaches is Bourgoyne and Young model, where the rate of penetration can be estimated by the drilling parameters as well as a wear index using an empirical correlation, provided all the constants and coefficients are accurately determined. This paper introduces a new methodology to estimate the eight coefficients for Bourgoyne and Young model using the gPROMS parameters estimation GPE (Version 4.2.0). Real data collected form similar formations (12 ¼’ sections) in two different fields in Libya are used to estimate the coefficients. The estimated coefficients are then used in the equations and applied to nearby wells in the same field to predict the bit wear.

Keywords: Bourgoyne and Young model, bit wear, gPROMS, rate of penetration

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Authors: Alexander Heckman, Rohan Goswami, Zachi Attia, Paul Friedman, Peter Noseworthy, Demilade Adedinsewo, Pablo Moreno-Franco, Rickey Carter, Tathagat Narula

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Background: Postoperative atrial fibrillation (POAF) is associated with adverse health consequences, higher costs, and longer hospital stays. Utilizing existing predictive models that rely on clinical variables and circulating biomarkers, multiple societies have published recommendations on the treatment and prevention of POAF. Although reasonably practical, there is room for improvement and automation to help individualize treatment strategies and reduce associated complications. Methods and Results: In this retrospective cohort study of solid organ transplant recipients, we evaluated the diagnostic utility of a previously developed AI-based ECG prediction for silent AF on the development of POAF within 30 days of transplant. A total of 2261 non-cardiac transplant patients without a preexisting diagnosis of AF were found to have a 5.8% (133/2261) incidence of POAF. While there were no apparent sex differences in POAF incidence (5.8% males vs. 6.0% females, p=.80), there were differences by race and ethnicity (p<0.001 and 0.035, respectively). The incidence in white transplanted patients was 7.2% (117/1628), whereas the incidence in black patients was 1.4% (6/430). Lung transplant recipients had the highest incidence of postoperative AF (17.4%, 37/213), followed by liver (5.6%, 56/1002) and kidney (3.6%, 32/895) recipients. The AUROC in the sample was 0.62 (95% CI: 0.58-0.67). The relatively low discrimination may result from undiagnosed AF in the sample. In particular, 1,177 patients had at least 1 AI-ECG screen for AF pre-transplant above .10, a value slightly higher than the published threshold of 0.08. The incidence of POAF in the 1104 patients without an elevated prediction pre-transplant was lower (3.7% vs. 8.0%; p<0.001). While this supported the hypothesis that potentially undiagnosed AF may have contributed to the diagnosis of POAF, the utility of the existing AI-ECG screening algorithm remained modest. When the prediction for POAF was made using the first postoperative ECG in the sample without an elevated screen pre-transplant (n=1084 on account of n=20 missing postoperative ECG), the AUROC was 0.66 (95% CI: 0.57-0.75). While this discrimination is relatively low, at a threshold of 0.08, the AI-ECG algorithm had a 98% (95% CI: 97 – 99%) negative predictive value at a sensitivity of 66% (95% CI: 49-80%). Conclusions: This study's principal finding is that the incidence of POAF is rare, and a considerable fraction of the POAF cases may be latent and undiagnosed. The high negative predictive value of AI-ECG screening suggests utility for prioritizing monitoring and evaluation on transplant patients with a positive AI-ECG screening. Further development and refinement of a post-transplant-specific algorithm may be warranted further to enhance the diagnostic yield of the ECG-based screening.

Keywords: artificial intelligence, atrial fibrillation, cardiology, transplant, medicine, ECG, machine learning

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Authors: Diwei Lin, Amanda Jia Hui Tan

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In 2012, testicular cancer was estimated to account for 940 disability adjusted life years in Australia; of these, 450 were years lost due to premature death and 500 were years of healthy life lost due to disease, disability or injury. Testicular choriocarcinoma is one of the rarest variants of testicular germ cell tumours, accounting for less than 1% of testicular germ cell tumours and only about 0.19% of all testicular tumours. Management involves radical orchiectomy followed by chemotherapy. Even then, the prognosis is extremely poor. This case report describes a 20-year-old male with pure testicular choriocarcinoma with pulmonary metastases.

Keywords: testicular cancer, choriocarcinoma, cryptorchidism, chemotherapy, metastatic testicular cancer

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Authors: Koe Han Beng, Khoo Boo Cheong

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The rising interest of stepped hull design has been led by the demand of more efficient high-speed boat. At the same time, the need of accurate prediction method for stepped planing hull is getting more important. By understanding the flow at high Froude number is the key in designing a practical step hull, the study surrounding stepped hull has been done mainly in the towing tank which is time-consuming and costly for initial design phase. Here the feasibility of predicting hydrodynamics of high-speed planing hull both with and without step using computational fluid dynamics (CFD) with the volume of fluid (VOF) methodology is studied in this work. First the flow around the prismatic body is analyzed, the force generated and its center of pressure are compared with available experimental and empirical data from the literature. The wake behind the transom on the keel line as well as the quarter beam buttock line are then compared with the available data, this is important since the afterbody flow of stepped hull is subjected from the wake of the forebody. Finally the calm water performance prediction of a conventional planing hull and its stepped version is then analyzed. Overset mesh methodology is employed in solving the dynamic equilibrium of the hull. The resistance, trim, and heave are then compared with the experimental data. The resistance is found to be predicted well and the dynamic equilibrium solved by the numerical method is deemed to be acceptable. This means that computational fluid dynamics will be very useful in further study on the complex flow around stepped hull and its potential usage in the design phase.

Keywords: planing hulls, stepped hulls, wake shape, numerical simulation, hydrodynamics

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Authors: M. Walmsley, S. Elmatarri, S. Mannion

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Introduction: Self-inflicted injury is a recognised cause of major trauma in adults and is an independent indicator of a reduced functional outcome compared to non-intentional major trauma. There is little literature available on the inpatient mental health (MH) management of this vulnerable group. A retrospective review was conducted of inpatient MH management of major trauma patients admitted to a UK regional Major Trauma Centre (MTC). Their outcomes were compared to all major trauma patients. This group of patients required multiple MH interventions whilst on the Major Trauma Ward (MTW) and a had worse functional outcome compared to non-intentional trauma. Method: The national TARN (Trauma Audit and Research Network) database was used to identify patients admitted to a regional MTC over a 2-year period from June 2018 to July 2020. Patients with an ISS (Injury Severity Score) of greater than 15 with a mechanism of either self-harm or high-risk behavior were included for further analysis. Inpatient medical notes were reviewed for MH interventions on the MTW. Further outcomes, including mortality, length of stay (LOS) and Glasgow Outcome Score (GOS) were compared with all major trauma patients for the same time period. Results: A total of 60 patients were identified in the time period and of those, 27 spent time on the MTW. A total of 23 (85%) had a prior MH diagnosis, with 11 (41%) under the care of secondary MH services. Adequate inpatient records for review were available for 24 patients. During their inpatient stay, 8 (33%) were reviewed on the ward by the inpatient MH team. There were 10 interventions required for 6 (25%) patients on the MTW including, sections under the Mental Health Act, transfer to specialist MH facility, pharmacological sedation and security being called to the MTW. When compared to all major trauma patients, those admitted due to self-harm or high-risk behavior had a statistically significantly higher ISS (31.43 vs 24.22, p=0.0001) and LOS (23.51d vs 16.06d, p=0.002). Functional outcomes using the GOS were reduced in this group of patients, GOS 5 (low disability) (51.66% vs. 61.01%) and they additionally had a higher level of mortality, GOS 1 (15.00% vs 11.67%). Discussion: Intentional self-harm is a recognised cause of major trauma in adults and this patient group sustains more severe injuries, requiring a longer hospital stay with worse outcomes compared to all major trauma patients. Inpatient MH interventions are required for a significant proportion of these patients and therefore, there needs to be a close relationship with MH services. There is limited available evidence for how this patient group is best managed as an inpatient to aid their recovery and further work is needed on how outcomes in this vulnerable group can be improved.

Keywords: adult major trauma, attempted suicide, self-inflicted major trauma, inpatient management

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Authors: Muhammad Luthfi, Sutikno Sutikno, Purhadi Purhadi

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Weather forecast has necessarily been improved to provide the communities an accurate and objective prediction as well. To overcome such issue, the numerical-based weather forecast was extensively developed to reduce the subjectivity of forecast. Yet the Numerical Weather Predictions (NWPs) outputs are unfortunately issued without taking dynamical weather behavior and local terrain features into account. Thus, NWPs outputs are not able to accurately forecast the weather quantities, particularly for medium and long range forecast. The aim of this research is to aid and extend the development of ensemble forecast for Meteorology, Climatology, and Geophysics Agency of Indonesia. Ensemble method is an approach combining various deterministic forecast to produce more reliable one. However, such forecast is biased and uncalibrated due to its underdispersive or overdispersive nature. As one of the parametric methods, Bayesian Model Averaging (BMA) generates the calibrated ensemble forecast and constructs predictive PDF for specified period. Such method is able to utilize ensemble of any size but does not take spatial correlation into account. Whereas space dependencies involve the site of interest and nearby site, influenced by dynamic weather behavior. Meanwhile, Geostatistical Output Perturbation (GOP) reckons the spatial correlation to generate future weather quantities, though merely built by a single deterministic forecast, and is able to generate an ensemble of any size as well. This research conducts both BMA and GOP to generate the calibrated ensemble forecast for the daily temperature at few meteorological sites nearby Indonesia international airport.

Keywords: Bayesian Model Averaging, ensemble forecast, geostatistical output perturbation, numerical weather prediction, temperature

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Authors: Pillay J. D., D. De Wit, J. F. Ducray

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Field hockey is a popular international sport which is played in more than 100 countries across the world. Due to the nature of hockey, players repeatedly perform a combination of forward flexion and rotational movements of the spine in order to strike the ball. These movements have been shown to increase the risk of pain and injury to the lumbar spine. The aim of this study was to determine the prevalence and incidence of low back pain (LBP) in male adolescent field hockey players and the characteristics of LBP in terms of location, chronicity, disability, and treatment sought, as well as its association with selected risk factors. A survey was conducted on 112 male adolescent field hockey players in the eThekwini Municipality of KwaZulu-Natal, South Africa. The questionnaire contained sections on the demographics of participants, general characteristics of participants, health and lifestyle characteristics, low back pain patterns, treatment of low back pain, and the level of disability associated with LBP. The data were statistically analysed using IBM SPSS version 25 with statistical significance set at p-value <0.05. Descriptive statistics such as mean and standard deviation were used to summarise responses to continuous variables as appropriate. Categorical variables were described using frequency tables. Associations between risk factors and low back pain were tested using Pearson’s chi-square test and t-tests as appropriate. A total of 68 questionnaires were completed for analysis (67% participation rate); the period prevalence of LBP was 63.2% (35.0%:beginning of the season, 32.4%:mid-season, 22.1%: end of season). Incidence was 38.2%. The most common location for LBP was the middle low back region (39.5%), and the most common duration of pain was a few hours (32.6%). Most participants (79.1%) did not classify their pain as a disability, and only 44.2% of participants received medical treatment for their LBP. An interesting finding was the association between hydration and LBP (p = 0.050), i.e., those individuals who did not hydrate frequently during matches and training were significantly more likely to experience LBP. The results of this study, although limited to a select group of adolescents, showed a higher prevalence of LBP than that of previous studies. More importantly, even though most participants did not experience LBP classified as a disability, LBP still had a large impact on participants, as nearly half of the participants consulted with a medical professional for treatment. Need for the application of further strategies in the prevention and management of LBP in field hockey, such as adequate warm-up and cool-down, stretching exercises, rest between sessions, etc., are recommended as simple strategies to reduce LBP prevalence.

Keywords: adolescents, field hockey players, incidence, low back pain, prevalence, risk factors

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Authors: Reinaldo Biscaro, Luciano R. Ferreira, Leonardo C. Biscaro, Raphael C. Biscaro, Isabela S. Vasconcelos, Laura C. R. Ferreira, Cristiano M. Galhardi, Erica P. Baciuk

Abstract:

Introduction: The present study had the objective to present the profile of workers with shoulder disorders related to labor lawsuits in Brazil. The study analyzed the association between the worker’s health and the actions performed by the companies related to injured professional. The research method performed a retrospective, cross-sectional and quantitative database analysis. The documents of labor lawsuits with shoulder injury registered at the Regional Labor Court in the 15^th region (Campinas - São Paulo) were submitted to the medical examination and evaluated during the period from 2012 until 2015. The data collected were age, gender, onset of symptoms, length of service, current occupation, type of shoulder injury, referred complaints, type of acromion, associated or related diseases, company actions as CAT (workplace accident communication), compliance of NR7 by the organization (Environmental Risk Prevention Program - PPRA and Medical Coordination Program in Occupational Health - PCMSO). Results: From the 93 workers evaluated, there was a prevalence of men (58.1%), with a mean age of 42.6 y-o, and 54.8% were included in the age group 35-49 years. Regarding the length of work time in the company, 66.7% have worked for more than 5 years. There was an association between gender and current occupational status (p < 0.005), with predominance of women in household occupation (13 vs. 2) and predominance of unemployed men in job search situation (24 vs. 10) and reintegrated to work by judicial decision (8 vs. 2). There was also a correlation between pain and functional limitation (p < 0.01). There was a positive association of PPRA with the complaint of functional limitation and negative association with pain (p < 0.04). There was also a correlation between the sedentary lifestyle and the presence of PCMSO and PPRA (p < 0.04), and the absence of CAT in the companies (p < 0.001). It was concluded that the appearance or aggravation of osseous and articular shoulder pathologies in workers who have undertaken labor law suits seem to be associated with individual habits or inadequate labor practices. These data can help preventing the occurrence of these lesions by implementing local health promotion policies at work.

Keywords: work-related accidents, cross-sectional study, shoulder lesions, labor lawsuits

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Authors: Manuela Villani, Anila Xhahysa, Christopher Brooks, Marco Pagani

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The geological structure of Western Balkans is strongly affected by the collision between Adria microplate and the southwestern Euroasia margin, resulting in a considerably active seismic region. The Harmonization of Seismic Hazard Maps in the Western Balkan Countries Project (BSHAP) (2007-2011, 2012-2015) by NATO supported the preparation of new seismic hazard maps of the Western Balkan, but when inspecting the seismic hazard models produced later by these countries on a national scale, significant differences in design PGA values are observed in the border, for instance, North Albania-Montenegro, South Albania- Greece, etc. Considering the fact that the catalogues were unified and seismic sources were defined within BSHAP framework, obviously, the differences arise from the Ground Motion Prediction Equations selection, which are generally the component with highest impact on the seismic hazard assessment. At the time of the project, a modest database was present, namely 672 three-component records, whereas nowadays, this strong motion database has increased considerably up to 20,939 records with Mw ranging in the interval 3.7-7 and epicentral distance distribution from 0.47km to 490km. Statistical analysis of the strong motion database showed the lack of recordings in the moderate-to-large magnitude and short distance ranges; therefore, there is need to re-evaluate the Ground Motion Prediction Equation in light of the recently updated database and the new generations of GMMs. In some cases, it was observed that some events were more extensively documented in one database than the other, like the 1979 Montenegro earthquake, with a considerably larger number of records in the BSHAP Analogue SM database when compared to ESM23. Therefore, the strong motion flat-file provided from the Harmonization of Seismic Hazard Maps in the Western Balkan Countries Project was merged with the ESM23 database for the polygon studied in this project. After performing the preliminary residual analysis, the candidate GMPE-s were identified. This process was done using the GMPE performance metrics available within the SMT in the OpenQuake Platform. The Likelihood Model and Euclidean Distance Based Ranking (EDR) were used. Finally, for this study, a GMPE logic tree was selected and following the selection of candidate GMPEs, model weights were assigned using the average sample log-likelihood approach of Scherbaum.

Keywords: residual analysis, GMPE, western balkan, strong motion, openquake

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Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and novel data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. To the best of our knowledge, this is the first work to apply modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

Procedia PDF Downloads 134

Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Cristian Pauna

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Trading the financial markets is a widespread activity today. A large number of investors, companies, public of private funds are buying and selling every day in order to make profit. Algorithmic trading is the prevalent method to make the trade decisions after the electronic trading release. The orders are sent almost instantly by computers using mathematical models. This paper will present a price prediction methodology based on a multi-dimensional neural network. Using the Fisher transform, the neural network will be instructed for a low-latency auto-adaptive process in order to predict the price evolution for the next period of time. The model is designed especially for algorithmic trading and uses the real-time price series. It was found that the characteristics of the Fisher function applied at the nodes scale level can generate reliable trading signals using the neural network methodology. After real time tests it was found that this method can be applied in any timeframe to trade the financial markets. The paper will also include the steps to implement the presented methodology into an automated trading system. Real trading results will be displayed and analyzed in order to qualify the model. As conclusion, the compared results will reveal that the neural network methodology applied together with the Fisher transform at the nodes level can generate a good price prediction and can build reliable trading signals for algorithmic trading.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, neural network

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Authors: A. Magno-França, A. M. Magalhães-Neto, F. Bachini, E. Cataldi, A. Bassini, L. C. Cameron

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Sprint canoeing (SC) is part of the Olympic Games since 1936. Athletes compete in solo or double races of 200m and 1000m (40 sec and 240 sec, respectively). Due to its high intensity and duration, SC is extremely useful to study the blood kinetics of some metabolites in high energetic demand. Sportomics is a field of study combining “-omics” sciences with classical biochemical analyses in order to understand sports induced systemic changes. Here, we compare Sportomics findings during SC training sessions to describe metabolic responses of five top-level canoeists. Five Olympic world-class male athletes were evaluated during two days of training.

Keywords: biochemistry of exercise, metabolomics, injury markers, sportomics

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Authors: Raptis Sotirios

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Health and social care (HSc) services planning and scheduling are facing unprecedented challenges due to the pandemic pressure and also suffer from unplanned spending that is negatively impacted by the global financial crisis. Data-driven can help to improve policies, plan and design services provision schedules using algorithms assist healthcare managers’ to face unexpected demands using fewer resources. The paper discusses services packing using statistical significance tests and machine learning (ML) to evaluate demands similarity and coupling. This is achieved by predicting the range of the demand (class) using ML methods such as CART, random forests (RF), and logistic regression (LGR). The significance tests Chi-Squared test and Student test are used on data over a 39 years span for which HSc services data exist for services delivered in Scotland. The demands are probabilistically associated through statistical hypotheses that assume that the target service’s demands are statistically dependent on other demands as a NULL hypothesis. This linkage can be confirmed or not by the data. Complementarily, ML methods are used to linearly predict the above target demands from the statistically found associations and extend the linear dependence of the target’s demand to independent demands forming, thus groups of services. Statistical tests confirm ML couplings making the prediction also statistically meaningful and prove that a target service can be matched reliably to other services, and ML shows these indicated relationships can also be linear ones. Zero paddings were used for missing years records and illustrated better such relationships both for limited years and in the entire span offering long term data visualizations while limited years groups explained how well patients numbers can be related in short periods or can change over time as opposed to behaviors across more years. The prediction performance of the associations is measured using Receiver Operating Characteristic(ROC) AUC and ACC metrics as well as the statistical tests, Chi-Squared and Student. Co-plots and comparison tables for RF, CART, and LGR as well as p-values and Information Exchange(IE), are provided showing the specific behavior of the ML and of the statistical tests and the behavior using different learning ratios. The impact of k-NN and cross-correlation and C-Means first groupings is also studied over limited years and the entire span. It was found that CART was generally behind RF and LGR, but in some interesting cases, LGR reached an AUC=0 falling below CART, while the ACC was as high as 0.912, showing that ML methods can be confused padding or by data irregularities or outliers. On average, 3 linear predictors were sufficient, LGR was found competing RF well, and CART followed with the same performance at higher learning ratios. Services were packed only if when significance level(p-value) of their association coefficient was more than 0.05. Social factors relationships were observed between home care services and treatment of old people, birth weights, alcoholism, drug abuse, and emergency admissions. The work found that different HSc services can be well packed as plans of limited years, across various services sectors, learning configurations, as confirmed using statistical hypotheses.

Keywords: class, cohorts, data frames, grouping, prediction, prob-ability, services

Procedia PDF Downloads 237

Authors: Erika Yamaguchi, Sirawadee Arunyanrt, Shunichi Ohmori, Kazuho Yoshimoto

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In this paper, we suggest a simulation tool to make a decision of monthly production planning for maximizing a profit of Specialty store retailer of Private label Apparel (SPA) firms. Most of SPA firms are fabless and make outsourcing deals for productions with factories of their subcontractors. Every month, SPA firms make a booking for production lines and manpower in the factories. The booking is conducted a few months in advance based on a demand prediction and a monthly production planning at that time. However, the demand prediction is updated month by month, and the monthly production planning would change to meet the latest demand prediction. Then, SPA firms have to change the capacities initially booked within a certain range to suit to the monthly production planning. The booking system is called “capacity-booking”. These days, though it is an issue for SPA firms to make precise monthly production planning, many firms are still conducting the production planning by empirical rules. In addition, it is also a challenge for SPA firms to match their products and factories with considering their demand predictabilities and regulation abilities. In this paper, we suggest a model for considering these two issues. An objective is to maximize a total profit of certain periods, which is sales minus costs of production, inventory, and capacity-booking penalty. To make a better monthly production planning at SPA firms, these points should be considered: demand predictabilities by random trends, previous and next month’s production planning of the target month, and regulation abilities of the capacity-booking. To decide matching products and factories for outsourcing, it is important to consider seasonality, volume, and predictability of each product, production possibility, size, and regulation ability of each factory. SPA firms have to consider these constructions and decide orders with several factories per one product. We modeled these issues as a linear programming. To validate the model, an example of several computational experiments with a SPA firm is presented. We suppose four typical product groups: basic, seasonal (Spring / Summer), seasonal (Fall / Winter), and spot product. As a result of the experiments, a monthly production planning was provided. In the planning, demand predictabilities from random trend are reduced by producing products which are different product types. Moreover, priorities to produce are given to high-margin products. In conclusion, we developed a simulation tool to make a decision of monthly production planning which is useful when the production planning is set every month. We considered the features of capacity-booking, and matching of products and factories which have different features and conditions.

Keywords: capacity-booking, SPA, monthly production planning, linear programming

Procedia PDF Downloads 520