Search results for: network protocol

Authors: Carolina S. Monteiro, Eugénia Pinto, Miguel A. Faria, Sara C. Cunha

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About half of the world's population eats rice daily, making it the primary food source for billions of people. Besides its nutrition potential, rice can be a significant route of exposure to many contaminants. Mycotoxins are an example of such contaminants that can be present in rice. Among them, ochratoxin (OTA), citrinin (CIT), and zearalenone (ZEN) are frequently reported in rice. During digestion, only a fraction of mycotoxins from food can be absorbed (bioaccessible fraction), influencing their ability to cause toxic effects. Insufficient knowledge of the bioavailability of mycotoxins, alone and in combination, may hinder an accurate risk assessment of contaminants ingested by humans. In this context, two different rice (Oryza sativa) varieties, Carolino white and Carolino brown, both with and without turmeric, were boiled and individually spiked with OTA, CIT, and ZEN plus with its combination. Subsequently, samples were submitted to the INFOGEST harmonized in vitro digestion protocol to evaluate the bioaccessibility of mycotoxins. Afterward, the in vitro intestinal transport of the mycotoxins, both alone and in combination, was evaluated in digests of Carolino white rice with and without turmeric. Assays were performed with a monolayers of of Caco-2 and HT-29 cells. Bioaccessibility of OTA and ZEN, alone and in combination, were similar in Carolino white and brown rice with or without turmeric. For CIT, when Carolino white rice was used, the bioaccessibility was higher alone than in combination (62.00% vs. 25.00%, without turmeric; 87.56% vs. 53.87%, with turmeric); however, with Carolino brown rice was the opposite (66.38% vs. 75.20%, without turmeric; 43.89% vs. 59.44%, with turmeric). All the mycotoxins, isolated, reached the higher bioaccessibility in the Carolino white rice with turmeric (CIT: 87.56%; OTA: 59.24%; ZEN: 58.05%). When mycotoxins are co-present, the higher bioaccessibility of each one varies with the type of rice. In general, when turmeric is present, bioaccessibility increases, except for CIT, using Carolino brown rice. Concerning the intestinal absorption in vitro, after 3 hours of transport, all mycotoxins were detected in the basolateral compartment being thus transported through the cells monolayer. ZEN presented the highest fraction absorbed isolated and combined, followed by CIT and OTA. These findings highlight that the presence of other components in the complex dietary matrix, like turmeric, and the co-presence of mycotoxins can affect its final bioavailability with obvious implications for health risk. This work provides new insights to qualitatively and quantitatively describe mycotoxin in rice fate during human digestion and intestinal absorption and further contribute to better risk assessment.

Keywords: bioaccessibility, digestion, intestinal absorption, mycotoxins

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Authors: Padma Chutoo, Elena Kulinskaya, Ilyas Bakbergenuly, Nicholas Steel, Dmitri Pchejetski

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Stroke is associated with a significant risk of morbidity and mortality. There is scarcity of research on the long-term survival after first-ever ischaemic stroke (IS) events in England with regards to effects of different medical therapies and comorbidities. The objective of this study was to model the all-cause mortality after an IS diagnosis in the adult population of England. Using a retrospective case-control design, we extracted the electronic medical records of patients born prior to or in year 1960 in England with a first-ever ischaemic stroke diagnosis from January 1986 to January 2017 within the Health and Improvement Network (THIN) database. Participants with a history of ischaemic stroke were matched to 3 controls by sex and age at diagnosis and general practice. The primary outcome was the all-cause mortality. The hazards of the all-cause mortality were estimated using a Weibull-Cox survival model which included both scale and shape effects and a shared random effect of general practice. The model included sex, birth cohort, socio-economic status, comorbidities and medical therapies. 20,250 patients with a history of IS (cases) and 55,519 controls were followed up to 30 years. From 2008 to 2015, the one-year all-cause mortality for the IS patients declined with an absolute change of -0.5%. Preventive treatments to cases increased considerably over time. These included prescriptions of statins and antihypertensives. However, prescriptions for antiplatelet drugs decreased in the routine general practice since 2010. The survival model revealed a survival benefit of antiplatelet treatment to stroke survivors with hazard ratio (HR) of 0.92 (0.90 – 0.94). IS diagnosis had significant interactions with gender and age at entry and hypertension diagnosis. IS diagnosis was associated with high risk of all-cause mortality with HR= 3.39 (3.05-3.72) for cases compared to controls. Hypertension was associated with poor survival with HR = 4.79 (4.49 - 5.09) for hypertensive cases relative to non-hypertensive controls, though the detrimental effect of hypertension has not reached significance for hypertensive controls, HR = 1.19(0.82-1.56). This study of English primary care data showed that between 2008 and 2015, the rates of prescriptions of stroke preventive treatments increased, and a short-term all-cause mortality after IS stroke declined. However, stroke resulted in poor long-term survival. Hypertension, a modifiable risk factor, was found to be associated with poor survival outcomes in IS patients. Antiplatelet drugs were found to be protective to survival. Better efforts are required to reduce the burden of stroke through health service development and primary prevention.

Keywords: general practice, hazard ratio, health improvement network (THIN), ischaemic stroke, multiple imputation, Weibull-Cox model.

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Authors: Fabiana Franceschi, Martha Cobo, Manuel Figueredo

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Bogotá, the capital of Colombia, is its largest city and one of the most polluted in Latin America due to the fast economic growth over the last ten years. Bogotá has been affected by high pollution events which led to the high concentration of PM10 and NO2, exceeding the local 24-hour legal limits (100 and 150 g/m3 each). The most important pollutants in the city are PM10 and PM2.5 (which are associated with respiratory and cardiovascular problems) and it is known that their concentrations in the atmosphere depend on the local meteorological factors. Therefore, it is necessary to establish a relationship between the meteorological variables and the concentrations of the atmospheric pollutants such as PM10, PM2.5, CO, SO2, NO2 and O3. This study aims to determine the interrelations between meteorological variables and air pollutants in Bogotá, using data mining techniques. Data from 13 monitoring stations were collected from the Bogotá Air Quality Monitoring Network within the period 2010-2015. The Principal Component Analysis (PCA) algorithm was applied to obtain primary relations between all the parameters, and afterwards, the K-means clustering technique was implemented to corroborate those relations found previously and to find patterns in the data. PCA was also used on a per shift basis (morning, afternoon, night and early morning) to validate possible variation of the previous trends and a per year basis to verify that the identified trends have remained throughout the study time. Results demonstrated that wind speed, wind direction, temperature, and NO2 are the most influencing factors on PM10 concentrations. Furthermore, it was confirmed that high humidity episodes increased PM2,5 levels. It was also found that there are direct proportional relationships between O3 levels and wind speed and radiation, while there is an inverse relationship between O3 levels and humidity. Concentrations of SO2 increases with the presence of PM10 and decreases with the wind speed and wind direction. They proved as well that there is a decreasing trend of pollutant concentrations over the last five years. Also, in rainy periods (March-June and September-December) some trends regarding precipitations were stronger. Results obtained with K-means demonstrated that it was possible to find patterns on the data, and they also showed similar conditions and data distribution among Carvajal, Tunal and Puente Aranda stations, and also between Parque Simon Bolivar and las Ferias. It was verified that the aforementioned trends prevailed during the study period by applying the same technique per year. It was concluded that PCA algorithm is useful to establish preliminary relationships among variables, and K-means clustering to find patterns in the data and understanding its distribution. The discovery of patterns in the data allows using these clusters as an input to an Artificial Neural Network prediction model.

Keywords: air pollution, air quality modelling, data mining, particulate matter

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Authors: Jisong Zhang, Yinghua Zhao

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The present study investigated the mechanical properties and microstructure of Ultra-High Performance Concrete (UHPC) containing supplementary cementitious materials (SCMs), such as fly ash (FA) and silica fume (SF), and to verify the synergistic effect in the ternary system. On the basis of 30% fly ash replacement, the incorporation of either 10% SF or 20% SF show a better performance compared to the reference sample. The efficiency factor (k-value) was calculated as a synergistic effect to predict the compressive strength of UHPC with these SCMs. The SEM of micrographs and pore volume from BJH method indicate a high correlation with compressive strength. Further, an artificial neural networks model was constructed for prediction of the compressive strength of UHPC containing these SCMs.

Keywords: artificial neural network, fly ash, mechanical properties, ultra-high performance concrete

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Authors: Umang Varshney, Amit Karamchandani, Rohit Kapoor

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Practitioners and researchers have realized that Blockchain is not limited to currency. Blockchain as a distributed ledger can ensure a transparent and traceable supply chain. Due to Blockchain-enabled IoTs, a firm’s information management system can now take inputs from other supply chain partners in real-time. This study aims to provide empirical evidence of dimensions responsible for blockchain implemented firm’s growth and highlight how sector (manufacturing or service), state's regulatory environment, and choice of blockchain network affect the blockchain's usefulness. This post-adoption study seeks to validate the findings of pre-adoption studies done on the blockchain. Data will be collected through a survey of managers working in blockchain implemented firms and analyzed through PLS-SEM.

Keywords: blockchain, information management system, PLS-SEM, firm's growth

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Authors: D. Szinay, O. Perski, A. Jones, T. Chadborn, J. Brown, F. Naughton

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Background: The uptake of health and wellbeing smartphone apps is largely influenced by popularity indicators (e.g., rankings), rather than evidence-based content. Rapid disengagement is common. This study aims to explore how and why potential users 1) select and 2) engage with such apps, and 3) how increased engagement could be promoted. Methods: Semi-structured interviews and a think-aloud approach were used to allow participants to verbalise their thoughts whilst searching for a health or wellbeing app online, followed by a guided search in the UK National Health Service (NHS) 'Apps Library' and Public Health England’s (PHE) 'One You' website. Recruitment took place between June and August 2019. Adults interested in using an app for behaviour change were recruited through social media. Data were analysed using the framework approach. The analysis is both inductive and deductive, with the coding framework being informed by the Theoretical Domains Framework. The results are further mapped onto the COM-B (Capability, Opportunity, Motivation - Behaviour) model. The study protocol is registered on the Open Science Framework (https://osf.io/jrkd3/). Results: The following targets were identified as playing a key role in increasing the uptake of and engagement with health and wellbeing apps: 1) psychological capability (e.g., reduced cognitive load); 2) physical opportunity (e.g., low financial cost); 3) social opportunity (e.g., embedded social media); 4) automatic motivation (e.g., positive feedback). Participants believed that the promotion of evidence-based apps on NHS-related websites could be enhanced through active promotion on social media, adverts on the internet, and in general practitioner practices. Future Implications: These results can inform the development of interventions aiming to promote the uptake of and engagement with evidence-based health and wellbeing apps, a priority within the UK NHS Long Term Plan ('digital first'). The targets identified across the COM-B domains could help organisations that provide platforms for such apps to increase impact through better selection of apps.

Keywords: behaviour change, COM-B model, digital health, mhealth

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Authors: Buhari Samaila, Sabiu Abdullahi, Buhari Maidamma

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Background: Contrast-enhanced computed tomography (CE-CT) is a technique that uses contrast media to improve image quality and diagnostic accuracy. It is a widely used imaging modality in medical diagnostics, offering high-resolution images for accurate diagnosis. However, concerns regarding the potential adverse effects of contrast media and radiation dose exposure have prompted ongoing investigation and assessment. It is important to assess the effects of contrast media and radiation dose in CE-CT procedures. Objective: This study aims to assess the effects of contrast media and radiation dose in contrast-enhanced computed tomography (CECT) procedures. Methods: A comprehensive review of the literature was conducted to identify studies related to contrast media effects and radiation dose assessment in CECT. Relevant data, including location, type of research, objective, method, findings, conclusion, authors, and year of publications, were extracted, analyzed, and reported. Results: The findings revealed that several studies have investigated the impacts of contrast media and radiation doses in CECT procedures, with iodinated contrast agents being the most commonly employed. Adverse effects associated with contrast media administration were reported, including allergic reactions, nephrotoxicity, and thyroid dysfunction, albeit at relatively low incidence rates. Additionally, radiation dose levels varied depending on the imaging protocol and anatomical region scanned. Efforts to minimize radiation exposure through optimization techniques were evident across studies. Conclusion: Contrast-enhanced computed tomography (CECT) remains an invaluable tool in medical imaging; however, careful consideration of contrast media effects and radiation dose exposure is imperative. Healthcare practitioners should weigh the diagnostic benefits against potential risks, employing strategies to mitigate adverse effects and optimize radiation dose levels for patient safety and effective diagnosis. Further research is warranted to enhance the understanding and management of contrast media effects and radiation dose optimization in CECT procedures.

Keywords: CT, contrast media, radiation dose, effect of radiation

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Authors: Chen Yao, Wang Ke

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Small towns are the most basic administrative institutions in our country, which are connected with cities and rural areas. Small towns play an important role in promoting local urban and rural economic development, providing the main public services and maintaining social stability in social governance. With the vigorous development of small towns and the transformation of industrial structure, the changes of social structure, spatial structure, and lifestyle are lagging behind, causing that the spatial form and landscape style do not belong to both cities and rural areas, and seriously affecting the quality of people’s life space and environment. The rural economy in Zhejiang Province has started, the society and the population are also developing in relative stability. In September 2016, Zhejiang Province set out the 'Technical Guidelines for Comprehensive Environmental Remediation of Small Towns in Zhejiang Province,' so as to comprehensively implement the small town comprehensive environmental remediation with the main content of strengthening the plan and design leading, regulating environmental sanitation, urban order and town appearance. In November 2016, Huzhou City started the comprehensive environmental improvement of small towns, strived to use three years to significantly improve the 115 small towns, as well as to create a number of high quality, distinctive and beautiful towns with features of 'clean and livable, rational layout, industrial development, poetry and painting style'. This paper takes Meixi Town, Zhangwu Town and Sanchuan Village in Huzhou City as the empirical cases, analyzes the small town public space by applying the relative theory of actor-network and space syntax. This paper also analyzes the spatial composition in actor and social structure elements, as well as explores the relationship of actor’s spatial practice and public open space by combining with actor-network theory. This paper introduces the relevant theories and methods of spatial syntax, carries out research analysis and design planning analysis of small town spaces from the perspective of quantitative analysis. And then, this paper proposes the effective updating strategy for the existing problems in public space. Through the planning and design in the building level, the dissonant factors produced by various spatial combination of factors and between landscape design and urban texture during small town development will be solved, inhabitant quality of life will be promoted, and town development vitality will be increased.

Keywords: small towns, urbanization, public space, updating

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Authors: Yong-Kyu Jung

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The fast growth in information technology has led to de-mands to access/process data. CPSs heavily depend on the time of hardware/software operations and communication over the network (i.e., real-time/parallel operations in CPSs (e.g., autonomous vehicles). Since data processing is an im-portant means to overcome the issue confronting data management, reducing the gap between the technological-growth and the data-complexity and channel-bandwidth. An adaptive perpetual data approximation method is intro-duced to manage the actual entropy of the digital spectrum. An ADAC implemented as an accelerator and/or apps for servers/smart-connected devices adaptively rescales digital contents (avg.62.8%), data processing/access time/energy, encryption/decryption overheads in AI/ML applications (facial ID/recognition).

Keywords: adaptive codec, AI, ML, HPC, cyber-physical, cybersecurity

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Authors: G. Vici, L. Cesanelli, L. Belli, R. Ceci, V. Polzonetti

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Several factors contribute to success in sport and diet is one of them. Evidence-based sport nutrition guidelines underline the importance of macro- and micro-nutrients’ balance and timing in order to improve athlete’s physical status and performance. Nevertheless, a high content of proteins is commonly found in resistance training athletes’ diet with carbohydrate intake that is not enough or not well planned. The aim of the study was to evaluate the impact of different protein and carbohydrate diet contents on body composition and sport performance on a group of resistance training athletes. Subjects were divided as study group (n=16) and control group (n=14). For a period of 4 months, both groups were subjected to the same resistance training fitness program with study group following a specific diet and control group following an ab libitum diet. Body compositions were evaluated trough anthropometric measurement (weight, height, body circumferences and skinfolds) and Bioimpedence Analysis. Physical strength and training status of individuals were evaluated through the One Repetition Maximum test (RM1). Protein intake in studied group was found to be lower than in control group. There was a statistically significant increase of body weight, free fat mass and body mass cell of studied group respect to the control group. Fat mass remains almost constant. Statistically significant changes were observed in quadriceps and biceps circumferences, with an increase in studied group. The MR1 test showed improvement in study group’s strength but no changes in control group. Usually people consume hyper-proteic diet to achieve muscle mass development. Through this study, it was possible to show that protein intake fixed at 1,7 g/kg/d can meet the individual's needs. In parallel, the increased intake of carbohydrates, focusing on quality and timing of assumption, has enabled the obtainment of desired results with a training protocol supporting a hypertrophic strategy. Therefore, the key point seems related to the planning of a structured program both from a nutritional and training point of view.

Keywords: body composition, diet, exercise, protein

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Authors: Supattra Kanchanopast

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The purpose of this research is to develop and design a management information system for 7 eleven shop in Bangkok. The system was designed and developed to meet users’ requirements via the internet network by use of application software such as My SQL for database management, Apache HTTP Server for Web Server and PHP Hypertext Preprocessor for an interface between web server, database and users. The system was designed into two subsystems as the main system, or system for head office, and the branch system for branch shops. These consisted of three parts which are classified by user management as shop management, inventory management and Point of Sale (POS) management. The implementation of the MIS for the mini-mart shop, can lessen the amount of paperwork and reduce repeating tasks so it may decrease the capital of the business and support an extension of branches in the future as well.

Keywords: convenience store, the management information system, inventory management, 7 eleven shop

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Authors: Skyler Kim

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An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.

Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning

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Authors: Mustafa M. Donma, Orkide Donma

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Metabolic syndrome (MetS) is a disease associated with obesity. It is a complicated clinical problem possibly affecting body composition as well as macrominerals. These parameters gain further attention, particularly in the pediatric population. The aim of this study is to investigate the amount of discrete body composition fractions in groups that differ in the severity of obesity. Also, the possible associations with calcium (Ca), phosphorus (P), magnesium (Mg) will be examined. The study population was divided into four groups. Twenty-eight, 29, 34, and 34 children were involved in Group 1 (healthy), 2 (obese), 3 (morbid obese), and 4 (MetS), respectively. Institutional Ethical Committee approved the study protocol. Informed consent forms were obtained from the participants. The classification of obese groups was performed based upon the recommendations of the World Health Organization. Metabolic syndrome components were defined. Serum Ca, P, Mg concentrations were measured. Within the scope of body composition, fat mass, fat-free mass, protein mass, mineral mass were determined by a body composition monitor using bioelectrical impedance analysis technology. Weight, height, waist circumference, hip circumference, head circumference, and neck circumference values were recorded. Body mass index, diagnostic obesity notation model assessment index, fat mass index, and fat-free mass index values were calculated. Data were statistically evaluated and interpreted. There was no statistically significant difference among the groups in terms of Ca and P concentrations. Magnesium concentrations differed between Group 1 and Group 4. Strong negative correlations were detected between P as well as Mg and fat mass index as well as diagnostic obesity notation model assessment index in Group 4, the group, which comprised morbid obese children with MetS. This study emphasized unique associations of P and Mg minerals with diagnostic obesity notation model assessment index and fat mass index during the evaluation of morbid obese children with MetS. It was also concluded that diagnostic obesity notation model assessment index and fat mass index were more proper indices in comparison with body mass index and fat-free mass index for the purpose of defining body composition in children.

Keywords: children, fat mass, fat-free mass, macrominerals, obesity

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: Dariush Jafari, Seyed Ali Jafari

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The recommended limit for cadmium concentration in potable water is less than 0.005 mg/L. A continuous biosorption process using indigenous red seaweed, Gracilaria corticata, was performed to remove cadmium from the potable water. The process was conducted under fixed conditions and the breakthrough curves were achieved for three consecutive sorption-desorption cycles. A modeling based on Artificial Neural Network (ANN) was employed to fit the experimental breakthrough data. In addition, a simplified semi empirical model, Thomas, was employed for this purpose. It was found that ANN well described the experimental data (R2>0.99) while the Thomas prediction were a bit less successful with R2>0.97. The adjusted design parameters using the nonlinear form of Thomas model was in a good agreement with the experimentally obtained ones. The results approve the capability of ANN to predict the cadmium concentration in potable water.

Keywords: ANN, biosorption, cadmium, packed-bed, potable water

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Authors: M. Caramihai, Irina Severin

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This paper deals with the design of an intelligent control structure for a bioprocess of Hansenula polymorpha yeast cultivation. The objective of the process control is to produce biomass in a desired physiological state. The work demonstrates that the designed Hybrid Control Techniques (HCT) are able to recognize specific evolution bioprocess trajectories using neural networks trained specifically for this purpose, in order to estimate the model parameters and to adjust the overall bioprocess evolution through an expert system and a fuzzy structure. The design of the control algorithm as well as its tuning through realistic simulations is presented. Taking into consideration the synergism of different paradigms like fuzzy logic, neural network, and symbolic artificial intelligence (AI), in this paper we present a real and fulfilled intelligent control architecture with application in bioprocess control.

Keywords: bioprocess, intelligent control, neural nets, fuzzy structure, hybrid techniques

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Authors: Sonia Ben Hamza, Sabra Habli, Nejla Mahjoub Saïd, Hervé Bournot, Georges Le Palec

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This paper spots light on the effect of a point source pollution on streams, stemming out from intentional release caused by unconscious facts. The consequences of such contamination on ecosystems are very serious. Accordingly, effective tools are highly demanded in this respect, which enable us to come across an accurate progress of pollutant and anticipate different measures to be applied in order to limit the degradation of the environmental surrounding. In this context, we are eager to model a pollutant dispersion of a free surface flow which is ejected by an outfall sewer of an urban sewerage network in coastal water taking into account the influence of climatic parameters on the spread of pollutant. Numerical results showed that pollutant dispersion is merely due to the presence of vortices and turbulence. Hence, it was realized that the pollutant spread in seawater is strongly correlated with climatic conditions in this region.

Keywords: coastal waters, numerical simulation, pollutant dispersion, turbulent flows

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Authors: Md, MCS, MPH Munyangi Wa Nkola Jerome

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The pandemic of COVID-19, a recently discovered contagious respiratory disease called SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus 2 Majority of people infected with SARS-CoV-2: Asymptomatic or mildly ill 14% of patients will develop severe illness requiring hospitalization and oxygen support, and 5% of these will be transferred to an intensive care unit, Urgent need for new treatments that can be used quickly to avoid transfer of patients to intensive care and death. Objective: To evaluate the clinical activity (efficacy) of ArtiCovid Hypothesis: Administration of 3 times a teaspoon per day by COVID patients (symptomatic, mild, or moderate forms) results in the disappearance of symptoms and improvement of biological parameters (including viral suppression). Clinical efficacy: the disappearance of clinical signs after seven days of treatment; reduction in the rate of patients transferred to intensive care units for mechanical ventilation and a decrease in mortality related to this infection Paraclinical efficacy: improvement of biological parameters (mainly d-dimer, CRP) Virological efficacy: suppression of the viral load after seven days of treatment (control test on the seventh day is negative) Pilot study using a standardized solution based on Artemisia annua (ARTICOVID) Obtaining authorization from the health authorities of the province of Central Kongo Recruitment of volunteer patients, mainly in the Kinkanda HospitalCarrying out tests before and after treatment as well as analyses before and after treatment. The protocol obtained the approval of the ethics committee 50 patients who completed the treatment were aged between 2 and 70 years, with an average age of 36 yearsMore half were male (56%). One in four patients was a health professional (25%) Of the 12 health professionals, 4 were physicians. For those who reported the date of onset of the disease, the average duration between the appearance of the first symptoms and the medical consultation was 5 days. The 50 patients put on ARTICOVID were discharged alive with CRP levels substantially normalizedAfter seven to eight days, the control test came back negative. This pilot study suggests that ARTICOVID may be effective against COVID-19 infection.

Keywords: artiCovid, DRC, Covid-19, SARS_COV_2

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Authors: Dingyang Hu, Dan Liu

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DNN (Deep Neural Network) deep learning models are widely used in classification, prediction, and other task scenarios. To address the difficulties of generic adversarial perturbation generation for deep learning models under black-box conditions, a generic adversarial ingestion generation method based on a saliency map (CJsp) is proposed to obtain salient image regions by counting the factors that influence the input features of an image on the output results. This method can be understood as a saliency map attack algorithm to obtain false classification results by reducing the weights of salient feature points. Experiments also demonstrate that this method can obtain a high success rate of migration attacks and is a batch adversarial sample generation method.

Keywords: adversarial sample, gradient, probability, black box

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Authors: Parikshit Gogo, N. N. Dutta

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The oxidative enzymes specially laccase (benzenediol: oxygen oxidoreductase, E.C.1.10.3.2) from bacteria, fungi and plants have been playing an important role in green technologies due to their specific advantageous properties. Laccase from different sources and in different forms was used as a biocatalyst in many oxidation and conjugation reactions starting from phenol to hydrocarbons. Tea polyphenols and its derivatives attract the scientific community because of their potential use as antioxidants in food, pharmaceutical and cosmetic industries. Conjugate of polyphenols emerged as a novel materials which shows better stability and antioxidant properties in applied fields. The conjugation reaction of catechin with poly (allylamine) has been studied using free, immobilized and cross-linked enzyme crystals (CLEC) of laccase from Trametes versicolor with particular emphasis on the effect of pertinent variables and kinetic aspects of the reaction. The stability and antioxidant property of the conjugated product was improved as compared to the unconjugated tea polyphenols. The reaction was studied in 11 different solvents in order to deduce the solvent effect through an attempt to correlate the initial reaction rate with solvent properties such as hydrophobicity (logP), water solubility (logSw), electron pair acceptance (ETN) and donation abilities (DNN), polarisibility and dielectric constant which exhibit reasonable correlations. The study revealed, in general that polar solvents favour the initial reaction rate. The kinetics of the conjugation reaction conformed to the so-called Ping-Pong-Bi-Bi mechanism with catechin inhibition. The stability as well as activity of the CLEC was better than the free enzymes and immobilized laccase for practical application. In case of immobilized laccase system marginal diffusional limitation could be inferred from the experimental data. The kinetic parameters estimated by non-linear regression analysis were found to be KmPAA(mM) = 0.75, 1.8967 and Kmcat (mM) = 11.769, 15.1816 for free and immobilized laccase respectively. An attempt has been made to assess the activity of the laccase for the conjugation reaction in relation to other reactions such as dimerisation of ferulic acids and develop a protocol to enhance polyphenol antioxidant activity.

Keywords: laccase, catechin, conjugation reaction, antioxidant properties

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Authors: Sanja Jovanovic, Aleksandar Simic, Ognjan Skrobic, Dragan Masulovic, Aleksandra Djuric-Stefanovic

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Introduction: Achalasia is an idiopathic primary esophageal motility disorder characterized by esophageal peristalsis and impaired swallow-induced relaxation of the lower esophageal sphincter (LES). It is a rare disease that affects both genders with an incidence of 1/100.000 and a prevalence rate of 10/100,000 per year. Objective: Laparoscopic Heller myotomy (LHM) represents a therapy of choice for patients with achalasia, providing excellent outcomes. The aim of this study was to evaluate the significance of computed tomography (CT) in analyzing achalasia subtypes and timed barium esophagogram (TBE) in evaluation of LHM success, as a part of standardized diagnostic protocol. Method: Fifty-one patients with achalasia, confirmed by manometric studies, in addition to standardized diagnostic methods, underwent CT and TBE. CT was done with multiplanar reconstruction, measuring the wall thickness above the esophago-gastric junction in the axial plane. TBE was performed preoperatively and two days postoperatively swallowing low-density barium sulfate, and plane upright frontal films were performed 1, 2 and 5 minutes after the ingestion. In all patients, LHM was done, and pre and postoperative height and weight of the barium column were compared. Results: According to CT findings we divided patients into 3 subtypes of achalasia according to wall thickness: < 4mm as subtype one, between 4 - 9mm as II, and > 10 mm as subtype 3. Correlation of manometric results, as a reference values, and CT findings indicated CT sensitivity of 90% and specificity of 70 % in establishing subtypes of achalasia. The preoperative values of TBE at 1, 2 and 5 minutes were: median barium column height 17.4 ± 7.4, 15.9 ± 6.2 and 13.9 ± 6.2 cm; median column width 5 ± 1.5, 4.7 ± 1.6 and 4.5 ± 1.8 cm respectively. LHM significantly reduced these values (height 7 ± 4.6, 5.8 ± 4.2, 3.7 ± 3.4 cm; width 2.9 ± 1.3, 2.6 ± 1.3 and 2.4 ± 1.4 cm), indicating the quantitative estimates of emptying as excellent (p value < 0.01). Conclusion: CT has high sensitivity and specificity in evaluation of achalasia subtypes, and can be introduced as an additional method for standardized evaluation of these patients. The quantitative assessment of TBE based on measurements of the barium column is an accurate and beneficial method, which adequately estimates esophageal emptying success of LHM.

Keywords: achalasia, computed tomography, esophagography, myotomy

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Authors: Avani Jain, S. Shelley, M. Indirani, Shilpa Kalal, Jaykanth Amalachandran

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Aim: To evaluate the prevalence of gall bladder dysfunction in patients with dyspepsia using In-House fatty meal cholescintigraphy. Materials & Methods: This study is a prospective cohort study. 59 healthy volunteers with no dyspeptic complaints and negative ultrasound and endoscopy were recruited in study. 61 patients having complaint of dyspepsia for duration of more than 6 months were included. All of them underwent 99mTc-Mebrofenin fatty meal cholescintigraphy following a standard protocol. Dynamic acquisitions were acquired for 120 minutes with an In-House fatty meal being given at 45th minute. Gall bladder emptying kinetics was determined with gall bladder ejection fractions (GBEF) calculated at 30minutes, 45minutes and at 60 minutes (30min, 45min & 60 min). Standardization of fatty meal was done for volunteers. Receiver operating characteristic (ROC) analysis was used assess the diagnostic accuracy of 3 time points (30min, 45min & 60 min) used for measuring gall bladder emptying. On the basis of cut off derived from volunteers, the patients were assessed for gall bladder dysfunction. Results: In volunteers, the GBEF at 30 min was 74.42±8.26 % (mean ±SD), at 45 min was 82.61 ± 6.5 % and at 60 min was 89.37±4.48%, compared to patients where at 30min it was 33.73±22.87%, at 45 min it was 43.03±26.97% and at 60 min it was 51.85±29.60%. The lower limit of GBEF in volunteers at 30 min was 60%, 45 min was 69% and at 60 min was 81%. ROC analysis showed that area under curve was largest for 30 min GBEF (0.952; 95% CI = 0.914-0.989) and that all the 3 measures were statistically significant (p < 0.005). Majority of the volunteers had 74% of gall bladder emptying by 30 minutes; hence it was taken as an optimum cutoff time to assess gall bladder contraction. > 60% GBEF at 30 min post fatty meal was considered as normal and < 60% GBEF as indicative of gall bladder dysfunction. In patients, various causes for dyspepsia were identified: GB dysfunction (63.93%), Peptic ulcer (8.19 %), Gastroesophageal reflux disease (8.19%), Gastritis (4.91%). In 18.03% of cases GB dysfunction coexisted with other gastrointestinal conditions. The diagnosis of functional dyspepsia was made in 14.75% of cases. Conclusions: Gall bladder dysfunction contributes significantly to the causation of dyspepsia. It could coexist with various other gastrointestinal diseases. Fatty meal was well tolerated and devoid of any side effects. Many patients who are labeled as functional dyspeptics could actually have gall bladder dysfunction. Hence as an adjunct to ultrasound and endoscopy, fatty meal cholescintigraphy can also be used as a screening modality in characterization of dyspepsia.

Keywords: in-house fatty meal, choescintigraphy, dyspepsia, gall bladder ejection fraction, functional dyspepsia

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Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

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Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: causal realtion extraction, relation extracton, convolutional neural network, text representation

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Authors: Kainat Affrin, P. Reshma, G. Narendra Kumar

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Optimization of timetable is the need of the day for the rescheduling and routing of trains in real time. Trains are scheduled in parallel with the road transport vehicles to the same destination. As the number of trains is restricted due to single track, customers usually opt for road transport to use frequently. The air pollution increases as the density of vehicles on road transport is increased. Use of an alternate mode of transport like train helps in reducing air-pollution. This paper mainly aims at attracting the passengers to Train transport by proper rescheduling of trains using hybrid of stop-skip algorithm and iterative convex programming algorithm. Rescheduling of train bi-directionally is achieved on a single track with dynamic dual time and varying stops. Introduction of more trains attract customers to use rail transport frequently, thereby decreasing the pollution. The results are simulated using Network Simulator (NS-2).

Keywords: air pollution, AODV, re-scheduling, WSNs

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Authors: Matthew Cameron, Stephen Yang, Latifa Al Kharusi, Adam Gosselin, Anissa Chirico, Pouya Gholipour Baradari

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Background: Iron supplementation has been evaluated in several randomized controlled trials (RCTs) for the potential to increase baseline hemoglobin and decrease the incidence of red blood cell (RBC) transfusion during cardiac surgery. This study's main objective was to evaluate the evidence for iron administration in cardiac surgery patients for its effect on the incidence of perioperative RBC transfusion. Methods: This systematic review protocol was registered with PROSPERO (CRD42020161927) on Dec. 19th, 2019, and was prepared as per the PRISMA guidelines. MEDLINE, EMBASE, CENTRAL, Web of Science databases, and Google Scholar were searched for RCTs evaluating perioperative iron administration in adult patients undergoing cardiac surgery. Each abstract was independently reviewed by two reviewers using predefined eligibility criteria. The primary outcome was perioperative RBC transfusion, with secondary outcomes of the number of RBC units transfused, change in ferritin level, reticulocyte count, hemoglobin, and adverse events, after iron administration. The risk of bias was assessed with the Cochrane Collaboration Risk of Bias Tool, and the primary and secondary outcomes were analyzed with a random-effects model. Results: Out of 1556 citations reviewed, five studies (n = 554 patients) met the inclusion criteria. The use of iron demonstrated no difference in transfusion incidence (RR 0.86; 95% CI 0.65 to 1.13). There was a low heterogeneity between studies (I²=0%). The trial sequential analysis suggested an optimal information size of 1132 participants, which the accrued information size did not reach. Conclusion: The current literature does not support the routine use of iron supplementation before cardiac surgery; however, insufficient data is available to draw a definite conclusion. A critical knowledge gap has been identified, and more robust RCTs are required on this topic.

Keywords: cardiac surgery, iron, iron supplementation, perioperative medicine, meta-analysis, systematic review, randomized controlled trial

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Authors: Anisuzzaman Khan, A. J. K. Masud

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Bangladesh is the largest delta in the world predominantly comprising large network of rives and wetlands. Wetlands in Bangladesh are represented by inland freshwater, estuarine brakishwater and tidal salt-water coastal wetlands. Bangladesh possesses enormous area of wetlands including rivers and streams, freshwater lakes and marshes, haors, baors, beels, water storage reservoirs, fish ponds, flooded cultivated fields and estuarine systems with extensive mangrove swamps. The past, present, and future of Bangladesh, and its people’s livelihoods are intimately connected to its relationship with water and wetlands. More than 90% of the country’s total area consists of alluvial plains, crisscrossed by a complex network of rivers and their tributaries. Floodplains, beels (low-lying depressions in the floodplain), haors (deep depression) and baors (oxbow lakes) represent the inland freshwater wetlands. Over a third of Bangladesh could be termed as wetlands, considering rivers, estuaries, mangroves, floodplains, beels, baors and haors. The country’s wetland ecosystems also offer critical habitats for globally significant biological diversity. Of these the deeply flooded basins of north-east Bangladesh, known as haors, are a habitat of wide range of wild flora and fauna unique to Bangladesh. The haor basin lies within the districts of Sylhet, Sunamgonj, Netrokona, Kishoregonj, Habigonj, Moulvibazar, and Brahmanbaria in the Northeast region of Bangladesh comprises the floodplains of the Meghna tributaries and is characterized by the presence of numerous large, deeply flooded depressions, known as haors. It covers about around 8,568 km2 area of Bangladesh. The topography of the region is steep at around foothills in the north and slopes becoming mild and milder gradually at downstream towards south. Haor is a great reservoir of aquatic biological resources and acts as the ecological safety net to the nature as well as to the dwellers of the haor. But in reality, these areas are considered as wastelands and to make these wastelands into a productive one, a one sided plan has been implementing since long. The programme is popularly known as Flood Control, Drainage and Irrigation (FCDI) which is mainly devoted to increase the monoculture rice production. However, haor ecosystem is a multiple-resource base which demands an integrated sustainable development approach. The ongoing management approach is biased to only rice production through FCDI. Thus this primitive mode of action is diminishing other resources having more economic potential ever thought.

Keywords: freshwater wetlands, biological diversity, biological resources, conservation and sustainable development

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Authors: El Asnaoui Khalid, Chawki Youness, Aksasse Brahim, Ouanan Mohammed

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In these recent decades, the important and fast growth in the development and demand of multimedia products is contributing to an insufficient in the bandwidth of device and network storage memory. Consequently, the theory of data compression becomes more significant for reducing the data redundancy in order to save more transfer and storage of data. In this context, this paper addresses the problem of the lossless and the near-lossless compression of images. This proposed method is based on Block SVD Power Method that overcomes the disadvantages of Matlab's SVD function. The experimental results show that the proposed algorithm has a better compression performance compared with the existing compression algorithms that use the Matlab's SVD function. In addition, the proposed approach is simple and can provide different degrees of error resilience, which gives, in a short execution time, a better image compression.

Keywords: image compression, SVD, block SVD power method, lossless compression, near lossless

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Authors: Kai Zhu, Shanhua He, Yujiao Chang

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This paper explores the virtual linguistic landscape of an emerging virtual language community in TikTok, a language community realizing immediate and non-immediate communication without a precise Spatio-temporal domain or a specific socio-cultural boundary or interpersonal network. This kind of language community generates a large number and various forms of virtual linguistic landscape, with which we conducted a virtual ethnographic survey together with telephone interviews to collect data from coping. We have been following two language communities in TikTok for several months so that we can illustrate the composition of the two language communities and some typical virtual language landscapes in both language communities first. Then we try to explore the reasons why and how they are formed through the organization, transcription, and analysis of the interviews. Our analysis reveals the richness and diversity of the virtual linguistic landscape, and finally, we summarize some of the characteristics of this language community.

Keywords: virtual linguistic landscape, virtual language community, virtual ethnographic survey, TikTok

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Authors: Chih-Fong Tsai, Ya-Han Hu

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The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets.

Keywords: data mining, feature selection, pattern classification, dimensionality reduction

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Authors: Tsu-Wang Shen, Shan-Chun Chang, Chih-Hsien Wang, Te-Chao Fang

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For electrocardiogram (ECG) biometrics system, it is a tedious process to pre-install user’s high-intensity heart rate (HR) templates in ECG biometric systems. Based on only resting enrollment templates, it is a challenge to identify human by using ECG with the high-intensity HR caused from exercises and stress. This research provides a heartbeat segment method with slope-oriented neural networks against the ECG morphology changes due to high intensity HRs. The method has overall system accuracy at 97.73% which includes six levels of HR intensities. A cumulative match characteristic curve is also used to compare with other traditional ECG biometric methods.

Keywords: high-intensity heart rate, heart rate resistant, ECG human identification, decision based artificial neural network

Procedia PDF Downloads 432