Search results for: gamma neural oscillation

Authors: Andreas Aceranti, Riccardo Dossena, Marco Colorato, Simonetta Vernocchi

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By the term “limbic resonance,” we usually refer to a state of deep connection, both emotional and physiological, between people who, when in resonance, find their limbic systems in tune with one another. Limbic resonance is not only about sharing emotions but also physiological states. In fact, people in such resonance can influence each other’s heart rate, blood pressure, and breathing. Limbic resonance is fundamental for human beings to connect and create deep bonds among a certain group. It is fundamental for our social skills. A relationship between gifted and resonant subjects is perceived as feeling safe, living the relation like an isle of serenity where it is possible to recharge, to communicate without words, to understand each others without giving explanations, to strengthen the balance of each member of the group. Within the circle, self-esteem is consolidated and makes it stronger to face what is outside, others, and reality. The idea that gifted people who are together may be unfit for the world does not correspond to the truth. The circle made up of people with high cognitive potential characterized by a limbic resonance is, in general, experienced as a solid platform from which you can safely move away and where you can return to recover strength. We studied 8 adults (between 21 and 47 years old). All of them with IQ higher than 130. We monitored their brain waves frequency (alpha, beta, theta, gamma, delta) by means of biosensing tracker along with their physiological states (heart beat frequency, blood pressure, breathing frequency, pO2, pCO2) and some blood works only (5-HT, dopamine, catecholamines, cortisol). The subjects of the study were asked to adhere to a protocol involving bonding activities (such as team building activities), role plays, meditation sessions, and group therapy. All these activities were carried out together. We observed that after about 4 months of activities, their brain waves frequencies tended to tune quicker and quicker. After 9 months, the bond among them was so important that they could “sense” each other inner states and sometimes also guess each others’ thoughts. According to our findings, it may be hypothesized that large synchronized outbursts of cortex neurons produces not only brain waves but also electromagnetic fields that may be able to influence the cortical neurons’ activity of other people’s brain by inducing action potentials in large groups of neurons and this is reasonably conceivable to be able to transmit information such as different emotions and cognition cues to the other’s brain. We also believe that upcoming research should focus on clarifying the role of brain magnetic particles in brain-to-brain communication. We also believe that further investigations should be carried out on the presence and role of cryptochromes to evaluate their potential roles in direct brain-to-brain communication.

Keywords: limbic resonance, psychotherapy, brain waves, emotion regulation, giftedness

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Authors: Muhammad Waleed Asfandyar, Akhtar Ahmed, Syed Adib-ul-Hasan Rizvi

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Objective: To evaluate the ability of serum concentration of prostate specific antigen between two cutting points considering it as a predictor of skeletal metastasis on bone scintigraphy in men with prostate cancer. Settings: This study was carried out in department of Nuclear Medicine at Sindh Institute of Urology and Transplantation (SIUT) Karachi, Pakistan. Materials and Method: From August 2013 to November 2013, forty two (42) consecutive patients with prostate cancer who underwent technetium-99m methylene diphosphonate (Tc-99mMDP) whole body bone scintigraphy were prospectively analyzed. The information was collected from the scintigraphic database at a Nuclear medicine department Sindh institute of urology and transplantation Karachi Pakistan. Patients who did not have a serum PSA concentration available within 1 month before or after the time of performing the Tc-99m MDP whole body bone scintigraphy were excluded from this study. A whole body bone scintigraphy scan (from the toes to top of the head) was performed using a whole-body Moving gamma camera technique (anterior and posterior) 2–4 hours after intravenous injection of 20 mCi of Tc-99m MDP. In addition, all patients necessarily have a pathological report available. Bony metastases were determined from the bone scan studies and no further correlation with histopathology or other imaging modalities were performed. To preserve patient confidentiality, direct patient identifiers were not collected. In all the patients, Prostate specific antigen values and skeletal scintigraphy were evaluated. Results: The mean age, mean PSA, and incidence of bone metastasis on bone scintigraphy were 68.35 years, 370.51 ng/mL and 19/42 (45.23%) respectively. According to PSA levels, patients were divided into 5 groups < 10ng/mL (10/42), 10-20 ng/mL (5/42), 20-50 ng/mL (2/42), 50-100 (3/42), 100- 500ng/mL (3/42) and more than 500ng/mL (0/42) presenting negative bone scan. The incidence of positive bone scan (%) for bone metastasis for each group were O1 patient (5.26%), 0%, 03 patients (15.78%), 01 patient (5.26%), 04 patients (21.05%), and 10 patients (52.63%) respectively. From the 42 patients 19 (45.23%) presented positive scintigraphic examination for the presence of bone metastasis. 1 patient presented bone metastasis on bone scintigraphy having PSA level less than 10ng/mL, and in only 1 patient (5.26%) with bone metastasis PSA concentration was less than 20 ng/mL. therefore, when the cutting point adopted for PSA serum concentration was 10ng/mL, a negative predictive value for bone metastasis was 95% with sensitivity rates 94.74% and the positive predictive value and specificities of the method were 56.53% and 43.48% respectively. When the cutting point of PSA serum concentration was 20ng/mL the observed results for Positive predictive value and specificity were (78.27% and 65.22% respectively) whereas negative predictive value and sensitivity stood (100% and 95%) respectively. Conclusion: Results of our study allow us to conclude that serum PSA concentration of higher than 20ng/mL was the most accurate cutting point than a serum concentration of PSA higher than 10ng/mL to predict metastasis in radionuclide bone scintigraphy. In this way, unnecessary cost can be avoided, since a considerable part of prostate adenocarcinomas present low serum PSA levels less than 20 ng/mL and for these cases radionuclide bone scintigraphy could be unnecessary.

Keywords: bone scan, cut off value, prostate specific antigen value, scintigraphy

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Authors: Rima Hleiss, Elie Bitar, Mahmoud Hassan, Mohamad Khalil

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A comprehensive study of object recognition in the human brain requires combining both spatial and temporal analysis of brain activity. Here, we are mainly interested in three issues: the time perception of visual objects, the ability of discrimination between two particular categories (objects vs. animals), and the possibility to identify a particular spatial representation of visual objects. Our experiment consisted of acquiring dense electroencephalographic (EEG) signals during a picture-naming task comprising a set of objects and animals’ images. These EEG responses were recorded from nine participants. In order to determine the time perception of the presented visual stimulus, we analyzed the Event Related Potentials (ERPs) derived from the recorded EEG signals. The analysis of these signals showed that the brain perceives animals and objects with different time instants. Concerning the discrimination of the two categories, the support vector machine (SVM) was applied on the instantaneous EEG (excellent temporal resolution: on the order of millisecond) to categorize the visual stimuli into two different classes. The spatial differences between the evoked responses of the two categories were also investigated. The results showed a variation of the neural activity with the properties of the visual input. Results showed also the existence of a spatial pattern of electrodes over particular regions of the scalp in correspondence to their responses to the visual inputs.

Keywords: brain activity, categorization, dense EEG, evoked responses, spatio-temporal analysis, SVM, time perception

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Authors: Sugandha Gupta, Arun Kumar Jha

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A large number of ferroelectric materials have been intensely investigated for applications in non-volatile ferroelectric random access memories (FeRAMs), piezoelectric transducers, actuators, pyroelectric sensors, high dielectric constant capacitors, etc. Bismuth layered ferroelectric materials such as Strontium Bismuth Tantalate (SBT) has attracted a lot of attention due to low leakage current, high remnant polarization and high fatigue endurance up to 1012 switching cycles. However, pure SBT suffers from various major limitations such as high dielectric loss, low remnant polarization values, high processing temperature, bismuth volatilization, etc. Significant efforts have been made to improve the dielectric and ferroelectric properties of this compound. Firstly, it has been reported that electrical properties vary with the Sr/ Bi content ratio in the SrBi2Ta2O9 compsition i.e. non-stoichiometric compositions with Sr-deficient / Bi excess content have higher remnant polarization values than stoichiometic SBT compositions. With the objective to improve structural, dielectric, ferroelectric and piezoelectric properties of SBT compound, rare earth holmium (Ho3+) was chosen as a donor cation for substitution onto the Bi2O2 layer. Moreover, hardly any report on holmium substitution in stoichiometric SrBi2Ta2O9 and non-stoichiometric Sr0.8Bi2.2Ta2O9 compositions were available in the literature. The holmium substituted SrBi2-xHoxTa2O9 (x= 0.00-2.0) and Sr0.8Bi2.2Ta2O9 (x=0.0 and 0.01) compositions were synthesized by the solid state reaction method. The synthesized specimens were characterized for their structural and electrical properties. X-ray diffractograms reveal single phase layered perovskite structure formation for holmium content in stoichiometric SBT samples up to x ≤ 0.1. The granular morphology of the samples was investigated using scanning electron microscope (Hitachi, S-3700 N). The dielectric measurements were carried out using a precision LCR meter (Agilent 4284A) operating at oscillation amplitude of 1V. The variation of dielectric constant with temperature shows that the Curie temperature (Tc) decreases on increasing the holmium content. The specimen with x=2.0 i.e. the bismuth free specimen, has very low dielectric constant and does not show any appreciable variation with temperature. The dielectric loss reduces significantly with holmium substitution. The polarization–electric field (P–E) hysteresis loops were recorded using a P–E loop tracer based on Sawyer–Tower circuit. It is observed that the ferroelectric property improve with Ho substitution. Holmium substituted specimen exhibits enhanced value of remnant polarization (Pr= 9.22 μC/cm²) as compared to holmium free specimen (Pr= 2.55 μC/cm²). Piezoelectric co-efficient (d33 values) was measured using a piezo meter system (Piezo Test PM300). It is observed that holmium substitution enhances piezoelectric coefficient. Further, the optimized holmium content (x=0.01) in stoichiometric SrBi2-xHoxTa2O9 composition has been substituted in non-stoichiometric Sr0.8Bi2.2Ta2O9 composition to obtain further enhanced structural and electrical characteristics. It is expected that a new class of ferroelectric materials i.e. Rare Earth Layered Structured Ferroelectrics (RLSF) derived from Bismuth Layered Structured Ferroelectrics (BLSF) will generate which can be used to replace static (SRAM) and dynamic (DRAM) random access memories with ferroelectric random access memories (FeRAMS).

Keywords: dielectrics, ferroelectrics, piezoelectrics, strontium bismuth tantalate

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Authors: Esther Acha, Jose Cambra, De Chen

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Hydrogen is considered an important energy vector for the 21st century. Nowadays there are some difficulties for hydrogen economy implantation, and one of them is the high purity required for hydrogen. This energy vector is still being mainly produced from fuels, from wich hydrogen is produced as a component of a mixture containing other gases, such as CO, CO2 and H2O. A forthcoming sustainable pathway for hydrogen is steam-reforming of bio-oils derived from biomass, e.g. via fast pyrolysis. Bio-oils are a mixture of acids, alcohols, aldehydes, esters, ketones, sugars phenols, guaiacols, syringols, furans, multi-functional compounds and also up to a 30 wt% of water. The sorption enhanced steam reforming (SESR) process is attracting a great deal of attention due to the fact that it combines both hydrogen production and CO2 separation. In the SESR process, carbon dioxide is captured by an in situ sorbent, which shifts the reversible reforming and water gas shift reactions to the product side, beyond their conventional thermodynamic limits, giving rise to a higher hydrogen production and lower cost. The hydrogen containing mixture has been obtained from the SESR of bio-oil type compounds. Different types of catalysts have been tested. All of them contain Ni at around a 30 wt %. Two samples have been prepared with the wet impregnation technique over conventional (gamma alumina) and non-conventional (olivine) supports. And a third catalysts has been prepared over a hydrotalcite-like material (HT). The employed sorbent is a commercial dolomite. The activity tests were performed in a bench-scale plant (PID Eng&Tech), using a stainless steel fixed bed reactor. The catalysts were reduced in situ in the reactor, before the activity tests. The effluent stream was cooled down, thus condensed liquid was collected and weighed, and the gas phase was analysed online by a microGC. The hydrogen yield, and process behavior was analysed without the sorbent (the traditional SR where a second purification step will be needed but that operates in steady state) and the SESR (where the purification step could be avoided but that operates in batch state). The influence of the support type and preparation method will be observed in the produced hydrogen yield. Additionally, the stability of the catalysts is critical, due to the fact that in SESR process sorption-desorption steps are required. The produced hydrogen yield and hydrogen purity has to be high and also stable, even after several sorption-desorption cycles. The prepared catalysts were characterized employing different techniques to determine the physicochemical properties of the fresh-reduced and used (after the activity tests) materials. The characterization results, together with the activity results show the influence of the catalysts preparation method, calcination temperature, or can even explain the observed yield and conversion.

Keywords: CO2 sorbent, enhanced steam reforming, hydrogen

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Authors: Mehmet Erdi Korkmaz, Mustafa Günay

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Nowadays, grinding is frequently replaced with hard turning for reducing set up time and higher accuracy. This paper focused on mathematical modeling of average surface roughness (Ra) in hard turning of AISI L2 grade (DIN 1.2210) cold work tool steel with ceramic tools. The steel was hardened to 60±1 HRC after the heat treatment process. Cutting speed, feed rate, depth of cut and tool nose radius was chosen as the cutting conditions. The uncoated ceramic cutting tools were used in the machining experiments. The machining experiments were performed according to Taguchi L27 orthogonal array on CNC lathe. Ra values were calculated by averaging three roughness values obtained from three different points of machined surface. The influences of cutting conditions on surface roughness were evaluated as statistical and experimental. The analysis of variance (ANOVA) with 95% confidence level was applied for statistical analysis of experimental results. Finally, mathematical models were developed using the artificial neural networks (ANN). ANOVA results show that feed rate is the dominant factor affecting surface roughness, followed by tool nose radius and cutting speed.

Keywords: ANN, hard turning, DIN 1.2210, surface roughness, Taguchi method

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Authors: Niklas Panten, Eberhard Abele

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This paper presents a novel approach for real-time and near-optimal control of industrial smart grids by deep reinforcement learning (DRL). To achieve highly energy-efficient factory systems, the energetic linkage of machines, technical building equipment and the building itself is desirable. However, the increased complexity of the interacting sub-systems, multiple time-variant target values and stochastic influences by the production environment, weather and energy markets make it difficult to efficiently control the energy production, storage and consumption in the hybrid industrial smart grids. The studied deep reinforcement learning approach allows to explore the solution space for proper control policies which minimize a cost function. The deep neural network of the DRL agent is based on a multilayer perceptron (MLP), Long Short-Term Memory (LSTM) and convolutional layers. The agent is trained within multiple Modelica-based factory simulation environments by the Advantage Actor Critic algorithm (A2C). The DRL controller is evaluated by means of the simulation and then compared to a conventional, rule-based approach. Finally, the results indicate that the DRL approach is able to improve the control performance and significantly reduce energy respectively operating costs of industrial smart grids.

Keywords: industrial smart grids, energy efficiency, deep reinforcement learning, optimal control

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Authors: Zhaohan Xie, Yining Yu, Mingliang Chen

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As research has shown a convergent tendency for aversion to AI recommendation, it is imperative to find a way to promote AI usage and better harness the technology. In the context of e-commerce, this study has found evidence that people show less avoidance of algorithms when recommending search products compared to experience products. This is due to people’s different attribution of mind to AI versus humans, as suggested by mind perception theory. While people hold the belief that an algorithm owns sufficient capability to think and calculate, which makes it competent to evaluate search product attributes that can be obtained before actual use, they doubt its capability to sense and feel, which is essential for evaluating experience product attributes that must be assessed after experience in person. The result of the behavioral investigation (Study 1, N=112) validated that consumers show low purchase intention to experience products recommended by AI. Further consumer neuroscience study (Study 2, N=26) using Event-related potential (ERP) showed that consumers have a higher level of cognitive conflict when faced with AI recommended experience product as reflected by larger N2 component, while the effect disappears for search product. This research has implications for the effective employment of AI recommenders, and it extends the literature on e-commerce and marketing communication.

Keywords: algorithm recommendation, consumer behavior, e-commerce, event-related potential, experience product, search product

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Authors: Mohamed Abdelrahman Abdalla

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Apart from their function in producing CSF, the brain ventricles have been recognized as the mere remnant of the embryological neural tube with no clear role. The lack of proper definition of the function of the brain ventricles and the central spinal canal has made it difficult to ascertain the pathophysiology of its different disease conditions or to treat them. This study aims to review the simple physics that could explain the basic function of the CNS ventricular system and to suggest new ways of approaching its pathology. There are probably more physical factors to consider than only the pressure. Monro-Killie hypothesis focuses on volume and subsequently pressure to direct our surgical management in different disease conditions. However, the enlarged volume of the ventricles in normal pressure hydrocephalus does not move any blood or brain outside the skull. Also, in idiopathic intracranial hypertension, the very high intracranial pressure rarely causes brain herniation. On this note, the continuum of the intracranial cavity with the spinal canal makes it a whole unit and hence the defect in the theory. In this study, adding different factors to the equation like brain and CSF density and positions of the brain in space, in addition to the volume and pressure, aims to identify how the ventricles are important in the CNS homeostasis. In addition, increasing the variables that we analyze to treat different CSF pathological conditions should increase our understanding and hence accuracy of treatment of such conditions.

Keywords: communicating hydrocephalus, functions of the ventricles, idiopathic intracranial hypertension physics of CSF

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Authors: Qais M. Yousef, Yasmeen A. Alshaer

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Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.

Keywords: artificial intelligence, data contents search, human active memory, mind wave, multi-objective optimization

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Authors: Daneal Rorke, Gueguim Kana

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The shift towards renewable energy sources for biofuel production has received increasing attention. However, the use and pre-treatment of lignocellulosic material are inundated with the generation of fermentation inhibitors which severely impact the feasibility of bioprocesses. This study reports the profiling of all volatile compounds generated during microwave assisted chemical pre-treatment of sorghum leaves. Furthermore, the optimization of reducing sugar (RS) from microwave assisted acid pre-treatment of sorghum leaves was assessed and gave a coefficient of determination (R2) of 0.76, producing an optimal RS yield of 2.74 g FS/g substrate. The development of an intelligent model to predict volatile compound fractions gave R2 values of up to 0.93 for 21 volatile compounds. Sensitivity analysis revealed that furfural and phenol exhibited high sensitivity to acid concentration, alkali concentration and S:L ratio, while phenol showed high sensitivity to microwave duration and intensity as well. These findings illustrate the potential of using an intelligent model to predict the volatile compound fraction profile of compounds generated during pre-treatment of sorghum leaves in order to establish a more robust and efficient pre-treatment regime for biofuel production.

Keywords: artificial neural networks, fermentation inhibitors, lignocellulosic pre-treatment, sorghum leaves

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Authors: Hanlin Liu, Hua Li, Yintan AI

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Network performance prediction (NPP) is essential for the management and optimization of software-defined networking (SDN) and contributes to improving the quality of service (QoS) in SDN to meet the requirements of users. Although current deep learning-based methods can achieve high effectiveness, they still suffer from some problems, such as difficulty in capturing global information of the network, inefficiency in modeling end-to-end network performance, and inadequate graph feature extraction. To cope with these issues, our proposed Graphormer-based architecture for NPP leverages the powerful graph representation ability of Graphormer to effectively model the graph structure data, and a node-edge transformation algorithm is designed to transfer the feature extraction object from nodes to edges, thereby effectively extracting the end-to-end performance characteristics of the network. Moreover, routing oriented centrality measure coefficient for nodes and edges is proposed respectively to assess their importance and influence within the graph. Based on this coefficient, an enhanced feature extraction method and an advanced centrality encoding strategy are derived to fully extract the structural information of the graph. Experimental results on three public datasets demonstrate that the proposed GraphNPP architecture can achieve state-of-the-art results compared to current NPP methods.

Keywords: software-defined networking, network performance prediction, Graphormer, graph neural network

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Authors: David Jurado, Carlos Ávila

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Detection of early signs of breast cancer development is crucial to quickly diagnose the disease and to define adequate treatment to increase the survival probability of the patient. Computer Aided Detection systems (CADs), along with modern data techniques such as Machine Learning (ML) and Neural Networks (NN), have shown an overall improvement in digital mammography cancer diagnosis, reducing the false positive and false negative rates becoming important tools for the diagnostic evaluations performed by specialized radiologists. However, ML and NN-based algorithms rely on datasets that might bring issues to the segmentation tasks. In the present work, an automatic segmentation and detection algorithm is described. This algorithm uses image processing techniques along with the Hough transform to automatically identify microcalcifications that are highly correlated with breast cancer development in the early stages. Along with image processing, automatic segmentation of high-contrast objects is done using edge extraction and circle Hough transform. This provides the geometrical features needed for an automatic mask design which extracts statistical features of the regions of interest. The results shown in this study prove the potential of this tool for further diagnostics and classification of mammographic images due to the low sensitivity to noisy images and low contrast mammographies.

Keywords: breast cancer, segmentation, X-ray imaging, hough transform, image analysis

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Authors: Shi-Bo Feng, Sheng-Jie Zhang, Jin Zhou

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The relationship between microbial diversity and algal bloom has received considerable attention for decades. Microbes undoubtedly affect annual bloom events and impact the physiology of both partners, as well as shape ecosystem diversity. However, knowledge about interactions and network correlations among broader-spectrum microbes that lead to the dynamics in a complete bloom cycle are limited. In this study, pyrosequencing and network approaches simultaneously assessed the associate patterns among bacteria, archaea, and microeukaryotes in surface water and sediments in response to a natural dinoflagellate (Alexandrium sp.) bloom. In surface water, among the bacterial community, Gamma-Proteobacteria and Bacteroidetes dominated in the initial bloom stage, while Alpha-Proteobacteria, Cyanobacteria, and Actinobacteria become the most abundant taxa during the post-stage. In the archaea biosphere, it clustered predominantly with Methanogenic members in the early pre-bloom period while the majority of species identified in the later-bloom stage were ammonia-oxidizing archaea and Halobacteriales. In eukaryotes, dinoflagellate (Alexandrium sp.) was dominated in the onset stage, whereas multiply species (such as microzooplankton, diatom, green algae, and rotifera) coexistence in bloom collapse stag. In sediments, the microbial species biomass and richness are much higher than the water body. Only Flavobacteriales and Rhodobacterales showed a slight response to bloom stages. Unlike the bacteria, there are small fluctuations of archaeal and eukaryotic structure in the sediment. The network analyses among the inter-specific associations show that bacteria (Alteromonadaceae, Oceanospirillaceae, Cryomorphaceae, and Piscirickettsiaceae) and some zooplankton (Mediophyceae, Mamiellophyceae, Dictyochophyceae and Trebouxiophyceae) have a stronger impact on the structuring of phytoplankton communities than archaeal effects. The changes in population were also significantly shaped by water temperature and substrate availability (N & P resources). The results suggest that clades are specialized at different time-periods and that the pre-bloom succession was mainly a bottom-up controlled, and late-bloom period was controlled by top-down patterns. Additionally, phytoplankton and prokaryotic communities correlated better with each other, which indicate interactions among microorganisms are critical in controlling plankton dynamics and fates. Our results supplied a wider view (temporal and spatial scales) to understand the microbial ecological responses and their network association during algal blooming. It gives us a potential multidisciplinary explanation for algal-microbe interaction and helps us beyond the traditional view linked to patterns of algal bloom initiation, development, decline, and biogeochemistry.

Keywords: microbial community, harmful algal bloom, ecological process, network

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Authors: Bi Zhao

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Neural network technology has greatly improved the output of machine translation in terms of both fluency and accuracy, which greatly increases its appeal for young users. The present exploratory study aims to find out how the Chinese undergraduates perceive and use machine translation in their daily life. A survey is conducted to collect data from 100 undergraduate students from multiple Chinese universities and with varied academic backgrounds, including arts, business, science, engineering, and medicine. The survey questions inquire about their use (including frequency, scenarios, purposes, and preferences) of and attitudes (including trust, quality assessment, justifications, and ethics) toward machine translation. Interviews and tasks of evaluating machine translation output are also employed in combination with the survey on a sample of selected respondents. The results indicate that Chinese undergraduate students use machine translation on a daily basis for a wide range of purposes in academic, communicative, and entertainment scenarios. Most of them have preferred machine translation tools, but the availability of machine translation tools within a certain scenario, such as the embedded machine translation tool on the webpage, is also the determining factor in their choice. The results also reveal that despite the reportedly limited trust in the accuracy of machine translation output, most students lack the ability to critically analyze and evaluate such output. Furthermore, the evidence is revealed of the inadequate awareness of ethical responsibility as machine translation users among Chinese undergraduate students.

Keywords: Chinese undergraduates, machine translation, trust, usage

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Authors: Arth Bohra, Marwa Mahmoud

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In this paper, we utilize computer vision to develop a tool for youth coaches to formulate set-piece tactics for their players. We used the Soccernet database to extract the ResNet features and camera calibration data for over 3000 corner kick across 500 professional matches in the top 6 European leagues (English Premier League, UEFA Champions League, Ligue 1, La Liga, Serie A, Bundesliga). Leveraging the provided homography matrix, we construct a feature vector representing the formation of players on these corner kicks. Additionally, labeling the videos manually, we obtained the pass-trajectory of each of the 3000+ corner kicks by segmenting the field into four zones. Next, after determining the localization of the players and ball, we used event data to give the corner kicks a rating on a 1-4 scale. By employing a Convolutional Neural Network, our model managed to predict the success of a corner kick given the formations of players. This suggests that with the right formations, teams can optimize the way they approach corner kicks. By understanding this, we can help coaches formulate set-piece tactics for their own teams in order to maximize the success of their play. The proposed model can be easily extended; our method could be applied to even more game situations, from free kicks to counterattacks. This research project also gives insight into the myriad of possibilities that artificial intelligence possesses in transforming the domain of sports.

Keywords: soccer, corner kicks, AI, computer vision

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Authors: Parisa Mansour

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The coronavirus disease 2019 (COVID-19) was discovered and rapidly spread to various countries around the world since the end of 2019. Computed tomography (CT) images have been used as an important alternative to the time-consuming RT. PCR test. However, manual segmentation of CT images alone is a major challenge as the number of suspected cases increases. Thus, accurate and automatic segmentation of COVID-19 infections is urgently needed. Because the imaging features of the COVID-19 infection are different and similar to the background, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to build a deep convolutional neural network adapted for the segmentation of chest CT images with COVID-19 infections. First, we maintain a large and novel chest CT image database containing 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of an infected lung can be improved by global intensity adjustment, we introduce a feature variable block into the proposed deep CNN, which adjusts the global features of features to segment the COVID-19 infection. The proposed PV array can effectively and adaptively improve the performance of functions in different cases. We combine features of different scales by proposing a progressive atrocious space pyramid fusion scheme to deal with advanced infection regions with various aspects and shapes. We conducted experiments on data collected in China and Germany and showed that the proposed deep CNN can effectively produce impressive performance.

Keywords: chest, COVID-19, chest Image, coronavirus, CT image, chest CT

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Authors: Jing Zhao, Dayong Liu, Shihao Wang, Xinghua Zhu, Delong Li

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Uhumanned Underwater Vehicles generally operate in the deep sea, which has its own unique working conditions. Lithium-ion power batteries should have the necessary stability and endurance for use as an underwater vehicle’s power source. Therefore, it is essential to accurately forecast how long lithium-ion batteries will last in order to maintain the system’s reliability and safety. In order to model and forecast lithium battery Remaining Useful Life (RUL), this research suggests a model based on Complete Ensemble Empirical Mode Decomposition with Adaptive noise-Temporal Convolutional Net (CEEMDAN-TCN). In this study, two datasets, NASA and CALCE, which have a specific gap in capacity data fluctuation, are used to verify the model and examine the experimental results in order to demonstrate the generalizability of the concept. The experiments demonstrate the network structure’s strong universality and ability to achieve good fitting outcomes on the test set for various battery dataset types. The evaluation metrics reveal that the CEEMDAN-TCN prediction performance of TCN is 25% to 35% better than that of a single neural network, proving that feature expansion and modal decomposition can both enhance the model’s generalizability and be extremely useful in industrial settings.

Keywords: lithium-ion battery, remaining useful life, complete EEMD with adaptive noise, temporal convolutional net

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Authors: Asghar Arif

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Lung cancer has been recognized as one of the greatest common cancers, causing the annual mortality rate of about 1.2 million people in the world. Lung cancer is the most prevalent cancer in men and the third-most common cancer among women (after breast and digestive cancers).Recent evidences have shown the inflammatory process as one of the potential factors of cancer. Tuberculosis (TB), pneumonia, and chronic bronchitis are among the most important inflammation-inducing factors in the lungs, among which TB has a more profound role in the emergence of cancer.TB is one of the important mortality factors throughout the world, and 205,000 death cases are reported annually due to this disease. Chronic inflammation and fibrosis due to TB can induce genetic mutation and alternations. Parenchyma tissue of lung is involved in both diseases of TB and lung cancer, and continuous cough in lung cancer, morphological vascular variations, lymphocytosis processes, and generation of immune system mediators such as interleukins, are all among the factors leading to the hypothesis regarding the role of TB in lung cancer Some reports have shown that the induction of necrosis and apoptosis or TB reactivation, especially in patients with immune-deficiency, may result in increasing IL-17 and TNF_α, which will either decrease P53 activity or increase the expression of Bcl-2, decrease Bax-T, and cause the inhibition of caspase-3 expression due to decreasing the expression of mitochondria cytochrome oxidase. It has been also indicated that following the injection of BCG vaccine, the host immune system will be reinforced, and in particular, the rates of gamma interferon, nitric oxide, and interleukin-2 are increased. Therefore, CD4 + lymphocyte function will be improved, and the person will be immune against cancer.Numerous prospective studies have so far been conducted on the role of TB in lung cancer, and it seems that this disease is effective in that particular cancer.One of the main challenges of lung cancer is its correct and timely diagnosis. Unfortunately, clinical symptoms (such as continuous cough, hemoptysis, weight loss, fever, chest pain, dyspnea, and loss of appetite) and radiological images are similar in TB and lung cancer. Therefore, anti-TB drugs are routinely prescribed for the patients in the countries with high prevalence of TB, like Pakistan. Regarding the similarity in clinical symptoms and radiological findings of lung cancer, proper diagnosis is necessary for TB and respiratory infections due to nontuberculousmycobacteria (NTM). Some of the drug resistive TB cases are, in fact, lung cancer or NTM lung infections. Acid-fast staining and histological study of phlegm and bronchial washing, culturing and polymerase chain reaction TB are among the most important solutions for differential diagnosis of these diseases. Briefly, it is assumed that TB is one of the risk factors for cancer. Numerous studies have been conducted in this regard throughout the world, and it has been observed that there is a significant relationship between previous TB infection and lung cancer. However, to prove this hypothesis, further and more extensive studies are required. In addition, as the clinical symptoms and radiological findings of TB, lung cancer, and non-TB mycobacteria lung infections are similar, they can be misdiagnosed as TB.

Keywords: TB and lung cancer, TB people, TB servivers, TB and HIV aids

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Authors: Spira A., Marabelle A., Kientop D., Moser E., Mumm J.

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Background: Both interleukin-2 (IL-2) and interleukin-10 (IL-10) have been extensively studied for their stimulatory function on T cells and their potential to obtain sustainable tumor control in RCC, melanoma, lung, and pancreatic cancer as monotherapy, as well as combination with PD-1 blockers, radiation, and chemotherapy. While approved, IL-2 retains significant toxicity, preventing its widespread use. The significant efforts undertaken to uncouple IL-2 toxicity from its anti-tumor function have been unsuccessful, and early phase clinical safety observed with PEGylated IL-10 was not met in a blinded Phase 3 trial. Deka Biosciences has engineered a novel molecule coupling wild-type IL-2 to a high affinity variant of Epstein Barr Viral (EBV) IL-10 via a scaffold (scFv) that binds to epidermal growth factor receptors (EGFR). This patented molecule, termed DK210 (EGFR), is retained at high levels within the tumor microenvironment for days after dosing. In addition to overlapping and non-redundant anti-tumor function, IL-10 reduces IL-2 mediated cytokine release syndrome risks and inhibits IL-2 mediated T regulatory cell proliferation. Methods: DK210 (EGFR) is being evaluated in an open-label, dose-escalation (Phase 1) study with 5 (0.025-0.3 mg/kg) monotherapy dose levels and (expansion cohorts) in combination with PD-1 blockers, or radiation or chemotherapy in patients with advanced solid tumors overexpressing EGFR. Key eligibility criteria include 1) confirmed progressive disease on at least one line of systemic treatment, 2) EGFR overexpression or amplification documented in histology reports, 3) at least a 4 week or 5 half-lives window since last treatment, and 4) excluding subjects with long QT syndrome, multiple myeloma, multiple sclerosis, myasthenia gravis or uncontrolled infectious, psychiatric, neurologic, or cancer disease. Plasma and tissue samples will be investigated for pharmacodynamic and predictive biomarkers and genetic signatures associated with IFN-gamma secretion, aiming to select subjects for treatment in Phase 2. Conclusion: Through successful coupling of wild-type IL-2 with a high affinity IL-10 and targeting directly to the tumor microenvironment, DK210 (EGFR) has the potential to harness IL-2 and IL-10’s known anti-cancer promise while reducing immunogenicity and toxicity risks enabling safe concomitant cytokine treatment with other anti-cancer modalities.

Keywords: cytokine, EGFR over expression, interleukine-2, interleukine-10, clinical trial

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Authors: Parker Townes, Prabdeep Panesar, Chunlin Liu, Soo Youn Lee, Dan Devoe, Paul D. Arnold, Jennifer Crosbie, Russell Schachar

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Attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are impairing childhood neurodevelopmental disorders with problems in executive functions. Executive functions are higher-level mental processes essential for daily functioning and goal attainment. There is genetic and neural overlap between ADHD and ASD. The aim of this meta-analysis was to evaluate if pediatric ASD and ADHD have distinct executive function profiles. This review was completed following Cochrane guidelines. Fifty-eight articles were identified through database searching, followed by a blinded screening in duplicate. A meta-analysis was performed for all task performance metrics evaluated by at least two articles. Forty-five metrics from 24 individual tasks underwent analysis. No differences were found between youth with ASD and ADHD in any domain under direct comparison. However, individuals with ASD and ADHD exhibited deficient attention, flexibility, visuospatial abilities, working memory, processing speed, and response inhibition compared to controls. No deficits in planning were noted in either disorder. Only 11 studies included a group with comorbid ASD+ADHD, making it difficult to determine whether common executive function deficits are a function of comorbidity. Further research is needed to determine if comorbidity accounts for the apparent commonality in executive function between ASD and ADHD.

Keywords: autism spectrum disorder, ADHD, neurocognition, executive function, youth

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: Nirupama Dixit, Anyaa Mittal, Neeru Sood

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Alzheimer’s disease (AD) is a progressive form of dementia, contributing to up to 70% of cases, mostly observed in elderly but is not restricted to old age. The pathophysiology of the disease is characterized by specific pathological changes in brain. The changes (i.e. accumulation of metal ions in brain, formation of extracellular β-amyloid (Aβ) peptide aggregates and tangle of hyper phosphorylated Tau protein inside neurons) damage the neuronal connections irreversibly. The current issues in improvement of life quality of Alzheimer's patient lies in the fact that the diagnosis is made at a late stage of the disease and the medications do not treat the basic causes of Alzheimer's. The targeted delivery of drug through the blood brain barrier (BBB) poses several limitations via traditional approaches for treatment. To overcome these drug delivery limitation, nanoparticles provide a promising solution. This review focuses on current strategies for efficient targeted drug delivery using nanoparticles and improving the quality of therapy provided to the patient. Nanoparticles can be used to encapsulate drug (which is generally hydrophobic) to ensure its passage to brain; they can be conjugated to metal ion chelators to reduce the metal load in neural tissue thus lowering the harmful effects of oxidative damage; can be conjugated with drug and monoclonal antibodies against BBB endogenous receptors. Finally this review covers how the nanoparticles can play a role in diagnosing the disease.

Keywords: Alzheimer's disease, β-amyloid plaques, blood brain barrier, metal chelators, nanoparticles

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Authors: Parsa Mahdavi, M. Amin Hariri-Ardebili

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The development of accurate machine learning and deep learning models traditionally demands hands-on expertise and a solid background to fine-tune hyperparameters. With the continuous expansion of datasets in various scientific and engineering domains, researchers increasingly turn to machine learning methods to unveil hidden insights that may elude classic regression techniques. This surge in adoption raises concerns about the adequacy of the resultant meta-models and, consequently, the interpretation of the findings. In response to these challenges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. AutoML encompasses crucial stages such as data preparation, feature engineering, hyperparameter optimization, and neural architecture search. This paper provides a comprehensive overview of the principles underpinning AutoML, surveying several widely-used AutoML platforms. Additionally, the paper offers a glimpse into the application of AutoML on various engineering datasets. By comparing these results with those obtained through classical machine learning methods, the paper quantifies the uncertainties inherent in the application of a single ML model versus the holistic approach provided by AutoML. These examples showcase the efficacy of AutoML in extracting meaningful patterns and insights, emphasizing its potential to revolutionize the way we approach and analyze complex datasets.

Keywords: automated machine learning, uncertainty, engineering dataset, regression

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

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Authors: B. Lokuketagoda, M. Weerakoon, U. Madushan, A. N. Senaratne, K. Y. Abeywardena

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Ransomware has become a common threat in past few years and the recent threat reports show an increase of growth in Ransomware infections. Researchers have identified different variants of Ransomware families since 2015. Lack of knowledge of the user about the threat is a major concern. Ransomware detection methodologies are still growing through the industry. Email is the easiest method to send Ransomware to its victims. Uninformed users tend to click on links and attachments without much consideration assuming the emails are genuine. As a solution to this in this paper R-Killer Ransomware detection tool is introduced. Tool can be integrated with existing email services. The core detection Engine (CDE) discussed in the paper focuses on separating suspicious samples from emails and handling them until a decision is made regarding the suspicious mail. It has the capability of preventing execution of identified ransomware processes. On the other hand, Sandboxing and URL analyzing system has the capability of communication with public threat intelligence services to gather known threat intelligence. The R-Killer has its own mechanism developed in its Proactive Monitoring System (PMS) which can monitor the processes created by downloaded email attachments and identify potential Ransomware activities. R-killer is capable of gathering threat intelligence without exposing the user’s data to public threat intelligence services, hence protecting the confidentiality of user data.

Keywords: ransomware, deep learning, recurrent neural networks, email, core detection engine

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Authors: Malkhaz Jokhadze, Vakhtang Mshvildadze, Levan Makaradze, Ekaterine Mosidze, Salome Barbaqadze, Mariam Murtazashvili, Dali Berashvili, Koba sivsivadze, Lasha Bakuridze, Aliosha Bakuridze

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Essential oils are one of the most important groups of biologically active substances present in plants. Due to the chemical diversity of components, essential oils and their preparations have a wide spectrum of pharmacological action. They have bactericidal, antiviral, fungicidal, antiprotozoal, anti-inflammatory, spasmolytic, sedative and other activities. They are expectorant, spasmolytic, sedative, hypotensive, secretion enhancing, antioxidant remedies. Based on preliminary pharmacological studies, we have developed a formulation called “Phytobiotic” containing essential oils, a feed additive for poultry as an alternative to antibiotics. Phytobiotic is a water-soluble powder containing a composition of essential oils of thyme, clary, monarda and auxiliary substances: dry extract of liquorice and inhalation lactose. On this stage of research, the goal was to study the chemical composition of provided phytobiotic, identify the main substances and determine their quantity, investigate the biological activity of phytobiotic through in vitro and in vivo studies. Using gas chromatography-mass spectrometry, 38 components were identified in phytobiotic, representing acyclic-, monocyclic-, bicyclic-, and sesquiterpenes. Together with identification of main active substances, their quantitative content was determined, including acyclic terpene alcohol β-linalool, acyclic terpene ketone linalyl acetate, monocyclic terpenes: D-limonene and γ-terpinene, monocyclic aromatic terpene thymol. Provided phytobiotic has pronounced and at the same time broad spectrum of antibacterial activity. In the cell model, phytobiotic showed weak antioxidant activity, and it was stronger in the ORAC (chemical model) tests. Meanwhile anti-inflammatory activity was also observed. When fowls were supplied feed enriched with phytobiotic, it was observed that gained weight of the chickens in the experimental group exceeded the same data for the control group during the entire period of the experiment. The survival rate of broilers in the experimental group during the growth period was 98% compared to -94% in the control group. As a result of conducted researches probable four different mechanisms which are important for the action of phytobiotics were identified: sensory, metabolic, antioxidant and antibacterial action. General toxic, possible local irritant and allergenic effects of phytobiotic were also investigated. Performed assays proved that formulation is safe.

Keywords: clary, essential oils, monarda, poultry, phytobiotics, thyme

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Authors: Kun Xu, Yuan Xu, Jia Qiao

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The document detection plays an important role in optical character recognition and text analysis. Because the traditional detection methods have weak generalization ability, and deep neural network has complex structure and large number of parameters, which cannot be well applied in mobile devices, this paper proposes a lightweight Document Corner Detection Network (DCDNet). DCDNet is a two-stage architecture. The first stage with Encoder-Decoder structure adopts depthwise separable convolution to greatly reduce the network parameters. After introducing the Feature Attention Union (FAU) module, the second stage enhances the feature information of spatial and channel dim and adaptively adjusts the size of receptive field to enhance the feature expression ability of the model. Aiming at solving the problem of the large difference in the number of pixel distribution between corner and non-corner, Weighted Binary Cross Entropy Loss (WBCE Loss) is proposed to define corner detection problem as a classification problem to make the training process more efficient. In order to make up for the lack of Dataset of document corner detection, a Dataset containing 6620 images named Document Corner Detection Dataset (DCDD) is made. Experimental results show that the proposed method can obtain fast, stable and accurate detection results on DCDD.

Keywords: document detection, corner detection, attention mechanism, lightweight

Procedia PDF Downloads 332

Authors: David Percy

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Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

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