Search results for: stochastic signals

Authors: Marianna Bolla

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The aim of this paper is to compare spectral, discrepancy, and degree properties of expanding graph sequences. As we can prove equivalences and implications between them and the definition of the generalized (multiclass) quasirandomness of Lovasz–Sos (2008), they can be regarded as generalized quasirandom properties akin to the equivalent quasirandom properties of the seminal Chung-Graham-Wilson paper (1989) in the one-class scenario. Since these properties are valid for deterministic graph sequences, irrespective of stochastic models, the partial implications also justify for low-dimensional embedding of large-scale graphs and for discrepancy minimizing spectral clustering.

Keywords: generalized random graphs, multiway discrepancy, normalized modularity spectra, spectral clustering

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Authors: Tet H. Yeap

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An analog restricted Hopfield Network is presented in this paper. It consists of two layers of nodes, visible and hidden nodes, connected by directional weighted paths forming a bipartite graph with no intralayer connection. An energy or Lyapunov function was derived to show that the proposed network will converge to stable states. By introducing hidden nodes, the proposed network can be trained to store patterns and has increased memory capacity. Training to be an associative memory, simulation results show that the associative memory performs better than a classical Hopfield network by being able to perform better memory recall when the input is noisy.

Keywords: restricted Hopfield network, Lyapunov function, simultaneous perturbation stochastic approximation

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Authors: E. M. Condori-Peñaloza, S. S. Costa

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Eleusine indica L, known as goose-grass, is considered a troublesome weed that can cause important economic losses in the agriculture worldwide. However, this grass is used as a medicinal plant in some regions of Brazil to treat influenza and pneumonia. In Africa and Asia, it is used to treat malaria and as diuretic, anti-helminthic, among other uses. Despite its therapeutic potential, little is known about the chemical composition and bioactive compounds of E. indica. Hitherto, two major flavonoids, schaftoside and vitexin, were isolated from aerial part of the species and showed inhibitory activity on lung neutrophil influxes in mice, suggesting a beneficial effect on airway inflammation. Therefore, the aim of this study was to analyze the chemical profile of aqueous extracts from aerial parts of Eleusine indica specimens using high performance liquid chromatography (HPLC-DAD) and 1H nuclear magnetic resonance spectroscopy (NMR), with emphasis on phenolic compounds. Specimens of E. indica were collected in Minas Gerais state, Brazil. Aerial parts of fresh plants were extracted by decoction (10% p/v). After spontaneous precipitation of the aqueous extract at 10-12°C for 24 hours, the supernatant obtained was frozen and lyophilized. After that, 1 g of the extract was dissolved into 25 mL of water and fractionated on a reverse phase chromatography column (RP-2), eluted with a gradient of H2O/EtOH. Five fractions were obtained. The extract and fractions had their chemical profile analyzed by using HPLC-DAD (C-18 column: 20 μL, 256 -365 nm; gradient water 0.01% phosphoric acid/ acetonitrile. The extract was also analyzed by NMR (1H, 500 MHz, D2O) in order to access its global chemical composition. HPLC-DAD analyses of crude extract allowed the identification of ten phenolic compounds. Fraction 1, eluted with 100% water, was poor in phenolic compounds and no major peak was detected. In fraction 2, eluted with 100% water, it was possible to observe one major peak at retention time (RT) of 23.75 minutes compatible with flavonoid; fraction 3, also eluted with 100% water, showed four peaks at RT= 21.47, 23.52, 24.33 and 25.84 minutes, all of them compatible with flavonoid. In fraction 4, eluted with 50%/ethanol/50% water, it was possible to observe 3 peaks compatible with flavonoids at RT=24.65, 26.81, 27.49 minutes, and one peak (28.83 min) compatible with a phenolic acid derivative. Finally, in fraction 5, eluted with 100% ethanol, no phenolic substance was detected. The UV spectra of all flavonoids detected were compatible with the flavone subclass (λ= 320-345 nm). The 1H NMR spectra of aerial parts extract showed signals in three regions: δ 0.8-3.0 ppm (aliphatic compounds), δ 3.0-5.5 ppm corresponding to carbohydrates (signals most abundant and overlapped), and δ 6.0-8.5 ppm (aromatic compounds). Signals compatible with flavonoids (rings A and B) could also be detected in the crude extract spectra. These results suggest the presence of several flavonoids in E. indica, which reinforces their therapeutic potential. The pharmacological activities of Eleusine indica extracts and fractions will be further evaluated.

Keywords: flavonoids, HPLC, NMR, phenolic compounds

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Authors: Fathi Abid, Bilel Kaffel

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The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

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Authors: Yi Yang, Jiasong Sun

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Almost every aspect of our daily lives is now intertwined with some degree of human-computer interaction (HCI). HCI courses draw on knowledge from disciplines as diverse as computer science, psychology, design principles, anthropology, and more. Our HCI courses, named the Media and Cognition course, are constantly updated to reflect state-of-the-art technological advancements such as virtual reality, augmented reality, and artificial intelligence-based interactions. For more than a decade, our course has used an interest-based approach to teaching, in which students proactively propose some research-based questions and collaborate with teachers, using course knowledge to explore potential solutions. Semantic communication plays a key role in facilitating understanding and interaction between users and computer systems, ultimately enhancing system usability and user experience. The advancements in AI-generated technology, which have gained significant attention from both academia and industry in recent years, are exemplified by language models like GPT-3 that generate human-like dialogues from given prompts. Our latest version of the Human-Computer Interaction course practices a semantic communication platform based on AI-generated techniques. The purpose of this semantic communication is twofold: to extract and transmit task-specific information while ensuring efficient end-to-end communication with minimal latency. An AI-generated semantic communication platform evaluates the retention of signal sources and converts low-retain ability visual signals into textual prompts. These data are transmitted through AI-generated techniques and reconstructed at the receiving end; on the other hand, visual signals with a high retain ability rate are compressed and transmitted according to their respective regions. The platform and associated research are a testament to our students' growing ability to independently investigate state-of-the-art technologies.

Keywords: human-computer interaction, media and cognition course, semantic communication, retainability, prompts

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Authors: Hadj Abd El Kader Benghenia, Fethi Bereksi Reguig

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In clinical medicine, blood pressure, raised blood hemodynamic monitoring is rich pathophysiological information of cardiovascular system, of course described through factors such as: blood volume, arterial compliance and peripheral resistance. In this work, we are interested in analyzing these signals to propose a detection algorithm to delineate the different sequences and especially systolic blood pressure (SBP), diastolic blood pressure (DBP), and the wave and dicrotic to do their analysis in order to extract the cardiovascular parameters.

Keywords: blood pressure, SBP, DBP, detection algorithm

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Authors: Laura Helena Caicedo Lopez, Luis Miguel Contreras Medina, Ramon Gerardo Guevara Gonzales, Juan E. Andrade

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In this study, we evaluated the effect of three different hydric stress conditions: Low (LHS), medium (MHS), and high (HHS) on capsaicinoid content and enzyme regulation of C. annuum plants. Five main peaks were detected using a 2 Hz resolution vibrometer laser (Polytec-B&K). These peaks or “characteristic frequencies” were used as acoustic emissions (AEs) treatment, transforming these signals into audible sound with the frequency (Hz) content of each hydric stress. Capsaicinoids (CAPs) are the main, secondary metabolites of chili pepper plants and are known to increase during hydric stress conditions or short drought-periods. The AEs treatments were applied in two plant stages: the first one was in the pre-anthesis stage to evaluate the genes that encode the transcription of enzymes responsible for diverse metabolic activities of C. annuum plants. For example, the antioxidant responses such as peroxidase (POD), superoxide dismutase (Mn-SOD). Also, phenyl-alanine ammonia-lyase (PAL) involved in the biosynthesis of the phenylpropanoid compounds. The chalcone synthase (CHS) related to the natural defense mechanisms and species-specific aquaporin (CAPIP-1) that regulate the flow of water into and out of cells. The second stage was at 40 days after flowering (DAF) to evaluate the biochemical effect of AEs related to hydric stress on capsaicinoids production. These two experiments were conducted to identify the molecular responses of C. annuum plants to AE. Moreover, to define AEs could elicit any increase in the capsaicinoids content after a one-week exposition to AEs treatments. The results show that all AEs treatment signals (LHS, MHS, and HHS) were significantly different compared to the non-acoustic emission control (NAE). Also, the AEs induced the up-regulation of POD (~2.8, 2.9, and 3.6, respectively). The gene expression of another antioxidant response was particularly treatment-dependent. The HHS induced and overexpression of Mn-SOD (~0.23) and PAL (~0.33). As well, the MHS only induced an up-regulation of the CHs gene (~0.63). On the other hand, CAPIP-1 gene gas down-regulated by all AEs treatments LHS, MHS, and HHS ~ (-2.4, -0.43 and -6.4, respectively). Likewise, the down-regulation showed particularities depending on the treatment. LHS and MHS induced downregulation of the SOD gene ~ (-1.26 and -1.20 respectively) and PAL (-4.36 and 2.05, respectively). Correspondingly, the LHS and HHS showed the same tendency in the CHs gene, respectively ~ (-1.12 and -1.02, respectively). Regarding the elicitation effect of AE on the capsaicinoids content, additional treatment controls were included. A white noise treatment (WN) to prove the frequency-selectiveness of signals and a hydric stressed group (HS) to compare the CAPs content. Our findings suggest that WN and NAE did not present differences statically. Conversely, HS and all AEs treatments induced a significant increase of capsaicin (Cap) and dihydrocapsaicin (Dcap) after one-week of a treatment. Specifically, the HS plants showed an increase of 8.33 times compared to the NAE and WN treatments and 1.4 times higher than the MHS, which was the AEs treatment with a larger induction of Capsaicinoids among treatments (5.88) and compared to the controls.

Keywords: acoustic emission, capsaicinoids, elicitors, hydric stress, plant signaling

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Authors: M. Cardenas-Garcia, P. P. Gonzalez-Perez

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Intercellular communication is a necessary condition for cellular functions and it allows a group of cells to survive as a population. Throughout this interaction, the cells work in a coordinated and collaborative way which facilitates their survival. In the case of cancerous cells, these take advantage of intercellular communication to preserve their malignancy, since through these physical unions they can send signs of malignancy. The Wnt/β-catenin signaling pathway plays an important role in the formation of intercellular communications, being also involved in a large number of cellular processes such as proliferation, differentiation, adhesion, cell survival, and cell death. The modeling and simulation of cellular signaling systems have found valuable support in a wide range of modeling approaches, which cover a wide spectrum ranging from mathematical models; e.g., ordinary differential equations, statistical methods, and numerical methods– to computational models; e.g., process algebra for modeling behavior and variation in molecular systems. Based on these models, different simulation tools have been developed from mathematical ones to computational ones. Regarding cellular and molecular processes in cancer, its study has also found a valuable support in different simulation tools that, covering a spectrum as mentioned above, have allowed the in silico experimentation of this phenomenon at the cellular and molecular level. In this work, we simulate and explore the complex interaction patterns of intercellular communication in cancer cells using the Cellulat bioinformatics tool, a computational simulation tool developed by us and motivated by two key elements: 1) a biochemically inspired model of self-organizing coordination in tuple spaces, and 2) the Gillespie’s algorithm, a stochastic simulation algorithm typically used to mimic systems of chemical/biochemical reactions in an efficient and accurate way. The main idea behind the Cellulat simulation tool is to provide an in silico experimentation environment that complements and guides in vitro experimentation in intra and intercellular signaling networks. Unlike most of the cell signaling simulation tools, such as E-Cell, BetaWB and Cell Illustrator which provides abstractions to model only intracellular behavior, Cellulat is appropriate for modeling both intracellular signaling and intercellular communication, providing the abstractions required to model –and as a result, simulate– the interaction mechanisms that involve two or more cells, that is essential in the scenario discussed in this work. During the development of this work we made evident the application of our computational simulation tool (Cellulat) for the modeling and simulation of intercellular communication between normal and cancerous cells, and in this way, propose key molecules that may prevent the arrival of malignant signals to the cells that surround the tumor cells. In this manner, we could identify the significant role that has the Wnt/β-catenin signaling pathway in cellular communication, and therefore, in the dissemination of cancer cells. We verified, using in silico experiments, how the inhibition of this signaling pathway prevents that the cells that surround a cancerous cell are transformed.

Keywords: cancer cells, in silico approach, intercellular communication, key molecules, modeling and simulation

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Authors: Arsalan Ansari, Muhammad Dawood Idrees, Maria Hafeez

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Use of mobile phones has been increasing considerably over the past decade. Currently, it is one of the main sources of communication and information. Initially, mobile phones were limited to calls and messages, but with the advent of new technology smart phones were being used for many other purposes including video games. Despite of positive outcomes, addiction to video games on mobile phone has become a leading cause of psychological and physiological problems among many people. Several researchers examined the different aspects of behavior addiction with the use of different scales. Objective of this study is to examine any distinction between mobile game addicted and non-addicted players with the use of electroencephalography (EEG), based upon psycho-physiological indicators. The mobile players were asked to play a mobile game and EEG signals were recorded by BIOPAC equipment with AcqKnowledge as data acquisition software. Electrodes were places, following the 10-20 system. EEG was recorded at sampling rate of 200 samples/sec (12,000samples/min). EEG recordings were obtained from the frontal (Fp1, Fp2), parietal (P3, P4), and occipital (O1, O2) lobes of the brain. The frontal lobe is associated with behavioral control, personality, and emotions. The parietal lobe is involved in perception, understanding logic, and arithmetic. The occipital lobe plays a role in visual tasks. For this study, a 60 second time window was chosen for analysis. Preliminary analysis of the signals was carried out with Acqknowledge software of BIOPAC Systems. From the survey based on CGS manual study 2010, it was concluded that five participants out of fifteen were in addictive category. This was used as prior information to group the addicted and non-addicted by physiological analysis. Statistical analysis showed that by applying clustering analysis technique authors were able to categorize the addicted and non-addicted players specifically on theta frequency range of occipital area.

Keywords: mobile game, addiction, psycho-physiology, EEG analysis

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Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: Maciej Zaręba, Sławomir Lasota

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The monitoring of manufacturing processes is an important issue in nowadays ERP systems. The identification and analysis of appropriate data for the units that take part in the production process are ones of the most crucial problems. In this paper, the authors introduce a new approach towards modelling the relation between production units, signals, and factors possible to obtain from the production system. The main idea for the system is based on the ontology of production units.

Keywords: manufacturing operation management, OWL, ontology implementation, ontology modeling

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Authors: Dilshad Ali Mohammed, Ziyad Nayef Shamsulddin Aldoski, Millet Salim Mohammed

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The increase in levels of traffic congestion along urban signalized arterials needs efficient traffic management. The application of traffic signal coordination can improve the traffic operation and safety for a series of signalized intersection along the arterials. The objective of this study is to evaluate the benefits achievable through actuated traffic signal coordination and make a comparison in control delay against the same signalized intersection in case of being isolated. To accomplish this purpose, a series of eight signalized intersections located on two major arterials in Duhok City was chosen for conducting the study. Traffic data (traffic volumes, link and approach speeds, and passenger car equivalent) were collected at peak hours. Various methods had been used for collecting data such as video recording technique, moving vehicle method and manual methods. Geometric and signalization data were also collected for the purpose of the study. The coupling index had been calculated to check the coordination attainability, and then time space diagrams were constructed representing one-way coordination for the intersections on Barzani and Zakho Streets, and others represented two-way coordination for the intersections on Zakho Street with accepted progression bandwidth efficiency. The results of this study show great progression bandwidth of 54 seconds for east direction coordination and 17 seconds for west direction coordination on Barzani Street under suggested controlled speed of 60 kph agreeable with the present data. For Zakho Street, the progression bandwidth is 19 seconds for east direction coordination and 18 seconds for west direction coordination under suggested controlled speed of 40 kph. The results show that traffic signal coordination had led to high reduction in intersection control delays on both arterials.

Keywords: bandwidth, congestion, coordination, traffic, signals, streets

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Authors: Mahdi Rahimi, Mohammad Mahdi Mojahedian, Mohammad Reza Aref

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In this paper, a cellular communication scenario with a transmitter and an authorized user is considered to analyze its secrecy in the face of eavesdroppers and the interferences propagated unintentionally through the communication network. It is also assumed that some D2D pairs and eavesdroppers are randomly located in the cell. Assuming hardware impairment, perfect connection probability is analytically calculated, and upper bound is provided for the secrecy outage probability. In addition, a method based on random activation of D2Ds is proposed to improve network security. Finally, the analytical results are verified by simulations.

Keywords: physical layer security, stochastic geometry, device-to-device, hardware impairment

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Authors: Hasik Lee, Seung-Young Kho

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Nowadays, automatic fare collection (AFC) system is widely used in many countries. However, smart card data from many of cities does not contain alighting information which is necessary to build OD matrices. Therefore, in order to utilize smart card data, destinations of passengers should be estimated. In this paper, kernel density estimation was used to forecast probabilities of alighting stations of bus passengers and applied to smart card data in Seoul, Korea which contains boarding and alighting information. This method was also validated with actual data. In some cases, stochastic method was more accurate than deterministic method. Therefore, it is sufficiently accurate to be used to build OD matrices.

Keywords: destination estimation, Kernel density estimation, smart card data, validation

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Authors: Hossein Bazmara, Kaamran Raahemifar, Mostafa Sefidgar, Madjid Soltani

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Angiogenesis is formation of new blood vessels from existing vessels. Due to flow of blood in vessels, during angiogenesis, blood flow plays an important role in regulating the angiogenesis process. Multiple mathematical models of angiogenesis have been proposed to simulate the formation of the complicated network of capillaries around a tumor. In this work, a multi-scale model of angiogenesis is developed to show the effect of blood flow on capillaries and network formation. This model spans multiple temporal and spatial scales, i.e. intracellular (molecular), cellular, and extracellular (tissue) scales. In intracellular or molecular scale, the signaling cascade of endothelial cells is obtained. Two main stages in development of a vessel are considered. In the first stage, single sprouts are extended toward the tumor. In this stage, the main regulator of endothelial cells behavior is the signals from extracellular matrix. After anastomosis and formation of closed loops, blood flow starts in the capillaries. In this stage, blood flow induced signals regulate endothelial cells behaviors. In cellular scale, growth and migration of endothelial cells is modeled with a discrete lattice Monte Carlo method called cellular Pott's model (CPM). In extracellular (tissue) scale, diffusion of tumor angiogenic factors in the extracellular matrix, formation of closed loops (anastomosis), and shear stress induced by blood flow is considered. The model is able to simulate the formation of a closed loop and its extension. The results are validated against experimental data. The results show that, without blood flow, the capillaries are not able to maintain their integrity.

Keywords: angiogenesis, endothelial cells, multi-scale model, cellular Pott's model, signaling cascade

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Authors: Junfeng Yan, Wenkui Hou

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Prognostics and Health Management (PHM) is a technology to monitor the equipment status and predict impending faults. It is used to predict the potential fault and provide fault information and track trends of system degradation by capturing characteristics signals. So how to detect characteristics signals is very important. The select of test point plays a very important role in detecting characteristics signal. Traditionally, we use dependency model to select the test point containing the most detecting information. But, facing the large complicated system, the dependency model is not built so easily sometimes and the greater trouble is how to calculate the matrix. Rely on this premise, the paper provide a highly effective method to select test point without dependency model. Because signal flow model is a diagnosis model based on failure mode, which focuses on system’s failure mode and the dependency relationship between the test points and faults. In the signal flow model, a fault information can flow from the beginning to the end. According to the signal flow model, we can find out location and structure information of every test point and module. We break the signal flow model up into serial and parallel parts to obtain the final relationship function between the system’s testability or prediction metrics and test points. Further, through the partial derivatives operation, we can obtain every test point’s importance degree in determining the testability metrics, such as undetected rate, false alarm rate, untrusted rate. This contributes to installing the test point according to the real requirement and also provides a solid foundation for the Prognostics and Health Management. According to the real effect of the practical engineering application, the method is very efficient.

Keywords: false alarm rate, importance degree, signal flow model, undetected rate, untrusted rate

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Authors: Abul B. M. M. K. Islam, Mandar Dave, Roderick V. Jensen, Ashok R. Amin

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A systems approach was applied to characterize differentially expressed transcripts, bioinformatics pathways, and proteins and prostaglandins (PGs) from lung fibroblasts procured from wild-type (WT), COX-1-/- and COX-2-/- mice to understand system level control mechanism. Bioinformatics analysis of COX-2 and COX-1 ablated cells induced COX-1 and COX-2 specific signature respectively, which significantly overlapped with an 'IL-1β induced inflammatory signature'. This defined novel cross-talk signals that orchestrated coordinated activation of pathways of sterile inflammation sensed by cellular stress. The overlapping signals showed significant over-representation of shared pathways for interferon y and immune responses, T cell functions, NOD, and toll-like receptor signaling. Gene Ontology Biological Process (GOBP) and pathway enrichment analysis specifically showed an increase in mRNA expression associated with: (a) organ development and homeostasis in COX-1-/- cells and (b) oxidative stress and response, spliceosomes and proteasomes activity, mTOR and p53 signaling in COX-2-/- cells. COX-1 and COX-2 showed signs of functional pathways committed to cell cycle and DNA replication at the genomics level. As compared to WT, metabolomics analysis revealed a significant increase in COX-1 mRNA and synthesis of basal levels of eicosanoids (PGE2, PGD2, TXB2, LTB4, PGF1α, and PGF2α) in COX-2 ablated cells and increase in synthesis of PGE2, and PGF1α in COX-1 null cells. There was a compensation of PGE2 and PGF1α in COX-1-/- and COX-2-/- cells. Collectively, these results support a broader, differential and collaborative regulation of both COX-1 and COX-2 pathways at the metabolic, signaling, and genomics levels in cellular homeostasis and sterile inflammation induced by cellular stress.

Keywords: cyclooxygenases, inflammation, lung fibroblasts, systemic

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Authors: Shuai Yuan, Minxia Shi, Xu Zhang, Jianzhi Yang, Kangqi Tian, Yuzheng Ma

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The precise measurement of muscle activities is essential for understanding the function of various body movements. This work aims to develop a muscle magnetic field signal detection system based on mathematical analysis. Medical research has underscored that early detection of muscle atrophy, coupled with lifestyle adjustments such as dietary control and increased exercise, can significantly enhance muscle-related diseases. Currently, surface electromyography (sEMG) is widely employed in research as an early predictor of muscle atrophy. Nonetheless, the primary limitation of using sEMG to forecast muscle strength is its inability to directly measure the signals generated by muscles. Challenges arise from potential skin-electrode contact issues due to perspiration, leading to inaccurate signals or even signal loss. Additionally, resistance and phase are significantly impacted by adipose layers. The recent emergence of optically pumped magnetometers introduces a fresh avenue for bio-magnetic field measurement techniques. These magnetometers possess high sensitivity and obviate the need for a cryogenic environment unlike superconducting quantum interference devices (SQUIDs). They detect muscle magnetic field signals in the range of tens to thousands of femtoteslas (fT). The utilization of magnetometers for capturing muscle magnetic field signals remains unaffected by issues of perspiration and adipose layers. Since their introduction, optically pumped atomic magnetometers have found extensive application in exploring the magnetic fields of organs such as cardiac and brain magnetism. The optimal operation of these magnetometers necessitates an environment with an ultra-weak magnetic field. To achieve such an environment, researchers usually utilize a combination of active magnetic compensation technology with passive magnetic shielding technology. Passive magnetic shielding technology uses a magnetic shielding device built with high permeability materials to attenuate the external magnetic field to a few nT. Compared with more layers, the coils that can generate a reverse magnetic field to precisely compensate for the residual magnetic fields are cheaper and more flexible. To attain even lower magnetic fields, compensation coils designed by Biot-Savart law are involved to generate a counteractive magnetic field to eliminate residual magnetic fields. By solving the magnetic field expression of discrete points in the target region, the parameters that determine the current density distribution on the plane can be obtained through the conventional target field method. The current density is obtained from the partial derivative of the stream function, which can be represented by the combination of trigonometric functions. Optimization algorithms in mathematics are introduced into coil design to obtain the optimal current density distribution. A one-dimensional linear regression analysis was performed on the collected data, obtaining a coefficient of determination R2 of 0.9349 with a p-value of 0. This statistical result indicates a stable relationship between the peak-to-peak value (PPV) of the muscle magnetic field signal and the magnitude of grip strength. This system is expected to be a widely used tool for healthcare professionals to gain deeper insights into the muscle health of their patients.

Keywords: muscle magnetic signal, magnetic shielding, compensation coils, trigonometric functions.

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Authors: Sven Tackenberg, Sönke Duckwitz, Andreas Petz, Christopher M. Schlick

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This paper introduces an extension to the well-established Resource-Constrained Project Scheduling Problem (RCPSP) to apply it to complex maintenance problems. The problem is to assign technicians to a team which has to process several tasks with multi-level skill requirements during a work shift. Here, several alternative activities for a task allow both, the temporal shift of activities or the reallocation of technicians and tools. As a result, switches from one valid work process variant to another can be considered and may be selected by the developed evolutionary algorithm based on the present skill level of technicians or the available tools. An additional complication of the observed scheduling problem is that the locations of the construction sites are only temporarily accessible during a day. Due to intensive rail traffic, the available time slots for maintenance and repair works are extremely short and are often distributed throughout the day. To identify efficient working periods, a first concept of a Bayesian network is introduced and is integrated into the extended RCPSP with pre-emptive and non-pre-emptive tasks. Thereby, the Bayesian network is used to calculate the probability of a maintenance task to be processed during a specific period of the shift. Focusing on the domain of maintenance of the railway infrastructure in metropolitan areas as the most unproductive implementation process at construction site, the paper illustrates how the extended RCPSP can be applied for maintenance planning support. A multi-criteria evolutionary algorithm with a problem representation is introduced which is capable of revising technician-task allocations, whereas the duration of the task may be stochastic. The approach uses a novel activity list representation to ensure easily describable and modifiable elements which can be converted into detailed shift schedules. Thereby, the main objective is to develop a shift plan which maximizes the utilization of each technician due to a minimization of the waiting times caused by rail traffic. The results of the already implemented core algorithm illustrate a fast convergence towards an optimal team composition for a shift, an efficient sequence of tasks and a high probability of the subsequent implementation due to the stochastic durations of the tasks. In the paper, the algorithm for the extended RCPSP is analyzed in experimental evaluation using real-world example problems with various size, resource complexity, tightness and so forth.

Keywords: maintenance management, scheduling, resource constrained project scheduling problem, genetic algorithms

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Authors: Funda Kul, İsmail Gür

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Pension scheme providers have to price mortality risk by accurate mortality forecasting method. There are many mortality-forecasting methods constructed and used in literature. The Lee-Carter model is the first model to consider stochastic improvement trends in life expectancy. It is still precisely used. Mortality forecasting is done by mortality index in the Lee-Carter model. It is assumed that mortality index fits ARIMA time series model. In this paper, we propose and use dynamic normal inverse gaussian distribution to modeling mortality indes in the Lee-Carter model. Using population mortality data for Italy, France, and Turkey, the model is forecasting capability is investigated, and a comparative analysis with other models is ensured by some well-known benchmarking criterions.

Keywords: mortality, forecasting, lee-carter model, normal inverse gaussian distribution

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Authors: S. Ghasemi, K. Khorasani

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In this paper, the problem of fault detection and isolation in the attitude control subsystem of spacecraft formation flying is considered. In order to design the fault detection method, an extended Kalman filter is utilized which is a nonlinear stochastic state estimation method. Three fault detection architectures, namely, centralized, decentralized, and semi-decentralized are designed based on the extended Kalman filters. Moreover, the residual generation and threshold selection techniques are proposed for these architectures.

Keywords: component, formation flight of satellites, extended Kalman filter, fault detection and isolation, actuator fault

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Authors: Jai Prakash, Promod Kumar, Chantel Swart, J. H. Neethling, A. Janse van Vuuren, H. C. Swart

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Ag nanoparticles (NPs) have been used as functional nanomaterials due to their optical and antibacterial properties. Similarly, TiO2 photocatalysts have also been used as suitable nanomaterials for killing cancer cells, viruses and bacteria. Here, we report on multifunctional plasmonic Ag-TiO2 nano-biocomposite synthesized by the sol-gel technique and their optical, surface enhanced Raman scattering (SERS) and antibacterial activities. The as-prepared composites of Ag–TiO2 with different silver content and TiO2 nanopowder were characterized by X-ray diffraction, scanning electron microscopy, high-resolution transmission electron microscopy, energy-dispersed X-ray analysis (EDX), UV-vis and Raman spectroscopy. The Ag NPs were found to be uniformly distributed and strongly attached to the TiO2 matrix. The novel optical response of the Ag-TiO2 nanocomposites is due to the strong electric field from the surface plasmon excitation of the Ag NPs. The Raman spectrum of Ag-TiO2 nanocomposite was found to be enhanced as compared to TiO2. The enhancement of the low frequency band is evident. This indicates the SERS effect of the TiO2 NPs in close vicinity of Ag NPs. In addition, nanocomposites showed enhancement in the SERS signals of methyl orange (MO) dye molecules with increasing Ag content. The localized electromagnetic field from the surface plasmon excitation of the Ag NPs was responsible for the SERS signals of the TiO2 NPs and MO molecules. The antimicrobial effect of the Ag–TiO2 nanocomposites with different silver content and TiO2 nanopowder were carried out against the bacterium Staphylococcus aureus. The Ag–TiO2 composites showed antibacterial activity towards S. aureus with increasing Ag content as compared to the TiO2 nanopowder. These results foresee promising applications of the functional plasmonic metal−semiconductor based nanobiocomposites for both chemical and biological samples.

Keywords: metal-Semiconductor, nano-Biocomposites, anti-microbial activity, surface enhanced Raman scattering

Procedia PDF Downloads 212

Authors: Norman Sinnigen, Igor Izyurov, Marina Krylova, Hamidreza Jamalabadi, Sarah Alizadeh, Martin Walter

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Background: EEG is a non-invasive brain activity recording technique with a high temporal resolution that allows the use of real-time applications, such as neurofeedback. However, EEG data are susceptible to electrooculographic (EOG) and electromyography (EMG) artifacts (i.e., jaw clenching, teeth squeezing and forehead movements). Due to their non-stationary nature, these artifacts greatly obscure the information and power spectrum of EEG signals. Many EEG artifact correction methods are too time-consuming when applied to low-density EEG and have been focusing on offline processing or handling one single type of EEG artifact. A software-only real-time method for correcting multiple types of EEG artifacts of high-density EEG remains a significant challenge. Methods: We demonstrate an improved approach for automatic real-time EEG artifact correction of EOG and EMG artifacts. The method was tested on three healthy subjects using 64 EEG channels (Brain Products GmbH) and a sampling rate of 1,000 Hz. Captured EEG signals were imported in MATLAB with the lab streaming layer interface allowing buffering of EEG data. EMG artifacts were detected by channel variance and adaptive thresholding and corrected by using channel interpolation. Real-time independent component analysis (ICA) was applied for correcting EOG artifacts. Results: Our results demonstrate that the algorithm effectively reduces EMG artifacts, such as jaw clenching, teeth squeezing and forehead movements, and EOG artifacts (horizontal and vertical eye movements) of high-density EEG while preserving brain neuronal activity information. The average computation time of EOG and EMG artifact correction for 80 s (80,000 data points) 64-channel data is 300 – 700 ms depending on the convergence of ICA and the type and intensity of the artifact. Conclusion: An automatic EEG artifact correction algorithm based on channel variance, adaptive thresholding, and ICA improves high-density EEG recordings contaminated with EOG and EMG artifacts in real-time.

Keywords: EEG, muscle artifacts, ocular artifacts, real-time artifact correction, real-time ICA

Procedia PDF Downloads 148

Authors: Johannes J. Schneider, Mathias S. Weyland, Peter Eggenberger Hotz, William D. Jamieson, Oliver Castell, Alessia Faggian, Rudolf M. Füchslin

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Agglomerations of polydisperse systems of spherical particles are created in computer simulations using a simplified stochastic-hydrodynamic model: Particles sink to the bottom of the cylinder, taking into account gravity reduced by the buoyant force, the Stokes friction force, the added mass effect, and random velocity changes. Two types of particles are considered, with one of them being able to create connections to neighboring particles of the same type, thus forming a network within the agglomeration at the bottom of a cylinder. Decreasing the fraction of these particles, a percolation transition occurs. The critical regime is determined by investigating the maximum cluster size and the percolation susceptibility.

Keywords: binary system, maximum cluster size, percolation, polydisperse

Procedia PDF Downloads 38

Authors: Benjamin Sonnenreich

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Brittle fracture failure probability is best described using a stochastic approach which is based on the 'weakest link concept' and the connection between a microstructure and macroscopic fracture scale. A general theoretical and experimental framework is presented to predict the uniaxial strength distribution according to independent uniaxial test data. The framework takes as input the applied stresses, the geometry, the materials, the defect distributions and the relevant random variables from uniaxial test results and gives as output an overall failure probability that can be used to improve the reliability of practical designs. Additionally, the method facilitates comparisons of strength data from several sources, uniaxial tests, and sample geometries.

Keywords: brittle fracture, strength distribution, uniaxial, weakest link concept

Procedia PDF Downloads 304

Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

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Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotive EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: brain computer interface, electroencephalogram, EEGLab, BCILab, emotive, emotions, interval features, spectral features, artificial neural network, control applications

Procedia PDF Downloads 300

Authors: P. Y. Chiang, Y. H. Chen, Y. J. Lin, C. C. Chang, W. C. Hsu

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The number of the elderly in the world population and the rate of falls in this increasing numbers of older people are increasing. Decreasing muscle strength and an increasing risk of falling are associated with the ageing process. Because the effects of seated stepping training on the walking performance in the elderly remain unclear, the main purpose of the proposed study is to perform electromyography analysis during walking and seated stepping in the elderly. Four surface EMG electrodes were sticked on the surface of lower limbs muscles, including vastus lateralis (VL), and gastrocnemius (GT) of both sides. Before test, maximal voluntary contraction (MVC) of the respective muscle was obtained using manual muscle testing. The analog raw data of EMG signals were digitized with a sampling frequency of 2000 Hz. The signals were fully rectified and the linear envelope were calculated. Stepping motion cycle was separated into two phases by stepping timing (ST) and pedal return timing (PRT). ST refer to the time when the pedal marker reached the highest height, representing the contra-lateral leg was going to release the pedal. PRT refer to the time when the pedal marker reached the lowest height, representing the contra-lateral leg was going to step the pedal. We assumed that ST acted the same role in initial contact during walking, and PRT for toe-off. The period from ST to next PRT was called pushing phase (PP), during which the leg would start to step with resistance, and we compare this phase with the stance phase in level walking. The period from PRT to next ST was called returning phase (RP), during which leg would not have any resistance in this phase, and we compare this phase with the swing phase in level walking. VL and Gastro muscular activation had similar patterns in both side. The ability may transfer to those needed during loading response, mid-stance and terminal swing phase. User needed to make more effort in stepping compared with walking with similar timing; thus the strengthening of the VL and Gastro may be helpful to improve the walking endurance and efficiency for the elderly.

Keywords: elderly, electromyography, seated stepping, walking

Procedia PDF Downloads 201

Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

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This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

Procedia PDF Downloads 91

Authors: Bin Feng, Zhulin Zong

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Radar systems are critical in the modern military, aviation, and maritime operations, and their proper functioning is essential for the success of these operations. However, due to the complexity and sensitivity of radar systems, they are susceptible to various faults that can significantly affect their performance. Traditional radar fault diagnosis strategies rely on expert knowledge and rule-based approaches, which are often limited in effectiveness and require a lot of time and resources. Deep learning has recently emerged as a promising approach for fault diagnosis due to its ability to learn features and patterns from large amounts of data automatically. In this paper, we propose a radar fault diagnosis strategy based on deep learning that can accurately identify and classify faults in radar systems. Our approach uses convolutional neural networks (CNN) to extract features from radar signals and fault classify the features. The proposed strategy is trained and validated on a dataset of measured radar signals with various types of faults. The results show that it achieves high accuracy in fault diagnosis. To further evaluate the effectiveness of the proposed strategy, we compare it with traditional rule-based approaches and other machine learning-based methods, including decision trees, support vector machines (SVMs), and random forests. The results demonstrate that our deep learning-based approach outperforms the traditional approaches in terms of accuracy and efficiency. Finally, we discuss the potential applications and limitations of the proposed strategy, as well as future research directions. Our study highlights the importance and potential of deep learning for radar fault diagnosis. It suggests that it can be a valuable tool for improving the performance and reliability of radar systems. In summary, this paper presents a radar fault diagnosis strategy based on deep learning that achieves high accuracy and efficiency in identifying and classifying faults in radar systems. The proposed strategy has significant potential for practical applications and can pave the way for further research.

Keywords: radar system, fault diagnosis, deep learning, radar fault

Procedia PDF Downloads 62

Authors: A. Bentabet, A. Aydin, N. Fenineche

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In this work, we try to determine the best analytical approximation of differential cross sections, used generally in Monte Carlo simulation, to study the electron/positron slowing down in solid targets in the energy range up to 10 keV. Actually, our comparative study was carried out on the angular distribution of the scattering angle, the elastic total and the first transport cross sections which are the essential quantities used generally in the electron/positron transport study by using both stochastic and deterministic methods. Indeed, the obtained results using the relativistic partial wave expansion method and the backscattering coefficient experimental data are used as criteria to evaluate the used model.

Keywords: differential cross-section, backscattering coefficient, Rutherford cross-section, Vicanek and Urbassek theory

Procedia PDF Downloads 542