In this study, we introduced a novel hybrid artificial intelligence approach of rotation forest (RF) as a Meta/ensemble classifier based on alternating decision tree (ADTree) as a base classifier called RF-ADTree in order to spatially predict gully erosion at Klocheh watershed of Kurdistan province, Iran. A total of 915 gully erosion locations along with 22 gully conditioning factors were used to construct a database. Some soft computing benchmark models (SCBM) including the ADTree, the Support Vector Machine by two kernel functions such as Polynomial and Radial Base Function (SVM-Polynomial and SVM-RBF), the Logistic Regression (LR), and the Naïve Bayes Multinomial Updatable (NBMU) models were used for comparison of the designed model. Results indicated that 19 conditioning factors were effective among which distance to river, geomorphology, land use, hydrological group, lithology and slope angle were the most remarkable factors for gully modeling process. Additionally, results of modeling concluded the RF-ADTree ensemble model could significantly improve (area under the curve (AUC) = 0.906) the prediction accuracy of the ADTree model (AUC = 0.882). The new proposed model had also the highest performance (AUC = 0.913) in comparison to the SVM-Polynomial model (AUC = 0.879), the SVM-RBF model (AUC = 0.867), the LR model (AUC = 0.75), the ADTree model (AUC = 0.861) and the NBMU model (AUC = 0.811).
In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results.
The need for power-efficient devices, such as smart sensor nodes, mobile devices, and portable digital gadgets, is markedly increasing and these devices are becoming commonly used in daily life. These devices continue to demand an energy-efficient cache memory designed on Static Random-Access Memory (SRAM) with enhanced speed, performance, and stability to perform on-chip data processing and faster computations. This paper presents an energy-efficient and variability-resilient 11T (E2VR11T) SRAM cell, which is designed with a novel Data-Aware Read-Write Assist (DARWA) technique. The E2VR11T cell comprises 11 transistors and operates with single-ended read and dynamic differential write circuits. The simulated results in a 45 nm CMOS technology exhibit 71.63% and 58.77% lower read energy than ST9T and LP10T and lower write energies of 28.25% and 51.79% against S8T and LP10T cells, respectively. The leakage power is reduced by 56.32% and 40.90% compared to ST9T and LP10T cells. The read static noise margin (RSNM) is improved by 1.94× and 0.18×, while the write noise margin (WNM) is improved by 19.57% and 8.70% against C6T and S8T cells. The variability investigation using the Monte Carlo simulation on 5000 samples highly validates the robustness and variability resilience of the proposed cell. The improved overall performance of the proposed E2VR11T cell makes it suitable for low-power applications.
The enzyme-linked Immunosorbent Assay (ELISA) is the gold standard clinical diagnostic tool for the detection and quantification of protein biomarkers. However, conventional ELISA tests have drawbacks in their requirement of time, expensive equipment and expertise for operation. Hence, for the purpose of rapid, high throughput screening and point-of-care diagnosis, researchers are miniaturizing sandwich ELISA procedures on Lab-on-a-Chip and Lab-on-Compact Disc (LOCD) platforms. This paper presents a novel integrated device to detect and interpret the ELISA test results on a LOCD platform. The system applies absorption spectrophotometry to measure the absorbance (optical density) of the sample using a monochromatic light source and optical sensor. The device performs automated analysis of the results and presents absorbance values and diagnostic test results via a graphical display or via Bluetooth to a smartphone platform which also acts as controller of the device. The efficacy of the device was evaluated by performing dengue antibody IgG ELISA on 64 hospitalized patients suspected of dengue. The results demonstrate high accuracy of the device, with 95% sensitivity and 100% specificity in detection when compared with gold standard commercial ELISA microplate readers. This sensor platform represents a significant step towards establishing ELISA as a rapid, inexpensive and automatic testing method for the purpose of point-of-care-testing (POCT) in resource-limited settings.
An inexpensive single-step carbon-assisted thermal evaporation method for the growth of SnO2-core/ZnO-shell nanostructures is described, and the ethanol sensing properties are presented. The structure and phases of the grown nanostructures are investigated by field-emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM) and X-ray diffraction (XRD) techniques. XRD analysis indicates that the core-shell nanostructures have good crystallinity. At a lower growth duration of 15 min, only SnO2 nanowires with a rectangular cross-section are observed, while the ZnO shell is observed when the growth time is increased to 30 min. Core-shell hierarchical nanostructures are present for a growth time exceeding 60 min. The growth mechanism for SnO2-core/ZnO-shell nanowires and hierarchical nanostructures are also discussed. The sensitivity of the synthesized SnO2-core/ZnO-shell nanostructures towards ethanol sensing is investigated. Results show that the SnO2-core/ZnO-shell nanostructures deposited at 90 min exhibit enhanced sensitivity to ethanol. The sensitivity of SnO2-core/ZnO-shell nanostructures towards 20 ppm ethanol gas at 400 °C is about ~5-times that of SnO2 nanowires. This improvement in ethanol gas response is attributed to high active sensing sites and the synergistic effect of the encapsulation of SnO2 by ZnO nanostructures.
Epileptic seizures are temporary episodes of convulsions, where approximately 70 percent of the diagnosed population can successfully manage their condition with proper medication and lead a normal life. Over 50 million people worldwide are affected by some form of epileptic seizures, and their accurate detection can help millions in the proper management of this condition. Increasing research in machine learning has made a great impact on biomedical signal processing and especially in electroencephalogram (EEG) data analysis. The availability of various feature extraction techniques and classification methods makes it difficult to choose the most suitable combination for resource-efficient and correct detection. This paper intends to review the relevant studies of wavelet and empirical mode decomposition-based feature extraction techniques used for seizure detection in epileptic EEG data. The articles were chosen for review based on their Journal Citation Report, feature selection methods, and classifiers used. The high-dimensional EEG data falls under the category of '3N' biosignals-nonstationary, nonlinear, and noisy; hence, two popular classifiers, namely random forest and support vector machine, were taken for review, as they are capable of handling high-dimensional data and have a low risk of over-fitting. The main metrics used are sensitivity, specificity, and accuracy; hence, some papers reviewed were excluded due to insufficient metrics. To evaluate the overall performances of the reviewed papers, a simple mean value of all metrics was used. This review indicates that the system that used a Stockwell transform wavelet variant as a feature extractor and SVM classifiers led to a potentially better result.
: Concrete failure will lead to serious safety concerns in the performance of a building structure. It is one of the biggest challenges for engineers to inspect and maintain the quality of concrete throughout the service years in order to prevent structural deterioration. To date, a lot of research is ongoing to develop different instruments to inspect concrete quality. Detection of moisture ingress is important in the structural monitoring of concrete. This paper presents a novel sensing technique using a smart antenna for the non-destructive evaluation of moisture content and deterioration inspection in concrete blocks. Two different standard concrete samples (United Kingdom and Malaysia) were investigated in this research. An electromagnetic (EM) sensor was designed and embedded inside the concrete to detect the moisture content within the structure. In addition, CST microwave studio was used to validate the theoretical model of the EM sensor against the test data. The results demonstrated that the EM sensor at 2.45 GHz is capable of detecting the moisture content in the concrete with linear regression of R² = 0.9752. Furthermore, identification of different mix ratios of concrete were successfully demonstrated in this paper. In conclusion, the EM sensor is capable of detecting moisture content non-destructively and could be a potential technique for maintenance and quality control of the building performance.
We present ARTYCUL (ARTifact popularitY for CULtural heritage), a machine learning(ML)-based framework that graphically represents the footfall around an artifact on display at a museum or a heritage site. The driving factor of this framework was the fact that the presence of security cameras has become universal, including at sites of cultural heritage. ARTYCUL used the video streams of closed-circuit televisions (CCTV) cameras installed in such premises to detect human figures, and their coordinates with respect to the camera frames were used to visualize the density of visitors around the specific display items. Such a framework that can display the popularity of artifacts would aid the curators towards a more optimal organization. Moreover, it could also help to gauge if a certain display item were neglected due to incorrect placement. While items of similar interest can be placed in vicinity of each other, an online recommendation system may also use the reputation of an artifact to catch the eye of the visitors. Artificial intelligence-based solutions are well suited for analysis of internet of things (IoT) traffic due to the inherent veracity and volatile nature of the transmissions. The work done for the development of ARTYCUL provided a deeper insight into the avenues for applications of IoT technology to the cultural heritage domain, and suitability of ML to process real-time data at a fast pace. While we also observed common issues that hinder the utilization of IoT in the cultural domain, the proposed framework was designed keeping in mind the same obstacles and a preference for backward compatibility.
Proliferative Diabetic Retinopathy (PDR) is a severe retinal disease that threatens diabetic patients. It is characterized by neovascularization in the retina and the optic disk. PDR clinical features contain highly intense retinal neovascularization and fibrous spreads, leading to visual distortion if not controlled. Different image processing techniques have been proposed to detect and diagnose neovascularization from fundus images. Recently, deep learning methods are getting popular in neovascularization detection due to artificial intelligence advancement in biomedical image processing. This paper presents a semantic segmentation convolutional neural network architecture for neovascularization detection. First, image pre-processing steps were applied to enhance the fundus images. Then, the images were divided into small patches, forming a training set, a validation set, and a testing set. A semantic segmentation convolutional neural network was designed and trained to detect the neovascularization regions on the images. Finally, the network was tested using the testing set for performance evaluation. The proposed model is entirely automated in detecting and localizing neovascularization lesions, which is not possible with previously published methods. Evaluation results showed that the model could achieve accuracy, sensitivity, specificity, precision, Jaccard similarity, and Dice similarity of 0.9948, 0.8772, 0.9976, 0.8696, 0.7643, and 0.8466, respectively. We demonstrated that this model could outperform other convolutional neural network models in neovascularization detection.
One obvious requirement for concerted action by a bacterial population is for an individual to be aware of and respond to the other individuals of the same species in order to form a response in unison. The term "quorum sensing" (QS) was coined to describe bacterial communication that is able to stimulate expression of a series of genes when the concentration of the signaling molecules has reached a threshold level. Here we report the isolation from aquatic environment of a bacterium that was later identified as Enterobacter sp.. Chromobacterium violaceum CV026 and Escherichia coli [pSB401] were used for preliminary screening of N-acyl homoserine lactone (AHL) production. The Enterobacter sp. isolated was shown to produce two types of AHLs as confirmed by analysis using high resolution tandem mass spectrometry. To the best of our knowledge, this is the first documentation of an Enterobacter sp. that produced both 3-oxo-C6-HSL and 3-oxo-C8-HSL as QS signaling molecules.
N-acylhomoserine lactones (AHL) plays roles as signal molecules in quorum sensing (QS) in most Gram-negative bacteria. QS regulates various physiological activities in relation with population density and concentration of signal molecules. With the aim of isolating marine water-borne bacteria that possess QS properties, we report here the preliminary screening of marine bacteria for AHL production using Chromobacterium violaceum CV026 as the AHL biosensor. Strain T33 was isolated based on preliminary AHL screening and further identified by using 16S rDNA sequence analysis as a member of the genus Vibrio closely related to Vibrio brasiliensis. The isolated Vibrio sp. strain T33 was confirmed to produce N-hexanoyl-L-homoserine lactone (C6-HSL) and N-(3-oxodecanoyl)-L-homoserine lactone (3-oxo-C10 HSL) through high resolution tandem mass spectrometry analysis. We demonstrated that this isolate formed biofilms which could be inhibited by catechin. To the best of our knowledge, this is the first report that documents the production of these AHLs by Vibrio brasiliensis strain T33.
The inherent complexities of Industrial Internet of Things (IIoT) architecture make its security and privacy issues becoming critically challenging. Numerous surveys have been published to review IoT security issues and challenges. The studies gave a general overview of IIoT security threats or a detailed analysis that explicitly focuses on specific technologies. However, recent studies fail to analyze the gap between security requirements of these technologies and their deployed countermeasure in the industry recently. Whether recent industry countermeasure is still adequate to address the security challenges of IIoT environment are questionable. This article presents a comprehensive survey of IIoT security and provides insight into today's industry countermeasure, current research proposals and ongoing challenges. We classify IIoT technologies into the four-layer security architecture, examine the deployed countermeasure based on CIA+ security requirements, report the deficiencies of today's countermeasure, and highlight the remaining open issues and challenges. As no single solution can fix the entire IIoT ecosystem, IIoT security architecture with a higher abstraction level using the bottom-up approach is needed. Moving towards a data-centric approach that assures data protection whenever and wherever it goes could potentially solve the challenges of industry deployment.
Quorum sensing (QS), acts as one of the gene regulatory systems that allow bacteria to regulate their physiological activities by sensing the population density with synchronization of the signaling molecules that they produce. Here, we report a marine isolate, namely strain T47, and its unique AHL profile. Strain T47 was identified using 16S rRNA sequence analysis confirming that it is a member of Vibrio closely clustered to Vibrio sinaloensis. The isolated V. sinaloensis strain T47 was confirmed to produce N-butanoyl-L-homoserine lactone (C4-HSL) by using high resolution liquid chromatography tandem mass spectrometry. V. sinaloensis strain T47 also formed biofilms and its biofilm formation could be affected by anti-QS compound (cathechin) suggesting this is a QS-regulated trait in V. sinaloensis strain T47. To our knowledge, this is the first documentation of AHL and biofilm production in V. sinaloensis strain T47.
Various parts of Piper nigrum, Piper betle and Gnetum gnemon are used as food sources by Malaysians. The purpose of this study is to examine the anti-quorum sensing (anti-QS) properties of P. nigrum, P. betle and G. gnemon extracts. The hexane, chloroform and methanol extracts of these plants were assessed in bioassays involving Pseudomonas aeruginosa PA01, Escherichia coli [pSB401], E. coli [pSB1075] and Chromobacterium violaceum CV026. It was found that the extracts of these three plants have anti-QS ability. Interestingly, the hexane, chloroform and methanol extracts from P. betle showed the most potent anti-QS activity as judged by the bioassays. Since there is a variety of plants that serve as food sources in Malaysia that have yet to be tested for anti-QS activity, future work should focus on identification of these plants and isolation of the anti-QS compounds.
Quorum sensing regulates bacterial virulence determinants, therefore making it an interesting target to attenuate pathogens. In this work, we screened edible, endemic plants in Malaysia for anti-quorum sensing properties. Extracts from Melicope lunu-ankenda (Gaertn.) T. G. Hartley, a Malay garden salad, inhibited response of Chromobacterium violaceum CV026 to N-hexanoylhomoserine lactone, thus interfering with violacein production; reduced bioluminescence expression of E. coli [pSB401], disrupted pyocyanin synthesis, swarming motility and expression of lecA::lux of Pseudomonas aeruginosa PAO1. Although the chemical nature of the anti-QS compounds from M. lunu-ankenda is currently unknown, this study proves that endemic Malaysian plants could serve as leads in the search for anti-quorum sensing compounds.
The use of the enzyme alanine dehydrogenase (AlaDH) for the determination of ammonium ion (NH(4)(+)) usually requires the addition of pyruvate substrate and reduced nicotinamide adenine dinucleotide (NADH) simultaneously to effect the reaction. This addition of reagents is inconvenient when an enzyme biosensor based on AlaDH is used. To resolve the problem, a novel reagentless amperometric biosensor using a stacked methacrylic membrane system coated onto a screen-printed carbon paste electrode (SPE) for NH(4)(+) ion determination is described. A mixture of pyruvate and NADH was immobilized in low molecular weight poly(2-hydroxyethyl methacrylate) (pHEMA) membrane, which was then deposited over a photocured pHEMA membrane (photoHEMA) containing alanine dehydrogenase (AlaDH) enzyme. Due to the enzymatic reaction of AlaDH and the pyruvate substrate, NH(4)(+) was consumed in the process and thus the signal from the electrocatalytic oxidation of NADH at an applied potential of +0.55 V was proportional to the NH(4)(+) ion concentration under optimal conditions. The stacked methacrylate membranes responded rapidly and linearly to changes in NH(4)(+) ion concentrations between 10-100 mM, with a detection limit of 0.18 mM NH(4)(+) ion. The reproducibility of the amperometrical NH(4)(+) biosensor yielded low relative standard deviations between 1.4-4.9%. The stacked membrane biosensor has been successfully applied to the determination of NH(4)(+) ion in spiked river water samples without pretreatment. A good correlation was found between the analytical results for NH(4)(+) obtained from the biosensor and the Nessler spectrophotometric method.
Quorum sensing (QS) is a mechanism adopted by bacteria to regulate expression of genes according to population density. N-acylhomoserine lactones (AHLs) are a type of QS signalling molecules commonly found in Gram-negative bacteria which have been reported to play a role in microbial spoilage of foods and pathogenesis. In this study, we isolated an AHL-producing Hafnia alvei strain (FB1) from spherical fish pastes. Analysis via high resolution triple quadrupole liquid chromatography/mass spectrometry (LC/MS) on extracts from the spent supernatant of H. alvei FB1 revealed the existence of two short chain AHLs: N-(3-oxohexanoyl) homoserine lactone (3-oxo-C6-HSL) and N-(3-oxo- octanoyl) homoserine lactone (3-oxo-C8-HSL). To our knowledge, this is the first report of the production of AHLs, especially 3-oxo-C8-HSL, by H. alvei.
Hand gesture recognition (HGR) is a crucial area of research that enhances communication by overcoming language barriers and facilitating human-computer interaction. Although previous works in HGR have employed deep neural networks, they fail to encode the orientation and position of the hand in the image. To address this issue, this paper proposes HGR-ViT, a Vision Transformer (ViT) model with an attention mechanism for hand gesture recognition. Given a hand gesture image, it is first split into fixed size patches. Positional embedding is added to these embeddings to form learnable vectors that capture the positional information of the hand patches. The resulting sequence of vectors are then served as the input to a standard Transformer encoder to obtain the hand gesture representation. A multilayer perceptron head is added to the output of the encoder to classify the hand gesture to the correct class. The proposed HGR-ViT obtains an accuracy of 99.98%, 99.36% and 99.85% for the American Sign Language (ASL) dataset, ASL with Digits dataset, and National University of Singapore (NUS) hand gesture dataset, respectively.
Clenbuterol (CLB) is an antibiotic and illegal growth promoter drug that has a long half-life and easily remains as residue and contaminates the animal-based food product that leads to various health problems. In this work, electrochemical immunosensor based on poly(3,4-ethylenedioxythiophene)/graphene oxide (PEDOT/GO) modified screen-printed carbon electrode (SPCE) for CLB detection was developed for antibiotic monitoring in a food product. The modification of SPCE with PEDOT/GO as a sensor platform was performed through electropolymerization, while the electrochemical assay was accomplished while using direct competitive format in which the free CLB and clenbuterol-horseradish peroxidase (CLB-HRP) in the solution will compete to form binding with the polyclonal anti-clenbuterol antibody (Ab) immobilized onto the modified electrode surface. A linear standard CLB calibration curve with R² = 0.9619 and low limit of detection (0.196 ng mL-1) was reported. Analysis of milk samples indicated that this immunosensor was able to detect CLB in real samples and the results that were obtained were comparable with enzyme-linked immunosorbent assays (ELISA).
A flow injection analysis (FIA) incorporating a thiosemicarbazone-based coated wire electrode (CWE) was developed method for the determination of mercury(II). A 0.1 M KNO(3) carrier stream with pH between 1 and 5 and flow rate of 1 mL·min(-1) were used as optimum parameters. A linear plot within the concentration range of 5 × 10(-6)–0.1 M Hg(II), slope of 27.8 ± 1 mV per decade and correlation coefficient (R2) of 0.984 were obtained. The system was successfully applied for the determination of mercury(II) in dental amalgam solutions and spiked environmental water samples. Highly reproducible measurements with relative standard deviation (RSD < 1% (n = 3)) were obtained, giving a typical throughput of 30 samples·h(-1).