Search results for: boron anomaly

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: Atinkut Atinafu Yilma, Eyob Messele Sefene

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Anomaly detection is one of the essential mechanisms to control and reduce production loss, especially in today's smart manufacturing. Quick anomaly detection aids in reducing the cost of production by minimizing the possibility of producing defective products. However, developing an anomaly detection model that can rapidly detect a production change is challenging. This paper proposes Gated Recurrent Unit (GRU) combined with Random Forest (RF) to detect anomalies in the production process in real-time quickly. The GRU is used as a feature detector, and RF as a classifier using the input features from GRU. The model was tested using various synthesis and real-world datasets against benchmark methods. The results show that the proposed GRU-RF outperforms the benchmark methods with the shortest time taken to detect anomalies in the production process. Based on the investigation from the study, this proposed model can eliminate or reduce unnecessary production costs and bring a competitive advantage to manufacturing industries.

Keywords: anomaly detection, multivariate time series data, smart manufacturing, gated recurrent unit network, random forest

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Authors: Christopher Baiyegunhi, Oswald Gwavava

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The location/source of the Beattie magnetic anomaly (BMA) and interconnectivity of geologic structures at depth have been a topic of investigation for over 30 years. Up to now, no relationship between geological structures (interconnectivity of dolerite intrusions) at depth has been established. Therefore, the environmental impact of fracking the Karoo for shale gas could not be assessed despite the fact that dolerite dykes are groundwater localizers in the Karoo. In this paper, we shed more light to the unanswered questions concerning the possible location of the source of the BMA, the connectivity of geologic structures like dolerite dykes and sills at depth and this relationship needs to be established before the tectonic evolution of the Karoo basin can be fully understood and related to fracking of the Karoo for shale gas. The result of the magnetic investigation and modelling of four gravity profiles that crosses the BMA in the study area reveals that the anomaly, which is part of the Beattie magnetic anomaly tends to divide into two anomalies and continue to trend in an NE-SW direction, the dominant gravity signatures is of long wavelength that is due to a deep source/interface inland and shallows towards the coast, the average depth to the top of the shallow and deep magnetic sources was estimated to be approximately 0.6 km and 15 km, respectively. The BMA become stronger with depth which could be an indication that the source(s) is deep possibly a buried body in the basement. The bean-shaped anomaly also behaves in a similar manner like the BMA thus it could possibly share the same source(s) with the BMA.

Keywords: Beattie magnetic anomaly, magnetic sources, modelling, Karoo Basin

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Authors: Gülşah Çelik Gül, Figen Kurtuluş

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Magnesium containing boron compounds with hexagonal structure have been drawn much attention due to their superconductive nature. The main target of this work is new modified microwave oven by on our own has an ability about passing through a gas in the oven medium for attainment of oxygen-free compounds such as c-BN.  Mg containing boride was synthesized by modified-microwave method under nitrogen atmosphere using amorphous boron and magnesium source in appropriate molar ratio. Microwave oven with oxygen free environment has been modified to aimed to obtain magnesium boride without oxygen. Characterizations were done by powder X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy. Mg containing boride, generally named magnesium boride, with amorphous character without oxygen is obtained via designed microwave oven system.

Keywords: magnesium containing boron compounds, modified microwave synthesis, powder X-ray diffraction, FTIR

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Authors: S. V. Boroznin, I. V. Zaporotskova, N. P. Polikarpova

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Studing of nanotubes sorption properties is very important for researching. These processes for carbon and boron nanotubes described in the high number of papers. But the sorption properties of boron containing nanotubes, susch as BC3-nanotubes haven’t been studied sufficiently yet. In this paper we present the results of theoretical research into the mechanism of atomic surface adsorption on the two types of boron-carbon nanotubes (BCNTs) within the framework of an ionic-built covalent-cyclic cluster model and an appropriately modified MNDO quantum chemical scheme and DFT method using B3LYP functional with 6-31G basis. These methods are well-known and the results, obtained using them, were in good agreement with the experiment. Also we studied three position of atom location above the nanotube surface. These facts suggest us to use them for our research and quantum-chemical calculations. We studied the mechanism of sorption of Cl, O and F atoms on the external surface of single-walled BC3 arm-chair nanotubes. We defined the optimal geometry of the sorption complexes and obtained the values of the sorption energies. Analysis of the band structure suggests that the band gap is insensitive to adsorption process. The electron density is located near atoms of the surface of the tube. Also we compared our results with others, which have been obtained earlier for pure carbon and boron nanotubes. The most stable adsorption complex has been between boron-carbon nanotube and oxygen atom. So, it suggests us to make a research of oxygen molecule adsorption on the BC3 nanotube surface. We modeled five variants of molecule orientation above the nanotube surface. The most stable sorption complex has been defined between the oxygen molecule and nanotube when the oxygen molecule is located above the nanotube surface perpendicular to the axis of the tube.

Keywords: Boron-carbon nanotubes, nanostructures, nanolayers, quantum-chemical calculations, nanoengineering

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

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

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

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Authors: Ji Chan Kim, Seok Hong Min, Tae Kwon Ha

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Effect of boron addition on the microstructure and mechanical properties of AZ91D Mg alloy was investigated in this study. Through calculation of phase equilibria, carried out by using FactSage® and FTLite database, solution treatment temperature was decided as 420 °C where supersaturated solid solution can be obtained. Solid solution treatment was conducted at 420 °C for 24 hrs followed by hot rolling at 420 °C and the total reduction was about 60%. Recrystallization heat treatment was followed at 420 °C for 6 hrs to obtain equiaxed microstructure. After recrystallization treatment, aging heat treatment was conducted at temperature of 200 °C for time intervals from 1 min to 200 hrs and hardness of each condition was measured by micro-Vickers method. Peak hardness was observed after 20 hrs. Tensile tests were also conducted on the specimens aged for various time intervals and the results were compared with hardness.

Keywords: AZ91D Mg alloy, boron, heat treatment, microstructure, mechanical properties, hardness

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Authors: Ola Abdel-Tawab Hussein

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Background: Boron is a naturally occurring agent and an essential trace element of human, animals and higher plants. It is released in the form of boric acid (BA) that is water soluble and biolologically available. Its largest uses are in glass, detergents, agriculture, leather tanning industries, cosmetics, photographic materials, soaps and cleaners. Human consume daily few milligrams in the water, fruits and vegetables. High doses of boron had been recorded to be developmental and reproductive toxin in animals(Only few studies on human had investigated the health effects associated with exposure to boron. Vitamin C is a major water soluble non-enzymatic antioxidant, acts to overcome the oxidative stress. Aim of the work: However , the liver is exposed to toxic substances that are absorbed, degraded or conjugated there were little information exists about the effects of boron that it would specifically have in the liver tissue of experimental rats. So the present work aimed to study the effects of long-term boron ingestion on histological structural of the liver of adult male albino rats and to evaluate the protective role of vitamin C against induced changes. Material and Methods: 30 adult male albino rats were divided into 3 equal groups; Group I: control, Group II: recieved drinking water containing 55x10-6 gm boron/liter for 90 days and Group III: recieved vitamin C (200mg/Kg.B.W) orally concomitant with boron for the same period. liver specimens were processed for light and electron microscopic(TEM) study. Results: Examination of the liver sections of group II revealed foci of severe dilatation and congestion of central and portal veins with mononuclear cellular infiltration and hepatocellular vacuolation. Increased collagen deposition specially around the portal areas. Marked electrolucent areas in the cytoplasm, heterochromatic nuclei and destroyed organelles of the hepatocytes. Apoptotic cells were observed and decreased lipid content of ito cells. In Group III the co administration of vitamin C improved most of the structural changes of the hepatocytes, Ito cells, increased binucleated cells and decreased collagen fibers deposition. Conclusion: Thus, the long term exposure to boron, induced histological changes on the structure of liver. The co administration of vitamin C improved most of these structural changes.

Keywords: boron, liver, vitamin C, rats

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Authors: I. Wlasny, Z. Klusek, A. Wysmolek

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Hexagonal boron nitride is a representative of a widely popular class of two-dimensional Van Der Waals materials. It finds its uses, among others, in construction of complexly layered heterostructures. Hexagonal boron nitride attracts great interest because of its properties characteristic for wide-gap semiconductors as well as an ultra-flat surface.Van Der Waals heterostructures composed of two-dimensional layered materials, such as transition metal dichalcogenides or graphene give hope for miniaturization of various electronic and optoelectronic elements. In our presentation, we will show the results of our investigations of the not previously reported modification of the hexagonal boron nitride layers with focused laser beam. The electrostatic force microscopy (EFM) images reveal that the irradiation leads to changes of the local electric fields for a wide range of laser wavelengths (from 442 to 785 nm). These changes are also accompanied by alterations of crystallographic structure of the material, as reflected by Raman spectra. They exhibit high stability and remain visible after at least five months. This behavior can be explained in terms of photoionization of the defect centers in h-BN which influence non-uniform electrostatic field screening by the photo-excited charge carriers. Analyzed changes influence local defect structure, and thus the interatomic distances within the lattice. These effects can be amplified by the piezoelectric character of hexagonal boron nitride, similar to that found in nitrides (e.g., GaN, AlN). Our results shed new light on the optical properties of the hexagonal boron nitride, in particular, those associated with electron-phonon coupling. Our study also opens new possibilities for h-BN applications in layered heterostructures where electrostatic fields can be used in tailoring of the local properties of the structures for use in micro- and nanoelectronics or field-controlled memory storage. This work is supported by National Science Centre project granted on the basis of the decision number DEC-2015/16/S/ST3/00451.

Keywords: atomic force microscopy, hexagonal boron nitride, optical properties, raman spectroscopy

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Authors: Ishay Wolf

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In this study, the financial position of pension actors in the market during the pension system transition toward a more funded capitalized scheme is explored, mainly via an option benefit model. This is enabled by not considering the economy as a single earning cohort. We analytically demonstrate a socio-economic anomaly in the funded pension system, which is in favor of high earning cohorts on at the expense of low earning cohorts. This anomaly is realized by a lack of insurance and exposure to financial and systemic risks. Furthermore, the anomaly might lead to pension re-reform back to unfunded scheme, mostly due to political pressure. We find that a minimum pension guarantee is a rebalance mechanism to this anomaly, which increases the probability to of the sustainable pension scheme. Specifically, we argue that implementing the guarantee with an intra-generational, risk-sharing mechanism is the most efficient way to reduce the effect of this abnormality. Moreover, we exhibit the convergence process toward implementing minimum pension guarantee in many countries which have capitalized their pension systems during the last three decades, particularly among Latin America and CEE countries.

Keywords: benefits, pension scheme, put option, social security

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Authors: Ia Kurashvili, Giorgi Darsavelidze, Giorgi Chubinidze, Marina Kadaria

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Boron-doped Czochralski (CZ) silicon of p-type, widely used in the photovoltaic industry is suffering from the light-induced-degradation (LID) of bulk electrophysical characteristics. This is caused by specific metastable B-O defects, which are characterized by strong recombination activity. In this regard, it is actual to suppress B-O defects in CZ silicon. One of the methods is doping of silicon by different isovalent elements (Ge, C, Sn). The present work deals with the investigations of the influence of germanium doping on the internal friction and shear modulus amplitude dependences in the temperature interval of 600-800⁰C and 0.5-5 Hz frequency range in boron-containing monocrystalline silicon. Experimental specimens were grown by Czochralski method (CZ) in [111] direction. Four different specimens were investigated: Si+0,5at%Ge:B (5.1015cm-3), Si+0,5at%Ge:B (1.1019cm-3), Si+2at%Ge:B (5.1015cm-3) and Si+2at%Ge:B (1.1019cm-3). Increasing tendency of dislocation density and inhomogeneous distribution in silicon crystals with high content of boron and germanium were revealed by metallographic studies on the optical microscope of NMM-80RF/TRF. Weak increase of current carriers-holes concentration and slight decrease of their mobility were observed by Van der Pauw method on Ecopia HMS-3000 device. Non-monotonous changes of dislocation origin defects mobility and microplastic deformation characteristics influenced by measuring temperatures and boron and germanium concentrations were revealed. Possible mechanisms of changes of mechanical characteristics in Si-Ge experimental specimens were discussed.

Keywords: dislocation, internal friction, microplastic deformation, shear modulus

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Authors: Pervaiz Ahmad, Mayeen Uddin Khandaker, Yusoff Mohd Amin

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A unique cone-like morphologies of boron nitride microtubes with larger internal space and thin walls structure are synthesized in a dual zone quartz tube furnace at 1200 ° C with ammonia as a reaction atmosphere. The synthesized microtubes are found to have diameter in the range of 1 to ̴ 2 μm with walls thickness estimated from 10 – 100 nm. XPS survey shows N 1s and B 1s peaks at 398.7 eV and 191 eV that represent h-BN in the sample. Raman spectroscopy indicates a high intensity peak at 1372.53 (cm-1) that corresponds to the E2g mode of h-BN.

Keywords: BNMTs, synthesis, reaction atmosphere, growth

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Authors: Edward Guillén, Jhordany Rodriguez, Rafael Páez

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Anomaly detection techniques have been focused on two main components: data extraction and selection and the second one is the analysis performed over the obtained data. The goal of this paper is to analyze the influence that each of these components has over the system performance by evaluating detection over network scenarios with different setups. The independent variables are as follows: the number of system inputs, the way the inputs are codified and the complexity of the analysis techniques. For the analysis, some approaches of artificial neural networks are implemented with different number of layers. The obtained results show the influence that each of these variables has in the system performance.

Keywords: network intrusion detection, machine learning, artificial neural network, anomaly detection module

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Authors: John Onyima, Ikechukwu Ezepue

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Virtualization and cloud computing are among the fast-growing computing innovations in recent times. Organisations all over the world are moving their computing services towards the cloud this is because of its rapid transformation of the organization’s infrastructure and improvement of efficient resource utilization and cost reduction. However, this technology brings new security threats and challenges about safety, reliability and data confidentiality. Evidently, no single security technique can guarantee security or protection against malicious attacks on a cloud computing network hence an integrated model of intrusion detection and prevention system has been proposed. Anomaly-based and signature-based detection techniques will be integrated to enable the network and its host defend themselves with some level of intelligence. The anomaly-base detection was implemented using the local deviation factor graph-based (LDFGB) algorithm while the signature-based detection was implemented using the snort algorithm. Results from this collaborative intrusion detection and prevention techniques show robust and efficient security architecture for cloud computing networks.

Keywords: anomaly-based detection, cloud computing, intrusion detection, intrusion prevention, signature-based detection

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Authors: Tammeen Siraj, Wenxing Zhou, Terry Huang, Mohammad Al-Amin

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The high resolution inline inspection (ILI) tool is used extensively in the pipeline industry to identify, locate, and measure metal-loss corrosion anomalies on buried oil and gas steel pipelines. Corrosion anomalies may occur singly (i.e. individual anomalies) or as clusters (i.e. a colony of corrosion anomalies). Although the ILI technology has advanced immensely, there are measurement errors associated with the sizes of corrosion anomalies reported by ILI tools due limitations of the tools and associated sizing algorithms, and detection threshold of the tools (i.e. the minimum detectable feature dimension). Quantifying the measurement error in the ILI data is crucial for corrosion management and developing maintenance strategies that satisfy the safety and economic constraints. Studies on the measurement error associated with the length of the corrosion anomalies (in the longitudinal direction of the pipeline) has been scarcely reported in the literature and will be investigated in the present study. Limitations in the ILI tool and clustering process can sometimes cause clustering error, which is defined as the error introduced during the clustering process by including or excluding a single or group of anomalies in or from a cluster. Clustering error has been found to be one of the biggest contributory factors for relatively high uncertainties associated with ILI reported anomaly length. As such, this study focuses on developing a consistent and comprehensive framework to quantify the measurement errors in the ILI-reported anomaly length by comparing the ILI data and corresponding field measurements for individual and clustered corrosion anomalies. The analysis carried out in this study is based on the ILI and field measurement data for a set of anomalies collected from two segments of a buried natural gas pipeline currently in service in Alberta, Canada. Data analyses showed that the measurement error associated with the ILI-reported length of the anomalies without clustering error, denoted as Type I anomalies is markedly less than that for anomalies with clustering error, denoted as Type II anomalies. A methodology employing data mining techniques is further proposed to classify the Type I and Type II anomalies based on the ILI-reported corrosion anomaly information.

Keywords: clustered corrosion anomaly, corrosion anomaly assessment, corrosion anomaly length, individual corrosion anomaly, metal-loss corrosion, oil and gas steel pipeline

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Authors: G. Zakharov, Z. Aslamazashvili, M. Chikhradze, D. Kvaskhvadze, N. Khidasheli, S. Gvazava

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Traditional technologies for processing iron-containing industrial waste, including steel-rolling production, are associated with significant energy costs, the long duration of processes, and the need to use complex and expensive equipment. Waste generated during the industrial process negatively affects the environment, but at the same time, it is a valuable raw material and can be used to produce new marketable products. The study of the effectiveness of self-propagating high-temperature synthesis (SHS) methods, which are characterized by the simplicity of the necessary equipment, the purity of the final product, and the high processing speed, is under the wide scientific and practical interest to solve the set problem. The work presents technological aspects of the production of Ferro boron by the method of SHS - metallurgy from iron-containing wastes of rolled production for alloying of cast iron and results of the effect of alloying element on the degree of boron assimilation with liquid cast iron. Features of Fe-B system combustion have been investigated, and the main parameters to control the phase composition of synthesis products have been experimentally established. Effect of overloads on patterns of cast ligatures formation and mechanisms structure formation of SHS products was studied. It has been shown that an increase in the content of hematite Fe₂O₃ in iron-containing waste leads to an increase in the content of phase FeB and, accordingly, the amount of boron in the ligature. Boron content in ligature is within 3-14%, and the phase composition of obtained ligatures consists of Fe₂B and FeB phases. Depending on the initial composition of the wastes, the yield of the end product reaches 91 - 94%, and the extraction of boron is 70 - 88%. Combustion processes of high exothermic mixtures allow to obtain a wide range of boron-containing ligatures from industrial wastes. In view of the relatively low melting point of the obtained SHS-ligature, the positive dynamics of boron absorption by liquid iron is established. According to the obtained data, the degree of absorption of the ligature by alloying gray cast iron at 1450°C is 80-85%. When combined with the treatment of liquid cast iron with magnesium, followed by alloying with the developed ligature, boron losses are reduced by 5-7%. At that, uniform distribution of boron micro-additives in the volume of treated liquid metal is provided. Acknowledgment: This work was supported by Shota Rustaveli Georgian National Science Foundation of Georgia (SRGNSFG) under the GENIE project (grant number № CARYS-19-802).

Keywords: self-propagating high-temperature synthesis, cast iron, industrial waste, ductile iron, structure formation

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Authors: Ching-Hua Li, Sheng-Hui Chen

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Sputtering is a popular technique with many advantages for thin film deposition. To fabricate a hydrogenated silicon thin film using sputtering process for solar cell applications, the ion bombardment during sputtering will generate microstructures (voids and columnar structures) to form silicon dihydride bodings as defects. The properties of heterojunction silicon solar cells were studied by using boron grains and silicon-boron targets. Finally, an 11.7% efficiency of solar cell was achieved by using all sputtering process.

Keywords: solar cell, sputtering process, pvd, alloy target

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Authors: Kleiner Marques Marra, Tércio Pedrosa

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This paper reports the production of uranium-molybdenum alloys, which have been considered promising fuel for test and research nuclear reactors. U-Mo alloys were produced in three molybdenum contents: 5 wt.%, 7 wt.%, and 10 wt.%, using an electric vacuum induction furnace. A boron nitride-coated graphite crucible was employed in the production of the alloys and, after melting, the material was immediately poured into a boron nitride-coated graphite mold. The incorporation of carbon was observed, but it happened in a lower intensity than in the case of the non-coated crucible/mold. It is observed that the carbon incorporation increased and alloys density decreased with Mo addition. It was also noticed that the increase in the carbon or molybdenum content did not seem to change the as-cast structure in terms of granulation. The three alloys presented body-centered cubic crystal structure (g phase), after solidification, besides a seeming negative microsegregation of molybdenum, from the center to the periphery of the grains. There were signs of macrosegregation, from the base to the top of the ingots.

Keywords: uranium-molybdenum alloys, incorporation of carbon, solidification, macrosegregation and microsegregation

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Authors: Majed Sanan, Mohammad Rammal, Wassim Rammal

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Today, security is a major concern. Intrusion Detection, Prevention Systems and Honeypot can be used to moderate attacks. Many researchers have proposed to use many IDSs ((Intrusion Detection System) time to time. Some of these IDS’s combine their features of two or more IDSs which are called Hybrid Intrusion Detection Systems. Most of the researchers combine the features of Signature based detection methodology and Anomaly based detection methodology. For a signature based IDS, if an attacker attacks slowly and in organized way, the attack may go undetected through the IDS, as signatures include factors based on duration of the events but the actions of attacker do not match. Sometimes, for an unknown attack there is no signature updated or an attacker attack in the mean time when the database is updating. Thus, signature-based IDS fail to detect unknown attacks. Anomaly based IDS suffer from many false-positive readings. So there is a need to hybridize those IDS which can overcome the shortcomings of each other. In this paper we propose a new approach to IDS (Intrusion Detection System) which is more efficient than the traditional IDS (Intrusion Detection System). The IDS is based on Honeypot Technology and Anomaly based Detection Methodology. We have designed Architecture for the IDS in a packet tracer and then implemented it in real time. We have discussed experimental results performed: both the Honeypot and Anomaly based IDS have some shortcomings but if we hybridized these two technologies, the newly proposed Hybrid Intrusion Detection System (HIDS) is capable enough to overcome these shortcomings with much enhanced performance. In this paper, we present a modified Hybrid Intrusion Detection System (HIDS) that combines the positive features of two different detection methodologies - Honeypot methodology and anomaly based intrusion detection methodology. In the experiment, we ran both the Intrusion Detection System individually first and then together and recorded the data from time to time. From the data we can conclude that the resulting IDS are much better in detecting intrusions from the existing IDSs.

Keywords: security, intrusion detection, intrusion prevention, honeypot, anomaly-based detection, signature-based detection, cloud computing, kfsensor

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Authors: Yang Yang, Dan Liu

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Anomaly detection models for network flow based on machine learning have poor detection performance under extremely unbalanced training data conditions and also have slow detection speed and large resource consumption when deploying on network edge devices. Embedding multi-teacher knowledge distillation (mKD) in anomaly detection can transfer knowledge from multiple teacher models to a single model. Inspired by this, we proposed a state-of-the-art model, mKDNAD, to improve detection performance. mKDNAD mine and integrate the knowledge of one-dimensional sequence and two-dimensional image implicit in network flow to improve the detection accuracy of small sample classes. The multi-teacher knowledge distillation method guides the train of the student model, thus speeding up the model's detection speed and reducing the number of model parameters. Experiments in the CICIDS2017 dataset verify the improvements of our method in the detection speed and the detection accuracy in dealing with the small sample classes.

Keywords: network flow anomaly detection (NAD), multi-teacher knowledge distillation, machine learning, deep learning

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Authors: Nikhil Jain, Bin Yu

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We explore the potential of using ultra-thin hexagonal boron nitride (h-BN) as both supporting substrate and gate dielectric for graphene-channel field effect transistors (GFETs). Different from commonly used oxide-based dielectric materials which are typically amorphous, very rough in surface, and rich with surface traps, h-BN is layered insulator free of dangling bonds and surface states, featuring atomically smooth surface. In a graphene-channel-last device structure with local buried metal gate electrode (TiN), thin h-BN multilayer is employed as both supporting “substrate” and gate dielectric for graphene active channel. We observed superior carrier mobility and electrical conduction, significantly improved from that in GFETs with SiO2 as substrate/gate insulator. In addition, we report excellent dielectric behavior of layered h-BN, including ultra-low leakage current and high critical electric field for breakdown.

Keywords: graphene, field-effect transistors, hexagonal boron nitride, dielectric strength, tunneling

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Authors: Jacky Liu, Dulani Jayasuriya, Ryan Elmore

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Match fixing and anomalous sports events have increasingly threatened the integrity of professional sports, prompting concerns about existing detection methods. This study addresses prior research limitations in match fixing detection, improving the identification of potential fraudulent matches by incorporating advanced anomaly detection techniques. We develop a novel method to identify anomalous matches and player performances by examining series of matches, such as playoffs. Additionally, we investigate bettors' potential profits when avoiding anomaly matches and explore factors behind unusual player performances. Our literature review covers match fixing detection, match outcome forecasting models, and anomaly detection methods, underscoring current limitations and proposing a new sports anomaly detection method. Our findings reveal anomalous series in the 2022 NBA playoffs, with the Phoenix Suns vs Dallas Mavericks series having the lowest natural occurrence probability. We identify abnormal player performances and bettors' profits significantly decrease when post-season matches are included. This study contributes by developing a new approach to detect anomalous matches and player performances, and assisting investigators in identifying responsible parties. While we cannot conclusively establish reasons behind unusual player performances, our findings suggest factors such as team financial difficulties, executive mismanagement, and individual player contract issues.

Keywords: anomaly match detection, match fixing, match outcome forecasting, problematic players identification

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Authors: Victor Breux, Jérôme Boutet, Alain Goret, Viviane Cattin

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Early detection of anomalies in data centers is important to reduce downtimes and the costs of periodic maintenance. However, there is little research on this topic and even fewer on the fusion of sensor data for the detection of abnormal events. The goal of this paper is to propose a method for anomaly detection in data centers by combining sensor data (temperature, humidity, power) and deep learning models. The model described in the paper uses one autoencoder per sensor to reconstruct the inputs. The auto-encoders contain Long-Short Term Memory (LSTM) layers and are trained using the normal samples of the relevant sensors selected by correlation analysis. The difference signal between the input and its reconstruction is then used to classify the samples using feature extraction and a random forest classifier. The data measured by the sensors of a data center between January 2019 and May 2020 are used to train the model, while the data between June 2020 and May 2021 are used to assess it. Performances of the model are assessed a posteriori through F1-score by comparing detected anomalies with the data center’s history. The proposed model outperforms the state-of-the-art reconstruction method, which uses only one autoencoder taking multivariate sequences and detects an anomaly with a threshold on the reconstruction error, with an F1-score of 83.60% compared to 24.16%.

Keywords: anomaly detection, autoencoder, data centers, deep learning

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Authors: Saad Bakkali

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Image processing is a powerful tool for the enhancement of edges in images used in the interpretation of geophysical potential field data. Arial and terrestrial gravimetric surveys were carried out in the region of Tangier-Tetuan. From the observed and measured data of gravity Bouguer gravity anomalies map was prepared. This paper reports the results and interpretations of the transformed maps of Bouguer gravity anomaly of the Tangier-Tetuan area using image processing. Filtering analysis based on classical image process was applied. Operator image process like logarithmic and gamma correction are used. This paper also present the results obtained from this image processing analysis of the enhancement edges of the Bouguer gravity anomaly map of the Tangier-Tetuan zone.

Keywords: bouguer, tangier, filtering, gamma correction, logarithmic enhancement edges

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Authors: Fouzi Harrou, Ying Sun, Sofiane Khadraoui

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Anomaly detection based on Principal Component Analysis (PCA) was studied intensively and largely applied to multivariate processes with highly cross-correlated process variables. Monitoring metrics such as the Hotelling's T2 and the Q statistics are usually used in PCA-based monitoring to elucidate the pattern variations in the principal and residual subspaces, respectively. However, these metrics are ill suited to detect small faults. In this paper, the Exponentially Weighted Moving Average (EWMA) based on the Q and T statistics, T2-EWMA and Q-EWMA, were developed for detecting faults in the process mean. The performance of the proposed methods was compared with that of the conventional PCA-based fault detection method using synthetic data. The results clearly show the benefit and the effectiveness of the proposed methods over the conventional PCA method, especially for detecting small faults in highly correlated multivariate data.

Keywords: data-driven method, process control, anomaly detection, dimensionality reduction

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Authors: I. Kerti, G. Sezen, S. Daglilar

Abstract:

This present work is focused on studying to improve low wetting behaviour between liquid metal and ceramic particles. Ceramic particles like SiC and B4C have attracted great attention because of their usability as reinforcement for composite materials. However, poor wettability of particles is one of the major drawbacks of metal matrix composite production. Various methods have been studied to enhance the wetting properties between ceramic materials and metal substrates during ceramic reinforced metal matrix composites. Among these methods, autocatalytic nickel deposition is a unique process for the enhancement of the surface properties of ceramic particles. In fact, it is difficult to obtain continuous and uniform metallic coating on ceramic powders. In this study deposition of nickel boron layer on ceramic particles via autocatalytic plating in borohydride baths were investigated. Firstly, powders with different particle sizes were sensitized and activated respectively in order to ensure catalytic properties. Following the pre-treatment operations, particles were transferred into the coating bath containing nickel sulphate or nickel chloride as the Ni2+ source. The results show that a better bonding and uniform coating layer were obtained for Ni-B coatings with the Ni2+ source of NiCl2.6H2O as compared to NiSO4.6H2O. With the progress of the time, both particle surfaces are completely covered by a continuous and thin nickel boron layer. The surface morphology of the coatings that were analysed using scanning electron microscopy (SEM) show that SiC and B4C particles both distributed and different thickness of Ni-B nanolayers have been successfully coated onto the particles. The particles were mounted into a polimeric resin and polished in order to observe the thickness and the continuity of the coating layer. The composition of the coating layers were also evaluated by EDS analyses. The SEM morphologies and the EDS results of the coatings at different reaction times were adopted for detailed discussion of the Ni-B electroless plating mechanism.

Keywords: boron carbide, electroless coating, nickel boron deposition, silicon carbide

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Authors: M. Kowalski, M. Brodowski, K. Dziabowska, E. Czaczyk, W. Bialobrzeska, N. Malinowska, S. Zoledowska, R. Bogdanowicz, D. Nidzworski

Abstract:

Boron-doped-carbon nanowall (BCNW) electrodes are recently in much interest among scientists. BCNWs are good candidates for biosensor purposes as they possess interesting electrochemical characteristics like a wide potential range and the low difference between redox peaks. Moreover, from technical parameters, they are mechanically resistant and very tough. The production process of the microwave plasma-enhanced chemical vapor deposition (MPECVD) allows boron to build into the structure of the diamond being formed. The effect is the formation of flat, long structures with sharp ends. The potential of these electrodes was checked in the biosensing field. The procedure of simple carbon electrodes modification by antibodies was adopted to BCNW for specific antigen recognition. Surface protein D deriving from H. influenzae pathogenic bacteria was chosen as a target analyte. The electrode was first modified with the aminobenzoic acid diazonium salt by electrografting (electrochemical reduction), next anti-protein D antibodies were linked via 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride/N-hydroxysuccinimide (EDC/NHS) chemistry, and free sites were blocked by BSA. Cyclic voltammetry measurements confirmed the proper electrode modification. Electrochemical impedance spectroscopy records indicated protein detection. The sensor was proven to detect protein D in femtograms. This work was supported by the National Centre for Research and Development (NCBR) TECHMATSTRATEG 1/347324/12/NCBR/ 2017.

Keywords: anti-protein D antibodies, boron-doped carbon nanowall, impedance spectroscopy, Haemophilus influenzae.

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Authors: M. M. S. Prahastomi, M. Muhajir Saputra, Axel Derian

Abstract:

Lapindo mud (LUSI ) phenomenon which occurred in Sidoarjo 2006 is a national scale of the geological phenomenon. This mudflow forms a mud volcano that spreads by time is in the need of serious treatment. Some further research has been conducted either by the application method of geodesy, geophysics, and subsurface geology, but still remains a mystery to this phenomenon. Sidoarjo Physiographic regions are included in the Kendeng zone flanked by Rembang zones in northern and Solo zones in southern. In this region revealed Kabuh formation, Jombang formation, and alluvium. In general, in the northern part of the area is composed of sedimentary rocks Sidoarjo klastika, epiklastic, pyroclastics, and older alluvium of the Early Pleistocene to Resen. The study was conducted with the literature study of the stratigraphy and regional geology as well as secondary data from observations coupled gravity method (Anomaly Bouger). The aim of the study is to reveal the subsurface geology structure pattern and the changes in mass flow. Gravity anomaly data were obtained from the calculation of the value of gravity and altitude, then processed into gravity anomaly contours which reflect changes in density of each group observed gravity. The gravity data could indicate a bottom surface which deformation occur the stronger or more intense to the south. Deformation in the form of gravity impairment was associated with a decrease in future density which is indicated by the presence of gas, water and gas bursts. Sectional analysis of changes in the measured value of gravity at different times indicates a change in the value of gravity caused by the presence of subsurface subsidence. While the gravity anomaly section describes the fault zone causes the zone to be unstable.

Keywords: mud volcano, Lumpur Sidoarjo, Bouger anomaly, Indonesia

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Authors: V. Geza, J. Vencels, G. Zageris, S. Pavlovs

Abstract:

One of the most perspective methods to produce SoG-Si is refinement via metallurgical route. The most critical part of this route is refinement from boron and phosphorus. Therefore, a new approach could address this problem. We propose an approach of creating surface waves on silicon melt’s surface in order to enlarge its area and accelerate removal of boron via chemical reactions and evaporation of phosphorus. A two dimensional numerical model is created which includes coupling of electromagnetic and fluid dynamic simulations with free surface dynamics. First results show behaviour similar to experimental results from literature.

Keywords: numerical modelling, silicon refinement, surface waves, VOF method

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Authors: Maziar Noei

Abstract:

Adsorption of a boron nitride nanotube (BNNT) was examined toward ethylacetylene (C4H6) molecule by using density functional theory (DFT) calculations at the B3LYP/6-31G (d) level, and it was found that the adsorption energy (Ead) of ethylacetylene the pristine nanotubes is about -1.60kcal/mol. But when nanotube have been doped with Si and Al atomes, the adsorption energy of ethylacetylene molecule was increased. Calculation showed that when the nanotube is doping by Al, the adsorption energy is about -24.19kcal/mol and also the amount of HOMO/LUMO energy gap (Eg) will reduce significantly. Boron nitride nanotube is a suitable adsorbent for ethylacetylene and can be used in separation processes ethylacetylene. It is seem that nanotube (BNNT) is a suitable semiconductor after doping, and the doped BNNT in the presence of ethylacetylene an electrical signal is generating directly and therefore can potentially be used for ethylacetylene sensors.

Keywords: sensor, nanotube, DFT, ethylacetylene

Procedia PDF Downloads 229