Search results for: voice liveness detection

Authors: Adesuyi Ayodeji Steve, Zahn Munch

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This study investigates the use of MODIS NDVI to identify agricultural land cover change areas on an annual time step (2007 - 2012) and characterize the trend in the study area. An ISODATA classification was performed on the MODIS imagery to select only the agricultural class producing 3 class groups namely: agriculture, agriculture/semi-natural, and semi-natural. NDVI signatures were created for the time series to identify areas dominated by cereals and vineyards with the aid of ancillary, pictometry and field sample data. The NDVI signature curve and training samples aided in creating a decision tree model in WEKA 3.6.9. From the training samples two classification models were built in WEKA using decision tree classifier (J48) algorithm; Model 1 included ISODATA classification and Model 2 without, both having accuracies of 90.7% and 88.3% respectively. The two models were used to classify the whole study area, thus producing two land cover maps with Model 1 and 2 having classification accuracies of 77% and 80% respectively. Model 2 was used to create change detection maps for all the other years. Subtle changes and areas of consistency (unchanged) were observed in the agricultural classes and crop practices over the years as predicted by the land cover classification. 41% of the catchment comprises of cereals with 35% possibly following a crop rotation system. Vineyard largely remained constant over the years, with some conversion to vineyard (1%) from other land cover classes. Some of the changes might be as a result of misclassification and crop rotation system.

Keywords: change detection, land cover, modis, NDVI

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Authors: Summer Isaacson

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Research on gender differences in environmental behavior has long claimed that women engage less than men in environmental political participation (EPP) (protests, petitions), despite their higher levels of environmental concern and vulnerability. Using recent data from the ISSP’s 2020 Environment module including 28 countries, we revisit the gender gap in EPP. Arguing that increasing gender equality and socio-economic development can allow women to voice their environmental grievances, we use multi-level models to examine the effects of macro-level gender equality on gender differences in environmental protests, petitions, and boycotts. By distinguishing individual from collective and non-confrontational from confrontational engagement forms, this study offers an encompassing understanding of gendered patterns of participation. Women do participate more than men, but mainly in individual and non-confrontational EPP forms (petitions, boycotts) and with substantial variation across countries. Moreover, considering how women have historically been restrained from participating in politics, we argue that structural gender inequality remains an important limitation to women’s engagement. Cross-level interactions indicate that in more egalitarian countries, women are more likely to engage in several types of EPP than men. The study offers new perspectives and findings on gender differences in EPP, highlighting the impact of gender inequality on women’s participation.

Keywords: environmental activism, political participation, gender equality, pro-environmental behavior

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Authors: Andrew Sutherland

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In many ways, opera is not a natural domain for children. It is hardly surprising that from the thousands of works, comparatively few include roles for children. There are several possibilities for this, the dramatic themes in opera are often about the human condition from the adult perspective; the need for developed voices to project in large, theatrical spaces underpinned by orchestral accompaniment does not naturally suit the child’s voice, and enabling children to cope with long runs of performances on top of their education requires vocal and physical stamina. In more recent times, the involvement of children contributes another layer of difficulty in terms of having access to young singers while adhering to laws that protect their working rights. Despite these points, children have been in opera since its inception in a variety of ways, but their contribution is often undervalued or ignored by musicologists and even the industry itself. In this paper, the phenomenon of children in opera from the late 16th century to the present day is explored through empirical, socio-musicological observations with reference to score analysis. Conclusions are drawn regarding the changing attitudes of composers when scoring for children’s voices in relation to societal developments. From the use of ‘kindertruppen’ in the pre-enlightenment period to Handel’s virtuosic writing for William Savage, to the darkness of the inter-war eras which saw a proliferation of operatic characters for children and the post-war era which saw children as the new frontier of building audiences for opera, the links between changes in society and the inclusion, portrayal and scoring for children in opera are largely congruent.

Keywords: children, musical analysis, opera, sociology

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Authors: Aanh Eduardo Dimate-Garcia, Diana Carolina Rodriguez-Romero, Edna Yuliana Gonzalez Rincon, Diana Marcela Pardo Lopez, Yessica Garibello Cubillos

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Musculoskeletal disorders (MD) are the new epidemic of chronic diseases, are multifactorial and affect the different productive sectors. Although there are multiple instruments to evaluate the static and dynamic load, the method of repetitive occupational action (OCRA) seems to be an attractive option. Objective: It is aimed to analyze the use of the OCRA method and the prevalence of MD in workers of various productive sectors according to the literature (2007-2018). Materials and Methods: A literature review (following the PRISMA statement) of studies aimed at assessing the level of biomechanical risk (OCRA) and the prevalence of MD in the databases Scielo, Science Direct, Scopus, ProQuest, Gale, PubMed, Lilacs and Ebsco was realized; 7 studies met the selection criteria; the majority are quantitative (cross section). Results: it was evidenced (gardening and flower-growers) in this review that 79% of the conditions related to the task require physical requirements and involve repetitive movements. In addition, of the high appearance of DM in the high-low back, upper and lower extremities that are produced by the frequency of the activities carried out (footwear production). Likewise, there was evidence of 'very high risks' of developing MD (salmon industry) and a medium index (OCRA) for repetitive movements that require special care (U-Assembly line). Conclusions: the review showed the limited use of the OCRA method for the detection of MD in workers from different sectors, and this method can be used for the detection of biomechanical risk and the appearance of MD.

Keywords: checklist, cumulative trauma disorders, musculoskeletal diseases, repetitive movements

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Authors: Swathi Gopakumar, Sruthi Krishna, Shivasubramani Krishnamoorthy

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Contactless, painless and radiation-free thermal imaging technology is one of the preferred screening modalities for detection of breast cancer. However, poor signal to noise ratio and the inexorable need to preserve edges defining cancer cells and normal cells, make the segmentation process difficult and hence unsuitable for computer-aided diagnosis of breast cancer. This paper presents key findings from a research conducted on the appraisal of two promising techniques, for the detection of breast cancer: (I) marker-controlled, Level-set segmentation of anisotropic diffusion filtered preprocessed image versus (II) Segmentation using marker-controlled level-set on a Gaussian-filtered image. Gaussian-filtering processes the image uniformly, whereas anisotropic filtering processes only in specific areas of a thermographic image. The pre-processed (Gaussian-filtered and anisotropic-filtered) images of breast samples were then applied for segmentation. The segmentation of breast starts with initial level-set function. In this study, marker refers to the position of the image to which initial level-set function is applied. The markers are generally placed on the left and right side of the breast, which may vary with the breast size. The proposed method was carried out on images from an online database with samples collected from women of varying breast characteristics. It was observed that the breast was able to be segmented out from the background by adjustment of the markers. From the results, it was observed that as a pre-processing technique, anisotropic filtering with level-set segmentation, preserved the edges more effectively than Gaussian filtering. Segmented image, by application of anisotropic filtering was found to be more suitable for feature extraction, enabling automated computer-aided diagnosis of breast cancer.

Keywords: anisotropic diffusion, breast, Gaussian, level-set, thermograms

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Muhammad Kamran Asif

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MPLS stands for Multi-Protocol Label Switching. It is the technology which replaces ATM (Asynchronous Transfer Mode) and frame relay. In this paper, we have designed a full fledge small scale MPLS based service provider network core network model, which provides communication services (e.g. voice, video and data) to the customer more efficiently using label switching technique. Using MPLS VPN provides security to the customers which are either on LAN or WAN. It protects its single customer sites from being attacked by any intruder from outside world along with the provision of concept of extension of a private network over an internet. In this paper, we tried to implement a service provider network using minimum available resources i.e. five 3800 series CISCO routers comprises of service provider core, provider edge routers and customer edge routers. The customers on the one end of the network (customer side) is capable of sending any kind of data to the customers at the other end using service provider cloud which is MPLS VPN enabled. We have also done simulation and emulation for the model using GNS3 (Graphical Network Simulator-3) and achieved the real time scenarios. We have also deployed a NMS system which monitors our service provider cloud and generates alarm in case of any intrusion or malfunctioning in the network. Moreover, we have also provided a video help desk facility between customers and service provider cloud to resolve the network issues more effectively.

Keywords: MPLS, VPN, NMS, ATM, asynchronous transfer mode

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Authors: Şeyma Özçirak Ergün, Ergün Şakalar, Emrah Yalazi̇, Nebahat Şahi̇n

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Food irradiation is a usage of exposing food to ionising radiation (IR) such as gamma rays. IR has been used to decrease the number of harmful microorganisms in the food such as spices. Excessive usage of IR can cause damage to both food and people who consuming food. And also it causes to damages on food DNA. Generally, IR detection techniques were utilized in literature for spices are Electron Spin Resonance (ESR), Thermos Luminescence (TL). Storage creates negative effect on IR detection method then analyses of samples have been performed without storage in general. In the experimental part, red pepper (Capsicum annuum) and mint (Mentha piperita) as spices were exposed to 0, 0.272, 0.497, 1.06, 3.64, 8.82, and 17.42 kGy ionize radiation. ESR was applied to samples irradiated. DNA isolation from irradiated samples was performed using GIDAGEN Multi Fast DNA isolation kit. The DNA concentration was measured using a microplate reader spectrophotometer (Infinite® 200 PRO-Life Science–Tecan). The concentration of each DNA was adjusted to 50 ng/µL. Genomic DNA was imaged by UV transilluminator (Gel Doc XR System, Bio-Rad) for the estimation of genomic DNA bp-fragment size after IR. Thus, agarose gel profiles of irradiated spices were obtained to determine the change of band profiles. Besides, samples were examined at three different time periods (0, 3, 6 months storage) to show the feasibility of developed method. Results of gel electrophoresis showed especially degradation of DNA of irradiated samples. In conclusion, this study with gel electrophoresis can be used as a basis for the identification of the dose of irradiation by looking at degradation profiles at specific amounts of irradiation. Agarose gel results of irradiated samples were confirmed with ESR analysis. This method can be applied widely to not only food products but also all biological materials containing DNA to predict radiation-induced damage of DNA.

Keywords: DNA, electrophoresis, gel electrophoresis, ionizeradiation

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Authors: Abdul Khalique Baloch, Ali Okatan

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The quality analysis of food and vegetable from image is hot topic now a day, where researchers make them better then pervious findings through different technique and methods. In this research we have review the literature, and find gape from them, and suggest better proposed approach, design the algorithm, developed a software to measure the quality from images, where accuracy of image show better results, and compare the results with Perouse work done so for. The Application we uses an open-source dataset and python language with tensor flow lite framework. In this research we focus to sort food and vegetable from image, in the images, the application can sorts and make them grading after process the images, it could create less errors them human base sorting errors by manual grading. Digital pictures datasets were created. The collected images arranged by classes. The classification accuracy of the system was about 94%. As fruits and vegetables play main role in day-to-day life, the quality of fruits and vegetables is necessary in evaluating agricultural produce, the customer always buy good quality fruits and vegetables. This document is about quality detection of fruit and vegetables using images. Most of customers suffering due to unhealthy foods and vegetables by suppliers, so there is no proper quality measurement level followed by hotel managements. it have developed software to measure the quality of the fruits and vegetables by using images, it will tell you how is your fruits and vegetables are fresh or rotten. Some algorithms reviewed in this thesis including digital images, ResNet, VGG16, CNN and Transfer Learning grading feature extraction. This application used an open source dataset of images and language used python, and designs a framework of system.

Keywords: deep learning, computer vision, image processing, rotten fruit detection, fruits quality criteria, vegetables quality criteria

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Authors: Katharine Pollock

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Feminist scholars assert that a defining aspect of feminist confessional literature is that it expresses both an individual and communal identity, one which is predicated on the commonly-shared aspects of female experience. Reading feminist confessional literature in this way accommodates a plurality of readerly experiences and textual interpretations. It affirms the individual whilst acknowledging those experiences which bind women together, and refuses traditional objective criticism. It invites readers to see themselves reflected in the text, and encourages them to share their own stories. Similarly, music which communicates women’s personal experience, fictive or not, expresses a dual identity. There is an inherent risk of imposing a confessional reading upon a musical or literary text. Understanding music as being multivocal in the same way as confessional literature negates this patriarchal tendency, and allows listeners to engage with both the subjective and collective aspects of a text. By hearing their own stories reflected in the music, listeners engage in an ongoing dialogic process in which female stories are prioritised. This refuses patriarchal silencing and ensures a diversity of female voices. To demonstrate the veracity of these claims, literary criticism is applied to Lily Allen’s music, and memoir My Thoughts Exactly.

Keywords: confession, female, feminist, literature, music

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Authors: Nadia El Alami El Hassani, Soukaina Motia, Benachir Bouchikhi, Nezha El Bari

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Doxycycline (DXy) is a cycline antibiotic, most frequently prescribed to treat bacterial infections in veterinary medicine. However, its broad antimicrobial activity and low cost, lead to an intensive use, which can seriously affect human health. Therefore, its spread in the food products has to be monitored. The scope of this work was to synthetize a sensitive and very selective molecularly imprinted polymer (MIP) for DXy detection in honey samples. Firstly, the synthesis of this biosensor was performed by casting a layer of carboxylate polyvinyl chloride (PVC-COOH) on the working surface of a gold screen-printed electrode (Au-SPE) in order to bind covalently the analyte under mild conditions. Secondly, DXy as a template molecule was bounded to the activated carboxylic groups, and the formation of MIP was performed by a biocompatible polymer by the mean of polyacrylamide matrix. Then, DXy was detected by measurements of differential pulse voltammetry (DPV). A non-imprinted polymer (NIP) prepared in the same conditions and without the use of template molecule was also performed. We have noticed that the elaborated biosensor exhibits a high sensitivity and a linear behavior between the regenerated current and the logarithmic concentrations of DXy from 0.1 pg.mL⁻¹ to 1000 pg.mL⁻¹. This technic was successfully applied to determine DXy residues in honey samples with a limit of detection (LOD) of 0.1 pg.mL⁻¹ and an excellent selectivity when compared to the results of oxytetracycline (OXy) as analogous interfering compound. The proposed method is cheap, sensitive, selective, simple, and is applied successfully to detect DXy in honey with the recoveries of 87% and 95%. Considering these advantages, this system provides a further perspective for food quality control in industrial fields.

Keywords: doxycycline, electrochemical sensor, food control, gold nanoparticles, honey, molecular imprinted polymer

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Authors: Vaibhav Budhiraja, Chandra Mouli Pandey

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The ultrasonic synthesis of nanostructured conducting copolymer is an effective technique to synthesize polymer with desired chemical properties. This tailored nanostructure, shows tremendous improvement in sensitivity and stability to detect a variety of analytes. The present work reports ultrasonically synthesized nanostructured conducting poly(o-phenylenediamine)-co-poly(1-naphthylamine) (POPD-co-PNA). The synthesized material has been characterized using Fourier transform infrared spectroscopy (FTIR), ultraviolet-visible spectroscopy, transmission electron microscopy, X-ray diffraction and cyclic voltammetry. FTIR spectroscopy confirmed random copolymerization, while UV-visible studies reveal the variation in polaronic states upon copolymerization. High crystallinity was achieved via ultrasonic synthesis which was confirmed by X-ray diffraction, and the controlled morphology of the nanostructures was confirmed by transmission electron microscopy analysis. Cyclic voltammetry shows that POPD-co-PNA has rather high electrochemical activity. This behavior was explained on the basis of variable orientations adopted by the conducting polymer chains. The synthesized material was electrophoretically deposited at onto indium tin oxide coated glass substrate which is used as cathode and parallel platinum plate as the counter electrode. The fabricated bioelectrode was further used for detection of glucose by crosslinking of glucose oxidase in the PODP-co-PNA film. The bioelectrode shows a surface-controlled electrode reaction with the electron transfer coefficient (α) of 0.72, charge transfer rate constant (ks) of 21.77 s⁻¹ and diffusion coefficient 7.354 × 10⁻¹⁵ cm²s⁻¹.

Keywords: conducting, electrophoretic, glucose, poly (o-phenylenediamine), poly (1-naphthylamine), ultrasonic

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Authors: David Polcari, Samuel C. Perry, Loredano Pollegioni, Matthias Geissler, Janine Mauzeroll

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ᴅ-serine acts as an endogenous co-agonist for N-methyl-ᴅ-aspartate receptors in neuronal synapses. This makes it a key component in the development and function of a healthy brain, especially given its role in several neurodegenerative diseases such as Alzheimer’s disease and dementia. Despite such clear research motivations, the primary site and mechanism of ᴅ-serine release is still currently unclear. For this reason, we are developing a biosensor for the detection of ᴅ-serine utilizing a microelectrode in combination with a ᴅ-amino acid oxidase enzyme, which produces stoichiometric quantities of hydrogen peroxide in response to ᴅ-serine. For the fabrication of a biosensor with good selectivity, we use a permselective poly(meta-phenylenediamine) film to ensure only the target molecule is reacted, according to the size exclusion principle. In this work, we investigated the effect of the electrodeposition conditions used on the biosensor’s response time and selectivity. Careful optimization of the fabrication process allowed for enhanced biosensor response time. This allowed for the real time sensing of ᴅ-serine in a bulk solution, and also provided in means to map the efflux of ᴅ-serine in real time. This was done using scanning electrochemical microscopy (SECM) with the optimized biosensor to measure localized release of ᴅ-serine from an agar filled glass capillary sealed in an epoxy puck, which acted as a model system. The SECM area scan simultaneously provided information regarding the rate of ᴅ-serine flux from the model substrate, as well as the size of the substrate itself. This SECM methodology, which provides high spatial and temporal resolution, could be useful to investigate the primary site and mechanism of ᴅ-serine release in other biological samples.

Keywords: ᴅ-serine, enzymatic biosensor, microelectrode, scanning electrochemical microscopy

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Authors: Salvatore Luongo, Carlo Luongo

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This paper discusses the implementation solutions to reduce the computational load for the Traffic Situational Awareness with Alerts (TSAA) application, based on Automatic Dependent Surveillance-Broadcast (ADS-B) technology. In 2008, there were 23 total mid-air collisions involving general aviation fixed-wing aircraft, 6 of which were fatal leading to 21 fatalities. These collisions occurred during visual meteorological conditions, indicating the limitations of the see-and-avoid concept for mid-air collision avoidance as defined in the Federal Aviation Administration’s (FAA). The commercial aviation aircraft are already equipped with collision avoidance system called TCAS, which is based on classic transponder technology. This system dramatically reduced the number of mid-air collisions involving air transport aircraft. In general aviation, the same reduction in mid-air collisions has not occurred, so this reduction is the main objective of the TSAA application. The major difference between the original conflict detection application and the TSAA application is that the conflict detection is focused on preventing loss of separation in en-route environments. Instead TSAA is devoted to reducing the probability of mid-air collision in all phases of flight. The TSAA application increases the flight crew traffic situation awareness providing alerts of traffic that are detected in conflict with ownship in support of the see-and-avoid responsibility. The relevant effort has been spent in the design process and the code generation in order to maximize the efficiency and performances in terms of computational load and memory consumption reduction. The TSAA architecture is divided into two high-level systems: the “Threats database” and the “Conflict detector”. The first one receives the traffic data from ADS-B device and provides the memorization of the target’s data history. Conflict detector module estimates ownship and targets trajectories in order to perform the detection of possible future loss of separation between ownship and each target. Finally, the alerts are verified by additional conflict verification logic, in order to prevent possible undesirable behaviors of the alert flag. In order to reduce the computational load, a pre-check evaluation module is used. This pre-check is only a computational optimization, so the performances of the conflict detector system are not modified in terms of number of alerts detected. The pre-check module uses analytical trajectories propagation for both target and ownship. This allows major accuracy and avoids the step-by-step propagation, which requests major computational load. Furthermore, the pre-check permits to exclude the target that is certainly not a threat, using an analytical and efficient geometrical approach, in order to decrease the computational load for the following modules. This software improvement is not suggested by FAA documents, and so it is the main innovation of this work. The efficiency and efficacy of this enhancement are verified using fast-time and real-time simulations and by the execution on a real device in several FAA scenarios. The final implementation also permits the FAA software certification in compliance with DO-178B standard. The computational load reduction allows the installation of TSAA application also on devices with multiple applications and/or low capacity in terms of available memory and computational capabilities

Keywords: traffic situation awareness, general aviation, aircraft conflict detection, computational load reduction, implementation solutions, software certification

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Authors: Tessa Verhallen, Mama Africa

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By co-constructing personal narratives of sex workers in Namibia this paper represents how sex workers experience their violation of rights in Namibia. It is written from an emic (as an advisor for a sex worker-led organization named Rights not Rescue Trust) and an etic (as an ethnographer) point of view, in collaboration with the staff of the organization Rights not Rescue Trust. This organization represents circa 3000 members. The paper describes the current deplorable situation of sex workers in Namibia, encompassing the stigma and discrimination they face, their struggle to have their work decriminalized and their urge to advocate for human rights and the end of violations. Based on a triangular research design (ethnography, narratives, literature study, human rights’ training and counseling sessions) the authors show that sex workers, particularly LGBTI sex workers, are extremely vulnerable to emotional, physical, and sexual violence in Namibia. The main perpetrators of violence turn out to be not only clients and intimate partners but also law enforcement officers and health care workers who are supposed to protect and support sex workers. The sex workers’ narratives voice their disgraceful circumstances regarding how their rights are violated. It also highlights their importance to fight for their rights and access to health care, legal services and education in order to improve the sexual reproductive health of sex workers.

Keywords: HIV/aids, LGBTI, methodological innovative, sex work

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Authors: Eliza. E. Camaso, Guiller. B. Damian, Miguelito. F. Isip, Ronaldo T. Alberto

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Land-cover maps were important for many scientific, ecological and land management purposes and during the last decades, rapid decrease of soil fertility was observed to be due to land use practices such as rice cultivation. High-precision land-cover maps are not yet available in the area which is important in an economy management. To assure   accurate mapping of land cover to provide information, remote sensing is a very suitable tool to carry out this task and automatic land use and cover detection. The study did not only provide high precision land cover maps but it also provides estimates of rice production area that had undergone chemical degradation due to fertility decline. Land-cover were delineated and classified into pre-defined classes to achieve proper detection features. After generation of Land-cover map, of high intensity of rice cultivation, soil fertility degradation assessment in rice production area due to fertility decline was created to assess the impact of soils used in agricultural production. Using Simple spatial analysis functions and ArcGIS, the Land-cover map of Municipality of Quezon in Nueva Ecija, Philippines was overlaid to the fertility decline maps from Land Degradation Assessment Philippines- Bureau of Soils and Water Management (LADA-Philippines-BSWM) to determine the area of rice crops that were most likely where nitrogen, phosphorus, zinc and sulfur deficiencies were induced by high dosage of urea and imbalance N:P fertilization. The result found out that 80.00 % of fallow and 99.81% of rice production area has high soil fertility decline.

Keywords: aerial image, landcover, LiDAR, soil fertility degradation

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Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

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Authors: Gaurav Bhanjana, Ganga Ram Chaudhary, Sandeep Kumar, Neeraj Dilbaghi

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Main sources of endocrine disrupting compounds in the ecosystem are hormones, pesticides, phthalates, flame retardants, dioxins, personal-care products, coplanar polychlorinated biphenyls (PCBs), bisphenol A, and parabens. These endocrine disrupting compounds are responsible for learning disabilities, brain development problems, deformations of the body, cancer, reproductive abnormalities in females and decreased sperm count in human males. Although discharge of these chemical compounds into the environment cannot be stopped, yet their amount can be retarded through proper evaluation and detection techniques. The available techniques for determination of these endocrine disrupting compounds mainly include high performance liquid chromatography (HPLC), mass spectroscopy (MS) and gas chromatography-mass spectrometry (GC–MS). These techniques are accurate and reliable but have certain limitations like need of skilled personnel, time consuming, interference and requirement of pretreatment steps. Moreover, these techniques are laboratory bound and sample is required in large amount for analysis. In view of above facts, new methods for detection of endocrine disrupting compounds should be devised that promise high specificity, ultra sensitivity, cost effective, efficient and easy-to-operate procedure. Nowadays, electrochemical sensors/biosensors modified with nanomaterials are gaining high attention among researchers. Bioelement present in this system makes the developed sensors selective towards analyte of interest. Nanomaterials provide large surface area, high electron communication feature, enhanced catalytic activity and possibilities of chemical modifications. In most of the cases, nanomaterials also serve as an electron mediator or electrocatalyst for some analytes.

Keywords: electrochemical, endocrine disruptors, microscopy, nanoparticles, sensors

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Authors: Ravinder Barn, Balbir Barn

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This paper outlines ethical and practical challenges in the building of social technology for use with socially excluded and marginalised groups. The primary aim of this study was to design, deploy and evaluate social technology that may help to promote better engagement between case workers and young people to help prevent recidivism, and support young people’s transition towards social inclusion in society. A total of 107 practitioners/managers (n=64), and young people (n=43) contributed to the data collection via surveys, focus groups and 1-1 interviews. Through a process of co-design where end-users are involved as key contributors to social technological design, this paper seeks to make an important contribution to the area of participatory methodologies by arguing that whilst giving ‘voice’ to key stakeholders in the research process is crucial, there is a risk that competing voices may lead to tensions and unintended outcomes. The paper is contextualized within a Foucauldian perspective to examine significant concepts including power, authority and surveillance. Implications for youth justice policy and practice are considered. The authors conclude that marginalized youth and over-stretched practitioners are better served when such social technology is perceived and adopted as a tool of empowerment within a framework of child welfare and child rights.

Keywords: youth justice, social technology, marginalization, participatory research, power

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Authors: Parvaneh Eskandari, Rachel O'Reilly

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In the last years, controlling the self-assembly behavior of stimuli-responsive nano-objects, including micelles, vesicles, worm-like, etc., at different conditions is considered a pertinent challenge in the polymer community. The aim of the project was to synthesize aggregation-induced emission (AIE)-active stimuli-responsive polymeric nano-objects to control the self-assemblies morphologies of the prepared nano-objects. Two types of nanoobjects, micelle and vesicles, including PDMAEMA-b-P(BzMA-TPEMA) [PDMAEMA: poly(N,Ndimethylaminoethyl methacrylate); P(BzMA-TPEMA): poly[benzyl methacrylate-co- tetraphenylethene methacrylate]] were synthesized by using reversible addition−fragmentation chain-transfer (RAFT)- mediated polymerization-induced self-assembly (PISA), which combines polymerization and self-assembly in a single step. Transmission electron microscope and dynamic light scattering (DLS) analysis were used to confirm the formed self-assemblies morphologies. The controlled self-assemblies were applied as nitrophenolic compounds (NPCs) adsorbents from wastewater, thanks to their CO2-responsive part, PDMAEMA. Moreover, the fluorescence-active part of the prepared nano-objects, P(BzMA-TPEMA), played a key role in the detection of the NPCs at the aqueous solution. The optical properties of the prepared nano-objects were studied by UV/Vis and fluorescence spectroscopies. For responsivity investigations, the hydrodynamic diameter and Zeta-potential (ζ-potential) of the sample's aqueous solution were measured by DLS. In the end, the prepared nano-objects were used for the detection and adsorption of different NPCs.

Keywords: aggregation-induced emission polymers, stimuli-responsive polymers, reversible addition−fragmentation chain-transfer polymerization, polymerization-induced self-assembly, wastewater treatment

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Authors: Valeriy M. Kondratev, Ekaterina A. Vyacheslavova, Talgat Shugabaev, Alexander S. Gudovskikh, Alexey D. Bolshakov

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Modern sensorics imposes strict requirements on the biosensors characteristics, especially technological feasibility, and selectivity. There is a growing interest in the analysis of human health biological markers, which indirectly testifying the pathological processes in the body. Such markers are acids and alkalis produced by the human, in particular - ammonia and hydrochloric acid, which are found in human sweat, blood, and urine, as well as in gastric juice. Biosensors based on modern nanomaterials, especially low dimensional, can be used for this markers detection. Most classical adsorption sensors based on metal and silicon oxides are considered non-selective, because they identically change their electrical resistance (or impedance) under the action of adsorption of different target analytes. This work demonstrates a feasible frequency-resistive method of electrical impedance spectroscopy data analysis. The approach allows to obtain of selectivity in adsorption sensors of a resistive type. The method potential is demonstrated with analyzis of impedance spectra of silicon nanowires in the presence of NH3 and HCl vapors with concentrations of about 125 mmol/L (2 ppm) and water vapor. We demonstrate the possibility of unambiguous distinction of the sensory signal from NH3 and HCl adsorption. Moreover, the method is found applicable for analysis of the composition of ammonia and hydrochloric acid vapors mixture without water cross-sensitivity. Presented silicon sensor can be used to find diseases of the gastrointestinal tract by the qualitative and quantitative detection of ammonia and hydrochloric acid content in biological samples. The method of data analysis can be directly translated to other nanomaterials to analyze their applicability in the field of biosensory.

Keywords: electrical impedance spectroscopy, spectroscopy data analysis, selective adsorption sensor, nanotechnology

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Authors: Saleh Gorji, Sahil Gulati, Ana Pakzad

Abstract:

Continuous electron diffraction tomography, also known as microcrystal electron diffraction (MicroED) or three-dimensional electron diffraction (3DED), is a powerful technique, which in combination with cryo-electron microscopy (cryo-ED), can provide atomic-scale 3D information about the crystal structure and composition of different classes of crystalline materials such as proteins, peptides, and small molecules. Unlike the well-established X-ray crystallography method, 3DED does not require large single crystals and can collect accurate electron diffraction data from crystals as small as 50 – 100 nm. This is a critical advantage as growing larger crystals, as required by X-ray crystallography methods, is often very difficult, time-consuming, and expensive. In most cases, specimens studied via 3DED method are electron beam sensitive, which means there is a limitation on the maximum amount of electron dose one can use to collect the required data for a high-resolution structure determination. Therefore, collecting data using a conventional scintillator-based fiber coupled camera brings additional challenges. This is because of the inherent noise introduced during the electron-to-photon conversion in the scintillator and transfer of light via the fibers to the sensor, which results in a poor signal-to-noise ratio and requires a relatively higher and commonly specimen-damaging electron dose rates, especially for protein crystals. As in other cryo-EM techniques, damage to the specimen can be mitigated if a direct detection camera is used which provides a high signal-to-noise ratio at low electron doses. In this work, we have used two classes of such detectors from Gatan, namely the K3® camera (a monolithic active pixel sensor) and Stela™ (that utilizes DECTRIS hybrid-pixel technology), to address this problem. The K3 is an electron counting detector optimized for low-dose applications (like structural biology cryo-EM), and Stela is also a counting electron detector but optimized for diffraction applications with high speed and high dynamic range. Lastly, data collection workflows, including crystal screening, microscope optics setup (for imaging and diffraction), stage height adjustment at each crystal position, and tomogram acquisition, can be one of the other challenges of the 3DED technique. Traditionally this has been all done manually or in a partly automated fashion using open-source software and scripting, requiring long hours on the microscope (extra cost) and extensive user interaction with the system. We have recently introduced Latitude® D in DigitalMicrograph® software, which is compatible with all pre- and post-energy-filter Gatan cameras and enables 3DED data acquisition in an automated and optimized fashion. Higher quality 3DED data enables structure determination with higher confidence, while automated workflows allow these to be completed considerably faster than before. Using multiple examples, this work will demonstrate how to direct detection electron counting cameras enhance 3DED results (3 to better than 1 Angstrom) for protein and small molecule structure determination. We will also show how Latitude D software facilitates collecting such data in an integrated and fully automated user interface.

Keywords: continuous electron diffraction tomography, direct detection, diffraction, Latitude D, Digitalmicrograph, proteins, small molecules

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Authors: Wafa Toumi, Alessandro Ripalti, Luigi Ricciardiello, Dalila Gargouri, Jamel Kharrat, Abderraouf Cherif, Ahmed Bouhafa, Slim Jarboui, Mohamed Zili, Ridha Khelifa

Abstract:

Background & aims: Colorectal cancer (CRC) is one of the most common malignancies throughout the world. Several risk factors, both genetic and environmental, including viral infections, have been linked to colorectal carcinogenesis. A few studies report the detection of human polyomavirus JC (JCV) DNA and transformation antigen (T-Ag) in a fraction of the colorectal tumors studied and suggest an association of this virus with CRC. In order to investigate whether such an association of JCV with CRC will hold in a different epidemiological setting, we looked for the presence of JCV DNA and T-Ag expression in a group of Tunisian CRC patients. Methods: Fresh colorectal mucosa biopsies were obtained from 17 healthy volunteers and from both colorectal tumors and adjacent normal tissues of 47 CRC patients. DNA was extracted from fresh biopsies or from formalin-fixed, paraffin-embedded tissue sections using the Invitrogen Purelink Genomic DNA mini Kit. A simple PCR and a nested PCR were used to amplify a region of the T-Ag gene. The obtained PCR products revealed a 154 bp and a 98 bp bands, respectively. Specificity was confirmed by sequencing of the PCR products. T-Ag expression was determined by immunohistochemical staining using a mouse monoclonal antibody (clone PAb416) directed against SV40 T-Ag that cross reacts with JCV T-Ag. Results: JCV DNA was found in 12 (25%) and 22 (46%) of the CRC tumors by simple PCR and by nested PCR, respectively. All paired adjacent normal mucosa biopsies were negative for viral DNA. Sequencing of the DNA amplicons obtained confirmed the authenticity of T-Ag sequences. Immunohistochemical staining showed nuclear T-Ag expression in all 22 JCV DNA- positive samples and in 3 additional tumor samples which appeared DNA-negative by PCR. Conclusions: These results suggest an association of JCV with a subpopulation of Tunisian colorectal tumors.

Keywords: colorectal cancer, immunohistochemistry, Polyomavirus JC, PCR

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Authors: Hossein Tavallali, Mohammad Ali Karimi, Gohar Deilamy-Rad

Abstract:

In this study, a new type of alumina-coated magnetite nanoparticles (Fe3O4/Al2O3 NPs) with sodium dodecyl sulfate- 1-(2-pyridylazo)-2-naphthol (SDS-PAN) as a new sorbent solid phase extraction (SPE) has been successfully synthesized and applied for preconcentration and separation of Cd and Pb in environmental samples. Compared with conventional SPE methods, the advantages of this new magnetic Mixed Hemimicelles Solid-Phase Extraction Procedure (MMHSPE) still include easy preparation and regeneration of sorbents, short times of sample pretreatment, high extraction yields, and high breakthrough volumes. It shows great analytical potential in preconcentration of Cd and Pb compounds from large volume water samples. Due to the high surface area of these new sorbents and the excellent adsorption capacity after surface modification by SDS-PAN, satisfactory concentration factor and extraction recoveries can be produced with only 0.05 g Fe3O4/Al2O3 NPs. The metals were eluted with 3mL HNO3 2 mol L-1 directly and detected with the detection system Flame Atomic Absorption Spectrometry (FAAS). Various influencing parameters on the separation and preconcentration of trace metals, such as the amount of PAN, pH value, sample volume, standing time, desorption solvent and maximal extraction volume, amount of sorbent and concentration of eluent, were studied. The detection limits of this method for Cd and Pb were 0.3 and 0.7 ng mL−1 and the R.S.D.s were 3.4 and 2.8% (C = 28.00 ng mL-1, n = 6), respectively. The preconcentration factor of the modified nanoparticles was 166.6. The proposed method has been applied to the determination of these metal ions at trace levels in soil, river, tap, mineral, spring and wastewater samples with satisfactory results.

Keywords: Alumina-coated magnetite nanoparticles, Magnetic Mixed Hemimicell Solid-Phase Extraction, Cd and Pb, soil sample

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Authors: Gulyas Erzsebet

Abstract:

The multimerisation of proteins is a common feature of many cellular processes; however, it could also impair protein functions and/or be associated with the occurrence of diseases. Thus, development of a research tool monitoring the appearance/presence of multimeric protein forms has great importance for a variety of research fields. Such a tool is potentially applicable in the ante-mortem diagnosis of certain conformational diseases, such as transmissible spongiform encephalopathies (TSE) and Alzheimer’s disease. These conditions are accompanied by the appearance of aggregated protein multimers, present in low concentrations in various tissues. This detection is particularly relevant for TSE where the handling of tissues derived from affected individuals and of meat products of infected animals have become an enormous health concern. Here we demonstrate the potential of such a multimer detection approach in TSE by developing a facile approach. The Biospiral-Detect system resembles a traditional sandwich ELISA, except that the capturing antibody that is attached to a solid surface and the detecting antibody is directed against the same or overlapping epitopes. As a consequence, the capturing antibody shields the epitope on the captured monomer from reacting with the detecting antibody, therefore monomers are not detected. Thus, MDS is capable of detecting only protein multimers with high specificity. We developed an alternative system as well, where RNA aptamers were employed instead of monoclonal antibodies. In order to minimize degradation, the 3' and 5' ends of the aptamer contained deoxyribonucleotides and phosphorothioate linkages. When compared the monoclonal antibodies-based system with the aptamers-based one, the former proved to be superior. Thus all subsequent experiments were conducted by employing the Biospiral -Detect modified sandwich ELISA kit. Our approach showed an order of magnitude higher sensitivity toward mulimers than monomers suggesting that this approach may become a valuable diagnostic tool for conformational diseases that are accompanied by multimerization.

Keywords: diagnosis, ELISA, Prion, TSE

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Authors: M. Bilal Ishfaq, Adnan N. Qureshi

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COVID-19 is the most infectious disease these days, it was first reported in Wuhan, the capital city of Hubei in China then it spread rapidly throughout the whole world. Later on 11 March 2020, the World Health Organisation (WHO) declared it a pandemic. Since COVID-19 is highly contagious, it has affected approximately 219M people worldwide and caused 4.55M deaths. It has brought the importance of accurate diagnosis of respiratory diseases such as pneumonia and COVID-19 to the forefront. In this paper, we propose a hybrid approach for the automated detection of COVID-19 using medical imaging. We have presented the hybridization of manually extracted and convolutional features. Our approach combines Haralick texture features and convolutional features extracted from chest X-rays and CT scans. We also employ a minimum redundancy maximum relevance (MRMR) feature selection algorithm to reduce computational complexity and enhance classification performance. The proposed model is evaluated on four publicly available datasets, including Chest X-ray Pneumonia, COVID-19 Pneumonia, COVID-19 CTMaster, and VinBig data. The results demonstrate high accuracy and effectiveness, with 0.9925 on the Chest X-ray pneumonia dataset, 0.9895 on the COVID-19, Pneumonia and Normal Chest X-ray dataset, 0.9806 on the Covid CTMaster dataset, and 0.9398 on the VinBig dataset. We further evaluate the effectiveness of the proposed model using ROC curves, where the AUC for the best-performing model reaches 0.96. Our proposed model provides a promising tool for the early detection and accurate diagnosis of COVID-19, which can assist healthcare professionals in making informed treatment decisions and improving patient outcomes. The results of the proposed model are quite plausible and the system can be deployed in a clinical or research setting to assist in the diagnosis of COVID-19.

Keywords: COVID-19, feature engineering, artificial neural networks, radiology images

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Authors: Evgeny A. Rybakov, Dmitry P. Starikov

Abstract:

This article contains information about creating special unit for scanning 3D objects different nature, different materials, for example plastic, plaster, cardboard, wood, metal and etc. The main part of the unit is infrared transducer, which is sends the wave to the object and receive back wave for calculating distance. After that, microcontroller send to PC data, and computer program create model for printing from the plastic, gypsum, brass, etc.

Keywords: clutch, infrared, microcontroller, plastic, shaft, stage

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Authors: Donna L. Roberts

Abstract:

Beyond the fundamental question of what is (and what isn’t) art, one then moves to the question of what about art, or a specific artwork, matters. If there is an agreement that something is art, the next step is to answer the obvious, ‘So what? What does it mean?’ In answering these questions, one must decide how to focus the proverbial microscope –i.e., what level of perspective is relevant as a point of view for this analysis- the artwork itself, the artist’s intention, the viewer’s interpretation, the artwork’s reflection of the larger artistic movement, the social, political, and historical context of art? One must determine what product and what contexts are meaningful when experiencing and interpreting art. Is beauty really in the eye of the beholder? Or is it more important what the creator was trying to say than what the critic or observer heard? The fact that so many artists –from Rembrandt to Van Gogh to Picasso- include among their works at least one self-portrait seems to scream their point –I matter. But, Is a piece more impactful because of the persona behind it? Or does that persona impose limits and close one’s mind to the possibilities of interpretation? In the popular art text visual culture, Richard Howells argues against a biographical focus on the artist in the analysis of art. Similarly, abstract expressionist Mark Rothko, along with several of his contemporaries of the genre, often did not title his paintings for the express purpose of not imposing a specific meaning or interpretation on the piece. And yet, he once said, ‘The people who weep before my pictures are having the same religious experience I had when I painted them,’ thus alluding to a desire for a shared connection and revelation. This research analyzes the arguments for differing levels of interpretation and points of view when considering a work of art and/or the artist who created it.

Keywords: art analysis, art interpretation, art theory, artistic perspective

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Authors: Mirco Tarozzi, Giacomo Pignagnoli, Andrea Benedetti

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The design of planning maintenance of civil structures often requires the evaluation of their level of safety in order to be able to choose which structure, and in which measure, it needs a structural retrofit. This work deals with the evaluation of the stiffness reduction of a scaled steel deck due to the presence of localized damages. The dynamic tests performed on it have shown the variability of its main frequencies linked to the gradual reduction of its rigidity. This deck consists in a steel grillage of four secondary beams and three main beams linked to a concrete slab. This steel deck is 6 m long and 3 m wide and it rests on two abutments made of concrete. By processing the signals of the accelerations due to a random excitation of the deck, the main natural frequencies of this bridge have been extracted. In order to assign more reliable parameters to the numerical model of the deck, some load tests have been performed and the mechanical property of the materials and the supports have been obtained. The two external beams have been cut at one third of their length and the structural strength has been restored by the design of a bolted plate. The gradual loss of the bolts and the plates removal have made the simulation of localized damage possible. In order to define the relationship between frequency variation and loss in stiffness, the identification of its natural frequencies has been performed, before and after the occurrence of the damage, corresponding to each step. The study of the relationship between stiffness losses and frequency shifts has been reported in this paper: the square of the frequency variation due to the presence of the damage is proportional to the ratio between the rigidities. This relationship can be used to quantify the loss in stiffness of a real scale bridge in an efficient way.

Keywords: damage detection, dynamic test, frequency shifts, operational modal analysis, steel bridge

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Authors: Rosalina Penaloza, Joanna Navarro, Pauline Jolly, Anna Junkins, Carlos Seas, Larissa Otero

Abstract:

Background: Tuberculosis (TB) case detection in Peru relies on passive case finding. This strategy relies on the assumption that the community is aware that a persistent cough is a possible symptom of TB and that formal health care needs to be sought. Despite its importance, health knowledge specific to TB is underexplored in Peru. This study aimed to assess health literacy and level of TB knowledge among outpatients attending a referral hospital in Lima, Peru. The goal was to ascertain knowledge gaps in key areas relating to TB, to identify and prioritize subgroups for intervention, and to provide insight for policy and community interventions considering health literacy. Methods: An observational cross-sectional study was conducted using a survey to measure sociodemographic factors, tuberculosis knowledge, and health literacy. Bivariate and Multivariate logistic regression was performed to study the associations between variables and to account for potential confounders. The study was conducted at Hospital Cayetano Heredia in Lima, Peru from June – August 2017. Results: 272 participants were included in the analysis. 57.7% knew someone who had had TB before, 9% had had TB in the past. Two weeks a cough was correctly identified as a symptom that could be TB by 69.1%. High TB knowledge was found among 149 (54.8%) participants. High health literacy was found among 193 (71.0%) participants. Health literacy and TB knowledge were not significantly associated (OR 0.9 (95%CI 0.5-1.5)). After controlling for sex, age, district, education, health insurance, frequency of hospital visits and previous TB diagnosis: High TB knowledge was associated with knowing someone with TB (aOR 2.7 (95%CI 1.6-4.7)) and being a public transport driver, (aOR 0.2 (95%CI 0.05-0.9)). Not being poor was the single factor associated with high health literacy (aOR 3.8 (95%CI 1.6-8.9)). Conclusions: TB knowledge was fair, though 30% did not know the most important symptom of TB. Tailoring educational strategies to risk groups may enhance passive case detection especially amongst transport workers in Lima, Peru.

Keywords: health literacy, Peru, tuberculosis, tuberculosis knowledge

Procedia PDF Downloads 473