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Abstract This article introduces a novel microstrip slot antenna array (SAA) configuration for radar applications. The proposed antenna is specifically designed for operation in the K-band, spanning from 23 to 24.3 GHz. The antenna structure comprises two substrates: the feed network and ground plane are on the bottom substrate, and the radiating slots are on the top layer of the first substrate. The incorporation of a unique grid feed configuration, featuring 50 Ohm center excitation for the first time, improves the feed mechanism of the microstrip SAA. This innovation contributes to achieving a compact size and high gain. To enhance the side lobe level, the design incorporates a substrate-integrated waveguide-backed cavity, which significantly reduces surface waves. The SAA consists of 25 radiating elements with a gain of 14 dBi. In the elevation and azimuth planes, the half-power beamwidths are measured at 12.1° and 69.1°, respectively. The proposed antenna array’s measured impedance bandwidth ranges from 23.15 to 24.75 GHz, guaranteeing a reflection coefficient (S11) of less than − 10 dB. The suggested antenna's applicability for automotive multi-input multi-output radar has been validated.

Abstract In the telecom sector, predicting customer churn has increased in importance in recent years. Developing a robust and accurate churn prediction model takes time, but it is crucial. Early churn prediction avoids revenue loss and improves customer retention. Telecom companies must identify these customers before they leave to solve this issue. Researchers have used a variety of applied machine-learning approaches to reveal the hidden relationships between different features. A key aspect of churn prediction is the accuracy level that affects the learning model's performance. This study aims to clarify several aspects of customer churn prediction accuracy and investigate state-of-the-art techniques' performance. However, no previous research has investigated performance using a hybrid framework combining the advantages of selecting suitable data preprocessing, ensemble learning, and resampling techniques. The study introduces a proposed hybrid framework that improves the accuracy of customer churn prediction in the telecom industry. The framework is built by integrating the XGBOOST classifier with the hybrid resampling method SMOTE-ENN, which concerns applying effective techniques for data preprocessing. The proposed framework is used for two experiments with three datasets in the telecom industry. This study determines which features are most crucial and influence customer churn, introduces the impact of data balancing, compares the classifiers' pre- and post-data balancing performances, and examines a speed-accuracy trade-off in hybrid classifiers. Many metrics, including accuracy, precision, recall, F1-score, and ROC curve, are used to analyze the results. All evaluation criteria are used to identify the most effective experiment. The results of the accuracy of the hybrid framework that respects balanced data outperformed applying the classifier only to imbalanced data. In addition, the results of the proposed hybrid framework are compared to previous studies on the same datasets, and the result of this comparison is offered. Compared with the review of the latest works, our proposed hybrid framework with the three datasets outperformed these works.

ABSTRACT The Solar Lake in Taba, Egypt, encompasses one of the few modern-day microbial mats’ systems metabolically analogous to Precambrian stromatolites. Solar Lake benthic communities and their adaptation to the Lake’s unique limnological cycle have not been described for over two decades. In this study, we revisit the flat mat and describe the summer’s shallow water versus exposed microbial community; the latter occurs in response to the seasonal partial receding of water. We employed metagenomic NovaSeq-6000 shotgun sequencing and 16S rRNA, mcrA, and dsrB quantitative PCR. A total of 292 medium-to-high-quality metagenome-assembled genomes (MAGs) were reconstructed. At the structural level, Candidatus Aenigmatarchaeota, Micrarchaeota, and Omnitrophota MAGs were exclusively detected in the shallow-water mats, whereas Halobacteria and Myxococcota MAGs were specific to the exposed microbial mat. Functionally, genes involved in reactive oxygen species (ROS) detoxification and osmotic pressure were more abundant in the exposed than in the shallow-water microbial mats, whereas genes involved in sulfate reduction/oxidation and nitrogen fixation were ubiquitously detected. Genes involved in the utilization of methylated amines for methane production were predominant when compared with genes associated with alternative methanogenesis pathways. Solar Lake methanogen MAGs belonged to Methanosarcinia, Bathyarchaeia, Candidatus Methanofastidiosales, and Archaeoglobales. The latter had the genetic capacity for anaerobic methane oxidation. Moreover, Coleofasciculus chthonoplastes, previously reported to dominate the winter shallow-water flat mat, had a substantial presence in the summer. These findings reveal the taxonomic and biochemical microbial zonation of the exposed and shallow-water Solar Lake flat mat benthic community and their capacity to ecologically adapt to the summer water recession.IMPORTANCEFifty-five years ago, the extremophilic “Solar Lake” was discovered on the Red Sea shores, garnering microbiologists’ interest worldwide from the 1970s to 1990s. Nevertheless, research on the lake paused at the turn of the millennium. In our study, we revisited the Solar Lake benthic community using a genome-centric approach and described the distinct microbial communities in the exposed versus shallow-water mat unveiling microbial zonation in the benthic communities surrounding the Solar Lake. Our findings highlighted the unique structural and functional adaptations employed by these microbial mat communities. Moreover, we report new methanogens and phototrophs, including an intriguing methanogen from the Archaeoglobales family. We describe how the Solar Lake’s flat mat microbial community adapts to stressors like oxygen intrusion and drought due to summer water level changes, which provides insights into the genomic strategies of microbial communities to cope with altered and extreme environmental conditions.

Abstract Introduction COVID-19 causes severe inflammatory respiratory distress syndrome. The global pandemic caused millions of cases of morbidity and mortality worldwide. Patients may present with variable symptoms including dyspnea, fever, and GIT manifestations. The HMOX-1 gene is located on the long (q) arm of chromosome 22 at position 12.3. HMOX-1 is expressed in all mammalian tissues at basal levels and is considered as a stress response enzyme. HMOX-1 has a specific polymorphic site with variable GT(n) repeats at the promotor region. Several authors evaluated the HMOX-1 GT(n) promoter polymorphism in different inflammatory conditions. We evaluated HMOX-1 promoter polymorphism in relation to serum Hemoxygenase level and inflammatory makers (CRP, Ferritin, PCT, IL-6 and D-dimer) in patients affected by SARS-COV-2 disease. Subjects and methods Ninety patients confirmed to be infected with COVID-19 were followed up till the study end point (recovery and discharge or death). HMOX-1 promotor GT(n) polymorphism was evaluated using Sanger sequencing. HMOX-1 enzyme serum level was measured by ELISA and the level of different inflammatory markers was assessed by available commercial kits. Results A novel Single nucleotide polymorphism (SNP) (A > G) - rs13057211 in the GT(n) region of HMOX-1 promoter gene was found in 40 (61.5%) COVID-19 patients out of the studied 65 patients. This (A > G) SNP was associated with higher mortality rate in COVID-19 as it was detected in 27 patients (75% of the patients who succumbed to the disease) (p = 0.021, Odds ratio = 3.7; 95% CI:1.29–10.56). Serum IL-6 (Interleuken-6) was positively correlated the length of Hospital Stay (LOHS) and procalcitonin (PCT); (p = 0.014, r: 0.651 and p G, rs13057211) in the GT(n) region of HMOX-1 promoter gene that was associated with mortality in COVID-19 patients, however no significant difference was found in HMOX-1 serum level or HMOX-1 (GT)n repeats within the studied groups.

Traditional methods of dealing with finding the relationship between the inputs data of simulation models and the outputs data fail or takes a long time to find this relationship. Artificial neural networks (ANNs) have the ability to learn complex relationships between inputs and outputs. Their use can greatly enhance simulation models and allow for more accurate representations of real life scenarios.This paper is concerned with the application of the mechanism of integrating simulation models with artificial neural network (ANN) model. This mechanism was tested by integrating simulation models of re-tubing heat exchangers line (RTHEL) with ANN model to schedule entering exchangers to inside retubing workshop. The result of applying this mechanism of integration in system (RTHEL) was in reducing completion time of re-tubing batches of heat exchangers by about (12.5%).

Candida species have long been attributed to various diseases like candidiasis and systemic diseases and exacerbate the symptoms of immunocompromised patients. Candida species have enzymes that could function as drug targets to decrease their pathogenicity and eradicate the fungi. This research aimed to investigate the potency of new bis-triazolothiadiazine derivatives contained in inhibiting important enzymes of C. albicans as an example, through molecular docking simulation. Thus, a novel series of bis-triazolo[3,4-b][1,3,4]thiadiazines were designed and prepared via the reaction of the most versatile, hitherto unreported 5,5′-(1,2,2-trimethylcyclopentane-1,3-diyl)bis(4-amino-2,4-dihydro-3H-1,2,4-triazole-3-thione) with the appropriate hydrazonoyl halides and phenacyl bromides. Various spectroscopic techniques were used to identify the structures of the synthesized derivatives. The synthesized derivatives were tested against different species of Candida spp. The most effective compound was 6f followed by 6b, 6d, and 6e, where the inhibition zone ranged from 45 mm to 38 mm. By using molecular docking, which highlighted the important interactions with the amino acid residues Lys57, Leu77, Glu116, Gly114, Phe36, Thr58, and Glu32 at the point of binding, it was possible to determine the binding interactions of the produced derivatives to the fluconazole target fungi. The binding interaction energy was discovered to be −6.494 kcal/mol for the fungi candida albicans (PDB ID: 1IA2). The derivatives 6f and 6d, which demonstrated the highest efficacy, displayed significant conserved interactions with the amino acid residues at the binding site of the fluconazole fungi, in conjunction with the PDB co-crystal ligand 1IA2. The study’s results also revealed that the dG scores of the novel bis-triazolo-thiadiazines 6b-f. The study shows good docking scores with acceptable binding interactions. Finally, molecular docking studies revealed the lowest binding activity of derivatives 6e and 6c with the target fungi.

Abstract Introduction Thyroid nodules are highly prevalent in the general population; therefore, it is crucial to discriminate benign from malignant nodules. A practical thyroid imaging reporting and data system (TI-RADS) for thyroid nodules and ultrasound elastography are valuable tools not only for characterization of nodules but also for the selection of tumors for fine-needle aspiration cytology (FNAC). Objective This study aimed to evaluate the diagnostic accuracy of US evaluation in the prediction of malignant thyroid nodules and evaluate the role of the elastography score, strain ratio (SR), and the TI-RADS scoring system as non-invasive tools in differentiation between malignant and benign thyroid nodules. Material and methods A total of 1269 patients were evaluated between February 2017 and April 2020 by a single expert thyroid ultrasound operator. The final diagnosis was achieved from cytological and/or histological evaluation and follow-up for at least 1 year. Results There were 1088 females and 181 males with a mean age of 44 ± 10 SD. The final diagnosis was 1197 benign nodules and 72 malignant nodules. Most malignant nodules were scored elastography score 4 (83.3%) and TI-RADS category 5 (86.11%). We found that nodules with antero-posterior to transverse (A-P/T) diameter > 1, have 21 times more risk to be malignant than those with A-P/T diameter

This paper help with leguminous seeds detection and smart farming. There are hundreds of kinds of seeds and it can be very difficult to distinguish between them. Botanists and those who study plants, however, can identify the type of seed at a glance. As far as we know, this is the first work to consider leguminous seeds images with different backgrounds and different sizes and crowding. Machine learning is used to automatically classify and locate 11 different seed types. We chose Leguminous seeds from 11 types to be the objects of this study. Those types are of different colors, sizes, and shapes to add variety and complexity to our research. The images dataset of the leguminous seeds was manually collected, annotated, and then split randomly into three sub-datasets train, validation, and test (predictions), with a ratio of 80%, 10%, and 10% respectively. The images considered the variability between different leguminous seed types. The images were captured on five different backgrounds: white A4 paper, black pad, dark blue pad, dark green pad, and green pad. Different heights and shooting angles were considered. The crowdedness of the seeds also varied randomly between 1 and 50 seeds per image. Different combinations and arrangements between the 11 types were considered. Two different image-capturing devices were used: a SAMSUNG smartphone camera and a Canon digital camera. A total of 828 images were obtained, including 9801 seed objects (labels). The dataset contained images of different backgrounds, heights, angles, crowdedness, arrangements, and combinations. The TensorFlow framework was used to construct the Faster Region-based Convolutional Neural Network (R-CNN) model and CSPDarknet53 is used as the backbone for YOLOv4 based on DenseNet designed to connect layers in convolutional neural. Using the transfer learning method, we optimized the seed detection models. The currently dominant object detection methods, Faster R-CNN, and YOLOv4 performances were compared experimentally. The mAP (mean average precision) of the Faster R-CNN and YOLOv4 models were 84.56% and 98.52% respectively. YOLOv4 had a significant advantage in detection speed over Faster R-CNN which makes it suitable for real-time identification as well where high accuracy and low false positives are needed. The results showed that YOLOv4 had better accuracy, and detection ability, as well as faster detection speed beating Faster R-CNN by a large margin. The model can be effectively applied under a variety of backgrounds, image sizes, seed sizes, shooting angles, and shooting heights, as well as different levels of seed crowding. It constitutes an effective and efficient method for detecting different leguminous seeds in complex scenarios. This study provides a reference for further seed testing and enumeration applications.

Introduction Pediatric patients presenting with atypical stroke symptoms pose a diagnostic challenge, which often causes delays in management. Around a quarter of pediatric stroke patients will present with new onset seizures. We report this case to emphasize the importance of early consideration of ischemia as a differential diagnosis. We also want to emphasize the collaborative approach between pediatric and adult neurologists in challenging cases. Methods Case report Results A 15‐year‐old male presented to an outside hospital for new onset seizure. The patient later mentioned that he had difficulty writing the morning of the seizure. Initial blood work, CBC, CMP, urine analysis and toxicology were unremarkable. A second seizure occurred while in the ED. CT head revealed a questionable area of decreased attenuation in the left frontal lobe, felt to be artifact. Patient was transferred to our hospital for higher level of care. MRI of the brain showed gyriform signal abnormality within the left frontal lobe with restricted diffusion, no abnormal enhancement. Serum MOG, NMDA, LGI1, NMO antibodies were sent. LP was done and there was no evidence of infection. MRI of the C, T and L spine were unremarkable. CTA/CTV of the head was done and there was no evidence of vessel abnormalities or sinus thrombosis. Patient was started on high dose steroids for possible autoimmune etiology on hospital day 2. On Hospital Day 3, the case was re‐evaluated, and the possibility of infarction was proposed. The case was discussed with an adult vascular neurology attending physician, thus further imaging was obtained. CT Perfusion of Brain revealed a subacute left frontal infarct and possible dural AVF. A diagnostic cerebral angiography revealed enhanced capillary blush consistent with shunt in region of left peri‐Sylvian infarct. There was no evidence of other vascular malformations. A hypercoagulable panel was unremarkable. The patient was discharged on Hospital Day 6 on Vimpat and aspirin with outpatient follow‐up. Echo revealed a PFO on Valsalva maneuver. MRI spectroscopy revealed depressed creatine peak, low NAA and a high choline peak in the left frontal opercular T2 hyperintensity ‐ findings consistent with an evolving infarct with increased choline due to gliosis. He is planned for PFO closure and has not had further recurrent seizures. Conclusion According to Hollist et al., pediatric stroke incidence is 2.5‐13 per 100,000 children per year [1]. There are thousands of stroke misdiagnoses every year [2]. Stroke misdiagnosis is more likely in younger patients [2, 3]. 60% of pediatric stroke patients will end up with disability and half will have a decreased quality of life [4]. Misdiagnosis also results in misutilization of health care resources. Similar to our case, a collaborative approach between adult stroke neurologists and pediatric neurologists, where pediatric stroke neurologists are not available, helps avoid stroke misdiagnosis. As a result, this helps better patient outcomes and avoids preventable morbidity and mortality.