Petrochemical plants use on-stream inspection often to detect and monitor the corrosion on the equipment and piping system. Compared to ultrasonic thickness gauging and pulse-echo A-scan, phased array corrosion mapping has better coverability and can scan a large area to detect general and localized corrosion. This paper's objective is to obtain documentary evidence for the accuracy of corrosion detection from 30 °C to 250 °C on A36 low-carbon steel by carrying out simulation experiments every 10 °C step. A minimum of three sets of phased array corrosion mapping data in each temperature were collected to study and evaluate the detectability. The data evidence could enhance the confidence level of the plant's end users in using phased array mapping in the future during inspections. The experiments were found to be insufficiently thorough despite addressing the initial concerns, leaving more area for discussion in further studies, such as expanding the investigation to thicker carbon steel, stainless steel, and wedge materials.
Cartilage damage is difficult to heal and poses a serious problem to human health as it can lead to osteoarthritis. In this work, we explore the application of biological 3D printing to manufacture new cartilage scaffolds to promote cartilage regeneration. The hydrogel made by mixing sodium alginate (SA) and gelatin (GA) has high biocompatibility, but its mechanical properties are poor. The addition of hydroxyapatite (HA) can enhance its mechanical properties. In this paper, the preparation scheme of the SA-GA-HA composite hydrogel cartilage scaffold was explored, the scaffolds prepared with different concentrations were compared, and better formulations were obtained for printing and testing. Mathematical modeling of the printing process of the bracket, simulation analysis of the printing process based on the mathematical model, and adjustment of actual printing parameters based on the results of the simulation were performed. The cartilage scaffold, which was printed using Bioplotter 3D printer, exhibited useful mechanical properties suitable for practical needs. In addition, ATDC-5 cells were seeded on the cartilage scaffolds and the cell survival rate was found to be higher after one week. The findings demonstrated that the fabricated chondrocyte scaffolds had better mechanical properties and biocompatibility, providing a new scaffold strategy for cartilage tissue regeneration.
This paper presents an experimental and numerical investigation of pultruded composite glass fibre-reinforced polymer (pGFRP) cross-arms subjected to flexural creep behaviour to assess their performance and sustainability in composite cross-arm structure applications. The primary objective of this study was to investigate the failure creep behaviour of pGFRP cross-arms with different stacking sequences. Specifically, the study aimed to understand the variations in strain rate exhibited during different stages of the creep process. Therefore, this study emphasizes a simplified approach within the experiment, numerical analysis, and mathematical modelling of three different pGFRP composites to estimate the stiffness reduction factors that determine the prediction of failure. The findings show that Findley's power law and the Burger model projected very different strains and diverged noticeably outside the testing period. Findley's model estimated a minimal increase in total strain over 50 years, while the Burger model anticipated PS-1 and PS-2 composites would fail within about 11 and 33 years, respectively. The Burger model's forecasts might be more reasonable due to the harsh environment the cross-arms are expected to withstand. The endurance and long-term performance of composite materials used in overhead power transmission lines may be predicted mathematically, and this insight into material property factors can help with design and maintenance.
Compared to conventional metal oxide nanoparticles, metal oxide nanocomposites have demonstrated significantly enhanced efficiency in various applications. In this study, we aimed to synthesize zinc oxide-copper oxide nanocomposites (ZnO-CuO NCs) using a green synthesis approach. The synthesis involved mixing 4 g of Zn(NO3)2·6H2O with different concentrations of mangosteen (G. mangostana) leaf extract (0.02, 0.03, 0.04 and 0.05 g/mL) and 2 or 4 g of Cu(NO3)2·3H2O, followed by calcination at temperatures of 300, 400 and 500 °C. The synthesized ZnO-CuO NCs were characterized using various techniques, including a UV-Visible spectrometer (UV-Vis), photoluminescence (PL) spectroscopy, Fourier Transform Infrared (FTIR) spectroscopy, X-ray powder diffraction (XRD) analysis and Field Emission Scanning Electron Microscope (FE-SEM) with an Energy Dispersive X-ray (EDX) analyzer. Based on the results of this study, the optical, structural and morphological properties of ZnO-CuO NCs were found to be influenced by the concentration of the mangosteen leaf extract, the calcination temperature and the amount of Cu(NO3)2·3H2O used. Among the tested conditions, ZnO-CuO NCs derived from 0.05 g/mL of mangosteen leaf extract, 4 g of Zn(NO3)2·6H2O and 2 g of Cu(NO3)2·3H2O, calcinated at 500 °C exhibited the following characteristics: the lowest energy bandgap (2.57 eV), well-defined Zn-O and Cu-O bands, the smallest particle size of 39.10 nm with highest surface area-to-volume ratio and crystalline size of 18.17 nm. In conclusion, we successfully synthesized ZnO-CuO NCs using a green synthesis approach with mangosteen leaf extract. The properties of the nanocomposites were significantly influenced by the concentration of the plant extract, the calcination temperature and the amount of precursor used. These findings provide valuable insights for researchers seeking innovative methods for the production and utilization of nanocomposite materials.
Scorodocarpus borneensis (Baill.) Becc. is attracting increased attention as a potential commercial medicinal plant product in Southeast Asia. This review summarizes the current knowledge on the taxonomy, habitat, distribution, medicinal uses, natural products, pharmacology, toxicology, and potential utilization of S. borneesis in the pharmaceutical/nutraceutical/functional cosmetic industries. All data in this review were compiled from Google Scholar, PubMed, Science Direct, Web of Science, ChemSpider, PubChem, and a library search from 1866 to 2022. A total of 33 natural products have been identified, of which 11 were organosulfur compounds. The main organosulfur compound in the seeds is bis-(methylthiomethyl)disulfide, which inhibited the growth of a broad spectrum of bacteria and fungi, T-lymphoblastic leukemia cells, as well as platelet aggregation. Organic extracts evoked anti-microbial, cytotoxic, anti-free radical, and termiticidal effects. S. borneensis and its natural products have important and potentially patentable pharmacological properties. In particular, the seeds have the potential to be used as a source of food preservatives, antiseptics, or termiticides. However, there is a need to establish acute and chronic toxicity, to examine in vivo pharmacological effects and to perform clinical studies.
Glucokinase plays an important role in regulating the blood glucose level and serves as an essential therapeutic target in type 2 diabetes management. Entada africana is a medicinal plant and highly rich source of bioactive ligands with the potency to develop new target drugs for glucokinase such as diabetes and obesity. Therefore, the study explored a computational approach to predict identified compounds from Entada africana following its intermolecular interactions with the allosteric binding site of the enzymes. We retrieved the three-dimensional (3D) crystal structure of glucokinase (PDB ID: 4L3Q) from the online protein data bank and prepared it using the Maestro 13.5, Schrödinger Suite 2022-3. The compounds identified were subjected to ADME, docking analysis, pharmacophore modeling, and molecular simulation. The results show the binding potential of the identified ligands to the amino acid residues, thereby suggesting an interaction of the amino acids with the ligand at the binding site of the glucokinase activator through conventional chemical bonds such as hydrogen bonds and hydrophobic interactions. The compatibility of the molecules was highly observed when compared with the standard ligand, thereby leading to structural and functional changes. Therefore, the bioactive components from Entada africana could be a good driver of glucokinase, thereby paving the way for the discovery of therapeutic drugs for the treatment of diabetes and its related complications.
Shale rock swelling poses a significant challenge during drilling a well, leading to issues related to wellbore instability. Water-based mud with specific shale inhibitors is preferred over oil-based drilling mud due to its lower environmental impact. Recently, ionic liquids (ILs) have emerged as potential shale inhibitors due to their adjustable properties and strong electrostatic attraction. However, research has shown that the most commonly used class of ILs (imidazolium) in drilling mud are toxic, non-biodegradable, and expensive. Deep Eutectic Solvents (DESs), the fourth generation of ionic liquids, have been proposed as a cheaper and non-toxic alternative to ILs. However, ammonium salt-based DESs are not truly environmentally friendly. This research explores the utilization of Natural Deep Eutectic Solvent (NADES) based on Epsom salt (a naturally occurring salt) and glycerine as a drilling fluid additive. The drilling mud is prepared according to API 13B-1 standards. Various concentrations of NADES-based mud are tested for yield point, plastic viscosity, and filtration properties for both aged and non-aged samples. The linear swell meter is used to determine the percentage swelling of the NADES-based mud, and the results are compared with the swelling caused by KCl- and EMIM-Cl-based mud. FTIR analysis is conducted to understand the interaction between NADES and clay, while surface tension, d-spacing (XRD), and zeta potential are measured to comprehend the mechanism of swelling inhibition by NADES. The findings reveal that NADES improves the yield point and plastic viscosity of the mud, resulting in a 26% reduction in mudcake thickness and a 30.1% decrease in filtrate volume at a concentration of 1%. NADES achieves a significant 49.14% inhibition of swelling at the optimal concentration of 1%, attributed to its ability to modify surface activity, zeta potential of clay surfaces, and d-spacing of clay layers. Consequently, NADES emerges as a non-toxic, cost-effective, and efficient shale inhibitor that can replace ILs and DESs.
Oil spill remediation plays a vital role in mitigating the environmental impacts caused by oil spills. The chemical method is one of the widely recognized approaches in chemical surfactants. However, the most commonly used chemical surfactants are toxic and non-biodegradable. Herein, two biocompatible and biodegradable surfactants were synthesized from orange peel using the ionic liquid 1-butyl-3-methylimidazolium chloride (BMIMCl) and organic solvent dimethylacetamide (CH3CN(CH3)2) as reaction media. The acronyms SOPIL and SOPOS refer to the surfactants prepared with BMIMCl and dimethylacetamide, respectively. The surface tension, dispersant effectiveness, optical microscopy, and emulsion stability test were conducted to examine the comparative performance of the synthesized surfactants. The Baffled flask test (BFT) was carried out to determine the dispersion effectiveness. The toxicity test was performed against zebrafish (Danio rerio), whereas the closed bottle test (CBT) evaluated biodegradability. The results revealed that the critical micelle concentration (CMC) value of SOPIL was lower (8.57 mg/L) than that of SOPOS (9.42 mg/L). The dispersion effectiveness values for SOPIL and SOPOS were 69.78% and 40.30%, respectively. The acute toxicity test demonstrated that SOPIL was 'practically non-toxic' with a median lethal concentration of more than 1000 mg/L after 96 h. The biodegradation rate was recorded as higher than 60% for both surfactants within 28 days, demonstrating their readily biodegradable nature. Considering these attributes, biocompatible and biodegradable surfactants derived from orange peel emerge as a promising and sustainable alternative for oil spill remediation.
Apium graveolens is an indigenous plant in the family Apiaceae, or Umbelliferae, that contains many active compounds. It has been used traditionally to treat arthritic conditions, gout, and urinary infections. The authors conducted a scoping review to assess the quality of available evidence on the overall effects of celery when treating neurological disorders. A systematic search was performed using predetermined keywords in selected electronic databases. The 26 articles included upon screening consisted of 19 in vivo studies, 1 published clinical trial, 4 in vitro studies and 2 studies comprising both in vivo and in vitro methods. A. graveolens and its bioactive phytoconstituent, 3-n-butylphthalide (NBP), have demonstrated their effect on neurological disorders such as Alzheimer's disease, Parkinson's disease, stroke-related neurological complications, depression, diabetes-related neurological complications, and epilepsy. The safety findings were minimal, showing that NBP is safe for up to 18 weeks at 15 mg/kg in animal studies, while there were adverse effects (7%) reported when consuming NBP for 24 weeks at 600 mg daily in human trials. In conclusion, the safety of A. graveolens extract and NBP can be further investigated clinically on different neurological disorders based on their potential role in different targeted pathways.
Organotin (IV) dithiocarbamate has recently received attention as a therapeutic agent among organotin (IV) compounds. The individual properties of the organotin (IV) and dithiocarbamate moieties in the hybrid complex form a synergy of action that stimulates increased biological activity. Organotin (IV) components have been shown to play a crucial role in cytotoxicity. The biological effects of organotin compounds are believed to be influenced by the number of Sn-C bonds and the number and nature of alkyl or aryl substituents within the organotin structure. Ligands target and react with molecules while preventing unwanted changes in the biomolecules. Organotin (IV) dithiocarbamate compounds have also been shown to have a broad range of cellular, biochemical, and molecular effects, with their toxicity largely determined by their structure. Continuing the investigation of the cytotoxicity of organotin (IV) dithiocarbamates, this mini-review delves into the appropriate method for synthesis and discusses the elemental and spectroscopic analyses and potential cytotoxic effects of these compounds from articles published since 2010.
Inflammation or inflamm-aging is a chronic low-grade inflammation that contributes to numerous types of degenerative diseases among the elderly and might be impeded by introducing an anti-inflammatory agent like Moringa oleifera Lam (moringa) and Zingiber officinale Roscoe (ginger). Therefore, this paper aims to review the role of moringa and ginger in suppressing inflamm-aging to prevent degenerative diseases. Various peer-reviewed publications were searched and downloaded using the reputed search engine "Pubmed" and "Google Scholar". These materials were reviewed and tabulated. A comparison between these previous findings was made based on the mechanism of action of moringa and ginger against degenerative diseases, focusing on their anti-inflammatory properties. Many studies have reported the efficacy of moringa and ginger in type 2 diabetes mellitus, neurodegenerative disease, cardiovascular disease, cancer, and kidney disease by reducing inflammatory cytokines activities, mainly of TNF-α and IL-6. They also enhanced the activity of antioxidant enzymes, including catalase, glutathione, and superoxide dismutase. The anti-inflammatory activities can be seen by inhibiting NF-κβ activity. Thus, the anti-inflammatory potential of moringa and ginger in various types of degenerative diseases due to inflamm-aging has been shown in many recent types of research.
Cedrus atlantica (Endl.) Manetti ex Carriere is an endemic tree possessing valuable health benefits which has been widely used since time immemorial in international traditional pharmacopoeia. The aim of this exploratory investigation is to determine the volatile compounds of C. atlantica essential oils (CAEOs) and to examine their in vitro antimicrobial, antioxidant, anti-inflammatory, and dermatoprotective properties. In silico simulations, including molecular docking and pharmacokinetics absorption, distribution, metabolism, excretion, and toxicity (ADMET), and drug-likeness prediction were used to reveal the processes underlying in vitro biological properties. Gas chromatography-mass spectrophotometry (GC-MS) was used for the chemical screening of CAEO. The antioxidant activity of CAEO was investigated using four in vitro complementary techniques, including ABTS and DPPH radicals scavenging activity, ferric reductive power, and inhibition of lipid peroxidation (β-carotene test). Lipoxygenase (5-LOX) inhibition and tyrosinase inhibitory assays were used for testing the anti-inflammatory and dermatoprotective properties. GC-MS analysis indicated that the main components of CAEO are β-himachalene (28.99%), α-himachalene (14.43%), and longifolene (12.2%). An in vitro antimicrobial activity of CAEO was examined against eleven strains of Gram-positive bacteria (three strains), Gram-negative bacteria (four strains), and fungi (four strains). The results demonstrated high antibacterial and antifungal activity against ten of them (>15 mm zone of inhibition) using the disc-diffusion assay. The microdilution test showed that the lowest values of MIC and MBC were recorded with the Gram-positive bacteria in particular, which ranged from 0.0625 to 0.25 % v/v for MIC and from 0.5 to 0.125 % v/v for MBC. The MIC and MFC of the fungal strains ranged from 0.5 to 4.0% (MIC) and 0.5 to 8.0% v/v (MFC). According to the MBC/MIC and MFC/MIC ratios, CAEO has bactericidal and fungicidal activity. The results of the in vitro antioxidant assays revealed that CAEO possesses remarkable antioxidant activity. The inhibitory effects on 5-LOX and tyrosinase enzymes was also significant (p < 0.05). ADMET investigation suggests that the main compounds of CAEO possess favorable pharmacokinetic properties. These findings provide scientific validation of the traditional uses of this plant and suggest its potential application as natural drugs.
Eurycomanone and eurycomalactone are known quassinoids present in the roots and stems of Eurycoma longifolia. These compounds had been reported to have cytotoxic effects, however, their mechanism of action in a few cancer cell lines have yet to be elucidated. This study was aimed at investigating the anticancer effects and mechanisms of action of eurycomanone and eurycomalactone in cervical (HeLa), colorectal (HT29) and ovarian (A2780) cancer cell lines via Sulforhodamine B assay. Their mechanism of cell death was evaluated based on Hoechst 33342 assay and in silico molecular docking toward DHFR and TNF-α as putative protein targets. Eurycomanone and eurycomalactone exhibited in vitro anticancer effects manifesting IC50 values of 4.58 ± 0.090 µM and 1.60 ± 0.12 µM (HeLa), 1.22 ± 0.11 µM and 2.21 ± 0.049 µM (HT-29), and 1.37 ± 0.13 µM and 2.46 ± 0.081 µM (A2780), respectively. They induced apoptotic cancer cell death in dose- and time-dependent manners. Both eurycomanone and eurycomalactone were also predicted to have good inhibitory potential as demonstrated by the docking into TNF-α with binding affinity of -8.83 and -7.51 kcal/mol, respectively, as well as into DHFR with binding affinity results of -8.05 and -8.87 kcal/mol, respectively. These results support the evidence of eurycomanone and eurycomalactone as anticancer agents via apoptotic cell death mechanism that could be associated with TNF-α and DHFR inhibition as among possible protein targets.
Blend copolymers (PVA/S) were grafted with polyethylene glycol methyl methacrylate (PEGMA) with different ratios. Potassium persulfate was used as an initiator. The blend copolymer (PVA/S) was created by combining poly(vinyl alcohol) (PVA) with starch (S) in various ratios. The main idea was to study the effect of different ratios of the used raw materials on the biodegradability of plastic films. The resulting polymers (PVA/S/PEGMA) were analyzed using FTIR spectroscopy to investigate the hydrogen bond interaction between PVA, S, and PEGMA in the mixtures. TGA and SEM analyses were used to characterize the polymers (PVA/S/AA). The biodegradability and mechanical properties of the PVA/S/PEGMA blend films were evaluated. The findings revealed that the mechanical properties of the blend films are highly influenced by PEGMA. The time of degradation of the films immersed in soil and Coca-Cola increases as the contents of PVA and S and the molecular weight (MW) of PEGMA increase in the terpolymer. The M8 sample (PVA/S/PEGMA in the ratio of 3:1:2, respectively) with a MW of 950 g/mol produced the lowest elongation at break (67.5%), whereas M1 (PVA/S/PEGMA in the ratio of 1:1:1, respectively) with a MW of 300 g/mol produced the most (150%). The film's tensile strength and elongation at break were improved by grafting PEGMA onto the blending polymer (PAV-b-S). Tg and Tm increased when the PEGMA MW increased from 300 to 950. Tg (48.4 °C) and Tm (190.9 °C) were the lowest in M1 (300), while Tg (84.8 °C) and Tm (190.9 °C) were greatest in M1 (950) at 209.3 °C. The increased chain and molecular weight of PEGMA account for the increase in Tg and Tm of the copolymers.
The swift advancement of the Internet of Things (IoT), coupled with the growing application of healthcare software in this area, has given rise to significant worries about the protection and confidentiality of critical health data. To address these challenges, blockchain technology has emerged as a promising solution, providing decentralized and immutable data storage and transparent transaction records. However, traditional blockchain systems still face limitations in terms of preserving data privacy. This paper proposes a novel approach to enhancing privacy preservation in IoT-based healthcare applications using homomorphic encryption techniques combined with blockchain technology. Homomorphic encryption facilitates the performance of calculations on encrypted data without requiring decryption, thus safeguarding the data's privacy throughout the computational process. The encrypted data can be processed and analyzed by authorized parties without revealing the actual contents, thereby protecting patient privacy. Furthermore, our approach incorporates smart contracts within the blockchain network to enforce access control and to define data-sharing policies. These smart contracts provide fine-grained permission settings, which ensure that only authorized entities can access and utilize the encrypted data. These settings protect the data from being viewed by unauthorized parties. In addition, our system generates an audit record of all data transactions, which improves both accountability and transparency. We have provided a comparative evaluation with the standard models, taking into account factors such as communication expense, transaction volume, and security. The findings of our experiments suggest that our strategy protects the confidentiality of the data while at the same time enabling effective data processing and analysis. In conclusion, the combination of homomorphic encryption and blockchain technology presents a solution that is both resilient and protective of users' privacy for healthcare applications integrated with IoT. This strategy offers a safe and open setting for the management and exchange of sensitive patient medical data, while simultaneously preserving the confidentiality of the patients involved.
MeSH terms: Delivery of Health Care; Humans; Computer Security; Privacy
This paper develops a novel approach for reliable vehicle-to-vehicle (V2V) communication in various environments. A switched beam antenna is deployed at the transmitting and receiving points, with a beam management system that concentrates the power in each beam using a low-computation algorithm and a potential mathematical model. The algorithm is designed to be flexible for various environments faced by vehicles. Additionally, an anti-failure system is proposed in case the intelligent transportation system (ITS) system fails to retrieve real-time Packet Delivery Ratio (PDR) values related to traffic density. Performance metrics include the time to collision in seconds, the bit error rate (BER), the packet error rate (PER), the average throughput (Mbps), the beam selection probability, and computational complexity factors. The proposed system is compared with traditional systems. Extensive experiments, simulations, and comparisons show that the proposed approach is excellent and reliable for vehicular systems. The proposed study demonstrates an average throughput of 1.7 Mbps, surpassing conventional methods' typical throughput of 1.35 Mbps. Moreover, the bit error rate (BER) of the proposed study is reduced by a factor of 0.1. Additionally, the proposed framework achieves a beam power efficiency of touching to 100% at computational factor of 34. These metrics indicate that the proposed method is both efficient and sufficiently robust.
Vidos from a first-person or egocentric perspective offer a promising tool for recognizing various activities related to daily living. In the egocentric perspective, the video is obtained from a wearable camera, and this enables the capture of the person's activities in a consistent viewpoint. Recognition of activity using a wearable sensor is challenging due to various reasons, such as motion blur and large variations. The existing methods are based on extracting handcrafted features from video frames to represent the contents. These features are domain-dependent, where features that are suitable for a specific dataset may not be suitable for others. In this paper, we propose a novel solution to recognize daily living activities from a pre-segmented video clip. The pre-trained convolutional neural network (CNN) model VGG16 is used to extract visual features from sampled video frames and then aggregated by the proposed pooling scheme. The proposed solution combines appearance and motion features extracted from video frames and optical flow images, respectively. The methods of mean and max spatial pooling (MMSP) and max mean temporal pyramid (TPMM) pooling are proposed to compose the final video descriptor. The feature is applied to a linear support vector machine (SVM) to recognize the type of activities observed in the video clip. The evaluation of the proposed solution was performed on three public benchmark datasets. We performed studies to show the advantage of aggregating appearance and motion features for daily activity recognition. The results show that the proposed solution is promising for recognizing activities of daily living. Compared to several methods on three public datasets, the proposed MMSP-TPMM method produces higher classification performance in terms of accuracy (90.38% with LENA dataset, 75.37% with ADL dataset, 96.08% with FPPA dataset) and average per-class precision (AP) (58.42% with ADL dataset and 96.11% with FPPA dataset).
Unmanned aerial vehicle (UAV) usage is increasing drastically worldwide as UAVs are used in various industries for many applications, such as inspection, logistics, agriculture, and many more. This is because performing a task using UAV makes the job more efficient and reduces the workload needed. However, for a UAV to be operated manually or autonomously, the UAV must be equipped with proper safety features. An anti-collision system is one of the most crucial and fundamental safety features that UAVs must be equipped with. The anti-collision system allows the UAV to maintain a safe distance from any obstacles. The anti-collision technologies are of crucial relevance to assure the survival and safety of UAVs. Anti-collision of UAVs can be varied in the aspect of sensor usage and the system's working principle. This article provides a comprehensive overview of anti-collision technologies for UAVs. It also presents drone safety laws and regulations that prevent a collision at the policy level. The process of anti-collision technologies is studied from three aspects: Obstacle detection, collision prediction, and collision avoidance. A detailed overview and comparison of the methods of each element and an analysis of their advantages and disadvantages have been provided. In addition, the future trends of UAV anti-collision technologies from the viewpoint of fast obstacle detection and wireless networking are presented.
Structural health monitoring is a popular inspection method that utilizes acoustic emission (AE) signals for fault detection in engineering infrastructures. Diagnosis based on the propagation of AE signals along any surface material offers an attractive solution for fault identification. However, the classification of AE signals originating from failure events, especially coating failure (coating disbondment), is a challenging task given the AE signature of each material. Thus, different experimental settings and analyses of AE signals are required to classify the various types of coating failures, and they are time-consuming and expensive. Hence, to address these issues, we utilized machine learning (ML) classification models in this work to evaluate epoxy-based-protective-coating disbondment based on the AE principle. A coating disbondment experiment consisting of coated carbon steel test panels for the collection of AE signals was implemented. The obtained AE signals were then processed to construct the final dataset to train various state-of-the-art ML classification models to divide the failure severity of coating disbondment into three classes. Consequently, methods for the extraction of useful features, the handling of data imbalance, and a reduction in the bias of ML models were also effectively utilized in this study. Evaluations of state-of-the-art ML classification models on the AE signal dataset in terms of standard metrics revealed that the decision forest classification model outperformed the other state-of-the-art models, with accuracy, precision, recall, and F1 score values of 99.48%, 98.76%, 97.58%, and 98.17%, respectively. These results demonstrate the effectiveness of utilizing ML classification models for the failure severity prediction of protective-coating defects via AE signals.
Autonomous vehicles are gaining popularity, and the development of automatic parking systems is a fundamental requirement. Detecting the parking slots accurately is the first step towards achieving an automatic parking system. However, modern parking slots present various challenges for detection task due to their different shapes, colors, functionalities, and the influence of factors like lighting and obstacles. In this comprehensive review paper, we explore the realm of vision-based deep learning methods for parking slot detection. We categorize these methods into four main categories: object detection, image segmentation, regression, and graph neural network, and provide detailed explanations and insights into the unique features and strengths of each category. Additionally, we analyze the performance of these methods using three widely used datasets: the Tongji Parking-slot Dataset 2.0 (ps 2.0), Sejong National University (SNU) dataset, and panoramic surround view (PSV) dataset, which have played a crucial role in assessing advancements in parking slot detection. Finally, we summarize the findings of each method and outline future research directions in this field.