Search Results (103)

In a recent investigation the corrosion-fighting potential of five benzaldehyde derivatives were explored: 4-Formylbenzonitrile (BA1), 4-Nitrobenzaldehyde (BA2), 2-Hydroxy-5-methoxy-3-nitrobenzaldehyde (BA3), 3,5-Bis(trifluoromethyl)benzaldehyde (BA4), and 4-Fluorobenzaldehyde (BA5). Benzaldehyde derivative (BA-2) showed a maximum inhibition efficiency of 93.3% at 500 ppm. Several techniques were used to evaluate these compounds’ ability to protect mild steel from corrosion in a 1 M HCl solution, including potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), adsorption isotherms, and computational methods. Supporting techniques Fourier transform infrared spectroscopy (FTIR) and ultraviolet–visible (UV-Vis) spectroscopy were also employed to validate the results. Despite sharing a common benzene ring, the molecules differ in their substituents, allowing for a comprehensive examination of the substituents’ impact on corrosion inhibition. PDP analysis disclosed that the inhibitors exhibited mixed-type inhibition behavior, interacting with anodic as well as cathodic reactions, influencing the corrosion process. EIS analysis revealed that benzaldehyde derivatives formed a protective passive film on the metal, exhibiting high corrosion resistance by shielding the alloy from corrosive attacks. The benzaldehyde inhibitors followed the Langmuir adsorption isotherm, with high R² values near one, indicating a monolayer adsorption mechanism. DFT results indicate that BA 2 is the most effective inhibitor. FTIR and UV-vis spectroscopy revealed the molecular interactions between metal and benzaldehyde derivative molecules, providing insight into the binding mechanism. Experimental results support the outcomes obtained from the molecular dynamic (MD) simulations. Full article

Natural sand and high OPC utilization in the concrete industry have affected our environment and caused climate change. This study developed a novel methodology to prepare modified sand by adding (50% R-Sand + 50% M-Sand) to replace 100% natural sand. The two SCMs (5–20% of FA) and 10% of optimized SF were added to the four newly developed concrete mixes. The developed sustainable design mix concrete achieved the design and target strength after a curing period of 28 days. The findings for flexural strength showed comparable trends. Significant strength improvement was also seen at later curing ages, till 182 days. The water absorption and sulfuric acid attacks of the design mix concrete at the hardened stage were also measured. The analysis reveals that water absorption percentages tend to decline as the curing age progresses. The developed mixes show better resistance against sulfuric acid attacks than the reference mix NAC*. A mass loss of around 5% was discovered, much closer to the published studies. The developed mix 15FASFRSC showed consistent results when the modified sand (50% R-Sand + 50% M-Sand) was combined with the SCMs of (15% FA + 10% SF). Hence, the mix 15FASFRSC is the best sustainable mix for the concrete industry. Full article

A software-defined network (SDN) is a new architecture approach for constructing and maintaining networks with the main goal of making the network open and programmable. This allows the achievement of specific network behavior by updating and installing software, instead of making physical changes to the network. Thus, SDNs allow far more flexibility and maintainability compared to conventional device-dependent architectures. Unfortunately, like their predecessors, SDNs are prone to distributed denial of service (DDoS) attacks. These attack paralyze networks by flooding the controller with bogus requests. The answer to this problem is to ignore machines in the network sending these requests. This can be achieved by incorporating classification algorithms that can distinguish between genuine and bogus requests. There is abundant literature on the application of such algorithms on conventional networks. However, because SDNs are relatively new, they lack such abundance both in terms of novel algorithms and effective datasets when it comes to DDoS attack detection. To address these issues, the present study analyzes several variants of the decision tree algorithm for detection of DDoS attacks while using two recently proposed datasets for SDNs. The study finds that a decision tree constructed with a hill climbing approach, termed the greedy decision tree, iteratively adds features on the basis of model performance and provides a simpler and more effective strategy for the detection of DDoS attacks in SDNs when compared with recently proposed schemes in the literature. Furthermore, stability analysis of the greedy decision tree provides useful insights about the performance of the algorithm. One edge that greedy decision tree has over several other methods is its enhanced interpretability in conjunction with higher accuracy. Full article

In tree-based algorithms like random forest and deep forest, due to the presence of numerous inefficient trees and forests in the model, the computational load increases and the efficiency decreases. To address this issue, in the present paper, a model called Automatic Deep Forest Shrinkage (ADeFS) is proposed based on shrinkage techniques. The purpose of this model is to reduce the number of trees, enhance the efficiency of the gcforest, and reduce computational load. The proposed model comprises four steps. The first step is multi-grained scanning, which carries out a sliding window strategy to scan the input data and extract the relations between features. The second step is cascade forest, which is structured layer-by-layer with a number of forests consisting of random forest (RF) and completely random forest (CRF) within each layer. In the third step, which is the innovation of this paper, shrinkage techniques such as LASSO and elastic net (EN) are employed to decrease the number of trees in the last layer of the previous step, thereby decreasing the computational load, and improving the gcforest performance. Among several shrinkage techniques, elastic net (EN) provides better performance. Finally, in the last step, the simple average ensemble method is employed to combine the remaining trees. The proposed model is evaluated by Monte Carlo simulation and three real datasets. Findings demonstrate the superior performance of the proposed ADeFS-EN model over both gcforest and RF, as well as the combination of RF with shrinkage techniques. Full article

Background: The optimal surgical approach for localized T1 medullary thyroid cancer remains unclear. Total thyroidectomy is standard, but lobectomy and subtotal thyroidectomy may minimize mortality while maintaining oncologic control. Methods: This retrospective analysis utilized the National Cancer Institute’s Surveillance, Epidemiology, and End Results registry to identify 2702 MTC patients including 398 patients with T1N0/1M0 MTC treated with total thyroidectomy or lobectomy/subtotal thyroidectomy from 2000 to 2019. Cox regression analyses assessed thyroid cancer-specific and overall mortality. Results: The majority (89.7%) underwent total thyroidectomy, while 10.3% had lobectomy/subtotal thyroidectomy. Nodal metastases were present in 29.6%. Over a median follow-up of 8.75 years, no significant difference was observed in cancer-specific mortality (5.7% vs. 8.1%, p = 0.47) or overall mortality (13.2% vs. 12.8%, p = 0.95). On multivariate analysis, undergoing cancer-directed surgery was associated with significantly improved overall survival (HR 0.18, p < 0.001) and cancer-specific survival (HR 0.17, p < 0.001) compared to no surgery. However, no significant survival difference was seen between total thyroidectomy and lobectomy/subtotal thyroidectomy for overall mortality (HR 0.77, p = 0.60) or cancer-specific mortality (HR 0.44, p = 0.23). The extent of surgery also did not impact outcomes within subgroups stratified by age, gender, T stage, or nodal status. Delayed surgery >1 month after diagnosis was associated with worse overall survival (p = 0.012). Conclusions: For localized T1 MTC, lobectomy/subtotal thyroidectomy appears to achieve comparable long-term survival to total thyroidectomy in this population-based analysis. The selective use of limited thyroidectomy may be reasonable for low-risk T1N0/1M0 MTC patients. Delayed surgery is associated with worse survival and additional neck dissection showed no benefit for this select group of patients. Full article

Background: Pyroptosis, an inflammatory cell death, is involved in the progression of atherosclerosis. Pyroptosis in endothelial cells (ECs) and its underlying mechanisms in atherosclerosis are poorly understood. Here, we investigated the role of a caspase-4/5-NF-κB pathway in pyroptosis in palmitic acid (PA)-stimulated ECs and EVs as players in pyroptosis. Methods: Human umbilical vein endothelial cells (HUVECs) were cultured in an endothelial cell medium, treated with Ox-LDL, PA, caspase-4/5 inhibitor, NF-κB inhibitor, and sEV release inhibitor for 24 h, respectively. The cytotoxicity of PA was determined using an MTT assay, cell migration using a scratch-wound-healing assay, cell morphology using bright field microscopy, and lipid deposition using oil red O staining. The mRNA and protein expression of GSDM-D, CASP4, CASP5, NF-κB, NLRP3, IL-1β, and IL-18 were determined with RT-PCR and Western blot. Immunofluorescence was used to determine NLRP3 and ICAM-1 expressions. Extracellular vesicles (EVs) were isolated using an exosome isolation kit and were characterized by Western blot and scanning electron microscopy. Results: PA stimulation significantly changed the morphology of the HUVECs characterized by cell swelling, plasma membrane rupture, and increased LDH release, which are features of pyroptosis. PA significantly increased lipid accumulation and reduced cell migration. PA also triggered inflammation and endothelial dysfunction, as evidenced by NLRP3 activation, upregulation of ICAM-1 (endothelial activation marker), and pyroptotic markers (NLRP3, GSDM-D, IL-1β, IL-18). Inhibition of caspase-4/5 (Ac-FLTD-CMK) and NF-κB (trifluoroacetate salt (TFA)) resulted in a significant reduction in LDH release and expression of caspase-4/5, NF-κB, and gasdermin D (GSDM-D) in PA-treated HUVECs. Furthermore, GW4869, an exosome release inhibitor, markedly reduced LDH release in PA-stimulated HUVECs. EVs derived from PA-treated HUVECs exacerbated pyroptosis, as indicated by significantly increased LDH release and augmented expression of GSDM-D, NF-κB. Conclusions: The present study revealed that inflammatory, non-canonical caspase-4/5-NF-κB signaling may be one of the crucial mechanistic pathways associated with pyroptosis in ECs, and pyroptotic EVs facilitated pyroptosis in normal ECs during atherosclerosis. Full article

Background: The worldwide misuse of antibiotics is one of the main factors in microbial resistance that is a serious threat worldwide. Alternative strategies are needed to overcome this issue. Objectives: In this study, a novel strategy was adopted to suppress the growth of resistant pathogens through immobilization of silver nanoparticles (AgNPs) in gum of Moringa oleifera. Methods: The AgNPs were prepared from the leaves of Moringa oleifera and subsequently characterized through UV-spectrophotometry, FTIR, SEM, and XRD. The differential ratios of characterized AgNPs were immobilized with gum of M. oleifera and investigated for antimicrobial potential against highly resistant pathogens. Results: The immobilized AgNPs displayed promising activities against highly resistant B. subtilis (23.6 mm; 50 µL:200 µL), E. coli (19.3 mm; 75 µL:200 µL), K. pneumoniae (22 mm; 200 µL:200 µL), P. mirabilis (16.3 mm; 100 µL:200 µL), P. aeruginosa (22 mm; 175 µL:200 µL), and S. typhi (19.3; 25 µL:200 µL) than either AgNPs alone or gum. The immobilized AgNPs released positive sliver ions that easily attached to negatively charged bacterial cells. After attachment and permeation to bacterial cells, the immobilized NPs alter the cell membrane permeability, protein/enzymes denaturation, oxidative stress (ROS), damage DNA, and change the gene expression level. It has been mechanistically considered that the immobilized AgNPs can kill bacteria by damaging their cell membranes, dephosphorylating tyrosine residues during their signal transduction pathways, inducing cell apoptosis, rupturing organelles, and inhibiting cell division, which finally leads to cell death. Conclusions: This study proposes a potential alternative drug for curing various infections. Full article

Background: Supernumerary teeth numerical anomalies and the early diagnosis of supernumerary teeth is very important to avoid potential complications. The study aim was to determine the prevalence, occurrence, and characteristics of supernumerary teeth among the Arabian population. Methods: A retrospective radiographic study was performed using panoramic radiographs of patients attending a teaching hospital from January 2018 to December 2020. Only healthy patients with clear radiographs were included in the study, and patients with syndromes, cleft lip, and palate, and unclear radiographs were excluded from the study. The details include the patient’s age and gender, supernumerary tooth number, location, orientation, and position. Only a single examiner was involved in the data collection and analysis. Results: Overall, 38 (2%) patients were observed with 47 supernumerary teeth. Among them, 76% were males and 24% were females, with a mean age of 16.1 ± 9.7 years. Mesiodens (87%) are the common type of supernumerary tooth, and the majority of the supernumerary teeth were impacted (66%). The majority of the patients presented with a single supernumerary tooth, while 24% of the patients presented with two supernumerary teeth. Sixty percent of the supernumerary teeth were conical in morphology, followed by a tuberculate morphology. In the study population, most of the supernumerary teeth were normal in orientation. Conclusions: The prevalence of supernumerary teeth was 2%. Among them, the majority were observed at the anterior region of the maxillary arch with a conical shape of normal orientation. The gender-based comparison of location, orientation, morphology, eruption, and number of supernumerary teeth showed male predilection. Full article

PPHN is a common cause of neonatal respiratory failure and is still a serious condition that is associated with high mortality. Objectives: To analyze the clinical characteristics and outcomes of SARS-CoV-2 infection in neonates with PPHN to identify neonatal cases at risk to develop severe illness. Methods: For this systematic review, we adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and searched Medline, Embase, CINAHL, and PubMed for studies on the development of COVID-19 in neonates with PPHN, published from 1 December 2019 to 29 February 2024, with an English language restriction. Results: Of the 2406 papers that were identified, 21 articles were included in the systematic review. Studies involving thirty-six neonates with PPHN and infected with SARS-CoV-2 were analyzed (twenty-nine survived, six died, and one is still hospitalized). The main causes of PPHN in neonates who had COVID-19 were neonatal respiratory distress syndrome (NRDS) (41.7%), meconium-stained amniotic fluid (MSAF) (16.7%), preterm premature rupture of membranes (PPROM) (11.1%), hypoxic ischemic encephalopathy (HIE) (5.5%), pneumonia (5.5%), and idiopathic (2.8%). Most of those neonates were male (33.3%), belonged to Indian ethnicity (50%), and were delivered via caesarean section (44.4%). COVID-19 in cases with PPHN commonly occurred in neonates born with a pregnancy range from 32 to <37 weeks (moderate to late preterm) (36.1%). The maternal severity of COVID-19 was reported to be severe in three cases only (8.3%); however, SARS-CoV-2 infection in neonates with PPHN was either severe (44.4%) or critical (22.2%). Most of these neonates experienced acute respiratory distress syndrome (ARDS) (58.3%). Early and late multisystem inflammatory syndrome in neonates (MIS-N) were reported in 50% and 11.1%, respectively. A high proportion of neonates were admitted to the intensive care unit (ICU) (58.3%) or needed mechanical ventilation (MV) (47.2%). Neonates with concurrent PPHN and SARS-CoV-2 infection who died had worse severity of COVID-19 [i.e., severity of COVID-19 was critical in 10% (neonates with PPHN who survived group) vs. 83.3% (neonates with PPHN who died group); p = 0.026]. Neonates with PPHN and COVID-19 had a higher relative risk of death if they received more antibiotics (RR 4.14, 95% CI 0.64–6.88) and if their COVID-19 was defined as critical (RR 2.84, 95% CI 0.86–9.39). Male neonates with PPHN and COVID-19 (RR 2.60, 95% CI 0.30–1.17) and those requiring prolonged invasive positive pressure ventilation (RR 2.22, 95% CI 0.64–7.73) also showed an increased relative risk for death. Conclusions: COVID-19 in neonates with PPHN is challenging and may be associated with increased mortality, severity, ICU admission, ARDS, MIS-N, and MV usage. The results should be interpreted with caution owing to the small number of studies and substantial heterogeneity and indicate a need for future research in this area. Due to its benefits, testing for SARS-CoV-2 should be encouraged for newborns with symptoms consistent with COVID-19, especially in neonates with a history of SARS-CoV-2 exposure. Effective protection measures should be implemented during delivery and post-delivery care as necessary. Full article

Smartphones are intricately connected to the modern society. The two widely used mobile phone operating systems, iOS and Android, profoundly affect the lives of millions of people. Android presently holds a market share of close to 71% among these two. As a result, if personal information is not securely protected, it is at tremendous risk. On the other hand, mobile malware has seen a year-on-year increase of more than 42% globally in 2022 mid-year. Any group of human professionals would have a very tough time detecting and removing all of this malware. For this reason, deep learning in particular has been used recently to overcome this problem. Deep learning models, however, were primarily created for picture analysis. Despite the fact that these models have shown promising findings in the field of vision, it has been challenging to fully comprehend what the characteristics recovered by deep learning models are in the area of malware. Furthermore, the actual potential of deep learning for malware analysis has not yet been fully realized due to the translation invariance trait of well-known models based on CNN. In this paper, we present ViTDroid, a novel model based on vision transformers for the deep learning-based analysis of opcode sequences of Android malware samples from large real-world datasets. We have been able to achieve a false positive rate of 0.0019 as compared to the previous best of 0.0021. However, this incremental improvement is not the major contribution of our work. Our model aims to make explainable predictions, i.e., it not only performs the classification of malware with high accuracy, but it also provides insights into the reasons for this classification. The model is able to pinpoint the malicious behavior-causing instructions in the malware samples. This means that our model can actually aid in the field of malware analysis itself by providing insights to human experts, thus leading to further improvements in this field. Full article

Multiphase fluids are common in many industries, such as oil and petrochemical, and volume fraction measurement of their phases is a vital subject. Hence, there are lots of scientists and researchers who have introduced many methods and equipment in this regard, for example, photon attenuation sensors, capacitance-based sensors, and so on. These approaches are non-invasive and for this reason, are very popular and widely used. In addition, nowadays, artificial neural networks (ANN) are very attractive in a lot of fields and this is because of their accuracy. Therefore, in this paper, to estimate volume proportion of a three-phase homogeneous fluid, a new system is proposed that contains an MLP ANN, standing for multilayer perceptron artificial neural network, a capacitance-based sensor, and a photon attenuation sensor. Through computational methods, capacities and mass attenuation coefficients are obtained, which act as inputs for the proposed network. All of these inputs were divided randomly in two main groups to train and test the presented model. To opt for a suitable network with the lowest rate of mean absolute error (MAE), a number of architectures with different factors were tested in MATLAB software R2023b. After receiving MAEs equal to 0.29, 1.60, and 1.67 for the water, gas, and oil phases, respectively, the network was chosen to be presented in the paper. Hence, based on outcomes, the proposed approach’s novelty is being able to predict all phases of a homogeneous flow with very low error. Full article

Background: The increasing prevalence of chronic diseases in Saudi Arabia has heightened the need for organ transplantation; however, the donor pool remains insufficient. This study explored awareness and willingness towards organ donation among Riyadh residents and examined the sociodemographic factors influencing these attitudes. Methods: A cross-sectional survey using convenience sampling was conducted among adults in Riyadh. The survey assessed demographic characteristics, awareness, willingness to donate, and sociodemographic factors. Statistical analyses included descriptive statistics and logistic regression. Results: Among the 645 respondents, 56.4% were willing to donate organs, with females showing a higher propensity than males (OR 2.9, 95% CI 1.7–5.1, p < 0.001). Awareness of organ donation centers was linked to increased willingness to donate (OR 1.5, 95% CI 1.1–2.5, p < 0.001). Higher educational level was strongly associated with donor registration (OR 36.8, 95% CI 14.7–91.9, p < 0.001). Despite their high willingness, only 9.5% were registered as donors, highlighting the gap between intention and action. Conclusions: Riyadh residents showed a significant willingness to donate organs, influenced by gender, education, and awareness. Low registration rates suggest barriers such as religious beliefs and lack of information. Targeted educational campaigns and policy evaluations, including an opt-out system, are recommended to enhance registration rates. Full article

The adoption of deep learning (DL) and machine learning (ML) has surged in recent years because of their imperative practicalities in different disciplines. Among these feasible workabilities are the noteworthy contributions of ML and DL, especially ant colony optimization (ACO) and whale optimization algorithm (WOA) ameliorated with neural networks (NNs) to identify specific categories of skin lesion disorders (SLD) precisely, supporting even high-experienced healthcare providers (HCPs) in performing flexible medical diagnoses, since historical patient databases would not necessarily help diagnose other patient situations. Unfortunately, there is a shortage of rich investigations respecting the contributory influences of ACO and WOA in the SLD classification, owing to the recent adoption of ML and DL in the medical field. Accordingly, a comprehensive review is conducted to shed light on relevant ACO and WOA functionalities for enhanced SLD identification. It is hoped, relying on the overview findings, that clinical practitioners and low-experienced or talented HCPs could benefit in categorizing the most proper therapeutical procedures for their patients by referring to a collection of abundant practicalities of those two models in the medical context, particularly (a) time, cost, and effort savings, and (b) upgraded accuracy, reliability, and performance compared with manual medical inspection mechanisms that repeatedly fail to correctly diagnose all patients. Full article

Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks. SSL techniques have evolved in response to the poor classification performance of conventional and even modern machine learning (ML) and DL models of enormous unlabeled data produced periodically in different disciplines. However, the literature does not fully address SSL’s practicalities and workabilities necessary for industrial engineering and medicine. Accordingly, this thorough review is administered to identify these prominent possibilities for prediction, focusing on industrial and medical fields. This extensive survey, with its pivotal outcomes, could support industrial engineers and medical personnel in efficiently predicting machinery faults and patients’ ailments without referring to traditional numerical models that require massive computational budgets, time, storage, and effort for data annotation. Additionally, the review’s numerous addressed ideas could encourage industry and healthcare actors to take SSL principles into an agile application to achieve precise maintenance prognostics and illness diagnosis with remarkable levels of accuracy and feasibility, simulating functional human thinking and cognition without compromising prediction efficacy. Full article

Solar thermal energy storage improves the practicality and efficiency of solar systems for space heating by addressing the intermittent nature of solar radiation, leading to enhanced energy utilization, cost reduction, and a more sustainable and environmentally friendly approach to meeting heating needs in residential, commercial, and industrial settings. In this study, an indoor experimental setup was employed to investigate the impact of a water-based Al₂O₃ nanofluid on the storage capacity of a flat plate solar collector under varying flow rates of the heat transfer fluid. The nanofluid, introduced at specific concentrations, was incorporated into a water-contained storage tank through which the hot heat transfer fluid circulated within a heat exchanger. This process resulted in the storage of thermal energy for future applications. The research identified that the optimal flow rate of the heat transfer fluid, corresponding to the maximum storage temperature, was 15 L per hour, and the ideal nanofluid concentration, associated with the maximum specific heat capacity of the storage medium, was 0.6%. Furthermore, the introduction of nanoparticles into the storage tank led to a significant increase in the specific heat of the water, reaching a maximum of 19% from 4.18 to 5.65 kJ/(kg·°C). Full article