Search results for: SQL injection attack classification

Authors: Younes El Khchine

Abstract:

A parametric study has been conducted to analyse the flow around S809 airfoil of a wind turbine in order to better understand the characteristics and effects of laminar separation bubble (LSB) on aerodynamic design for maximizing wind turbine efficiency. Numerical simulations were performed at low Reynolds numbers by solving the Unsteady Reynolds Averaged Navier-Stokes (URANS) equations based on C-type structural mesh and using the γ-Reθt turbulence model. A two-dimensional study was conducted for the chord Reynolds number of 1×105 and angles of attack (AoA) between 0 and 20.15 degrees. The simulation results obtained for the aerodynamic coefficients at various angles of attack (AoA) were compared with XFoil results. A sensitivity study was performed to examine the effects of Reynolds number and free-stream turbulence intensity on the location and length of the laminar separation bubble and the aerodynamic performances of wind turbines. The results show that increasing the Reynolds number leads to a delay in the laminar separation on the upper surface of the airfoil. The increase in Reynolds number leads to an accelerated transition process, and the turbulent reattachment point moves closer to the leading edge owing to an earlier reattachment of the turbulent shear layer. This leads to a considerable reduction in the length of the separation bubble as the Reynolds number is increased. The increase in the level of free-stream turbulence intensity leads to a decrease in separation bubble length and an increase in the lift coefficient while having negligible effects on the stall angle. When the AoA increased, the bubble on the suction airfoil surface was found to move upstream to the leading edge of the airfoil, causing earlier laminar separation.

Keywords: laminar separation bubble, turbulence intensity, s809 airfoil, transition model, Reynolds number

Procedia PDF Downloads 65

Authors: Chin-Hsien Yeh, Shao-Wen Chen, Wen-Shu Huang, Chun-Fu Huang, Jong-Rong Wang, Jung-Hua Yang, Yuh-Ming Ferng, Chunkuan Shih

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In this research, a Fukushima-like conditions is simulated with TRACE and RELAP5. Fukushima Daiichi Nuclear Power Plant (NPP) occurred the disaster which caused by the earthquake and tsunami. This disaster caused extended loss of all AC power (ELAP). Hence, loss of ultimate heat sink (LUHS) happened finally. In order to handle Fukushima-like conditions, Taiwan Atomic Energy Council (AEC) commanded that Taiwan Power Company should propose strategies to ensure the nuclear power plant safety. One of the diverse and flexible coping strategies (FLEX) is a different water injection strategy. It can execute core injection at 20 Kg/cm2 without depressurization. In this study, TRACE and RELAP5 were used to simulate Maanshan nuclear power plant, which is a three loops PWR in Taiwan, under Fukushima-like conditions and make sure the success criteria of FLEX. Reducing core cooling ability is due to failure of emergency core cooling system (ECCS) in extended loss of all AC power situation. The core water level continues to decline because of the seal leakage, and then FLEX is used to save the core water level and make fuel rods covered by water. The result shows that this mitigation strategy can cool the reactor pressure vessel (RPV) as soon as possible under Fukushima-like conditions, and keep the core water level higher than Top of Active Fuel (TAF). The FLEX can ensure the peak cladding temperature (PCT) below than the criteria 1088.7 K. Finally, the FLEX can provide protection for nuclear power plant and make plant safety.

Keywords: TRACE, RELAP5/MOD3.3, ELAP, FLEX

Procedia PDF Downloads 246

Authors: Douglas N. Simoes, Michele Pittol, Vanda F. Ribeiro, Daiane Tomacheski, Ruth M. C. Santana

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Thermoplastic elastomers (TPE) compounds are used in a wide range of applications, like home appliances, automotive components, medical devices, footwear, and others. These materials are susceptible to microbial attack, causing a crack in polymer chains compounds based on SEBS copolymers, poly (styrene-b-(ethylene-co-butylene)-b-styrene, are a class of TPE, largely used in domestic appliances like refrigerator seals (gaskets), bath mats and sink squeegee. Moisture present in some areas (such as shower area and sink) in addition to organic matter provides favorable conditions for microbial survival and proliferation, contributing to the spread of diseases besides the reduction of product life cycle due the biodegradation process. Zinc oxide (ZnO) has been studied as an alternative antibacterial additive due its biocidal effect. It is important to know the influence of these additives in the properties of the compounds, both at the beginning and during the life cycle. In that sense, the aim of this study was to evaluate the effect of accelerated aging in oven on antibacterial and mechanical properties of ZnO loaded SEBS based TPE compounds. Two different comercial zinc oxide, named as WR and Pe were used in proportion of 1%. A compound with no antimicrobial additive (standard) was also tested. The compounds were prepared using a co-rotating double screw extruder (L/D ratio of 40/1 and 16 mm screw diameter). The extrusion parameters were kept constant for all materials, screw rotation rate was set at 226 rpm, with a temperature profile from 150 to 190 ºC. Test specimens were prepared using the injection molding machine at 190 ºC. The Standard Test Method for Rubber Property—Effect of Liquids was applied in order to simulate the exposition of TPE samples to detergent ingredients during service. For this purpose, ZnO loaded TPE samples were immersed in a 3.0% w/v detergent (neutral) and accelerated aging in oven at 70°C for 7 days. Compounds were characterized by changes in mechanical (hardness and tension properties) and mass. The Japan Industrial Standard (JIS) Z 2801:2010 was applied to evaluate antibacterial properties against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). The microbiological tests showed a reduction up to 42% in E. coli and up to 49% in S. aureus population in non-aged samples. There were observed variations in elongation and hardness values with the addition of zinc The changes in tensile at rupture and mass were not significant between non-aged and aged samples.

Keywords: antimicrobial, domestic appliance, sebs, zinc oxide

Procedia PDF Downloads 242

Authors: M. Abuqebitah, H. Tanyildizi, N. Yeyin, I. Cavdar, M. Demir, L. Kabasakal

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Aim: Targeted radionuclide therapy (TRT) is an increasingly used treatment modality for wide range of cancers. Presently dosimetry is highly required either to plan treatment or to ascertain the absorbed dose delivered to critical organs during treatment. Methods and Materials: The study comprised 7 patients suffered from prostate cancer with progressive disease and candidate to undergo Lu-177-DOTA-617 therapy following to PSMA- PET/CT imaging for all patients. (5.2±0.3 mCi) was intravenously injected. To evaluate bone marrow absorbed dose 2 cc blood samples were withdrawn in short variable times (3, 15, 30, 60, 180 minutes) after injection. Furthermore, whole body scans were performed using scintillation gama camera in 4, 24, 48, and 120 hours after injection and in order to quantify the activity taken up in the body, kidneys , liver, right parotid, and left parotid the geometric mean of anterior and posterior counts were determined through ROI analysis, after that background subtraction and attenuation correction were applied using patients PSMA- PET/CT images taking in a consideration: organ thickness, body thickness, and Hounsfield unites from CT scan. OLINDA/EXM dosimetry program was used for curve fitting, residence time calculation, and absorbed dose calculations. Findings: Absorbed doses of bone marrow, left kidney, right kidney, liver, left parotid, right parotid, total body were 1.28±0.52, 32.36±16.36, 32.7±13.68, 10.35±3.45, 38.67±21.29, 37.55±19.77, 2.25±0.95 (mGy/mCi), respectively. Conclusion: Our first results clarify that Lu-177-DOTA-617 is safe and reliable therapy as there were no complications seen. In the other hand, the observable variation in the absorbed dose of the critical organs among the patients necessitate patient-specific dosimetry approach to save body organs and particularly highly exposed kidneys and parotid gland.

Keywords: Lu-177-PSMA, prostate cancer, radionuclide therapy

Procedia PDF Downloads 477

Authors: Aaron Walayat

Abstract:

Protection of the environment in international law has been one of the most developed in the context of international humanitarian law. This paper examines the history of the protection of the environment in times of armed conflict, beginning with the traditional notion of restraint observed in antiquity towards the obligation to protect the environment, examining the treaties and agreements, both binding and non-binding which have contributed to environmental protection in war. The paper begins with a discussion of the ancient concept of restraint. This section examines the social norms in favor of protection of the environment as observed in the Bible, Greco-Roman mythology, and even more contemporary literature. The study of the traditional rejection of total war establishes the social foundation on which the current legal regime has stemmed. The paper then studies the principle of restraint as codified in international humanitarian law. It mainly examines Additional Protocol I of the Geneva Convention of 1949 and existing international law concerning civilian objects and the principles of international humanitarian law in the classification between civilian objects and military objectives. The paper then explores the environment’s classification as both a military objective and as a civilian object as well as explores arguments in favor of the classification of the whole environment as a civilian object. The paper will then discuss the current legal regime surrounding the protection of the environment, discussing some declarations and conventions including the 1868 Declaration of St. Petersburg, the 1907 Hague Convention No. IV, the Geneva Conventions, and the 1976 Environmental Modification Convention. The paper concludes with the outline noting the movement from codification of the principles of restraint into the various treaties, agreements, and declarations of the current regime of international humanitarian law. This paper provides an analysis of the history and significance of the relationship between international humanitarian law as a major contributor to the growing field of international environmental law.

Keywords: armed conflict, environment, legal regime, restraint

Procedia PDF Downloads 195

Authors: Galal Omer, Onisimo Mutanga, Elfatih M. Abdel-Rahman, Elhadi Adam

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Threatened tree species (TTS) play a significant role in ecosystem functioning and services, land use dynamics, and other socio-economic aspects. Such aspects include ecological, economic, livelihood, security-based, and well-being benefits. The development of techniques for mapping and monitoring TTS is thus critical for understanding the functioning of ecosystems. The advent of advanced imaging systems and supervised learning algorithms has provided an opportunity to classify TTS over fragmenting landscape. Recently, vegetation maps have been produced using advanced imaging systems such as WorldView-2 (WV-2) and robust classification algorithms such as support vectors machines (SVM) and artificial neural network (ANN). However, delineation of TTS in a fragmenting landscape using high resolution imagery has widely remained elusive due to the complexity of the species structure and their distribution. Therefore, the objective of the current study was to examine the utility of the advanced WV-2 data for mapping TTS in the fragmenting Dukuduku indigenous forest of South Africa using SVM and ANN classification algorithms. The results showed the robustness of the two machine learning algorithms with an overall accuracy (OA) of 77.00% (total disagreement = 23.00%) for SVM and 75.00% (total disagreement = 25.00%) for ANN using all eight bands of WV-2 (8B). This study concludes that SVM and ANN classification algorithms with WV-2 8B have the potential to classify TTS in the Dukuduku indigenous forest. This study offers relatively accurate information that is important for forest managers to make informed decisions regarding management and conservation protocols of TTS.

Keywords: artificial neural network, threatened tree species, indigenous forest, support vector machines

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Authors: Youhang Zhou, Weimin Zeng, Qi Xie

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Abstract: In order to solve the wear recognition problem of the machine tool guide surface, a new machine tool guide surface recognition method based on the radar-graph barycentre feature is presented in this paper. Firstly, the gray mean value, skewness, projection variance, flat degrees and kurtosis features of the guide surface image data are defined as primary characteristics. Secondly, data Visualization technology based on radar graph is used. The visual barycentre graphical feature is demonstrated based on the radar plot of multi-dimensional data. Thirdly, a classifier based on the support vector machine technology is used, the radar-graph barycentre feature and wear original feature are put into the classifier separately for classification and comparative analysis of classification and experiment results. The calculation and experimental results show that the method based on the radar-graph barycentre feature can detect the guide surface effectively.

Keywords: guide surface, wear defects, feature extraction, data visualization

Procedia PDF Downloads 516

Authors: Samson Mil'shtein, Dhawal Asthana, Benjamin Sullivan

Abstract:

The wide energy gap of GaN is the major parameter justifying the design and fabrication of high-power electronic components made of this material. However, the existence of a piezo-electrics in nature sheet charge at the AlGaN/GaN interface complicates the control of carrier injection into the intrinsic channel of GaN HEMTs (High Electron Mobility Transistors). As a result, most of the transistors created as R&D prototypes and all of the designs used for mass production are D-mode devices which introduce challenges in the design of integrated circuits. This research presents the design and modeling of an E-mode GaN HEMT with a very low turn-on voltage. The proposed device includes two critical elements allowing the transistor to achieve zero conductance across the channel when Vg = 0V. This is accomplished through the inclusion of an extremely thin, 2.5nm intrinsic Ga₀.₇₄Al₀.₂₆N spacer layer. The added spacer layer does not create piezoelectric strain but rather elastically follows the variations of the crystal structure of the adjacent GaN channel. The second important factor is the design of a gate metal with a high work function. The use of a metal gate with a work function (Ni in this research) greater than 5.3eV positioned on top of n-type doped (Nd=10¹⁷cm⁻³) Ga₀.₇₄Al₀.₂₆N creates the necessary built-in potential, which controls the injection of electrons into the intrinsic channel as the gate voltage is increased. The 5µm long transistor with a 0.18µm long gate and a channel width of 30µm operate at Vd=10V. At Vg =1V, the device reaches the maximum drain current of 0.6mA, which indicates a high current density. The presented device is operational at frequencies greater than 10GHz and exhibits a stable transconductance over the full range of operational gate voltages.

Keywords: compound semiconductors, device modeling, enhancement mode HEMT, gallium nitride

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Authors: Younes El Khchine, Mohammed Sriti

Abstract:

A parametric study has been conducted to analyse the flow around S809 airfoil of wind turbine in order to better understand the characteristics and effects of laminar separation bubble (LSB) on aerodynamic design for maximizing wind turbine efficiency. Numerical simulations were performed at low Reynolds number by solving the Unsteady Reynolds Averaged Navier-Stokes (URANS) equations based on C-type structural mesh and using γ-Reθt turbulence model. Two-dimensional study was conducted for the chord Reynolds number of 1×105 and angles of attack (AoA) between 0 and 20.15 degrees. The simulation results obtained for the aerodynamic coefficients at various angles of attack (AoA) were compared with XFoil results. A sensitivity study was performed to examine the effects of Reynolds number and free-stream turbulence intensity on the location and length of laminar separation bubble and aerodynamic performances of wind turbine. The results show that increasing the Reynolds number leads to a delay in the laminar separation on the upper surface of the airfoil. The increase in Reynolds number leads to an accelerate transition process and the turbulent reattachment point move closer to the leading edge owing to an earlier reattachment of the turbulent shear layer. This leads to a considerable reduction in the length of the separation bubble as the Reynolds number is increased. The increase of the level of free-stream turbulence intensity leads to a decrease in separation bubble length and an increase the lift coefficient while having negligible effects on the stall angle. When the AoA increased, the bubble on the suction airfoil surface was found to moves upstream to leading edge of the airfoil that causes earlier laminar separation.

Keywords: laminar separation bubble, turbulence intensity, S809 airfoil, transition model, Reynolds number

Procedia PDF Downloads 76

Authors: Aisulu Tileukulova, Zhexebay Dauren

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Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

Procedia PDF Downloads 349

Authors: Rajesh Ranjan, Marimuthu Palaniswami, A. A. Hashmi

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With the changing lifestyle and environment around us, the prevalence of the critical and incurable disease has proliferated. One such condition is the neurological disorder which is rampant among the old age population and is increasing at an unstoppable rate. Most of the neurological disorder patients suffer from some movement disorder affecting the movement of their body parts. Tremor is the most common movement disorder which is prevalent in such patients that infect the upper or lower limbs or both extremities. The tremor symptoms are commonly visible in Parkinson’s disease patient, and it can also be a pure tremor (essential tremor). The patients suffering from tremor face enormous trouble in performing the daily activity, and they always need a caretaker for assistance. In the clinics, the assessment of tremor is done through a manual clinical rating task such as Unified Parkinson’s disease rating scale which is time taking and cumbersome. Neurologists have also affirmed a challenge in differentiating a Parkinsonian tremor with the pure tremor which is essential in providing an accurate diagnosis. Therefore, there is a need to develop a monitoring and assistive tool for the tremor patient that keep on checking their health condition by coordinating them with the clinicians and caretakers for early diagnosis and assistance in performing the daily activity. In our research, we focus on developing a system for automatic classification of tremor which can accurately differentiate the pure tremor from the Parkinsonian tremor using a wearable accelerometer-based device, so that adequate diagnosis can be provided to the correct patient. In this research, a study was conducted in the neuro-clinic to assess the upper wrist movement of the patient suffering from Pure (Essential) tremor and Parkinsonian tremor using a wearable accelerometer-based device. Four tasks were designed in accordance with Unified Parkinson’s disease motor rating scale which is used to assess the rest, postural, intentional and action tremor in such patient. Various features such as time-frequency domain, wavelet-based and fast-Fourier transform based cross-correlation were extracted from the tri-axial signal which was used as input feature vector space for the different supervised and unsupervised learning tools for quantification of severity of tremor. A minimum covariance maximum correlation energy comparison index was also developed which was used as the input feature for various classification tools for distinguishing the PT and ET tremor types. An automatic system for efficient classification of tremor was developed using feature extraction methods, and superior performance was achieved using K-nearest neighbors and Support Vector Machine classifiers respectively.

Keywords: machine learning approach for neurological disorder assessment, automatic classification of tremor types, feature extraction method for tremor classification, neurological movement disorder, parkinsonian tremor, essential tremor

Procedia PDF Downloads 151

Authors: Uswah Khairuddin, Rubiyah Yusof, Nenny Ruthfalydia Rosli

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This paper presents a comparative study between k-Nearest Neighbour (k-NN) and Multi-Layer Perceptron Neural Network (MLP-NN) classifier using Genetic Algorithm (GA) as feature selector for wood recognition system. The features have been extracted from the images using Grey Level Co-Occurrence Matrix (GLCM). The use of GA based feature selection is mainly to ensure that the database used for training the features for the wood species pattern classifier consists of only optimized features. The feature selection process is aimed at selecting only the most discriminating features of the wood species to reduce the confusion for the pattern classifier. This feature selection approach maintains the ‘good’ features that minimizes the inter-class distance and maximizes the intra-class distance. Wrapper GA is used with k-NN classifier as fitness evaluator (GA-kNN). The results shows that k-NN is the best choice of classifier because it uses a very simple distance calculation algorithm and classification tasks can be done in a short time with good classification accuracy.

Keywords: feature selection, genetic algorithm, optimization, wood recognition system

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Authors: Yunwei Zhang, Na Li, Yuhong Niu

Abstract:

Bone tissue engineering scaffold was regarded as an important clinical technology of curing bony defect. The patent protection of bone tissue engineering scaffold had been paid more attention and strengthened all over the world. This study analyzed the future development trends of international technologies in the field of bone tissue engineering scaffold and its patent protection. This study used the methods of data classification and classification indexing to analyze 2718 patents retrieved in the patent database. Results showed that the patents coming from United States had a competitive advantage over other countiries in the field of bone tissue engineering scaffold. The number of patent applications by a single company in U.S. was a quarter of that of the world. However, the capability of R&D in China was obviously weaker than global level, patents mainly coming from universities and scientific research institutions. Moreover, it would be predicted that synthetic organic materials as new materials would be gradually replaced by composite materials. The patent technology protections of composite materials would be more strengthened in the future.

Keywords: bone tissue engineering, patent analysis, Scaffold material, patent protection

Procedia PDF Downloads 131

Authors: Sonia Anand Knowlton

Abstract:

With the rise of artificial intelligence, computer science is becoming increasingly integrated in virtually every area of life. Of course, the law is no exception. Through argumentation frameworks (AFs), computer scientists have used abstract algebra to structure the legal reasoning process in a way that allows conclusions to be drawn from a formalized system of arguments. In AFs, arguments compete against each other for logical success and are related to one another through the binary operation of the attack. The prevailing arguments make up the preferred extension of the given argumentation framework, telling us what set of arguments must be accepted from a logical standpoint. There have been several developments of AFs since its original conception in the early 90’s in efforts to make them more aligned with the human reasoning process. Generally, these developments have sought to add nuance to the factors that influence the logical success of competing arguments (e.g., giving an argument more logical strength based on the underlying value it promotes). The most cogent development was that of the Extended Argumentation Framework (EAF), in which attacks can themselves be attacked by other arguments, and the promotion of different competing values can be formalized within the system. This article applies the logical structure of EAFs to current theoretical understandings of judicial reasoning to contribute to theories of judging and to the evolution of AFs simultaneously. The argument is that the main limitation of EAFs, when applied to judicial reasoning, is that they require judges to themselves assign values to different arguments and then lexically order these values to determine the given framework’s preferred extension. Drawing on John Rawls’ Theory of Justice, the examination that follows is whether values are lexical and commensurable to this extent. The analysis that follows then suggests a potential extension of the EAF system with an approach that formalizes different “planes of attack” for competing arguments that promote lexically ordered values. This article concludes with a summary of how these insights contribute to theories of judging and of legal reasoning more broadly, specifically in indeterminate cases where judges must turn to value-based approaches.

Keywords: computer science, mathematics, law, legal theory, judging

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Authors: Meng H. Lean, Wei-Ping L. Chu

Abstract:

The effects of ferroelectric nanofiller size, shape, loading, and polarization, on bipolar charge injection, transport, and recombination through amorphous and semicrystalline polymers are studied. A 3D particle-in-cell model extends the classical electrical double layer representation to treat ferroelectric nanoparticles. Metal-polymer charge injection assumes Schottky emission and Fowler-Nordheim tunneling, migration through field-dependent Poole-Frenkel mobility, and recombination with Monte Carlo selection based on collision probability. A boundary integral equation method is used for solution of the Poisson equation coupled with a second-order predictor-corrector scheme for robust time integration of the equations of motion. The stability criterion of the explicit algorithm conforms to the Courant-Friedrichs-Levy limit. Trajectories for charge that make it through the film are curvilinear paths that meander through the interspaces. Results indicate that charge transport behavior depends on nanoparticle polarization with anti-parallel orientation showing the highest leakage conduction and lowest level of charge trapping in the interaction zone. Simulation prediction of a size range of 80 to 100 nm to minimize attachment and maximize conduction is validated by theory. Attached charge fractions go from 2.2% to 97% as nanofiller size is decreased from 150 nm to 60 nm. Computed conductivity of 0.4 x 1014 S/cm is in agreement with published data for plastics. Charge attachment is increased with spheroids due to the increase in surface area, and especially so for oblate spheroids showing the influence of larger cross-sections. Charge attachment to nanofillers and nanocrystallites increase with vol.% loading or degree of crystallinity, and saturate at about 40 vol.%.

Keywords: nanocomposites, nanofillers, electrical double layer, bipolar charge transport

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Authors: Apostolos Kalatzis, Ashish Teotia, Vishnunarayan Girishan Prabhu, Laura Stanley

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Emotions are functional behaviors influenced by thoughts, stimuli, and other factors that induce neurophysiological changes in the human body. Understanding and classifying emotions are challenging as individuals have varying perceptions of their environments. Therefore, it is crucial that there are publicly available databases and virtual reality (VR) based environments that have been scientifically validated for assessing emotional classification. This study utilized two commercially available VR applications (Guided Meditation VR™ and Richie’s Plank Experience™) to induce acute stress and calm state among participants. Subjective and objective measures were collected to create a validated multimodal dataset and classification scheme for affective state classification. Participants’ subjective measures included the use of the Self-Assessment Manikin, emotional cards and 9 point Visual Analogue Scale for perceived stress, collected using a Virtual Reality Assessment Tool developed by our team. Participants’ objective measures included Electrocardiogram and Respiration data that were collected from 25 participants (15 M, 10 F, Mean = 22.28  4.92). The features extracted from these data included heart rate variability components and respiration rate, both of which were used to train two machine learning models. Subjective responses validated the efficacy of the VR applications in eliciting the two desired affective states; for classifying the affective states, a logistic regression (LR) and a support vector machine (SVM) with a linear kernel algorithm were developed. The LR outperformed the SVM and achieved 93.8%, 96.2%, 93.8% leave one subject out cross-validation accuracy, precision and recall, respectively. The VR assessment tool and data collected in this study are publicly available for other researchers.

Keywords: affective computing, biosignals, machine learning, stress database

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Authors: John Farr Rothrock

Abstract:

Although migraine traditionally has been assumed to convey a relative decrease in libido, sexual activity and sexual performance, recent data have suggested that the female migraine population is far from homogenous in this regard. We sought to determine the levels of libido, sexual activity and sexual performance in the female migraine patient population both generally and according to clinical phenotype. In this single-blind study, a consecutive series of sexually active new female patients ages 25-55 initially presenting to a university-based headache clinic and having a >1 year history of migraine were asked to complete anonymously a survey assessing their sexual histories generally and as they related to their headache disorder and the 19-item Female Sexual Function Index (FSFI). To serve as 2 separate control groups, 100 sexually active females with no history of migraine and 100 female migraineurs from the general (non-clinic) population but matched for age, marital status, educational background and socioeconomic status completed a similar survey. Over a period of 3 months, 188 consecutive migraine patients were invited to participate. Twenty declined, and 28 of the remaining 160 potential subjects failed to meet the inclusion criterion utilized for “sexually active” (ie, heterosexual intercourse at a frequency of > once per month in each of the preceding 6 months). In all groups younger age (p<.005), higher educational level attained (p<.05) and higher socioeconomic status (p<.025) correlated with a higher monthly frequency of intercourse and a higher likelihood of intercourse resulting in orgasm. Relative to the 100 control subjects with no history of migraine, the two migraine groups (total n=232) reported a lower monthly frequency of intercourse and recorded a lower FSFI score (both p<.025), but the contribution to this difference came primarily from the chronic migraine (CM) subgroup (n=92). Patients with low frequency episodic migraine (LFEM) and mid frequency episodic migraine (MFEM) reported a higher FSFI score, higher monthly frequency of intercourse, higher likelihood of intercourse resulting in orgasm and higher likelihood of multiple active sex partners than controls. All migraine subgroups reported a decreased likelihood of engaging in intercourse during an active migraine attack, but relative to the CM subgroup (8/92=9%), a higher proportion of patients in the LFEM (12/49=25%), MFEM (14/67=21%) and high frequency episodic migraine (HFEM: 6/14=43%) subgroups reported utilizing intercourse - and orgasm specifically - as a means of potentially terminating a migraine attack. In the clinic vs no-clinic groups there were no significant differences in the dependent variables assessed. Research subjects with LFEM and MFEM may report a level of libido, frequency of intercourse and likelihood of orgasm-associated intercourse that exceeds what is reported by age-matched controls free of migraine. Many patients with LFEM, MFEM and HFEM appear to utilize intercourse/orgasm as a means to potentially terminate an acute migraine attack.

Keywords: migraine, female, libido, sexual activity, phenotype

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Authors: Mariem Kchaou, Wiem Khlif, Faiez Gargouri

Abstract:

Modeling business processes is an essential task when evaluating, improving, or documenting existing business processes. To be efficient in such tasks, a business process model (BPM) must have high structural quality and high performance. Evidently, evaluating the performance of a business process model is a necessary step to reduce time, cost, while assessing the structural quality aims to improve the understandability and the modifiability of the BPMN model. To achieve these objectives, a set of structural and performance measures have been proposed. Since the diversity of measures, we propose a framework that integrates both structural and performance aspects for classifying them. Our measure classification is based on business process model perspectives (e.g., informational, functional, organizational, behavioral, and temporal), and the elements (activity, event, actor, etc.) involved in computing the measures. Then, we implement this framework in a tool assisting the structural quality and the performance of a business process. The tool helps the designers to select an appropriate subset of measures associated with the corresponding perspective and to calculate and interpret their values in order to improve the structural quality and the performance of the model.

Keywords: performance, structural quality, perspectives, tool, classification framework, measures

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Authors: Nagui A. Abdel-Khalek, Khaled A. Selim, Ahmed Hamdy

Abstract:

Kaolin is naturally occurring ore predominantly containing kaolinite mineral in addition to some gangue minerals. Typical impurities present in kaolin ore are quartz, iron oxides, titanoferrous minerals, mica, feldspar, organic matter, etc. The main coloring impurity, particularly in the ultrafine size range, is titanoferrous minerals. Kaolin is used in many industrial applications such as sanitary ware, table ware, ceramic, paint, and paper industries, each of which should be of certain specifications. For most industrial applications, kaolin should be processed to obtain refined clay so as to match with standard specifications. For example, kaolin used in paper and paint industries need to be of high brightness and low yellowness. Egyptian kaolin is not subjected to any beneficiation process and the Egyptian companies apply selective mining followed by, in some localities, crushing and size reduction only. Such low quality kaolin can be used in refractory and pottery production but not in white ware and paper industries. This paper aims to study the amenability of beneficiation of an Egyptian kaolin ore of El-Teih locality, Sinai, to be suitable for different industrial applications. Attrition scrubbing and classification followed by magnetic separation are applied to remove the associated impurities. Attrition scrubbing and classification are used to separate the coarse silica and feldspars. Wet high intensity magnetic separation was applied to remove colored contaminants such as iron oxide and titanium oxide. Different variables affecting of magnetic separation process such as solid percent, magnetic field, matrix loading capacity, and retention time are studied. The results indicated that substantial decrease in iron oxide (from 1.69% to 0.61% ) and TiO2 (from 3.1% to 0.83%) contents as well as improving iso-brightness (from 63.76% to 75.21% and whiteness (from 79.85% to 86.72%) of the product can be achieved.

Keywords: Kaolin, titanoferrous minerals, beneficiation, magnetic separation, attrition scrubbing, classification

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Authors: Taha SamadSoltani, Peyman Rezaei Hachesu, Marjan GhaziSaeedi, Maryam Zolnoori

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Introduction: Data mining defined as a process to find patterns and relationships along data in the database to build predictive models. Application of data mining extended in vast sectors such as the healthcare services. Medical data mining aims to solve real-world problems in the diagnosis and treatment of diseases. This method applies various techniques and algorithms which have different accuracy and precision. The purpose of this study was to apply knowledge discovery and data mining techniques for the diagnosis of asthma based on patient symptoms and history. Method: Data mining includes several steps and decisions should be made by the user which starts by creation of an understanding of the scope and application of previous knowledge in this area and identifying KD process from the point of view of the stakeholders and finished by acting on discovered knowledge using knowledge conducting, integrating knowledge with other systems and knowledge documenting and reporting.in this study a stepwise methodology followed to achieve a logical outcome. Results: Sensitivity, Specifity and Accuracy of KNN, SVM, Naïve bayes, NN, Classification tree and CN2 algorithms and related similar studies was evaluated and ROC curves were plotted to show the performance of the system. Conclusion: The results show that we can accurately diagnose asthma, approximately ninety percent, based on the demographical and clinical data. The study also showed that the methods based on pattern discovery and data mining have a higher sensitivity compared to expert and knowledge-based systems. On the other hand, medical guidelines and evidence-based medicine should be base of diagnostics methods, therefore recommended to machine learning algorithms used in combination with knowledge-based algorithms.

Keywords: asthma, datamining, classification, machine learning

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Authors: Rodrigo Arnaldo Scarpel

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Trend following is a widely accepted investment strategy that adopts a rule-based trading mechanism that rather than striving to predict market direction or on information gathering to decide when to buy and when to sell a stock. Thus, in trend following one must respond to market’s movements that has recently happen and what is currently happening, rather than on what will happen. Optimally, in trend following strategy, is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. For applying the trend following strategy one needs to find the trend and identify trade signals. In order to avoid false signals, i.e., identify fluctuations of short, mid and long terms and to separate noise from real changes in the trend, most academic works rely on moving averages and other technical analysis indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI) to uncover intelligible stock trading rules following trend following strategy philosophy. Recently, some works has applied machine learning techniques for trade rules discovery. In those works, the process of rule construction is based on evolutionary learning which aims to adapt the rules to the current environment and searches for the global optimum rules in the search space. In this work, instead of focusing on the usage of machine learning techniques for creating trading rules, a time series trend classification employing a semi-supervised approach was used to early identify both the beginning and the end of upward and downward trends. Such classification model can be employed to identify trade signals and the decision-making procedure is that if an up-trend (down-trend) is identified, a buy (sell) signal is generated. Semi-supervised learning is used for model training when only part of the data is labeled and Semi-supervised classification aims to train a classifier from both the labeled and unlabeled data, such that it is better than the supervised classifier trained only on the labeled data. For illustrating the proposed approach, it was employed daily trade information, including the open, high, low and closing values and volume from January 1, 2000 to December 31, 2022, of the São Paulo Exchange Composite index (IBOVESPA). Through this time period it was visually identified consistent changes in price, upwards or downwards, for assigning labels and leaving the rest of the days (when there is not a consistent change in price) unlabeled. For training the classification model, a pseudo-label semi-supervised learning strategy was used employing different technical analysis indicators. In this learning strategy, the core is to use unlabeled data to generate a pseudo-label for supervised training. For evaluating the achieved results, it was considered the annualized return and excess return, the Sortino and the Sharpe indicators. Through the evaluated time period, the obtained results were very consistent and can be considered promising for generating the intended trading signals.

Keywords: evolutionary learning, semi-supervised classification, time series data, trading signals generation

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Authors: Mandayachepa C. Nyando, Lauren Suchman, Innocencia Mtalimanja, Address Malata, Tamanda Jumbe, Martha Kamanga, Peter Waiswa

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Background: Subcutaneous depot medroxyprogesterone acetate (DMPA-SC) is a new innovation in contraceptive methods that allow users to inject themselves with a hormonal contraceptive in their own homes. Self-injection (SI) of DMPA-SC has the potential to improve the accessibility of family planning to women who want it and who are capable of injecting themselves. Malawi started implementing this new innovation in 2018. SI was incorporated into the DMPA-SC delivery strategy from its outset. Methodology: This study involved two districts in Malawi where DMPA-SC SI was rolled out: Mulanje and Ntchisi. We used a qualitative cross-sectional study design where 60 in-depth interviews were conducted with women of reproductive age group stratified as 15-45 age band. These included women who were SI users, non-users, and any woman who was on any contraceptive methods. The women participants were tape-recorded, and data were transcribed and then analysed using Dedoose software, where themes were categorised into mother and child themes. Results: Women perceived DMPA SC SI as uniquely private, convenient, and less painful when self-injected. In terms of privacy, women in Mulanje and Ntchisi especially appreciated that self-injecting allowed them to use covertly from partners. Some men do not allow their spouses to use modern contraceptive methods; hence women prefer to use them covertly. “… but I first reach out to men because the strongest power is answered by men (MJ015).” In addition, women reported that SI offers privacy from family/community and less contact with healthcare providers. These aspects of privacy were especially valued in areas where there is a high degree of mistrust around family planning and among those who feel judged or antagonized purchasing contraception, such as young unmarried women. Women also valued the convenience SI provided in terms of their ability to save time by injecting themselves at home rather than visiting a healthcare provider and having more reliable access to contraception, particularly in the face of stockouts. SI allows for stocking up on doses to accommodate shifting work schedules in case of future stockouts or hard times, such as the period of COVID-19, where there was a limitation in the movement of the people. Conclusion: Our findings suggest that SI may meet the needs of many women in Malawi as long as the barriers are eliminated. The barriers women mentioned include fear of self-inject and proper storage of the DMPA SC SI, and these barriers can be eliminated by proper training. The findings also set the scene for policy revision and direction at a national level and integrate the approach with national family planning strategies in Malawi. Findings provide insights that may guide future implementation strategies, strengthen non-clinic family planning access programs and stimulate continued research.

Keywords: family planning, Malawi, Sayana press, self-injection

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Authors: Worawanna Panyakotkaew, Falan Srisuriyachai

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Cyclic steam injection (CSI) is a thermal recovery technique performed by injecting periodically heated steam into heavy oil reservoir. Oil viscosity is substantially reduced by means of heat transferred from steam. Together with gas pressurization, oil recovery is greatly improved. Nevertheless, prediction of effectiveness of the process is difficult when reservoir contains degree of heterogeneity. Therefore, study of heterogeneity together with interest reservoir properties must be evaluated prior to field implementation. In this study, thermal reservoir simulation program is utilized. Reservoir model is firstly constructed as multi-layered with coarsening upward sequence. The highest permeability is located on top layer with descending of permeability values in lower layers. Steam is injected from two wells located diagonally in quarter five-spot pattern. Heavy oil is produced by adjusting operating parameters including soaking period and steam quality. After selecting the best conditions for both parameters yielding the highest oil recovery, effects of degree of heterogeneity (represented by Lorenz coefficient), vertical permeability and permeability sequence are evaluated. Surprisingly, simulation results show that reservoir heterogeneity yields benefits on CSI technique. Increasing of reservoir heterogeneity impoverishes permeability distribution. High permeability contrast results in steam intruding in upper layers. Once temperature is cool down during back flow period, condense water percolates downward, resulting in high oil saturation on top layers. Gas saturation appears on top after while, causing better propagation of steam in the following cycle due to high compressibility of gas. Large steam chamber therefore covers most of the area in upper zone. Oil recovery reaches approximately 60% which is of about 20% higher than case of heterogeneous reservoir. Vertical permeability exhibits benefits on CSI. Expansion of steam chamber occurs within shorter time from upper to lower zone. For fining upward permeability sequence where permeability values are reversed from the previous case, steam does not override to top layers due to low permeability. Propagation of steam chamber occurs in middle of reservoir where permeability is high enough. Rate of oil recovery is slower compared to coarsening upward case due to lower permeability at the location where propagation of steam chamber occurs. Even CSI technique produces oil quite slowly in early cycles, once steam chamber is formed deep in the reservoir, heat is delivered to formation quickly in latter cycles. Since reservoir heterogeneity is unavoidable, a thorough understanding of its effect must be considered. This study shows that CSI technique might be one of the compatible solutions for highly heterogeneous reservoir. This competitive technique also shows benefit in terms of heat consumption as steam is injected periodically.

Keywords: cyclic steam injection, heterogeneity, reservoir simulation, thermal recovery

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Authors: Stefano Zappacosta, Cristiano Bove, Maria Carmela Marinelli, Paola di Lauro, Katarina Spasenovic, Lorenzo Ostano, Giuseppe Aiello, Marco Pietrosanto

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This paper proposes a novel neural network approach for assessing flood hazard mapping. The core of the model is a machine learning component fed by frequency ratios, namely statistical correlations between flood event occurrences and a selected number of topographic properties. The proposed hybrid model can be used to classify four different increasing levels of hazard. The classification capability was compared with the flood hazard mapping River Basin Plans (PAI) designed by the Italian Institute for Environmental Research and Defence, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale). The study area of Piemonte, an Italian region, has been considered without loss of generality. The frequency ratios may be used as a standalone block to model the flood hazard mapping. Nevertheless, the mixture with a neural network improves the classification power of several percentage points, and may be proposed as a basic tool to model the flood hazard map in a wider scope.

Keywords: flood modeling, hazard map, neural networks, hydrogeological risk, flood risk assessment

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Authors: Mukesh Singh Boori, Vít Voženílek

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Olomouc is a unique and complex landmark with widespread forestation and land use. This research work was conducted to assess important and complex land use change trajectories in Olomouc region. Multi-temporal satellite data from 1991, 2001 and 2013 were used to extract land use/cover types by object oriented classification method. To achieve the objectives, three different aspects were used: (1) Calculate the quantity of each transition; (2) Allocate location based landscape pattern (3) Compare land use/cover evaluation procedure. Land cover change trajectories shows that 16.69% agriculture, 54.33% forest and 21.98% other areas (settlement, pasture and water-body) were stable in all three decade. Approximately 30% of the study area maintained as a same land cove type from 1991 to 2013. Here broad scale of political and socio-economic factors was also affect the rate and direction of landscape changes. Distance from the settlements was the most important predictor of land cover change trajectories. This showed that most of landscape trajectories were caused by socio-economic activities and mainly led to virtuous change on the ecological environment.

Keywords: remote sensing, land use/cover, change trajectories, image classification

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Authors: Madhuri S. Ghate, Rahul P. Chavhan, Shriya S. Nahar

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Learning objective: •To describe Magnetic Resonance Imaging (MRI) imaging appearances of typical and atypical uterine fibroids with emphasis on differentiating it from other similar conditions. •To classify uterine fibroids according to International Federation of Gynecology and Obstetrics (FIGO) Sub-classifications system and emphasis on its clinical significance. •To show cases with atypical imaging appearances atypical fibroids Material and methods: MRI of Pelvis had been performed in symptomatic women of child bearing age group on 1.5T and 3T MRI using T1, T2, STIR, FAT SAT, DWI sequences. Contrast was administered when degeneration was suspected. Imaging appearances of Atypical fibroids and various degenerations in fibroids were studied. Fibroids were classified using FIGO Sub-classification system. Its impact on surgical decision making and clinical outcome were also studied qualitatively. Results: Intramural fibroids were most common (14 patients), subserosal 7 patients, submucosal 5 patients . 6 patients were having multiple fibroids. 7 were having atypical fibroids. (1 hyaline degeneration, 1 cystic degeneration, 1 fatty, 1 necrosis and hemorrhage, 1 red degeneration, 1 calcification, 1 unusual large bilobed growth). Fibroids were classified using FIGO system. In uterus conservative surgeries, the lesser was the degree of myometrial invasion of fibroid, better was the fertility outcome. Conclusion: Relationship of fibroid with mucosal and serosal layers is important in the management of symptomatic fibroid cases. Risk to fertility involved in uterus conservative surgeries in women of child bearing age group depends on the extent of myometrial invasion of fibroids. FIGO system provides better insight into the degree of myometrial invasion. Knowledge about the atypical appearances of fibroids is important to avoid diagnostic confusion and untoward treatment.

Keywords: degeneration, FIGO sub-classification, MRI pelvis, uterine fibroids

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Authors: Maha Shamseddine, Amjad Nusayr, Wassim Itani

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In this paper, we present a Platform-as-a-Service (PaaS) model for controlling the privacy enforcement mechanisms applied on user data when stored and processed in Cloud data centers. The proposed architecture consists of establishing user configurable ‘sticky’ policies on the Graphical User Interface (GUI) data-bound components during the application development phase to specify the details of privacy enforcement on the contents of these components. Various privacy classification classes on the data components are formally defined to give the user full control on the degree and scope of privacy enforcement including the type of execution containers to process the data in the Cloud. This not only enhances the privacy-awareness of the developed Cloud services, but also results in major savings in performance and energy efficiency due to the fact that the privacy mechanisms are solely applied on sensitive data units and not on all the user content. The proposed design is implemented in a real PaaS cloud computing environment on the Microsoft Azure platform.

Keywords: privacy enforcement, platform-as-a-service privacy awareness, cloud computing privacy

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Authors: Lubna Khondker

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Background: Melasma is a common pigmentary dermatosis, manifested by hyperpigmented macules or patches on the face, commonly occurring in females due to an acquired disorder in the melanogenesis process. Although several treatments are currently used, it remains a great challenge due to recurrence and refractory nature. It was recently reported that tranexamic acid (TA-plasmin inhibitor) is an effective treatment for melasma. Objective: This study aims to compare the efficacy and side effects of intralesional injection of Tranexamic acid with oral Tranexamic acid in the treatment of melasma. Methods: A clinical trial was done in the Department of Dermatology and Venereology, Bangabandhu Sheikh Mujib Medical University, for a period of 4 years. A total of 100 patients with melasma who did not respond to topical therapy were included in the study as group A and group B. Group A Patients were administered intralesional injection (10 mg/ml) of Tranexamic acid( TA) weekly for 6 weeks, and group B patients were treated with oral tranexamic acid 250 mg 12 hourly for 12 weeks after taking informed consent. The severity and extent of pigmentation were assessed by the modified melasma area severity index (MASI). The response to treatment was assessed by MASI at 4 weeks, 8 weeks, and 12 weeks after stopping treatment. Results: The study showed the MASI scores at the baseline, 4 weeks, 8 weeks, and 12 weeks in group A were 18.23±1.22, 6.14±3.26, 3.21±2.14 and 2.11±2.01 respectively, and in group B, 17.87±1.12, 11.21±6.25, 6.57±4.26 and 6.41±4.17 respectively. The mean MASI significantly reduced in group A compared to group B in the 4th, 8th, and 12th weeks. The present study showed that among group A patients, 56% rated excellent (>75% reduction) in outcome, 32% good (50-75% reduction), 8% moderate (25-50% reduction) and only 4% (<25% reduction) was unsatisfactory and among group B patients, 14% rated excellent in outcome, 28% good, 36% moderate and 22% was unsatisfactory. Overall improvement in our study in group A was 96% and in group B 78%. Side effects were negligible, and all the patients tolerated the treatment well. Conclusion: Based on our results, intralesional Tranexamic acid (10 mg/ml) is more effective and safer than oral Tranexamic acid in the treatment of melasma.

Keywords: intralesional tranexamic acid, melasma, oral tranexamic acid, MASI score

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Authors: Zyad Elkhadir, Khalid Chougdali, Mohammed Benattou

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Most of the existing intrusion detection systems works on quantitative network traffic data with many irrelevant and redundant features, which makes detection process more time’s consuming and inaccurate. A several feature extraction methods, such as linear discriminant analysis (LDA), have been proposed. However, LDA suffers from the small sample size (SSS) problem which occurs when the number of the training samples is small compared with the samples dimension. Hence, classical LDA cannot be applied directly for high dimensional data such as network traffic data. In this paper, we propose two solutions to solve SSS problem for LDA and apply them to a network IDS. The first method, reduce the original dimension data using principal component analysis (PCA) and then apply LDA. In the second solution, we propose to use the pseudo inverse to avoid singularity of within-class scatter matrix due to SSS problem. After that, the KNN algorithm is used for classification process. We have chosen two known datasets KDDcup99 and NSLKDD for testing the proposed approaches. Results showed that the classification accuracy of (PCA+LDA) method outperforms clearly the pseudo inverse LDA method when we have large training data.

Keywords: LDA, Pseudoinverse, PCA, IDS, NSL-KDD, KDDcup99

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Authors: Atanu K Samanta, Asim Ali Khan

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Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good.

Keywords: brain tumor, computer-aided diagnostic (CAD) system, gray-level co-occurrence matrix (GLCM), tumor segmentation, level set method

Procedia PDF Downloads 505