Search results for: metal detection

Authors: M. Malekian, M. E. Heydari, M. Irani Estyar

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Human papillomavirus (HPV) is one of the most common sexually transmitted infection, which may lead to cervical cancer as the main cause of it. With early diagnosis and treatment in health care services, cervical cancer and its complications are considered to be preventable. This study was aimed to compare the efficiency, sensitivity, and specificity of Pap smear and polymerase chain reaction (PCR) in detecting HPV. A literature search was performed in Google Scholar, PubMed and SID databases using the keywords 'human papillomavirus', 'pap smear' and 'polymerase change reaction' to identify studies comparing Pap smear and PCR methods for the detection. No restrictions were considered.10 studies were included in this review. All samples that were positive by pop smear were also positive by PCR. However, there were positive samples detected by PCR which was negative by pop smear and in all studies, many positive samples were missed by pop smear technique. Although The Pap smear had high specificity, PCR based HPV detection was more sensitive method and had the highest sensitivity. In order to promote the quality of detection and high achievement of the maximum results, PCR diagnostic methods in addition to the Pap smear are needed and Pap smear method should be combined with PCR techniques according to the high error rate of Pap smear in detection.

Keywords: human papillomavirus, cervical cancer, pap smear, polymerase chain reaction

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Authors: Raphael Odoh, Obida J. Oko, Mary S. Dauda

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The determination of Cd, Cr, Cu, Fe,Mn, Ni, Pb and Zn contents in edible oils (palm oil, ground-nut oil and soybean oil) bought from various markets of Benue and Taraba state were carried out with flame atomic absorption spectrophotometric technique. The method 3031 developed acid digestion of oils for metal analysis by atomic absorption or ICP spectrometry was used in the preparation of the edible oil samples for the determination of total metal content in this study. The overall results (µg/g) in palm oil sample ranged from 0.028-0.076, 0.035-0.092, 1.011-1.955, 2.101-4.892, 0.666-0.922, 0.054-0.095, 0.031-0.068 and 1.987-2.971 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn respectively, while in ground-nut oil the overall results ranged from 0.011-0.042, 0.011-0.052, 0.133-0.788, 1.789-2.511, 0.078-0.765, 0.045-0.092, 0.011-0.028 and 1.098-1.997 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn respectively. Of the heavy metals considered Cd and Ni showed the highest contamination in the soybean oil sample. The overall results in soybean oil samples ranged from 0.011-0.015, 0.017-0.032, 0.453-0.987, 1.789-2.511, 0.089-0.321, 0.011-0.016, 0.012-0.065 and 1.011-1.997 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn respectively. The concentration of Pb was the highest. The degree of contamination by each metal was estimated by the transfer factor. The transfer factors obtained for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn in edible oils (palm oil, ground-nut oil and soybean oil) were 10.800, 16.500, 16.000, 18.813, 15.115, 14.230, 23.000 and 9.418 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn in palm oil, and 7.000, 12.500, 8.880, 11.333, 7.708, 10.833, 15.00 and 6.608 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn in ground-nut oil while for soybean oil the transfer factors were 13.000, 11.000, 7.642, 11.578, 4.486, 13.00, 12.333 and 4.412 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn respectively. The inter-element correlation was found among metals in edible oil samples using Pearson’s correlation co-efficient. There were positive and negative correlations among the metals determined. All Metals determined showed degree of contamination but concentrations lower than the USP specification.

Keywords: Benue State, contamination, edible oils, heavy metals, markets, Taraba State

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Authors: Saleh H. Abud, Z. Hassan, F. K. Yam

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Characteristics of MSM photodetector based on a porous In0.08Ga0.92N thin film were reported. Nanoporous structures of n-type In0.08Ga0.92N/AlN/Si thin films were synthesized by photoelectrochemical (PEC) etching at a ratio of 1:4 of HF:C2H5OH solution for 15 min. The structural and optical properties of pre- and post-etched thin films were investigated. Field emission scanning electron microscope and atomic force microscope images showed that the pre-etched thin film has a sufficiently smooth surface over a large region and the roughness increased for porous film. Blue shift has been observed in photoluminescence emission peak at 300 K for porous sample. The photoluminescence intensity of the porous film indicated that the optical properties have been enhanced. A high work function metals (Pt and Ni) were deposited as a metal contact on the porous films. The rise and recovery times of the devices were investigated at 390 nm chopped light. Finally, the sensitivity and quantum efficiency were also studied.

Keywords: porous InGaN, photoluminescence, SMS photodetector, atomic force microscopy

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Authors: Trupti Chavan, Ramachandra Guda, Kameshwar Rao

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The lack of data is a pain problem in medical image analysis using a convolutional neural network (CNN). This work uses various spatial transformation techniques to address the medical image augmentation issue for knee detection and localization using an enhanced single shot detector (SSD) network. The spatial transforms like a negative, histogram equalization, power law, sharpening, averaging, gaussian blurring, etc. help to generate more samples, serve as pre-processing methods, and highlight the features of interest. The experimentation is done on the OpenKnee dataset which is a collection of knee images from the openly available online sources. The CNN called enhanced single shot detector (SSD) is utilized for the detection and localization of the knee joint from a given X-ray image. It is an enhanced version of the famous SSD network and is modified in such a way that it will reduce the number of prediction boxes at the output side. It consists of a classification network (VGGNET) and an auxiliary detection network. The performance is measured in mean average precision (mAP), and 99.96% mAP is achieved using the proposed enhanced SSD with spatial transformations. It is also seen that the localization boundary is comparatively more refined and closer to the ground truth in spatial augmentation and gives better detection and localization of knee joints.

Keywords: data augmentation, enhanced SSD, knee detection and localization, medical image analysis, openKnee, Spatial transformations

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Authors: Naser Safdarian, Nader Jafarnia Dabanloo

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In this paper we used four features i.e. Q-wave integral, QRS complex integral, T-wave integral and total integral as extracted feature from normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our research we focused on detection and localization of MI in standard ECG. We use the Q-wave integral and T-wave integral because this feature is important impression in detection of MI. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI. Because these methods have good accuracy for classification of normal and abnormal signals. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 80% for accuracy in test data for localization and over 95% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve accuracy of classification by adding more features in this method. A simple method based on using only four features which extracted from standard ECG is presented which has good accuracy in MI localization.

Keywords: ECG signal processing, myocardial infarction, features extraction, pattern recognition

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Authors: Md Fazle Alam, Amaj Ahmed Laskar, Hina Younus

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Melamine toxicity which causes renal failure and death of humans and animals have recently attracted worldwide attention. Developing an easy, fast and sensitive method for the routine melamine detection is the need of the hour. Herein, we have developed a rapid, sensitive, one step and selective colorimetric method for the detection of melamine in milk samples based upon in-situ formation of silver nanoparticles (AgNPs) via tannic acid at room temperature. These AgNPs thus formed were characterized by UV-VIS spectrophotometer, transmission electron microscope (TEM), zetasizer and dynamic light scattering (DLS). Under optimal conditions, melamine could be selectively detected within the concentration range of 0.05-1.4 µM with a limit of detection (LOD) of 10.1 nM, which is lower than the strictest melamine safety requirement of 1 ppm. This assay does not utilize organic cosolvents, enzymatic reactions, light sensitive dye molecules and sophisticated instrumentation, thereby overcoming some of the limitations of conventional methods.

Keywords: milk adulteration, melamine, silver nanoparticles, tannic acid

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Authors: Abe Degale D., Cheng Jian

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When creating automated video surveillance systems, violent action recognition is crucial. In recent years, hand-crafted feature detectors have been the primary method for achieving violence detection, such as the recognition of fighting activity. Researchers have also looked into learning-based representational models. On benchmark datasets created especially for the detection of violent sequences in sports and movies, these methods produced good accuracy results. The Hockey dataset's videos with surveillance camera motion present challenges for these algorithms for learning discriminating features. Image recognition and human activity detection challenges have shown success with deep representation-based methods. For the purpose of detecting violent images and identifying aggressive human behaviours, this research suggested a deep representation-based model using the transfer learning idea. The results show that the suggested approach outperforms state-of-the-art accuracy levels by learning the most discriminating features, attaining 99.34% and 99.98% accuracy levels on the Hockey and Movies datasets, respectively.

Keywords: violence detection, faster RCNN, transfer learning and, surveillance video

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Authors: Antoni Ivanov, Nikolay Dandanov, Nicole Christoff, Vladimir Poulkov

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Spectrum underutilization has made cognitive radio a promising technology both for current and future telecommunications. This is due to the ability to exploit the unused spectrum in the bands dedicated to other wireless communication systems, and thus, increase their occupancy. The essential function, which allows the cognitive radio device to perceive the occupancy of the spectrum, is spectrum sensing. In this paper, the performance of modern adaptations of the four most widely used spectrum sensing techniques namely, energy detection (ED), cyclostationary feature detection (CSFD), matched filter (MF) and eigenvalues-based detection (EBD) is compared. The implementation has been accomplished through the PlutoSDR hardware platform and the GNU Radio software package in very low Signal-to-Noise Ratio (SNR) conditions. The optimal detection performance of the examined methods in a realistic implementation-oriented model is found for the common relevant parameters (number of observed samples, sensing time and required probability of false alarm).

Keywords: cognitive radio, dynamic spectrum access, GNU Radio, spectrum sensing

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Authors: Xinwen Zhu, Xingguang Li, Sun Yi

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Crack is one of the most common damages in buildings, bridges, roads and so on, which may pose safety hazards. However, cracks frequently happen in structures of various materials. Traditional methods of manual detection and measurement, which are known as subjective, time-consuming, and labor-intensive, are gradually unable to meet the needs of modern development. In addition, crack detection and measurement need be safe considering space limitations and danger. Intelligent crack detection has become necessary research. In this paper, an efficient method for crack detection and quantification using a 3D sensor, LiDAR, and depth camera is proposed. This method works even in a dark environment, which is usual in real-world applications. The LiDAR rapidly spins to scan the surrounding environment and discover cracks through lasers thousands of times per second, providing a rich, 3D point cloud in real-time. The LiDAR provides quite accurate depth information. The precision of the distance of each point can be determined within around  ±3 cm accuracy, and not only it is good for getting a precise distance, but it also allows us to see far of over 100m going with the top range models. But the accuracy is still large for some high precision structures of material. To make the depth of crack is much more accurate, the depth camera is in need. The cracks are scanned by the depth camera at the same time. Finally, all data from LiDAR and Depth cameras are analyzed, and the size of the cracks can be quantified successfully. The comparison shows that the minimum and mean absolute percentage error between measured and calculated width are about 2.22% and 6.27%, respectively. The experiments and results are presented in this paper.

Keywords: LiDAR, depth camera, real-time, detection and measurement

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Authors: Sukhwinder Singh Jolly

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Metal spinning is one such process in which the stresses are localized to a small area and the material is made to flow or move over the mandrel with the help of spinning tool. Spinning of tubular products can be performed by two techniques, forward spinning and backward spinning. Many researchers have studied the process both experimentally and analytically. An effort has been made to apply the process to the spinning of thin wall, highly precision, small bore long tube in hard-to-work materials such as titanium.

Keywords: metal spinning, hard-to-work materials, roller diameter, power consumption

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Authors: Kay Thinzar Phu, Lwin Lwin Oo

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In an intelligent transport system and advanced driver assistance system, the developing of real-time traffic sign detection and recognition (TSDR) system plays an important part in recent research field. There are many challenges for developing real-time TSDR system due to motion artifacts, variable lighting and weather conditions and situations of traffic signs. Researchers have already proposed various methods to minimize the challenges problem. The aim of the proposed research is to develop an efficient and effective TSDR in real time. This system proposes an adaptive thresholding method based on RGB color for traffic signs detection and new features for traffic signs recognition. In this system, the RGB color thresholding is used to detect the blue and yellow color traffic signs regions. The system performs the shape identify to decide whether the output candidate region is traffic sign or not. Lastly, new features such as termination points, bifurcation points, and 90’ angles are extracted from validated image. This system uses Myanmar Traffic Sign dataset.

Keywords: adaptive thresholding based on RGB color, blue color detection, feature extraction, yellow color detection

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Authors: S. Ramesh, A. S. Sasiraaju, K. Sidhaarth, N. Sudhan Rajkumar, V. Manivel Muralidaran

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This work presents the result of investigations aimed at determining the hardness of the welded Chromoly (A 4130) steel plate of 2” thickness. Multi pass welding for the thick sections was carried out and analyzed for the Chromoly alloy steel plates. The study of hardness at the weld metal reveals that there is the presence of different micro structure products which yields diverse properties. The welding carried out using GMAW with ER70s-2 electrode. Single V groove design was selected for the butt joint configuration. The presence of hydrogen has been suppressed by selecting low hydrogen electrode. Preheating of the plate prior to welding reduces the cooling rate which also affects the weld metal microstructure. The shielding gas composition used in this analysis is 80% Ar-20% CO2. The experimental analysis gives the detailed study of the hardness of the material.

Keywords: chromoly, gas metal arc weld (GMAW), hardness, multi pass weld, shielding gas composition

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Authors: Hyun-Woo Cho

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Earlier detection of incipient abnormal operations in terms of plant-wide process management is quite necessary in order to improve product quality and process safety. And generating warning signals or alarms for operating personnel plays an important role in process automation and intelligent plant health monitoring. Various methodologies have been developed and utilized in this area such as expert systems, mathematical model-based approaches, multivariate statistical approaches, and so on. This work presents a nonlinear empirical monitoring methodology based on the real-time analysis of massive process data. Unfortunately, the big data includes measurement noises and unwanted variations unrelated to true process behavior. Thus the elimination of such unnecessary patterns of the data is executed in data processing step to enhance detection speed and accuracy. The performance of the methodology was demonstrated using simulated process data. The case study showed that the detection speed and performance was improved significantly irrespective of the size and the location of abnormal events.

Keywords: detection, monitoring, process data, noise

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Authors: Vaishnavi Shivde, Shrishti Sinha, Trapti Mishra

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Traffic light detection from a moving vehicle is an important technology both for driver safety assistance functions as well as for autonomous driving in the city. This paper proposed a deep-learning-based traffic light recognition method that consists of a pixel-wise image segmentation technique and a fully convolutional network i.e., UNET architecture. This paper has used a method for detecting the position and recognizing the state of the traffic lights in video sequences is presented and evaluated using Traffic Light Dataset which contains masked traffic light image data. The first stage is the detection, which is accomplished through image processing (image segmentation) techniques such as image cropping, color transformation, segmentation of possible traffic lights. The second stage is the recognition, which means identifying the color of the traffic light or knowing the state of traffic light which is achieved by using a Convolutional Neural Network (UNET architecture).

Keywords: traffic light detection, image segmentation, machine learning, classification, convolutional neural networks

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Authors: Mahmoud H. Aboul-Ela, Riham R. Mohamed, Magdy W. Sabaa

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Synthesis of cross-linked hydrogels composed of trimethyl chitosan (TMC) and poly(vinyl alcohol) (PVA) in different weight ratios in presence of glutaraldehyde as cross-linking agent. Characterization of the prepared hydrogels was done using FTIR, XRD, SEM and TGA. The prepared hydrogels were investigated as adsorbent materials for some transition metal ions from their aqueous solutions. Moreover, the swell ability of the prepared hydrogels was also investigated in both acidic and alkaline pHs, as well as in simulated body fluid (SBF).

Keywords: trimethyl chitosan, hydrogels, metal uptake, superabsorbent materials

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Authors: Rui Tu, Yakui Bai, Huailin Li

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The lead-bismuth eutectic (LBE) alloy is a promising candidate of coolant in the fast neutron reactors and accelerator-driven systems (ADS) because of its good properties, such as low melting point, high neutron yields and high thermal conductivity. Although the corrosion of the structure materials caused by the liquid metal (LM) coolant is a challenge to the safe operating of a lead-bismuth eutectic nuclear reactor. Thermodynamic theories, experiential formulas and experimental data can be used for explaining the maintenance of the protective oxide layers on stainless steels under satisfaction oxygen concentration, but the atomic scale insights of such anti-corrosion mechanisms are little known. In the present work, the first-principles calculations are carried out to study the effects of oxygen partial pressure on the formation energies of the liquid metal coolant relevant impurity defects in the anti-corrosion oxide films on the surfaces of the structure materials. These approaches reveal the microscope mechanisms of the corrosion of the structure materials, especially for the influences from the oxygen partial pressure. The results are helpful for identifying a crucial oxygen concentration for corrosion control, which can ensure the systems to be operated safely under certain temperatures.

Keywords: oxygen partial pressure, liquid metal coolant, TDDFT, anti-corrosion layer, formation energy

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Authors: Deeman Yousif Mahmood, Mohammed Abdullah Hussein

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The goal of a network-based intrusion detection system is to classify activities of network traffics into two major categories: normal and attack (intrusive) activities. Nowadays, data mining and machine learning plays an important role in many sciences; including intrusion detection system (IDS) using both supervised and unsupervised techniques. However, one of the essential steps of data mining is feature selection that helps in improving the efficiency, performance and prediction rate of proposed approach. This paper applies unsupervised K-means clustering algorithm with information gain (IG) for feature selection and reduction to build a network intrusion detection system. For our experimental analysis, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 60.0% for the training set and the remainder for the testing set, a 2 class classifications have been implemented (Normal, Attack). Weka framework which is a java based open source software consists of a collection of machine learning algorithms for data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with using the full features of the dataset with the same algorithm.

Keywords: information gain (IG), intrusion detection system (IDS), k-means clustering, Weka

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Authors: Omoruyi Gold Idemudia, Alexander P. Sadimenko

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The synthesis of N-heterocycles with novel properties, having broad spectrum biological activities that may become alternative medicinal drugs, have been attracting a lot of research attention due to the emergence of medicinal drug’s limitations such as disease resistance and their toxicity effects among others. Acylpyrazolones have been employed as pharmaceuticals as well as analytical reagent and their application as coordination complexes with transition metal ions have been well established. By way of a condensation reaction with amines acylpyrazolone ketones form a more chelating and superior group of compounds known as azomethines. 4-propyl-3-methyl-1-phenyl-2-pyrazolin-5-one was reacted with phenylhydrazine to get a new phenylhydrazone which was further reacted with aqueous solutions of palladium and platinum salts, in an effort towards the discovery of transition metal based synthetic drugs. The compounds were characterized by means of analytical, spectroscopic, thermogravimetric analysis TGA, as well as x-ray crystallography. 4-propyl-3-methyl-1-phenyl-2-pyrazolin-5-one phenylhydrazone crystallizes in a triclinic crystal system with a P-1 (No. 2) space group based on x-ray crystallography. The bidentate ON ligand formed a square planar geometry on coordinating with metal ions based on FTIR, electronic and NMR spectra as well as magnetic moments. Reported compounds showed antibacterial activities against the nominated bacterial isolates using the disc diffusion technique at 20 mg/ml in triplicates. The metal complexes exhibited a better antibacterial activity with platinum complex having an MIC value of 0.63 mg/ml. Similarly, ligand and complexes also showed antioxidant scavenging properties against 2, 2-diphenyl-1-picrylhydrazyl DPPH radical at 0.5mg/ml relative to ascorbic acid (standard drug).

Keywords: acylpyrazolone, antibacterial studies, metal complexes, phenylhydrazone, spectroscopy

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Authors: M. Kamel, A. Hoayek, M. Batton-Hubert

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With the increase of network virtualization and the disparity of vendors, the continuous monitoring and detection of anomalies cannot rely on static rules. An advanced analytical methodology is needed to discriminate between ordinary events and unusual anomalies. In this paper, we focus on log data (textual data), which is a crucial source of information for network performance. Then, we introduce an algorithm used as a pipeline to help with the pretreatment of such data, group it into patterns, and dynamically label each pattern as an anomaly or not. Such tools will provide users and experts with continuous real-time logs monitoring capability to detect anomalies and failures in the underlying system that can affect performance. An application of real-world data illustrates the algorithm.

Keywords: logs, anomaly detection, ML, scoring, NLP

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Authors: Edmaritz Hernandez-Pagan, Kanjana Laosuntisuk, Alex Harris, Allison Haynes, David Buitrago, Michael Kudenov, Colleen Doherty

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Rare Earth Elements (REEs) are critical metals for modern electronics, green technologies, and defense systems. However, due to their dispersed nature in the Earth’s crust, frequent co-occurrence with radioactive materials, and similar chemical properties, acquiring and purifying REEs is costly and environmentally damaging, restricting access to these metals. Plants could serve as resources for bioengineering REE mining systems. Although there is limited information on how REEs affect plants at a cellular and molecular level, plants with high REE tolerance and hyperaccumulation have been identified. This dissertation aims to develop a plant-based system for harvesting REEs from industrial waste material with a focus on Acid Mine Drainage (AMD), a toxic coal mining product. The objectives are 1) to develop a non-destructive, in vivo detection method for REE detection in Phytolacca plants (REE hyperaccumulator) plants utilizing fluorescence spectroscopy and with a primary focus on dysprosium, 2) to characterize the uptake of REE and Heavy Metals in Phytolacca americana and Phytolacca acinosa (REE hyperaccumulator) in AMD for potential implementation in the plant-based system, 3) to implement the REE detection method to identify REE-binding proteins and peptides for potential enhancement of uptake and selectivity for targeted REEs in the plants implemented in the plant-based system. The candidates are known REE-binding peptides or proteins, orthologs of known metal-binding proteins from REE hyperaccumulator plants, and novel proteins and peptides identified by comparative plant transcriptomics. Lanmodulin, a high-affinity REE-binding protein from methylotrophic bacteria, is used as a benchmark for the REE-protein binding fluorescence assays and expression in A. thaliana to test for changes in REE plant tolerance and uptake.

Keywords: phytomining, agromining, rare earth elements, pokeweed, phytolacca

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Authors: A. Shoiynbek, K. Kozhakhmet, P. Menezes, D. Kuanyshbay, D. Bayazitov

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Speech emotion recognition has received increasing research interest all through current years. There was used emotional speech that was collected under controlled conditions in most research work. Actors imitating and artificially producing emotions in front of a microphone noted those records. There are four issues related to that approach, namely, (1) emotions are not natural, and it means that machines are learning to recognize fake emotions. (2) Emotions are very limited by quantity and poor in their variety of speaking. (3) There is language dependency on SER. (4) Consequently, each time when researchers want to start work with SER, they need to find a good emotional database on their language. In this paper, we propose the approach to create an automatic tool for speech emotion extraction based on facial emotion recognition and describe the sequence of actions of the proposed approach. One of the first objectives of the sequence of actions is a speech detection issue. The paper gives a detailed description of the speech detection model based on a fully connected deep neural network for Kazakh and Russian languages. Despite the high results in speech detection for Kazakh and Russian, the described process is suitable for any language. To illustrate the working capacity of the developed model, we have performed an analysis of speech detection and extraction from real tasks.

Keywords: deep neural networks, speech detection, speech emotion recognition, Mel-frequency cepstrum coefficients, collecting speech emotion corpus, collecting speech emotion dataset, Kazakh speech dataset

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Authors: Muhammad Hassan Khalil, Xu Jiadong

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Early detection through screening is the best tool short of a perfect treatment against the malignant tumor inside the breast of a woman. By detecting cancer in its early stages, it can be recognized and treated before it has the opportunity to spread and change into potentially dangerous. Microwave tomography is a new imaging method based on contrast in dielectric properties of materials. The mathematical theory of microwave tomography involves solving an inverse problem for Maxwell’s equations. In this paper, we present designed antenna for breast cancer detection, which will use in microwave tomography configuration.

Keywords: microwave imaging, inverse scattering, breast cancer, malignant tumor detection

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Authors: Lenka Mynaříková, Hedvika Boukalová

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The study looks at the ability to detect nonverbal deception among police officers and management students in the Czech Republic. Respondents from police departments (n=197) and university students of human resources (n=161) completed a deception detection task and evaluated veracity of the statements of suspects in 21 video clips from real crime investigations. Their evaluations were based on nonverbal behavior. Voices in the video clips were modified so that words were not recognizable, yet paraverbal voice characteristics were preserved. Results suggest that respondents have a tendency to lie bias based on their profession. In the evaluation of video clips, stereotypes also played a significant role. The statements of suspects of a different ethnicity, younger age or specific visual features were considered deceitful more often. Research might be beneficial for training in professions that are in need of deception detection techniques.

Keywords: deception detection, police officers, human resources, forensic psychology, forensic studies, organizational psychology

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Authors: Majid Farsadrooh, Najmeh Sabbaghi, Seyed Mohammad Mostashari, Abolhasan Moradi

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Phenolic compounds are chief environmental contaminants on account of their hazardous and toxic nature on human health. The preparation of sensitive and potent chemosensors to monitor emerging pollution in water and effluent samples has received great consideration. A novel and versatile nanocomposite sensor based on poly pyrogallol is presented for the first time in this study, and its electrochemical behavior for simultaneous detection of hydroquinone (HQ), catechol (CT), and resorcinol (RS) in the presence of nitrite is evaluated. The physicochemical characteristics of the fabricated nanocomposite were investigated by emission-scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDS), and Brunauer-Emmett-Teller (BET). The electrochemical response of the proposed sensor to the detection of HQ, CT, RS, and nitrite is studied using cyclic voltammetry (CV), chronoamperometry (CA), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). The kinetic characterization of the prepared sensor showed that both adsorption and diffusion processes can control reactions at the electrode. In the optimized conditions, the new chemosensor provides a wide linear range of 0.5-236.3, 0.8-236.3, 0.9-236.3, and 1.2-236.3 μM with a low limit of detection of 21.1, 51.4, 98.9, and 110.8 nM (S/N = 3) for HQ, CT and RS, and nitrite, respectively. Remarkably, the electrochemical sensor has outstanding selectivity, repeatability, and stability and is successfully employed for the detection of RS, CT, HQ, and nitrite in real water samples with the recovery of 96.2%–102.4%, 97.8%-102.6%, 98.0%–102.4% and 98.4%–103.2% for RS, CT, HQ, and nitrite, respectively. These outcomes illustrate that poly pyrogallol is a promising candidate for effective electrochemical detection of dihydroxybenzene isomers in the presence of nitrite.

Keywords: electrochemical sensor, poly pyrogallol, phenolic compounds, simultaneous determination

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Authors: Justina I. R. Udotong, Essien U. Essien

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Diagnostic enzymes like aspartate aminotransferase (AST), alanine aminotransferase (ALT) and alkaline phosphatase (ALP) were determined as indices of heavy metal pollution in Tilapia guinensis. Three different sets of fishes treated with lead (Pb), iron (Fe) and copper (Cu) were used for the study while a fourth group with no heavy metal served as a control. Fishes in each of the groups were exposed to 2.65 mg/l of Pb, 0.85 mg/l of Fe and 0.35 mg/l of Cu in aerated aquaria for 96 hours. Tissue fractionation of the liver tissues was carried out and the three diagnostic enzymes (AST, ALT, and ALP) were estimated. Serum levels of the same diagnostic enzymes were also measured. The mean values of the serum enzyme activity for ALP in each experimental group were 19.5±1.62, 29.67±2.17 and 1.15±0.27 IU/L for Pb, Fe and Cu groups compared with 9.99±1.34 IU/L enzyme activity in the control. This result showed that Pb and Fe caused increased release of the enzyme into the blood circulation indicating increased tissue damage while Cu caused a reduction in the serum level as compared with the level in the control group. The mean values of enzyme activity obtained in the liver were 102.14±6.12, 140.17±2.06 and 168.23±3.52 IU/L for Pb, Fe and Cu groups, respectively compared to 91.20±9.42 IU/L enzyme activity for the control group. The serum and liver AST and ALT activities obtained in Pb, Fe, Cu and control groups are reported. It was generally noted that the presence of the heavy metal caused liver tissues damage and consequent increased level of the diagnostic enzymes in the serum.

Keywords: diagnostic enzymes, enzyme activity, heavy metals, tissues investigations

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Authors: Shaher Bano, Samia Fida, Asif Israr

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The synergistic effect of properties of metals and fibers reinforced laminates has diverted attention of the world towards use of robust composite materials known as fiber-metal laminates in many high performance applications. In this study, modification of an adhesively bonded joint as a single lap joint of carbon fibers based CARALL FML has done to increase interlaminar shear strength of the joint. The effect of different configurations of joint designs such as spews, stepped and modification in adhesive by addition of nano-fillers was studied. Both experimental and simulation results showed that modified joint design have superior properties as maximum force experienced stepped joint was 1.5 times more than the simple lap joint. Addition of carbon nano-tubes as nano-fillers in the adhesive joint increased the maximum force due to crack deflection mechanism.

Keywords: adhesive joint, Carbon Reinforced Aluminium Laminate (CARALL), fiber metal laminates, spews

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Authors: F. Fakheri Raof, F. Zobeidizadeh

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The present study examined the potential capability of Spirogyra algae remove heavy metals Zn, Pb, Cu, and Cr from the water. For this purpose, the water treatment No. 3 of Ahvaz County in Khuzestan Province of Iran was selected as a case study. From 8 sampling stations, 4 stations were dedicated to the water samples and 4 stations to the algae samples. According to the obtained results, the concentration of the heavy metals Cr, Cu, Pb, and Zn in water samples were within the ranges of 1.98-19.53, 0.67-13.45, 1-23.18, and 2.12-83.04 µg/L. Besides, the concentration of heavy metal Cr, Pb, Cu, and Zn in spirogyra algae samples varied between the ranges 2.30-3.61, 2.06-3.43, 2.29-2.56, and 9.88-10.84 µg/L. The highest amount of metal absorption in spirogyra algae samples was related to the zinc. The obtained results also indicated that the last spirogyra algae sample which was at the inlet of Tank 4 absorbed the lowest concentration of metals. This would be due to the treatment process along the course of ponds resulted in completely pure water at the outlet without the existence of algae on the sides. The paper also provides some useful recommendations on this issue.

Keywords: absorption, Ahvaz, heavy metal, spirogyra algae, water treatment plants

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Authors: Ehsan Khademi, Mahdi Jalili

Abstract:

Despite the special features of bipartite networks, they are common in many systems. Real-world bipartite networks may show community structure, similar to what one can find in one-mode networks. However, the interpretation of the community structure in bipartite networks is different as compared to one-mode networks. In this manuscript, we compare a number of available methods that are frequently used to discover community structure of bipartite networks. These networks are categorized into two broad classes. One class is the methods that, first, transfer the network into a one-mode network, and then apply community detection algorithms. The other class is the algorithms that have been developed specifically for bipartite networks. These algorithms are applied on a model network with prescribed community structure.

Keywords: community detection, bipartite networks, co-clustering, modularity, network projection, complex networks

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Authors: Shilpa Khobragade, Carlos Da Silva Granja, Niklas Sandström, Igor Efimov, Victor P. Ostanin, Wouter van der Wijngaart, David Klenerman, Sourav K. Ghosh

Abstract:

Rapid, label-free and direct detection of pathogenic bacteria is critical for the prevention of disease outbreaks. This paper for the first time attempts to probe the nonlinear acoustic response of quartz crystal resonator (QCR) functionalized with specific DNA aptamers for direct detection and quantification of viable E. coli KCTC 2571 bacteria. DNA aptamers were immobilized through biotin and streptavidin conjugation, onto the gold surface of QCR to capture the target bacteria and the detection was accomplished by shift in amplitude of the peak 3f signal (3 times the drive frequency) upon binding, when driven near fundamental resonance frequency. The developed nonlinear acoustic aptasensor system demonstrated better reliability than conventional resonance frequency shift and energy dissipation monitoring that were recorded simultaneously. This sensing system could directly detect 10⁽⁵⁾ cells/mL target bacteria within 30 min or less and had high specificity towards E. coli KCTC 2571 bacteria as compared to the same concentration of S.typhi bacteria. Aptasensor response was observed for the bacterial suspensions ranging from 10⁽⁵⁾-10⁽⁸⁾ cells/mL. Conclusively, this nonlinear acoustic aptasensor is simple to use, gives real-time output, cost-effective and has the potential for rapid, specific, label-free direction detection of bacteria.

Keywords: acoustic, aptasensor, detection, nonlinear

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Authors: E. E. Tereshatov, M. Yu. Boltoeva, V. Mazan, M. F. Volia, C. M. Folden III

Abstract:

Room temperature ionic liquids (RTILs) are a class of liquid organic salts with melting points below 20 °C that are considered to be environmentally friendly ‘designers’ solvents. Pure hydrophobic ILs are known to extract metallic species from aqueous solutions. The closest analogues of ionic liquids are deep eutectic solvents (DESs), which are a eutectic mixture of at least two compounds with a melting point lower than that of each individual component. DESs are acknowledged to be attractive for organic synthesis and metal processing. Thus, these non-volatile and less toxic compounds are of interest for critical metal extraction. The US Department of Energy and the European Commission consider indium as a key metal. Its chemical homologue, thallium, is also an important material for some applications and environmental safety. The aim of this work is to systematically investigate In and Tl extraction from aqueous solutions into pure fluorinated ILs and hydrophobic DESs. The dependence of the Tl extraction efficiency on the structure and composition of the ionic liquid ions, metal oxidation state, and initial metal and aqueous acid concentrations have been studied. The extraction efficiency of the TlXz3–z anionic species (where X = Cl– and/or Br–) is greater for ionic liquids with more hydrophobic cations. Unexpectedly high distribution ratios (> 103) of Tl(III) were determined even by applying a pure ionic liquid as receiving phase. An improved mathematical model based on ion exchange and ion pair formation mechanisms has been developed to describe the co-extraction of two different anionic species, and the relative contributions of each mechanism have been determined. The first evidence of indium extraction into new quaternary ammonium- and menthol-based hydrophobic DESs from hydrochloric and oxalic acid solutions with distribution ratios up to 103 will be provided. Data obtained allow us to interpret the mechanism of thallium and indium extraction into ILs and DESs media. The understanding of Tl and In chemical behavior in these new media is imperative for the further improvement of separation and purification of these elements.

Keywords: deep eutectic solvents, indium, ionic liquids, thallium

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