Search

Abstract Background Newer generation ultrathin strut stents are associated with less incidence of target lesion failure (TLF) in patients undergoing percutaneous coronary intervention (PCI) in the short term. However, its long-term effect on different cardiovascular outcomes remains unknown. Objectives We aim to identify the effects of newer-generation ultrathin-strut stents vs. standard thickness second-generation drug-eluting stents (DES) on long-term outcomes of revascularization in coronary artery disease. Methods We searched PubMed, Web of Science, Cochrane Library databases, and Scopus for randomized controlled trials (RCTs) and registries that compare newer-generation ultrathin-strut ( 70 mm) DES to evaluate cardioprotective effects over a period of up to 5 years. Primary outcome was TLF, a composite of cardiac death, target vessel myocardial infarction (TVMI) or target lesion revascularization (TLR). Secondary outcomes included the components of TLF, stent thrombosis (ST), and all-cause death were pooled as the standardized mean difference between the two groups from baseline to endpoint. Results We included 19 RCTs and two prospective registries (103,101 patients) in this analysis. The overall effect on the primary outcome was in favor of second-generation ultrathin struts stents in terms of TLF at ≥ 1 year, ≥ 2 years, and ≥ 3 years (P value = 0.01, 95% CI [0.75, 0.96]), P value = 0.003, 95% CI [0.77, 0.95]), P value = 0.007, 95% CI [0.76, 0.96]), respectively. However, there was no reported benefit in terms of TLF when we compared the two groups at ≥ 5 years (P value = 0.21), 95% CI [0.85, 1.04]). Some of the reported components of the primary and secondary outcomes, such as TLR, target vessel revascularization (TVR), and TVMI, showed the same pattern as the TLF outcome. Conclusion Ultrathin-strut DES showed a beneficial effect over thicker strut stents for up to 3 years. However, at the 5-year follow-up, the ultrathin strut did not differ in terms of TLF, TLR, TVR, and TVMI compared with standard-thickness DES, with similar risks of patient-oriented composite endpoint (POCE), MI, ST, cardiac death, and all-cause mortality.

Abstract Centrifugal water pumps account for more than 20% of energy use all over the world. Even a little increase in centrifugal pump operating efficiency may result in billions of dollars in savings each year and help reduce carbon dioxide emissions. This study creates a design tool that integrates one-dimensional equations and computational fluid dynamics (CFD) simulations to design centrifugal pump impellers. This examines different combinations of design factors by utilizing both the powerful numerical tools that allow the modeling of numerous scenarios and the large number of empirical equations and coefficients that represent the significant knowledge accumulated in this field. The design tool runs through a series of equations to select the beginning candidates for the different impeller design variables. After that, a design space is established by generating upper and lower bounds for each design variable. The CFD tool executes numerous scenarios inside the design space in order to find the best candidates with the maximum operating efficiency and meet the design objectives. A low specific speed centrifugal pump with a semi-open impeller is used as a case study. The design tool is successful in designing it, fulfilling the design requirement, and increasing operational efficiency when compared to a simple single-arc comparable impeller.

Abstract Horizontal drilling and multistage hydraulic fracturing have seen widespread application in shale formations during the past decade, leading to significant economic productivity gains through the creation of extensive fracture surfaces. The determination of the ideal cluster spacing in shale gas wells is contingent upon the unique geological and formation characteristics. Generally, reducing the spacing between clusters has the potential to augment gas recovery, albeit at the expense of higher drilling and completion costs, as well as the influence of stress shadows on fracture propagation. This study introduces an integrated methodology designed to explore the impact of cluster interference on well performance. Commencing with a fracture propagation model accommodating stress shadow effects for an equivalent slurry volume injection, analytical rate transient analysis (RTA) was amalgamated with reservoir numerical simulation to compute the effective fracture surface area (Aca.) for hydrocarbon production. The correlation between the effective fracture surface area determined by RTA and the actual stimulated fracture area (Aca.) derived from numerical simulations was established in relation to cluster spacing. The findings of this research reveal that wells featuring a greater number of stages and tighter cluster spacing tend to exhibit elevated cluster interference, resulting in a lower effective-to-actual fracture surface area ratio and heightened stress shadow effects impeding fracture propagation. A cluster spacing of 33 feet with six clusters per stage emerges as the optimal choice at formation permeability of 0.00005 md that decreased to 18 ft at formation permeability of 0.00001 md. ACe either stabilizes or decreases above the optimal value, suggesting that more clusters would not have a major impact on increasing the effective stimulated area. Allowing 20% interference, regardless of the permeability of the formation, maximized cumulative production while preventing thief zones and excessive cluster interference. The insights gained from this study will serve as a valuable resource for completion and reservoir engineers, enabling them to fine-tune cluster spacing to maximize well revenue in the dynamic landscape of shale gas extraction.

Abstract Reservoir characterization, essential for understanding subsurface heterogeneity, often faces challenges due to scale-dependent variations. This study addresses this issue by utilizing hydraulic flow unit (HFU) zonation to group rocks with similar petrophysical and flow characteristics. Flow Zone Indicator (FZI), a crucial measure derived from pore throat size, permeability, and porosity, serves as a key parameter, but its determination is time-consuming and expensive. The objective is to employ supervised and unsupervised machine learning to predict FZI and classify the reservoir into distinct HFUs. Unsupervised learning using K-means clustering and supervised algorithms including Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were employed. FZI values from RCAL data formed the basis for model training and testing, then the developed models were used to predict FZI in unsampled locations. A methodical approach involves 3 k-fold cross-validation and hyper-parameter tuning, utilizing the random search cross-validation technique over 50 iterations was applied to optimize each model. The four applied algorithms indicate high performance with coefficients determination (R2) of 0.89 and 0.91 in training and testing datasets, respectively. RF showed the heist performance with training and testing R2 values of 0.957 and 0.908, respectively. Elbow analysis guided the successful clustering of 212 data points into 10 HFUs using k-means clustering and Gaussian mixture techniques. The high-quality reservoir zone was successfully unlocked using the unsupervised technique. It has been discovered that the areas between 2370–2380 feet and 2463–2466 feet are predicted to be high-quality reservoir potential areas, with average FZI values of 500 and 800, consecutively. The application of machine learning in reservoir characterization is deemed highly valuable, offering rapid, cost-effective, and precise results, revolutionizing decision-making in field development compared to conventional methods.

Abstract The uniaxial compressive strength (UCS) and tensile strength (T0) are crucial parameters in field development and excavation projects. Traditional lab-based methods for directly measuring these properties face practical challenges. Therefore, non-destructive techniques like machine learning have gained traction as innovative tools for predicting these parameters. This study leverages machine learning methods, specifically random forest (RF) and decision tree (DT), to forecast UCS and T0 using real well-logging data sourced from a Middle East reservoir. The dataset comprises 2600 data points for model development and over 600 points for validation. Sensitivity analysis identified gamma-ray, compressional time (DTC), and bulk density (ROHB) as key factors influencing the prediction. Model accuracy was assessed using the correlation coefficient (R) and the absolute average percentage error (AAPE) against actual parameter profiles. For UCS prediction, both RF and DT achieved R values of 0.97, with AAPE values at 0.65% for RF and 0.78% for DT. In T0 prediction, RF yielded R values of 0.99, outperforming DT's 0.93, while AAPE stood at 0.28% for RF and 1.4% for DT. These outcomes underscore the effectiveness of both models in predicting strength parameters from well-logging data, with RF demonstrating superior performance. These models offer the industry an economical and rapid tool for accurately and reliably estimating strength parameters from well-logging data.

Abstract Accurate estimation of fracture half-lengths in shale gas and oil reservoirs is critical for optimizing stimulation design, evaluating production potential, monitoring reservoir performance, and making informed economic decisions. Assessing the dimensions of hydraulic fractures and the quality of well completions in shale gas and oil reservoirs typically involves techniques such as chemical tracers, microseismic fiber optics, and production logs, which can be time-consuming and costly. This study demonstrates an alternative approach to estimate fracture half-lengths using the Gaussian pressure transient (GPT) Method, which has recently emerged as a novel technique for quantifying pressure depletion around single wells, multiple wells, and hydraulic fractures. The GPT method is compared to the well-established rate transient analysis (RTA) method to evaluate its effectiveness in estimating fracture parameters. The study used production data from 11 wells at the hydraulic fracture test site 1 in the Midland Basin of West Texas from Upper and Middle Wolfcamp (WC) formations. The data included flow rates and pressure readings, and the fracture half-lengths of the 11 wells were individually estimated by matching the production data to historical records. The GPT method can calculate the fracture half-length from daily production data, given a certain formation permeability. Independently, the traditional RTA method was applied to separately estimate the fracture half-length. The results of the two methods (GPT and RTA) are within an acceptable, small error margin for all 5 of the Middle WC wells studied, and for 5 of the 6 Upper WC wells. The slight deviation in the case of the Upper WC well is due to the different production control and a longer time for the well to reach constant bottomhole pressure. The estimated stimulated surface area for the Middle and Upper WC wells was correlated to the injected proppant volume and the total fluid production. Applying RTA and GPT methods to the historic production data improves the fracture diagnostics accuracy by reducing the uncertainty in the estimation of fracture dimensions, for given formation permeability values of the stimulated rock volume.

Abstract Despite significant advancements in geomodelling technologies, accurately estimating hydraulic fracture half-length remains a challenging task. This paper introduces a detailed estimation approach using the Gaussian Pressure Transient (GPT) method, which is relatively new. The GPT method is iterative, ensuring fast convergence and providing reliable estimations of hydraulic fracture half-length based on a predetermined hydraulic diffusivity value obtained from Gaussian Decline Curve Analysis (DCA). To validate the GPT results, production data from two case study wells in the Wolfcamp Shale Formation, located in the Midland Basin of West Texas, are utilized alongside the traditional Rate-Transient Analysis (RTA) method. Moreover, the GPT method offers the capability to probabilistically estimate hydraulic fracture half-lengths, presenting two innovative approaches to evaluate the robustness of this newly developed method for both deterministic and probabilistic estimations. The simulation results demonstrate a close correlation between the Gaussian method and micro-seismic fracture half-lengths, with separate confirmation from the classic RTA-method. Through the case studies presented in this paper, the GPT-method showcases its utility in estimating hydraulic fracture half-lengths for two Wolfcamp case study wells, effectively demonstrating the validity and practical applicability of this novel method.

Every year, a substantial number of children sustain injuries and fatalities in motor vehicle crashes in Wyoming. Understanding the factors contributing to child injury is crucial for the development of appropriate mitigation measures that aid in alleviating the severity of such injuries. In this study, a hierarchical Bayesian binary logit regression model was developed to investigate the factors that contribute to children's injuries resulting from crashes while accounting for possible intra-class correlation effects (those of unobserved factors common to children involved in the same crash). A strong correlation among crashes justified the use of the hierarchical Bayesian logit model. As per the modeling results, the children's ages, safety restraint types, vehicle types, drivers' ages, alcohol/drug involvement, drivers' seat belt use habits, drivers' actions, manners of collision and environmental conditions contributed to child injury risk. The child's age was found to be inversely related to the risk of injury. Similarly, among safety restraint types, rear-facing car seats and forward-facing car seats were found to reduce injury likelihoods in crashes. When it comes to the drivers' characteristics, the probability of incurring injuries among the child population increased in the presence of young, unbuckled and impaired drivers. Furthermore, improper driving actions, such as running off the road, raised the risk of incurring injuries to children. The findings of this study may be beneficial to authorities regarding developing and implementing road safety programs aimed at ameliorating child injury concerns.

Abstract The significance of CO2 wetting behavior in shale formations has been emphasized in various CO2 sequestration applications. Traditional laboratory experimental techniques used to assess shale wettability are complex and time-consuming. To overcome these limitations, the study proposes the use of machine learning (ML); artificial neural networks (ANN), support vector machines (SVM), and adaptive neuro-fuzzy inference systems (ANFIS) tools to estimate the contact angle, a key indicator of shale wettability, providing a more efficient alternative to conventional laboratory methods. A dataset comprising various shale samples under different conditions was collected to predict shale-water-CO2 wettability by considering shale properties, operating pressure and temperature, and brine salinity. Pearson’s correlation coefficient (R) was utilized to assess the linearity between the contact angle (CA) value and other input parameters. Initial data analysis showed that the elements affecting the shale wettability are primarily reliant on the pressure and temperature at which it operates, the total organic content (TOC), and the mineral composition of the rock. Between the different ML models, the artificial neural network (ANN) model performed the best, achieving a training R2 of 0.99, testing R2 of 0.98 and a validation R2 of 0.96, with an RMSE below 5. The adaptive neuro-fuzzy inference system (ANFIS) model also accurately predicted the contact angle, obtaining a training R2 of 0.99, testing R2 of 0.97 and a validation R2 of 0.95. Conversely, the support vector machine (SVM) model displayed signs of overfitting, as it achieved R2 values of 0.99 in the training dataset, which decreased to 0.94 in the testing dataset, and 0.88 in the validation dataset. To avoid rerunning the ML models, an empirical correlation was developed based on the optimized weights and biases obtained from the ANN model to predict contact angle values using input parameters and the validation data set revealed R2 of 0.96. The parametric study showed that, among the factors influencing shale wettability at a constant TOC, pressure had the most significant impact, and the dependency of the contact angle on pressure increased when TOC values were high.

Abstract Formation resistivity is crucial for calculating water saturation, which, in turn, is used to estimate the stock-tank oil initially in place. However, obtaining a complete resistivity log can be challenging due to high costs, equipment failure, or data loss. To overcome this issue, this study introduces novel machine learning models that can be used to predict the electrical resistivity of oil wells, using conventional well logs. The analysis utilized gamma-ray (GR), delta time compressional logs (DTC), sonic shear log (DSTM), neutron porosity, and bulk density. The study utilized a dataset of 3529 logging data points from horizontal oil carbonate wells which were used to develop different machine learning models using random forest (RF) and decision tree (DT) algorithms. The obtained results showed that both models can predict electrical resistivity with high accuracy, over 0.94 for training and testing data. Comparing the models based on accuracy and consistency revealed that the RF model had a slight advantage over the DT model. Based on the data analysis, it was found that the formation resistivity is more significantly impacted by GR logs compared to DTC logs. These new ML models offer a low-cost and practical alternative to estimate well resistivity in oil wells, providing valuable information for geophysical and geological interpretation.

Abstract Background Arterial spin labeling (ASL) is a recently used magnetic resonance imaging (MRI) perfusion technique in acute cerebrovascular stroke conditions; it can detect the hypo perfused areas on basis of qualitative and quantitative measurements and also identify the area at risk known as penumbra by detecting the diffusion/perfusion mismatch. The purpose of this study was to assess the role of ASL perfusion technique in management of acute ischemic stroke and its ability to predict the clinical outcome of acute stroke patients. The study was prospectively carried out on 33 patients clinically presented with acute stroke from the first of August 2020 till the first of August 2021. All cases were clinically assessed by stroke consultant followed by brain imaging including conventional MRI and ASL perfusion technique, based upon which management was established. These imaging data were correlated with the clinical outcome after 3 months using Modified Rankin Scale. Results Sixteen cases (48.48%) showed ischemic penumbra with diffusion perfusion mismatch with three cases presenting within the first 4 h managed by intravenous thrombolytic therapy and 13 cases presenting later than 4 h, 10 of whom were managed by endovascular intervention. The group with ischemic penumbra showed significant positive correlation with favorable clinical outcome while the group without ischemic penumbra showed significant positive correlation with poor clinical outcome. Quantitative ASL values were statistically significantly higher (p ≤ 0.05) in patients with favorable clinical outcome than those with poor clinical outcome. The estimated cut off values of ASL absolute cerebral blood flow and relative cerebral blood flow to predict favorable or poor outcome using ROC curve analysis were 19 ml/100gm/min and 74% compared to the contralateral side respectively. Conclusion The use of MRI as a primary diagnostic tool in arterial ischemic stroke with the application of non-contrast ASL perfusion sequence allows precise detection of perfusion deficit and diffusion perfusion mismatch (penumbra) and provides a reliable insight into outcome prediction.

Abstract Comprehensive and precise knowledge about rocks' mechanical properties facilitate the drilling performance optimization, and hydraulic fracturing design and reduces the risk of wellbore-related problems. This paper is concerned with the failure parameters, namely, cohesion and friction angle which are conventionally estimated using Mohr's cycles that are drawn using compressional tests on rock samples. The availability, continuity and representability, and cost of acquiring those samples are major concerns. The objective of this paper is to investigate an alternative technique to estimate these parameters from the drilling data. In this work, more than 2200 data points were used to develop and test the correlations built by the artificial neural network. Each data point comprises the failure parameters and five drilling records that are available instantaneously in drilling rigs such as rate of penetration, weight on bit, and torque. The data were grouped into three datasets, training, testing, and validation with a corresponding percentage of 60/20/20, the former two sets were utilized in the models' building while the last one was hidden as a final check afterward. The models were optimized and evaluated using the correlation coefficient (R) and average absolute percentage error (AAPE). In general, the two models yielded good fits with the actual values. The friction angle model yielded R values around 0.86 and AAPE values around 4% for the three datasets. While the model for cohesion resulted in R values around 0.89 and APPE values around 6%. The equation and the parameters of those models are reported in the paper. These results show the ability of in-situ and instantaneous rock mechanical properties estimation with good reliability and at no additional costs.

Abstract This study proposes the use of different machine learning techniques to predict the estimated ultimate recovery (EUR) as a function of the hydraulic fracturing design. A set of data includes 200 well production data, and completion designs were collected from oil production wells in the Niobrara shale formation. The completion design parameters include the lateral length, the number of stages, the total injected proppant and slurry volumes, and the maximum treating pressure measured during the fracturing operations. The data set was randomly split into training and testing with a ratio of 75:25. Different machine learning methods were to predict EUR from the completion design including linear regression, random forest (RF), and decision tree (DT) in addition to gradient boosting regression (GBR). EUR prediction from the completion data showed a low accuracy. As result, an intermediate step of estimating the well IP30 (the initial well production rate for the first month) from the completion data was carried out; then, the IP30 and the completion design were used as input parameters to predict the EUR. The linear regression showed some linear relationship between the output and the inputs, where the EUR can be predicted with a linear relationship with an R-value of 0.84. In addition, a linear correlation was developed based on the linear regression model. Moreover, the other ML tools including RF, DT, and GBR presented high accuracy of EUR prediction with correlation coefficient (R) values between actual and predicted EUR from the ML model higher than 0.9. This study provides ML application with an empirical correlation to predict the EUR from the completion design parameters at an early time without the need for complex numerical simulation analysis. Unlike the available empirical DCA models that require several months of production to build a sound prediction of EUR, the main advantage of the developed models in this study is that it requires only an initial flow rate along with the completion design to predict EUR with high certainty.

Abstract Background Heart rate variability (HRV) is a valuable indicator of autonomic nervous system integrity and can be a prognostic tool of COVID-19 induced myocardial affection. This study aimed to compare HRV indices between patients who developed myocardial injury and those without myocardial injury in COVID-19 patients who were admitted to intensive care unit (ICU). Methods In this retrospective study, the data from 238 COVID-19 adult patients who were admitted to ICU from April 2020 to June 2021 were collected. The patients were assigned to myocardial injury and non-myocardial injury groups. The main collected data were R-R intervals, standard deviation of NN intervals (SDANN) and the root mean square of successive differences between normal heartbeats (RMSSD) that were measured daily during the first five days of ICU admission. Results The R-R intervals, the SDANN and the RMSSD were significantly shorter in the myocardial injury group than the non-myocardial group at the first, t second, third, fourth and the fifth days of ICU admission. There were no significant differences between the myocardial injury and the non-myocardial injury groups with regard the number of patients who needed mechanical ventilation, ICU length of stay and the number of ICU deaths. Conclusions From the results of this retrospective study, we concluded that the indices of HRV were greatly affected in COVID-19 patients who developed myocardial injury.

This study focused on the phytochemical, antioxidant, and antimicrobial activities of the ethanol extracts of golden berry calyx and fruits. The phytochemical analysis of calyx and fruit extracts revealed high total phenols and flavonoids. High antioxidant capacity was recorded for fruit extracts. Fruit extracts also gave the highest antibacterial activity against Pseudomonas fluorescens, Escherichia coli, and Salmonella typhimurium. Furthermore, tomato paste was prepared using sodium benzoate and golden berry extract as food preservatives. The incorporation of golden berry extract and chemical preservatives maintained the paste quality indicators over the control samples. A rapid decline in total antioxidant capacity was also observed in control samples (21.6%) as compared with the treated samples (15.21% and 15.41% for sodium benzoate and golden berry extract, respectively) after 30 days of storage. This study demonstrated that dried fruit extract with high phytochemical and antimicrobial properties could be used as a natural food preservative.

Intersections are commonly recognized as crash hot spots on roadway networks. Therefore, intersection safety is a major concern for transportation professionals. Identifying and quantifying the impact of crash contributing factors is crucial to planning and implementing the appropriate countermeasures. This study covered the analysis of nine years of intersection crash records in the State of Wyoming to identify the contributing factors to crash injury severity at intersections. The study involved the investigation of the influence of roadway (intersection) and environmental characteristics on crash injury severity. The results demonstrated that several parameters related to intersection attributes (pavement friction; urban location; roadway functional classification; guardrails; right shoulder width) and two environmental conditions (road surface condition and lighting) influence the injury severity of intersection crashes. This study identified the significant roadway characteristics influencing crash severity and explored the key role of pavement friction, which is a commonly omitted variable.

Abstract Safe mud window (SMW) defines the allowable limits of the mud weights that can be used while drilling O&G wells. Controlling the mud weight within the SMW limits would help avoid many serious problems such as wellbore instability issues, loss of circulation, etc. SMW can be defined by the minimum mud weight below which shear failure (breakout) may occur (MWBO) and the maximum mud weight above which tensile failure (breakdown) may occur (MWBD). These limits can be determined from the geomechanical analysis of downhole formations. However, such analysis is not always accessible for most drilled wells. Therefore, in this study, a new approach is introduced to develop a new data-driven model to estimate the safe mud weight range in no time and without additional cost. New models were developed using an artificial neural network (ANN) to estimate both MWBO and MWBD directly from the logging data that are usually available for most wells. The ANN-based models were trained using actual data from a Middle Eastern field before being tested by an unseen dataset. The models achieved high accuracy exceeding 92% upon comparing the predicted and observed output values. Additionally, new equations were established based on the optimized ANN models’ weights and biases whereby both MWBO and MWBD can be calculated without the need for any complicated codes. Finally, another dataset from the same field was then used to validate the new equations and the results demonstrated the high robustness of the new equations to estimate MWBO and MWBD with a low mean absolute percentage error of 0.60% at maximum. So, unlike the costly conventional approaches, the newly developed equations would facilitate determining the SMW limits in a timely and economically effective way, with high accuracy whenever the logging data are available.

Abstract Background In locally advanced rectal cancer, many imaging modalities are used, for example 18F-2-fluoro-2-deoxy-d-glucose (18F-FDG) positron emission tomography-computed tomography (PET-CT) and MRI. The aim of our study is to compare the diagnostic accuracy of 18 F-FDG-PET/CT & pelvic MRI; as well as to investigate the possible added value of using combined pelvic MRI and PET-CT for assessment of tumor response. Results Regarding the presence of local tumor, both PET CT and MRI showed perfect agreement with 97.1% overall accuracy, while in N category, PET CT showed higher specificity but lower sensitivity than MRI. MRI was superior to PET/CT in detecting extension to nearby organs; owing to the more anatomical details of MRI regarding the involvement of mesorectal fascia and EMVI. Almost total agreement of both MRI and PET/CT was noticed in evaluating post-therapy and postoperative complications. Conclusion For locally advanced rectal cancer (pT3–4 N0 M0 or any T N1 M0), a multimodality strategy has been shown to be the best option to evaluate local disease process, using the diagnostic criteria that were based on morphology, as well as glucose uptake, instead of the SUV alone for reassessment of post-therapy or postoperative changes.

Two-lane highways comprise a substantial proportion of the nation’s highways. One of their critical aspects is the passing sight distance (PSD), which is the minimum distance required for executing a safe passing maneuver using the opposing travel lane. Sight distances that are shorter than the available PSD warrant the designation of no-passing zones. The PSDs, documented in the Manual on Uniform Traffic Control Devices, are developed based on the premise that only one slower moving impeding vehicle is being overtaken. Yet, the oil and gas industry in Wyoming advises its truck drivers to travel in platoons creating a complicated situation for trailing vehicles. Passing a single impeding vehicle is cumbersome, not to mention passing multiple trucks. The PSD is augmented by the length of each truck in the platoon and the spacing among the trucks. In this research, an existing mathematical kinematics PSD model is implemented to compute the PSDs needed to pass multiple oil and gas trucks for a variety of scenarios belonging to two-lane highway segments with posted speed limits of 70 mph. The PSDs computed range from 1385 to 1915 ft depending on the number of trucks in the platoon and spacings between the trucks. The results are compared with those of VISSIM microsimulation models, which are validated using field traffic volume data. The discrepancies between the computed PSD results and those of the simulations are less than 3%. Furthermore, it is recommended to deploy passing lanes to mitigate the effects of truck platoons on traffic operations.

Abstract Background Thyroid cancer considered the 6th common cancer in female and represents approximately 1% of all cancers. Thyroid cancer is generally characterized by good prognosis, long term survival and low aggressiveness. Its prognosis is related to tumor dimensions, extra capsular extensions, the age at diagnosis and distant metastases sites. Combination between positron emission tomography and computed tomography allow anatomic, functional & molecular information. Aim To demonstrate Fluorine 18 fluorodeoxyglucose positron emission tomography)/computed tomography role for evaluation of post-thyroidectomy recurrence either local or distant metastatic lesions in differentiated cancer thyroid patients with negative radio-isotope iodine scan, yet showing elevated serum thyroglobulin level. Procedure A prospective study included twenty patients with previous history of differentiated thyroid cancer. All patients after history taking and revising the medical sheet underwent Serum Thyroglobulin level & I-131 whole body scan examinations and then Fluorine 18 Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography study. The findings of Positron Emission Tomography/Computed Tomography imaging compared with results of histopathology or follow-up clinical results as a gold standard. Results Fluorine 18 fluorodeoxyglucose positron emission tomography/computed tomography based analysis showed that nineteen true positives and one was true negative as confirmed by the gold standard (Histopathology and clinical follow-up). Thirteen patients had either local recurrence or lymph node metastases without distant metastatic disease, six patients had different distant metastasis. Fluorine 18 fluorodeoxyglucose positron emission tomography/computed tomography based analysis showed that nineteen true positives and one was true negative as confirmed by the gold standard (histopathology and clinical follow-up). The accuracy and sensitivity of Fluorine 18 fluorodeoxyglucose positron emission tomography/computed tomography (95%) were significantly better than those of the Computed Tomography alone (84.2% and 80%, respectively). Conclusions Fluorine 18 Fluorodeoxyglucose positron emission tomography/computed tomography provided a critical role in assessment and management of patients with suspected differentiated thyroid recurrence, presenting with high serum thyroglobulin level and negative radio-isotope iodine scan. It enhances diagnostic accuracy through giving exact anatomical localization of recurrent and/or metastatic tumor foci.

Abstract Determination of in-situ stresses is essential for subsurface planning and modeling, such as horizontal well planning and hydraulic fracture design. In-situ stresses consist of overburden stress (σv), minimum (σh), and maximum (σH) horizontal stresses. The σh and σH are difficult to determine, whereas the overburden stress can be determined directly from the density logs. The σh and σH can be estimated either from borehole injection tests or theoretical finite elements methods. However, these methods are complex, expensive, or need unavailable tectonic stress data. This study aims to apply different machine learning (ML) techniques, specifically, random forest (RF), functional network (FN), and adaptive neuro-fuzzy inference system (ANFIS), to predict the σh and σH using well-log data. The logging data includes gamma-ray (GR) log, formation bulk density (RHOB) log, compressional (DTC), and shear (DTS) wave transit-time log. A dataset of 2307 points from two wells (Well-1 and Well-2) was used to build the different ML models. The Well-1 data was used in training and testing the models, and the Well-2 data was used to validate the developed models. The obtained results show the capability of the three ML models to predict accurately the σh and σH using the well-log data. Comparing the results of RF, ANFIS, and FN models for minimum horizontal stress prediction showed that ANFIS outperforms the other two models with a correlation coefficient (R) for the validation dataset of 0.96 compared to 0.91 and 0.88 for RF, and FN, respectively. The three models showed similar results for predicting maximum horizontal stress with R values higher than 0.98 and an average absolute percentage error (AAPE) less than 0.3%. a20 index for the actual versus the predicted data showed that the three ML techniques were able to predict the horizontal stresses with a deviation less than 20% from the actual data. For the validation dataset, the RF, ANFIS, and FN models were able to capture all changes in the σh and σH trends with depth and accurately predict the σh and σH values. The outcomes of this study confirm the robust capability of ML to predict σh and σH from readily available logging data with no need for additional costs or site investigation.

There is a growing interest in the application of the machine learning techniques in predicting the motorcycle crash severity. This is partly due to a progress in autonomous vehicles technology, and machine learning technique, which as a main component of autonomous vehicle could be implemented for traffic safety enhancement. Wyoming's motorcycle crash fatalities constitute a concern since the count of riders being killed in motorcycle crashes in 2014 was 11% of the total road fatalities in the state. The first step of crash reduction could be achieved through identification of contributory factors to crashes. This could be accomplished by using a right model with high accuracy in predicting crashes. Thus, this study adopted random forest, support vector machine, multivariate adaptive regression splines and binary logistic regression techniques to predict the injury severity outcomes of motorcycle crashes. Even though researchers applied all the aforementioned techniques to model motorcycle injury severities, a comparative analysis to assess the predictive power of such modeling frameworks is limited. Hence, this study contributes to the road safety literature by comparing the performance of the discussed techniques. In this study, Wyoming's motorcycle crash injury severities are modeled as functions of the characteristics that give rise to crashes. Before conducting any analyses, feature reduction was used to identify a best number of predictors to be included in the model. Also to have an unbiased estimation of the performance of different machine learning techniques, 5-fold cross-validation was used for model performance evaluation. Two measure, Area under the curve (AUC), and confusion matrix were used to compare different models' performance. The machine learning results indicate that random forest model outperformed the other models with the least misclassification and higher AUC. It was also revealed that a dichotomous response variable, with fatality and incapacitation injury in one category, along with all other categories in another group would result in a lower misclassification rate than a polychotomous response variable. This might result from the nature of motorcycle crashes, lacking a protection compared with passenger cars, preventing machine learning technique to get trained properly. Moreover, the most important variables identified by the random forest model are those related to the operating speed, resentful other party, traffic volume, truck traffic volume, riding under the influence, horizontal curvature, wide roadway with more than two lanes and rider's age.

One of the critical areas of road safety is motorcycle safety. Motorcyclists are more vulnerable to injuries than occupants of other motor vehicles when involved in crashes. Researchers have studied the relationships between motorcycle crash severity and crash contributing factors. They are crash characteristics, roadway geometric design features, traffic characteristics, socio-demographics and environmental conditions. However, few researchers considered unobserved heterogeneity effects when modeling motorcycle crash injury severities, let alone interaction effects. In this research, motorcycle crashes in Wyoming that occurred from 2008 to 2017 were analyzed. Specifically, the injury severities of single motorcycle crashes and multiple vehicle crashes involving motorcycles were modeled. The response was whether the motorcycle crash incurred an incapacitating injury or fatality or not. The binary logistic regression and mixed binary logistic regression modeling structures were implemented. The mixed models revealed effects that otherwise would have been undisclosed in the binary logistic regression models’ results. According to the results of single motorcycle crashes, the majority of motorcycle-animal crashes and of motorcycle-barrier crashes were likely to be severe relative to other single motorcycle crashes. It was also found that horizontal curves increased the risk of severe injuries. Young riders were found to be less at risk of being gravely injured in single motorcycle crashes than older riders as well. Furthermore, riding under the influence and high posted speed limits increased the odds of severe crashes regardless of whether the crashes were single motorcycle crashes or multiple vehicle crashes involving motorcycles. Additionally, the mixed models uncovered interaction effects and unobserved effects pertaining to speed limits.

In this work we proposed a new four junction AlAs/GaAs/GaAs0.94Bi0.0583/GaAs0.91Bi0.0587 based solar cell, where AlAs (Eg=2.17eV) and GaAs0.91Bi0.0587 (Eg=0.7eV) were used as uppermost and bottom layer respectively. All the calculations were done based on spectral p-n junction model (modified version) for three air mass conditions (AM 1.5D, AM 1.5G and AM 0) using Matlab simulation package. The four latticed-matched sub-layers were considered to be connected in series for producing optical transparency and maximum current conduction through top to bottom layer. All the calculated solar cell parameters such as efficiency, short-circuit current density, open-circuit voltage, fill factor showed better results for air mass zero spectral condition. The J-V characteristic curves were plotted to visualize the performance of the multijunction solar cell. The maximum photo-current conversion efficiency, open-circuit voltage and fill factor for AM 0 condition were found to be 64.8%, 3.6463 Volt and 0.9578 respectively. Thus, the proposed high efficiency multijunction solar cell will contribute in solar cell industry to aid in design and selection of proper solar cells for practical application.

Multistage hydraulic fracturing is a technique to extract hydrocarbon from tight and unconventional reservoirs. Although big advancements occurred in this field, understanding of the created fractures location, size, complexity, and proppant distribution is in its infancy. This study provides the recent advances in the methods and techniques used to diagnose hydraulic fractures in unconventional formations. These techniques include tracer flowback analysis, fiber optics such as distributed temperature sensing (DTS) and distributed acoustic sensing (DAS), tiltmeters, microseismic monitoring, and diagnostic fracture injection tests (DFIT). These techniques are used to estimate the fracture length, height, width, complexity, azimuth, cluster efficiency, fracture spacing between laterals, and proppant distribution. Each technique has its advantages and limitations, while integrating more than one technique in fracture diagnostics might result in synergies, leading to a more informative fracture description. DFIT analysis is critical and subjected to the interpreter’s understanding of the process and the formation properties. Hence, the applications of machine learning in fracture diagnostics and DFIT analysis were discussed. The current study presents an extensive review and comparison between different multistage fracture diagnostic methods, and their applicability is provided. The advantages and the limitations of each technique were highlighted, and the possible areas of future research were suggested.

Aim: Native rabbits in smallholder system are considered as important genetic resources, and the present study was aimed to study the genetic and phenotypic characterization and detection of the maternal origin of the native rabbit populations located at the Middle of Egypt. Materials and Methods: A survey of native rabbit populations was conducted in three governorates (Fayum [FY], Beni Suef [BN], and El Menia [MN]). The phenotypic characterization of rabbits included the profile body of the head, ears, eyes, neck, and legs and the coat colors. The blood samples were collected for genetic characterization based on mitochondrial (cytochrome b) and the microsatellite markers. Results: The phenotypic characterization of the body parts in the three populations was almost similar. The body weight of the mature rabbits in MN government was significantly heaviest, and the measurements for the main body parts (body length, chest circumference, and abdominal girth) were the highest compared to the two populations. The results of mitochondrial (cytochrome b) analysis revealed that the rabbits from the three governments belonged to lineage A except one animal was recorded as lineage G from MN's rabbit population. The results of the microsatellite markers revealed that the genetic diversity between the three populations showed genetic interferences; however, a closer genetic relationship was observed between BN and MN than FY. The majority of the genetic diversity was the individual variability. Conclusion: The mitochondrial lineage A is the major lineage in rabbit populations in the area of the Middle Egypt understudy. The genetic populations' structure is the interferences among the three populations. A large-scale survey should be done on native rabbit populations for the sustainable management and conservation of the local breeds' genetic resources.

Abstract Background Junctional ectopic tachycardia is a serious arrhythmia that frequently occurs after tetralogy of Fallot repair. Arrhythmia prophylaxis is not feasible for all pediatric cardiac surgery patients and identification of high risk patients is required. The objectives of this study were to characterize patients with JET, identify its predictors and subsequent complications and the effect of various treatment strategies on the outcomes in selected TOF patients undergoing total repair before 2 years of age. Methods From 2003 to 2017, 609 patients had Tetralogy of Fallot repair, 322 were included in our study. We excluded patients above 2 years and patients with preoperative arrhythmia. 29.8% of the patients (n = 96) had postoperative JET. Results JET patients were younger and had higher preoperative heart rate. Independent predictors of JET were younger age, higher preoperative heart rate, cyanotic spells, non-use of B-blockers and low Mg and Ca (p = 0.011, 0.018, 0.024, 0.001, 0.004 and 0.001; respectively). JET didn’t affect the duration of mechanical ventilation nor hospital stay (p = 0.12 and 0.2 respectively) but prolonged the ICU stay (p = 0.011). JET resolved in 39.5% (n = 38) of patients responding to conventional measures. Amiodarone was used in 31.25% (n = 30) of patients and its use was associated with longer ICU stay (p = 0.017). Ventricular pacing was required in 4 patients (5.2%). Median duration of JET was 30.5 h and 5 patients had recurrent JET episode. Timing of JET onset didn’t affect ICU (p = 0.43) or hospital stay (p = 0.14) however, long duration of JET increased ICU and hospital stay (p = 0.02 and 0.009; respectively). Conclusion JET increases ICU stay after TOF repair. Preoperative B-blockers significantly reduced JET. Patients with preoperative risk factors could benefit from preoperative arrhythmia prophylaxis and aggressive management of postoperative electrolyte disturbance is essential.