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An accurate prediction of fetal birth weight is crucial in ensuring safe delivery without health complications for the mother and baby. The uncertainty surrounding the fetus’s birth situation, including its weight range, can lead to significant risks for both mother and baby. As there is a standard birth weight range, if the fetus exceeds or falls below this range, it can result in considerable health problems. Although ultrasound imaging is commonly used to predict fetal weight, it does not always provide accurate readings, which may lead to unnecessary decisions such as early delivery and cesarian section. Besides that, no supporting system is available to predict the weight range in Saudi Arabia. Therefore, leveraging the available technologies to build a system that can serve as a second opinion for doctors and health professionals is essential. Machine learning (ML) offers significant advantages to numerous fields and can address various issues. As such, this study aims to utilize ML techniques to build a predictive model to predict the birthweight range of infants into low, normal, or high. For this purpose, two datasets were used: one from King Fahd University Hospital (KFHU), Saudi Arabia, and another publicly available dataset from the Institute of Electrical and Electronics Engineers (IEEE) data port. KFUH’s best result was obtained with the Extra Trees model, achieving an accuracy, precision, recall, and F1-score of 98%, with a specificity of 99%. On the other hand, using the Random Forest model, the IEEE dataset attained an accuracy, precision, recall, and F1-score of 96%, respectively, with a specificity of 98%. These results suggest that the proposed ML system can provide reliable predictions, which could be of significant value for doctors and health professionals in Saudi Arabia.

Objectives: Gross description of gastro-intestinal malignancy is facilitated by endoscopy of the upper and lower gastro-intestinal tract and also provides tissue for definitive diagnosis. Currently, direct vision endoscopic forceps biopsy & FNAC are two standard techniques used to establish a preoperative diagnosis in gastro-intestinal malignancies. The objective of our study is to evaluate the diagnostic accuracy of endoscopic findings when combined with fine needle aspiration and endoscopic biopsy in the diagnosis of gastrointestinal malignancies. So, we compare both techniques (endoscopic forceps biopsy & endoscopic FNAC) to each other and how accurately both techniques are diagnosing the lesion in combination with each other. Material and Method: In our study, 40 cases of suspected gastro-intestinal malignancies underwent endoscopic biopsy & FNAC in a period of one and half years. Result: Out of 40 cases, total malignant lesions were accounting for 32 cases. 27 cases were diagnosed as malignant by biopsy. FNAC showed positive results in 28 cases. The FNAC was found to be 81.82% Sensitive and 85.71% Specific & an accuracy of 82.5%. Biopsy had Sensitivity 81.82% Specificity 100% & Accuracy 85%. The Combined accuracy of both endoscopic FNAC and biopsy was 93.93%. Conclusion: The advent of endoscopic biopsy & FNAC have facilitated the detection & diagnosis of gastrointestinal malignancies. Biopsy though is considered to be diagnostic, Fine Needle Aspiration along with Biopsy can help in the detection of additional cases of malignancy. Thus, both procedures applied together can detect more malignant cases & can establish a more definitive diagnosis. Keywords: Endoscopy, FNAC, Biopsy, Gastrointestinal Malignancies

Anaplastic thyroid cancer (ATC) is a rare, aggressive form of undifferentiated thyroid cancer, which exhibits rapid progression and is almost universally fatal. At least a subset of ATC is thought to arise from pre-existing well-differentiated thyroid cancer, most frequently papillary thyroid cancer (PTC). While PIK3CA mutations are rare in PTC, they are common in ATC and tend to co-occur with BRAF mutations. This provided the rationale for our study to identify the potential role of PIK3CA mutations in the progression from well-differentiated to undifferentiated thyroid cancer. We introduced PIK3CAE545K into the LAM1 PTC cell line, which carries a BRAFV600E mutation. In culture, the engineered cell line (LAM1:PIK3CAE545K) proliferated faster and demonstrated increased clonogenic potential relative to the parental line carrying an empty vector (LAM1EV). Both the LAM1EV and LAM1:PIK3CAE545K edited lines were implanted into hind flanks of athymic nude mice for in vivo determination of disease progression. While tumour weights and volumes were not significantly higher in LAM1:PIK3CAE545K mice, there was a decrease in expression of thyroid differentiation markers TTF-1, thyroglobulin, PAX8 and B-catenin, suggesting that introduction of PIK3CAE545K led to dedifferentiation in vivo. Collectively, this study provides evidence of a role for PIK3CAE545K in driving disease progression from a well-differentiated to an undifferentiated thyroid cancer; however, over-expression was not a determinant of an accelerated growth phenotype in ATC.

Background: Overprescribing of potentially harmful medication in UK general practice has a complex association with socioeconomic deprivation. Aim: To assess trends in general practice prescribing of five high-risk medications and their relationship with deprivation. Design & setting: An observational study was conducted using general practice data from three English regions with varied sociodemographic factors: West Yorkshire and Harrogate (WY), Black Country and West Birmingham (BC), and Surrey and East Sussex (SE). Method: Practice-level prescribing data were obtained from 2016–2021 for five drug classes: opioids, hypnotics, gabapentinoids, non-steroidal anti-inflammatory drugs (NSAIDs), and antibacterials. Prescribing trends were demonstrated using a linear model. Results: Reduction in NSAID, opioid, hypnotic and antibacterial prescriptions, and the increase in gabapentinoid prescriptions, were significant at each financial year time period. Index of Multiple Deprivation (IMD) was positively associated with all drug classes except antibacterials, which showed a positive association when incorporating the interaction term between IMD and age. When adjusting for IMD and population, region was independently associated with prescribing rate. Compared with WY, IMD had a smaller association with prescribing in BC for NSAIDs (coefficient = −0.01578, P = 0.004) and antibacterials (coefficient = −0.02769, P = 0.007), whereas IMD had a greater association with prescribing in SE for NSAIDs (coefficient = 0.02443, P

In this study, we designed a 6T-SRAM cell using 16-nm CMOS process and analyzed the performance in terms of read-speed latency. The temperaturedependent Cu and multilayered graphene nanoribbon (MLGNR)-based nanointerconnect materials is used throughout the circuit (primarily bit/bit-bars [red lines] and word lines [write lines]). Here, the read speed analysis is performed with four different chip operating temperatures (150K, 250K, 350K, and 450K) using both Cu and graphene nanoribbon (GNR) nano-interconnects with different interconnect lengths (from 10 um to 100 um), for reading-0 and reading-1 operations. To execute the reading operation, the CMOS technology, that is, the16-nm PTM-HPC model, and the16-nm interconnect technology, that is, ITRS-13, are used in this application. The complete design is simulated using TSPICE simulation tools (by Mentor Graphics). The read speed latency increases rapidly as interconnect length increases for both Cu and GNR interconnects. However, the Cu interconnect has three to six times more latency than the GNR. In addition, we observe that the reading speed latency for the GNR interconnect is ~10.29 ns for wide temperature variations (150K to 450K), whereas the reading speed latency for the Cu interconnect varies between ~32 ns and 65 ns for the same temperature ranges. The above analysis is useful for the design of next generation, high-speed memories using different nano-interconnect materials.

Laryngeal squamous cell carcinoma (LSCC) from different subsites have distinct presentations and prognosis. In this study, we carried out a multiomic comparison of LSCC subsites. The Cancer Genome Atlas (TCGA) LSCC cohort was analyzed in the R statistical environment for differences between supraglottic and glottic cancers in single nucleotide variations (SNVs), copy number alterations (CNAs), mRNA abundance, protein abundance, pathway overrepresentation, tumor microenvironment (TME), hypoxia status, and patient outcome. Supraglottic cancers had significantly higher overall and smoking-associated SNV mutational load. Pathway analysis revealed upregulation of muscle related pathways in glottic cancer and neural pathways in supraglottic cancer. Proteins involved in cancer relevant signaling pathways including PI3K/Akt/mTOR, the cell cycle, and PDL1 were differentially abundant between subsites. Glottic and supraglottic tumors have different molecular profiles, which may partially account for differences in presentation and response to therapy.

Loss of the 3p chromosome arm has previously been reported to be a biomarker of poorer outcome in both human papillomavirus (HPV)-positive and HPV-negative head and neck cancer. However, the precise operational measurement of 3p arm loss is unclear and the mutational profile associated with the event has not been thoroughly characterized. We downloaded the clinical, single nucleotide variation (SNV), copy number aberration (CNA), RNA sequencing, and reverse phase protein assay (RPPA) data from The Cancer Genome Atlas (TCGA) and The Cancer Proteome Atlas HNSCC cohorts. Survival data and hypoxia scores were downloaded from published studies. In addition, we report the inclusion of an independent Memorial Sloan Kettering cohort. We assessed the frequency of loci deletions across the 3p arm separately in HPV-positive and -negative disease. We found that deletions on chromosome 3p were almost exclusively an all or none event in the HPV-negative cohort; patients either had 97% of the arm deleted. 3p arm loss, defined as >97% deletion in HPV-positive patients and >50% in HPV-negative patients, had no impact on survival (p > 0.05). However, HPV-negative tumors with 3p arm loss presented at a higher N-category and overall stage and developed more distant metastases (p < 0.05). They were enriched for SNVs in TP53, and depleted for point mutations in CASP8, HRAS, HLA-A, HUWE1, HLA-B, and COL22A1 (false discovery rate, FDR < 0.05). 3p arm loss was associated with CNAs across the whole genome (FDR < 0.1), and pathway analysis revealed low lymphoid-non-lymphoid cell interactions and cytokine signaling (FDR < 0.1). In the tumor microenvironment, 3p arm lost tumors had low immune cell infiltration (FDR < 0.1) and elevated hypoxia (FDR < 0.1). 3p arm lost tumors had lower abundance of proteins phospho-HER3 and ANXA1, and higher abundance of miRNAs hsa-miR-548k and hsa-miR-421, which were all associated with survival. There were no molecular differences by 3p arm status in HPV-positive patients, at least at our statistical power level. 3p arm loss is largely an all or none phenomenon in HPV-negative disease and does not predict poorer survival from the time of diagnosis in TCGA cohort. However, it produces tumors with distinct molecular characteristics and may represent a clinically useful biomarker to guide treatment decisions for HPV-negative patients.

Laryngeal squamous cell carcinoma (LSCC) from different subsites have distinct presentations and prognosis. In this study, we carried out a multiomic comparison of LSCC subsites. The Cancer Genome Atlas (TCGA) LSCC cohort was analyzed in the R statistical environment for differences between supraglottic and glottic cancers in single nucleotide variations (SNVs), copy number alterations (CNAs), mRNA abundance, protein abundance, pathway overrepresentation, tumor microenvironment (TME), hypoxia status, and patient outcome. Supraglottic cancers had significantly higher overall and smoking-associated SNV mutational load. Pathway analysis revealed upregulation of muscle related pathways in glottic cancer and neural pathways in supraglottic cancer. Proteins involved in cancer relevant signaling pathways including PI3K/Akt/mTOR, the cell cycle, and PDL1 were differentially abundant between subsites. Glottic and supraglottic tumors have different molecular profiles, which may partially account for differences in presentation and response to therapy.

BackgroundThe genomic landscape of head and neck cancer has been reported through The Cancer Genome Atlas project. We attempt to determine if high-risk human papillomavirus (HPV) or frequently mutated genes are correlated with survival in an oral cancer cohort.MethodsPatient demographic data along with data from final pathology was collected. Tumor DNA was analyzed using a custom Illumina targeted sequencing panel. Five high-risk HPV types were tested by qPCR. Statistical analyses were used to identify associations between patient outcome and mutational status.ResultsHigh-risk HPV types were identified in 7% of cases; HPV status was not associated with survival. Mutations were identified in TP53, TERT promoter, & PIK3CA. Mutations in TP53 were significantly associated with poorer overall survival on multi-variate analysis (p = 0.03).ConclusionsMutations in TP53 were associated with poor patient survival. Expanding our sample size may identify further predictors of outcome to direct customized cancer care.

Enhanced resolution of 7 T magnetic resonance imaging (MRI) scanners has considerably advanced our knowledge of structure and function in human and animal brains. Post-industrialized countries are particularly prone to an ever-increasing number of ageing individuals and ageing-associated neurodegenerative diseases. Neurodegenerative diseases are associated with volume loss in the affected brain. MRI diagnoses and monitoring of subtle volume changes in the ageing/diseased brains have the potential to become standard diagnostic tools. Even with the superior resolution of 7 T MRI scanners, the microstructural changes comprising cell types, cell numbers, and cellular processes, are still undetectable. Knowledge of origin, nature, and progression for microstructural changes are necessary to understand pathogenetic stages in the relentless neurodegenerative diseases, as well as to develop therapeutic tools that delay or stop neurodegenerative processes at their earliest stage. We illustrate the gap in resolution by comparing the identical regions of the post-mortem in situ 7 T MR images (virtual autopsy or virtopsy) with the histological observations in serial sections through the same brain. We also described the protocols and limitations associated with these comparisons, as well as the necessity of supercomputers and data management for "Big data". Analysis of neuron and/or glial number by using a body of mathematical tools and guidelines (stereology) is time-consuming, cumbersome, and still restricted to trained human investigators. Development of tools based on machine learning (ML) and artificial intelligence (AI) could considerably accelerate studies on localization, onset, and progression of neuron loss. Finally, these observations could disentangle the mechanisms of volume loss into stages of reversible atrophy and/or irreversible fatal cell death. This AI- and ML-based cooperation between virtopsy and histology could bridge the present gap between virtual reality and neuropathology. It could also culminate in the creation of an imaging-associated comprehensive database. This database would include genetic, clinical, epidemiological, and technical aspects that could help to alleviate or even stop the adverse effects of neurodegenerative diseases on affected individuals, their families, and society.

To identify potential genotypes for location specific breeding programmes of Andhra Pradesh, genetic diversity among 537 chickpea germplasm accessions was assessed for yield attributing traits during rabi 2020-21 at Regional Agricultural Research Station, Acharya N.G. Ranga Agricultural University, Nandyal, Andhra Pradesh. K-means clustering was utilised to ascertain genetic distances among 537 chickpea germplasm accessions which were grouped into 12 different clusters. Cluster I was the biggest and had the most genotypes (73), followed by clusters XII (66), VII (60), XI (58), X (49), V & VII (43), III (40), IV (36), II (27), IX (22) and VI (20). Clusters IV and IX, as well as clusters VI and VII, were the most divergent, with the maximum inter-cluster distances. The germplasm accessions viz., NBeG- 169, RVSSG-40, CP 117-5-23 and ICCV 07101 were identified with high per se performance from diverse clusters IV, IX, VI, and VII for inclusion as parents in a hybridization program to achieve desirable transgressive segregants.

The performance of self-compacting concrete (SCC) is gaining popularity in construction due to its exceptional strength and durability. However, the properties of combined steel and concrete at elevated temperatures lack experimental data from previous research. This study aimed to investigate the behavior of the SCC core with a steel tube at ambient and elevated temperatures varying from 100 °C to 800 °C with 100 °C intervals for each test specimen. Tests were conducted on circular steel tubes filled with SCC for different grades (M25, M30, and M40) under compression at elevated temperatures. Experimental observations revealed that the stress–strain curve increased with increasing the cross-sectional area and grade of concrete. However, increasing the temperature and length-to-diameter ratio reduced the stress–strain curve. At elevated temperatures, confined SCC experienced a smaller decrease in the overall modulus of elasticity when compared to unconfined concrete. Within the compressive elastic region (from 30 °C to 400 °C), there was a significant relationship between lateral strain and longitudinal strain, which was followed by a sudden increase beyond 400 °C. Equations for various design parameters were proposed based on the peak load and confinement factor of confined SCC-filled steel tubes (SCCFSTs) via multiple regression. Moreover, this study developed load–axial shortening curves, identifying significant properties such as the yield strength of confined SCCFSTs, including the load-carrying capacity. The predicted numerical analysis results were well aligned with the experimental results, and the findings contributed valuable insights for designing resilient and durable combined SCC and steel tube infrastructures.

Smoking has historically been recognized as a negative prognostic factor in head and neck squamous cell carcinoma (HNSCC). This study aimed to assess the mutational differences between heavy smokers (>20 pack years) and never smokers among the HNSCC patients within The Cancer Genome Atlas (TCGA). Single nucleotide variation and copy number aberration differences between heavy smokers and never smokers were compared within human papillomavirus-positive (HPV-positive) (n = 67) and HPV-negative (n = 431) TCGA cohorts with HNSCC, and the impact of these mutations on survival were assessed. No genes were differentially mutated between smoking and never-smoking patients with HPV-positive tumors. By contrast, in HPV-negative tumors, NSD1 and COL1A11 were found to be more frequently mutated in heavy smokers, while CASP8 was more frequently altered in never smokers. HPV-negative patients with NSD1 mutations experienced significantly improved overall survival compared with NSD1 WT patients. This improved prognosis was validated in an independent cohort of 77 oral cavity cancer patients and a meta-analysis that included 2 additional data sets (688 total patients, hazard ratio for death 0.44, 95% CI, 0.30-0.65). NSD1 mutations are more common in HPV-negative heavy smokers, define a cohort with favorable prognosis, and may represent a clinically useful biomarker to guide treatment deintensification for HPV-negative patients.

Genetic diversity was assessed in working germplasm collection comprising of 207 kabuli chickpea accessions for seven yield attributes. Wide genetic variation was seen in the kabuli germplasm for seven important yield and yield traits. Genetic diversity in the germplasm was evident in the form of 13 distinct clusters, out of which cluster I accommodated maximum number with 67 genotypes followed by cluster III with 62 genotypes. The diversity of the genotypes was mainly due to phenological traits days to 50 % flowering, which contributed 39.41 per cent to the total diversity followed by days to maturity (35.76 %). Variation is seed size (100- seed weight) contributed 14.29 per cent to the total diversity measured. The genotypes in cluster IX were the most divergent which was shown by their higher intra cluster distance (9.04) followed by the genotypes in cluster XII, VI and V. The diversity between cluster X and cluster VI was the highest as their inter cluster distance was the highest (27.25) followed by cluster XII and VI (26.05). Low inter cluster distance (3.86) was observed between clusters X and VII. The most promising and divergent kabuli germplasm lines LBeG 7, NBeG 855, ICCV 08313, ICCV 083308, ICC 15568 and NBeG 731 were identified for inclusion in hybridization for realizing desirable transgressive segregants.

Developmental dysplasia of the hip (DDH) is a disorder characterized by abnormal hip development that frequently manifests in infancy and early childhood. Preventing DDH from occurring relies on a timely and accurate diagnosis, which requires careful assessment by medical specialists during early X-ray scans. However, this process can be challenging for medical personnel to achieve without proper training. To address this challenge, we propose a computational framework to detect DDH in pelvic X-ray imaging of infants that utilizes a pipelined deep learning-based technique consisting of two stages: instance segmentation and keypoint detection models to measure acetabular index angle and assess DDH affliction in the presented case. The main aim of this process is to provide an objective and unified approach to DDH diagnosis. The model achieved an average pixel error of 2.862 ± 2.392 and an error range of 2.402 ± 1.963° for the acetabular angle measurement relative to the ground truth annotation. Ultimately, the deep-learning model will be integrated into the fully developed mobile application to make it easily accessible for medical specialists to test and evaluate. This will reduce the burden on medical specialists while providing an accurate and explainable DDH diagnosis for infants, thereby increasing their chances of successful treatment and recovery.

Drowsy driving is a widespread cause of traffic accidents, especially on highways. It has become an essential task to seek an understanding of the situation in order to be able to take immediate remedial actions to detect driver drowsiness and enhance road safety. To address the issue of road safety, the proposed model offers a method for evaluating the level of driver fatigue based on changes in a driver’s eyeball movement using a convolutional neural network (CNN). Further, with the help of CNN and VGG16 models, facial sleepiness expressions were detected and classified into four categories (open, closed, yawning, and no yawning). Subsequently, a dataset of 2900 images of eye conditions associated with driver sleepiness was used to test the models, which include a different range of features such as gender, age, head position, and illumination. The results of the devolved models show a high degree of accountability, whereas the CNN model achieved an accuracy rate of 97%, a precision of 99%, and recall and F-score values of 99%. The VGG16 model reached an accuracy rate of 74%. This is a considerable contrast between the state-of-the-art methods in the literature for similar problems.

Abstract Anaplastic thyroid cancer (ATC) is a rare, aggressive form of undifferentiated thyroid cancer, which exhibits rapid progression and is almost universally fatal. At least a subset of ATC is thought to arise from pre-existing well-differentiated thyroid cancer, most frequently papillary thyroid cancer (PTC). While PIK3CA mutations are rare in PTC, they are common in ATC and tend to co-occur with BRAF mutations. This provided the rationale for our study to identify the potential role of PIK3CA mutations in the progression from well-differentiated to undifferentiated thyroid cancer. We introduced PIK3CA E545K into the LAM1 PTC cell line, which carries a BRAF V600E mutation. In culture, the engineered cell line (LAM1:PIK3CA E545K) proliferated faster and demonstrated increased clonogenic potential relative to the parental line carrying an empty vector (LAM1EV). Both the LAM1EV and LAM1:PIK3CA E545K edited lines were implanted into hind flanks of athymic nude mice for in vivo determination of disease progression. While tumour weights and volumes were not significantly higher in LAM1:PIK3CA E545K mice, there was a decrease in expression of thyroid differentiation markers TTF-1, thyroglobulin, PAX8 and B-catenin, suggesting that introduction of PIK3CA E545K led to dedifferentiation in vivo. Collectively, this study provides evidence of a role for PIK3CA E545K in driving disease progression from a well-differentiated to an undifferentiated thyroid cancer; however, over-expression was not a determinant of an accelerated growth phenotype in ATC. Graphical Abstract

Background: COVID 19 resurgence in multiple waves and the highly infectious variant of SARS-CoV-2 (B.1.617) has wreaked havoc across healthcare systems in India. We conducted a study to delineate the clinico-epidemiological profile of COVID-19 patients in this second wave of the pandemic. Methods: This was a retrospective, single centre, observational study at the Emergency Department(ED) of a teaching institute in North India. The ED health records were screened for patient files with the diagnosis of COVID -19, age > 14 years, presenting from 10th April to 30th of May. ED clinical notes, disposition, and mortality data were retrieved and analysed. Results: 1647 files were screened and 253 eligible patients of COVID-19 were included in the study. 60% patients were male, mean age (+ SD) was 54 (+ 14.8 years). Shortness of breath (74.7%), fever (71.9%), cough (57.7%) were the common presenting symptoms. 20% of patients were nil comorbid; Diabetes (44.7%) and Hypertension (41.5%) were the common comorbid illnesses. 73.1% patients had severe COVID illness, 39.9% had oxygen saturation

Pneumonia, COVID-19, and tuberculosis are some of the most fatal and common lung diseases in the current era. Several approaches have been proposed in the literature for the diagnosis of individual diseases, since each requires a different feature set altogether, but few studies have been proposed for a joint diagnosis. A patient being diagnosed with one disease as negative may be suffering from the other disease, and vice versa. However, since said diseases are related to the lungs, there might be a likelihood of more than one disease being present in the same patient. In this study, a deep learning model that is able to detect the mentioned diseases from the chest X-ray images of patients is proposed. To evaluate the performance of the proposed model, multiple public datasets have been obtained from Kaggle. Consequently, the proposed model achieved 98.72% accuracy for all classes in general and obtained a recall score of 99.66% for Pneumonia, 99.35% for No-findings, 98.10% for Tuberculosis, and 96.27% for COVID-19, respectively. Furthermore, the model was tested using unseen data from the same augmented dataset and was proven to be better than state-of-the-art studies in the literature in terms of accuracy and other metrics.

Over the decades, a tremendous increase has been witnessed in the production of documents available in digital form. The increased production of documents has gained so much momentum that their rate of production jumps two-fold every five years. These articles are searched over the internet via search engines, digital libraries, and citation indexes. However, the retrieval of relevant research papers for user queries is still a pipedream. This is because scientific documents are not indexed based on some subject classification hierarchies. Hence, the classification of these documents becomes a challenging task for the researchers. Classification of the documents can be two-fold: one way is to assign a single label to each document and the other is to assign multi-labels to each document based on its belonging domains. Classification of the documents can be performed by using either the available metadata or the whole content of the documents. While performing classification, there are many challenges which may belong to the dataset, feature selection technique, preprocessing methodology, and which classification model is suitable for the classification of the documents. This paper highlights the issues for single-label and multi-label classification by using either metadata or content of the documents and why metadata-based approaches are better than content-based approaches in terms of feasibility.

The efficient monitoring of the environment is currently gaining a continuous growing interest in view of finding solutions for the global pollution issues and their associated climate change. In this sense, two-dimensional (2D) materials appear as one of highly attractive routes for the development of efficient sensing devices due, in particular, to the interesting blend of their superlative properties. For instance, graphene (Gr) and graphitic carbon nitride g-C3N4 (g-CN) have specifically attracted great attention in several domains of sensing applications owing to their excellent electronic and physical-chemical properties. Despite the high potential they offer in the development and fabrication of high-performance gas-sensing devices, an exhaustive comparison between Gr and g-CN is not well established yet regarding their electronic properties and their sensing performances such as sensitivity and selectivity. Hence, this work aims at providing a state-of-the-art overview of the latest experimental advances in the fabrication, characterization, development, and implementation of these 2D materials in gas-sensing applications. Then, the reported results are compared to our numerical simulations using density functional theory carried out on the interactions of Gr and g-CN with some selected hazardous gases’ molecules such as NO2, CO2, and HF. Our findings conform with the superior performances of the g-CN regarding HF detection, while both g-CN and Gr show comparable detection performances for the remaining considered gases. This allows suggesting an outlook regarding the future use of these 2D materials as high-performance gas sensors.

To constructively ameliorate and enhance traffic safety measures in Saudi Arabia, a prolific number of AI (Artificial Intelligence) traffic surveillance technologies have emerged, including Saher, throughout the past years. However, rapidly detecting a vehicle incident can play a cardinal role in ameliorating the response speed of incident management, which in turn minimizes road injuries that have been induced by the accident’s occurrence. To attain a permeating effect in increasing the entailed demand for road traffic security and safety, this paper presents a real-time traffic incident detection and alert system that is based on a computer vision approach. The proposed framework consists of three models, each of which is integrated within a prototype interface to fully visualize the system’s overall architecture. To begin, the vehicle detection and tracking model utilized the YOLOv5 object detector with the DeepSORT tracker to detect and track the vehicles’ movements by allocating a unique identification number (ID) to each vehicle. This model attained a mean average precision (mAP) of 99.2%. Second, a traffic accident and severity classification model attained a mAP of 83.3% while utilizing the YOLOv5 algorithm to accurately detect and classify an accident’s severity level, sending an immediate alert message to the nearest hospital if a severe accident has taken place. Finally, the ResNet152 algorithm was utilized to detect the ignition of a fire following the accident’s occurrence; this model achieved an accuracy rate of 98.9%, with an automated alert being sent to the fire station if this perilous event occurred. This study employed an innovative parallel computing technique for reducing the overall complexity and inference time of the AI-based system to run the proposed system in a concurrent and parallel manner.