Comparing fatal crash risk factors by age and crash type by using machine learning techniques.

This study aims to use machine learning methods to examine the causative factors of significant crashes, focusing on accident type and driver's age. In this study, a wide-ranging data set from Jeddah city is employed to look into various factors, such as whether the driver was male or female, where the vehicle was situated, the prevailing weather conditions, and the efficiency of four machine learning algorithms, specifically XGBoost, Catboost, LightGBM and RandomForest. The results show that the XGBoost Model (accuracy of 95.4%), the CatBoost model (94% accuracy), and the LightGBM model (94.9% accuracy) were superior to the random forest model with 89.1% accuracy. It is worth noting that the LightGBM had the highest accuracy of all models. This shows various subtle changes in models, illustrating the need for more analyses while assessing vehicle accidents. Machine learning is also a transforming tool in traffic safety analysis while providing vital guidelines for developing accurate traffic safety regulations.

Shear strength and particle breakage of construction and demolition waste as a function of moisture state and compaction level: Insights for sustainable highway engineering.

In this study, the variation of shear strength behavior and particle breakage (after shearing), as a function of moisture state and compaction level, is investigated for recycled concrete aggregate blended with recycled clay masonry. Recycled masonry was blended with concrete aggregate in percentages ranging from 0% to 30% by total weight. Tests include; basic engineering characteristics (particle size, modified compaction, hydraulic conductivity, and California Bearing Ratio, CBR) as well as unconsolidated undrained static triaxial testing. In triaxial tests, moisture levels ranged from 60% to 100% of optimum moisture content, but compaction levels ranged from 90% to 98% of maximum dry density. The hydraulic conductivity for blends is approximately 2x10-6 cm/s, which indicates a relatively low hydraulic conductivity. Results show a proportional linear relationship between the shear strength of blends and the level of compaction. Despite this, both apparent cohesion and shear strength exhibited reverse linear trends. As expected, more compaction effort resulted in more particle breakage. Strict control should be performed over the compaction process to achieve the required compaction level which resulting in pavement materials being stiffer.

A surrogate safety analysis at sharp gore areas of diverging freeway ramps using micro simulation under congested traffic conditions.

Safety at the gore areas near diverging ramps is very crucial during planning and implementation of highway safety improvement programs. Limited research has been conducted on safety at the gore areas on arterial roads. This study aims at investigating the impact of improving a sharp gore area in Lincoln, Nebraska by performing a micro simulation before-and-after study with respect to its underlying state of safety and congestion. Data on travel times and traffic volumes for peak hours are incorporated after successful calibration to find out how a geometric intervention can decrease mobility issues as well as the likelihood of crash involvement. This study has utilized VISSIM software package to run the simulation however, to perform safety analysis, Surrogate Safety Assessment Model (SSAM) is used which is developed by the Federal Highway Administration (FHWA). During the model calibration, custom driving behaviors are created to represent driving tendencies of familiar drivers. The simulation results indicated that by adding an auxiliary lane near the gore area, the mobility issues such as bottle necks, lane changing dilemmas and queue lengths are substantially decreased. However, geometric interventions such as provision of a separate lane, increasing ramp spacing, nose spacing, deceleration area and queue storage area considerably reduced the likelihood of rear-end and lane changing crashes. Surrogate safety assessment in diverging ramps, particularly for sharp gores, has not previously been studied, and this study can serve as a primary footmark for future research on ramp-gores safety.

A hybrid Cycle GAN-based lightweight road perception pipeline for road dataset generation for Urban mobility.

One of the major problems that cause continual trouble in deep learning networks is that training a large network requires massive labelled datasets. The preparation of a massive labelled dataset is a cumbersome task and requires lot of human interventions. This paper proposes a novel generator network 'Sim2Real' transfer is a recent and fast-developing field in machine learning used to bridge the gap between simulated and real data. Training with simulated datasets often converges due to its size but fails to generalize real-world applications. Simulated datasets can be used to train and test deep learning models, enables the development and evaluation of new algorithms and architectures. By simulating road dataset, researchers can generate large amounts of realistic road-traffic dataset that can be used to study and understand several problems such as vehicular object tracking and classification, traffic situation analysis etc. The main advantage of such a transfer algorithm is to use the abundance of a simulated dataset to generate huge realistic-looking datasets to solve data-intense tasks. This work presents a novel, robust sim2real algorithm that converts the labels of a semantic segmentation map to a realistic-looking street view using the Cityscapes dataset and aims to achieve robust urban mobility for smart cities. Further, the generalizability of the Cycle Generative Adversarial Network (CycleGAN) architecture was tested by using an origami robot dataset for sim2real transfer. We show that the results were found to be qualitatively satisfactory for different traffic analysis applications. In addition, road perception was done using a lightweight SVM pipeline and evaluated on the KITTI dataset. We have incorporated Cycle Consistency Loss and Identity Loss as the metrics to evaluate the performance of the proposed Cycle GAN model. We inferred that the proposed Cycle GAN model provides an Identity loss of less than 0.2 in both the Cityscapes dataset and KITTI datasets. Also, we understand that the super-pixel resolution has a good impact on the quantitative results of the proposed Cycle GAN models.

Modeling resilient modulus of subgrade soils using LSSVM optimized with swarm intelligence algorithms.

Resilient modulus (Mr) of subgrade soils is one of the crucial inputs in pavement structural design methods. However, the spatial variability of soil properties and the nature of test protocols, the laboratory determination of Mr has become inexpedient. This paper aims to design an accurate soft computing technique for the prediction of Mr of subgrade soils using the hybrid least square support vector machine (LSSVM) approaches. Six swarm intelligence algorithms, namely particle swarm optimization (PSO), grey wolf optimizer (GWO), symbiotic organisms search (SOS), salp swarm algorithm (SSA), slime mould algorithm (SMA), and Harris hawks optimization (HHO) have been applied and compared to optimize the LSSVM parameters. For this purpose, a literature dataset (891 datasets) of different types of soils has been used to design and evaluate the proposed models. The input variables in all of the proposed models included confining stress, deviator stress, unconfined compressive strength, degree of soil saturation, soil moisture content, optimum moisture content, plasticity index, liquid limit, and percent of soil particles (P #200). The accuracy of the proposed models was assessed by comparing the predicted with the observed of Mr values with respect to different statistical analyses, i.e., root means square error (RMSE) and determination coefficient (R2). For modeling the Mr of subgrade soils, percent passing No. 200 sieve, optimum moisture content, and unconfined compressive strength were found to be the most significant variables. It is observed that the performance of LSSVM-GWO, LSSVM-SOS, and LSSVM-SSA outperforms other models in predicting accurate values of Mr. The (RMSE and R2) of the LSSVM-GWO, LSSVM-SSA, and LSSVM-SOS are (6.79 MPa and 0.940), (6.78 MPa and 0.940), and (6.72 MPa and 0.942), respectively, and hence, LSSVM-SOS can be used for high estimating accuracy of Mr of subgrade soils.

Biomedical applications of metallic nanoparticles in cancer: Current status and future perspectives.

The current advancements in nanotechnology are as an outcome of the development of engineered nanoparticles. Various metallic nanoparticles have been extensively explored for various biomedical applications. They attract lot of attention in biomedical field due to their significant inert nature, and nanoscale structures, with size similar to many biological molecules. Their intrinsic characteristics which include electronic, optical, physicochemical and, surface plasmon resonance, that can be changed by altering certain particle characteristics such as size, shape, environment, aspect ratio, ease of synthesis and functionalization properties have led to numerous applications in various fields of biomedicine. These include targeted drug delivery, sensing, photothermal and photodynamic therapy, imaging, as well as the modulation of two or three applications. The current article also discusses about the various properties of metallic nanoparticles and their applications in cancer imaging and therapeutics. The associated bottlenecks related to their clinical translation are also discussed.

Geopolymer Concrete with Lightweight Fine Aggregate: Material Performance and Structural Application.

Limited information and data are available on the material and structural performance of GC incorporating lightweight fine aggregate. In this research, three types of lightweight fine materials were utilized to partially replace sand volume of GC. These lightweight materials were rubber, vermiculite, or lightweight expanded clay aggregate (LECA) and they were used in contents of 20%, 40%, 60%, and 100%. The variables were applied to better investigate the efficiency of each lightweight material in GC and to recommend GC mixes for structural applications. The concrete workability, compressive strength, indirect tensile strength, freezing and thawing performance, and impact resistance were measured in this study. In addition, three reinforced concrete slabs were made from selected mixes with similar compressive strength of 32 MPa and then tested under a 4-point bending loading regime. The results showed that using LECA as sand replacement in GC increased its compressive strength at all ages and all replacement ratios. Compared with the control GC mix, using 60% LECA increased the compressive strength by up to 44%, 39%, and 27%, respectively at 3, 7, and 28 days. The slabs test showed that partial or full replacement of GC sand adversely affected the shear resistance of concrete and caused premature failure of slabs. The slab strength and deflection capacities decreased by 9% and 30%, respectively when using rubber, and by 23% and 59%, respectively when using LECA, compared with control GC slab. The results indicated the applicability of GC mix with 60% LECA in structures subjected to axial loads. However, rubber would be the best lightweight material to recommend for resisting impact and flexural loads.

Discovery, Development, Inventions, and Patent Trends on Mobocertinib Succinate: The First-in-Class Oral Treatment for NSCLC with EGFR Exon 20 Insertions.

The majority of lung cancers are non-small-cell lung cancer (NSCLC) having a low survival rate. Recent studies have indicated the involvement of epidermal growth factor receptor (EGFR) oncogene mutations like EGFR exon 20 insertions (EGFRex20ins) mutation among NSCLC patients. The response of patients of NSCLC with the EGFRex20ins mutation to the currently available EGFR inhibitor is negligible. Mobocertinib is the first oral treatment that has been approved by the USFDA, on 15 September 2021, to treat NSCLC with the EGFRex20ins mutation. This patent review discusses the inventions and patent literature of mobocertinib that will help the scientific community to develop additional and improved inventions related to mobocertinib. The structure of mobocertinib was first reported in 2015. Therefore, this article covered the patents/patent applications related to mobocertinib from 2015 to 25 October 2021. The patent search revealed 27 patents/patent applications related to compound, method of treatment, salt, polymorph, process, composition, and drug combinations of mobocertinib. The authors foresee an exciting prospect for developing a treatment for NSCLC with EGFRex20ins mutation, and other cancers employing a combination of mobocertinib with other approved anticancer agents. The inventions related to novel dosage forms, processes, and intermediates used in the synthesis of mobocertinib are also anticipated.

Optimized Polyethylene Glycolylated Polymer-Lipid Hybrid Nanoparticles as a Potential Breast Cancer Treatment.

PURPOSE: The aim of this work is to optimize a polyethylene glycolated (PEGylated) polymer-lipid hybrid nanoparticulate system for the delivery of anastrozole (ANS) to enhance its biopharmaceutical attributes and overall efficacy. METHODS: ANS loaded PEGylated polymer-lipid hybrid nanoparticles (PLNPs) were prepared by a direct emulsification solvent evaporation method. The physical incorporation of PEG was optimized using variable ratios. The produced particles were evaluated to discern their particle size and shape, zeta-potential, entrapment efficiency, and physical stability. The drug-release profiles were studied, and the kinetic model was analyzed. The anticancer activity of the ANS PLNPs on estrogen-positive breast cancer cell lines was determined using flow cytometry. RESULTS: The prepared ANS-PLNPs showed particle sizes in the range of 193.6 ± 2.9 to 218.2 ± 1.9 nm, with good particle size uniformity (i.e., poly-dispersity index of around 0.1). Furthermore, they exhibited relatively low zeta-potential values ranging from -0.50 ± 0.52 to 6.01 ± 4.74. The transmission electron microscopy images showed spherical shape of ANS-PLNPs and the compliance with the sizes were revealed by light scattering. The differential scanning calorimetry DSC patterns of the ANS PLNPs revealed a disappearance of the characteristic sharp melting peak of pure ANS, supporting the incorporation of the drug into the polymeric matrices of the nanoparticles. Flow cytometry showed the apoptosis of MCF-7 cell lines in the presence of ANS-PLNPs. CONCLUSION: PEGylated polymeric nanoparticles presented a stable encapsulated system with which to incorporate an anticancer drug (ANS) with a high percentage of entrapment efficiency (around 80%), good size uniformity, and induction of apoptosis in MCF-7 cells.