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Abstract The COVID-19 pandemic brought forth an urgent need for widespread genomic surveillance for rapid detection and monitoring of emerging SARS-CoV-2 variants. It necessitated design, development, and deployment of a nationwide infrastructure designed for sequestration, consolidation, and characterization of patient samples that disseminates de-identified information to public authorities in tight turnaround times. Here, we describe our development of such an infrastructure, which sequenced 594,832 high coverage SARS-CoV-2 genomes from isolates we collected in the United States (U.S.) from March 13th 2020 to July 3rd 2023. Our sequencing protocol (‘Virseq’) utilizes wet and dry lab procedures to generate mutation-resistant sequencing of the entire SARS-CoV-2 genome, capturing all major lineages. We also characterize 379 clinically relevant SARS-CoV-2 multi-strain co-infections and ensure robust detection of emerging lineages via simulation. The modular infrastructure, sequencing, and analysis capabilities we describe support the U.S. Centers for Disease Control and Prevention national surveillance program and serve as a model for rapid response to emerging pandemics at a national scale.

Abstract We are witnessing a steep increase in model development initiatives in genomics that employ high-end machine learning methodologies. Of particular interest are models that predict certain genomic characteristics based solely on DNA sequence. These models, however, treat the DNA as a mere collection of four, A, T, G and C, letters, dismissing the past advancements in science that can enable the use of more intricate information from nucleic acid sequences. Here, we provide a comprehensive database of quantum mechanical (QM) and geometric features for all the permutations of 7-meric DNA in their representative B, A and Z conformations. The database is generated by employing the applicable high-cost and time-consuming QM methodologies. This can thus make it seamless to associate a wealth of novel molecular features to any DNA sequence, by scanning it with a matching k-meric window and pulling the pre-computed values from our database for further use in modelling. We demonstrate the usefulness of our deposited features through their exclusive use in developing a model for A->C mutation rates.

Abstract Background Studies have been conducted worldwide to investigate the level of adherence to personal protective measures or fear of COVID-19 among healthcare providers. However, few studies have examined the relationship between adherence to personal protective measures and fear of COVID-19. There is also a need for more information on this topic from Iran. This study investigated the relationship between adherence to personal protective measures against COVID-19 and fear of COVID-19 in the healthcare providers at Pastor Hospital of Bam, Iran, in 2022. Methods This cross-sectional study was conducted in August and September 2022 with 199 healthcare providers of Pastor Hospital of Bam, Iran. The study included medical, nursing, and paramedical staff at Pastor Hospital at the time of the study. Incomplete responses and failure to return the questionnaire to the researcher were exclusion criteria. The fear of COVID-19 scale and a checklist of personal protective measures were used to collect data. Descriptive statistics, t-tests, analysis of variance, and Pearson’s correlation coefficient were used to analyze the data. Results Of the 199 participants, 67.3% were female, and their mean age was 31 ± 4.55 years. The mean score for adherence to personal protective measures was 14.46 ± 3.39 (out of 23), and the mean score for fear of COVID-19 was 17.04 ± 4.58 (out of 35). Adherence to personal protective measures was higher among females than males (14.96 ± 2.99 vs. 13.43 ± 3.92, p = 0.003), in individuals who had attended infection control courses than in those who had not (15.57 ± 2.88 vs. 13.30 ± 3.50, p

Abstract Wellbore stability analysis is a critical component of petroleum engineering, evaluating the risks of sanding, reservoir compaction, and casing failures. Laboratory rock mechanical measurements must be scaled up to reservoir scales to achieve accurate results. One challenge lies in upscaling dynamic measurements from petrophysical logs to pseudo-static elastic properties, which has significant implications for oil and gas operations. We present a novel approach that combines laboratory rock mechanical measurements with well-log data to develop a mechanical earth model (MEM) for an Iranian oilfield with over 350 wells. We conducted static elastic property measurements on 40 core samples from various layers and depths of carbonate and sandstone rocks, demonstrating the practical application of our approach. By integrating these measurements with dynamic log data and static-dynamic correlations, we established a framework for evaluating the mechanical properties of different layers. Our findings indicate that the safe mud weight window ranges from 41.5 to 118.59 pcf, while the stable mud weight window ranges from 41.5 to 156 pcf. We demonstrate the importance of conducting parallel rock mechanical studies on cores and logs to reduce uncertainties, costs, and risks during oil and gas operations. We also propose a novel methodology combining lithological characteristics, abnormally high pressure, and borehole instability mechanisms to evaluate the stability of borehole walls. This framework provides a fresh perspective on wellbore stability analysis and offers practical solutions for the industry. Essential novel techniques include developing a geomechanical model that integrates laboratory rock mechanical measurements with well-log data to evaluate mechanical properties and calculate safe and stable mud-weight windows. Our study advances wellbore stability analysis by providing a new method for addressing this long-standing challenge. It offers valuable insights for petroleum engineers working in the oil and gas industry.

Abstract High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software programs for image analysis workflows typically do not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, nucleus registration, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new analysis modules for existing analysis pipelines and to adding new analysis modules. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI software platform for a variety of cell biology applications.

Abstract Purpose Child marriage remains a significant socio-cultural phenomenon with profound implications for the quality of marital relationships, particularly among young women. This study investigates the association between child marriage and the quality of marriage among young married women in Iran. Methods This study employed a population-based cross-sectional research design. A total of 70 young women, aged 18–20 years, who had married before reaching 18 years of age, were selected from four primary health care (PHC) centers in Papi District, Lorestan Province, Iran, employing a systematic random sampling approach. Subjects were categorized into women experiencing relationship distress and those without such distress using the Quality of Marriage Index (QMI). Concurrently, their level of sexual satisfaction was assessed utilizing the Hudson’s Index of Sexual Satisfaction (ISS). Furthermore, Social Support was evaluated using the Social Support Questionnaire (SSQ). Results The mean age (SD) of women was 18.9 (0.7) years, and their mean age (SD) of marriage was 15.2 (2.1) years. Women experiencing distress showed significant differences in age of menarche (P = 0.006), education levels (P = 0.039), spouses’ education (P = 0.025), spouses’ occupations (P = 0.004), household income satisfaction (P = 0.041), and household structure (P = 0.045). Pearson’s correlation coefficient analysis revealed significant and positive correlations between sexual self-efficacy and social support with marital quality (r = 0.73, p

Context: Triple-negative breast cancer (TNBC) has poor prognosis, high mortality, and recurrence rates. Ixabepilone or ixempra could be a promising agent for TNBC, especially in the taxane- and/or anthracycline-resistant population. Aims: To evaluate the efficacy and safety of ixabepilone-based chemotherapy compared to ixabepilone-free chemotherapy in TNBC patients. Methods: This review followed the preferred reporting items for systematic reviews and meta-analysis guidelines. The eligibility criteria included randomized control trials (RCTs) in 2013-2023 comparing ixabepilone-based with ixabepilone-free chemotherapy regimen in TNBC. Results: This study identified four eligible RCTs. One study showed that patients treated with ixabepilone had a significant improvement in disease-free survival, had a better response, and a lower risk of disease recurrence compared to those treated with taxane-based regimen. However, two studies showed no differences in terms of overall survival (OS), disease-free survival, and pathological complete responses rates in both treatment arms. Ixabepilone addition to capecitabine significantly prolonged progression-free survival and objective response rate in TNBC patients, but did not significantly improve OS (n = 1). Conclusions: The effectiveness and safety of ixabepilone-based chemotherapy versus ixabepilone-free treatment for triple-negative breast cancer (TNBC) patients vary among studies. More research is needed to better understand the effectiveness and safety of ixabepilone-based chemotherapy versus ixabepilone-free treatment in TNBC patients.

This research aimed to determine the effectiveness of intervention program based on resilience and forgiveness in irritability and learning behavior in Bullying Victims. The research design was a semi-experimental pre-test and post-test type with a control group and a three-month follow-up period. The research sample included 30 bullying victims’ students, in the second secondary school in Bostan Abad city (Iran) in the academic year 2024-2023, which were selected through the purposeful sampling method. The measurement instruments included the Bullying Victim Questionnaire, Irritability Scale, and Learning Behavior Scale. The intervention program based on resilience and forgiveness was implemented in eight 45-minute sessions, during eight weeks in a group separated by gender for the experimental group, and the control group did not receive any training. Analysis of variance with repeated measure and Tukey and Bonferroni post hoc tests were used to analyze the data. The results showed that the intervention program based on resilience and forgiveness significantly reduced irritability and increased the learning behavior of bullying victims’ students. Therefore, given these findings using such comprehensive programs by education authorities at the schools is recommended.

Research on grocery shopping channel preferences has been growing in the wake of the Covid-19 pandemic. However, few studies have utilized the discrete choice experiment (DCE) to elicit choices in hypothetical scenarios. Moreover, attitudinal factors, which may better explain preference heterogeneity, are rarely considered. Given that the evolution of shopping behavior in the context of the Covid-19 pandemic has huge implications for transportation planning and modeling, this study aims to examine consumers’ grocery shopping channel preferences through a DCE that was constructed with three grocery shopping channels (home delivery, curbside pickup, and in-store) and five time–cost attributes (product price, shopping time, delivery time, delivery cost, and travel time). 8 603 responses were elicited from 1 229 Florida residents between February and April 2021. Information on various aspects of respondents’ shopping attitudes as well as their socio-demographic and household attributes, grocery shopping activities, and distance to the grocery store were also collected. Using mixed logit modeling for analyses, results indicate that individuals with low education, in low- to middle-income earning households, with three or more household vehicles, and having full access to a vehicle tended to prefer in-store shopping. Also, perceived security risk, pro-alternative mobility options, pro-local store shopping, and shorter distances to grocery stores predisposed individuals toward in-store shopping. Alternatively, females, young and middle-aged individuals, workers, and individuals in large households tended to prefer home delivery and curbside pickup. Technology savviness, pro-environment, pro-online shopping, and shopping enjoyment were also drivers of home delivery and curbside pickup purchases, while cost and time consciousness did not show significant effects. Overall, the findings in this study have implications for retailers, transportation planners, and policymakers.

Abstract Utilizing a preliminary interatomic potential, this work represents an initial exploration into the thermomechanical behavior of NbCr solid solutions. Specifically, it examines the effect of different amounts of Cr solute, for which information in the literature is limited. The employed interatomic potential was developed according to the embedded atom model (EAM), and was trained on data derived from density functional theory calculations. While the potential demonstrated reasonable accuracy and predictive power when tested, various results highlight deficiencies and encourage further development and training. Mechanical strength, heat capacities, thermal expansion coefficients, and thermal conductivities were found to decrease with Cr content. Elastic coefficients, too, were observed to be strongly dependent on Cr composition. The Pugh embrittlement criterion was not satisfied for any of the compositions and temperatures explored. Gibbs free energy calculations performed on C14, C15, and C36 NbCr $$_{2}$$ 2 allotropes predicted the C36 structure to be the most thermodynamically favorable across all investigated temperatures and it was found that C36 becomes increasingly more stable relative to the other two phases with increased pressure. The inability of this work to accurately capture the stability of the different Laves phases is most likely due to the shortcomings in the developed potential.

Abstract Background Several studies have evaluated the effects of zinc supplementation on glycemic biomarkers in humans and have demonstrated varying results. We systematically evaluated the literature and performed an umbrella meta-analysis of the effects of zinc supplementation on type 2 diabetes biomarkers. Methods A comprehensive literature search was conducted in the following databases; PubMed, Embase, Embase, Cochrane Library, Scopus, and Web of Science for studies published up to March 10, 2024. Results Zinc supplementation was effective in reducing serum FBS (WMD: − 13.58, 95% CI: − 17.38, − 9.77; p

PURPOSE: Compared with people living without HIV (PWOH), people living with HIV (PWH) and cancer have traditionally been excluded from immune checkpoint inhibitor (ICI) trials. Furthermore, there is a paucity of real-world data on the use of ICIs in PWH and cancer. METHODS: This retrospective study included PWH treated with anti-PD-1- or anti-PD-L1-based therapies for advanced cancers. Kaplan-Meier method was used to estimate overall survival (OS) and progression-free survival (PFS). Objective response rates (ORRs) were measured per RECIST 1.1 or other tumor-specific criteria, whenever feasible. Restricted mean survival time (RMST) was used to compare OS and PFS between matched PWH and PWOH with metastatic NSCLC (mNSCLC). RESULTS: Among 390 PWH, median age was 58 years, 85% (n = 331) were males, 36% (n = 138) were Black; 70% (n = 274) received anti-PD-1/anti-PD-L1 monotherapy. Most common cancers were NSCLC (28%, n = 111), hepatocellular carcinoma ([HCC]; 11%, n = 44), and head and neck squamous cell carcinoma (HNSCC; 10%, n = 39). Seventy percent (152/216) had CD4+ T cell counts ≥200 cells/µL, and 94% (179/190) had HIV viral load

This is the first study to use discrete choice experiment in exploring the stated choice preference of blended learning preference among the university students in the context of Bangladesh. As a prerequisite in developing student engagement learning strategies, we investigate the choice preference of university students towards different types of blended learning to explore relevant concerns and challenges in order to plan for successful implementation of this option. Around 306 responses from the students belonging to Bangladesh University of Professionals (BUP) and North South University (NSU) are considered representing both public and private universities in Bangladesh. Conditional logit model is used to explore the choice preference of the respondents based on the attributes. We find that university students explicitly dislike recorded videos as the primary mode of instruction as there is minimum human interaction using this method. Students with mobile internet also prefer offline classes to online classes, whereas students with broadband internet prefer the opposite choices. The policy implications of these findings hold global relevance in devising student engagement strategies towards blended learning such as for other developing economies in South Asia which were forced to transition to online learning as an adaptation response to the COVID-19 pandemic.

This study aims to explore the political cartoons that highlight the displacement of Palestinians in the village of Sheikh Jarrah. It investigates the way this discourse is portrayed as well as the predominant themes of these cartoons. The data of this study consists of eight political cartoons drawn from Palestinian online newspapers from May to July 2021, featuring the displacement issue of Sheik Jarrah by well-known local cartoonists. Multimodal discourse analysis (MDA) in which language analysis is combined with the analysis of other semiotic resources including colors, gestures, images, etc. for interpreting meaning (O'Halloran et al., 2011), is adopted for the analysis. Visual Social Semiotics of Kress and Van Leeuwen (2006) is also used as a linguistic framework. The study asserts that understanding the three different aspects of the visual compositions, namely, representational, interactive and compositional, that serve as a semiotic mode of communication, is essential for conveying the intended message of the visual compositions. The study also identifies the predominant themes or messages of the selected cartoons, which mainly focus on two main points: (i) the tyranny and oppression of Israel over Palestinians through eradication and displacement; and (ii) the Palestinians' struggle, steadfastness and resistance in the event of Israel's forced displacement.

The present quantitative study intended to investigate the effect of Iranian EFL learners' pronunciation anxiety (PA) and pronunciation motivation (PM) on their L2 willingness to communicate (L2 WTC) in English classes. Additionally, it aimed at identifying which of these two independent variables (PA or PM) could better predict their L2 WTC. To these aims, a convenience sample of 134 upper-intermediate EFL learners were recruited from two private language institutes in one of the large cities of Iran. Their proficiency level had already been determined by the institutes using written and oral placement tests. To gather the necessary data, three questionnaires, namely, Baran-Lucarz's (2017) PA, and PM, and Simic's (2014) WTC questionnaires were used. Descriptive and inferential statistics were utilized to answer the research question of the study. Findings indicated that both variables under study, PA and PM, had significant correlations with learners' L2 WTC. However, although results suggested that both independent variables predicted the learners' L2 WTC, PA was found to be a stronger predictor. Implications of the study findings and suggestions for further research are also offered.

Identifying mathematical equations from real-world math problems presents a unique and challenging task within the field of Natural Language Processing (NLP). It has a wide range of applications in various areas, such as academics, digital content design, and the development of automatic or interactive learning systems. However, the accurate understanding of these equations still needs to be enhanced due to the intrinsic complexity of mathematical symbols and various structural formats. Additionally, the unique syntax, diverse symbols, and complex structure of mathematical equations present significant obstacles that traditional NLP methods and Optical Character Recognition (OCR) systems need help to overcome. In this research, we utilize deep transformer architecture to recognize mathematical equations, and we have utilized our novel dataset with 3433 distinct observations. This dataset, which we have collected to include a diverse range of mathematical equations, is used to predict six (z=x+y, z=x−y, z=xy, z=x/y, y=x! and y=x) basic mathematical equations. We applied different transformer-based architectures, such as BERT, ELECTRA, XLNet, RoBERTa, and DistilBERT, and BERT performs best with 99.80% accuracy. To the best of our knowledge, this is the first NLP work in any language where we recognize the equation from the mathematical text.

Lubricating oil disposal is considered hazardous waste and has adverse effects on the environment and human health. It is crucial to implement tight measures to regulate and minimize these impacts, or explore opportunities for oil regeneration. The paper offers an extensive review of worldwide demand for lubricating oil and its market share, with a specific emphasis on the viewpoint of Bangladesh. Furthermore, the work presents a concise overview of various re-refining methods, including acid/clay and solvent extraction, and compares them based on physical and chemical attributes such as neutralization number, viscosity, and metal contents. It is also emphasized the implementation of design modifications and the integration of current and newly developed technologies like vaxon, ecohuile, etc. to enhance productivity and durability in terms of economic and environmental aspects, to increase the quality of products with a comparison in chemical composition from FTIR and GC-MS analysis of the samples that are obtained via recycling procedures. The feasibility of using spent motor oil as a fuel with similar properties to diesel is also evaluated based on the findings from tests conducted on engine performance and emission attributes. Using nanoparticles to enhance the characteristics of lubricating oil is very promising, particularly in reconditioning old motor oil, which is currently underexplored. The primary goal of the review is to assist the re-refining industries in improving their diverse range of potential uses.

The means of implementing cadre formation carried out in an organization are very important for cadres who want to know and understand more deeply about the organization. The researcher is trying to find out the model of cadre education carried out at MTs Muhammadiyah Sei Apung Jaya in the context of implementing Muhammadiyah Student Association cadre formation which is carried out for students at MTS Muhammadiyah Sei Apung Jaya. The type of research carried out is qualitative research using field research methods (field research) in which data collection is carried out through observation, interviews and documentation. It was found that the cadre education model implemented at MTs Muhammadiyah Sei Apung Jaya included MABICA as a pre-cadre activity and after the implementation of MABICA the cadres could proceed to the first cadre stage, namely PKTM I.

Pengabdian ini dilatarbelakangi oleh kegiatan Kuliah Kerja Nyata yang merupakan sarana penerapan serta pengembangan ilmu pengetahuan dan teknologi, yang dilaksanakan di luar kampus dalam batas waktu, cara kerja dan persyaratan tertentu. Taman Pendidikan Al-Qur’an (TPQ) merupakan Lembaga Pendidikan Islam yang penting selain sekolah formal yang bertujuan untuk membentuk akhlak dan kepribadian qur’ani pada anak. Program ini didasari atas kurangnya pengetahuan anak tentang pembelajaran fasholatan dan semangat dalam melaksanakan ibadah salat. Tujuan pengabdian ini adalah untuk menambah pengetahuan dan semangat anak dalam pembelajaran fasholatan. Manfaat pengabdian ini adalah anak mampu mempraktikkan materi pembelajaran fasholatan yang telah dipelajari dalam kehidupan sehari-hari serta menumbuhkan semangat beribadah pada diri anak. Metode yang digunakan pada pengabdian ini adalah observasi, wawancara dan dokumentasi untuk mengumpulkan data terkait pemahaman anak pada materi fasholatan. Program dan kegiatan yang dilakukan adalah bantuan mengajar di kelas dan penguatan materi fasholatan pada anak kelas 1 dan 2. Ketika mengajar di kelas, kami mendapati sebagian besar anak kurang bersemangat dalam belajar materi fasholatan. Pengabdian ini menunjukkan bahwa anak kelas 1 mampu menghafal materi fasholatan melalui lagu dan anak kelas 2 mampu mempraktikkan materi fasholatan yang telah dipelajari dalam kehidupan sehari-hari. Kegiatan ini memberikan pemahaman kepada anak tentang materi fasholatan dengan metode yang menyenangkan.

ABSTRACT Drug-induced lung disease (DILD) encompasses a broad, highly heterogeneous group of conditions that may occur as a result of exposure to numerous agents, such as antineoplastic drugs, conventional or biological disease-modifying antirheumatic drugs, antiarrhythmics, and antibiotics. Between 3% and 5% of prevalent cases of interstitial lung diseases are reported as DILDs. The pathogenesis of lung injury in DILD is variable, multifactorial, and often unknown. Acute presentation is the most common, can occur from days to months after the start of treatment, and ranges from asymptomatic to acute respiratory failure. The CT patterns are varied and include ground-glass opacities, organizing pneumonia, and diffuse alveolar damage. Notably, there are no clinical manifestations or CT patterns specific to DILD, which makes the diagnosis quite challenging and necessitates a high index of suspicion, as well as the exclusion of alternative causes such as infection, cardiac-related pulmonary edema, exacerbation of a preexisting ILD, and neoplastic lung involvement. Discontinuation of the offending medication constitutes the cornerstone of treatment, and corticosteroid treatment is usually necessary after the onset of clinical manifestations. The prognosis varies widely, with high mortality rates in severe cases. A history of medications related to pulmonary toxicity in patients with new-onset respiratory symptoms should prompt consideration of DILD as a potential underlying cause.

Biomedical implants have revolutionized modern medicine, providing diverse treatment options for various medical conditions. Ensuring the long-term success of certain materials used in various applications requires careful consideration of their ability to interact with biological systems and withstand harsh biological conditions. Optimizing surface properties is crucial for successfully integrating biomedical implants into the human body, ensuring biocompatibility, durability, and functionality. Chemical Vapor Deposition (CVD) has become a crucial technology in surface engineering, offering a precise technique for applying thin films with customized properties. This article provides a comprehensive study of surface engineering for biomedical implants, specifically emphasizing the CVD coating technique. By carefully manipulating chemical reactions in the vapor phase, CVD allows for the creation of coatings that enhance wear resistance, minimize friction, and improve biocompatibility. This review also explores the underlying principles of CVD, the various process parameters involved, and the subsequent enhancements in implant performance. Using case studies and experimental findings, it showcases the ability of CVD to greatly enhance the durability and effectiveness of biomedical implants.

Vehicles are frequently impacted objects during floods, susceptible to being washed away when the flow velocity and water depth surpass specific thresholds. Thus, understanding the characteristics of vehicle instability in floodwaters is crucial for establishing effective safety guidelines. This paper presents a comprehensive review, delving into previous studies on flooded vehicle stability during flood events and assessing progress in guideline formulation spanning from 2010 to 2023. According to the literature, 78 % of the previous investigations focused on the stability of static vehicles, while only 22 % considered vehicles in motion. Previous studies on vehicle stability during tsunamis have not been well-explored. Moreover, emergency vehicles, including police cars, fire trucks, and ambulances, received little attention and were only considered in two studies. On the other hand, extensive research has been conducted on defining the critical vehicle orientation with respect to incoming flow, which is reported to occur when the flow direction is perpendicular to the longitudinal vehicle axis. However, it is expected that this review will serve as a foundational resource, inspiring researchers to identify areas requiring attention and delve deeper into producing general safety guidelines. The ultimate goal is to prevent fatalities related to flooded vehicles and contribute to the broader field of flood risk mitigation.

The aviation industry plays a critical role in global transportation, facilitating economic growth and revolutionizing travel. However, flight delays have become a growing concern, impacting both airlines and passengers. This study aims to study the Naïve Bayes algorithm for flight delay prediction. The objective is to develop a reliable flight delay prediction model using the Naïve Bayes algorithm and evaluate its performance. The data set that records flight delay and cancellation data from U.S Department of Transportation’s (DOT) was used for the prediction. This study has modified the parameter tuning for Gaussian Naïve Bayes to identify optimum values specifically to construct model for this flight delay dataset. The performance of parameters tuning Gaussian Naïve Bayes model was compared with another two well-known algorithms which are K-Nearest Neighbors (KNN) and Support Vector Machine (SVM)). The KNN and SVM algorithms were also trained and tested to complete the binary classification of flight delays for benchmarking purposes. The evaluation of algorithms was fulfilled by comparing the values of accuracy, specificity and ROC AUC score. The comparative analysis showed that the Gaussian Naïve Bayes has the best performance with an accuracy of 93% and KNN has the worst performance with ROC AUC score 63%.

The main aim of this paper is to introduce a new high step-up/down buck-boost converter with a minimum number of switches which provides the benefit of having the continuity of the input/output current. This single-switch semi-quadratic converter is suitable for high step-up applications while being able to provide step-down voltage gains. Also, by applying some minor changes to the circuit elements, another single-switch buck-boost converter is suggested which has two operative outputs with different voltage gains. One of the outputs of this topology has quadratic buck-boost converter voltage gain which is appropriate for high step-up/step-down applications. The other output could vary from input voltage to minus infinity, ideally. After studying the steady-state operation of the proposed converters in Continuous Conduction Mode (C.C.M), the simulation results are presented. In addition, a comparison among related converters proposed in the literature is made. Finally, experimental results are evaluated by implementing a laboratory prototype of the proposed converter in both step-down and step-up modes. Theoretical, simulation, and experimental results are compatible with each other.

MXenes are a group of 2D transition metal carbonitrides, nitrides and carbides that have become widely recognized as useful materials since they were first discovered in 2011. MXenes, with their exceptional layered structures and splendid external chemistries, have excellent electrical, optical, and thermal properties, making them suitable for catalysis, biomedical uses, environmental remediation, energy storage, and EMI shielding. Over forty MXene compounds with surface terminations like hydroxyl, oxygen, or fluorine are hydrophilic and easily integrated into various applications. Advanced synthesis methods, including selective etching and etchant modifications, have broadened MXene surface chemistries for customized mechanical, thermal, and electrical applications. Integrating MXenes into polymer composites has demonstrated notable promise, enhancing the host polymers' electrical conductivity, thermal stability and mechanical strength. The MXene-polymer composites demonstrate remarkable prospective on behalf of advanced purposes, including flexible electronics, high-performance EMI shielding materials, and lightweight structural components. MXenes have the desirable characteristic of being able to create flexible and translucent films, as well as improve the properties of polymer matrices. This makes them very suitable for use in advanced technological applications. This review summarizes MXene research, methods, and insights, highlighting key discoveries and future directions. This also highlights the importance of ongoing research to fill in the gaps in current knowledge and improve the practical uses of MXenes.

The synergistic effect of adding Silicon Carbide (SiC), Aluminum Oxide (Al2O3), and Graphene (GR) nanoparticles into Kevlar fiber-reinforced epoxy composites is investigated in this study. Within the epoxy matrix and Kevlar fibre reinforcement, these nanoparticles come in a variety of compositions. The mechanical and flammability features of the composite are revealed by experimental evaluation. Of the nanoparticles, graphene performs the best; the composite reinforced with graphene shows 324 MPa of tensile strength, 8 J/mm of impact strength, and 75.2 HRC of hardness. The composite layers' interior structure is investigated using scanning electron microscopy examination. To evaluate thermal stability of the composition the thermogravimetric (TG) and the differential thermal analysis (DTA) investigations were performed. Thermal stability is achieved by the graphene-reinforced composite at 775 °C. Additionally, corrosion tests and water absorption tests were conducted to assess the material's moisture sensitivity. The overall goal of the research is to ascertain whether adding various nanoparticles to Kevlar produces the best outcomes; graphene has demonstrated promising results in terms of improving mechanical and thermal stability.