Search results for: contextual toxicity detection

Authors: Apurva Singh, S. P. Jaiswar, Apala Priyadarshini, Akancha Pandey

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Objective: The aim of this study is to find the association of PON1 gene polymorphism with pesticides In RPL subjects. Background: Recurrent pregnancy loss (RPL) is defined as three or more sequential abortions before the 20th week of gestation. Pesticides and its derivatives (organochlorine and organophosphate) are proposed to accommodate a ruler chemical for RPL in the sub-humid region of India. The paraoxonase-1 enzyme (PON1) plays an important role in the toxicity of some organophosphate pesticides, with low PON1 activity being associated with higher pesticide sensitivity Methodology: This is a case-control study done in Department of Obstetrics & Gynaecology & Department of Biochemistry, K.G.M.U, Lucknow, India. The subjects were enrolled after fulfilling the inclusion & exclusion criteria. Inclusion criteria: Cases- Subject having two or more spontaneous abortions & Control- Healthy female having one or more alive child was selected. Exclusion criteria: Cases & Control- Subject having the following disease will be excluded from the study Diabetes mellitus, Hypertension, Tuberculosis, Immunocompromised patients, any endocrine disorder and genital, colon or breast cancer any other malignancies. Blood samples were collected in EDTA tubes from cases & healthy control women & genomic DNA was extracted by phenol-chloroform method. The estimation of pesticides residue from blood was done by HPLC. Biochemical estimation was also performed. Genotyping of PON1 gene polymorphism was performed by RFLP. Statistical analysis of the data was performed using the SPSS16.3 software. Results: A sum of total 14 pesticides (12 organochlorine and 2 organophosphate) selected on the basis of their persistent nature and consumption rate. The significant level of pesticide (ppb) estimated by the Mann whiney test and it was found to be significant at higher level of β-HCH (p:0.04), γ-HCH (p:0.001), δ-HCH (p: 0.002), chloropyrifos (p:0.001), pp-DDD (p:0.001) and fenvalrate (p: 0.001) in case group compare to its control. The level of antioxidant enzymes were found to be significantly decreased among the cases. Wild homozygous TT was more frequent and prevalent among control groups. However, heterozygous group (Tt) was more in cases than control groups (CI-0.3-1.3) (p=0.06). Conclusion: Higher levels of pesticides with endocrine disrupting potential in cases indicate the possible role of these compounds as one of the causes of recurrent pregnancy loss. Possibly, increased pesticide level appears to indicate increased levels of oxidative damage that has been associated with the possible cause of Recurrent Miscarriage, it may reflect indirect evidence of toxicity rather than the direct cause. Since both factors are reported to increase risk, individuals with higher levels of these 'Toxic compounds' especially in 'high-risk genotypes' might be more susceptible to recurrent pregnancy loss.

Keywords: paraoxonase, pesticides, PON1, RPL

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Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

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Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

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Authors: Madhushi Bandara, Dharshana Kasthurirathna, Danaja Maldeniya, Mahendra Piraveenan

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Community detection is an extremely useful technique in understanding the structure and function of a social network. Louvain algorithm, which is based on Newman-Girman modularity optimization technique, is extensively used as a computationally efficient method extract the communities in social networks. It has been suggested that the nodes that are in close geographical proximity have a higher tendency of forming communities. Variants of the Newman-Girman modularity measure such as dist-modularity try to normalize the effect of geographical proximity to extract geographically dispersed communities, at the expense of losing the information about the geographically proximate communities. In this work, we propose a method to extract geographically dispersed communities while preserving the information about the geographically proximate communities, by analyzing the ‘community network’, where the centroids of communities would be considered as network nodes. We suggest that the inter-community link strengths, which are normalized over the community sizes, may be used to identify and extract the ‘overlay communities’. The overlay communities would have relatively higher link strengths, despite being relatively apart in their spatial distribution. We apply this method to the Gowalla online social network, which contains the geographical signatures of its users, and identify the overlay communities within it.

Keywords: social networks, community detection, modularity optimization, geographically dispersed communities

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Authors: Thilini M. Yatanwala

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CCTV (Closed-Circuit-Television) Surveillance System is being used in public places over decades and a large variety of data is being produced every moment. However, most of the CCTV data is stored in isolation without having integrity. As a result, identification of the behavior of suspicious people along with their location has become strenuous. This research was conducted to acquire more accurate and reliable timely information from the CCTV video records. The implemented system can identify human objects in public places based on outfit colors. Inter-process communication technologies were used to implement the CCTV camera network to track people in the premises. The research was conducted in three stages and in the first stage human objects were filtered from other movable objects available in public places. In the second stage people were uniquely identified based on their outfit colors and in the third stage an individual was continuously tracked in the CCTV network. A face detection algorithm was implemented using cascade classifier based on the training model to detect human objects. HAAR feature based two-dimensional convolution operator was introduced to identify features of the human face such as region of eyes, region of nose and bridge of the nose based on darkness and lightness of facial area. In the second stage outfit colors of human objects were analyzed by dividing the area into upper left, upper right, lower left, lower right of the body. Mean color, mod color and standard deviation of each area were extracted as crucial factors to uniquely identify human object using histogram based approach. Color based measurements were written in to XML files and separate directories were maintained to store XML files related to each camera according to time stamp. As the third stage of the approach, inter-process communication techniques were used to implement an acknowledgement based CCTV camera network to continuously track individuals in a network of cameras. Real time analysis of XML files generated in each camera can determine the path of individual to monitor full activity sequence. Higher efficiency was achieved by sending and receiving acknowledgments only among adjacent cameras. Suspicious incidents such as a person staying in a sensitive area for a longer period or a person disappeared from the camera coverage can be detected in this approach. The system was tested for 150 people with the accuracy level of 82%. However, this approach was unable to produce expected results in the presence of group of people wearing similar type of outfits. This approach can be applied to any existing camera network without changing the physical arrangement of CCTV cameras. The study of human identification and suspicious incident detection using outfit color analysis can achieve higher level of accuracy and the project will be continued by integrating motion and gait feature analysis techniques to derive more information from CCTV videos.

Keywords: CCTV surveillance, human detection and identification, image processing, inter-process communication, security, suspicious detection

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Authors: Peter Modregger, Özgül Öztürk

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Glass fabric reinforced thermoplastic (GFRT) are composite materials, which combine low weight and resilient mechanical properties rendering them especially suitable for automobile construction. However, defects in the glass fabric as well as in the polymer matrix can occur during manufacturing, which may compromise component lifetime or even safety. One type of these defects is local fiber shifts, which can be difficult to detect. Recently, we have experimentally demonstrated the reliable detection of local fiber shifts by X-ray scattering based on the edge-illumination (EI) principle. EI constitutes a novel X-ray imaging technique that utilizes two slit masks, one in front of the sample and one in front of the detector, in order to simultaneously provide absorption, phase, and scattering contrast. The principle of contrast formation is as follows. The incident X-ray beam is split into smaller beamlets by the sample mask, resulting in small beamlets. These are distorted by the interaction with the sample, and the distortions are scaled up by the detector masks, rendering them visible to a pixelated detector. In the experiment, the sample mask is laterally scanned, resulting in Gaussian-like intensity distributions in each pixel. The area under the curves represents absorption, the peak offset refraction, and the width of the curve represents the scattering occurring in the sample. Here, scattering is caused by the numerous glass fiber/polymer matrix interfaces. In our recent publication, we have shown that the standard deviation of the absorption and scattering values over a selected field of view can be used to distinguish between intact samples and samples with local fiber shift defects. The quantification of defect detection performance was done by using p-values (p=0.002 for absorption and p=0.009 for scattering) and contrast-to-noise ratios (CNR=3.0 for absorption and CNR=2.1 for scattering) between the two groups of samples. This was further improved for the scattering contrast to p=0.0004 and CNR=4.2 by utilizing a harmonic decomposition analysis of the images. Thus, we concluded that local fiber shifts can be reliably detected by the X-ray scattering contrasts provided by EI. However, a potential application in, for example, production monitoring requires fast data acquisition times. For the results above, the scanning of the sample masks was performed over 50 individual steps, which resulted in long total scan times. In this paper, we will demonstrate that reliable detection of local fiber shift defects is also possible by using single images, which implies a speed up of total scan time by a factor of 50. Additional performance improvements will also be discussed, which opens the possibility for real-time acquisition. This contributes a vital step for the translation of EI to industrial applications for a wide variety of materials consisting of numerous interfaces on the micrometer scale.

Keywords: defects in composites, X-ray scattering, local fiber shifts, X-ray edge Illumination

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Authors: Antònio Inês, Davide Silva, Filipa Carvalho, Luís Filipe-Riberiro, Fernando M. Nunes, Luís Abrunhosa, Fernanda Cosme

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The presence of mycotoxins in foodstuff is a matter of concern for food safety. Mycotoxins are toxic secondary metabolites produced by certain molds, being ochratoxin A (OTA) one of the most relevant. Wines can also be contaminated with these toxicants. Several authors have demonstrated the presence of mycotoxins in wine, especially ochratoxin A. Its chemical structure is a dihydro-isocoumarin connected at the 7-carboxy group to a molecule of L-β-phenylalanine via an amide bond. As these toxicants can never be completely removed from the food chain, many countries have defined levels in food in order to attend health concerns. OTA contamination of wines might be a risk to consumer health, thus requiring treatments to achieve acceptable standards for human consumption. The maximum acceptable level of OTA in wines is 2.0 μg/kg according to the Commission regulation No. 1881/2006. Therefore, the aim of this work was to reduce OTA to safer levels using different fining agents, as well as their impact on white wine physicochemical characteristics. To evaluate their efficiency, 11 commercial fining agents (mineral, synthetic, animal and vegetable proteins) were used to get new approaches on OTA removal from white wine. Trials (including a control without addition of a fining agent) were performed in white wine artificially supplemented with OTA (10 µg/L). OTA analyses were performed after wine fining. Wine was centrifuged at 4000 rpm for 10 min and 1 mL of the supernatant was collected and added of an equal volume of acetonitrile/methanol/acetic acid (78:20:2 v/v/v). Also, the solid fractions obtained after fining, were centrifuged (4000 rpm, 15 min), the resulting supernatant discarded, and the pellet extracted with 1 mL of the above solution and 1 mL of H2O. OTA analysis was performed by HPLC with fluorescence detection. The most effective fining agent in removing OTA (80%) from white wine was a commercial formulation that contains gelatin, bentonite and activated carbon. Removals between 10-30% were obtained with potassium caseinate, yeast cell walls and pea protein. With bentonites, carboxymethylcellulose, polyvinylpolypyrrolidone and chitosan no considerable OTA removal was verified. Following, the effectiveness of seven commercial activated carbons was also evaluated and compared with the commercial formulation that contains gelatin, bentonite and activated carbon. The different activated carbons were applied at the concentration recommended by the manufacturer in order to evaluate their efficiency in reducing OTA levels. Trial and OTA analysis were performed as explained previously. The results showed that in white wine all activated carbons except one reduced 100% of OTA. The commercial formulation that contains gelatin, bentonite and activated carbon reduced only 73% of OTA concentration. These results may provide useful information for winemakers, namely for the selection of the most appropriate oenological product for OTA removal, reducing wine toxicity and simultaneously enhancing food safety and wine quality.

Keywords: wine, ota removal, food safety, fining

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Authors: Esraa Moustafa, Gaetan Rey, Stephane Lavirotte, Jean-Yves Tigli

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With the development of Internet of Things and Web of Things, computing becomes more pervasive, invisible and present everywhere. In fact, in our environment, we are surrounded by multiple devices that deliver (web) services that meet the needs of the users. However, the mobility of these devices as the users has important repercussions that challenge software design of these applications because the variability of the environment cannot be anticipated at the design time. Thus, it will be interesting to dynamically discover the environment and adapt the application during its execution to the new contextual conditions. We, therefore, propose a model of a context-aware middleware that can address this issue through a monitoring service that is capable of reasoning and observation channels capable of calculating the context during the runtime. The monitoring service evaluates the pre-defined X-Query predicates in the context manager and uses Prolog to deduce the services needed to respond back. An independent Observation Channel for each different predicate is then dynamically generated by the monitoring service depending on the current state of the environment. Each channel sends its result directly to the context manager which consequently calculates the context based on all the predicates’ results while preserving the reactivity of the self-adaptive system.

Keywords: auto-adaptation, context-awareness, middleware, reasoning engine

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Authors: Vladimir Messerle, Alfred Mosse, Alexandr Ustimenko, Oleg Lavrichshev

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Hygiene and sanitary study of typical medical-biological waste made in Kazakhstan, Russia, Belarus and other countries show that their risk to the environment is much higher than that of most chemical wastes. For example, toxicity of solid waste (SW) containing cytotoxic drugs and antibiotics is comparable to toxicity of radioactive waste of high and medium level activity. This report presents the results of the thermodynamic analysis of thermal processing of SW and experiments at the developed plasma unit for SW processing. Thermodynamic calculations showed that the maximum yield of the synthesis gas at plasma gasification of SW in air and steam mediums is achieved at a temperature of 1600K. At the air plasma gasification of SW high-calorific synthesis gas with a concentration of 82.4% (СO – 31.7%, H2 – 50.7%) can be obtained, and at the steam plasma gasification – with a concentration of 94.5% (СO – 33.6%, H2 – 60.9%). Specific heat of combustion of the synthesis gas produced by air gasification amounts to 14267 kJ/kg, while by steam gasification - 19414 kJ/kg. At the optimal temperature (1600 K), the specific power consumption for air gasification of SW constitutes 1.92 kWh/kg, while for steam gasification - 2.44 kWh/kg. Experimental study was carried out in a plasma reactor. This is device of periodic action. The arc plasma torch of 70 kW electric power is used for SW processing. Consumption of SW was 30 kg/h. Flow of plasma-forming air was 12 kg/h. Under the influence of air plasma flame weight average temperature in the chamber reaches 1800 K. Gaseous products are taken out of the reactor into the flue gas cooling unit, and the condensed products accumulate in the slag formation zone. The cooled gaseous products enter the gas purification unit, after which via gas sampling system is supplied to the analyzer. Ventilation system provides a negative pressure in the reactor up to 10 mm of water column. Condensed products of SW processing are removed from the reactor after its stopping. By the results of experiments on SW plasma gasification the reactor operating conditions were determined, the exhaust gas analysis was performed and the residual carbon content in the slag was determined. Gas analysis showed the following composition of the gas at the exit of gas purification unit, (vol.%): СO – 26.5, H2 – 44.6, N2–28.9. The total concentration of the syngas was 71.1%, which agreed well with the thermodynamic calculations. The discrepancy between experiment and calculation by the yield of the target syngas did not exceed 16%. Specific power consumption for SW gasification in the plasma reactor according to the results of experiments amounted to 2.25 kWh/kg of working substance. No harmful impurities were found in both gas and condensed products of SW plasma gasification. Comparison of experimental results and calculations showed good agreement. Acknowledgement—This work was supported by Ministry of Education and Science of the Republic of Kazakhstan and Ministry of Education and Science of the Russian Federation (Agreement on grant No. 14.607.21.0118, project RFMEF160715X0118).

Keywords: coal, efficiency, ignition, numerical modeling, plasma-fuel system, plasma generator

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Authors: Youngsun Moon, Yeong-Ju Go, Jong-Soo Choi

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Most drones that currently have surveillance/reconnaissance missions are basically equipped with optical equipment, but we also need to use a microphone array to estimate the location of the acoustic source. This can provide additional information in the absence of optical equipment. The purpose of this study is to estimate Direction of Arrival (DOA) based on Time Difference of Arrival (TDOA) estimation of the acoustic source in the drone. The problem is that it is impossible to measure the clear target acoustic source because of the drone noise. To overcome this problem is to separate the drone noise and the target acoustic source using Blind Source Separation(BSS) based on Independent Component Analysis(ICA). ICA can be performed assuming that the drone noise and target acoustic source are independent and each signal has non-gaussianity. For maximized non-gaussianity each signal, we use Negentropy and Kurtosis based on probability theory. As a result, we can improve TDOA estimation and DOA estimation of the target source in the noisy environment. We simulated the performance of the DOA algorithm applying BSS algorithm, and demonstrated the simulation through experiment at the anechoic wind tunnel.

Keywords: aeroacoustics, acoustic source detection, time difference of arrival, direction of arrival, blind source separation, independent component analysis, drone

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Authors: Laura Jenkins, Tim Eschle, Joanne Ciafone, Colin Hamilton

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An original working memory model suggested the separation of visual and verbal systems in working memory architecture, in which only visual working memory components were used during visual working memory tasks. It was later suggested that the visuo spatial sketch pad was the only memory component at use during visual working memory tasks, and components such as the phonological loop were not considered. In more recent years, a contrasting approach has been developed with the use of an executive resource to incorporate both visual and verbal representations in visual working memory paradigms. This was supported using research demonstrating the use of verbal representations and an executive resource in a visual matrix patterns task. The aim of the current research is to investigate the working memory architecture during both a quantitative and a qualitative visual working memory task. A dual task method will be used. Three secondary tasks will be used which are designed to hit specific components within the working memory architecture – Dynamic Visual Noise (visual components), Visual Attention (spatial components) and Verbal Attention (verbal components). A comparison of the visual working memory tasks will be made to discover if verbal representations are at use, as the previous literature suggested. This direct comparison has not been made so far in the literature. Considerations will be made as to whether a domain specific approach should be employed when discussing visual working memory tasks, or whether a more domain general approach could be used instead.

Keywords: semantic organisation, visual memory, change detection

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Authors: Michael Sunday Afolayan

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This research work examined the challenges bedevilling the development of legal framework for electronic commercial transactions (e-commerce) in Nigeria. Nigeria does not have a clear-cut legislation regulating electronic commerce in its jurisdiction despite the geometrical rate of growth and adoption of this method of trade. It specifically posed a great challenge looking at taxation, evidential and jurisdictional issues in e-commerce in Nigeria. The author in a broader research work which is abridged here, traced the origin and development of e-commerce and the attendant laws applicable in Nigeria, examining their sufficiency or otherwise. In carrying out the research work, doctrinal mode of legal research was adopted, examining both primary and secondary sources of legal research materials within their contextual meanings. It was found that the failure to enact a law which has direct regulatory bearing on e-commerce in Nigeria has led to adoption and application of circumstantial laws, rules and common law principles to tackle the problems arising out of electronic commercial transactions, especially in the areas of taxation, evidential and jurisdictional challenges. It was ultimately suggested that there is urgent need to sign into law, the Electronic Transaction Bill which had already been passed by the National Assembly since 2017.

Keywords: e-commerce, legislation, taxation, evidential, jurisdiction

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Authors: Vriddhi Prasad

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The paper briefly outlines the nature of contemporary Architecture Education in India and its present challenges with theoretically feasible solutions. It explores in detail the arduous position of architecture education owing to, privatization of higher education institutes in India, every changing demand of the technology driven industry and discipline, along with regional and cultural resources that should be explored academically for the enrichment of graduates. With the government's education policy of supporting privatization, a comprehensive role for the regulating body of Architecture Education becomes imperative. The paper provides key insights through empirical research into the nature of these roles and the areas which need attention in light of the problems. With the aid of critically acclaimed education model like Design Build, contextual retrofits for Indian institutes can be stressed for inclusion in the curriculum. The pairing of a private institute and public industry/research body and vice versa can lead to pro-economic and pro-social research environment. These reforms if stressed by an autonomous nationwide regulating body rather than the state will lead to uniformity and flexibility of curriculum which promotes the creation of fresh graduates who are adaptable to the changing needs.

Keywords: architecture education, building information modelling, design build, pedagogy

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Authors: S. Merazga, F. Boudeffar, A. Bouaoua, A. Cheriet, M. Berouaken, M. Mebarki, K. Ayouz, N. Gabouze

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Several anode materials as transition metal oxides (Fe3O4, SnO2 a, SnO2, LiCoO2, and Li₄Ti₅O₁₂) has been used. Although titanium oxide has attracted great attention as a; superior electrode for Li-ion batteries due tohis excellent characteristic such as: high capacity, low cost and non-toxicity. In this work, the Synthesis and Characterization of Si Doped Li₄Ti₅O₁₂ with hydrothermal Method was electrochemically evaluated. The SEM images shows that the morphology of LTO powders sizes in the range 70nm.The electrochemical properties of synthesizer nanopowders are investigated for use as an anode active material for lithium-ion batteries by galvanostatic techniques in Li-half cells, obtaining reversible discharge capacity of 173.8 mAh/g at 0.1C even upon 100 cycles.Though the doped powders exhibit an upgrade in The electrical conductivity , This is suitable for use as a high-power cathode material for lithium-ion batteries.

Keywords: LTO, li-ion, battteries, anode

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Authors: Ramona Zharfpeykan

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The concept of sustainability is generally considered as a key topic in many countries, and sustainability reporting is becoming an important tool for companies to communicate their sustainability plans and performance to their stakeholders. There have been various studies on factors that may influence sustainability reporting in companies. This study examines the possible effect of some of the organisational factors on corporate sustainability reporting. The organisational factors included in this study are a company’s type (public or private), industry, and size as well as managers’ perception of the level of importance of indicators in reporting these indicators. A survey was conducted from 240 Australian and New Zealand companies in various industries. They were asked about their perception of the importance of sustainability indicators in their performance and if they report these indicators. The GRI indicators used to develop the survey. A multiple regression model was developed using reporting strategy score as dependent and type, size, industry categorisation, and managers’ perception of the level of importance of the GRI indicators as independent factors. The results show that among all the factors included in the model, size of a company and the perception of managers of the level of importance of environmental and labour practice indicators can affect the sustainability scores of these companies.

Keywords: sustainability reporting, global reporting initiative, sustainability reporting strategy, organisational features

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Authors: Hee-Jae Suh, Mi-Na Hong, Min-Ji Kim, Yeon-Seong Jeong, Ok-Hwan Lee, Jae-Wook Shin, Hyang-Sook Chun, Chan Lee

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Brown HT is a bis-azo dye which is permitted in EU as a food colorant. So far, many studies have focused on HPLC using diode array detection (DAD) analysis for detection of this food colorant with different columns and mobile phases. Even though these methods make it possible to detect Brown HT, low recovery, reproducibility, and linearity are still the major limitations for the application in foods. The purpose of this study was to compare various methods for the analysis of Brown HT and to develop an improved analytical methods including pretreatment. Among tested analysis methods, best resolution of Brown HT was observed when the following solvent was applied as a eluent; solvent A of mobile phase was 0.575g NH4H2PO4, and 0.7g Na2HPO4 in 500mL water added with 500mL methanol. The pH was adjusted using phosphoric acid to pH 6.9 and solvent B was methanol. Major peak for Brown HT appeared at the end of separation, 13.4min after injection. This method exhibited relatively high recovery and reproducibility compared with other methods. LOD (0.284 ppm), LOQ (0.861 ppm), resolution (6.143), and selectivity (1.3) of this method were better than those of ammonium acetate solution method which was most frequently used. Precision and accuracy were verified through inter-day test and intra-day test. Various methods for sample pretreatments were developed for different foods and relatively high recovery over 80% was observed in all case. This method exhibited high resolution and reproducibility of Brown HT compared with other previously reported official methods from FSA and, EU regulation.

Keywords: analytic method, Brown HT, food colorants, pretreatment method

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Authors: K. Bresilla, L. Manfrini, B. Morandi, A. Boini, G. Perulli, L. C. Grappadelli

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Image/video processing for fruit in the tree using hard-coded feature extraction algorithms have shown high accuracy during recent years. While accurate, these approaches even with high-end hardware are computationally intensive and too slow for real-time systems. This paper details the use of deep convolution neural networks (CNNs), specifically an algorithm (YOLO - You Only Look Once) with 24+2 convolution layers. Using deep-learning techniques eliminated the need for hard-code specific features for specific fruit shapes, color and/or other attributes. This CNN is trained on more than 5000 images of apple and pear fruits on 960 cores GPU (Graphical Processing Unit). Testing set showed an accuracy of 90%. After this, trained data were transferred to an embedded device (Raspberry Pi gen.3) with camera for more portability. Based on correlation between number of visible fruits or detected fruits on one frame and the real number of fruits on one tree, a model was created to accommodate this error rate. Speed of processing and detection of the whole platform was higher than 40 frames per second. This speed is fast enough for any grasping/harvesting robotic arm or other real-time applications.

Keywords: artificial intelligence, computer vision, deep learning, fruit recognition, harvesting robot, precision agriculture

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Authors: Avalloy McCarthy-Curvin

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For over fifty years there has been widespread dissatisfaction with the teaching of Mathematics in Jamaica. Studies, done in the Jamaican context highlight that teachers at the end of training do not have a deep understanding of the mathematics content they teach. Little research has been done in the Jamaican context that targets the advancement of contextual knowledge on the problem to ultimately provide a solution. The aim of the study is to identify what influences this outcome of teacher education in Jamaica so as to remedy the problem. This study formatively evaluated the curriculum documents, assessments and the delivery of the curriculum that are being used in teacher training institutions in Jamaica to determine their rigor -the extent to which written document, instruction, and the assessments focused on enabling pre-service teachers to develop deep understanding of mathematics and relevance- the extent to which the curriculum document, instruction, and the assessments are focus on developing the requisite knowledge for teaching mathematics. The findings show that neither the curriculum document, instruction nor assessments ensure rigor and enable pre-service teachers to develop the knowledge and skills they need to teach mathematics effectively.

Keywords: relevance, rigor, deep understanding, formative evaluation

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Authors: Elena Maria Scalisi

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Currently, photodegradation by nanoparticles (NPs) is a common solution for wastewater treatment. Nanoparticles are efficient for removing organic and inorganic pollutants, heavy metals from wastewater and killing microorganisms through environmentally friendly. In this context, the major representative of photocatalytic technology for industrial wastewater treatment are TiO₂ nanoparticles (TiO₂-NPs). TiO₂-NPs have a strong catalytic activity that depends to their physicochemical properties. Thanks to their small size (between 1-100 nm), nanoparticles occupy less volume, then their surface area increases. The increase in the surface-to-volume ratio results in the increase of the particle surface energy, which improve their reactivity potential. However, these unique properties represent risks to the ecosystems and organisms when unintentionally TiO₂-NPs are release into the environment and absorbed by living organisms. Several studies confirm that there is a high level of interest concerning the safety of TiO₂-NPs in the aquatic environment, furthermore, ecotoxicological tools are useful to correctly evaluate their toxicity. In the current study, we aimed to characterize potential toxic effects of TiO₂-NP suspension to zebrafish during embryo-larval stages to evaluate parameters such as survival rates, malformation, hatching, the overall length of the larvae heartbeat, and biochemical biomarkers that reflect the acute toxicity and sublethal effects of TiO₂-NPs. Zebrafish embryos were exposed to titanium dioxide nanoparticles (TiO₂-NPs at 1mg/L, 2mg/L, and 4mg/L) from fertilization to the free swimming stage (144hpf). Every day, we recorded the toxicological endpoints, moreover, immunohistochemical analysis has been performed at the end of the exposure. In particular, we have evaluate the expression of the following biomarkers: Heat Shock Protein 70 (HSP70), Poly ADP-Ribose Polymerase-1 (PARP-1), Metallothioneins (MTs). Our results have shown that hatch ability, survival, and malformation rate were not affected by TiO₂ NPs at these exposure levels. However, TiO₂-NPs caused an increase of heartbeat and reduction of body length; at the same time, TiO₂-NPs have inducted the production of ROS and the expression of oxidative stress biomarkers HSP70 and PARP-1. Hight positivity for PARP-1 at all concentration tested was observed. As regards MT, positivity was found in the expression of this biomarker in the whole body of the embryo, with the exception of the end of the tail. Metallothioneins (MT) are biomarkers widely used in environmental monitoring programs for aquatic creatures. At the light of our results i.e. no death until the end of the experiment (144hpf), no malformation and expression of the biomarkers mentioned, it is evident that zebrafish larvae with their natural detoxification pathways are able to resist the presence of toxic substances and then they can tolerate the presence of metal concentrations. However, an excessive oxidative state can compromise cell function, therefore the uncontrolled release of nanoparticles into the environment is severe and must be constantly monitored.

Keywords: nanoparticles, embryo zebrafish, HSP70, PARP-1

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Authors: Sayed Obeidullah Abrar

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Nanosensor is the combination of two terms nanoparticles and sensors. These are chemical or physical sensor constructed using nanoscale components, usually microscopic or submicroscopic in size. These sensors are very sensitive and can detect single virus particle or even very low concentrations of substances that could be potentially harmful. Nanosensors have a large scope of research especially in the field of medical sciences, military applications, pharmaceuticals etc.

Keywords: HIV/AIDS, nanosensors, DNA, RNA

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Authors: Raziyeh Mohammadi

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At present, nanoparticles are used for various biomedical applications where they facilitate laboratory diagnostics and therapeutics. The performance of nanoparticles for biomedical applications is often assessed by their narrow size distribution, suitable magnetic saturation, and low toxicity effects. Superparamagnetic iron oxide nanoparticles have received great attention due to their applications as contrast agents for magnetic resonance imaging (MRI. (Processes in the tissue where the blood brain barrier is intact in this way shielded from the contact to this conventional contrast agent and will only reveal changes in the tissue if it involves an alteration in the vasculature. This technique is very useful for detecting tumors and can even be used for detecting metabolic functional alterations in the brain, such as epileptic activity.SPIONs have found application in Magnetic Resonance Imaging (MRI) and magnetic hyperthermia. Unlike bulk iron, SPIONs do not have remnant magnetization in the absence of the external magnetic field; therefore, a precise remote control over their action is possible.

Keywords: nanoparticles, MRI, biomedical, iron oxide, spions

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Authors: L. Oufni, M. Amrane

Abstract:

In this work, different river water samples have been collected and analyzed from different locations along Oum Er-Rabia River in Morocco. The uranium (238U) and thorium (232Th) concentrations were investigated in the studied river and dam water samples using Solid State Nuclear Track Detector (SSNTD). Mean activity concentrations of uranium and thorium in water were found to be between 12 – 37 Bq m^-3 and 2-10 Bq m^-3, respectively. The pH measured at all river water samples was slightly alkaline and ranged from 7.5 to 8.75. The electrical conductivity ranged from 2790 to 794 µS cm^-1. It was found that uranium and thorium concentrations were correlated with some chemical parameters in Oum Er-Rabia River water. The uranium concentrations found in river water are insignificant from the radiological point of view. The recommended value for uranium in drinking water based on its toxicity given by the Federal Environment Agency. This corresponds to an activity concentration of 238U of 123.5 mBq L^-1. In none of the samples, the uranium activity exceeds this value.

Keywords: uranium, thorium, conductivity, water, SSNTD

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Authors: João Madureira, Ricardo Lagido, Inês Sousa, Fraunhofer Portugal

Abstract:

Surf is an increasingly popular sport and its performance evaluation is often qualitative. This work aims at using a smartphone to collect and analyze the GPS and inertial sensors data in order to obtain quantitative metrics of the surfing performance. Two approaches are compared for detection of wave rides, computing the number of waves rode in a surfing session, the starting time of each wave and its duration. The first approach is based on computing the velocity from the Global Positioning System (GPS) signal and finding the velocity thresholds that allow identifying the start and end of each wave ride. The second approach adds information from the Inertial Measurement Unit (IMU) of the smartphone, to the velocity thresholds obtained from the GPS unit, to determine the start and end of each wave ride. The two methods were evaluated using GPS and IMU data from two surfing sessions and validated with similar metrics extracted from video data collected from the beach. The second method, combining GPS and IMU data, was found to be more accurate in determining the number of waves, start time and duration. This paper shows that it is feasible to use smartphones for quantification of performance metrics during surfing. In particular, detection of the waves rode and their duration can be accurately determined using the smartphone GPS and IMU.

Keywords: inertial measurement unit (IMU), global positioning system (GPS), smartphone, surfing performance

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Authors: Ling Qi, Matloob Khushi, Josiah Poon

Abstract:

This paper introduces a pre-trained Masked Candlestick Model (MCM) for trading time-series data. The pre-trained model is based on three core designs. First, we convert trading price data at each data point as a set of normalized elements and produce embeddings of each element. Second, we generate a masked sequence of such embedded elements as inputs for self-supervised learning. Third, we use the encoder mechanism from the transformer to train the inputs. The masked model learns the contextual relations among the sequence of embedded elements, which can aid downstream classification tasks. To evaluate the performance of the pre-trained model, we fine-tune MCM for three different downstream classification tasks to predict future price trends. The fine-tuned models achieved better accuracy rates for all three tasks than the baseline models. To better analyze the effectiveness of MCM, we test the same architecture for three currency pairs, namely EUR/GBP, AUD/USD, and EUR/JPY. The experimentation results demonstrate MCM’s effectiveness on all three currency pairs and indicate the MCM’s capability for signal extraction from trading data.

Keywords: masked language model, transformer, time series prediction, trading prediction, embedding, transfer learning, self-supervised learning

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Authors: Amr A. Fouad, Waleed H. Albuali, Iyad Jresat

Abstract:

The protective effect of thymoquinone (TQ) was investigated in rats exposed to testicular injury induced by sodium arsenite (10mg/kg/day, orally, for two days). TQ treatment (10mg/kg/day, intraperitoneal injection) was applied for five days, starting three day before arsenic administration. TQ significantly attenuated the arsenic-induced decreases of serum testosterone, and testicular reduced glutathione level, and significantly decreased the elevations of testicular malondialdehyde and nitric oxide levels resulted from arsenic administration. Also, TQ ameliorated the arsenic-induced testicular tissue injury observed by histopathological examination. In addition, TQ decreased the arsenic-induced expression of inducible nitric oxide synthase and caspase-3 in testicular tissue. It was concluded that TQ may represent a potential candidate to protect against arsenic-induced testicular injury.

Keywords: thymoquinone, arsenic, testes, rats

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Authors: Omer Cahana, Maya Herman, Ofer Levi

Abstract:

Magnetic resonance imaging (MRI) is a long medical scan that stems from a long acquisition time. This length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach, such as compress sensing (CS) or parallel imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. In order to achieve that, two properties have to exist: i) the signal must be sparse under a known transform domain, ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm needs to be applied to recover the signal. While the rapid advance in the deep learning (DL) field, which has demonstrated tremendous successes in various computer vision task’s, the field of MRI reconstruction is still in an early stage. In this paper, we present an extension of the state-of-the-art model in MRI reconstruction -VarNet. We utilize VarNet by using dilated convolution in different scales, which extends the receptive field to capture more contextual information. Moreover, we simplified the sensitivity map estimation (SME), for it holds many unnecessary layers for this task. Those improvements have shown significant decreases in computation costs as well as higher accuracy.

Keywords: MRI, deep learning, variational network, computer vision, compress sensing

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Authors: Qiang Zhang, Xiaojian Hu

Abstract:

In multi-vehicle tracking, based on feature selection, the tracking system efficiently tracks vehicles in a video with minimal error in combination with color attribution, which focuses on presenting a simple and fast, yet accurate and robust solution to the problem such as inaccurately and untimely responses of statistics-based adaptive traffic control system in the intersection scenario. In this study, a real-time tracking system is proposed for multi-vehicle tracking in the intersection scene. Considering the complexity and application feasibility of the algorithm, in the object detection step, the detection result provided by virtual loops were post-processed and then used as the input for the tracker. For the tracker, lightweight methods were designed to extract and select features and incorporate them into the adaptive color tracking (ACT) framework. And the approbatory online feature selection algorithms are integrated on the mature ACT system with good compatibility. The proposed feature selection methods and multi-vehicle tracking method are evaluated on KITTI datasets and show efficient vehicle tracking performance when compared to the other state-of-the-art approaches in the same category. And the system performs excellently on the video sequences recorded at the intersection. Furthermore, the presented vehicle tracking system is suitable for surveillance applications.

Keywords: real-time, multi-vehicle tracking, feature selection, color attribution

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Authors: Monica Racha, Siva Chandrasekaran, Alex Stojcevski

Abstract:

The engineering programs focus on promoting students' personal and professional development by ensuring that students acquire technical and professional competencies during four-year studies. The traditional engineering laboratory provides an opportunity for students to "practice by doing," and laboratory facilities aid them in obtaining insight and understanding of their discipline. Due to rapid technological advancements and the current COVID-19 outbreak, the traditional labs were transforming into virtual learning environments. Aim: To better understand the limitations of the physical laboratory, this research study aims to use a Machine Learning (ML) algorithm that interfaces with the Augmented Reality HoloLens and predicts the image behavior to classify and detect the electronic components. The automated electronic components error classification and detection automatically detect and classify the position of all components on a breadboard by using the ML algorithm. This research will assist first-year undergraduate engineering students in conducting laboratory practices without any supervision. With the help of HoloLens, and ML algorithm, students will reduce component placement error on a breadboard and increase the efficiency of simple laboratory practices virtually. Method: The images of breadboards, resistors, capacitors, transistors, and other electrical components will be collected using HoloLens 2 and stored in a database. The collected image dataset will then be used for training a machine learning model. The raw images will be cleaned, processed, and labeled to facilitate further analysis of components error classification and detection. For instance, when students conduct laboratory experiments, the HoloLens captures images of students placing different components on a breadboard. The images are forwarded to the server for detection in the background. A hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm will be used to train the dataset for object recognition and classification. The convolution layer extracts image features, which are then classified using Support Vector Machine (SVM). By adequately labeling the training data and classifying, the model will predict, categorize, and assess students in placing components correctly. As a result, the data acquired through HoloLens includes images of students assembling electronic components. It constantly checks to see if students appropriately position components in the breadboard and connect the components to function. When students misplace any components, the HoloLens predicts the error before the user places the components in the incorrect proportion and fosters students to correct their mistakes. This hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm automating electronic component error classification and detection approach eliminates component connection problems and minimizes the risk of component damage. Conclusion: These augmented reality smart glasses powered by machine learning provide a wide range of benefits to supervisors, professionals, and students. It helps customize the learning experience, which is particularly beneficial in large classes with limited time. It determines the accuracy with which machine learning algorithms can forecast whether students are making the correct decisions and completing their laboratory tasks.

Keywords: augmented reality, machine learning, object recognition, virtual laboratories

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Authors: Guillaume Craveur

Abstract:

This study highlights the impact of an onboard fire for the evacuation of a rolling stock. Two fires models are achieved. The first one is a zone model realized with the CFAST software. Then, this fire is imported in a building EXODUS model in order to determine the evacuation time with effects of fire effluents (temperature, smoke opacity, smoke toxicity) on passengers. The second fire is achieved with Fire Dynamics Simulator software. The fire defined is directly imported in the FDS+Evac model which will permit to determine the evacuation time and effects of fire effluents on passengers. These effects will be compared with tenability criteria defined in some standards in order to see if the situation is acceptable. Different power of fire will be underlined to see from what power source the hazard become unacceptable.

Keywords: fire safety engineering, numerical tools, rolling stock, evacuation

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Authors: M. A. I Perera, C. R. Wijesinghe, A. R. Weerasinghe

Abstract:

his study was conducted to review and identify the unsupervised techniques that can be employed to analyze gene expression data in order to identify better subtypes of tumors. Identifying subtypes of cancer help in improving the efficacy and reducing the toxicity of the treatments by identifying clues to find target therapeutics. Process of gene expression data analysis described under three steps as preprocessing, clustering, and cluster validation. Feature selection is important since the genomic data are high dimensional with a large number of features compared to samples. Hierarchical clustering and K Means are often used in the analysis of gene expression data. There are several cluster validation techniques used in validating the clusters. Heatmaps are an effective external validation method that allows comparing the identified classes with clinical variables and visual analysis of the classes.

Keywords: cancer subtypes, gene expression data analysis, clustering, cluster validation

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Authors: Jeanne Leblond, Warren Viricel, Amira Mbarek

Abstract:

Although several products have reached the market, gene therapeutics are still in their first stages and require optimization. It is possible to improve their lacking efficiency by the use of carefully engineered vectors, able to carry the genetic material through each of the biological barriers they need to cross. In particular, getting inside the cell is a major challenge, because these hydrophilic nucleic acids have to cross the lipid-rich plasmatic and/or endosomal membrane, before being degraded into lysosomes. It takes less than one hour for newly endocytosed liposomes to reach highly acidic lysosomes, meaning that the degradation of the carried gene occurs rapidly, thus limiting the transfection efficiency. We propose to use a new pH-sensitive lipid able to change its conformation upon protonation at endosomal pH values, leading to the disruption of the lipidic bilayer and thus to the fast release of the nucleic acids into the cytosol. It is expected that this new pH-sensitive mechanism promote endosomal escape of the gene, thereby its transfection efficiency. The main challenge of this work was to design a preparation presenting fast-responding lipidic bilayer destabilization properties at endosomal pH 5 while remaining stable at blood pH value and during storage. A series of pH-sensitive lipids able to perform a conformational switch upon acidification were designed and synthesized. Liposomes containing these switchable lipids, as well as co-lipids were prepared and characterized. The liposomes were stable at 4°C and pH 7.4 for several months. Incubation with siRNA led to the full entrapment of nucleic acids as soon as the positive/negative charge ratio was superior to 2. The best liposomal formulation demonstrated a silencing efficiency up to 10% on HeLa cells, very similar to a commercial agent, with a lowest toxicity than the commercial agent. Using flow cytometry and microscopy assays, we demonstrated that drop of pH was required for the transfection efficiency, since bafilomycin blocked the transfection efficiency. Additional evidence was brought by the synthesis of a negative control lipid, which was unable to switch its conformation, and consequently exhibited no transfection ability. Mechanistic studies revealed that the uptake was mediated through endocytosis, by clathrin and caveolae pathways, as reported for previous lipid nanoparticle systems. This potent system was used for the treatment of hypercholesterolemia. The switchable lipids were able to knockdown PCSK9 expression on human hepatocytes (Huh-7). Its efficiency is currently evaluated on in vivo mice model of PCSK9 KO mice. In summary, we designed and optimized a new cationic pH-sensitive lipid for gene delivery. Its transfection efficiency is similar to the best available commercial agent, without the usually associated toxicity. The promising results lead to its use for the treatment of hypercholesterolemia on a mice model. Anticancer applications and pulmonary chronic disease are also currently investigated.

Keywords: liposomes, siRNA, pH-sensitive, molecular switch

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