Search results for: Additive White Gaussian Noise

Authors: Franca De Sarno, Alfonso Maria Ponsiglione, Enza Torino

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Recent advances in diagnostic imaging technology have significantly contributed to a better understanding of specific changes associated with diseases progression. Among different imaging modalities, Magnetic Resonance Imaging (MRI) represents a noninvasive medical diagnostic technique, which shows low sensitivity and long acquisition time and it can discriminate between healthy and diseased tissues by providing 3D data. In order to improve the enhancement of MRI signals, some imaging exams require intravenous administration of contrast agents (CAs). Recently, emerging research reports a progressive deposition of these drugs, in particular, gadolinium-based contrast agents (GBCAs), in the body many years after multiple MRI scans. These discoveries confirm the need to have a biocompatible system able to boost a clinical relevant Gd-chelate. To this aim, several approaches based on engineered nanostructures have been proposed to overcome the common limitations of conventional CAs, such as the insufficient signal-to-noise ratios due to relaxivity and poor safety profile. In particular, nanocarriers, labeling or loading with CAs, capable of carrying high payloads of CAs have been developed. Currently, there’s no a comprehensive understanding of the thermodynamic contributions enable of boosting the efficacy of conventional CAs by using biopolymers matrix. Thus, considering the importance of MRI in diagnosing diseases, here it is reported a successful example of the next generation of these drugs where the commercial gadolinium chelate is incorporate into a biopolymer nanostructure, formed by cross-linked hyaluronic acid (HA), with improved relaxation properties. In addition, they are highlighted the basic principles ruling biopolymer-CA interactions in the perspective of their influence on the relaxometric properties of the CA by adopting a multidisciplinary experimental approach. On the basis of these discoveries, it is clear that the main point consists in increasing the rigidification of readily-available Gd-CAs within the biopolymer matrix by controlling the water dynamics, the physicochemical interactions, and the polymer conformations. In the end, the acquired knowledge about polymer-CA systems has been applied to develop of Gd-based HA nanoparticles with enhanced relaxometric properties.

Keywords: biopolymers, MRI, nanoparticles, contrast agent

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Authors: Alina Muradyan, Lina Babayan, Arsen Nanyan, Gohar Galstyan, Vigen Khachatryan

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We present a photometric method for identifying stars, galaxies and quasars in multi-color surveys, which uses a library of ∼> 65000 color templates for comparison with observed objects. The method aims for extracting the information content of object colors in a statistically correct way, and performs a classification as well as a redshift estimation for galaxies and quasars in a unified approach based on the same probability density functions. For the redshift estimation, we employ an advanced version of the Minimum Error Variance estimator which determines the redshift error from the redshift dependent probability density function itself. The method was originally developed for the Calar Alto Deep Imaging Survey (CADIS), but is now used in a wide variety of survey projects. We checked its performance by spectroscopy of CADIS objects, where the method provides high reliability (6 errors among 151 objects with R < 24), especially for the quasar selection, and redshifts accurate within σz ≈ 0.03 for galaxies and σz ≈ 0.1 for quasars. For an optimization of future survey efforts, a few model surveys are compared, which are designed to use the same total amount of telescope time but different sets of broad-band and medium-band filters. Their performance is investigated by Monte-Carlo simulations as well as by analytic evaluation in terms of classification and redshift estimation. If photon noise were the only error source, broad-band surveys and medium-band surveys should perform equally well, as long as they provide the same spectral coverage. In practice, medium-band surveys show superior performance due to their higher tolerance for calibration errors and cosmic variance. Finally, we discuss the relevance of color calibration and derive important conclusions for the issues of library design and choice of filters. The calibration accuracy poses strong constraints on an accurate classification, which are most critical for surveys with few, broad and deeply exposed filters, but less severe for surveys with many, narrow and less deep filters.

Keywords: VO, ArVO, DFBS, FITS, image processing, data analysis

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Authors: Brooke Sweeney, David White, Felix Amparano, Nick A. Clark, Amy R. Beck, Mathew Lindquist, Lora Edwards, Julie Vandal, Jennifer Lisondra, Katie Cox, Renee Arensberg, Allen Cummins, Jazmine Cedeno, Jason D. Fraser, Kelsey Dean, Helena H. Laroche, Cristina Fernandez

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Background: National organizations call for standardized pre-surgical requirements and education to optimize postoperative outcomes. Since 2017 our surgery program has used defined protocols and educational curricula pre- and post-surgery. In response to patient outcomes, our educational content was refined to include quizzes to assess patient knowledge and surgical preparedness. We aim to optimize adolescent pre-bariatric surgery preparedness by improving overall aggregate pre-surgical assessment performance from 68% to 80% within 12 months. Methods: A multidisciplinary improvement team was developed within the weight management clinic (WMC) of our tertiary care, free-standing children’s hospital. A manual has been utilized since 2017, with limitations in consistent delivery and patient uptake of information. The curriculum has been improved to include quizzes administered during WMC visits prior to bariatric surgery. The initial outcome measure is the pre-surgical quiz score of adolescents preparing for bariatric surgery. Process measure was the number of questions answered correctly to test the questions. Baseline performance was determined by a patient assessment survey of pre-surgical preparedness at patient visits. Plan-Do-Study-Act cycles (PDSA) included: 1) creation and implementation of a refined curriculum, 2) development of 5 new quizzes based upon learning objectives, and 3) improving provider-lead teaching and quiz administration within clinic workflow. Run charts assessed impact over time. Results: A total of 346 quiz questions were administered to 34 adolescents. The outcome measure improved from a baseline mean of 68% to 86% following PDSA 2 cycles, and it was sustained. Conclusion/Implication: Patient/family comprehension of surgical preparedness improved with standardized education via team member-led teaching and assessment using quizzes during pre-surgical clinic visits. The next steps include launching redesigned teaching materials with modules correlated to quizzes and assessment of comprehension and outcomes post-surgically.

Keywords: bariatric surgery, adolescent, clinic, pre-bariatric training

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Authors: Patience Manjengwa-Hungwe, Carmen White

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Background: Odyssean malaria refers to malaria acquired by infected mosquito bites from malaria endemic to non-endemic regions by mechanical modes of transport, such as airplanes, water vessels, trains and vehicles. Odyssean Malaria is rare and is characterised by absence of travel history to malaria endemic areas. As not anticipated in non-endemic areas, late diagnosis and treatment lead to a high case fatality rate. On 26 September 2014, the Outbreak Response Unit at the National Institute of Communicable Diseases was notified of a suspected death from Odyssean Malaria in Johannesburg, Gauteng Province, a non-endemic area. The main objective of this investigation was to identify the etiological agent's mode and source of transmission. Methods: Epidemiological surveys were conducted with the deceased’s family and clinical details were obtained from doctors who treated the victim in Southrand, Johannesburg. Blood samples were collected prior to death and sent to the National Health Laboratory Services, Johannesburg laboratory for a full blood count, urea electrolytes, creatinine, and C-reactive protein. Environmental assessments and entomological investigations, including collection of mosquito and larvae, were conducted at the deceased’s home and surrounding areas and sent to the laboratory for analysis. Results: Epidemiological surveys revealed no travel history, no mechanical transmission through blood transfusion and no previous possible exposure of the victim to malaria mosquitoes. Laboratory findings indicated that the platelet count was low. A further smear revealed that the malaria parasite was present and malaria antigen for P. falciparum was positive. Entomological findings revealed that none of the six adult or larval mosquitoes collected on site were malaria vectors. Dumping sites found at the back of the house were identified as possible sites where mosquitoes from endemic places could possibly breed. Conclusion: Given that there was no travel history or the possibility of mechanical transmission (blood transfusion or needle), the research team concluded that it is highly probable that the infection was acquired through an infective Anopheles mosquito inadvertently translocated from a Malaria endemic area by mechanical modes of transport. We recommend that clinicians in non-endemic malaria areas be aware of this type of malaria and test for malaria in patients showing malaria-like symptoms.

Keywords: Odyssean Malaria, vector Bourne, malaria, epidemiological surveys

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Authors: Joanna Estkowska

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Sensory integration methods are very useful and improve daily functioning autistic and mentally disabled children. Autism is a neurobiological disorder that impairs one's ability to communicate with and relate to others as well as their sensory system. Children with autism, even highly functioning kids, can find it difficult to process language with surrounding noise or smells. They are hypersensitive to things we can ignore such as sight, sounds and touch. Adolescents with highly functioning autism or Asperger Syndrome can study Science and Math but the social aspect is difficult for them. Nature science is an area of study that attracts many of these kids. It is a systematic field in which the children can focus on a small aspect. If you follow these rules you can come up with an expected result. Sensory integration program and systematic classroom observation are quantitative methods of measuring classroom functioning and behaviors from direct observations. These methods specify both the events and behaviors that are to be observed and how they are to be recorded. Our students with and without autism attended the lessons in the classroom of nature science in the school and in the laboratory of University of Science and Technology in Bydgoszcz. The aim of this study is investigation the effects of sensory integration methods in teaching to students with autism. They were observed during experimental lessons in the classroom and in the laboratory. Their physical characteristics, sensory dysfunction, and behavior in class were taken into consideration by comparing their similarities and differences. In the chemistry classroom, every autistic student is paired with a mentor from their school. In the laboratory, the children are expected to wear goggles, gloves and a lab coat. The chemistry classes in the laboratory were held for four hours with a lunch break, and according to the assistants, the children were engaged the whole time. In classroom of nature science, the students are encouraged to use the interactive exhibition of chemical, physical and mathematical models constructed by the author of this paper. Our students with and without autism attended the lessons in those laboratories. The teacher's goals are: to assist the child in inhibiting and modulating sensory information and support the child in processing a response to sensory stimulation.

Keywords: autism spectrum disorder, science education, sensory integration techniques, student with special educational needs

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Authors: Mohammad Shajid Rahman, Tarik Kaya, Edgar Matida

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Electronic cigarettes, also known as e-cigarettes, may have become a tool to improve smoking cessation due to their ability to provide nicotine at a selected rate. Unlike traditional cigarettes, which produce toxic elements from tobacco combustion, e-cigarettes generate aerosols by heating a liquid solution (commonly a mixture of propylene glycol, vegetable glycerin, nicotine and some flavoring agents). However, caution still needs to be taken when using e-cigarettes due to the presence of addictive nicotine and some harmful substances produced from the heating process. Particle size distribution (PSD) and associated velocities generated by e-cigarettes have significant influence on aerosol deposition in different regions of human respiratory tracts. On another note, low actuation power is beneficial in aerosol generating devices since it exhibits a reduced emission of toxic chemicals. In case of e-cigarettes, lower heating powers can be considered as powers lower than 10 W compared to a wide range of powers (0.6 to 70.0 W) studied in literature. Due to the importance regarding inhalation risk reduction, deeper understanding of particle size characteristics of e-cigarettes demands thorough investigation. However, comprehensive study on PSD and velocities of e-cigarettes with a standard testing condition at relatively low heating powers is still lacking. The present study aims to measure particle number count and size distribution of undiluted aerosols of a latest fourth-generation e-cigarette at low powers, within 6.5 W using real-time particle counter (time-of-flight method). Also, temporal and spatial evolution of particle size and velocity distribution of aerosol jets are examined using phase Doppler anemometry (PDA) technique. To the authors’ best knowledge, application of PDA in e-cigarette aerosol measurement is rarely reported. In the present study, preliminary results about particle number count of undiluted aerosols measured by time-of-flight method depicted that an increase of heating power from 3.5 W to 6.5 W resulted in an enhanced asymmetricity in PSD, deviating from log-normal distribution. This can be considered as an artifact of rapid vaporization, condensation and coagulation processes on aerosols caused by higher heating power. A novel mathematical expression, combining exponential, Gaussian and polynomial (EGP) distributions, was proposed to describe asymmetric PSD successfully. The value of count median aerodynamic diameter and geometric standard deviation laid within a range of about 0.67 μm to 0.73 μm, and 1.32 to 1.43, respectively while the power varied from 3.5 W to 6.5 W. Laser Doppler velocimetry (LDV) and PDA measurement suggested a typical centerline streamwise mean velocity decay of aerosol jet along with a reduction of particle sizes. In the final submission, a thorough literature review, detailed description of experimental procedure and discussion of the results will be provided. Particle size and turbulent characteristics of aerosol jets will be further examined, analyzing arithmetic mean diameter, volumetric mean diameter, volume-based mean diameter, streamwise mean velocity and turbulence intensity. The present study has potential implications in PSD simulation and validation of aerosol dosimetry model, leading to improving related aerosol generating devices.

Keywords: E-cigarette aerosol, laser doppler velocimetry, particle size distribution, particle velocity, phase Doppler anemometry

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Authors: Idowu Olugbenga Adewumi, Foluke Iyabo Oluwatoyinbo

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Presently, there are several discussions on the food security with increase in yield of arable crop throughout the world. This article, briefly present research efforts to create digital interfaces to nature, in particular to area of crop production in agriculture with increase in yield with interest on pervasive computing. The approach goes beyond the use of sensor networks for environmental monitoring but also by emphasizing the development of a system architecture that detect intruder (Intrusion Process) which reduce the yield of the farmer at the end of the planting/harvesting period. The objective of the work is to set a model for setting up the hand held or portable device for increasing the quality and quantity of arable crop. This process incorporates the use of infrared motion image sensor with security alarm system which can send a noise signal to intruder on the farm. This model of the portable image sensing device in monitoring or scaring human, rodent, birds and even pests activities will reduce post harvest loss which will increase the yield on farm. The nano intelligence technology was proposed to combat and minimize intrusion process that usually leads to low quality and quantity of produce from farm. Intranet system will be in place with wireless radio (WLAN), router, server, and client computer system or hand held device e.g PDAs or mobile phone. This approach enables the development of hybrid systems which will be effective as a security measure on farm. Since, precision agriculture has developed with the computerization of agricultural production systems and the networking of computerized control systems. In the intelligent plant production system of controlled greenhouses, information on plant responses, measured by sensors, is used to optimize the system. Further work must be carry out on modeling using pervasive computing environment to solve problems of agriculture, as the use of electronics in agriculture will attracts more youth involvement in the industry.

Keywords: pervasive computing, intrusion detection, precision agriculture, security, arable crop

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Authors: Hakimullah Hakim, Chiharu Toyofuku, Mari Ota, Mayuko Suzuki, Miyuki Komura, Masashi Yamada, Md. Shahin Alam, Natthanan Sangsriratanakul, Dany Shoham, Kazuaki Takehara

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Disinfectants and their application are essential part of infection control strategies and enhancement of biosecurity at farms, worldwide. Alkaline agents are well known for their strong and long term antimicrobial capacities and most frequently are applied at farms for control and prevention of biological hazards. However, inadequate information regarding such materials’ capacities to inactivate pathogens and their improper applications fail farmers to achieve the mentioned goal. Thus, this requires attention to further evaluate their efficacies, under different conditions and in different ways. Here in this study we evaluated bactericidal efficacies of food additive grade of calcium hydroxide (FdCa(OH)2) powder derived from natural calcium carbonates obtained from limestone (Fine Co., Ltd., Tokyo, Japan), and bioceramic powder (BCX) derived from chicken feces at pH 13 (NMG environmental development Co., Ltd., Tokyo, Japan), for their efficacies to inactivate bacteria in feces. [Materials & Methods] Chicken feces were inoculated by 100 µl Escherichia coli and Salmonella Infantis in falcon tubes, individually, then FdCa(OH)2 or BCX powders were individually added to make final concentration of 0, 5, 10, 20 and 30% (w/w) in total weight of 0.5g, followed by properly mixing and incubating at room temperature for certain periods of time, in a dark place. Afterwards, 10 ml 1M Tris-HCl (pH 7.2) was added onto them to reduce their pH, in order to stop powders’ activities and to harvest the remained viable bacteria, whereas using normal medium or dW2 to recover bacteria increases the mixture pH, and as a result bacteria would be inactivated soon; therefore, the latter practice brings about incorrect and misleading results. Samples were then inoculated on DHL agar plates in order to calculate colony forming units (CFU)/ml of viable bacteria. [Results and Discussion] FdCa(OH)2 powder at 10% and 5% required 3 hr and 6 hr exposure times, respectively, while BCX powder at 20% concentrations required 6 hr exposure time to kill the mentioned bacteria in feces down to lower than detectable level (≤ 3.6 log10 CFU/ml). This study confirmed capacities of FdCa(OH)2 and BCX powders to inactivate bacteria in feces, and both materials are environment friendly materials, with no risk to human or animal’s health. This finding helps farmers to properly apply alkaline agents in appropriate concentrations and exposure times in their farms, in order to prevent and control infectious diseases outbreaks and to enhance biosecurity. Finally, this finding may help farmers to implement better strategies for infections control in their livestock farms.

Keywords: bacterial inactivation, bioceramic, biosecurity at livestock farms, chicken feces

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Authors: Shinsuke Udagawa, Masato Yamagishi, Masanori Ota

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The Schlieren method, which has been conventionally used to visualize high-speed flows, has disadvantages such as the complexity of the experimental setup and the inability to quantitatively analyze the amount of refraction of light. The Background Oriented Schlieren (BOS) method proposed by Meier is one of the measurement methods that solves the problems, as mentioned above. The refraction of light is used for BOS method same as the Schlieren method. The BOS method is characterized using a digital camera to capture the images of the background behind the observation area. The images are later analyzed by a computer to quantitatively detect the amount of shift of the background image. The experimental setup for BOS does not require concave mirrors, pinholes, or color filters, which are necessary in the conventional Schlieren method, thus simplifying the experimental setup. However, the defocusing of the observation results is caused in case of using BOS method. Since the focus of camera on the background image leads to defocusing of the observed object. The defocusing of object becomes greater with increasing the distance between the background and the object. On the other hand, the higher sensitivity can be obtained. Therefore, it is necessary to adjust the distance between the background and the object to be appropriate for the experiment, considering the relation between the defocus and the sensitivity. The purpose of this study is to experimentally clarify the effect of defocus on density field reconstruction. In this study, the visualization experiment of underexpanded jet using BOS measurement system with ronchi ruling as the background that we constructed, have been performed. The reservoir pressure of the jet and the distance between camera and axis of jet is fixed, and the distance between background and axis of jet has been changed as the parameter. The images have been later analyzed by using personal computer to quantitatively detect the amount of shift of the background image from the comparison between the background pattern and the captured image of underexpanded jet. The quantitatively measured amount of shift have been reconstructed into a density flow field using the Abel transformation and the Gradstone-Dale equation. From the experimental results, it is found that the reconstructed density image becomes blurring, and noise becomes decreasing with increasing the distance between background and axis of underexpanded jet. Consequently, it is cralified that the sensitivity constant should be greater than 20, and the circle of confusion diameter should be less than 2.7mm at least in this experimental setup.

Keywords: BOS method, underexpanded jet, abel transformation, density field visualization

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Authors: Pamela R. Jackson, Andrea Hawkins-Daarud, Cassandra R. Rickertsen, Kamala Clark-Swanson, Scott A. Whitmire, Kristin R. Swanson

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Glioblastoma Multiforme (GBM), the most common primary brain tumor, often presents with hyperintensity on T2-weighted or T2-weighted fluid attenuated inversion recovery (T2/FLAIR) magnetic resonance imaging (MRI). This hyperintensity corresponds with vasogenic edema, however there are likely many infiltrating tumor cells within the hyperintensity as well. While MRIs do not directly indicate tumor cells, MRIs do reflect the microenvironmental water abnormalities caused by the presence of tumor cells and edema. The inherent heterogeneity and resulting MRI features of GBMs complicate assessing disease response. To understand how hyperintensity on T2/FLAIR MRI may correlate with edema in the extracellular space (ECS), a multi-compartmental MRI signal equation which takes into account tissue compartments and their associated volumes with input coming from a mathematical model of glioma growth that incorporates edema formation was explored. The reasonableness of two possible extracellular space schema was evaluated by varying the T2 of the edema compartment and calculating the possible resulting T2s in tumor and peripheral edema. In the mathematical model, gliomas were comprised of vasculature and three tumor cellular phenotypes: normoxic, hypoxic, and necrotic. Edema was characterized as fluid leaking from abnormal tumor vessels. Spatial maps of tumor cell density and edema for virtual tumors were simulated with different rates of proliferation and invasion and various ECS expansion schemes. These spatial maps were then passed into a multi-compartmental MRI signal model for generating simulated T2/FLAIR MR images. Individual compartments’ T2 values in the signal equation were either from literature or estimated and the T2 for edema specifically was varied over a wide range (200 ms – 9200 ms). T2 maps were calculated from simulated images. T2 values based on simulated images were evaluated for regions of interest (ROIs) in normal appearing white matter, tumor, and peripheral edema. The ROI T2 values were compared to T2 values reported in literature. The expanding scheme of extracellular space is had T2 values similar to the literature calculated values. The static scheme of extracellular space had a much lower T2 values and no matter what T2 was associated with edema, the intensities did not come close to literature values. Expanding the extracellular space is necessary to achieve simulated edema intensities commiserate with acquired MRIs.

Keywords: extracellular space, glioblastoma multiforme, magnetic resonance imaging, mathematical modeling

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Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Harbor protection systems are so important. The need for automatic protection systems has increased over the last years. Diver detection active sonar has great significance. It used to detect underwater threats such as divers and autonomous underwater vehicle. To automatically detect such threats the sonar image is processed by algorithms. These algorithms used to detect, track and classify of underwater objects. In this work, divers tracking and classification algorithm is improved be proposing a robust wake detection method. To detect objects the sonar images is normalized then segmented based on fixed threshold. Next, the centroids of the segments are found and clustered based on distance metric. Then to track the objects linear Kalman filter is applied. To reduce effect of noise and creation of false tracks, the Kalman tracker is fine tuned. The tuning is done based on our active sonar specifications. After the tracks are initialed and updated they are subjected to a filtering stage to eliminate the noisy and unstable tracks. Also to eliminate object with a speed out of the diver speed range such as buoys and fast boats. Afterwards the result tracks are subjected to a classification stage to deiced the type of the object been tracked. Here the classification stage is to deice wither if the tracked object is an open circuit diver or a close circuit diver. At the classification stage, a small area around the object is extracted and a novel wake detection method is applied. The morphological features of the object with his wake is extracted. We used support vector machine to find the best classifier. The sonar training images and the test images are collected by ARMELSAN Defense Technologies Company using the portable diver detection sonar ARAS-2023. After applying the algorithm to the test sonar data, we get fine and stable tracks of the divers. The total classification accuracy achieved with the diver type is 97%.

Keywords: harbor protection, diver detection, active sonar, wake detection, diver classification

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Authors: Ju-Hong Lee, Ching-Wei Liao

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Adaptive beamforming using antenna array of sensors is useful in the process of adaptively detecting and preserving the presence of the desired signal while suppressing the interference and the background noise. For conventional adaptive array beamforming, we require a prior information of either the impinging direction or the waveform of the desired signal to adapt the weights. The adaptive weights of an antenna array beamformer under a steered-beam constraint are calculated by minimizing the output power of the beamformer subject to the constraint that forces the beamformer to make a constant response in the steering direction. Hence, the performance of the beamformer is very sensitive to the accuracy of the steering operation. In the literature, it is well known that the performance of an adaptive beamformer will be deteriorated by any steering angle error encountered in many practical applications, e.g., the wireless communication systems with massive antennas deployed at the base station and user equipment. Hence, developing effective signal processing techniques to deal with the problem due to steering angle error for array beamforming systems has become an important research work. In this paper, we present an effective signal processing technique for constructing an adaptive beamformer against the steering angle error. The proposed array beamformer adaptively estimates the actual direction of the desired signal by using the presumed steering vector and the received array data snapshots. Based on the presumed steering vector and a preset angle range for steering mismatch tolerance, we first create a matrix related to the direction vector of signal sources. Two projection matrices are generated from the matrix. The projection matrix associated with the desired signal information and the received array data are utilized to iteratively estimate the actual direction vector of the desired signal. The estimated direction vector of the desired signal is then used for appropriately finding the quiescent weight vector. The other projection matrix is set to be the signal blocking matrix required for performing adaptive beamforming. Accordingly, the proposed beamformer consists of adaptive quiescent weights and partially adaptive weights. Several computer simulation examples are provided for evaluating and comparing the proposed technique with the existing robust techniques.

Keywords: adaptive beamforming, robustness, signal blocking, steering angle error

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Authors: Irakli Gamkrelidze, David Shengelia, Tamara Tsutsunava, Giorgi Chichinadze, Giorgi Beridze, Ketevan Tedliashvili, Tamara Tsamalashvili

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The Loki crystalline massif crops out in the Caucasian region and the geological retrospective represent the northern marginal part of the Baiburt-Sevanian terrain (island arc), bordering with the Paleotethys oceanic basin in the north. The pre-Alpine basement of the massif is built up of Lower-Middle Paleozoic metamorphic complex (metasedimentary and metabasite rocks), Upper Devonian quartz-diorites and Late Variscan granites. Earlier metamorphic complex was considered as an indivisible set including suites with different degree of metamorphism. Systematic geologic, petrologic and geochemical investigations of the massif’s rocks suggest the different conception on composition, structure and formation conditions of the massif. In particular, there are two main rock types in the Loki massif: the oldest autochthonous series of gneissic quartz-diorites and cutting them granites. The massif is flanked on its western side by a volcano-sedimentary sequence, metamorphosed to low-T facies. Petrologic, metamorphic and structural differences in this sequence prove the existence of a number of discrete units (overthrust sheets). One of them, the metabasic sheet represents the fragment of ophiolite complex. It comprises transition types of the second and third layers of the Paleooceanic crust: the upper noncumulated part of the third layer gabbro component and the following lowest part of the parallel diabase dykes of the second layer. The ophiolites are represented by metagabbros, metagabbro-diabases, metadiabases and amphibolite schists. According to the content of petrogenic components and additive elements in metabasites is stated that the protolith of metabasites belongs to petrochemical type of tholeiitic series of basalts. The parental magma of metaophiolites is of E-MORB composition, and by petrochemical parameters, it is very close to the composition of intraplate basalts. The dykes of hypabissal leucocratic siliceous and medium magmatic rocks associated with the metaophiolite sheet form the separate complex. They are granitoids with the extremely low content of CaO and quartz-diorite porphyries. According to various petrochemical parameters, these rocks have mixed characteristics. Their formation took place in spreading conditions or in the areas of manifestation of plumes most likely of island arc type. The metamorphism degree of the metaophiolites corresponds to a very low stage of green schist facies. The rocks of the metaophiolite complex are obducted from the Paleotethys Ocean. Geological and paleomagnetic data show that the primary location of the ocean is supposed to be to the north of the Loki crystalline massif.

Keywords: the Caucasus, crystalline massif, ophiolites, tectonic sheet

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Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

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Authors: Saheli Mitra, Ramesh Karri, Praveen K. Mylapalli, Arka. B. Dey, Gourav Bhattacharya, Gouriprasanna Roy, Syed M. Kamil, Surajit Dhara, Sunil K. Sinha, Sajal K. Ghosh

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The most well-known structures of lyotropic liquid crystalline systems are the two dimensional hexagonal phase of cylindrical micelles with a positive interfacial curvature and the lamellar phase of flat bilayers with zero interfacial curvature. In aqueous solution of surfactants, the concentration dependent phase transitions have been investigated extensively. However, instead of changing the surfactant concentrations, the local curvature of an aggregate can be altered by tuning the electrostatic interactions among the constituent molecules. Intermediate phases with non-uniform interfacial curvature are still unexplored steps to understand the route of phase transition from hexagonal to lamellar. Understanding such structural evolution in lyotropic liquid crystalline systems is important as it decides the complex rheological behavior of the system, which is one of the main interests of the soft matter industry. Sodium dodecyl sulfate (SDS) is an anionic surfactant and can be considered as a unique system to tune the electrostatics by cationic additives. In present study, imidazolium-based ionic liquids (ILs) with different number of carbon atoms in their single hydrocarbon chain were used as the additive in the aqueous solution of SDS. At a fixed concentration of total non-aqueous components (SDS and IL), the molar ratio of these components was changed, which effectively altered the electrostatic interactions between the SDS molecules. As a result, the local curvature is observed to modify, and correspondingly, the structure of the hexagonal liquid crystalline phases are transformed into other phases. Polarizing optical microscopy of SDS and imidazole-based-IL systems have exhibited different textures of the liquid crystalline phases as a function of increasing concentration of the ILs. The small angle synchrotron x-ray diffraction (SAXD) study has indicated the hexagonal phase of direct cylindrical micelles to transform to a rectangular phase at the presence of short (two hydrocarbons) chain IL. However, the hexagonal phase is transformed to a lamellar phase at the presence of long (ten hydrocarbons) chain IL. Interestingly, at the presence of a medium (four hydrocarbons) chain IL, the hexagonal phase is transformed to another hexagonal phase of direct cylindrical micelles through the lamellar phase. To the best of our knowledge, such a phase sequence has not been reported earlier. Even though the small angle x-ray diffraction study has revealed the lattice parameters of these phases to be similar to each other, their rheological behavior has been distinctly different. These rheological studies have shed lights on how these phases differ in their viscoelastic behavior. Finally, the packing parameters, calculated for these phases based on the geometry of the aggregates, have explained the formation of the self-assembled aggregates.

Keywords: lyotropic liquid crystals, polarizing optical microscopy, rheology, surfactants, small angle x-ray diffraction

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Authors: Z. Razi, S. M. J. Mortazavi, N. Shokrpour, Z. Shayan, F. Amiri

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Long-term exposure to low doses of ionizing radiation occurs in radiation health care workplaces. Although doses in health professions are generally very low, there are still matters of concern. The radiation safety program promotes occupational radiation safety through accurate and reliable monitoring of radiation workers in order to effectively manage radiation protection. To achieve this goal, it has become mandatory to implement health examination periodically. As a result, based on the hematological alterations, working populations with a common occupational radiation history are screened. This paper calls into question the effectiveness of blood component analysis as a screening program which is mandatory for medical radiation workers in some countries. This study details the distribution and trends of changes in blood components, including white blood cells (WBCs), red blood cells (RBCs) and platelets as well as received cumulative doses from occupational radiation exposure. This study was conducted among 199 participants and 100 control subjects at the medical imaging departments at the central hospital of Shiraz University of Medical Sciences during the years 2006–2010. Descriptive and analytical statistics, considering the P-value<0.05 as statistically significance was used for data analysis. The results of this study show that there is no significant difference between the radiation workers and controls regarding WBCs and platelet count during 4 years. Also, we have found no statistically significant difference between the two groups with respect to RBCs. Besides, no statistically significant difference was observed with respect to RBCs with regards to gender, which has been analyzed separately because of the lower reference range for normal RBCs levels in women compared to men and. Moreover, the findings confirm that in a separate evaluation between WBCs count and the personnel’s working experience and their annual exposure dose, results showed no linear correlation between the three variables. Since the hematological findings were within the range of control levels, it can be concluded that the radiation dosage (which was not more than 7.58 mSv in this study) had been too small to stimulate any quantifiable change in medical radiation worker’s blood count. Thus, use of more accurate method for screening program based on the working profile of the radiation workers and their accumulated dose is suggested. In addition, complexity of radiation-induced functions and the influence of various factors on blood count alteration should be taken into account.

Keywords: blood cell count, mandatory testing, occupational exposure, radiation

Procedia PDF Downloads 462

Authors: Shankha Sanyal, Archi Banerjee, Sayan Biswas, Sourya Sengupta, Sayan Nag, Ranjan Sengupta, Dipak Ghosh

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The term ‘gestalt’ has been widely used in the field of psychology which defined the perception of human mind to group any object not in part but as a 'unified' whole. Music, in general, is polyphonic - i.e. a combination of a number of pure tones (frequencies) mixed together in a manner that sounds harmonious. The study of human brain response due to different frequency groups of the acoustic signal can give us an excellent insight regarding the neural and functional architecture of brain functions. Hence, the study of music cognition using neuro-biosensors is becoming a rapidly emerging field of research. In this work, we have tried to analyze the effect of different frequency bands of music on the various frequency rhythms of human brain obtained from EEG data. Four widely popular Rabindrasangeet clips were subjected to Wavelet Transform method for extracting five resonant frequency bands from the original music signal. These frequency bands were initially analyzed with Detrended/Adaptive Fractal analysis (DFA/AFA) methods. A listening test was conducted on a pool of 100 respondents to assess the frequency band in which the music becomes non-recognizable. Next, these resonant frequency bands were presented to 20 subjects as auditory stimulus and EEG signals recorded simultaneously in 19 different locations of the brain. The recorded EEG signals were noise cleaned and subjected again to DFA/AFA technique on the alpha, theta and gamma frequency range. Thus, we obtained the scaling exponents from the two methods in alpha, theta and gamma EEG rhythms corresponding to different frequency bands of music. From the analysis of music signal, it is seen that loss of recognition is proportional to the loss of long range correlation in the signal. From the EEG signal analysis, we obtain frequency specific arousal based response in different lobes of brain as well as in specific EEG bands corresponding to musical stimuli. In this way, we look to identify a specific frequency band beyond which the music becomes non-recognizable and below which in spite of the absence of other bands the music is perceivable to the audience. This revelation can be of immense importance when it comes to the field of cognitive music therapy and researchers of creativity.

Keywords: AFA, DFA, EEG, gestalt in music, Hurst exponent

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Authors: Dino Carpentras, Alejandro Dinkelberg, Michael Quayle

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Opinion dynamics models use an agent-based modeling approach to model people’s opinions. Model's properties are usually explored by testing the two 'degrees of freedom': the interaction rule and the network topology. The latter defines the connection, and thus the possible interaction, among agents. The interaction rule, instead, determines how agents select each other and update their own opinion. Here we show the existence of the third degree of freedom. This can be used for turning one model into each other or to change the model’s output up to 100% of its initial value. Opinion dynamics models represent the evolution of real-world opinions parsimoniously. Thus, it is fundamental to know how real-world opinion (e.g., supporting a candidate) could be turned into a number. Specifically, we want to know if, by choosing a different opinion-to-number transformation, the model’s dynamics would be preserved. This transformation is typically not addressed in opinion dynamics literature. However, it has already been studied in psychometrics, a branch of psychology. In this field, real-world opinions are converted into numbers using abstract objects called 'scales.' These scales can be converted one into the other, in the same way as we convert meters to feet. Thus, in our work, we analyze how this scale transformation may affect opinion dynamics models. We perform our analysis both using mathematical modeling and validating it via agent-based simulations. To distinguish between scale transformation and measurement error, we first analyze the case of perfect scales (i.e., no error or noise). Here we show that a scale transformation may change the model’s dynamics up to a qualitative level. Meaning that a researcher may reach a totally different conclusion, even using the same dataset just by slightly changing the way data are pre-processed. Indeed, we quantify that this effect may alter the model’s output by 100%. By using two models from the standard literature, we show that a scale transformation can transform one model into the other. This transformation is exact, and it holds for every result. Lastly, we also test the case of using real-world data (i.e., finite precision). We perform this test using a 7-points Likert scale, showing how even a small scale change may result in different predictions or a number of opinion clusters. Because of this, we think that scale transformation should be considered as a third-degree of freedom for opinion dynamics. Indeed, its properties have a strong impact both on theoretical models and for their application to real-world data.

Keywords: degrees of freedom, empirical validation, opinion scale, opinion dynamics

Procedia PDF Downloads 155

Authors: Hanadi Mohamed Shawgi Gamal, Ashraf Mohamed Ahmed Abdalla

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Sudan is endowed by a great diversity of tree species; nevertheless, the utilization of wood resources has traditionally concentrated on a few number of species. With the great variation in the climatic zones of Sudan, great variations are expected in the anatomical properties between and within species. This variation needs to be fully explored in order to suggest the best uses for the species. Modern research on wood has substantiated that the climatic condition where the species grow has significant effect on wood properties. Understanding the extent of variability of wood is important because the uses for each kind of wood are related to its characteristics; furthermore, the suitability or quality of wood for a particular purpose is determined by the variability of one or more of these characteristics. The present study demonstrates the effect of rainfall zones in some anatomical properties of Acacia seyal var. seyal growing in Sudan. For this purpose, twenty healthy trees were collected randomly from two zones (ten trees per zone). One zone with relatively low rainfall (273mm annually) which represented by North Kordofan state and White Nile state and the second with relatively high rainfall (701 mm annually) represented by Blue Nile state and South Kordofan state. From each sampled tree, a stem disc (3 cm thick) was cut at 10% from stem height. One radius was obtained in central stem dices. Two representative samples were taken from each disc, one at 10% distance from pith to bark, the second at 90% in order to represent the juvenile and mature wood. The investigated anatomical properties were fibers length, fibers and vessels diameter, lumen diameter, and wall thickness as well as cell proportions. The result of the current study reveals significant differences between zones in mature wood vessels diameter and wall thickness, as well as juvenile wood vessels, wall thickness. The higher values were detected in the drier zone. Significant differences were also observed in juvenile wood fiber length, diameter as well as wall thickness. Contrary to vessels diameter and wall thickness, the fiber length, diameter as well as wall thickness were decreased in the drier zone. No significant differences have been detected in cell proportions of juvenile and mature wood. The significant differences in some fiber and vessels dimension lead to expect significant differences in wood density. From these results, Acacia seyal var. seyal seems to be well adapted with the change in rainfall and may survive in any rainfall zone.

Keywords: Acacia seyal var. seyal, anatomical properties, rainfall zones, variation

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Authors: Rigers Dodaj

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Nowadays, the environment is a critical goal for civil engineers, human activity, construction projects, economic growth, and whole national development. Regarding the development of Albania's economy, people's living standards are increasing, and the requirements for the living environment are also increasing. Under these circumstances, environmental protection and sustainability this is the critical issue. The rising industrialization, urbanization, and energy demand affect the environment by emission of carbon dioxide gas (CO2), a significant parameter known to impact air pollution directly. Consequently, many governments and international organizations conducted policies and regulations to address environmental degradation in the pursuit of economic development, for instance in Albania, the CO2 emission calculated in metric tons per capita has increased by 23% in the last 20 years. This paper analyzes the importance of civil engineering and economic growth in the sustainability of the environment focusing on CO2 emission. The analyzed data are time series 2001 - 2020 (with annual frequency), based on official publications of the World Bank. The statistical approach with vector error correction model and time series forecasting model are used to perform the parameter’s estimations and long-run equilibrium. The research in this paper adds a new perspective to the evaluation of a sustainable environment in the context of carbon emission reduction. Also, it provides reference and technical support for the government toward green and sustainable environmental policies. In the context of low-carbon development, effectively improving carbon emission efficiency is an inevitable requirement for achieving sustainable economic and environmental protection. Also, the study reveals that civil engineering development projects impact greatly the environment in the long run, especially in areas of flooding, noise pollution, water pollution, erosion, ecological disorder, natural hazards, etc. The potential for reducing industrial carbon emissions in recent years indicates that reduction is becoming more difficult, it needs another economic growth policy and more civil engineering development, by improving the level of industrialization and promoting technological innovation in industrial low-carbonization.

Keywords: CO₂ emission, civil engineering, economic growth, environmental sustainability

Procedia PDF Downloads 86

Authors: Sonia Tamanna, Akramul Hassan, Mohammad Shakil Mahmood, Farzana Ansari, Gowhar Rashid, Mir Fahim Faisal, M. Zakir Hossain Howlader

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Background: Preeclampsia (PE), a pregnancy-specific hypertensive disorder, significantly impacts maternal and fetal health. It is particularly prevalent in underdeveloped countries and is linked to preterm delivery and fetal growth. The renin-angiotensin system (RAS) plays a crucial role in ensuring a successful pregnancy outcome, with Angiotensin-Converting Enzyme 2 (ACE2) being a key component. ACE2 converts ANG II to Ang-(1-7), offering protection against ANG II-induced stress and inflammation while regulating blood pressure and osmotic balance during pregnancy. The reduced maternal plasma angiotensin-converting enzyme 2 (ACE2) seen in preeclampsia might contribute to its pathogenesis. However, there has been a dearth of comprehensive research into the association between ACE2 gene polymorphism and preeclampsia. In the South Asian population, hypertension is strongly linked to two SNPs: rs2285666 and rs879922. This genotype was therefore considered, and the possible association of maternal and fetal ACE2 gene polymorphism with preeclampsia within the Bangladeshi population was evaluated. Method: DNA was extracted from peripheral white blood cells (WBCs) using the organic method, and SNP genotyping was done via PCR-RFLP. Odds ratios (OR) with 95% confidence intervals (95% CI) were calculated using logistic regression to determine relative risk. Result: A comprehensive case-control study was conducted on 51 PE patients and their infants, along with 56 control subjects and their infants. Maternal single nuvleotide polymorphisms (SNP) (rs2285666) analysis revealed a strong association between the TT genotype and preeclampsia, with a four-fold increased risk in mothers (P=0.024, OR=4.00, 95% CI=1.36-11.37) compared to their ancestral genotype CC. However, the CT genotype (rs2285666) showed no significant difference (P=0.46, OR=1.54, 95% CI=0.57-4.14). Notably, no significant correlation was found in infants, regardless of their gender. For rs879922, no significant association was observed in both mothers and infants. This pioneering study suggests that mothers carrying the ACE2 gene variant rs2285666 (TT allele) may be at higher risk for preeclampsia, potentially influencing hypertension characteristics, whereas rs879922 does not appear to be associated with developing preeclampsia. Conclusion: This study sheds light on the role of ACE2 gene polymorphism, particularly the rs2285666 TT allele, in maternal susceptibility to preeclampsia. However, rs879922 does not appear to be linked to the risk of PE. This research contributes to our understanding of the genetic underpinnings of preeclampsia, offering insights into potential avenues for prevention and management.

Keywords: ACE2, PCR-RFLP, preeclampsia, single nuvleotide polymorphisms (SNPs)

Procedia PDF Downloads 61

Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

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Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

Procedia PDF Downloads 130

Authors: Iñaki Milton-Laskibar, Leixuri Aguirre, Maria P. Portillo

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Introduction: Energy restriction is an effective approach in preventing liver steatosis. However, due to social and economic reasons among others, compliance with this treatment protocol is often very poor, especially in the long term. Resveratrol, a natural polyphenolic compound that belongs to stilbene group, has been widely reported to imitate the effects of energy restriction. Objective: To analyze the effects of resveratrol under normoenergetic feeding conditions and under a mild energy restriction on liver fat accumulation and hepatic fatty acid oxidation. Methods: 36 male six-week-old rats were fed a high-fat high-sucrose diet for 6 weeks in order to induce steatosis. Then, rats were divided into four groups and fed a standard diet for 6 additional weeks: control group (C), resveratrol group (RSV, resveratrol 30 mg/kg/d), restricted group (R, 15 % energy restriction) and combined group (RR, 15 % energy restriction and resveratrol 30 mg/kg/d). Liver triacylglycerols (TG) and total cholesterol contents were measured by using commercial kits. Carnitine palmitoyl transferase 1a (CPT 1a) and citrate synthase (CS) activities were measured spectrophotometrically. TFAM (mitochondrial transcription factor A) and peroxisome proliferator-activator receptor alpha (PPARα) protein contents, as well as the ratio acetylated peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1α)/Total PGC1α were analyzed by Western blot. Statistical analysis was performed by using one way ANOVA and Newman-Keuls as post-hoc test. Results: No differences were observed among the four groups regarding liver weight and cholesterol content, but the three treated groups showed reduced TG when compared to the control group, being the restricted groups the ones showing the lowest values (with no differences between them). Higher CPT 1a and CS activities were observed in the groups supplemented with resveratrol (RSV and RR), with no difference between them. The acetylated PGC1α /total PGC1α ratio was lower in the treated groups (RSV, R and RR) than in the control group, with no differences among them. As far as TFAM protein expression is concerned, only the RR group reached a higher value. Finally, no changes were observed in PPARα protein expression. Conclusions: Resveratrol administration is an effective intervention for liver triacylglycerol content reduction, but a mild energy restriction is even more effective. The mechanisms of action of these two strategies are different. Thus resveratrol, but not energy restriction, seems to act by increasing fatty acid oxidation, although mitochondriogenesis seems not to be induced. When both treatments (resveratrol administration and a mild energy restriction) were combined, no additive or synergic effects were appreciated. Acknowledgements: MINECO-FEDER (AGL2015-65719-R), Basque Government (IT-572-13), University of the Basque Country (ELDUNANOTEK UFI11/32), Institut of Health Carlos III (CIBERobn). Iñaki Milton is a fellowship from the Basque Government.

Keywords: energy restriction, fat, liver, oxidation, resveratrol

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Authors: Massimiliano Frezza, Sergio Bianchi, Augusto Pianese

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The relation between stock price volatility and trading volume represents a controversial issue which has received a remarkable attention over the past decades. In fact, an extensive literature shows a positive relation between price volatility and trading volume in the financial markets, but the causal relationship which originates such association is an open question, from both a theoretical and empirical point of view. In this regard, various models, which can be considered as complementary rather than competitive, have been introduced to explain this relationship. They include the long debated Mixture of Distributions Hypothesis (MDH); the Sequential Arrival of Information Hypothesis (SAIH); the Dispersion of Beliefs Hypothesis (DBH); the Noise Trader Hypothesis (NTH). In this work, we analyze whether stock market efficiency can explain the diversity of results achieved during the years. For this purpose, we propose an alternative measure of market efficiency, based on the pointwise regularity of a stochastic process, which is the Hurst–H¨older dynamic exponent. In particular, we model the stock market by means of the multifractional Brownian motion (mBm) that displays the property of a time-changing regularity. Mostly, such models have in common the fact that they locally behave as a fractional Brownian motion, in the sense that their local regularity at time t0 (measured by the local Hurst–H¨older exponent in a neighborhood of t0 equals the exponent of a fractional Brownian motion of parameter H(t0)). Assuming that the stock price follows an mBm, we introduce and theoretically justify the Hurst–H¨older dynamical exponent as a measure of market efficiency. This allows to measure, at any time t, markets’ departures from the martingale property, i.e. from efficiency as stated by the Efficient Market Hypothesis. This approach is applied to financial markets; using data for the SP500 index from 1978 to 2017, on the one hand we find that when efficiency is not accounted for, a positive contemporaneous relationship emerges and is stable over time. Conversely, it disappears as soon as efficiency is taken into account. In particular, this association is more pronounced during time frames of high volatility and tends to disappear when market becomes fully efficient.

Keywords: volume–volatility relationship, efficient market hypothesis, martingale model, Hurst–Hölder exponent

Procedia PDF Downloads 79

Authors: Oluwafunmibi Omotayo Fasanya, Augustine Kena Adjei

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To develop a machine learning model to predict diabetes (DM) prevalence in the U.S. population using demographic characteristics, physical indicators, and lifestyle habits, and to analyze how these factors contribute to the likelihood of diabetes. We analyzed data from 23,546 participants aged 20 and older, who were non-pregnant, from the 2009-2018 National Health and Nutrition Examination Survey (NHANES). The dataset included key demographic (age, sex, ethnicity), physical (BMI, leg length, total cholesterol [TCHOL], fasting plasma glucose), and lifestyle indicators (smoking habits). A weighted sample was used to account for NHANES survey design features such as stratification and clustering. A classification machine learning model was trained to predict diabetes status. The target variable was binary (diabetes or non-diabetes) based on fasting plasma glucose measurements. The following models were evaluated: Logistic Regression (baseline), Random Forest Classifier, Gradient Boosting Machine (GBM), Support Vector Machine (SVM). Model performance was assessed using accuracy, F1-score, AUC-ROC, and precision-recall metrics. Feature importance was analyzed using SHAP values to interpret the contributions of variables such as age, BMI, ethnicity, and smoking status. The Gradient Boosting Machine (GBM) model outperformed other classifiers with an AUC-ROC score of 0.85. Feature importance analysis revealed the following key predictors: Age: The most significant predictor, with diabetes prevalence increasing with age, peaking around the 60s for males and 70s for females. BMI: Higher BMI was strongly associated with a higher risk of diabetes. Ethnicity: Black participants had the highest predicted prevalence of diabetes (14.6%), followed by Mexican-Americans (13.5%) and Whites (10.6%). TCHOL: Diabetics had lower total cholesterol levels, particularly among White participants (mean decline of 23.6 mg/dL). Smoking: Smoking showed a slight increase in diabetes risk among Whites (0.2%) but had a limited effect in other ethnic groups. Using machine learning models, we identified key demographic, physical, and lifestyle predictors of diabetes in the U.S. population. The results confirm that diabetes prevalence varies significantly across age, BMI, and ethnic groups, with lifestyle factors such as smoking contributing differently by ethnicity. These findings provide a basis for more targeted public health interventions and resource allocation for diabetes management.

Keywords: diabetes, NHANES, random forest, gradient boosting machine, support vector machine

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Authors: Efe Gumuslu, Guclu Insel, Gülten Yuksek, Nilay Sayi Ucar, Emine Ubay Cokgor, Tuğba Olmez Hanci, Didem Okutman Tas, Fatoş Germirli Babuna, Derya Firat Ertem, Ökmen Yildirim, Özge Erturan, Betül Kirci

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Although water reclamation and reuse are inseparable parts of sustainable production concept all around the world, current levels of reuse constitute only a small fraction of the total volume of industrial effluents. Nowadays, within the perspective of serious climate change, wastewater reclamation and reuse practices should be considered as a requirement. Industrial sector is one of the largest users of water sources. The OECD Environmental Outlook to 2050 predicts that global water demand for manufacturing will increase by 400% from 2000 to 2050 which is much larger than any other sector. Metal finishing industry is one of the industries that requires high amount of water during the manufacturing. Therefore, actions regarding the improvement of wastewater treatment and reuse should be undertaken on both economic and environmental sustainability grounds. Process wastewater can be reused for more purposes if the appropriate treatment systems are installed to treat the wastewater to the required quality level. Recent studies showed that membrane separation techniques may help in solving the problem of attaining a suitable quality of water that allows being recycled back to the process. The metal finishing factory where this study is conducted is one of the biggest white-goods manufacturers in Turkey. The sheet metal parts used in the cookers production have to be exposed to surface pre-treatment processes composed of degreasing, rinsing, nanoceramics coating and deionization rinsing processes, consecutively. The wastewater generating processes in the factory are enamel coating, painting and styrofoam processes. In the factory, the main source of water is the well water. While some part of the well water is directly used in the processes after passing through resin treatment, some portion of it is directed to the reverse osmosis treatment to obtain required water quality for enamel coating and painting processes. In addition to these processes another important source of water that can be considered as a potential water source is rainwater (3660 tons/year). In this study, process profiles as well as pollution profiles were assessed by a detailed quantitative and qualitative characterization of the wastewater sources generated in the factory. Based on the preliminary results the main water sources that can be considered for reuse in the processes were determined as painting and styrofoam processes.

Keywords: enamel coating, painting, reuse, wastewater

Procedia PDF Downloads 380

Authors: Mario Tauzene Afonso Matangue, Ivan Andres Sanchez Ortiz

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The Metropolitan area of Maputo (Mozambique Capital City) is located in semi-arid zone (800 mm annual rainfall) with 1101170 million inhabitants. On the west side, there are the flatlands of Infulene where the Mulauze River flows towards to the Indian Ocean, receiving at this site, the storm water contaminated with sanitary sewage from Maputo, transported through a concrete open channel. In Infulene, local communities grow salads crops such as tomato, onion, garlic, lettuce, and cabbage, which are then commercialized and consumed in several markets in Maputo City. Lettuce is the most daily consumed salad crop in different meals, generally in fast-foods, breakfasts, lunches, and dinners. However, the risk of infection by several pathogens due to the consumption of lettuce, using the Quantitative Microbial Risk Assessment (QMRA) tools, is still unknown since there are few studies or publications concerning to this matter in Mozambique. This work is aimed at determining the annual risk arising from the consumption of lettuce grown in Infulene valley, in Maputo, using QMRA tools. The exposure model was constructed upon the volume of contaminated water remaining in the lettuce leaves, the empirical relations between the number of pathogens and the indicator of microorganisms (E. coli), the consumption of lettuce (g) and reduction of pathogens (days). The reference pathogens were Vibrio cholerae, Cryptosporidium, norovirus, and Ascaris. The water quality samples (E. coli) were collected in the storm water channel from January 2016 to December 2018, comprising 65 samples, and the urban lettuce consumption data were collected through inquiry in Maputo Metropolis covering 350 persons. A non-parametric bootstrap was performed involving 10,000 iterations over the collected dataset, namely, water quality (E. coli) and lettuce consumption. The dose-response models were: Exponential for Cryptosporidium, Kummer Confluent hypergeomtric function (1F1) for Vibrio and Ascaris Gaussian hypergeometric function (2F1-(a,b;c;z) for norovirus. The annual infection risk estimates were performed using R 3.6.0 (CoreTeam) software by Monte Carlo (Latin hypercubes), a sampling technique involving 10,000 iterations. The annual infection risks values expressed by Median and the 95th percentile, per person per year (pppy) arising from the consumption of lettuce are as follows: Vibrio cholerae (1.00, 1.00), Cryptosporidium (3.91x10⁻³, 9.72x 10⁻³), nororvirus (5.22x10⁻¹, 9.99x10⁻¹) and Ascaris (2.59x10⁻¹, 9.65x10⁻¹). Thus, the consumption of the lettuce would result in greater risks than the tolerable levels ( < 10⁻³ pppy or 10⁻⁶ DALY) for all pathogens, and the Vibrio cholerae is the most virulent pathogens, according to the hit-single models followed by the Ascaris lumbricoides and norovirus. The sensitivity analysis carried out in this work pointed out that in the whole QMRA, the most important input variable was the reduction of pathogens (Spearman rank value was 0.69) between harvest and consumption followed by water quality (Spearman rank value was 0.69). The decision-makers (Mozambique Government) must strengthen the prevention measures related to pathogens reduction in lettuce (i.e., washing) and engage in wastewater treatment engineering.

Keywords: annual infections risk, lettuce, non-parametric bootstrapping, quantitative microbial risk assessment tools

Procedia PDF Downloads 122

Authors: Arinze Ezieke, Antonio Serrano, William Clarke, Denys Villa-Gomez

Abstract:

Berry cultivation results in discharge of high organic strength putrescible solid waste which potentially contributes to environmental degradation, making it imperative to assess options for its complete management. Anaerobic digestion (AD) could be an ideal option when the target is energy generation; however, due to berry fruit characteristics high carbohydrate composition, the technology could be limited by its high alkalinity requirement which suggests dosing of additives such as buffers and trace elements supplement. Overcoming this limitation in an economically viable way could entail replacement of synthetic additives with recycled by-product waste. Consequently, ash from co-smouldering of high COD characteristic AD digestate and coco-coir could be a promising material to be used to enhance the AD of berry fruit waste, given its characteristic high pH, alkalinity and metal concentrations which is typical of synthetic additives. Therefore, the aim of the research was to evaluate the stability and process performance from the AD of BFW when ash from co-smoldered digestate and coir are supplemented as alkalinity and trace elements (TEs) source. Series of batch experiments were performed to ascertain the necessity for alkalinity addition and to see whether the alkalinity and metals in the co-smouldered digestate ash can provide the necessary buffer and TEs for AD of berry fruit waste. Triplicate assays were performed in batch systems following I/S of 2 (in VS), using serum bottles (160 mL) sealed and placed in a heated room (35±0.5 °C), after creating anaerobic conditions. Control experiment contained inoculum and substrates only, and inoculum, substrate and NaHCO3 for optimal total alkalinity concentration and TEs assays, respectively. Total alkalinity concentration refers to alkalinity of inoculum and the additives. The alkalinity and TE potential of the ash were evaluated by supplementing ash (22.574 g/kg) of equivalent total alkalinity concentration to that of the pre-determined optimal from NaHCO3, and by dosing ash (0.012 – 7.574 g/kg) of varying concentrations of specific essential TEs (Co, Fe, Ni, Se), respectively. The result showed a stable process at all examined conditions. Supplementation of 745 mg/L CaCO3 NaHCO3 resulted to an optimum TAC of 2000 mg/L CaCO3. Equivalent ash supplementation of 22.574 g/kg allowed the achievement of this pre-determined optimum total alkalinity concentration, resulting to a stable process with a 92% increase in the methane production rate (323 versus 168 mL CH4/ (gVS.d)), but a 36% reduction in the cumulative methane production (103 versus 161 mL CH4/gVS). Addition of ashes at incremental dosage as TEs source resulted to a reduction in the Cumulative methane production, with the highest dosage of 7.574 g/kg having the highest effect of -23.5%; however, the seemingly immediate bioavailability of TE at this high dosage allowed for a +15% increase in the methane production rate. With an increased methane production rate, the results demonstrated that the ash at high dosages could be an effective supplementary material for either a buffered or none buffered berry fruit waste AD system.

Keywords: anaerobic digestion, alkalinity, co-smoldered digestate ash, trace elements

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Authors: Larasati Zuhro, Ida F Priyanto

Abstract:

Background: In March 2020, Indonesia started to be affected by Covid-19, and the number of victims increased slowly but surely until finally, the highest number of victims reached the highest—about 50,000 persons—for the daily cases in the middle of 2021. Like other institutions, schools and universities were suddenly closed in March 2020, and students had to change their ways of studying from face-to-face to online. This sudden changed affected students and faculty, including LIS students and faculty because they never experienced online classes in Indonesia due to the previous regulation that academic and school activities were all conducted onsite. For almost two years, school and academic activities were held online. This indeed has affected the way students learned and faculty delivered their courses. This raises the question of whether students are now ready for their new learning activities due to the covid-19 disruption. Objectives: this study was conducted to find out the impact of covid-19 pandemic on the LIS learning process and the effectiveness of online classes for students of LIS in Indonesia. Methodology: This was qualitative research conducted among LIS students at UIN Sunan Kalijaga, Yogyakarta, Indonesia. The population are students who were studying for masters’program during covid-19 pandemic. Results: The study showed that students were ready with the online classes because they are familiar with the technology. However, the Internet and technology infrastructure do not always support the process of learning. Students mention slow WIFI is one factor that causes them not being able to study optimally. They usually compensate themselves by visiting a public library, a café, or any other places to get WIFI network. Noises come from the people surrounding them while they are studying online.Some students could not concentrate well when attending the online classes as they studied at home, and their families sometimes talk to other family members, or they asked the students while they are attending the online classes. The noise also came when they studied in a café. Another issue is that the classes were held in shorter time than that in the face-to-face. Students said they still enjoyed the onsite classes instead of online, although they do not mind to have hybrid model of learning. Conclusion: Pandemic of Covid-19 has changed the way students of LIS in Indonesia learn. They have experienced a process of migrating the way they learn from onsite to online. They also adapted their learning with the condition of internet access speed, infrastructure, and the environment. They expect to have hybrid classes in the future.

Keywords: learning, LIS students, pandemic, covid-19

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Authors: Aylin W Sahin, Alice Jaeger, Laura Nyhan, Gregory Belt, Steffen Münch, Elke K. Arendt

Abstract:

Our current food systems cause an increase in malnutrition resulting in more people being overweight or obese in the Western World. Additionally, our natural resources are under enormous pressure and the greenhouse gas emission increases yearly with a significant contribution to climate change. Hence, transforming our food systems is of highest priority. Plant-based food products have a lower environmental impact compared to their animal-based counterpart, representing a more sustainable protein source. However, most plant-based protein ingredients, such as soy and pea, are lacking indispensable amino acids and extremely limited in their functionality and, thus, in their food application potential. They are known to have a low solubility in water and change their properties during processing. The low solubility displays the biggest challenge in the development of milk alternatives leading to inferior protein content and protein quality in dairy alternatives on the market. Moreover, plant-based protein ingredients often possess an off-flavour, which makes them less attractive to consumers. EverPro, a plant-protein isolate originated from Brewer’s Spent Grain, the most abundant by-product in the brewing industry, represents the missing piece in the plant protein portfolio. With a protein content of >85%, it is of high nutritional value, including all indispensable amino acids which allows closing the protein quality gap of plant proteins. Moreover, it possesses high techno-functional properties. It is fully soluble in water (101.7 ± 2.9%), has a high fat absorption capacity (182.4 ± 1.9%), and a foaming capacity which is superior to soy protein or pea protein. This makes EverPro suitable for a vast range of food applications. Furthermore, it does not cause changes in viscosity during heating and cooling of dispersions, such as beverages. Besides its outstanding nutritional and functional characteristics, the production of EverPro has a much lower environmental impact compared to dairy or other plant protein ingredients. Life cycle assessment analysis showed that EverPro has the lowest impact on global warming compared to soy protein isolate, pea protein isolate, whey protein isolate, and egg white powder. It also contributes significantly less to freshwater eutrophication, marine eutrophication and land use compared the protein sources mentioned above. EverPro is the prime example of sustainable ingredients, and the type of plant protein the food industry was waiting for: nutritious, multi-functional, and environmentally friendly.

Keywords: plant-based protein, upcycled, brewers' spent grain, low environmental impact, highly functional ingredient

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