Search results for: metal ion detection

Authors: Dmitry Dikarev, Ajit Nimbalker, Alexei Davydov

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Polar coding is a novel example of error correcting codes, which can achieve Shannon limit at block length N→∞ with log-linear complexity. Active research is being carried to adopt this theoretical concept for using in practical applications such as 5th generation wireless communication systems. Cyclic redundancy check (CRC) error detection code is broadly used in conjunction with successive cancellation list (SCL) decoding algorithm to improve finite-length polar code performance. However, there are two issues: increase of code block payload overhead by CRC bits and decrease of CRC error-detection capability. This paper proposes a method to control CRC overhead and false alarm rate of polar decoding. As shown in the computer simulations results, the proposed method provides the ability to use any set of CRC polynomials with any list size while maintaining the desired level of false alarm rate. This level of flexibility allows using polar codes in 5G New Radio standard.

Keywords: 5G New Radio, channel coding, cyclic redundancy check, list decoding, polar codes

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Authors: G. Medkour Ishak-Boushaki, A. Taibi, M. Allab

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Quantitative environmental radioactivity measurements are needed to determine the level of exposure of a population to ionizing radiations and for the assessment of the associated risks. Gamma spectrometry remains a very powerful tool for the analysis of radionuclides present in an environmental sample but the basic problem in such measurements is the low rate of detected events. Using large environmental samples could help to get around this difficulty but, unfortunately, new issues are raised by gamma rays attenuation and self-absorption. Recently, a new method has been suggested, to detect and identify without quantification, in a short time, a gamma ray of a low count source. This method does not require, as usually adopted in gamma spectrometry measurements, a pulse height spectrum acquisition. It is based on a chronological record of each detected photon by simultaneous measurements of its energy ε and its arrival time τ on the detector, the pair parameters [ε,τ] defining an event mode sequence (EMS). The EMS serials are analyzed sequentially by a Bayesian approach to detect the presence of a given radioactive source. The main object of the present work is to test the applicability of this sequential approach in radioactive environmental materials detection. Moreover, for an appropriate health oversight of the public and of the concerned workers, the analysis has been extended to get a reliable quantification of the radionuclides present in environmental samples. For illustration, we consider as an example, the problem of detection and quantification of 238U. Monte Carlo simulated experience is carried out consisting in the detection, by a Ge(Hp) semiconductor junction, of gamma rays of 63 keV emitted by 234Th (progeny of 238U). The generated EMS serials are analyzed by a Bayesian inference. The application of the sequential Bayesian approach, in environmental radioactivity analysis, offers the possibility of reducing the measurements time without requiring large environmental samples and consequently avoids the attached inconvenient. The work is still in progress.

Keywords: Bayesian approach, event mode sequence, gamma spectrometry, Monte Carlo method

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Authors: El Harraj Abdeslam, Raissouni Naoufal

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The detection of moving objects from a video image sequences is very important for object tracking, activity recognition, and behavior understanding in video surveillance. The most used approach for moving objects detection / tracking is background subtraction algorithms. Many approaches have been suggested for background subtraction. But, these are illumination change sensitive and the solutions proposed to bypass this problem are time consuming. In this paper, we propose a robust yet computationally efficient background subtraction approach and, mainly, focus on the ability to detect moving objects on dynamic scenes, for possible applications in complex and restricted access areas monitoring, where moving and motionless persons must be reliably detected. It consists of three main phases, establishing illumination changes in variance, background/foreground modeling and morphological analysis for noise removing. We handle illumination changes using Contrast Limited Histogram Equalization (CLAHE), which limits the intensity of each pixel to user determined maximum. Thus, it mitigates the degradation due to scene illumination changes and improves the visibility of the video signal. Initially, the background and foreground images are extracted from the video sequence. Then, the background and foreground images are separately enhanced by applying CLAHE. In order to form multi-modal backgrounds we model each channel of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture Model (GMM). Finally, we post process the resulting binary foreground mask using morphological erosion and dilation transformations to remove possible noise. For experimental test, we used a standard dataset to challenge the efficiency and accuracy of the proposed method on a diverse set of dynamic scenes.

Keywords: video surveillance, background subtraction, contrast limited histogram equalization, illumination invariance, object tracking, object detection, behavior understanding, dynamic scenes

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Authors: Rizka Ardhiyana, Liesbetini Haditjaroko, Sri Mulijani, Reki Ashadi Wicaksono, Raafqi Ranasasmita

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Designing a sensitive, specific and easy to use method to detect pork contamination in the food industry remains a major challenge. In the current study, we developed a sensitive thiol-bond AuNP-Probe biosensor that will change color when detecting pork DNA in the Cytochrome B region. The interaction between the biosensors and DNA sample is measured by spectrophotometer at 540 nm. The biosensor is made by reducing gold with trisodium citrate to produce gold nanoparticle with 39.05 nm diameter. The AuNP-Probe biosensor (gold nanoprobe) achieved 16.04 ng DNA/µl limit of detection and 53.48 ng DNA/µl limit of quantification. The linearity (R2) between color absorbance changes and DNA concentration is 0.9916. The biosensor has a good specificty as it does not cross-react with DNA of chicken and beef. To verify specificity towards the target sequence, PCR was tested to the target sequence and reacted to the PCR product with the biosensor. The PCR DNA isolate resulted in a 2.7 fold higher absorbance compared to pork-DNA isolate alone (without PCR). The sensitivity and specificity of the method show the promising application of the thiol-bond AuNP biosensor in pork-detection.

Keywords: biosensor, DNA probe, gold nanoparticle (AuNP), pork meat, qPCR

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Authors: Ranjit A. Patil, Yung Liou, Yuan-Ron Ma

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It has been observed that when the size of metals such as, Au, Zn, Ag, Cu, Te, and metal oxides is reduced to several nano-meters, it starts to show further interesting properties. These new properties boost the use of nano-structures to produce attractive functional materials or used as promising building blocks in electronic devices. Present work describes the synthesis of bismuth (Bi) nanoparticles (NP’s) having uniform morphology, high crystallinity, and single phase purity by the temperature assisted pulsed laser deposition (TAPLD). Pulsed Laser deposition (PLD) technique is one of the promising methods to synthesize nano-structures. It can provide the stable nucleation sites in orders of magnitudes higher than for MBE and sputtering deposition. The desired size of purely metallic Bi NP’s of can be easily controlled by adjusting the temperature of the substrate varying from 1000 C to 250 0C. When the temperatures of the substrate raised step wise the average size of Bi NP’s appeared to be increased by maintaining the uniform distribution of NP’s on the Si surfaces. The diameter range of NP’s is ~33-84 nm shows size distribution constrained in the limited range. The EDS results show that the 0D Bi NP’s synthesized at high temperature (250 0C) at a high vacuum still remained in a metallic phase. Moreover, XRD, TEM and SAED results showed that these Bi NP’s are hexagonal in crystalline in a space group R -3 m and no traces of bismuth oxide, confirming that Bi NP’s synthesized at wide range of temperatures persisted of the pure Bi-metallic phase.

Keywords: metal nano particles, bismuth, pulsed laser deposition (PLD), nano particles, temperature assisted growth

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Authors: Said Abdul Ahad Ahadi, Elyas Baray, Nitin Rakesh, Sudeep Varshney

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Wi-Fi is a widely used internet source that is used to provide internet access in many areas such as Stores, Cafes, University campuses, Restaurants and so on. This technology brought more facilities in communication and networking. On the other hand, due to the transmission of data over the air, which makes the network vulnerable, so it becomes prone to various threats such as Evil Twin and etc. The Evil Twin is a kind of adversary which impersonates a legitimate access point (LAP) as it can happen by spoofing the name (SSID) and MAC address (BSSID) of a legitimate access point (LAP). And this attack can cause many threats such as MITM, Service Interruption, Access point service blocking. Various Evil Twin Attack Detection Techniques are proposed, but they require additional hardware, or they require protocol modification. In this paper, we proposed a new technique based on Access Point’s two fingerprints, Received Signal Strength Indicator (RSSI) and Hop Count, that is hard to copy by an adversary. And we implemented the technique in a system called “ETDetector,” which can detect and prevent the attack.

Keywords: evil twin, LAP, SSID, Wi-Fi security, signal variation, ETAD, kali linux, scapy, python

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Authors: Mehrnaz Mostafavi

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The current research aims to explore a method for creating nano-ring structures through chemical reduction. By employing a direct reduction process at a controlled, slow pace, and concurrently introducing specific reduction agents, the goal is to fabricate these unique nano-ring formations. The deliberate slow reduction of nanoparticles within this process helps prevent spatial hindrances caused by the reduction agents. The timing of the reduction of metal atoms, facilitated by these agents, emerges as a crucial factor influencing the creation of nano-ring structures. In investigation involves a chemical approach utilizing bovine serum albumin and human serum albumin as organic reducing agents to produce gold nano-rings. The controlled reduction of metal atoms at a slow pace and under specific pH conditions plays a pivotal role in the successful fabrication of these nanostructures. Optical spectroscopic analyses revealed distinctive plasmonic behavior in both visible and infrared spectra, owing to the collective movement of electrons along the inner and outer walls of the gold nano-rings. Importantly, these ring-shaped nanoparticles exhibit customizable plasmon resonances in the near-infrared spectrum, a characteristic absent in solid particles of similar sizes. This unique attribute makes the generated samples valuable for applications in Nanomedicine and Nanobiotechnology, leveraging the distinct optical properties of these nanostructures.

Keywords: nano-ring structure, nano-particles, reductant agents, plasmon resonace

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Authors: V. M. Varagyan, G. A. Gevorgyan, K. V. Grigoryan, A. L. Varagyan

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Alaverdi region is situated in the northern part of the Republic of Armenia. Previous studies (1989) in Alaverdi region showed that due to soil irrigation with the highly polluted waters of the Debed and Shnogh rivers, the content of heavy metals in the brown forest steppe soils was significantly higher than the maximum permissible concentration as a result of which the fermentative activity in all the layers of the soils was stressed. Compared to the non-polluted soils, the activity of ferments in the plough layers of the highly polluted soils decreased by 44 - 68% (invertase – 60%, phosphatase – 44%, urease – 66%, catalase – 68%). In case of the soil irrigation with the polluted waters, a decrease in the intensity of fermentative reactions was conditioned by the high content of heavy metals in the soils and changes in chemical composition, physical and physicochemical properties. 20-year changes in the fermentative activity in the brown forest steppe soils in Alaverdi region were investigated. The activity of extracellular ferments in the soils was determined by the unification methods. The study has confirmed that self-recovery process occurs in soils previously polluted with heavy metals which can be revealed by fermentative activity. The investigations revealed that during 1989 – 2009, the activity of ferments in the plough layers of the medium and highly polluted soils increased by 31.2 – 52.6% (invertase – 31.2%, urease – 52.6%, phosphatase – 33.3%, catalase – 41.8%) and 24.1 – 87.0% (invertase – 40.4%, urease – 76.9%, phosphatase – 24.1%, catalase – 87.0%) respectively which indicated that the dynamic properties of the soils, which had been broken due to heavy metal pollution, were improved. In 1989, the activity of the Alaverdi copper smelting plant was temporarily stopped due to financial problems caused by the economic crisis and the absence of market, and the factory again started operation in 1997 and isn’t currently running at full capacity. As a result, the Debed river water has obtained a new chemical composition and comparatively good irrigation properties. Due to irrigation with this water, the gradually recovery of the soil dynamic properties, which had been broken due to irrigation with the waters polluted with heavy metals, was occurred. This is also explained by the fact that in case of irrigation with the partially cleaned water, the soil protective function against pollutants rose due to a content increase in humus and silt fractions. It is supposed that in case of the soil irrigation with the partially cleaned water, the intensity of fermentative reactions wasn’t directly affected by heavy metals.

Keywords: alaverdi region, heavy metal pollution, self-recovery, soil fermentative activity

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Authors: Dylan Elliott, Katya Pertsova

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Preposition errors are some of the most common errors created by L2 speakers. In addition, improving error correction and detection methods remains an open issue in the realm of Natural Language Processing (NLP). This research investigates whether the bidirectional encoder representations from the transformers model (BERT) have the potential to correct preposition errors accurately enough to be useful in error correction software. This research finds that BERT performs strongly when the scope of its error correction is limited to preposition choice. The researchers used an open-source BERT model and over three hundred thousand edited sentences from Wikipedia, tagged for part of speech, where only a preposition edit had occurred. To test BERT’s ability to detect errors, a technique known as multi-level masking was used to generate suggestions based on sentence context for every prepositional environment in the test data. These suggestions were compared with the original errors in the data and their known corrections to evaluate BERT’s performance. The suggestions were further analyzed to determine if BERT more often agreed with the judgements of the Wikipedia editors. Both the untrained and fined-tuned models were compared. Finetuning led to a greater rate of error-detection which significantly improved recall, but lowered precision due to an increase in false positives or falsely flagged errors. However, in most cases, these false positives were not errors in preposition usage but merely cases where more than one preposition was possible. Furthermore, when BERT correctly identified an error, the model largely agreed with the Wikipedia editors, suggesting that BERT’s ability to detect misused prepositions is better than previously believed. To evaluate to what extent BERT’s false positives were grammatical suggestions, we plan to do a further crowd-sourcing study to test the grammaticality of BERT’s suggested sentence corrections against native speakers’ judgments.

Keywords: BERT, grammatical error correction, preposition error detection, prepositions

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Authors: Sanjin Kršćanski, Josip Brnić

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Virtual Crack Closure Technique (VCCT) is a method used for calculating stress intensity factor (SIF) of a cracked body that is easily implemented on top of basic finite element (FE) codes and as such can be applied on the various component geometries. It is a relatively simple method that does not require any special finite elements to be used and is usually used for calculating stress intensity factors at the crack tip for components made of brittle materials. This paper studies applicability and accuracy of VCCT applied on standard metal specimens containing trough thickness crack, subjected to an in-plane bending load. Finite element analyses were performed using regular 4-node, regular 8-node and a modified quarter-point 8-node 2D elements. Stress intensity factor was calculated from the FE model results for a given crack length, using data available from FE analysis and a custom programmed algorithm based on virtual crack closure technique. Influence of the finite element size on the accuracy of calculated SIF was also studied. The final part of this paper includes a comparison of calculated stress intensity factors with results obtained from analytical expressions found in available literature and in ASTM standard. Results calculated by this algorithm based on VCCT were found to be in good correlation with results obtained with mentioned analytical expressions.

Keywords: VCCT, stress intensity factor, finite element analysis, 2D finite elements, bending

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Authors: C. Gopi Jinimole, A. Harsha

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For detecting strokes, Computed Tomography (CT) scan is preferred for imaging the abnormalities or infarction in the brain. Because of the problems in the window settings used to evaluate brain CT images, they are very poor in the early stage infarction detection. This paper presents an automatic estimation method for the window settings of the CT images for proper contrast of the hyper infarction present in the brain. In the proposed work the window width is estimated automatically for each slice and the window centre is changed to a new value of 31HU, which is the average of the HU values of the grey matter and white matter in the brain. The automatic window width estimation is based on the average of median of statistical central moments. Thus with the new suggested window centre and estimated window width, the hyper infarction or post-stroke regions in CT brain images are properly detected. The proposed approach assists the radiologists in CT evaluation for early quantitative signs of delayed stroke, which leads to severe hemorrhage in the future can be prevented by providing timely medication to the patients.

Keywords: computed tomography (CT), hyper infarction or post stroke region, Hounsefield Unit (HU), window centre (WC), window width (WW)

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Authors: Perumal Siddhuraju, Arumugam Abirami, Gunasekaran Nagarani, Marimuthu Sangeethapriya

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Aqueous ethanol and aqueous acetone extracts of Moringa oleifera (outer pericarp of immature fruit and flower) and Sesbania grandiflora white variety (flower and leaf) were examined for radical scavenging capacities and antioxidant activities. Ethanol extract of S. grandiflora (flower and leaf) and acetone extract of M. oleifera (outer pericarp of immature fruit and flower) contained relatively higher levels of total dietary phenolics than the other extracts. The antioxidant potential of the extracts were assessed by employing different in vitro assays such as reducing power assay, DPPH˙, ABTS˙+ and ˙OH radical scavenging capacities, antihemolytic assay by hydrogen peroxide induced method and metal chelating ability. Though all the extracts exhibited dose dependent reducing power activity, acetone extract of all the samples were found to have more hydrogen donating ability in DPPH˙ (2.3% - 65.03%) and hydroxyl radical scavenging systems (21.6% - 77.4%) than the ethanol extracts. The potential of multiple antioxidant activity was evident as it possessed antihemolytic activity (43.2 % to 68.0 %) and metal ion chelating potency (45.16 - 104.26 mg EDTA/g sample). The result indicate that acetone extract of M. oleifera (OPIF and flower) and S. grandiflora (flower and leaf) endowed with polyphenols, could be utilized as natural antioxidants/nutraceuticals.

Keywords: antioxidant activity, Moringa oleifera, polyphenolics, Sesbania grandiflora, underutilized vegetables

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Authors: Peter Thissen

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In this work, we report about the influence of the chemical potential of water on the carbonation reaction of wollastonite (CaSiO3) as model surface of cement and concrete. Total energy calculations based on density functional theory (DFT) combined with kinetic barrier predictions based on nudge elastic band (NEB) method show that the exposure of the water-free wollastonite surface to CO2 results in a barrier-less carbonation. CO2 reacts with the surface oxygen and forms carbonate (CO32-) complexes together with a major reconstruction of the surface. The reaction comes to a standstill after one carbonate monolayer has been formed. In case one water monolayer is covering the wollastonite surface, the carbonation is no more barrier-less, yet ending in a localized monolayer. Covered with multilayers of water, the thermodynamic ground state of the wollastonite completely changes due to a metal-proton exchange reaction (MPER, also called early stage hydration) and Ca2+ ions are partially removed from solid phase into the H2O/wollastonite interface. Mobile Ca2+ react again with CO2 and form carbonate complexes, ending in a delocalized layer. By means of high resolution time-of-flight secondary-ion mass-spectroscopy images (ToF-SIMS), we confirm that hydration can lead to a partially delocalization of Ca2+ ions on wollastonite surfaces. Finally, we evaluate the impact of our model surface results by means of Low Energy Ion Scattering (LEIS) spectroscopy combined with careful discussion about the competing reactions of carbonation vs. hydration.

Keywords: Calcium-silicate, carbonation, hydration, metal-proton exchange reaction

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Authors: Gopi Kandaswamy, P. Balamuralidhar

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Fully autonomous small Unmanned Aerial Vehicles (UAVs) are increasingly being used in many commercial applications. Although a lot of research has been done to develop safe, reliable and durable UAVs, accidents due to electronic and structural failures are not uncommon and pose a huge safety risk to the UAV operators and the public. Hence there is a strong need for an automated health monitoring system for UAVs with a view to minimizing mission failures thereby increasing safety. This paper describes our approach to monitoring the electronic and structural components in a small UAV without the need for additional sensors to do the monitoring. Our system monitors data from four sources; sensors, navigation algorithms, control inputs from the operator and flight controller outputs. It then does statistical analysis on the data and applies a rule based engine to detect failures. This information can then be fed back into the UAV and a decision to continue or abort the mission can be taken automatically by the UAV and independent of the operator. Our system has been verified using data obtained from real flights over the past year from UAVs of various sizes that have been designed and deployed by us for various applications.

Keywords: fault detection, health monitoring, unmanned aerial vehicles, vibration analysis

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Authors: T. Patcharawit, C. Kansomket, N. Wongnaree, W. Kritsrikan, T. Yingnakorn, S. Khumkoa

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Recovery of pure copper and silver from end-of-life photovoltaic panels was investigated in this paper using an effective hybrid pyro-hydrometallurgical process. In the first step of waste treatment, solar panel waste was first dismantled to obtain a PV sheet to be cut and calcined at 500°C, to separate out PV ribbon from glass cullet, ash, and volatile while the silicon wafer containing silver finger was collected for recovery. In the second step of metal recovery, copper recovery from photovoltaic ribbon was via 1-3 M HCl leaching with SnCl₂ and H₂O₂ additions in order to remove the tin-lead coating on the ribbon. The leached copper band was cleaned and subsequently melted as an anode for the next step of electrorefining. Stainless steel was set as the cathode with CuSO₄ as an electrolyte, and at a potential of 0.2 V, high purity copper of 99.93% was obtained at 96.11% recovery after 24 hours. For silver recovery, the silicon wafer containing silver finger was leached using HNO₃ at 1-4 M in an ultrasonic bath. In the next step of precipitation, silver chloride was then obtained and subsequently reduced by sucrose and NaOH to give silver powder prior to oxy-acetylene melting to finally obtain pure silver metal. The integrated recycling process is considered to be economical, providing effective recovery of high purity metals such as copper and silver while other materials such as aluminum, copper wire, glass cullet can also be recovered to be reused commercially. Compounds such as PbCl₂ and SnO₂ obtained can also be recovered to enter the market.

Keywords: electrorefining, leaching, calcination, PV ribbon, silver finger, solar panel

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Authors: Haidy S. Ghali, Engy Serag, A. Samer Ezeldin

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Copper is an essential raw material used in the construction industry. During the year 2021 and the first half of 2022, the global market suffered from a significant fluctuation in copper raw material prices due to the aftermath of both the COVID-19 pandemic and the Russia-Ukraine war, which exposed its consumers to an unexpected financial risk. Thereto, this paper aims to develop two ANN-LSTM price prediction models, using Python, that can forecast the average monthly copper prices traded in the London Metal Exchange; the first model is a multivariate model that forecasts the copper price of the next 1-month and the second is a univariate model that predicts the copper prices of the upcoming three months. Historical data of average monthly London Metal Exchange copper prices are collected from January 2009 till July 2022, and potential external factors are identified and employed in the multivariate model. These factors lie under three main categories: energy prices and economic indicators of the three major exporting countries of copper, depending on the data availability. Before developing the LSTM models, the collected external parameters are analyzed with respect to the copper prices using correlation and multicollinearity tests in R software; then, the parameters are further screened to select the parameters that influence the copper prices. Then, the two LSTM models are developed, and the dataset is divided into training, validation, and testing sets. The results show that the performance of the 3-Month prediction model is better than the 1-Month prediction model, but still, both models can act as predicting tools for diverse economic situations.

Keywords: copper prices, prediction model, neural network, time series forecasting

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Authors: Simiao Ren, En Wei

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Drowning is a significant safety issue worldwide, and a robust computer vision-based alert system can easily prevent such tragedies in swimming pools. However, due to domain shift caused by the visual gap (potentially due to lighting, indoor scene change, pool floor color etc.) between the training swimming pool and the test swimming pool, the robustness of such algorithms has been questionable. The annotation cost for labeling each new swimming pool is too expensive for mass adoption of such a technique. To address this issue, we propose a domain-aware data augmentation pipeline based on Gaussian Poisson Generative Adversarial Network (GP-GAN). Combined with YOLOv8, we demonstrate that such a domain adaptation technique can significantly improve the model performance (from 0.24 mAP to 0.82 mAP) on new test scenes. As the augmentation method only require background imagery from the new domain (no annotation needed), we believe this is a promising, practical route for preventing swimming pool drowning.

Keywords: computer vision, deep learning, YOLOv8, detection, swimming pool, drowning, domain adaptation, generative adversarial network, GAN, GP-GAN

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Authors: Mojtaba Valinataj

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Reversible logic as a new favorable design domain can be used for various fields especially creating quantum computers because of its speed and intangible power consumption. However, its susceptibility to a variety of environmental effects may lead to yield the incorrect results. In this paper, because of the importance of multiplication operation in various computing systems, some novel reversible logic array multipliers are proposed with error detection capability by incorporating the parity-preserving gates. The new designs are presented for two main parts of array multipliers, partial product generation and multi-operand addition, by exploiting the new arrangements of existing gates, which results in two signed parity-preserving array multipliers. The experimental results reveal that the best proposed 4×4 multiplier in this paper reaches 12%, 24%, and 26% enhancements in the number of constant inputs, number of required gates, and quantum cost, respectively, compared to previous design. Moreover, the best proposed design is generalized for n×n multipliers with general formulations to estimate the main reversible logic criteria as the functions of the multiplier size.

Keywords: array multipliers, Baugh-Wooley method, error detection, parity-preserving gates, quantum computers, reversible logic

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Authors: Yves Mann Elate Lea Mbassi, Marie Solange Evehe Bebandoue, Wilfred Fon Mbacham

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Improving the catalytic performance of enzymes has been a long-standing theme of analytical biochemistry research. Induction of peroxidase activity by metals is a common reaction in higher plants. We thought that this increase in peroxidase activity may be due, on the one hand, to the stimulation of the gene expression of these enzymes but also to a modification of their chemical reactivity following the binding of some metal ions on their active site. We tested the effect of some metal salts (MgCl₂, MnCl₂, ZnCl₂, CaCl₂ and CuSO₄) on the activity and thermostability of peroxidase A6, a thermostable peroxidase that we discovered and purified in a previous study. The chromogenic substrate used was 3,3′,5,5′-tetramethylbenzidine. Of all the metals tested for their effect on A6, only magnesium and copper had a significant effect on the activity of the enzyme at room temperature. The Mann-Whitney test shows a slight inhibitory effect of activity by the magnesium salt (P = 0.043), while the activity of the enzyme is 5 times higher in the presence of the copper salt (P = 0.002). Moreover, the thermostability of peroxidase A6 is increased when calcium and copper salts are present. The activity in the presence of CaCl₂ is 8 times higher than the residual activity of the enzyme alone after incubation at 80°C for 10 min and 35 times higher in the presence of CuSO4 under the same conditions. In addition, manganese and zinc salts slightly reduce the thermostability of the enzyme. The activity and structural stability of peroxidase A6 can clearly be activated by Cu₂+, which therefore enhance the oxidation of 3,3′,5,5′-tetramethylbenzidine, which was used in this study as a chromogenic substrate. Ca₂+ likely has a more stabilizing function for the catalytic site.

Keywords: peroxidase activity, copper ions, calcium ions, thermostability

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Authors: Alireza Karimi

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Artificial Intelligence (AI) is reshaping various industries, and accounting is no exception. With the ability to process vast amounts of data quickly and accurately, AI is revolutionizing how financial professionals manage, analyze, and report financial information. In this article, we will explore the diverse applications of AI in accounting and its profound impact on the field. Automation of Repetitive Tasks: One of the most significant contributions of AI in accounting is automating repetitive tasks. AI-powered software can handle data entry, invoice processing, and reconciliation with minimal human intervention. This not only saves time but also reduces the risk of errors, leading to more accurate financial records. Pattern Recognition and Anomaly Detection: AI algorithms excel at pattern recognition. In accounting, this capability is leveraged to identify unusual patterns in financial data that might indicate fraud or errors. AI can swiftly detect discrepancies, enabling auditors and accountants to focus on resolving issues rather than hunting for them. Real-Time Financial Insights: AI-driven tools, using natural language processing and computer vision, can process documents faster than ever. This enables organizations to have real-time insights into their financial status, empowering decision-makers with up-to-date information for strategic planning. Fraud Detection and Prevention: AI is a powerful tool in the fight against financial fraud. It can analyze vast transaction datasets, flagging suspicious activities and reducing the likelihood of financial misconduct going unnoticed. This proactive approach safeguards a company's financial integrity. Enhanced Data Analysis and Forecasting: Machine learning, a subset of AI, is used for data analysis and forecasting. By examining historical financial data, AI models can provide forecasts and insights, aiding businesses in making informed financial decisions and optimizing their financial strategies. Artificial Intelligence is fundamentally transforming the accounting profession. From automating mundane tasks to enhancing data analysis and fraud detection, AI is making financial processes more efficient, accurate, and insightful. As AI continues to evolve, its role in accounting will only become more significant, offering accountants and finance professionals powerful tools to navigate the complexities of modern finance. Embracing AI in accounting is not just a trend; it's a necessity for staying competitive in the evolving financial landscape.

Keywords: artificial intelligence, accounting automation, financial analysis, fraud detection, machine learning in finance

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Authors: Marta Skwarecka, Patrycja Bloch, Rafal Walkusz, Oliwia Urbanowicz, Grzegorz Zielinski, Sabina Zoledowska, Dawid Nidzworski

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Upper respiratory tract infections are one of the most common reasons for visiting a general doctor. Streptococci are the most common bacterial etiological factors in these infections. There are many different types of Streptococci and infections vary in severity from mild throat infections to pneumonia. For example, S. pyogenes mainly contributes to acute pharyngitis, palatine tonsils and scarlet fever, whereas S. Streptococcus pneumoniae is responsible for several invasive diseases like sepsis, meningitis or pneumonia with high mortality and dangerous complications. There are only a few diagnostic tests designed for detection Streptococci from the infected throat of patients. However, they are mostly based on lateral flow techniques, and they are not used as a standard due to their low sensitivity. The diagnostic standard is to culture patients throat swab on semi selective media in order to multiply pure etiological agent of infection and subsequently to perform antibiogram, which takes several days from the patients visit in the clinic. Therefore, the aim of our studies is to develop and implement to the market a Point of Care device for the rapid identification of Streptococcus pyogenes and Streptococcus pneumoniae with simultaneous identification of antibiotic resistance genes. In the course of our research, we successfully selected genes for to-species identification of Streptococci and genes encoding antibiotic resistance proteins. We have developed a reaction to amplify these genes, which allows detecting the presence of S. pyogenes or S. pneumoniae followed by testing their resistance to erythromycin, chloramphenicol and tetracycline. What is more, the detection of β-lactamase-encoding genes that could protect Streptococci against antibiotics from the ampicillin group, which are widely used in the treatment of this type of infection is also developed. The test is carried out directly from the patients' swab, and the results are available after 20 to 30 minutes after sample subjection, which could be performed during the medical visit.

Keywords: antibiotic resistance, Streptococci, respiratory infections, diagnostic test

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Authors: Varsha Gupta, Priya Datta, Gursimran Mohi, Jagdish Chander

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Fluoroquinolones form the mainstay of therapy for the treatment of infections due to Salmonella enterica subsp. enterica. There is a complex interplay between several resistance mechanisms for quinolones and various fluoroquinolones discs, giving varying results, making detection and interpretation of fluoroquinolone resistance difficult. For detection of fluoroquinolone resistance in Salmonella ssp., we compared the use of pefloxacin and nalidixic acid discs as surrogate marker. Using MIC for ciprofloxacin as the gold standard, 43.5% of strains showed MIC as ≥1 μg/ml and were thus resistant to fluoroquinoloes. Based on the performance of nalidixic acid and pefloxacin discs as surrogate marker for ciprofloxacin resistance, both the discs could correctly detect all the resistant phenotypes; however, use of nalidixic acid disc showed false resistance in the majority of the sensitive phenotypes. We have also tested newer antimicrobial agents like cefixime, imipenem, tigecycline and azithromycin against Salmonella spp. Moreover, there was a comeback of susceptibility to older antimicrobials like ampicillin, chloramphenicol, and cotrimoxazole. We can also use cefixime, imipenem, tigecycline and azithromycin in the treatment of multidrug resistant S. typhi due to their high susceptibility.

Keywords: salmonella, pefloxacin, surrogate marker, chloramphenicol

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Authors: Mouhamadou Diop, Mohamed I. Hassan

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Molten metallic flow in metallurgical plant is highly turbulent and presents a complex coupling with heat transfer, phase transfer, chemical reaction, momentum transport, etc. Molten silicon flow has significant effect in directional solidification of multicrystalline silicon by affecting the temperature field and the emerging crystallization interface as well as the transport of species and impurities during casting process. Owing to the complexity and limits of reliable measuring techniques, computational models of fluid flow are useful tools to study and quantify these problems. The overall objective of this study is to investigate the potential of a traveling magnetic field for an efficient operating control of the molten metal flow. A multidimensional numerical model will be developed for the calculations of Lorentz force, molten metal flow, and the related phenomenon. The numerical model is implemented in a laboratory-scale silicon crystallization furnace. This study presents the potential of traveling magnetic field approach for an efficient operating control of the molten flow. A numerical model will be used to study the effects of magnetic force applied on the molten flow, and their interdependencies. In this paper, coupled and decoupled, steady and unsteady models of molten flow and crystallization interface will be compared. This study will allow us to retrieve the optimal traveling magnetic field parameter range for crystallization furnaces and the optimal numerical simulations strategy for industrial application.

Keywords: multidimensional, numerical simulation, solidification, multicrystalline, traveling magnetic field

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: Davidson E. Egirani, Nanfe R. Poyi, Napoleon Wessey

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This paper would report on the effect of solid waste on water resource quality in the Gbarain catchment of the Niger Delta Region of Nigeria. The Gbarain catchment presently hosts two waste-dump sites located along the flanks of a seasonal flow stream and perennially waterlogged terrain. The anthropogenic activity has significantly affected the quality of surface and groundwater in the Gbarain catchment. These wastes have made the water resource environment toxic leading to the poisoning of aquatic life. The contaminated water resources could lead to serious environmental and human health challenges such as low agricultural yields to loss of vital human organs. The contamination is via geological processes such as seepage and direct infiltration of contaminants into watercourses. The results obtained from field and experimental investigations followed by modeling, and graphical interpretation indicate heavy metal load and fecal pollution in some of the groundwater. The metal load, Escherichia coli, and total coliforms counts exceed the international and regional recommended limits. The contaminate values include Lead (> 0.01 mg/L), Mercury (> 0.006 mg/L), Manganese (> 0.4 mg/L and Escherichia coli (> 0 per 100ml) of the samples. Land use planning, enactment, and implementation of environmental laws are necessary for this region, for effective surface water and groundwater resource management.

Keywords: aquatic life, solid waste, environmental health, human health, waste-dump site, water-resource environment

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Authors: Gholba Niranjan Dilip, Anil Kumar

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Conduct of accurate reliable mapping of oil palm plantations and census of individual palm trees is a huge challenge. This study addresses this challenge and developed an optimized solution implemented deep learning techniques on remote sensing data. The oil palm is a very important tropical crop. To improve its productivity and land management, it is imperative to have accurate census over large areas. Since, manual census is costly and prone to approximations, a methodology for automated census using panchromatic images from Cartosat-2, SkySat and World View-3 satellites is demonstrated. It is selected two different study sites in Indonesia. The customized set of training data and ground-truth data are created for this study from Cartosat-2 images. The pre-trained model of Single Shot MultiBox Detector (SSD) Lite MobileNet V2 Convolutional Neural Network (CNN) from the TensorFlow Object Detection API is subjected to transfer learning on this customized dataset. The SSD model is able to generate the bounding boxes for each oil palm and also do the counting of palms with good accuracy on the panchromatic images. The detection yielded an F-Score of 83.16 % on seven different images. The detections are buffered and dissolved to generate polygons demarcating the boundaries of the oil palm plantations. This provided the area under the plantations and also gave maps of their location, thereby completing the automated census, with a fairly high accuracy (≈100%). The trained CNN was found competent enough to detect oil palm crowns from images obtained from multiple satellite sensors and of varying temporal vintage. It helped to estimate the increase in oil palm plantations from 2014 to 2021 in the study area. The study proved that high-resolution panchromatic satellite image can successfully be used to undertake census of oil palm plantations using CNNs.

Keywords: object detection, oil palm tree census, panchromatic images, single shot multibox detector

Procedia PDF Downloads 157

Authors: Chen-En Lin, Lih-Rou Rau, Jiunn-Woei Liaw, Shiao-Wen Tsai

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The unique optical properties of plasmon resonance metallic particles have attracted considerable applications in the fields of physics, chemistry and biology. Metal-Enhanced Fluorescence (MEF) effect is one of the useful applications. MEF effect stated that fluorescence intensity can be quenched or be enhanced depending on the distance between fluorophores and the metal nanoparticles. Silver nanoparticles have used widely in antibacterial studies. However, the major limitation for silver nanoparticles (AgNPs) in biomedical application is well-known cytotoxicity on cells. There were numerous literatures have been devoted to overcome the disadvantage. The aim of the study is to evaluate the cytotoxicity and biocompatibility of silica coated AgNPs against NIH-3T3 cells. The results were shown that NIH-3T3 cells started to detach, shrink, become rounded and finally be irregular in shape after 24 h of exposure at 10 µg/ml AgNPs. Besides, compared with untreated cells, the cell viability significantly decreased to 60% and 40% which were exposed to 10 µg/ml and 20 µg/ml AgNPs respectively. The result was consistent with previously reported findings that AgNPs induced cytotoxicity was concentration dependent. However, the morphology and cell viability of cells appeared similar to the control group when exposed to 20 µg/ml of silica coated AgNPs. We further utilized the dark-field hyperspectral imaging system to analysis the optical properties of the intracellular nanoparticles. The image displayed that the red shift of the surface plasmonic resonances band of the enclosed AgNPs further confirms the agglomerate of the AgNPs rather than their distribution in cytoplasm. In conclusion, the study demonstrated the silica coated of AgNPs showed well biocompatibility and significant lower cytotoxicity compared with bare AgNPs.

Keywords: silver nanoparticles, silica, cell viability, morphology

Procedia PDF Downloads 390

Authors: Syed Beenish Rufai, Sarman Singh

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Background: Performance of Genotype MTBDRsl (Hain Life science GmbH Germany) for detection of mutations associated with second-line drug resistance is well known. However, less evidence regarding the association of mutations and genetic background of strains is known which, in the future, is essential for clinical management of anti-tuberculosis drugs in those settings where the probability of particular genotype is predominant. Material and Methods: During this retrospective study, a total of 259 MDR-TB isolates obtained from pulmonary TB patients were tested for second-line drug susceptibility testing (DST) using Genotype MTBDRsl VER 1.0 and compared with BACTEC MGIT-960 as a reference standard. All isolates were further characterized using spoligotyping. The spoligo patterns obtained were compared and analyzed using SITVIT_WEB. Results: Of total 259 MDR-TB isolates which were screened for second-line DST by Genotype MTBDRsl, mutations were found to be associated with gyrA, rrs and emb genes in 82 (31.6%), 2 (0.8%) and 90 (34.7%) isolates respectively. 16 (6.1%) isolates detected mutations associated with both FQ as well as to AG/CP drugs (XDR-TB). No mutations were detected in 159 (61.4%) isolates for corresponding gyrA and rrs genes. Genotype MTBDRsl showed a concordance of 96.4% for detection of sensitive isolates in comparison with second-line DST by BACTEC MGIT-960 and 94.1%, 93.5%, 60.5% and 50% for detection of XDR-TB, FQ, EMB, and AMK/CAP respectively. D94G was the most prevalent mutation found among (38 (46.4%)) OFXR isolates (37 FQ mono-resistant and 1 XDR-TB) followed by A90V (23 (28.1%)) (17 FQ mono-resistant and 6 XDR-TB). Among AG/CP resistant isolates A1401G was the most frequent mutation observed among (11 (61.1%)) isolates (2 AG/CP mono-resistant isolates and 9 XDR-TB isolates) followed by WT+A1401G (6 (33.3%)) and G1484T (1 (5.5%)) respectively. On spoligotyping analysis, Beijing strain (46%) was found to be the most predominant strain among pre-XDR and XDR TB isolates followed by CAS (30%), X (6%), Unique (5%), EAI and T each of 4%, Manu (3%) and Ural (2%) respectively. Beijing strain was found to be strongly associated with D94G (47.3%) and A90V mutations by (47.3%) and 34.8% followed by CAS strain by (31.6%) and 30.4% respectively. However, among AG/CP resistant isolates, only Beijing strain was found to be strongly associated with A1401G and WT+A1401G mutations by 54.5% and 50% respectively. Conclusion: Beijing strain was found to be strongly associated with the most prevalent mutations among pre-XDR and XDR TB isolates. Acknowledgments: Study was supported with Grant by All India Institute of Medical Sciences, New Delhi reference No. P-2012/12452.

Keywords: tuberculosis, line probe assay, XDR TB, drug susceptibility

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Authors: G. Purushotham, Joel Hemanth

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An investigation has been carried out to fabricate and evaluate the strength and soundness of chilled composites consisting of nickel matrix and fused silica particles (size 40–150 μm) in the matrix. The dispersoid added ranged from 3 to 12 wt. % in steps of 3%. The resulting composites cast in moulds containing metallic and non-metallic chill blocks (MS, SiC, and Cu) were tested for their microstructure and mechanical properties. The main objective of the present research is to obtain fine grain Ni/SiO2 chilled sound composite having very good mechanical properties. Results of the investigation reveal the following: (1) Strength of the composite developed is highly dependent on the location of the casting from where the test specimens are taken and also on the dispersoid content of the composite. (2) Chill thickness and chill material, however, does significantly affect the strength and soundness of the composite. (3) Soundness of the composite developed is highly dependent on the chilling rate as well as the dispersoid content. An introduction of chilling and increase in the dispersoid content of the material both result in an increase in the ultimate tensile strength (UTS) of the material. The temperature gradient developed during solidification and volumetric heat capacity (VHC) of the chill used is the important parameters controlling the soundness of the composite. (4) Thermal properties of the end chills are used to determine the magnitude of the temperature gradient developed along the length of the casting solidifying under the influence of chills.

Keywords: metal matrix composite, mechanical properties, corrosion behavior, nickel alloy, fused silica, chills

Procedia PDF Downloads 396

Authors: Manal Kamel, Sara Maher

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Development of sensitive non-invasive tests for detection of SARS-CoV-2 antigens is imperative to manage the extent of infection throughout the population, yet, it is still challenging. Here, we designed and optimized a sandwich enzyme-linked immunosorbent assay (ELISA) for SARS-CoV-2 S1 antigen detection in saliva. Both saliva samples and nasopharyngeal swapswere collected from 170 PCR-confirmed positive and negative cases. Gold nanoparticles (AuNPs) were conjugated with S1protein receptor binding domain (RBD) nanobodies. Recombinant S1 monoclonal antibodies (S1mAb) as primery antibody and gold conjugated nanobodies as secondary antibody were employed in sandwich ELISA. Our developed system were optimized to achieve 87.5 % sensitivity and 100% specificity for saliva samples compared to 89 % and 100% for nasopharyngeal swaps, respectively. This means that saliva could be a suitable replacement for nasopharyngeal swaps No cross reaction was detected with other corona virus antigens. These results revealed that our developed ELISAcould be establishedas a new, reliable, sensitive, and non-invasive test for diagnosis of SARS-CoV-2 infection, using the easily collected saliva samples.

Keywords: COVID 19, diagnosis, ELISA, nanobodies

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