Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region.
The Internet of Things (IoT) has brought significant advancements that have connected our world more closely than ever before. However, the growing number of connected devices has also increased the vulnerability of IoT networks to several types of attacks. In this paper, we present an approach for detecting attacks on IoT networks using a combination of two convolutional neural networks (CNN-CNN). The first CNN model is leveraged to select the significant features that contribute to IoT attack detection from the raw data on network traffic. The second CNN utilizes the features identified by the first CNN to build a robust detection model that accurately detects IoT attacks. The proposed approach is evaluated using the BoT IoT 2020 dataset. The results reveal that the proposed approach achieves 98.04% detection accuracy, 98.09% precision, 99.85% recall, 98.96% recall, and a 1.93% false positive rate (FPR). Furthermore, the proposed approach is compared with other deep learning algorithms and feature selection methods; the results show that it outperforms these algorithms.
The revolution generated by the Internet of Things (IoT) has radically changed the world; countless objects with remote sensing, actuation, analysis and sharing capabilities are interconnected over heterogeneous communication networks. Consequently, all of today's devices can connect to the internet and can provide valuable information for decision making. However, the data collected by different devices are in different formats, which makes it necessary to develop a solution that integrates comprehensive semantic tools to represent, integrate and acquire knowledge, which is a major challenge for IoT environments. The proposed solution addresses this challenge by using IoT semantic data to reason about actionable knowledge, combining next-generation semantic technologies and artificial intelligence through a set of cognitive components that enables easy interoperability and integration for both legacy systems and emerging technologies, such as IoT, to generate business value in terms of faster analytics and improved decision making. Thus, combining IoT environments with cognitive artificial intelligence services, COSIBAS builds an abstraction layer between existing platforms for IoT and AI technologies to enable cognitive solutions and increase interoperability across multiple domains. The resulting low-cost cross platform supports scalability and the evolution of large-scale heterogeneous systems and allows the modernization of legacy infrastructures with cognitive tools and communication mechanisms while reusing assets.
Quorum sensing enables bacteria to control the gene expression in response to the cell density. It regulates a variety of bacterial physiological functions such as biofilm formation, bioluminescence, virulence factors and swarming which has been shown contribute to bacterial pathogenesis. The use of quorum sensing inhibitor would be of particular interest in treating bacterial pathogenicity and infections. In this work, we have tested caffeine as quorum sensing inhibitor by using Chromobacterium violaceum CV026 as a biosensor. We verified that caffeine did not degrade the N-acyl homoserine lactones tested. In this work, it is shown that caffeine could inhibit N-acyl homoserine lactone production and swarming of a human opportunistic pathogen, namely Pseudomonas aeruginosa PA01. To the best of our knowledge, this is the first documentation providing evidence on the presence of anti-quorum sensing activity in caffeine. Our work will allow caffeine to be explored as anti-infective drugs.
Many types of materials such as inorganic semiconductors have been employed as detectors for nuclear radiation, the importance of which has increased significantly due to recent nuclear catastrophes. Despite the many advantages of this type of materials, the ability to measure direct cellular or biological responses to radiation might improve detector sensitivity. In this context, semiconducting organic materials such as deoxyribonucleic acid or DNA have been studied in recent years. This was established by studying the varying electronic properties of DNA-metal or semiconductor junctions when exposed to radiation. In this work, we investigated the electronics of aluminium (Al)/DNA/silicon (Si) rectifying junctions using their current-voltage (I-V) characteristics when exposed to alpha radiation. Diode parameters such as ideality factor, barrier height and series resistance were determined for different irradiation times. The observed results show significant changes with exposure time or total dosage received. An increased deviation from ideal diode conditions (7.2 to 18.0) was observed when they were bombarded with alpha particles for up to 40 min. Using the conventional technique, barrier height values were observed to generally increase after 2, 6, 10, 20 and 30 min of radiation. The same trend was seen in the values of the series resistance (0.5889-1.423 Ω for 2-8 min). These changes in the electronic properties of the DNA/Si junctions could therefore be utilized in the construction of sensitive alpha particle detectors.
Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O₃), nitrogen dioxide (NO₂), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O₃ measurements due to the lack of a reference instrument for CO and NO₂. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO₂) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.
In recent years, artificial intelligence (AI) has started to manifest itself at an unprecedented pace. With highly sophisticated capabilities, AI has the potential to dramatically change our cities and societies. Despite its growing importance, the urban and social implications of AI are still an understudied area. In order to contribute to the ongoing efforts to address this research gap, this paper introduces the notion of an artificially intelligent city as the potential successor of the popular smart city brand-where the smartness of a city has come to be strongly associated with the use of viable technological solutions, including AI. The study explores whether building artificially intelligent cities can safeguard humanity from natural disasters, pandemics, and other catastrophes. All of the statements in this viewpoint are based on a thorough review of the current status of AI literature, research, developments, trends, and applications. This paper generates insights and identifies prospective research questions by charting the evolution of AI and the potential impacts of the systematic adoption of AI in cities and societies. The generated insights inform urban policymakers, managers, and planners on how to ensure the correct uptake of AI in our cities, and the identified critical questions offer scholars directions for prospective research and development.
This work investigates a new interrogation method of a fiber Bragg grating (FBG) sensor based on longer and shorter wavelengths to distinguish between transversal forces and temperature variations. Calibration experiments were carried out to examine the sensor's repeatability in response to the transversal forces and temperature changes. An automated calibration system was developed for the sensor's characterization, calibration, and repeatability testing. Experimental results showed that the FBG sensor can provide sensor repeatability of 13.21 pm and 17.015 pm for longer and shorter wavelengths, respectively. The obtained calibration coefficients expressed in the linear model using the matrix enabled the sensor to provide accurate predictions for both measurements. Analysis of the calibration and experiment results implied improvements for future work. Overall, the new interrogation method demonstrated the potential to employ the FBG sensing technique where discrimination between two/three measurands is needed.
Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producer's Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions.
Liver transplantation is the most common treatment for patients with end-stage liver failure. However, liver transplantation is greatly limited by a shortage of donors. Liver tissue engineering may offer an alternative by providing an implantable engineered liver. Currently, diverse types of engineering approaches for in vitro liver cell culture are available, including scaffold-based methods, microfluidic platforms, and micropatterning techniques. Active cell patterning via dielectrophoretic (DEP) force showed some advantages over other methods, including high speed, ease of handling, high precision and being label-free. This article summarizes liver function and regenerative mechanisms for better understanding in developing engineered liver. We then review recent advances in liver tissue engineering techniques and focus on DEP-based cell patterning, including microelectrode design and patterning configuration.
Wireless Sensor Networks (WSNs) contain several small, autonomous sensor nodes (SNs) able to process, transfer, and wirelessly sense data. These networks find applications in various domains like environmental monitoring, industrial automation, healthcare, and surveillance. Node Localization (NL) is a major problem in WSNs, aiming to define the geographical positions of sensors correctly. Accurate localization is essential for distinct WSN applications comprising target tracking, environmental monitoring, and data routing. Therefore, this paper develops a Chaotic Mapping Lion Optimization Algorithm-based Node Localization Approach (CMLOA-NLA) for WSNs. The purpose of the CMLOA-NLA algorithm is to define the localization of unknown nodes based on the anchor nodes (ANs) as a reference point. In addition, the CMLOA is mainly derived from the combination of the tent chaotic mapping concept into the standard LOA, which tends to improve the convergence speed and precision of NL. With extensive simulations and comparison results with recent localization approaches, the effectual performance of the CMLOA-NLA technique is illustrated. The experimental outcomes demonstrate considerable improvement in terms of accuracy as well as efficiency. Furthermore, the CMLOA-NLA technique was demonstrated to be highly robust against localization error and transmission range with a minimum average localization error of 2.09%.
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 cladded conductor is broadly utilized in engineering fields, such as aerospace, energy, and petrochemical; however, it is vulnerable to thickness loss occurring in the clad layer and nonconductive protection coating due to abrasive and corrosive environments. Such a flaw severely undermines the integrity and safety of the mechanical structures. Therefore, evaluating the thickness loss hidden inside cladded conductors via reliable nondestructive evaluation techniques is imperative. This paper intensively investigates the pulse-modulation eddy current technique (PMEC) for the assessment of thickness loss in a cladded conductor. An analytical model of the ferrite-cored probe is established for analyzing PMEC signals and characteristics of lift-off intersection (LOI) in testing signals. Experiments are conducted for evaluation of the thickness loss in cladded conductors. An inverse scheme based on LOI for estimation of the thickness-loss depth is proposed and further verified. Through simulations and experiments, it is found that the influences of the thickness loss in the clad layer and protective coating on the PMEC signals can be decoupled in virtue of the LOI characteristics. Based on LOI, the hidden thickness loss can be efficiently evaluated without much of a reduction in accuracy by using the PMEC probe for dedicated inspection of the cladded conductor.
Capacitive biosensors are an emerging technology revolutionizing wearable sensing systems and personal healthcare devices. They are capable of continuously measuring bioelectrical signals from the human body while utilizing textiles as an insulator. Different textile types have their own unique properties that alter skin-electrode capacitance and the performance of capacitive biosensors. This paper aims to identify the best textile insulator to be used with capacitive biosensors by analysing the characteristics of 6 types of common textile materials (cotton, linen, rayon, nylon, polyester, and PVC-textile) while evaluating their impact on the performance of a capacitive biosensor. A textile-insulated capacitive (TEX-C) biosensor was developed and validated on 3 subjects. Experimental results revealed that higher skin-electrode capacitance of a TEX-C biosensor yields a lower noise floor and better signal quality. Natural fabric such as cotton and linen were the two best insulating materials to integrate with a capacitive biosensor. They yielded the lowest noise floor of 2 mV and achieved consistent electromyography (EMG) signals measurements throughout the performance test.
Microstructural, topology, inner morphology, and gas-sensitivity of mixed xWO(3)(1-x)Y(2)O(3) nanoparticles (x = 1, 0.95, 0.9, 0.85, 0.8) thick-film semiconductor gas sensors were studied. The surface topography and inner morphological properties of the mixed powder and sensing film were characterized with X-ray diffraction (XRD), atomic force microscopy (AFM), transmission electron microscopy (TEM), and scanning electron microscopy (SEM). Also, gas sensitivity properties of the printed films were evaluated in the presence of methane (CH(4)) and butane (C(4)H(10)) at up to 500 °C operating temperature of the sensor. The results show that the doping agent can modify some structural properties and gas sensitivity of the mixed powder.
We report the production and degradation of quorum sensing N-acyl-homoserine lactones by bacteria isolated from Malaysian montane forest soil. Phylogenetic analysis indicated that these isolates clustered closely to the genera of Arthrobacter, Bacillus and Pseudomonas. Quorum quenching activity was detected in six isolates of these three genera by using a series of bioassays and rapid resolution liquid chromatography analysis. Biosensor screening and high resolution liquid chromatography-mass spectrometry analysis revealed the production of N-dodecanoyl-L-homoserine lactone (C12-HSL) by Pseudomonas frederiksbergensis (isolate BT9). In addition to degradation of a wide range of N-acyl-homoserine lactones, Arthrobacter and Pseudomonas spp. also degraded p-coumaroyl-homoserine lactone. To the best of our knowledge, this is the first documentation of Arthrobacter and Pseudomonas spp. capable of degrading p-coumaroyl-homoserine lactone and the production of C12-HSL by P. frederiksbergensis.
In this paper, we investigate the use of fat tissue as a communication channel between in-body, implanted devices at R-band frequencies (1.7⁻2.6 GHz). The proposed fat channel is based on an anatomical model of the human body. We propose a novel probe that is optimized to efficiently radiate the R-band frequencies into the fat tissue. We use our probe to evaluate the path loss of the fat channel by studying the channel transmission coefficient over the R-band frequencies. We conduct extensive simulation studies and validate our results by experimentation on phantom and ex-vivo porcine tissue, with good agreement between simulations and experiments. We demonstrate a performance comparison between the fat channel and similar waveguide structures. Our characterization of the fat channel reveals propagation path loss of ∼0.7 dB and ∼1.9 dB per cm for phantom and ex-vivo porcine tissue, respectively. These results demonstrate that fat tissue can be used as a communication channel for high data rate intra-body networks.
Optical fibers have been involved in the area of sensing applications for more than four decades. Moreover, interferometric optical fiber sensors have attracted broad interest for their prospective applications in sensing temperature, refractive index, strain measurement, pressure, acoustic wave, vibration, magnetic field, and voltage. During this time, numerous types of interferometers have been developed such as Fabry-Perot, Michelson, Mach-Zehnder, Sagnac Fiber, and Common-path interferometers. Fabry-Perot interferometer (FPI) fiber-optic sensors have been extensively investigated for their exceedingly effective, simple fabrication as well as low cost aspects. In this study, a wide variety of FPI sensors are reviewed in terms of fabrication methods, principle of operation and their sensing applications. The chronology of the development of FPI sensors and their implementation in various applications are discussed.
A simple, compact sickle-shaped printed antenna with a slotted ground plane is designed and developed for broadband circularly polarized (CP) radiation. The sickle-shaped radiator with a tapered feed line and circular slotted square ground plane are utilized to realize the wideband CP radiation feature. With optimized dimensions of 0.29λ × 0.29λ × 0.012λ at 2.22 GHz frequency for the realized antenna parameters, the measured results display that the antenna has a 10 dB impedance bandwidth of 7.70 GHz (126.85%; 2.22⁻9.92 GHz) and a 3 dB axial ratio (AR) bandwidth of 2.64 GHz (73.33%; 2.28⁻4.92 GHz). The measurement agrees well with simulation, which proves an excellent circularly polarized property. For verification, the mechanism of band improvement and circular polarization are presented, and the parametric study is carried out. Since, the proposed antenna is a simple design structure with broad impedance and AR bandwidth, which is a desirable feature as a candidate for various wireless communication systems. Because of the easy printed structure and scaling the dimension with broadband CP characteristics, the realized antenna does incorporate in a number of CP wireless communication applications.
Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier.