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(1) Background: Neck pain intensity, psychosocial factors, and physical function have been identified as potential predictors of neck disability. Machine learning algorithms have shown promise in classifying patients based on their neck disability status. So, the current study was conducted to identify predictors of neck disability in patients with neck pain based on clinical findings using machine learning algorithms. (2) Methods: Ninety participants with chronic neck pain took part in the study. Demographic characteristics in addition to neck pain intensity, the neck disability index, cervical spine contour, and surface electromyographic characteristics of the axioscapular muscles were measured. Participants were categorised into high disability and low disability groups based on the median value (22.2) of their neck disability index scores. Several regression and classification machine learning models were trained and assessed using a 10-fold cross-validation method; also, MANCOVA was used to compare between the two groups. (3) Results: The multilayer perceptron (MLP) revealed the highest adjusted R2 of 0.768, while linear discriminate analysis showed the highest receiver characteristic operator (ROC) area under the curve of 0.91. Pain intensity was the most important feature in both models with the highest effect size of 0.568 with p < 0.001. (4) Conclusions: The study findings provide valuable insights into pain as the most important predictor of neck disability in patients with cervical pain. Tailoring interventions based on pain can improve patient outcomes and potentially prevent or reduce neck disability. [ABSTRACT FROM AUTHOR]

This study presented an extensive assessment of risky driving behavior through Structural Equation Modeling (SEM) technique and explored the applicability of this method in identifying contributing factors influencing drivers' risk-taking behavior in clear and adverse weather. Drivers' questionnaire responses as well as vehicle trajectories of their completed trips in clear and adverse weather were utilized from the SHRP2 Naturalistic Driving Study (NDS). Factor analyses were conducted to identify the number of unobserved "latent" variables. Subsequently, two SEM models in clear and adverse weather were developed to attain the relationships between the observed and the latent variables. "Human Factors" and "Driving Skills" were determined as exogenous latent variables in both models to investigate their impacts on an endogenous latent variable (i.e., risky driving). The results suggested that "Human Factors" was the most significant latent variable affecting drivers' risk-taking behavior in clear and adverse weather conditions. Moreover, speeding was found to have a significant impact on risky behavior in adverse weather conditions. The findings could help safety practitioners with better understanding of the influencing factors affecting risky driving to improve safety through proper enforcement and necessary training programs, particularly targeting young and inexperienced drivers. [ABSTRACT FROM AUTHOR]

Coronaviruses cause respiratory and intestinal infections in animals and humans. By the end of 2019, there was an epidemic of novel coronavirus (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Coronaviruses have a highly mutable genome that makes them genetically and phenotypically modifiable with a potential transmission to new host species. Based on current sequence databases, all human coronaviruses have animal origins, so animals have important roles in virus spillover to humans. The aim of this study is to investigate the role of different animal species in the epidemiology of SARS-CoV-2 in Egypt. A pan-coronaviruses RT-PCR has been used for detection of possible coronaviruses infection in different species including bats, humans, birds, and dogs in Egypt during the period of November 2020 till June 2021. Ninety-two samples (46 from Rousettus aegyptiacus bats, 10 from human, 26 from wild birds, and 10 from dogs) were screened for SARS-CoV-2. Our results revealed that only human samples were SARS-CoV-2 positive for SARS-CoV-2 while all other animal and bird samples were negative. To recapitulate, our results suggest that animals may not actively transmit SARS-CoV-2 among people in Egypt during the current COVID-19 pandemic. Further structural surveillance and follow up screening for SARS-CoV-2 among domestic and wild animal populations in Egypt is crucially needed. [ABSTRACT FROM AUTHOR]

The main focus of this study was to develop a robust prediction model based on deep learning capable of providing timely predictions of injury and fatal crashes in adverse weather on rural mountainous freeways. This study leveraged a promising deep learning technique named ResNet18. To apply the proposed deep learning model, the numeric crash data were converted to images utilizing a cutting-edge method, called DeepInsight. In addition, considering the imbalanced nature of the crash data, this study leveraged two data balancing techniques, namely Random Under Sampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE); and experimented with several data sampling ratios. The best prediction performance was found using a ratio of 1:2:2 (Fatal:Injury:PDO) coupled with both RUS and SMOTE, which produced an overall prediction accuracy of 99.3% and 80.5% for fatal and injury crashes, respectively. This study also investigated the importance of variables on crash severity, which revealed that driver residency, vehicle damage extent, airbag deployment, driver conditions, weather, and road surface conditions were the most important variables contributing to the severity of crashes. The proposed deep learning framework can provide an accurate prediction of fatal and injury crashes, which is crucial to ensuring effective traffic collision management. [ABSTRACT FROM AUTHOR]

The current study uses three different pin eccentricities (e) and six different welding speeds to investigate the impact of pin eccentricity on friction stir welding (FSW) of AA5754-H24. To simulate and forecast the impact of (e) and welding speed on the mechanical properties of friction stir welded joints for (FSWed) AA5754-H24, an artificial neural network (ANN) model was developed. The input parameters for the model in this work are welding speed (WS) and tool pin eccentricity (e). The outputs of the developed ANN model include the mechanical properties of FSW AA5754-H24 (ultimate tensile strength, elongation, hardness of the thermomechanically affected zone (TMAZ), and hardness of the weld nugget zone (NG)). The ANN model yielded a satisfactory performance. The model has been used to predict the mechanical properties of the FSW AA5754 aluminum alloy as a function of TPE and WS with excellent reliability. Experimentally, the tensile strength is increased by increasing both the (e) and the speed, which was already captured from the ANN predictions. The R2 values are higher than 0.97 for all the predictions, reflecting the output quality. [ABSTRACT FROM AUTHOR]

Friction stir spot welding (FSSW) of similar AA5052-H32 joints has numerous benefits in shipbuilding, aerospace, and automotive structural applications. In addition, studying the role of tool rotation speed on the microstructure features, achieved textures, and joint performance of the friction stir spot-welded (FSSWed) joint still needs more systematic research. Different FSSWed AA5052-H32 lap joints of 4 mm thickness were produced at different heat inputs using three tool rotation speeds of 1500, 1000, and 500 rpm at a constant dwell time of 2 s. The applied thermal heat inputs for achieving the FSSW processes were calculated. The produced joints were characterized by their appearance, macrostructures, microstructures, and mechanical properties (hardness contour maps and maximum tensile–shear load) at room temperature. The grain structure and texture developed for all the FSSWed joints were deeply investigated using an advanced electron backscattering diffraction (EBSD) technique and compared with the base material (BM). The main results showed that the average hardness value of the stir zone (SZ) in the welded joints is higher than that in the AA5052-H32 BM for all applied rotation speeds, and it decreases as the rotation speed increases from 500 to 1000 rpm. This SZ enhancement in hardness compared to the BM cold-rolled grain structure is caused by the high grain refining due to the dynamic recrystallization associated with the FSSW. The average grain size values of the stir zones are 11, 9, and 4 µm for the FSSWed joints processed at 1500, 1000, and 500 rpm, respectively, while the BM average grain size is 40 µm. The simple shear texture with B/-B components mainly dominates the texture. Compared to the welded joints, the joint processed at 500 rpm and a 2 s duration time attains the highest tensile-shear load value of 4330 N. This value decreases with increasing rotation speed to reach 2569 N at a rotation speed of 1500. After tensile testing of the FSSWed joints, the fracture surface was also examined and discussed. [ABSTRACT FROM AUTHOR]

The use of the friction stir welding (FSW) process as a relatively new solid-state welding technology in the aerospace industry has pushed forward several developments in different related aspects of this strategic industry. In terms of the FSW process itself, due to the geometric limitations involved in the conventional FSW process, many variants have been required over time to suit the different types of geometries and structures, which has resulted in the development of numerous variants such as refill friction stir spot welding (RFSSW), stationary shoulder friction stir welding (SSFSW), and bobbin tool friction stir welding (BTFSW). In terms of FSW machines, significant development has occurred in the new design and adaptation of the existing machining equipment through the use of their structures or the new and specially designed FSW heads. In terms of the most used materials in the aerospace industry, there has been development of new high strength-to-weight ratios such as the 3rd generation aluminum–lithium alloys that have become successfully weldable by FSW with fewer welding defects and a significant improvement in the weld quality and geometric accuracy. The purpose of this article is to summarize the state of knowledge regarding the application of the FSW process to join materials used in the aerospace industry and to identify gaps in the state of the art. This work describes the fundamental techniques and tools necessary to make soundly welded joints. Typical applications of FSW processes are surveyed, including friction stir spot welding, RFSSW, SSFSW, BTFSW, and underwater FSW. Conclusions and suggestions for future development are proposed. [ABSTRACT FROM AUTHOR]

Friction stir-spot welding (FSSW) as a solid-state joining process for local welding offers a number of benefits for applications in the automotive, aerospace, and marine industries. In these industries, and from an economic point of view, producing spot welds at a low rotating speed and in a short time is critical for saving energy and enhancing productivity. This investigation helped fill a knowledge gap in the literature about FSSW of 4 mm similar lap joints of AA5052-H32 sheet materials, in which welding takes place over a short time period with a slow tool rotation speed. Consequently, the purpose of this work was to investigate the feasibility of FSSW 2 mm thick AA5052-H32 aluminum alloy sheets to produce 4 mm thick similar spot lap joints at various low dwell times of 1, 2, and 3 s and a constant relatively low tool rotation speed of 500 rpm. The introduced heat input for the friction stir-spot welded (FSSWed) lap joints was calculated based on the applied processing parameters. Joint appearance, cross-section macrostructures, and microstructure features of all the spot welds were evaluated. The mechanical properties (hardness contour maps and maximum tensile shear loads) were also examined. The results show that joining 2 mm sheet thickness AA5052-H32 at a low heat input in defect-free similar lap joints could be successfully achieved. The stir zone (SZ) region became wider as the dwell time increased from 1 to 3 s. The hardness value of the SZ was higher than that attained by the AA5052-H32 base material (BM) for all applied dwell times. Especially at 2 s, the hardness of the SZ was approximately 48% higher than that of the BM. This increase in hardness may be attributed to the high grain refinement of the new dynamically recrystallized grain (4 µm) in the SZ compared to the cold-rolled BM grain size (40 µm). Among the tried FSSW process variables, the dwell time of 2 s at a rotation rate of 500 rpm also produced the maximum tensile shear load of 4330 N. Finally, the locations and features of the fracture surfaces of the FSSWed joints were examined using a scanning electron microscope (SEM) and the obtained results were discussed. [ABSTRACT FROM AUTHOR]

This study investigates the effect of tool pin eccentricity and welding speed on the grain structure, crystallographic texture, and mechanical properties of friction stir welded (FSWed) AA5754-H24. Three tool pin eccentricities of 0, 0.2, and 0.8 mm at different welding speeds ranging from 100 mm/min to 500 mm/min and a constant tool rotation rate of 600 rpm were investigated. High-resolution electron backscattering diffraction (EBSD) data were acquired from each weld's center of the nugget zone (NG) and processed to analyze the grain structure and texture. In terms of mechanical properties, both hardness and tensile properties were investigated. The grain structure in the NG of the joints produced at 100 mm/min, 600 rpm, and different tool pin eccentricities showed significant grain refining due to dynamic recrystallization with average grain sizes of 18, 15, and 18 µm at 0, 0.2, and 0.8 mm pin eccentricities, respectively. Increasing the welding speed from 100 to 500 mm/min further reduced the average grain size of the NG zone to 12.4, 10, and 11 µm at 0, 0.2, and 0.8 mm eccentricity, respectively. The simple shear texture dominates the crystallographic texture with both B ¯ /B texture component with the C component at their ideal positions after rotating the data to align the shear reference frame with the FSW reference frame in both the PFs and ODF sections. The tensile properties of the welded joints were slightly lower than the base material due to the hardness reduction in the weld zone. However, the ultimate tensile strength and the yield stress for all welded joints increased by increasing the friction stir welding (FSW) speed from 100 to 500 mm/min. Welding using the pin eccentricity of 0.2 mm resulted in the highest tensile strength; at a welding speed of 500 mm/min, it reached 97% of the base material strength. The hardness profile showed the typical W shape with a reduction in the hardness of the weld zone and a slight recovery of the hardness in the NG zone. [ABSTRACT FROM AUTHOR]

Traffic crashes impose tremendous socio-economic losses on societies. To alleviate these concerns, countless traffic safety researches have shed light on the cognition of observable crash/crash severity contributing factors. Nonetheless, some influential factors might not be observable or measurable, referred to as unobserved heterogeneity, that could be accounted for by structuring random intercepts and slopes in hierarchical models. With this respect, although it is known random slopes can capture more unobserved heterogeneity, most previous studies utilized random intercepts to simplify result interpretations, indicating an inconsistency in the literature considering the hierarchical modeling specification. This study delves into the mentioned confusion within an empirical real-time clustering critical crashes, involving fatal or incapacitating injuries, versus non-critical crashes throughout 402-miles of Interstate-80 in Wyoming. The crash dataset was conflated with real-time traffic-related and environmental contributing factors. Regarding the inclusion of random intercepts and slopes, eleven Logistic regressions were conducted. As a data-dependent matter, results depicted random slopes, compared to random intercepts, do not necessarily enhance models' out-of-sample predictive performance because they impose much more complexity on the models' structure. Besides, considering the type of unobserved heterogeneity, if random slopes are required, random intercepts should be accompanied to allow data showing their true patterns. [ABSTRACT FROM AUTHOR]

In this manuscript, we study a new non-linear system which represents a ...-type five level atom interacting with a quantized four-mode electromagnetic field. A non-linear Stark shift is introduced, through the elimination of intermediate levels (two and four) using the adiabatic elimination technique. By using the Schrodinger equation we obtain the analytic solution this model. Some statistical aspects through the effective Hamiltonian are presented such as the collapses-revivals phenomenon, degree of purity, concurrence, and the squeezing phenomenon with respect to study the effect of Stark shift parameters on these statistical aspects. For small values of the Stark shift parameter, the collapse times increase and the atomic inversion symmetry axis shifts upward, while the entanglement decreases significantly. It has been noted that the system is affected by the Stark shift parameter. [ABSTRACT FROM AUTHOR]

The current research designed a statistical model for the bobbin tool friction stir processing (BT-FSP) of AA1050 aluminum alloy using the Response Surface Method (RSM). The analysis studied the influence of tool travel speeds of 100, 200, and 300 mm/min and different pin geometries (triangle, square, and cylindrical) at a constant tool rotation speed (RS) of 600 rpm on processing 8 mm thickness AA1050. The developed mathematical model optimizes the effect of the applied BT-FSP parameters on machine torque, processing zone (PZ) temperature, surface roughness, hardness values, and ultimate tensile strength (UTS). The experimental design is based on the Face Central Composite Design (FCCD), using linear and quadratic polynomial equations to develop the mathematical models. The results show that the proposed model adequately predicts the responses within the processing parameters, and the pin geometry is the most influential parameter during the BT-FSP of AA1050. The analysis of variance exhibit that the developed mathematical models can effectively predict the values of the machine torque, PZ temperature, surface roughness, hardness, and UTS with a confidence level of over 95% for the AA1050 BT-FSP. The optimization process shows that the optimum parameters to attain the highest mechanical properties in terms of hardness and tensile strength at the lowest surface roughness and machine torque are travel speed (TS) of 200 mm/min using cylindrical (Cy) pin geometry at the constant RS of 600 rpm. The PZ temperature of the processed specimens decreased with increasing TS at different pin geometries. Meanwhile, the surface roughness of the processed passes and machine torque increased with increasing the TS at different pin geometries. Increasing TS from 100 to 300 mm/min increases the hardness values of the processed materials using different pin geometries. The highest UTS of 79 MPa for the processed specimens was attained at the TS of 200 mm/min and RS of 600 rpm using the Cy pin geometry. [ABSTRACT FROM AUTHOR]

Joining dissimilar sheet thicknesses of AA6082-T6 alloys by friction stir spot welding (FSSW) provides many advantages in automotive and aerospace applications. The formed keyhole at the end of the FSSW process is one of the typical features after the welding process, which owns the same size as the rotating pin that remains at the joint center. This keyhole destroys the joint continuity and can stimulate serious stress concentration when the FSSW joint bears an external force. To solve this issue, a novel refilling technique was developed for the FSSW keyholes using a friction stir deposition (FSD) technique. The FSSW joints of AA6082-T6 sheets were welded at various rotation speeds from 400 to 1000 rpm and a constant dwell time of 3 s, where a 2 mm sheet thickness was an upper sheet, and a 1 mm sheet thickness was a lower sheet. All the keyhole refilling processes were achieved using a specially designed AA2011-T6 consumable rod to be used for friction stir deposition of continuous layers at a constant deposition parameter of 400 rpm consumable rod rotation speed and a 1 mm/min feed rate. The heat input energy for both the FSSW and refilled FSSW lap joints was calculated. In addition, the FSSW and the FSD temperatures were measured. Macrostructure, microstructure, and mechanical properties in terms of hardness and tensile shear maximum load were evaluated for both the friction stir spot welded (FSSWed) and the refilled FSSW lap joints. The obtained results showed that the keyhole could be successfully refilled with defect-free continuous multilayers after the refill friction stir spot welding (RFSSW) process. All the RFSSW lap joints showed higher tensile shear loads than that given by the FSSW (before refill) lap joints. The RFSSW joint (welded at 600 rpm/3 s and refilled at 400 rpm/1 mm/min) showed a higher tensile shear load of 5400 N ± 100 compared with that recorded by the unrefilled joint (4300 N ± 80). The fracture location and fracture surface of the FSSW and RFSSW were examined and discussed. [ABSTRACT FROM AUTHOR]

AA1050 plates of 8 mm thickness were processed via bobbin-tool friction stir processing technique at a constant rotation speed of 600 rpm and different travel speeds ranging from 50 to 300 mm/min using three-pin geometries of triangle, square, and cylindrical. The temperatures of the processed zone, the advancing side, and the retreating side were measured; the machine torque during processing was also recorded. The processed materials were evaluated in terms of surface roughness, macrostructure, tensile properties, and hardness measurements. The fracture surfaces of the tensile fractured specimens were investigated using SEM. The results indicated that the pin geometry and processing speed significantly affect the generated heat input and the morphology of the processed zone. The peak temperature in the center of the processed zone decreases with increasing the travel speed from 50 to 300 mm/min at all applied pin geometries. The maximum temperature of ~400 °C was reached using the cylindrical pin geometry. The machine torque increases with increasing the travel speed at all applied pin geometries, and the highest torque value of 73 N.m is recorded using the square pin geometry at 300 mm/min travel speed. The top surface roughness of the processed area using the cylindrical pin is lower than that given by the other pin geometries. Under all applied conditions, the hardness of the processed area increases with increasing travel speed, and the cylindrical pin shows a higher hardness than the other pin geometries with 19% enhancement over the BM. The AA1050 processed using a cylindrical pin at 200 mm/min travel speed and a rotation speed of 600 rpm produces a sound processing zone with the highest ultimate tensile strength of 79 MPa. [ABSTRACT FROM AUTHOR]

Real-time risk assessment studies have investigated a limited length of corridors. However, the necessity of assessing the safety performance of Connected Vehicles (CVs) requires looking into an entire corridor. Aligned with the CV Pilot Program on 402-miles Interstate-80 in Wyoming, this study serves as a baseline to quantify the safety performance of the corridor during CV pre-deployment. Real-time traffic-related predictors were characterized to capture the spatial variation in traffic characteristics, both longitudinally and laterally. Nine Crash Prediction Models (CPMs) were conducted following the matched-case control design within two main parts. First, important predictors were detected using three feature selection techniques; Corrected-Impurity Importance (CII), Mean Decrease Impurity, and Mean Decrease Accuracy. Secondly, for each of the three sets of selected features, three different Logistic Regression models were developed; the Generalized Additive Model (GAM), Generalized Linear Model, and Generalized Nonlinear Model. The combined GAM and CII outperformed other CPMs by obtaining minimum error, maximum prediction performance, and detecting a larger number of significant predictors, which would enhance the safety performance measurement of the few numbers of CVs by comparing CVs pre- to post-deployment. Findings showed that investigating individual lanes is beneficial to comprehend crash patterns on corridors with comparatively less traffic volume. [ABSTRACT FROM AUTHOR]

Simple Summary: Acrylamide (ACR) is a vital manufacturing chemical used in polymer and copolymer production. ACR has neurotoxic, genotoxic, and carcinogenic effects. ACR monomer causes reproductive toxicity in males and diminished function by reducing sperm concentration and increasing the sperm deformity index. Earthworms have many therapeutic indications including antihypertensive, anti-allergic, anti-ulcer activities, anti-inflammatory, antitumor, antibacterial, and anti-oxidative effects. Our study showed that earthworms in a dose of 300 mg/kg prevented ACR-induced reproductive toxicity through increasing sperm motility and viability with the enhancement of sperm count, decreasing oxidative stress, and normalizing expression of p53 and Ki-67. In summary, supplementation of EE at the level of 300 mg/kg showed a protective potential effect against testicular toxicity induced by ACR. This study examined the protective effect of earthworm extract (EE) on acrylamide (ACR)-induced reproductive dysfunction. Forty male rats were allocated into four groups (n = 10). The G I (control) group received distilled water (D.W.). The G II group received ACR (5 mg kg−1 B.W. in D.W.) 5 days per week, orally, for 3 weeks. The G III group was administered EE (300 mg kg−1 B.W in D.W.) 5 days per week, orally, for 3 weeks. The G IV group was pretreated with EE for 3 weeks and then co-treated with EE and ACR for an additional 3 weeks. ACR decreased the number of sperm, sperm viability, and total motility. However, it increased testosterone levels with no effect on the FSH or LH levels. Moreover, ACR increased the concentrations of malondialdehyde (MDA) and nitric oxide (NO). Meanwhile, it decreased the glutathione (GSH) concentration in testicular tissues. Notably, the expression levels of p53 and Ki-67 were increased in the degenerated spermatogenic cells and in the hyperplastic Leydig cells of the testis of the ACR-treated group, respectively. Acrylamide induced alterations in the testicular tissue architecture. Interestingly, EE restored the sperm parameters and recovered the testicular histological structures and the biochemical alterations induced by ACR. In conclusion, earthworm extract ameliorated ACR-induced reproductive toxicity via restoring the testicular antioxidant balance and suppressing p53 and Ki-67 expressions in testicular tissues. [ABSTRACT FROM AUTHOR]

Globally, obesity has become one of the major health problems. This study was conducted to evaluate the anti-obesity potential of Cymbopogon schoenanthus methanolic extract (CS) in rats. Fifty male Wistar rats of six to eight weeks old, 100–120 g body weight (BW) were randomly assigned into 5 groups (n = 10): The control group was fed a basal diet. CS-group was supplied with basal diet and orally given CS (200 mg/kg BW) for 12 weeks. HFD-group was fed a high-fat diet (HFD) for 18 weeks. HFD + CS-group was fed on HFD and CS HFD then CS-group was fed HFD for 12 weeks then shifted to basal diet and CS for another 6 weeks. Phytochemical analysis of CS indicated the presence of various terpenes and flavonoid compounds. Among the compounds characterized are quercetin, apigenin, luteolin, orientin, eudesmene, cymbopogonol, caffeic acid, coumaric acid, and linolenic acid. Supplementation of HFD significantly increased the body weight, levels of serum triacylglycerol, total cholesterol, very low-density lipoprotein, low-density lipo-protein (HDL), glucose, serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activities. In addition, HFD up-regulated the protein expression of uncoupling protein (UCP)-1 in both brown and white adipose tissue; and the expression of hepatic mRNA of sterol regulatory element-binding protein (SREBP)-1c and SREBP-2. However, it decreased the serum level of HDL, and protein expression level of UCP-1 in both brown and white adipose tissue. Treatment of HFD-fed animals with CS extract either concurrently (HFD + CS-group), or after obesity induction (HFD then CS-group) significantly reversed all HFD-induced alterations in body weight; food intake; serum biochemical profile (including hyperglycemia, dyslipidemia); and tissue gene expressions. These results indicate that CS methanolic extract ameliorated HFD-induced obesity, serum biochemical, hepatic, and adipose tissue gene expression alterations. CS extract accomplished these effects mostly through its various identified bioactive compounds which have been proven to have anti-obesity and anti-diabetic activities. [Display omitted] [ABSTRACT FROM AUTHOR]

Sound joints of a far apart property, pure aluminum and mild steel, and dissimilar materials were obtained by friction stir spot welding (FSSW) solid state joining process. Sheets of 2 mm thickness were overlapped and fixed with a fixture device then welded. Welding parameters that produced the sound joint were identified. Joints microstructure and mechanical properties were investigated. A microstructural investigation has revealed a creation of mechanical interlocking and discontinuous formation of the intermetallic compounds found at the interface. The intermetallic compound (IMC) layer thickness ranged from 6 μm to 17 μm with the optimum condition specimen, 800 rpm and 5 Sec dwell time. Tensile-shear test showed that failure load has increased with increasing tool plunge depth and rotational speed. After the optimum value of rotational speed, the shear failure load decreased. The maximum shear failure load of 2.15 KN was attained at 800 rpm and 2.4 mm plunge depth. [ABSTRACT FROM AUTHOR]

• Developing a realistic microsimulation model requires adjusting driving behavior parameters. • This study updated lane change models in various weather using trajectory-level SHRP2 NDS dataset. • Representative key lane change parameters were extracted from an automated algorithm. • The demonstrated methodology could be used to develop weather-specific microsimulation models. Introduction: Adverse weather has a considerable negative impact on safety and mobility of transportation networks. Microsimulation models are one of the potential tools that could be used to evaluate the safety and operational impacts of adverse weather. The development of a realistic microsimulation model requires the adjustment of driving behavior parameters with disaggregate trajectory-level data. This study presented a novel approach to update and adjust lane change model parameters for the development of realistic microsimulation models in different weather conditions by leveraging the trajectory-level data from SHRP2 Naturalistic Driving Study (NDS). Method: Representative key lane change parameters in various weather conditions were extracted from an automatic identification algorithm. These lane change parameters were used to develop microsimulation models in VISSIM in an attempt to assess the safety and operational impacts of adverse weather on a freeway weaving segment. Results: The evaluation of safety impacts of adverse weather with regard to three Surrogate Measures of Safety (SMoS) namely Time-to-Collision (TTC), Post Encroachment Time (PET), and Deceleration Rate to Avoid Collision (DRAC) suggested that extreme adverse weather (including heavy rain, heavy snow, and heavy fog) produced a higher total number of simulated conflicts compared to clear weather. The operational analysis results revealed that adjusted parameters in most of the adverse weather produced lower average speeds with higher total travel times and total delays than clear weather. Conclusions: The outcomes of safety and operational assessments for the adjusted parameters showed that the development of microsimulation models should be based on weather-specific, rather than default parameters. Practical Applications: The methodology presented in this study could be adopted by transportation agencies to develop weather-specific microsimulation models. Moreover, the demonstrated approach could be used to evaluate different Connected Vehicle (CV) applications related to lane change in terms of safety and operations in microsimulation platforms. [ABSTRACT FROM AUTHOR]

Friction stir spot welding (FSSW) is one of the important variants of the friction stir welding (FSW) process. FSSW has been developed mainly for automotive applications where the different thickness sheets spot welding is essential. In the present work, different thin thickness sheets (1 mm and 2 mm) of AA6082-T6 were welded using FSSW at a constant dwell time of 3 s and different rotation speeds of 400, 600, 800, and 1000 rpm. The FSSW heat input was calculated, and the temperature cycle experience during the FSSW process was recorded. Both starting materials and produced FSSW joints were investigated by macro- and microstructural investigation, a hardness test, and a tensile shear test, and the fractured surfaces were examined using a scanning electron microscope (SEM). The macro examination showed that defect-free spot joints were produced at a wide range of rotation speeds (400–1000 rpm). The microstructural results in terms of grain refining of the stir zone (SZ) of the joints show good support for the mechanical properties of FSSW joints. It was found that the best welding condition was 600 rpm for achieving different thin sheet thicknesses spot joints with the SZ hardness of 95 ± 2 HV0.5 and a tensile shear load of 4300 ± 30 N. [ABSTRACT FROM AUTHOR]

In the current work, mild steel used in shipbuilding applications was friction-stir-welded (FSWed) with the aim of investigating the microstructure and mechanical properties of the FSWed joints. Mild steel of 5 mm thickness was friction-stir-welded at a constant tool rotation rate of 500 rpm and two different welding speeds of 20 mm/min and 50 mm/min and 3° tool tilt angle. The microstructure of the joints was investigated using optical and scanning electron microscopes. Additionally, the grain structure and crystallographic texture of the nugget (NG) zone of the FSWed joints was investigated using electron backscattering diffraction (EBSD). Furthermore, the mechanical properties were investigated using both tensile testing and hardness testing. The microstructure of the low-welding-speed joint was found to consist of fine-grain ferrite and bainite (acicular ferrite) with an average grain size of 3 µm, which indicates that the temperature experienced above A1, where a ferrite and austenite mixture is formed, and upon cooling, the austenite transformed into bainite. The joint produced using high welding speed resulted in a microstructure consisting mainly of polygonal ferrite and pearlite. This could be due to the temperature far below A1 experienced during FSW. In terms of joint efficiency expressed in terms of relative ultimate tensile, the stress of the joint to the base material was found to be around 92% for the low-speed joint and 83% for the high-welding-speed joint. A reduction in welding was attributed to the microstructure, as well as the microtunnel defect formed near the advancing side of the joint. The tensile strain was preserved at 18% for low welding speed and increased to 24% for the high welding speed. This can be attributed to the NG zone microstructural constituents. In terms of crystallographic texture, it is dominated by a simple shear texture, with increased intensity achieved by increasing the welding speed. In both joints, the hardness was found to be significantly increased in the NG zone of the joints, with a greater increase in the case of the low-welding-speed joint. This hardness increase is mainly attributed to the fine-grained structure formed after FSW. [ABSTRACT FROM AUTHOR]

This work aims to optimize the performance evaluation characteristics such as the temperature at the weld center of the lap joint (Tw), the tensile shear load (TSL), and the hardness using an experimental design experiment for bobbin tool friction stir welding (BT-FSW) of AA1050 lap joints. BT-FSW is characterized by a fully penetrated pin and double-sided shoulder that promote symmetrical solid-state welds. This study contributes to improving the quality of 10 mm thick lap joints and addressing challenges to obtaining a sound weld deprived of any defects. Taguchi L9 orthogonal array (OA) experimental design was performed. Three different pin shapes (cylindrical, triangular, and square) and three levels of welding travel speeds of 200, 400, and 600 mm/min were selected as input controllable process parameters at a constant tool rotation speed of 600 rpm. A travel speed of 200 mm/min with square pin geometry significantly improves the TSL of the joint up to 6491 N. However, the hardness characteristic is optimized by using 600 mm/min travel speed and a cylindrical tool pin. The minimum temperature in the weld joint can be obtained using 600 mm/min or more with triangular pin geometry. From ANOVA results, it was seen that the BT-FSW of AA 1050 thick lap joints performance in terms of TLS and Tw were greatly influenced by travel speed; however, the tool shape influences the hardness more. For the validation of the models, BT-FSW experiments have been carried out for AA1050 using the applied processing parameters. Furthermore, regression models were developed to predict the Tw, TSL, and hardness. The calculated performance properties from the mathematical models were in an acceptable range compared to the measured experimental values. [ABSTRACT FROM AUTHOR]

The knot of endogeneity and unobserved heterogeneity are causes of rendering parameter estimates inconsistent in real-time crash prediction. This study intends to alleviate the potential sources of these issues in detecting critical crashes, involving fatal or incapacitating injuries, versus non-critical crashes through a 402-mile Interstate-80 in Wyoming. Among different types of endogeneity, the problem of errors-in-variables and simultaneity was respectively mitigated by conflating disaggregated real-time traffic observations with aggregated environmental conditions and removing secondary crashes from the dataset. The possibility of omitted variables and unobserved heterogeneity were dealt by using random intercepts in hierarchical modeling under Bayesian inference. Three models were calibrated. Model-1 treated all predictors as fixed parameters. Model-2 and Model-3, respectively, considered weather and road surface conditions as random intercepts. Model-2 outperformed the others where the Intraclass Correlation Coefficients confirmed that the crash dataset is more nested within weather conditions. Results indicated that critical crashes require more interaction between vehicles, and shaping backward shockwave reduces their risk on Interstate-80 with a comparatively less traffic volume. Furthermore, considering different categories of weather and road surface conditions, critical crashes are more likely to occur on dry pavement and cloudy conditions compared to the wet surface and rainy days. [ABSTRACT FROM AUTHOR]

This paper provides valuable insights into lane-changing characteristics based on driver behavior in fog and clear weather conditions using the Naturalistic Driving Study (NDS) data collected by the second Strategic Highway Research Program (SHRP2). While a few studies focused on lane-changing behavior based on driver type, the impact of adverse weather conditions was not addressed. This study tested several hypotheses to identify significant differences in number of lane-changing events per mile and lane-changing durations in fog and clear weather under different traffic regimes. Additionally, different distributions of lane-changing durations were fitted. Using K-means cluster analysis technique, drivers were classified into two categories: conservative and aggressive. It was found that in heavy fog the mean lane-changing durations were significantly higher than clear weather under mixed-flow conditions. Moreover, the cluster analysis results revealed that conservative drivers had longer lane-changing durations in heavy fog conditions compared to clear weather. The results of this study could be used in microsimulation model calibration and validation related to lane change in reduced visibility due to fog and various traffic conditions. [ABSTRACT FROM AUTHOR]

The objective of the present study is to perform fine-grained Large Eddy Simulations (LES) of a canonical 30-cm-diameter methanol pool fire configuration in order to evaluate the ability of current fire models to predict the flame structure and the rate of heat transfer to the liquid fuel surface. The simulations are performed using an LES solver called FireFOAM, a prescribed fuel evaporation rate, and a "wall-resolved" approach; the simulations also include a description of the fuel pan lip. Great attention is paid to the design of the computational grid and to the control of spatial resolution. Less attention is paid to the LES model formulation: except for the treatment of radiation for which we consider both a simplified model using a prescribed global radiant fraction and a more advanced model based on the Weighted-Sum-of-Gray-Gases approach, the simulations use baseline FireFOAM treatments to describe subgrid-scale turbulence and combustion. Results suggest that grid-converged solutions are achieved at a spatial resolution of 2 mm in the near-pool-surface region and 5 mm in the bulk of the flame region. Consistent with experimental observations, the simulated flames feature a strong instability characterized by the cyclic formation of thin boundary layer flames at the liquid pool surface and vortex rings at the burner rim that grow into large puffs and determine the structure of the entire flame. Predictions of the mean and root-mean-square temperature and velocity are found to be in good agreement with measurements taken from the literature. Equally encouraging results are obtained for the mean radiative and convective heat fluxes near the pool surface; quantitatively, simulations are found to overestimate the intensity of the thermal feedback by 25%. These results suggest that provided that the computational grid is fine enough, current fire models can be used to predict the gas-to-fuel thermal feedback. [ABSTRACT FROM AUTHOR]

This article investigates the safety effectiveness of weather-based variable speed limit (VSL) systems on a rural mountainous interstate in Wyoming using a before-after study with Empirical Bayes (EB). VSL is considered one of the important countermeasures that help in reducing crashes by regulating and minimizing the variability for the motorists' operating speed. Although the multiple benefits result from the VSL, studies evaluating its safety effectiveness using Highway Safety Manual (HSM) methodologies are scarce in literature. Interstate 80 (I-80) in Wyoming, which has a total length of 402 miles, is known by its challenging roadway geometry, adverse weather, and high truck traffic. It comprises four weather-based VSL corridors totaling 147 miles. Full SPFs were developed to account for the variability in weather, traffic, roadway geometry, and implemented countermeasures. Negative binomial (NB) and multivariate adaptive regression splines (MARS) were used to develop the SPFs. Six years in the before period and 5 years in the after period were utilized to develop the crash modification factors (CMFs). Results showed that the MARS model outperformed the NB model. Moreover, the VSL showed a significant reduction in all the investigated crash types. The safety effectiveness of VSL for the examined severity levels ranged between 15% to 29% and 12% to 34% using NB and MARS, respectively. [ABSTRACT FROM AUTHOR]

The current study aimed to investigate the protective potential of Chlorella Vulgaris (CV) extract against the reproductive dysfunction induced by sodium nitrite toxicity. Forty‐five male Wistar albino rats were assigned into five groups (n = 9). Control group received normal saline orally for 3 months, CV‐treated: administered CV extract (70 mg/kg.BW) orally for 3 months, sodium nitrite‐treated: received sodium nitrite (80 mg/kg.BW) orally for 3 months, co‐treated: simultaneously received CV along with sodium nitrite treatment, orally, daily for 3 months, and CV‐pre‐treated: pre‐treated with CV extract for 4 weeks followed by simultaneous treatment with sodium nitrite and CV extract for additional 8 weeks. Treatment with sodium nitrite significantly decreased serum testosterone and follicle‐stimulating hormone concentrations, sperm count, motility, and viability. Besides, it decreased testicular superoxide dismutase and glutathione peroxidase activities while increased malondialdehyde concentration. This effect of sodium nitrite was associated with degenerative, necrotic, vascular, and inflammatory changes in testicular tissues. Treatment of sodium nitrite‐intoxicated rats with CV in co‐treated and pre‐treated groups significantly prevented sodium nitrite‐induced alterations of sperm parameters, hormonal concentrations, testicular oxidative–antioxidant status, and histological architecture. This study indicates that CV extract ameliorates the reproductive dysfunction induced by sodium nitrite toxicity via improving reproductive hormonal levels and testicular antioxidant activities. [ABSTRACT FROM AUTHOR]

The environmental impact of crop wastes and the high cost of peat moss (PM) force scientists to find alternative growing media. In a pot experiment, peanut shell (PS) and corn wastes (CWs) were evaluated as growing media in comparison with PM in three different mixing ratios with washed sand. The mixing ratio were M1 = 1:1 raw material to sand, M2 = 1:2 raw material to sand, and M3 = 1:3 raw material to sand. The tested plant was lettuce (Lactuca sativa L.). The obtained results showed that PS medium contained available nitrogen (N) and phosphorus (P) higher by 121% and 38% above the PM medium. Availability of potassium (K) in CW medium was higher by 167% than PM. The EC, pH, OC, and C/N ratio of PM were higher by 227%, 4%, 128%, and 99% above the CW and by 1,473%, 9%, 74%, and193% above the PS, respectively. The highest significant values of growth parameters were recorded in PM medium. The highest total cost and lowest net profit were recorded with the use of PMM1, whereas the lowest cost and the highest net profit were obtained with PSM2. Physiochemical characteristics of peanut shell suggested that it can be economical alternative growing media for PM. [ABSTRACT FROM AUTHOR]

Crashes involving heavy trucks on rural mountainous freeways are known to result in severe injuries and fatalities, particularly under challenging driving conditions. This study aims to develop a robust model to accurately predict fatal and injury crashes involving heavy trucks on rural mountainous freeways. The crash database of Interstate-80 in Wyoming was used to extract a wide range of variables related to environmental, roadway, crash, occupant, and vehicle characteristics. This study employed a state-of-the-art deep neural network architecture named ResNet-50 using transfer learning to develop crash severity prediction models. The numeric crash data were converted to images utilizing DeepInsight to facilitate the application of the proposed deep learning model. Due to the imbalanced nature of the crash severity data, this study employed random undersampling (RUS) and synthetic minority oversampling technique (SMOTE) data balancing techniques and investigated several data sampling ratios. A ratio of 1:2:2 (Fatal: Injury: PDO) combined with both RUS and SMOTE produced the best performance with recall values of 99.7%, 79.7%, and 79.3% for fatal, injury, and PDO crashes, respectively. This study also employed Boruta and extreme gradient boosting (XGBoost) to examine the significance of variables on crash severity. The findings revealed that the deployment of airbags, use of seatbelts, driver distraction, and driver conditions such as inattentiveness and fatigue, vehicle type, vertical grades, weather, and road surface conditions were the most critical variables contributing to the severity of crashes involving heavy trucks. Furthermore, this study developed several reduced truck-involved crash severity prediction models without significantly compromising the prediction performance. The proposed deep neural network model can provide accurate and timely prediction of fatal and injury crashes involving heavy trucks, which is beneficial for ensuring efficient collision management, avoiding secondary pile-up crashes, and facilitating prompt medical assistance. [ABSTRACT FROM AUTHOR]

This study investigated the effect of a hemispherical friction stir welding (FSW) tool on the heat generation and mechanical properties of dissimilar butt welded AA5083 and AA7075 alloys. FSW was performed on the dissimilar aluminum alloys AA5083-H111 and AA7075-T6 using welding speeds of 25, 50, and 75 mm/min. The tool rotation rate was kept constant at 500 rpm. An analytical model was developed to calculate heat generation and temperature distribution during the FSW process utilizing a hemispherical tool. The experimental results were compared to the calculated data. The latter confirms the accuracy of the analytical model, demonstrating a high degree of agreement. Sound FSW dissimilar joints were achieved at welding speeds of 50 and 25 mm/min. Meanwhile, joints created at a welding speed of 75 mm/min exhibited a tunnel-like defect, which can be attributed to the minimal heat generated at this particular welding speed. At a lower welding speed of 25 mm/min, a higher tensile strength of the dissimilar FSWed joints AA5083 and AA7075 was achieved with a joint efficiency of over 97%. [ABSTRACT FROM AUTHOR]

Promoting the safety of commercial trucks by identifying countermeasures that eliminate/reduce the effect of factors that increase the severity of truck-related crashes is crucial. Crash causal factors for rural interstate roads, located within the mountain plains, are inherently unique compared to urban interstate roads. This is due to the presence of challenging road geometry coupled with severe weather conditions and high truck traffic volumes. This study investigated Interstate 80 in Wyoming using decision trees, as a data mining approach, and structural equation model (SEM) as a latent factor modeling approach. SEM was employed to clarify the direct and indirect relationships between endogenous and exogenous variables while accounting for the variation and covariation within and between the constructed measurement models. Crash severity data were processed to account for factors affecting single vehicles and multivehicle trucks. The results showed that the interaction with surrounding traffic was the most significant latent variable affecting the crash severity of multivehicle truck crashes, while adverse weather conditions were the most significant latent variable affecting the crash severity of single-truck crashes. The results of this study highlighted the importance of increasing the situational awareness of commercial truck drivers with upcoming hazardous events. This could be performed by communicating information using variable message signs, the 511 application, the commercial vehicle operator portal (CVOP), or the connected vehicle (CV) technologies. [ABSTRACT FROM AUTHOR]

• Utilization of trajectory-level real-time data in rainfall related near-crash risk studies. • The detection of evasive maneuvers was done using a sequence of vehicle kinematics. • A Time range of an aggressive driving behavior Warning messages was estimated. • The results from this study might be used in Advanced Driver Warning Systems (ADAS). • This is an early investigation study for SMoS that can be collected from CV in future. A better understanding of what happened before and during a crash event could lead to development of countermeasures that reduce crash risk on freeways. This paper focused on detecting near-crashes on freeways by comparing environmental conditions and vehicle kinematics signatures of near-crash events to normal driving using data collected from the Second Strategic Highway Research Program (SHRP2) – Naturalist Driving Study. The study considered near-crash events as surrogate measures of crash risk only in rainy and clear weather conditions due to a limited number of near-crashes in other weather conditions. A time chunking technique was used with different aggregation levels to monitor changes in vehicle kinematics on a timescale. Both parametric and non-parametric techniques were used to detect near-crashes on freeways. The Binary Logistic Regression model was used as a parametric detection model, while Decision Tree , K-Nearest Neighbors (K-NN), and Deep Learning Artificial Neural Network were used as non-parametric detection models. The results showed that the logistic regression model provided a good fit of the input data and can detect near-crashes with outstanding discrimination. However, the time zone of interest identified by non-parametric models led to higher accurancy than that identified by the logistic regression model. In addition, Decision Tree and Deep Learning ANN machine learning algorithms showed higher detection accuracy of near-crashes compared to the K-NN algorithm. [ABSTRACT FROM AUTHOR]

• Multilevel modeling was utilized to capture regional heterogeneity. • Effect of driver's locality-related factors on driver speeding behavior was evaluated. • The geographical location accounted for about 7.7% of the variability in speeding. Disparities in traffic safety among different regions and driver populations have been investigated in previous studies. These studies found significant differences in crashes between population groups and geographical locations with various socio-economic characteristics. However, driver-behavioral factors, specifically speeding behavior, which is a critical aspect of traffic safety, have received less attention in case of analyzing the impact of local characteristics on driver-behavioral choices that might increase the risk of crashes. The impact of local characteristics on various driver behaviors is getting even more important as the data from the connected and automated vehicles as well as similar second-by-second trajectory level data from naturalistic driving studies are becoming more available. In fact, neglecting mentioned impact might lead to erroneous inferences due to the disparities in socioeconomic characteristics in different regions. Therefore, this paper, for the first time, utilized multilevel logistic regression modeling approach to evaluate the effect of driver's locality-related factors on driver speeding behavior using naturalistic driving data collected from the SHRP2 project in six US states. The results showed that the geographical location of drivers accounted for about 7.7% of the variability in the likelihood of a driver driving over the posted speed, regardless of other explanatory variables. The methodology and the results from this study can pave the road for future human factor studies utilizing trajectory-level data from different geographical locations to reduce the heterogeneity and increase the transferability of the results without introducing a bias in inferences. [ABSTRACT FROM AUTHOR]

• The presence of fog has a significant negative impact on lane-keeping behavior. • The study utilized big trajectory-level SHRP2 Naturalistic Driving Study datasets. • Association rules mining method was adopted to examine driver lane-keeping ability. • Affected visibility was highly associated with poor lane-keeping performance. The presence of fog has a significant adverse impact on driving. Reduced visibility due to fog obscures the driving environment and greatly affects driver behavior and performance. Lane-keeping ability is a lateral driver behavior that can be very crucial in run-off-road crashes under reduced visibility conditions. A number of data mining techniques have been adopted in previous studies to examine driver behavior including lane-keeping ability. This study adopted an association rules mining method, a promising data mining technique, to investigate driver lane-keeping ability in foggy weather conditions using big trajectory-level SHRP2 Naturalistic Driving Study (NDS) datasets. A total of 124 trips in fog with their corresponding 248 trips in clear weather (i.e., 2 clear trips: 1 foggy weather trip) were considered for the study. The results indicated that affected visibility was associated with poor lane-keeping performance in several rules. Furthermore, additional factors including male drivers, a higher number of lanes, the presence of horizontal curves, etc. were found to be significant factors for having a higher proportion of poor lane-keeping performance. Moreover, drivers with more miles driven last year were found to have better lane-keeping performance. The findings of this study could help transportation practitioners to select effective countermeasures for mitigating run-off-road crashes under limited visibility conditions. [ABSTRACT FROM AUTHOR]

Highlights • This study serves as a baseline analysis to quantify the impact of connected vehicle technologies. • Safety performance functions (SPFs) for 402-mile rural mountainous interstate were developed. • Multivariate adaptive regression splines (MARS) and negative binomial (NB) were compared. • Transferability analysis was performed to examine the need to calibrate the jurisdiction-specific SPFs to be used for the whole corridor. • SPFs were not transferable within the same corridor. SPFs must be calibrated individually for VSL vs non-VSL corridors. Abstract Interstate 80 in Wyoming is one of the busiest freight corridors that is characterized with harsh winter conditions and challenging mountainous roadway sections. The fatality rates in Wyoming are always typically higher than the national level. The 402-mile I-80 corridor in Wyoming was selected by the USDOT FHWA for piloting connected vehicle technology to improve the safety and mobility of heavy trucks. To accurately quantify the effectiveness of the pilot, evaluation of the pre-deployment safety performance is essential. Unlike other studies, the full 402-mile of I-80 corridor passing through Wyoming was investigated as a requirement of the USDOT. Homogeneous segmentation was used to divide the corridor based on horizontal and vertical roadway characteristics. A transferability analysis was conducted to investigate whether a short portion of the corridor would be representative of the whole 402-miles of I-80. Results showed that the whole 402 miles should be considered in the analysis due to the radical changes throughout the corridor. Several SPFs were developed using three models; negative binomial (NB) model, spatial autoregressive (SAR) model, and non-parametric multivariate adaptive regression splines (MARS). Comparisons were performed for the developed models. Crash prediction models for total crashes and Fatal and Injury (F + I) crashes in addition to truck crashes were calibrated utilizing five years of crash data from 2012 to 2016. The results obtained from the three statistical approaches showed that MARS model provided a better model fit compared to NB and SAR models, given the lower AIC values for the developed models. Yet, SAR models showed the significant spatial dependency between the neighbor roadway segments. Additionally, the NB model showed its superiority on SAR when the spatial correlation was not significant. Parametric and non-parametric techniques should be interchangeably used in developing SPFs according to the modeling needs. [ABSTRACT FROM AUTHOR]

Roadway safety is an integral part of a functioning infrastructure. A major use of the highway system is the transport of goods. The United States has experienced constant growth in the amount of freight transported by truck in the last few years. Wyoming is experiencing a large increase in truck traffic on its local and county roads due to an increase in oil and gas production. This study explores the involvement of heavy trucks in crashes and their significance as a predictor of crash severity and addresses the effect that large truck traffic is having on the safety of roadways for various road classifications. Studies have been done on the factors involved in and the causation of heavy truck crashes, but none address the causation and effect of roadway classifications on truck crashes. Binary Logit Models (BLM) with Bayesian inferences were utilized to classify heavy truck involvement in severe and non-severe crashes using ten years (2002–2011) of historical crash data in the State of Wyoming. From the final main effects model, various interactions proved to be significant in predicting the severity of crashes and varied depending on the roadway classification. The results indicated the odds of a severe crash increase to 2.3 and 4.5 times when a heavy truck is involved on state and interstate highways respectively. The severity of crashes is significantly increased when road conditions were not clear, icy, and during snowy weather conditions. [ABSTRACT FROM AUTHOR]

Chemotherapy-induced peripheral neuropathy is a common adverse effect associated with a number of chemotherapeutic agents including paclitaxel (PTX) which is used in a wide range of solid tumors. Development of PTX-induced peripheral neuropathy (PIPN) during cancer treatment requires dose reduction which limits its clinical benefits. This study is conducted to investigate the role of toll like receptor-4 (TLR4) /p38 signaling and Klotho protein expression in PIPN and the role of trimetazidine (TMZ) in this pathway. Sixty-four male Swiss albino mice were divided into 4 groups (n = 16); Group (1) injected intraperitoneally (IP) with ethanol/tween 80/saline for 8 successive days. Group (2) received TMZ (5 mg/kg, IP, day) for 8 successive days. Group (3) treated with 4 doses of PTX (4.5 mg/kg, IP) every other day over a period of 7 days. Group (4) received a combination of TMZ as group 2 and PTX as group 3. The Effect of TMZ on the antitumor activity of PTX was studied in another set of solid Ehrlich carcinoma (SEC)-bearing mice that was similarly divided as the above-mentioned set. TMZ mitigated tactile allodynia, thermal hypoalgesia, numbness and fine motor discoordination associated with PTX in Swiss mice. The results of the current study show that the neuroprotective effect of TMZ can be attributed to inhibition of TLR4/p38 signaling which also includes a reduction in matrix metalloproteinase-9 (MMP9) protein levels as well as the proinflammatory interleukin-1β (IL-1β) and preserving the levels of the anti-inflammatory IL-10. Moreover, the current study is the first to demonstrate that PTX reduces the neuronal levels of klotho protein and showed its modulation via cotreatment with TMZ. In addition, this study showed that TMZ neither alter the growth of SEC nor the antitumor activity of PTX. In conclusion, we suggest that (1) Inhibition of Klotho protein and upregulation of TLR4/p38 signals in nerve tissues may contribute to PIPN. (2) TMZ attenuates PIPN by modulating TLR4/p38 and Klotho protein expression without interfering with its antitumor activity. • Trimetazidine possesses a protective effect against paclitaxel induced-peripheral neuropathy. • oNeuroprotective effect of trimetazidine includes inactivation of TLR4/p38/NF-κB pathway. • oThe study shows the possible ameliorative role of klotho in paclitaxel induced-peripheral neuropathy. [ABSTRACT FROM AUTHOR]

Adverse weather conditions can significantly impact roadways by influencing roadway conditions, vehicle performance and driver behavior. Vehicle user characteristics and behavior can be considered as the most important factors affecting the driving task. The ability to see objects in motion, so called “dynamic visual acuity”, and the proper reaction process, such as headway and speed selection, are imperative factors for safe driving. In this study, data from the SHRP2 naturalistic driving study (NDS) are used to provide better understanding of driver speed and headway selection behaviors in clear and rainy weather conditions. A unique procedure to identify rain-related trips from the massive SHRP2 database was introduced in this study. In addition, roadway information database (RID) and NDS were utilized to compare driver behavior in clear and heavy rain conditions using matching trips. Matching trips were defined as trips with same driver, same vehicle, and same traversed routes. Preliminary descriptive statistics, partial proportional odds model, as well as geographical information system analyses showed significant differences between driver behavior and performance in clear and rainy weather conditions. One interesting finding of this research is that drivers were less likely to drive above the speed limits on road segments with higher posted speed limits. In addition, it was found that the probability of reducing speed more than 5 kph below the speed limits were 23% and 29% higher in light rain and heavy rain, respectively. Not only will the findings of the study help in providing better insights on drivers’ behavior and performance in rainy weather conditions, but it will also serve as a foundation for further studies to investigate driver behavioral factors in other weather conditions using naturalistic driving data. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the protective potential of Royal jelly (RJ) against cadmium (Cd)- induced testicular dysfunction in rats. Thirty-five adult male Wistar rats were assigned into five groups. G I; (control) injected intraperitoneally with saline, G II injected intraperitoneally with a single dose of CdCl2 (1 mg/kg BW), G III received RJ (100 mg/kg BW/day) orally, G IV was pre- treated with RJ for 1 week then, treated with CdCl2, and G V was co- treated with RJ and CdCl2. After day 56, serum and tissue samples were collected and analysed. The results showed decreased serum testosterone, luteinising hormone (LH), follicle- stimulating hormone (FSH), superoxide dismutase, glutathione reductase, sperm motility and count while increased malondialdehyde, nitric oxide, tumour necrosis factor-α (TNF-α) and sperm abnormalities, along with a severely damaged seminiferous tubules epithelium with cytoplasmic and nuclear disruptions following Cd toxicity. Additionally, Cd stimulated testicular mRNA expression of TNF-α while inhibited those of steroidogenic acute regulatory protein, cytochrome P450 cholesterol side chain cleavage enzyme androgen binding protein, FSH-receptor, LH-receptor, androgen receptor, 3β-hydroxysteroid dehydrogenase (HSD), 17β-HSD, and cytochrome P450 17A1. These negative alterations of cadmium were greatly reduced by RJ treatment. This study concluded that RJ protects against Cd-induced testicular toxicity. [ABSTRACT FROM AUTHOR]

It is known that adverse weather conditions can affect driver performance due to reduction in visibility and slippery surface conditions. Lane keeping is one of the main factors that might be affected by weather conditions. Most of the previous studies on lane keeping have investigated driver lane-keeping performance from driver inattention perspective. In addition, the majority of previous lane-keeping studies have been conducted in controlled environments such as driving simulators. Therefore, there is a lack of studies that investigate driver lane-keeping ability considering adverse weather conditions in naturalistic settings. In this study, the relationship between weather conditions and driver lane-keeping performance was investigated using the SHRP2 naturalistic driving data for 141 drivers between 19 and 89 years of age. Moreover, a threshold was introduced to differentiate lane keeping and lane changing in naturalistic driving data. Two lane-keeping models were developed using the logistic regression and multivariate adaptive regression splines (MARS) to better understand factors affecting driver lane-keeping ability considering adverse weather conditions. The results revealed that heavy rain can significantly increase the standard deviation of lane position (SDLP), which is a very widely used method for analyzing lane-keeping ability. It was also found that traffic conditions, driver age and experience, and posted speed limits have significant effects on driver lane-keeping ability. An interesting finding of this study is that drivers have a better lane-keeping ability in roadways with higher posted speed limits. The results from this study might provide better insights into understanding the complex effect of adverse weather conditions on driver behavior. [ABSTRACT FROM AUTHOR]

Transportation agencies across the nation are continuously evaluating the safety and operation of their rural two-lane highways. The three most common countermeasures are to do nothing, add passing lanes to target sections, or to upgrade to four-lane. The goal of this article is to update the current body of knowledge available to agencies on the process of implementing countermeasures and realistically evaluating their safety effectiveness. The Wyoming Department of Transportation (WYDOT) constructed nine passing-lane segments on a 26-mile stretch of rural two-lane highway. Preliminary analysis using 7-year crash data showed that the safety benefit was negligible at best. However, utilizing Empirical Bayes and Wyoming-specific safety performance functions, it was shown that the basic approach of comparing crash rates underestimated fatal and injury (F+I), and lane-departure crashes and that the passing lanes had a significant safety benefit. Furthermore, the authors looked at what would have happened from a safety and cost perspective had no countermeasure been implemented and then also if the road had been upgraded to four-lane undivided instead of just adding passing lanes. It was shown that passing lanes are the best option from a safety and cost perspective especially when agencies have constrained budgets. [ABSTRACT FROM PUBLISHER]

The article discusses a molecular study of Egyptian pomegranates juice which reveals that its anti-obesity potential in obese rats model. Topics discussed include the use of pomegranate juice in decreasing the body weight gain, serum levels of lipid, and serum insulin level; the expression of fatty acid synthase and sterol regulatory elementbinding protein-1c all types of juices; and the effect of pomegranate juice in treatmenting on the ghrelin.

Red light cameras may have a demonstrable impact on reducing the frequency of red light running violations; however, their effect on the overall safety at intersections is still up for debate. This paper examined the safety impacts of Red Light Cameras (RLCs) on traffic crashes at signalized intersections using the Empirical Bayes (EB) method. Data were obtained from the Florida Department of Transportation for twenty-five RLC equipped intersections in Orange County, Florida. Additional fifty intersections that remained with no photo enforcement in the vicinity of the treated sites were collected to examine the spillover effects on the same corridors. The safety evaluation was performed at three main levels; only target approaches where RLCs were installed, all approaches on RLC intersections, and non-RLC intersections located on the same travel corridors as the camera equipped intersections. Moreover, the spatial spillover effects of RLCs were also examined on an aggregate level to evaluate the safety impacts on driver behavior at a regional scale. The results from this study indicated that there was a consistent significant reduction in angle and left-turn crashes and a significant increase in rear-end crashes on target approaches, in addition, the magnitude and the direction of these effects, to a lesser degree, were found similar on the whole intersection. Similar trends in shift of crash types were spilled-over to non-RLC intersections in the proximity of the treated sites. On an aggregate county level, there was a moderate spillover benefits with a notable crash migration to the boundary of the county. [ABSTRACT FROM AUTHOR]

The effect of reduction of visibility on crash occurrence has recently been a major concern. Although visibility detection systems can help to mitigate the increased hazard of limited-visibility, such systems are not widely implemented and many locations with no systems are experiencing considerable number of fatal crashes due to reduction in visibility caused by fog and inclement weather. On the other hand, airports’ weather stations continuously monitor all climate parameters in real-time, and the gathered data may be utilized to mitigate the increased risk for the adjacent roadways. This study aims to examine the viability of using airport weather information in real-time road crash risk assessment in locations with recurrent fog problems. Bayesian logistic regression was utilized to link six years (2005–2010) of historical crash data to real-time weather information collected from eight airports in the State of Florida, roadway characteristics and aggregate traffic parameters. The results from this research indicate that real-time weather data collected from adjacent airports are good predictors to assess increased risk on highways. [ABSTRACT FROM AUTHOR]

Recently fiber - reinforced polymer (FRP) has become a practical alternative construction material for replacing steel bars as reinforcement in concrete structures to overcome the corrosion related problems . The recently developed basalt fiber - reinforced polymer (BFRP) is a valid alternative to carbon fibers for their lower cost and to glass fibers for their strength. However, the performance of Basalt reinforced concrete elements subjected to repeated loading , which is a critical design limit for bridge beams, has not been fully explored. Also the brittleness and low modulus of elasticity of BFRP greatly reduces the stiffness of BFRP - reinforced concrete (BFRPRC) beams . The aim of this experimental study is to investigate the flexural behavior of concrete beams reinforced with Basalt fiber reinforced polymer (BFRP) bars when they subjected to repeated loadings and to improve their stiffness . For this purpose an experimental program was set up on eight specimens consisting of pure Basalt FRPRC beams and hybrid B FRPRC beams with 205 0 mm length and a cross - section of 15 0 x 2 0 0 m m were carried out and tested under static or repeated loading followed by static loading up to failure . The results were discussed and analyzed. The test results indicated the contribution of adding steel reinforcement to concrete beams reinforced with BFRP bars. Finally some valuable conclusions and recommendations were given. [ABSTRACT FROM AUTHOR]

This research studies the shear behavior of high strength fiber reinforced concrete beams longitudinally reinforced with Basalt Fiber Reinforced Plastic (BFRP) bars with and without stirrups. Eight high - strength reinforced concrete beams were tested. Steel fibers and stirrups were used either separately or together as shear reinforcement. The test variables were steel fiber content ( V f )% with and without minimum shear reinforcement. All beams were tested under a four point static loading up to failure to investigate the shear behavior of high strength fiber reinforced beams reinforced with BFRP rebars. The shear span - to - depth ratio (a/d ) was kept constant at 2.5. Crack pattern and mode of failure, c racking and ultimate shear strength and mid - span deflection are presented to provide useful insights on the shear failure mechanism of such beam s. The experimental study shows that addition of steel fiber improves cracking, ultimate shear strength and ductility of the tested beams, and transfers the mode of failure of these beams into a more ductile one. The test results were compared with different proposed equations and design guidelines. Imam M. A, gave a reasonable equation to predict the shear strength of fiber concrete beams reinforced with BFRP bars with or without stirrups. Also the proposed equation predict ed relatively well the ultimate shear strength of the experimental results than other predictions [ABSTRACT FROM AUTHOR]

The investigation on the energy transfer mechanism from soils to buildings during earthquakes is critical for the design of earthquake resistant structures and for upgrading existing structures. Thus the need for research into Soil-Structure Interaction (SSI) problems is gr eater than ever. Moreover, recent studies show that the effects of SSI may be detrimental to the seismic response of structure and neglecting SSI in analysis may lead to un-conservative design. Despite this, the conventional design procedure usually involves assumption of fixity at the base of foundation neglecting the flexibility of the foundation, the compressibility of soil mass and consequently the effect of foundation settlement on further redistribution of bending moment an d shear force demands. Hence the soil-structure interaction analysis of multi-story buildings is the main focus of this study; the effects of SSI are analyzed for typical multi-story building resting o n raft foundation. Three methods of analysis are used for seismic demands evaluation of th e target moment resistant frame buildings: equivalent static load (ESL); response spectrum (RS) methods and nonlinear time history (TH) analysis with suit of nine time history records. Three-dimensional FEM model is constructed to analyze the effects of different soil conditions and number of stories on the vibration characteristics and seismic response demands of building structures. Numerical results obtained using soil structure interaction model conditions are compared to those corresponding to fixed-base support conditions. The peak responses of story shear, story moment, story displacement, story drift, moments at beam ends, as well as force of inner columns are analyzed. The analysis results of different approaches are used to evaluate the advantages, limitations, and ease of application of each approach for seismic analysis. [ABSTRACT FROM AUTHOR]

One of the most important structural elements, the column is primarily intended to support compressive loads. The strengthening is carried out for existing reinforced concrete columns to increase the ductility of the column and to increase load capacity to sustain the applied load as sometimes there may be a use change, which leads to increased loads. This work aims to study the behavior and efficiency of RC columns strengthened with steel jackets and subjected to axial load. An experimental program was performed on columns specimen till failure. Aspect ratio (t is the long side of the cross-section /b is the short side of the cross-section), the volume of steel straps, and the spacing of steel straps at centers(S) were the primary research variables in this work. Ten specimens were rectangular concrete columns with aspect ratios equal to 1, 1.56, and 2.04. the cross sections of the column were (200*200mm), (160*250 mm) and (140*286 mm) respectively. the total height of the specimens was 1200 mm,960mm, and 840mm respectively. Under an axial load, three control columns and seven strengthened columns were tested. The results showed that using this method of strengthening is very effective and an increase in the axial load capacity of the strengthened columns is obtained. This increase was due to the confining effect of the steel jacket, and the ability of the steel angle to withstand a large part of the applied axial load. The failure mode in most of the strengthened specimens was due to the buckling of the steel angles. [ABSTRACT FROM AUTHOR]

Steel corrosion is thought to be one of the primary causes of the inadequate durability of concrete buildings in the maritime environment. Because of this, adopting Fiber-Reinforced Polymer (FRP) bars in harsh settings has attracted a lot of attention for its appealing mechanical properties as well as to prevent corrosion issues. But because there hasn't been much research in this area, we don't fully understand how fiber-reinforced polymer (FRP) bars behave when they are compressed. This work's goal is to assess the expected axial compressive strength of columns when hybrid reinforcement is used in place of steel reinforcement. Hybrid bars are steel bars surrounded by a cover shell of Glass or Carbon FRP (hybrid-steel) for longitudinal reinforcement and/or transverse reinforcement. 17 column specimens were included in an experimental study program that was created. The specimens were tested to failure with an axial loading condition. The parameters studied were the type of fibers, the percentage of steel in the hybrid reinforcement for longitudinal main reinforcement ρL (0.96, 0.44, and 0.25), the ratio of the web reinforcement (internal ties), the proportion of fiber in the hybrid bars, as well as the columns' cross sections' rectangularity. Based on the data, mathematical models were devised and assessed to forecast the load bearing capability of the column. The findings indicate that hybrid reinforced concrete columns have acceptable levels of dependability index in general. [ABSTRACT FROM AUTHOR]

• Use of PRISMA approach for comprehensive review of naturalistic driving studies. • NDSs have provided numerous insightful findings in area of traffic safety and operation. • Most studied topic using NDS data is driver behavior. • NDSs could be effectively leveraged to perfect the behavior cloning. The state of practice of investigating traffic safety and operation is primarily based on traditional data sources, such as spot sensors, loop detectors, and historical crash data. Recently, researchers have utilized transportation data from instrumented vehicles, driving simulators, and microsimulation modeling. However, these data sources might not represent the actual driving environment at a trajectory level and might introduce bias due to their experimental control. The shortcomings of these data sources can be overcome via Naturalistic Driving Studies (NDSs) considering the fact that NDS provides detailed real-time driving data that would help investigate the safety and operational impacts of human behavior along with other factors related to weather, traffic, and roadway geometry in a naturalistic setting. With the enormous potential of the NDS data, this study leveraged the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) approach to shortlist the most relevant naturalistic studies out of 2304 initial studies around the world with a focus on traffic safety and operation over the past fifteen years (2005–2020). A total of 117 studies were systematically reviewed, which were grouped into seven relevant topics, including driver behavior and performance, crash/near-crash causation, driver distraction, pedestrian/bicycle safety, intersection/traffic signal related studies, detection and prediction using NDSs data, based on their frequency of appearance in the keywords of these studies. The proper deployment of Connected and Autonomous Vehicles (CAV) require an appropriate level of human behavior integration, especially at the intimal stages where both CAV and human-driven vehicles will interact and share the same roadways in a mixed traffic environment. In order to integrate the heterogeneous nature of human behavior through behavior cloning approach, real-time trajectory-level NDS data is essential. The insights from this study revealed that NDSs could be effectively leveraged to perfect the behavior cloning to facilitate rapid and safe implementation of CAV. [ABSTRACT FROM AUTHOR]