Search results for: constant false alarm

Authors: Liliia N. Butymova, Vladimir Ya Modorskii

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To ensure the gas transmittal GCU's efficient operation, leakages through the labyrinth packings (LP) should be minimized. Leakages can be minimized by decreasing the LP gap, which in turn depends on thermal processes and possible rotor vibrations and is designed to ensure absence of mechanical contact. Vibration mitigation allows to minimize the LP gap. It is advantageous to research influence of processes in the dynamic gas-structure system on LP vibrations. This paper considers influence of rotor vibrations on LP gas dynamics and influence of the latter on the rotor structure within the FSI unidirectional dynamical coupled problem. Dependences of nonstationary parameters of gas-dynamic process in LP on rotor vibrations under various gas speeds and pressures, shaft rotation speeds and vibration amplitudes, and working medium features were studied. The programmed multi-processor ANSYS CFX was chosen as a numerical computation tool. The problem was solved using PNRPU high-capacity computer complex. Deformed shaft vibrations are replaced with an unyielding profile that moves in the fixed annulus "up-and-down" according to set harmonic rule. This solves a nonstationary gas-dynamic problem and determines time dependence of total gas-dynamic force value influencing the shaft. Pressure increase from 0.1 to 10 MPa causes growth of gas-dynamic force oscillation amplitude and frequency. The phase shift angle between gas-dynamic force oscillations and those of shaft displacement decreases from 3π/4 to π/2. Damping constant has maximum value under 1 MPa pressure in the gap. Increase of shaft oscillation frequency from 50 to 150 Hz under P=10 MPa causes growth of gas-dynamic force oscillation amplitude. Damping constant has maximum value at 50 Hz equaling 1.012. Increase of shaft vibration amplitude from 20 to 80 µm under P=10 MPa causes the rise of gas-dynamic force amplitude up to 20 times. Damping constant increases from 0.092 to 0.251. Calculations for various working substances (methane, perfect gas, air at 25 ˚С) prove the minimum gas-dynamic force persistent oscillating amplitude under P=0.1 MPa being observed in methane, and maximum in the air. Frequency remains almost unchanged and the phase shift in the air changes from 3π/4 to π/2. Calculations for various working substances (methane, perfect gas, air at 25 ˚С) prove the maximum gas-dynamic force oscillating amplitude under P=10 MPa being observed in methane, and minimum in the air. Air demonstrates surging. Increase of leakage speed from 0 to 20 m/s through LP under P=0.1 MPa causes the gas-dynamic force oscillating amplitude to decrease by 3 orders and oscillation frequency and the phase shift to increase 2 times and stabilize. Increase of leakage speed from 0 to 20 m/s in LP under P=1 MPa causes gas-dynamic force oscillating amplitude to decrease by almost 4 orders. The phase shift angle increases from π/72 to π/2. Oscillations become persistent. Flow rate proved to influence greatly on pressure oscillations amplitude and a phase shift angle. Work medium influence depends on operation conditions. At pressure growth, vibrations are mostly affected in methane (of working substances list considered), and at pressure decrease, in the air at 25 ˚С.

Keywords: aeroelasticity, labyrinth packings, oscillation phase shift, vibration

Procedia PDF Downloads 296

Authors: Bouchou Aïssa, Mohamed Akbi

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Contact materials used for electrical breakers are often made with silver alloys. Mechanical and thermo dynamical properties as well as electron emission of such complicated alloys present a lack of reliable and accurate experimental data. This paper deals mainly with electron work function (EWF) measurements about silver-metal oxide (Ag-MeO) electrical contacts (Ag-ZnO (92/8), before and after surface heat treatments at 296 K  813 K, under UHV conditions (residual gas pressure of 1.4 x 10-7 mbar). The electron work function (EWF) of silver zinc oxide materials was measured photoelectrically, using both Fowler’s method of isothermal curves and linearized Fowler plots. In this paper, we present the development of a method for measuring photoelectric work function of contact materials. Also reported in this manuscript are the results of experimental work whose purpose has been the buildup of a reliable photoelectric system and associated monochromatic ultra-violet radiations source, and the photoelectric measurement of the electron work functions (EWF) of contact materials. In order to study the influence of annealing temperature on the EWF, a vacuum furnace was used for heating the metallic samples up to 800 K. The EWF of the silver – zinc oxide materials were investigated to study the influence of annealing temperature on the EWF. In the present study, the photoelectric measurements about Ag-ZnO(92/8) contacts have shown a linear decrease of the EWF with increasing temperature, i.e. the temperature coefficient is constant and negative: for the first annealing # 1, in the temperature range [299 K  823 K]. On the contrary, a linear increase was observed with increasing temperature (i.e. , being constant and positive), for the next annealing # 2, in the temperature range [296 K  813 K]. The EWFs obtained for silver-zinc oxide Ag-ZnO(92/8) show an obvious dependence on the annealing temperature which is strongly associated with the evolution of the arrangement on ZnO nano particles on the Ag-ZnO contact surface as well as surface charge distribution.

Keywords: Photoemission, Electron work function, Fowler methods, Ag-ZnO contact materials, Vacuum heat treatment

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Authors: Himanshu Singh, Rishi Kant, Shantanu Bhattacharya

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This paper adopts a top-down approach for mathematical modeling to predict the size reduction from micro to nano-scale through persistent etching. The process is simulated using a finite difference approach. Previously, various researchers have simulated the etching process for 1-D and 2-D substrates. It consists of two processes: 1) Convection-Diffusion in the etchant domain; 2) Chemical reaction at the surface of the particle. Since the process requires analysis along moving boundary, partial differential equations involved cannot be solved using conventional methods. In 1-D, this problem is very similar to Stefan's problem of moving ice-water boundary. A fixed grid method using finite volume method is very popular for modelling of etching on a one and two dimensional substrate. Other popular approaches include moving grid method and level set method. In this method, finite difference method was used to discretize the spherical diffusion equation. Due to symmetrical distribution of etchant, the angular terms in the equation can be neglected. Concentration is assumed to be constant at the outer boundary. At the particle boundary, the concentration of the etchant is assumed to be zero since the rate of reaction is much faster than rate of diffusion. The rate of reaction is proportional to the velocity of the moving boundary of the particle. Modelling of the above reaction was carried out using Matlab. The initial particle size was taken to be 50 microns. The density, molecular weight and diffusion coefficient of the substrate were taken as 2.1 gm/cm3, 60 and 10-5 cm2/s respectively. The etch-rate was found to decline initially and it gradually became constant at 0.02µ/s (1.2µ/min). The concentration profile was plotted along with space at different time intervals. Initially, a sudden drop is observed at the particle boundary due to high-etch rate. This change becomes more gradual with time due to declination of etch rate.

Keywords: particle size reduction, micromixer, FDM modelling, wet etching

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Authors: Jin-Uk Park

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Recently, the semiconductor industry is rapidly demanding complicated structures and mass production. From the point of view of mass production, the ETCH industry is concentrating on maintaining the ER (Etch rate) of the wafer edge constant regardless of changes over time. In this study, two major thermal factors affecting process were identified and controlled. First, the filler of the thermal pad was studied. Second, the significant difference of handling the thermal pad during PM was studied.

Keywords: etcher, thermal pad, wet cleaning, thermal conductivity

Procedia PDF Downloads 195

Authors: In-Hwan Baek

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Valsartan is a potent and highly selective antagonist of the angiotensin II type 1 receptor, and is widely used for the treatment of hypertension. The aim of this study was to investigate the pharmacokinetic properties of the valsartan in dogs following oral administration of a single dose using quantitative modeling approaches. Forty beagle dogs were randomly divided into two group. Group A (n=20) was administered a single oral dose of valsartan 80 mg (Diovan® 80 mg), and group B (n=20) was administered a single oral dose of valsartan 160 mg (Diovan® 160 mg) in the morning after an overnight fast. Blood samples were collected into heparinized tubes before and at 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 12 and 24 h following oral administration. The plasma concentrations of the valsartan were determined using LC-MS/MS. Non-compartmental pharmacokinetic analyses were performed using WinNonlin Standard Edition software, and modeling approaches were performed using maximum-likelihood estimation via the expectation maximization (MLEM) algorithm with sampling using ADAPT 5 software. After a single dose of valsartan 80 mg, the mean value of maximum concentration (Cmax) was 2.68 ± 1.17 μg/mL at 1.83 ± 1.27 h. The area under the plasma concentration-versus-time curve from time zero to the last measurable concentration (AUC24h) value was 13.21 ± 6.88 μg·h/mL. After dosing with valsartan 160 mg, the mean Cmax was 4.13 ± 1.49 μg/mL at 1.80 ± 1.53 h, the AUC24h was 26.02 ± 12.07 μg·h/mL. The Cmax and AUC values increased in proportion to the increment in valsartan dose, while the pharmacokinetic parameters of elimination rate constant, half-life, apparent of total clearance, and apparent of volume of distribution were not significantly different between the doses. Valsartan pharmacokinetic analysis fits a one-compartment model with first-order absorption and elimination following a single dose of valsartan 80 mg and 160 mg. In addition, high inter-individual variability was identified in the absorption rate constant. In conclusion, valsartan displays the dose-dependent pharmacokinetics in dogs, and Subsequent quantitative modeling approaches provided detailed pharmacokinetic information of valsartan. The current findings provide useful information in dogs that will aid future development of improved formulations or fixed-dose combinations.

Keywords: dose-dependent, modeling, pharmacokinetics, valsartan

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Authors: Sinan Yapici, Hayrettin Eroglu

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The discharge of the aqueous radionuclides wastes used for the diagnoses of diseases and treatments of patients in nuclear medicine can cause fatal health problems when the radionuclides and its stable daughter component mix with underground water. Tl-201, which is one of the radionuclides commonly used in the nuclear medicine, is a toxic substance and is converted to its stable daughter component Hg-201, which is also a poisonous heavy metal: Tl201 → Hg201 + Gamma Ray [135-167 Kev (12%)] + X Ray [69-83 Kev (88%)]; t1/2 = 73,1 h. The purpose of the present work was to remove Tl-201 radionuclides from aqueous solution by biosorption on the solid bio wastes of food and cosmetic industry as bio sorbents of prina from an olive oil plant, rose residue from a rose oil plant and tea residue from a tea plant, and to make a comparison of the biosorption efficiencies. The effects of the biosorption temperature, initial pH of the aqueous solution, bio sorbent dose, particle size and stirring speed on the biosorption yield were investigated in a batch process. It was observed that the biosorption is a rapid process with an equilibrium time less than 10 minutes for all the bio sorbents. The efficiencies were found to be close to each other and measured maximum efficiencies were 93,30 percent for rose residue, 94,1 for prina and 98,4 for tea residue. In a temperature range of 283 and 313 K, the adsorption decreased with increasing temperature almost in a similar way. In a pH range of 2-10, increasing pH enhanced biosorption efficiency up to pH=7 and then the efficiency remained constant in a similar path for all the biosorbents. Increasing stirring speed from 360 to 720 rpm enhanced slightly the biosorption efficiency almost at the same ratio for all bio sorbents. Increasing particle size decreased the efficiency for all biosorbent; however the most negatively effected biosorbent was prina with a decrease in biosorption efficiency from about 84 percent to 40 with an increase in the nominal particle size 0,181 mm to 1,05 while the least effected one, tea residue, went down from about 97 percent to 87,5. The biosorption efficiencies of all the bio sorbents increased with increasing biosorbent dose in the range of 1,5 to 15,0 g/L in a similar manner. The fit of the experimental results to the adsorption isotherms proved that the biosorption process for all the bio sorbents can be represented best by Freundlich model. The kinetic analysis showed that all the processes fit very well to pseudo second order rate model. The thermodynamics calculations gave ∆G values between -8636 J mol-1 and -5378 for tea residue, -5313 and -3343 for rose residue, and -5701 and -3642 for prina with a ∆H values of -39516 J mol-1, -23660 and -26190, and ∆S values of -108.8 J mol-1 K-1, -64,0, -72,0 respectively, showing spontaneous and exothermic character of the processes. An empirical biosorption model in the following form was derived for each biosorbent as function of the parameters and time, taking into account the form of kinetic model, with regression coefficients over 0.9990 where At is biosorbtion efficiency at any time and Ae is the equilibrium efficiency, t is adsorption period as s, ko a constant, pH the initial acidity of biosorption medium, w the stirring speed as s-1, S the biosorbent dose as g L-1, D the particle size as m, and a, b, c, and e are the powers of the parameters, respectively, E a constant containing activation energy and T the temperature as K.

Keywords: radiation, diosorption, thallium, empirical modelling

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Authors: Jonathan Heng, Yoong Cheah Huei

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A strategic model that does not trigger any false alarms to detect anomalies in Secure Water Treatment (SWaT) test bed is presented. This model uses machine learning invariants formulated from streamlining the general form of Auto-Regressive models with eXogenous input. A creative generalized CUSUM algorithm to integrate the invariants and the detection strategy technique is successfully developed and tested in the SWaT Programmable Logic Controllers (PLCs). Three steps to fine-tune parameters, b and τ in the generalized algorithm are stated and an example used to demonstrate the tuning process is discussed. This approach can swiftly and effectively detect various scopes of cyber-attacks such as multiple points single stage and multiple points multiple stages in SWaT. This technique can be applied in water treatment plants and other cyber physical systems like power and gas plants too.

Keywords: machine learning invariants, generalized CUSUM algorithm with invariants and detection strategy, scope of cyber attacks, strategic model, tuning parameters

Procedia PDF Downloads 181

Authors: Wanyi Zhu, Xia Ming, Huafeng Wang, Junda Chen, Lu Liu, Jiangwei Jiang, Guohua Liu

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Anomaly detection is a first and imperative step needed to respond to unexpected problems and to assure high performance and security in large data center management. This paper presents an online anomaly detection system through an innovative approach of ensemble machine learning and adaptive differentiation algorithms, and applies them to performance data collected from a continuous monitoring system for multi-tier web applications running in Alibaba data centers. We evaluate the effectiveness and efficiency of this algorithm with production traffic data and compare with the traditional anomaly detection approaches such as a static threshold and other deviation-based detection techniques. The experiment results show that our algorithm correctly identifies the unexpected performance variances of any running application, with an acceptable false positive rate. This proposed approach has already been deployed in real-time production environments to enhance the efficiency and stability in daily data center operations.

Keywords: Alibaba data centers, anomaly detection, big data computation, dynamic ensemble learning

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Authors: Ahmed S. Afifi, Ahmed Magdy

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Satellite monitoring is an essential tool to study, understand, and map large-scale environmental changes that affect humans, climate, and biodiversity. The Sentinel-1 Synthetic Aperture Radar (SAR) instrument provides a high collection of data in all-weather, short revisit time, and high spatial resolution that can be used effectively in flood management. Floods occur when an overflow of water submerges dry land that requires to be distinguished from flooded areas. In this study, we use global flood mapper (GFM), a new google earth engine application that allows users to quickly map floods using Sentinel-1 SAR. The GFM enables the users to adjust manually the flood map parameters, e.g., the threshold for Z-value for VV and VH bands and the elevation and slope mask threshold. The composite R:G:B image results by coupling the bands of Sentinel-1 (VH:VV:VH) reduces false classification to a large extent compared to using one separate band (e.g., VH polarization band). The flood mapping algorithm in the GFM and the Otsu thresholding are compared with Sentinel-2 optical data. And the results show that the GFM algorithm can overcome the misclassification of a flooded area in An Giang, Vietnam.

Keywords: SAR backscattering, Sentinel-1, flood mapping, disaster

Procedia PDF Downloads 107

Authors: Naser Kazemi Eilaki, Ilona Heldal, Carolyn Ahmer, Bjarne Christian Hagen

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Elderly people and people with disabilities are recognized as at-risk groups when it comes to egress and travel from hazard zone to a safe place. One's disability can negatively influence her or his escape time, and this becomes even more important when people from this target group live alone. While earlier studies have frequently addressed quantitative measurements regarding at-risk groups' physical characteristics (e.g., their speed of travel), this paper considers the influence of at-risk groups’ characteristics on their decision and determining better escape routes. Most of evacuation models are based on mapping people's movement and their behaviour to summation times for common activity types on a timeline. Usually, timeline models estimate required safe egress time (RSET) as a sum of four timespans: detection, alarm, premovement, and movement time, and compare this with the available safe egress time (ASET) to determine what is influencing the margin of safety.This paper presents a cross-sectional study for identifying the most critical items on RSET and people's decision-making and with possibilities to include safety knowledge regarding people with physical or cognitive functional impairments. The result will contribute to increased knowledge on considering at-risk groups and disabilities for designing and developing safe escape routes. The expected results can be an asset to predict the probabilistic behavioural pattern of at-risk groups and necessary components for defining a framework for understanding how stakeholders can consider various disabilities when determining the margin of safety for a safe escape route.

Keywords: fire safety, evacuation, decision-making, at-risk groups

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Authors: Donglan Zha, Jiansong Si

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Because of its flexibility, CES attracts much interest in economic growth and programming models, and the macroeconomics or micro-macro models. This paper focuses on the development, estimating methods of CES production function considering energy as an input. We leave for future research work of relaxing the assumption of constant returns to scale, the introduction of potential input factors, and the generalization method of the optimal nested form of multi-factor production functions.

Keywords: bias of technical change, CES production function, elasticity of substitution, energy input

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Authors: Lalita, Niladri Sarkar, Subhasis Ghosh

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Since the discovery of high temperature superconductivity (HTSC) in cuprates, several aspects of this phenomena have fascinated physics community. The most debated one is the linear temperature dependence of normal state resistivity over wide range of temperature in violation of with Fermi liquid theory. The linear-in-T resistivity (LITR) is the indication of strongly correlated metallic, known as “strange metal”, attributed to non Fermi liquid theory (NFL). The proximity of superconductivity to LITR suggests that there may be underlying common origin. The LITR has been shown to be due to unknown dissipative phenomena, restricted by quantum mechanics and commonly known as ‘‘Planckian dissipation” , the term first coined by Zaanen and the associated inelastic scattering time τ and given by 1/τ=αkBT/ℏ, where ℏ, kB and α are reduced Planck’s constant, Boltzmann constant and a dimensionless constant of order of unity, respectively. Since the first report, experimental support for α ~ 1 is appearing in literature. There are several striking issues which remain to be resolved if we desire to find out or at least get a clue towards microscopic origin of maximal dissipation in cuprates. (i) Universality of α ~ 1, recently some doubts have been raised in some cases. (ii) So far, Planckian dissipation has been demonstrated in overdoped Cuprates, but if the proximity to quantum criticality is important, then Planckian dissipation should be observed in optimally doped and marginally underdoped cuprates. The link between Planckian dissipation and quantum criticality still remains an open problem. (iii) Validity of Planckian dissipation in all cuprates is an important issue. Here, we report reversible change in the superconducting behavior of high temperature superconductor Bi2Sr2Ca2Cu3O10+δ (Bi-2223) under dynamic doping induced by photo-excitation. Two doped Bi-223 samples, which are x = 0.16 (optimal-doped), x = 0.145 (marginal-doped) have been used for this investigation. It is realized that steady state photo-excitation converts magnetic Cu2+ ions to nonmagnetic Cu1+ ions which reduces superconducting transition temperature (Tc) by killing superfluid density. In Bi-2223, one would expect the maximum of suppression of Tc should be at charge transfer gap. We have observed suppression of Tc starts at 2eV, which is the charge transfer gap in Bi-2223. We attribute this transition due to Cu-3d9(Cu2+) to Cu-3d10(Cu+), known as d9 − d10 L transition, photoexcitation makes some Cu ions in CuO2 planes as spinless non-magnetic potential perturbation as Zn2+ does in CuO2 plane in case Zn-doped cuprates. The resistivity varies linearly with temperature with or without photo-excitation. Tc can be varied by almost by 40K be photoexcitation. Superconductivity can be destroyed completely by introducing ≈ 2% of Cu1+ ions for this range of doping. With this controlled variation of Tc and resistivity, detailed investigation has been carried out to reveal Planckian dissipation underdoped to optimally doped Bi-2223. The most important aspect of this investigation is that we could vary Tc dynamically and reversibly, so that LITR and associated Planckian dissipation can be studied over wide ranges of Tc without changing the doping chemically.

Keywords: linear resistivity, HTSC, Planckian dissipation, strange metal

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Authors: J. Carlos Kuri, Varun Vij, Raymond J. Turner, Orly Yadid-Pecht

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Celiac disease (CD) is an immune system disorder that is triggered by ingesting gluten. As a gluten-free (GF) diet has become a concern of many people for health reasons, a gold standard had to be nominated. Enzyme-linked immunosorbent assay (ELISA) has taken the seat of this role. However, multiple limitations were discovered, and with that, the desire for an alternative method now exists. Nucleic acid-based aptamers have become of great interest due to their selectivity, specificity, simplicity, and rapid-testing advantages. However, fluorescence-based aptasensors have been tagged as unstable, but lifespan details are rarely stated. In this work, the lifespan stability of a fluorescence-based aptasensor is shown over an 8-week-long study displaying the accuracy of the sensor and false negatives. This study follows 22 different samples, including GF and gluten-rich (GR) and soy sauce products, off-the-shelf products, and reference material from laboratories, giving a total of 836 tests. The analysis shows an accuracy of correctly classifying GF and GR products of 96.30% and 100%, respectively when the protocol is augmented with molecular sieves. The overall accuracy remains around 94% within the first four weeks and then decays to 63%.

Keywords: aptasensor, PEG, rGO, FAM, RM, ELISA

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Authors: Babak Bashari Rad

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A metamorphic computer virus uses different code transformation techniques to mutate its body in duplicated instances. Characteristics and function of new instances are mostly similar to their parents, but they cannot be easily detected by the majority of antivirus in market, as they depend on string signature-based detection techniques. The purpose of this research is to propose a Hidden Markov Model for classification of metamorphic viruses in executable files. In the proposed solution, portable executable files are inspected to extract the instructions opcodes needed for the examination of code. A Hidden Markov Model trained on portable executable files is employed to classify the metamorphic viruses of the same family. The proposed model is able to generate and recognize common statistical features of mutated code. The model has been evaluated by examining the model on a test data set. The performance of the model has been practically tested and evaluated based on False Positive Rate, Detection Rate and Overall Accuracy. The result showed an acceptable performance with high average of 99.7% Detection Rate.

Keywords: malware classification, computer virus classification, metamorphic virus, metamorphic malware, Hidden Markov Model

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Authors: Diana Katherine Gonzalez Galindo, David Rolando Suarez Mora

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With this study, we used data mining as a new alternative of the solution to evaluate the comments of the customers in order to find a pattern that helps us to determine some behaviors to reduce the deactivation of the partners of the LEVEL app. In one of the greatest business created in the last times, the partners are being affected due to an internal process that compensates the customer for a bad experience, but these comments could be false towards the driver, that’s why we made an investigation to collect information to restructure this process, many partners have been disassociated due to this internal process and many of them refuse the comments given by the customer. The main methodology used in this case study is the observation, we recollect information in real time what gave us the opportunity to see the most common issues to get the most accurate solution. With this new process helped by data mining, we could get a prediction based on the behaviors of the customer and some basic data recollected such as the age, the gender, and others; this could help us in future to improve another process. This investigation gives more opportunities to the partner to keep his account active even if the customer writes a message through the app. The term is trying to avoid a recession of drivers in the future offering improving in the processes, at the same time we are in search of stablishing a strategy which benefits both the app’s managers and the associated driver.

Keywords: agent, driver, deactivation, rider

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Authors: Tolulope Aremu

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The key process steps to produce liquid detergent products will introduce potential defects, such as formulation, mixing, filling, and packaging, which might compromise product quality, consumer safety, and operational efficiency. Real-time identification and characterization of such defects are of prime importance for maintaining high standards and reducing waste and costs. Usually, defect detection is performed by human inspection or rule-based systems, which is very time-consuming, inconsistent, and error-prone. The present study overcomes these limitations in dealing with optimization in defect characterization within the process for making liquid detergents using Machine Learning algorithms. Performance testing of various machine learning models was carried out: Support Vector Machine, Decision Trees, Random Forest, and Convolutional Neural Network on defect detection and classification of those defects like wrong viscosity, color deviations, improper filling of a bottle, packaging anomalies. These algorithms have significantly benefited from a variety of optimization techniques, including hyperparameter tuning and ensemble learning, in order to greatly improve detection accuracy while minimizing false positives. Equipped with a rich dataset of defect types and production parameters consisting of more than 100,000 samples, our study further includes information from real-time sensor data, imaging technologies, and historic production records. The results are that optimized machine learning models significantly improve defect detection compared to traditional methods. Take, for instance, the CNNs, which run at 98% and 96% accuracy in detecting packaging anomaly detection and bottle filling inconsistency, respectively, by fine-tuning the model with real-time imaging data, through which there was a reduction in false positives of about 30%. The optimized SVM model on detecting formulation defects gave 94% in viscosity variation detection and color variation. These values of performance metrics correspond to a giant leap in defect detection accuracy compared to the usual 80% level achieved up to now by rule-based systems. Moreover, this optimization with models can hasten defect characterization, allowing for detection time to be below 15 seconds from an average of 3 minutes using manual inspections with real-time processing of data. With this, the reduction in time will be combined with a 25% reduction in production downtime because of proactive defect identification, which can save millions annually in recall and rework costs. Integrating real-time machine learning-driven monitoring drives predictive maintenance and corrective measures for a 20% improvement in overall production efficiency. Therefore, the optimization of machine learning algorithms in defect characterization optimum scalability and efficiency for liquid detergent companies gives improved operational performance to higher levels of product quality. In general, this method could be conducted in several industries within the Fast moving consumer Goods industry, which would lead to an improved quality control process.

Keywords: liquid detergent manufacturing, defect detection, machine learning, support vector machines, convolutional neural networks, defect characterization, predictive maintenance, quality control, fast-moving consumer goods

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Authors: Jeelka Ray

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Survey of the papers revealed that there is no practical design available for packaging machine based on Embedded system, so the need arose for the development of the prototype model. In this paper, author has worked on the development of an ARM7 based Embedded Controller for controlling the sequence of packaging machine. The unit is made user friendly with TFT and Touch Screen implementing human machine interface (HMI). The different system components are briefly discussed, followed by a description of the overall design. The major functions which involve bag forming, sealing temperature control, fault detection, alarm, animated view on the home screen when the machine is working as per different parameters set makes the machine performance more successful. LPC2478 ARM 7 Embedded Microcontroller controls the coordination of individual control function modules. In back gone days, these machines were manufactured with mechanical fittings. Later on, the electronic system replaced them. With the help of ongoing technologies, these mechanical systems were controlled electronically using Microprocessors. These became the backbone of the system which became a cause for the updating technologies in which the control was handed over to the Microcontrollers with Servo drives for accurate positioning of the material. This helped to maintain the quality of the products. Including all, RS 485 MODBUS Communication technology is used for synchronizing AC Drive & Servo Drive. These all concepts are operated either manually or through a Graphical User Interface. Automatic tuning of heaters, sealers and their temperature is controlled using Proportional, Integral and Derivation loops. In the upcoming latest technological world, the practical implementation of the above mentioned concepts is really important to be in the user friendly environment. Real time model is implemented and tested on the actual machine and received fruitful results.

Keywords: packaging machine, embedded system, ARM 7, micro controller, HMI, TFT, touch screen, PID

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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: Zhe Li, Aihua Cai

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Aiming at the problem of high-density clutter and interference affecting radar detection target track initiation in ECM and complex radar mission, the traditional radar target track initiation method has been difficult to adapt. To this end, we propose a multi-algorithm fusion learning track initiation algorithm, which transforms the track initiation problem into a true-false track discrimination problem, and designs an algorithm based on 1DCNN(One-Dimensional CNN)combined with Bi-LSTM (Bi-Directional Long Short-Term Memory )for fusion classification. The experimental dataset consists of real trajectories obtained from a certain type of three-coordinate radar measurements, and the experiments are compared with traditional trajectory initiation methods such as rule-based method, logical-based method and Hough-transform-based method. The simulation results show that the overall performance of the multi-algorithm fusion learning track initiation algorithm is significantly better than that of the traditional method, and the real track initiation rate can be effectively improved under high clutter density with the average initiation time similar to the logical method.

Keywords: track initiation, multi-algorithm fusion, 1DCNN, Bi-LSTM

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Authors: Kankeyanathan Kannan

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On approximation properties of group C* algebras is everywhere; it is powerful, important, backbone of countless breakthroughs. For a discrete group G, let A(G) denote its Fourier algebra, and let M₀A(G) denote the space of completely bounded Fourier multipliers on G. An approximate identity on G is a sequence (Φn) of finitely supported functions such that (Φn) uniformly converge to constant function 1 In this paper we prove that approximation property pass to free product.

Keywords: approximation property, weakly amenable, strong invariant approximation property, invariant approximation property

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Authors: Giselda Cristina Ferreira, Carlos Alberto Kamienski, Ana Lígia Scott

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The COVID-19 pandemic has enhanced the anti-vaccination movement in Brazil, supported by unscientific theories and false news and the possibility of wide communication through social networks such as Twitter, Facebook, and YouTube. The World Health Organization (WHO) classified the large volume of information on the subject against COVID-19 as an Infodemic. In this paper, we present a protocol to identify polarizing users (called polarizers) and study the profiles of Brazilian polarizers on Twitter (renamed to X some weeks ago). We analyzed polarizing interactions on Twitter (in Portuguese) to identify the main polarizers and how the conflicts they caused influenced the COVID-19 vaccination rate throughout the pandemic. This protocol uses data from this social network, graph theory, Java, and R-studio scripts to model and analyze the data. The information about the vaccination rate was obtained in a public database for the government called OpenDataSus. The results present the profiles of Twitter’s Polarizer (political position, gender, professional activity, immunization opinions). We observed that social and political events influenced the participation of these different profiles in conflicts and the vaccination rate.

Keywords: Twitter, polarization, vaccine, Brazil

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Authors: María José Núñez-Ruiz, Luis Álvarez-González, Cristian Olivares-Rodriguez, Benjamin Lazo-Letelier

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This paper begins with some concept of formative assessment and the relationship with learning objective: contents objectives, processes objectives, and metacognitive objectives. Two methodologies are describes Evidence-Based teaching and Question Drive Instruction. To do formative assessments in larges classes a Classroom Response System (CRS) is needed. But most of CRS use only Multiple Choice Questions (MCQ), True/False question, or text entry; however, this is insufficient to formative assessment. To do that a new CRS, call FAMA was developed. FAMA support six types of questions: Choice, Order, Inline choice, Text entry, Associated, and Slider. An experiment participated in 149 students from four engineering careers. For results, Kendall's Range Correlation Analysis and descriptive analysis was done. In conclusion, there is a strong relation between contents question, process questions (ask in formative assessment without a score) and metacognitive questions, asked in summative assessment. As future work, the lecturer can do personalized teaching, because knows the behavior of all students in each formative assessment

Keywords: Python language, formative assessment, classroom response systems, evidence-Based teaching, question drive instruction

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Authors: Ya-Ching Fu, An-Kuo Chou, Boon-Fatt Tan, Chi-Nien Chen, Wen-Chien Yang, Pou-Leng Cheong

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Blind oro-/naso-pharyngeal suction and feeding tube placement are very common practices in neonatal intensive care unit. Though esophageal perforation is a rare complication of these instrumentations, its prevalence is highest in extremely premature neonates. Due to its association with significant morbidity (including respiratory deterioration, pneumothorax, and sepsis) and even mortality, it is an important issue to prevent this iatrogenic complication in the field of premature care. We demonstrate an esophageal perforation in an extreme-low-birth-weight neonate after oro-gastric tube placement. This female baby weighing 680 grams was delivered by caesarean section at 25 weeks of gestational age. She initially received oro-tracheal intubation with mechanical ventilation which was smoothly weaned to non-invasive positive-pressure ventilation at 7-day-old. However, after insertion of a 5-French oro-gastric tube, the baby’s condition suddenly worsened with apnea requiring mechanical ventilation. Her chest radiogram showed the oro-gastric tube in right pleural space, and thus another oro-gastric tube was replaced, and its position was radiographically confirmed. The malpositioned tube was then removed. The baby received 2-week course of intravenous antibiotics for her esophageal perforation. Feeding was then reintroduced and increased to full feeds in a smooth course. She was discharged at 107-day-old. Esophageal perforation in newborn is very rare. Sudden respiratory deterioration in a neonate after naso-/oro-gastric tube placement should alarm us to consider esophageal perforation, and further radiological investigation is required for the diagnosis. Tube materials, patient condition, and age are major risk factors of esophageal perforation. The use of softer tube material, such as silicone, in extreme premature baby might prevent this fetal complication.

Keywords: esophageal perforation, preterm, newborn, feeding tube

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Authors: Jelvin Tom Sebastian, Vinod Yeldho Baby

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In space, electronic devices are constantly attacked with radiation, which causes certain parts to fail or behave in unpredictable ways. To advance the thermal controllability for microsatellites, we need a new approach and thermal control system that is smaller than that on conventional satellites and that demand no electric power. Heat exchange inside the microsatellites is not that easy as conventional satellites due to the smaller size. With slight mass gain and no electric power, accommodating heat using phase change materials (PCMs) is a strong candidate for solving micro satellites' thermal difficulty. In other words, PCMs can absorb or produce heat in the form of latent heat, changing their phase and minimalizing the temperature fluctuation around the phase change point. The main restriction for these systems is thermal conductivity weakness of common PCMs. As PCM is having low thermal conductivity, it increases the melting and solidification time, which is not suitable for specific application like electronic cooling. In order to increase the thermal conductivity nanoparticles are introduced. Adding the nanoparticles in base PCM increases the thermal conductivity. Increase in weight concentration increases the thermal conductivity. This paper numerically investigates the thermal energy storage panel with nanoparticle enhanced phase change material. Silver nanostructure have increased the thermal properties of the base PCM, eicosane. Different weight concentration (1, 2, 3.5, 5, 6.5, 8, 10%) of silver enhanced phase change material was considered. Both steady state and transient analysis was performed to compare the characteristics of nanoparticle enhanced phase material at different heat loads. Results showed that in steady state, the temperature near the front panel reduced and temperature on NePCM panel increased as the weight concentration increased. With the increase in thermal conductivity more heat was absorbed into the NePCM panel. In transient analysis, it was found that the effect of nanoparticle concentration on maximum temperature of the system was reduced as the melting point of the material reduced with increase in weight concentration. But for the heat load of maximum 20W, the model with NePCM did not attain the melting point temperature. Therefore it showed that the model with NePCM is capable of holding more heat load. In order to study the heat load capacity double the load is given, maximum of 40W was given as first half of the cycle and the other is given constant OW. Higher temperature was obtained comparing the other heat load. The panel maintained a constant temperature for a long duration according to the NePCM melting point. In both the analysis, the uniformity of temperature of the TESP was shown. Using Ag-NePCM it allows maintaining a constant peak temperature near the melting point. Therefore, by altering the weight concentration of the Ag-NePCM it is possible to create an optimum operating temperature required for the effective working of the electronics components.

Keywords: carbon-fiber-reinforced polymer, micro/nano-satellite, nanoparticle phase change material, thermal energy storage

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Authors: Cheng-Ying Li, Sheng-Yuan Chu

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This article deposited LZO-doped Mg piezoelectric thin films via RF sputtering and observed microstructure and electrical characteristics by varying the deposition temperature. The XRD analysis results indicate that LZO-doped Mg exhibits excellent (002) orientation, and there is no presence of ZnO(100), Influenced by the temperature's effect on the lattice constant, the (002) peak intensity increases with rising temperature. Finally, we conducted deformation intensity analysis on the films, revealing an over fourfold increase in deformation at a processing temperature of 500°C.

Keywords: RF sputtering, piezoelectricity, ZnO, Mg

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Authors: Daniel M. Wolff, Patricia Gray, Rafael de la Parra Venegas

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An unsupervised machine listening system is constructed and applied to a dataset of 17,195 30-second marine hydrophone recordings. The system is then heuristically supplemented with anecdotal listening, contextual recording information, and supervised learning techniques to reduce the number of false positives. Features for classification are assembled by extracting the following data from each of the audio files: the spectral centroid, root-mean-squared values for each frequency band of a 10-octave filter bank, and mel-frequency cepstral coefficients in 5-second frames. In this way both time- and frequency-domain information are contained in the features to be passed to a clustering algorithm. Classification is performed using the k-means algorithm and then a k-nearest neighbors search. Different values of k are experimented with, in addition to different combinations of the available feature sets. Hypothesized class labels are 'primarily anthrophony' and 'primarily biophony', where the best class result conforming to the former label has 104 members after heuristic pruning. This demonstrates how a large audio dataset has been made more tractable with machine learning techniques, forming the foundation of a framework designed to acoustically monitor and gauge biological and anthropogenic activity in a marine environment.

Keywords: anthrophony, hydrophone, k-means, machine learning

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Authors: Daniel Karthik, Vijay Baheti, Jiri Militky, Sundaramurthy Palanisamy

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Fibre based nano particles are presently considered as one of the potential filler materials for the improvement of mechanical and physical properties of polymer composites. Due to high matrix-filler interfacial area there will be uniform and homogeneous dispersion of nanoparticles. In micro/nano filler reinforced composites, resin material is usually tailored by organic or inorganic nanoparticles to have improved matrix properties. The objective of this study was to compare the potential of reinforcement of different organic and inorganic micro/nano fillers in epoxy composites. Industrial and agricultural waste of fibres like Agave Americana, cornhusk, jute, basalt, carbon, glass and fly ash was utilized to prepare micro/nano particles. Micro/nano particles were obtained using high energy planetary ball milling process in dry condition. Milling time and ball size were kept constant throughout the ball milling process. Composites were fabricated by hand lay method. Particle loading was kept constant to 3% wt. for all composites. In present study, loading of fillers was selected as 3 wt. % for all composites. Dynamic mechanical properties of the nanocomposite films were performed in three-point bending mode with gauge length and sample width of 50 mm and 10 mm respectively. The samples were subjected to an oscillating frequency of 1 Hz, 5 Hz and 10 Hz and 100 % oscillating amplitude in the temperature ranges of 30°C to 150°C at the heating rate of 3°C/min. Damping was found to be higher with the jute composites. Amongst organic fillers lowest damping factor was observed with Agave Americana particles, this means that Agave americana fibre particles have betters interface adhesion with epoxy resin. Basalt, fly ash and glass particles have almost similar damping factors confirming better interface adhesion with epoxy.

Keywords: ball milling, damping factor, matrix-filler interface, particle reinforcements

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Authors: Aileen F. Wang

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Mammography has been one of the most reliable methods for early detection and diagnosis of breast cancer. However, mammography misses about 17% and up to 30% of breast cancers due to the subtle and unstable appearances of breast cancer in their early stages. Recent computer-aided diagnosis (CADx) technology using Zernike moments has improved detection accuracy. However, it has several drawbacks: it uses manual segmentation, Zernike moments are not robust, and it still has a relatively high false negative rate (FNR)–17.6%. This project will focus on the development of a novel breast cancer detection algorithm to automatically segment the breast mass and further reduce FNR. The algorithm consists of automatic segmentation of a single breast mass using Point Region Growing Segmentation, reconstruction of the segmented breast mass using Pseudo-Zernike moments, and classification of the breast mass using the root mean square (RMS). A comparative study among the various algorithms on the segmentation and reconstruction of breast masses was performed on randomly selected mammographic images. The results demonstrated that the newly developed algorithm is the best in terms of accuracy and cost effectiveness. More importantly, the new classifier RMS has the lowest FNR–6%.

Keywords: computer aided diagnosis, mammography, point region growing segmentation, pseudo-zernike moments, root mean square

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Authors: Sambit Supriya Dash, Rahul Ravi R, Vikram Ramanan, Vinayak Malhotra

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Combustion in itself is a complex phenomenon that involves the interaction and interplay of multiple phenomena, the combined effect of which gives rise to the common flame that we see and use in our daily life applications from cooking to propelling our vehicles to space. The most important thing that goes unnoticed about these flames is the effect of the various phenomena from its surrounding environment that affects its behavior and properties. These phenomena cause a variety of energy interactions that lead to various types of energy transformations which in turn affect the flame behavior. This paper focuses on experimentally investigating the effect of one such phenomenon, which is the acoustics or sound energy on diffusion flames. The subject in itself is extensively studied upon as thermo-acoustics globally, whereas the current work focuses on studying its effect on soot formation on diffusion flames. The said effect is studied in this research work by the use of a butane as fuel, fitted with a nozzle that houses 3 arrays consisting of 4 holes each that are placed equidistant to each other and the resulting flame impinged with sound from two independent and similar sound sources that are placed equidistant from the centre of the flame. The entire process is systematically video graphed using a 60 fps regular CCD and analysed for variation in flame heights and flickering frequencies where the fuel mass flow rate is maintained constant and the configuration of entrainment holes and frequency of sound are varied, whilst maintaining constant ambient atmospheric conditions. The current work establishes significant outcomes on the effect of acoustics on soot formation; it is noteworthy that soot formation is the main cause of pollution and a major cause of inefficiency of current propulsion systems. This work is one of its kinds, and its outcomes are widely applicable to commercial and domestic appliances that utilize combustion for energy generation or propulsion and help us understand them better, so that we can increase their efficiency and decrease pollution.

Keywords: thermoacoustics, entrainment, propulsion system, efficiency, pollution

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Authors: C. Lanzerstorfer, M. Hinterberger

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The iron content of the ore used is crucial for the productivity and coke consumption rate in blast furnace pig iron production. Therefore, most iron ore deposits are processed in beneficiation plants to increase the iron content and remove impurities. In several comminution stages, the particle size of the ore is reduced to ensure that the iron oxides are physically liberated from the gangue. Subsequently, physical separation processes are applied to concentrate the iron ore. The fine-grained ore concentrates produced need to be transported, stored, and processed. For smooth operation of these processes, the flow properties of the material are crucial. The flowability of powders depends on several properties of the material: grain size, grain size distribution, grain shape, and moisture content of the material. The flowability of powders can be measured using ring shear testers. In this study, the influence of the moisture content on the flowability for the Krivoy Rog magnetite iron ore concentrate was investigated. Dry iron ore concentrate was mixed with varying amounts of water to produce samples with a moisture content in the range of 0.2 to 12.2%. The flowability of the samples was investigated using a Schulze ring shear tester. At all measured values of the normal stress (1.0 kPa – 20 kPa), the flowability decreased significantly from dry ore to a moisture content of approximately 3-5%. At higher moisture contents, the flowability was nearly constant, while at the maximum moisture content the flowability improved for high values of the normal stress only. The results also showed an improving flowability with increasing consolidation stress for all moisture content levels investigated. The wall friction angle of the dust with carbon steel (S235JR), and an ultra-high molecule low-pressure polyethylene (Robalon) was also investigated. The wall friction angle increased significantly from dry ore to a moisture content of approximately 3%. For higher moisture content levels, the wall friction angles were nearly constant. Generally, the wall friction angle was approximately 4° lower at the higher wall normal stress.

Keywords: iron ore concentrate, flowability, moisture content, wall friction angle

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