Search results for: disease forecast

Authors: M. A. Sohaly, M. A. Elfouly

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Parkinson's disease (PD) is a heterogeneous movement disorder that often appears in the elderly. PD is induced by a loss of dopamine secretion. Some drugs increase the secretion of dopamine. In this paper, we will simply study the stability of PD models as a nonlinear delay differential equation. After a period of taking drugs, these act as positive feedback and increase the tremors of patients, and then, the differential equation has positive coefficients and the system is unstable under these conditions. We will present a set of suggested modifications to make the system more compatible with the biodynamic system. When giving a set of numerical examples, this research paper is concerned with the mathematical analysis, and no clinical data have been used.

Keywords: Parkinson's disease, stability, simulation, two delay differential equation

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Authors: Surachai Ampawasuvan, Supornchai Utainarumol

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Most of fuel station’ customers, who drive on the major arterial road wants to use the stations to fill fuel to their vehicle during their journey to destinations. According to the survey of traffic volume of the vehicle using fuel stations by video cameras, automatic counting tools, or questionnaires, it was found that most users prefer to use fuel stations on holiday rather than on working day. They also prefer to use fuel stations in the morning rather than in the evening. When comparing the ratio of the distribution pattern of traffic volume of the vehicle using fuel stations by video cameras, automatic counting tools, there is no significant difference. However, when comparing the ratio of peak hour (peak hour rate) of the results from questionnaires at 13 to 14 percent with the results obtained by using the methods of the Institute of Transportation Engineering (ITE), it is found that the value is similar. However, it is different from a survey by video camera and automatic traffic counting at 6 to 7 percent of about half. So, this study suggests that in order to forecast trip generation of vehicle using fuel stations on major arterial road which is mostly characterized by Though Traffic, it is recommended to use the value of half of peak hour rate, which would make the forecast for trips generation to be more precise and accurate and compatible to surrounding environment.

Keywords: peak rate, trips generation, fuel station, arterial road

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Authors: Amin Mehrabi, Mohsen Salesi, Pourya Pasavand

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Context and objective: The purpose of this research is to study the effect of exercise time (morning/evening) on amount of Troponin T in males' plasma suffering from cardiovascular disease. Method: 15 cardiovascular patients selected as the research subjects. At 7 a.m. pretest blood samples taken from the subjects and they did the exercise protocol in presence of a doctor. Immediately after and 3 hours after that blood measurements done. A week later, the subjects did the same steps at 7 p.m. The SPSS v.20 software used to analyze data. Findings: This study proved that circadian rhythm does not have any effect on the response of myocarditis tissue to exercise and cardiovascular patients allowed to exercise in any times of a day.

Keywords: cardiovascular disease, time of exercise, troponin T (cTnT), myocarditis

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Authors: Ahmed M. AlHasem

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The Kuwaiti society has long been aware of climatic changes and their annual dates and trying to link them to astronomy in an attempt to forecast the future weather conditions. The reason for this concern is that many of the economic, social and living activities of the society depend deeply on the nature of the weather conditions directly and indirectly. In other words, Kuwaiti society, like the case of many human societies, has in the past tried to predict climatic conditions by linking them to astronomy or popular statements to indicate the timing of climate changes. Accordingly, this study was devoted to scientific investigation based on the statistical analysis of climatic data to show the accuracy and compatibility of some of the most important elements of the cultural heritage in relation to climate change and to relate it scientifically to precise climatic measurements for decades. The research has been divided into 10 topics, each topic has been focused on one legacy, whether by linking climate changes to the appearance/disappearance of star or a popular statement inherited through generations, through explain the nature and timing and thereby statistical analysis to indicate the proportion of accuracy based on official climatic data since 1962. The study's conclusion is that the relationship is weak and, in some cases, non-existent between the popular heritage and the actual climatic data. Therefore, it does not have a dependable relationship and a reliable scientific prediction between both the popular heritage and the forecast of weather conditions.

Keywords: astronomy, cultural heritage, statistical analysis, weather prediction

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Authors: Fernando L. P. Santos, Luiz G. Lyra, Helenice O. Florentino, Daniela R. Cantane

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Dengue is a febrile infectious disease caused by a virus of the family Flaviridae. It is transmitted by the bite of mosquitoes, usually of the genus Aedes aegypti. It occurs in tropical and subtropical areas of the world. This disease has been a major public health problem worldwide, especially in tropical countries such as Brazil, and its incidence has increased in recent years. Dengue is a subject of intense research. Efficient forms of mosquito control must be considered. In this work, the mono-objective optimal control problem was solved for analysing the dengue disease problem. Chemical and biological controls were considered in the mathematical aspect. This model describes the dynamics of mosquitoes in water and winged phases. We applied the genetic algorithms (GA) to obtain optimal strategies for the control of dengue. Numerical simulations have been performed to verify the versatility and the applicability of this algorithm. On the basis of the present results we may recommend the GA to solve optimal control problem with a large region of feasibility.

Keywords: genetic algorithm, dengue, Aedes aegypti, biological control, chemical control

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Authors: Sultan Mahmud, Qamar Zaman, Travis Esau, Young Chang

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Strawberry powdery mildew (PM) is a serious disease that has a significant impact on strawberry production. Field scouting is still a major way to find PM disease, which is not only labor intensive but also almost impossible to monitor disease severity. To reduce the loss caused by PM disease and achieve faster automatic detection of the disease, this paper proposes an approach for detection of the disease, based on image texture and classified with support vector machines (SVMs) and k-nearest neighbors (kNNs). The methodology of the proposed study is based on image processing which is composed of five main steps including image acquisition, pre-processing, segmentation, features extraction and classification. Two strawberry fields were used in this study. Images of healthy leaves and leaves infected with PM (Sphaerotheca macularis) disease under artificial cloud lighting condition. Colour thresholding was utilized to segment all images before textural analysis. Colour co-occurrence matrix (CCM) was introduced for extraction of textural features. Forty textural features, related to a physiological parameter of leaves were extracted from CCM of National television system committee (NTSC) luminance, hue, saturation and intensity (HSI) images. The normalized feature data were utilized for training and validation, respectively, using developed classifiers. The classifiers have experimented with internal, external and cross-validations. The best classifier was selected based on their performance and accuracy. Experimental results suggested that SVMs classifier showed 98.33%, 85.33%, 87.33%, 93.33% and 95.0% of accuracy on internal, external-I, external-II, 4-fold cross and 5-fold cross-validation, respectively. Whereas, kNNs results represented 90.0%, 72.00%, 74.66%, 89.33% and 90.3% of classification accuracy, respectively. The outcome of this study demonstrated that SVMs classified PM disease with a highest overall accuracy of 91.86% and 1.1211 seconds of processing time. Therefore, overall results concluded that the proposed study can significantly support an accurate and automatic identification and recognition of strawberry PM disease with SVMs classifier.

Keywords: powdery mildew, image processing, textural analysis, color co-occurrence matrix, support vector machines, k-nearest neighbors

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Ana Corberan-Vallet, Karen C. Florez, Ingrid C. Marino, Jose D. Bermudez

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In 2016, the Region of the Americas was declared free of measles, a viral disease that can cause severe health problems. However, since 2017, measles has reemerged in Venezuela and has subsequently reached neighboring countries. In 2018, twelve American countries reported confirmed cases of measles. Governmental and health authorities in Colombia, a country that shares the longest land boundary with Venezuela, are aware of the need for a strong response to restrict the expanse of the epidemic. In this work, we apply a Bayesian hierarchical Poisson model with an underlying cluster structure to describe disease incidence in Colombia. Concretely, the proposed methodology provides relative risk estimates at the department level and identifies clusters of disease, which facilitates the implementation of targeted public health interventions. Socio-demographic factors, such as the percentage of migrants, gross domestic product, and entry routes, are included in the model to better describe the incidence of disease. Since the model does not impose any spatial correlation at any level of the model hierarchy, it avoids the spatial confounding problem and provides a suitable framework to estimate the fixed-effect coefficients associated with spatially-structured covariates.

Keywords: Bayesian analysis, cluster identification, disease mapping, risk estimation

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Authors: Nattapong Techarattanased

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The purposes of this research are to make comparisons in respect of the behaviors on the use of the services of metered taxi classified by the demographic factor and to study the influence of the recognition on service quality having the effect on usage behaviors of metered taxi services of consumers in Bangkok Metropolitan Areas. The samples used in this research are 400 metered taxi service users in Bangkok Metropolitan Areas and use a questionnaire as the tool for collecting the data. Analysis statistics is mean and multiple regression analysis. Results of the research revealed that the consumers recognize the overall quality of services in each aspect include tangible aspects of the service, responses to customers, assurance on the confidence, understanding and knowing of customers which is rated at the moderate level except the aspect of the assurance on the confidence and trustworthiness which are rated at a high level. For the result of a hypothetical test, it is found that the quality in providing the services on the aspect of the assurance given to the customers has the effect on the usage behaviors of metered taxi services and the aspect of the frequency on the use of the services per month which in this connection. Such variable can forecast at one point nine percent (1.9%). In addition, quality in providing the services and the aspect of the responses to customers have the effect on the behaviors on the use of metered taxi services on the aspect of the expenses on the use of services per month which in this connection, such variable can forecast at two point one percent (2.1%).

Keywords: consumer behavior, metered taxi service, satisfaction, service quality

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Authors: Rania A. R. Soudan, Easter Joury

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Introduction: Psoriasis is a common chronic dermatologic disease. It may affect the mucous membranes. The presence of oral mucosal lesions has been a subject of controversy. The aim: To determine possible association between oral mucosal lesions and psoriasis, and to correlate the same with different types of psoriasis and severity of the disease. Materials and Methods: The oral mucosa was clinically examined in 100 randomly selected Syrian psoriatic patients presented to the Dermatological Diseases Hospital in Damascus University, Syria (February 2009 - December 2010), and in 100 matched controls. PASI index was used to evaluate the disease severity. Chi-square and Student t-test were used to compare differences between groups. Results: Oral mucosal lesions were observed in 72% of the psoriasis cases, while 46% of the control group’s subjects had oral lesions. Fissured tongue, geographic tongue, and red lesions were detected in 36%, 25%, and 7% of the examined psoriatics, respectively. These lesions were significantly more frequent in the psoriatics than in the controls. A correlation was found between furred tongue and the age of the psoriasis patients. However, an association was observed for fissured tongue, furred tongue with the severity of the disease, and for fissured tongue, white lesions, cheilitis with nail involvement. However, no correlation with the psoriasis types was recorded. Conclusion: Some oral mucosal lesions were associated with psoriasis, so these lesions may be considered as oral manifestations of this disease, and should be taken into account in new studies as possible predictors or markers of this dermatitis. Further studies are recommended to confirm these oral manifestations.

Keywords: psoriasis, tongue, mucosa, lesions

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Authors: Septiawan A. Saputro, Purnami Widyaningsih, Sutanto Sastraredja

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Measles is a disease which can spread caused by a virus and has been a priority’s Ministry of Health in Indonesia to be solved. Each infected person can be recovered and get immunity so that the spread of the disease can be constructed with susceptible infected recovered (SIR). To prevent the spread of measles transmission, the Ministry of Health holds vaccinations program. The aims of the research are to derive susceptible vaccinated infected recovered (SVIR) model, to determine the patterns of disease spread with SVIR model, and also to apply the SVIR model on the spread of measles in Indonesia. Based on the article, it can be concluded that the spread model of measles with vaccinations, that is SVIR model. It is a first-order differential equation system. The patterns of disease spread is determined by solution of the model. Based on that model Indonesia will be a measles-free nation in 2186 with the average of vaccinations scope about 88% and the average score of vaccinations failure about 4.9%. If it is simulated as Ministry of Health new programs with the average of vaccinations scope about 95% and the average score of vaccinations failure about 3%, then Indonesia will be a measles-free nation in 2184. Even with the average of vaccinations scope about 100% and no failure of vaccinations, Indonesia will be a measles-free nation in 2183. Indonesia’s target as a measles-free nation in 2020 has not been reached.

Keywords: measles, vaccination, susceptible infected recovered (SIR), susceptible vaccinated infected recovered (SVIR)

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Authors: Eyup Ozkan, Ozkan U. Nalbantoglu, Aycan Gundogdu, Mehmet Hora, A. Emre Onuk

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The human microbiome has been associated with cardiological conditions and this relationship is becoming to be defined beyond the gastrointestinal track. In this study, we investigate the alteration in circulatory microbiota in the context of Coronary Artery Disease (CAD). We received circulatory blood samples from suspected CAD patients and maintain 16S ribosomal RNA sequencing to identify each patient’s microbiome. It was found that Corynebacterium and Methanobacteria genera show statistically significant differences between healthy and CAD patients. The overall biodiversities between the groups were observed to be different revealed by machine learning classification models. We also achieve and demonstrate the performance of a diagnostic method using circulatory blood microbiome-based estimation.

Keywords: coronary artery disease, blood microbiome, machine learning, angiography, next-generation sequencing

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Authors: H. C. Chinwenyi, H. D. Ibrahim, J. O. Adekunle

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The incubation period is defined as the time from infection with a microorganism to development of symptoms. In this research, two disease models: one with incubation period and another without incubation period were studied. The study involves the use of a  mathematical model with a single incubation period. The test for the existence and stability of the disease free and the endemic equilibrium states for both models were carried out. The fourth order Runge-Kutta method was used to solve both models numerically. Finally, a computer program in MATLAB was developed to run the numerical experiments. From the results, we are able to show that the endemic equilibrium state of the model with incubation period is locally asymptotically stable whereas the endemic equilibrium state of the model without incubation period is unstable under certain conditions on the given model parameters. It was also established that the disease free equilibrium states of the model with and without incubation period are locally asymptotically stable. Furthermore, results from numerical experiments using empirical data obtained from Nigeria Centre for Disease Control (NCDC) showed that the overall population of the infected people for the model with incubation period is higher than that without incubation period. We also established from the results obtained that as the transmission rate from susceptible to infected population increases, the peak values of the infected population for the model with incubation period decrease and are always less than those for the model without incubation period.

Keywords: asymptotic stability, Hartman-Grobman stability criterion, incubation period, Routh-Hurwitz criterion, Runge-Kutta method

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Authors: Yishu Gong, Liangliang Yang, Jianyu Zhang, Zhengyu Chen, Sihong He, Xusheng Zhang, Wei Zhang

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide and is characterized by cognitive decline and behavioral changes. People living with Alzheimer’s disease often find it hard to complete routine tasks. However, there are limited objective assessments that aim to quantify the difficulty of certain tasks for AD patients compared to non-AD people. In this study, we propose to use speech emotion recognition (SER), especially the frustration level, as a potential biomarker for quantifying the difficulty patients experience when describing a picture. We build an SER model using data from the IEMOCAP dataset and apply the model to the DementiaBank data to detect the AD/non-AD group difference and perform longitudinal analysis to track the AD disease progression. Our results show that the frustration level detected from the SER model can possibly be used as a cost-effective tool for objective tracking of AD progression in addition to the Mini-Mental State Examination (MMSE) score.

Keywords: Alzheimer’s disease, speech emotion recognition, longitudinal biomarker, machine learning

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Authors: Lu Anne A. Godinez, May Claudine I. Gador, Preacious G. Gumolon, Louiechi Von R. Mendoza, Neil Bryan N. Moninio

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Gender equality and women empowerment is the third of eight Millennium Development Goals. Understanding corruption’s linkages to gender equality issues and how it impacts women’s empowerment is part of the broader process of advancing women’s rights and understanding the gender dimensions of democratic governance. Taking a long view of political (corruption index) and the social (women empowerment) dimension — a view from 2015 to 2030, a context based forecast was conducted to forecast the ASEAN corruption index in the next 15 years, answering the question: “Does women political involvement decrease corruption rate index of ASEAN countries in the next 15 years?” The study have established that there will be an increase women political involvement in the ASEAN countries in the next 15 years that will cause a drop on corruption rate index. There will be a significant decline on corruption rate index in 2030. This change entails reform not only in the political aspect of progress, but to the social aspect as well. Finally, the political aspect is increasing at a constant rate however a double or triple increase of the social aspect is seen to be the key solution for corruption.

Keywords: women, women political involvement, corruption, gender equity index, economic participation, educational attainment, political empowerment, control of corruption, regulatory quality, rule of law, voice and accountability government effectiveness, political stability and corruption perception index

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Authors: Diana C. El Assal, Fatima Monteiro, Caroline May, Peter Barbuti, Silvia Bolognin, Averina Nicolae, Hulda Haraldsdottir, Lemmer R. P. El Assal, Swagatika Sahoo, Longfei Mao, Jens Schwamborn, Rejko Kruger, Ines Thiele, Kathrin Marcus, Ronan M. T. Fleming

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Degeneration of substantia nigra pars compacta dopaminergic neurons is one of the hallmarks of Parkinson's disease. These neurons have a highly complex axonal arborisation and a high energy demand, so any reduction in ATP synthesis could lead to an imbalance between supply and demand, thereby impeding normal neuronal bioenergetic requirements. Synaptic mitochondria exhibit increased vulnerability to dysfunction in Parkinson's disease. After biogenesis in and transport from the cell body, synaptic mitochondria become highly dependent upon oxidative phosphorylation. We applied a systems biochemistry approach to identify the metabolic pathways used by neuronal mitochondria for energy generation. The mitochondrial component of an existing manual reconstruction of human metabolism was extended with manual curation of the biochemical literature and specialised using omics data from Parkinson's disease patients and controls, to generate reconstructions of synaptic and somal mitochondrial metabolism. These reconstructions were converted into stoichiometrically- and fluxconsistent constraint-based computational models. These models predict that Parkinson's disease is accompanied by an increase in the rate of glycolysis and a decrease in the rate of oxidative phosphorylation within synaptic mitochondria. This is consistent with independent experimental reports of a compensatory switching of bioenergetic pathways in the putamen of post-mortem Parkinson's disease patients. Ongoing work, in the context of the SysMedPD project is aimed at computational prediction of mitochondrial drug targets to slow the progression of neurodegeneration in the subset of Parkinson's disease patients with overt mitochondrial dysfunction.

Keywords: bioenergetics, mitochondria, Parkinson's disease, systems biochemistry

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Authors: Hami Ashraf, Farid Kosari

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Graft-versus-host disease (GvHD) is a common complication of allogeneic hematopoietic stem cell transplantation that can lead to significant morbidity and mortality. Histopathological examination of affected tissues is an essential tool for diagnosing and grading GvHD in animal models, which are used to study disease mechanisms and evaluate new therapies. In this systematic review, we identified and analyzed original research articles in PubMed, Scopus, Web of Science, and Google Scholar that described grading systems for GvHD in animal models based on histopathological features. We found that several grading systems have been developed, which vary in the tissues and criteria they assess, the severity scoring scales they use, and the level of detail they provide. Skin, liver, and gut are the most commonly evaluated tissues, but lung and thymus are also included in some systems. Our analysis highlights the need for standardized criteria and consistent use of grading systems to enable comparisons between studies and facilitate the translation of preclinical findings to clinical practice.

Keywords: graft-versus-host disease, GvHD, animal model, histopathology, grading system

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Authors: Kostas Kouvaris, Anastasia Eleftheriou, Georgios A. Sarantitis, Apostolos Chondronasios

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To achieve the objective of almost zero carbon energy solutions by 2050, the EU needs to accelerate the development of integrated, highly efficient and environmentally friendly solutions. In this direction, district heating and cooling (DHC) emerges as a viable and more efficient alternative to conventional, decentralized heating and cooling systems, enabling a combination of more efficient renewable and competitive energy supplies. In this paper, we develop a forecasting tool for near real-time local weather and thermal energy demand predictions for an entire DHC network. In this fashion, we are able to extend the functionality and to improve the energy efficiency of the DHC network by predicting and adjusting the heat load that is distributed from the heat generation plant to the connected buildings by the heat pipe network. Two case-studies are considered; one for Vransko, Slovenia and one for Montpellier, France. The data consists of i) local weather data, such as humidity, temperature, and precipitation, ii) weather forecast data, such as the outdoor temperature and iii) DHC operational parameters, such as the mass flow rate, supply and return temperature. The external temperature is found to be the most important energy-related variable for space conditioning, and thus it is used as an external parameter for the energy demand models. For the development of the forecasting tool, we use state-of-the-art deep neural networks and more specifically, recurrent networks with long-short-term memory cells, which are able to capture complex non-linear relations among temporal variables. Firstly, we develop models to forecast outdoor temperatures for the next 24 hours using local weather data for each case-study. Subsequently, we develop models to forecast thermal demand for the same period, taking under consideration past energy demand values as well as the predicted temperature values from the weather forecasting models. The contributions to the scientific and industrial community are three-fold, and the empirical results are highly encouraging. First, we are able to predict future thermal demand levels for the two locations under consideration with minimal errors. Second, we examine the impact of the outdoor temperature on the predictive ability of the models and how the accuracy of the energy demand forecasts decreases with the forecast horizon. Third, we extend the relevant literature with a new dataset of thermal demand and examine the performance and applicability of machine learning techniques to solve real-world problems. Overall, the solution proposed in this paper is in accordance with EU targets, providing an automated smart energy management system, decreasing human errors and reducing excessive energy production.

Keywords: machine learning, LSTMs, district heating and cooling system, thermal demand

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Authors: P. S. Akami, H. Nahunnaro, A. Zubainatu

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Cercospora leaf spot (Cercospora sesami. Zimm) has been identified as one of the most prevalent diseases, posing serious constraints to sesame production in producing areas. Two sets of experiments were carried out. The first and second experiments were conducted in the Modibbo Adama University of Technology Yola at the Crop Production and Horticulture and Plant Science Departments, respectively. The field experiment was carried out using a Randomized Complete Block Design and was replicated three times on a plot size of 4m x 5m with four sesame varieties and three Mancob-M fungicide levels (0g, 2g and 4g) to give a total of Twelve treatments. The laboratory experiment involved the isolation of the pathogens from diseased leaves with symptoms of Cercospora leaf spot, which was identified as Cercospora sesami. Data collected were subjected to analysis of variance for a randomized complete block design using SAS (1999) statistical package. The treatment means that are significantly different were separated using the Least Significant Difference at P=0.05. The result revealed that 4g Mancob M recorded the lowest mean value for disease incidence and severity at 8WAS, which was 90.30% and 35.60%, respectively, while the control (0g) recorded the highest mean value for disease incidence and severity at 90.30% and 59.80% respectively. Ex-Sudan recorded the lowest value of 720 kg/ha, while NCRIBEN 03 recorded the highest yield of 834 kg/ha-¹. For the concentrations, 2g recorded a higher yield of 843 kg/ha-¹ followed by 0g, which recorded 765 kg/ha-¹. Conclusively, Cercospora leaf spot of sesame was found to be prevalent. E8 has a higher resistance to the disease, while NCRIBEN 03 tends to be more susceptible. It is therefore recommended that further trials should be carried out using different varieties in different locations.

Keywords: disease, evaluation, prevalence, treatment, resistance

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Authors: Duanghathai Saipinta, Tanittian Panyamongkol, Witaya Suriyasathaporn

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Animal management aims to provide a suitable environment for animals allowing maximal productivity in those animals. Prevention of disease is an important part of animal management. Foot-and-mouth disease (FMD) is a highly contagious viral disease in cattle and is an economically important animal disease worldwide. Monitoring the FMD virus in farms is useful management for the prevention of the FMD outbreak. A recent publication indicated collection samples from nasal swabs can be used for monitoring FMD in symptomatic cows. Therefore, the objectives of this study were to determine the FMD virus in asymptomatic dairy cattle using nasal swab samples during the absence of an FMD outbreak. The study was conducted from December 2020 to June 2021 using 185 asymptomatic signs of FMD dairy cattle in Chiang Mai Province, Thailand. By random cow selection, nasal mucosal swabs were used to collect samples from the selected cows and then were to evaluate the presence of FMD viruses using the real-time rt-PCR assay. In total, 4.9% of dairy cattle detected FMD virus, including 2 dairy farms in Mae-on (8 samples; 9.6%) and 1 farm in the Chai-Prakan district (1 sample; 1.2%). Interestingly, both farms in Mae-on were the outbreak of the FMD after this detection for 6 months. This indicated that the FMD virus presented in asymptomatic cattle might relate to the subsequent outbreak of FMD. The outbreak demonstrates the presence of the virus in the environment. In conclusion, monitoring of FMD can be performed by nasal swab collection. Further investigation is needed to show whether the FMD virus presented in asymptomatic FMD cattle could be the cause of the subsequent FMD outbreak or not.

Keywords: cattle, foot-and-mouth disease, nasal swab, real-time rt-PCR assay

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Authors: Corentin Jankowiak, Aggelos Zacharopoulos, Caterina Brandoni

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In a context of increasing stress put on the electricity network by the decarbonization of many sectors, energy storage is likely to be the key mitigating element, by acting as a buffer between production and demand. In particular, the highest potential for storage is when connected closer to the loads. Yet, low voltage storage struggles to penetrate the market at a large scale due to the novelty and complexity of the solution, and the competitive advantage of fossil fuel-based technologies regarding regulations. Strong and reliable numerical simulations are required to show the benefits of storage located near loads and promote its development. The present study was restrained from excluding aggregated control of storage: it is assumed that the storage units operate independently to one another without exchanging information – as is currently mostly the case. A computationally light battery model is presented in detail and validated by direct comparison with a domestic battery operating in real conditions. This model is then used to develop Peak-Shaving (PS) control strategies as it is the decentralized service from which beneficial impacts are most likely to emerge. The aggregation of flatter, peak- shaved consumption profiles is likely to lead to flatter and arbitraged profile at higher voltage layers. Furthermore, voltage fluctuations can be expected to decrease if spikes of individual consumption are reduced. The crucial part to achieve PS lies in the charging pattern: peaks depend on the switching on and off of appliances in the dwelling by the occupants and are therefore impossible to predict accurately. A performant PS strategy must, therefore, include a smart charge recovery algorithm that can ensure enough energy is present in the battery in case it is needed without generating new peaks by charging the unit. Three categories of PS algorithms are introduced in detail. First, using a constant threshold or power rate for charge recovery, followed by algorithms using the State Of Charge (SOC) as a decision variable. Finally, using a load forecast – of which the impact of the accuracy is discussed – to generate PS. A performance metrics was defined in order to quantitatively evaluate their operating regarding peak reduction, total energy consumption, and self-consumption of domestic photovoltaic generation. The algorithms were tested on load profiles with a 1-minute granularity over a 1-year period, and their performance was assessed regarding these metrics. The results show that constant charging threshold or power are far from optimal: a certain value is not likely to fit the variability of a residential profile. As could be expected, forecast-based algorithms show the highest performance. However, these depend on the accuracy of the forecast. On the other hand, SOC based algorithms also present satisfying performance, making them a strong alternative when the reliable forecast is not available.

Keywords: decentralised control, domestic integrated batteries, electricity network performance, peak-shaving algorithm

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Authors: Ali Fuad Ashour

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Introduction: Multiple sclerosis (MS) is a chronic, progressive and degenerative disease that affects the central nervous system (CNS). MS has been described to result in the debilitating symptom of the disease. It is reported to have a negative impact on the patient’s mental activities, brings a lower quality of life, leads to unemployment, causes distress and psychological disorders, generates low levels of motivation and self-esteem, and result in disability and neurological impairment. The aim of this study was to compare the effects of MS on patients from Britain and Kuwait. Methodology: A questionnaire was distributed to 200 individuals with MS (100 Kuwaiti and 100 British). The questionnaire consists of three parts; 1. General demographics, 2. Disease-specific data (symptoms, severity levels, relapse frequency, and support system), and 3. Attitudes towards physical exercise. Results: A response rate of 62% from the British sample and 50% from the Kuwaiti sample was achieved. 84% of the sample (n=52) were 41 years old or over. The duration of the disease was less than 10 years in 43.4% of British and 68% of Kuwaiti respondents. The majority of British respondents (56.5%) reported the disease severity to be moderate, while the majority of Kuwaitis was mild (72%). The annual relapse rates in Kuwait were relatively low, with 82% of the Kuwaiti sample had one relapse per year, compared to the 64.5% of British. The most common symptoms reported by British respondents were balance (75.8%), fatigue (74.2%), and weakness (71%), and by Kuwaiti respondents were fatigue (86%), balance (76%), and weakness (66%). The help and support for MS were by far more diverse for the British than Kuwaiti respondents. Discussion: The results unveiled marked differences between two groups of British and Kuwaiti MS patients in terms of patients’ age and disease duration, and severity. The overwhelming majority of Kuwaiti patients are young individuals who have been with the disease for a relatively short period of time, and their MS in most cases was mild. On the other hand, British patients were relatively older, many have been with the disease for a long period of time, and their average MS condition was more serious than that of their Kuwaiti counterparts. The main support in Kuwait comes from the neurologist, who primarily prescribe medications and advise patients to try to be active. The Kuwaiti respondents thought that lack of encouragement was the main reason for them not to engage in social activities.

Keywords: multiple sclerosis, Kuwait, exercise, demographic

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Authors: Mai M. Farid, Ximeng Yang, Tomoharu Kuboyama, Yuna Inada, Chihiro Tohda

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Intro: Trigonella foenum-graecum (Fenugreek), from Fabaceae family is a well-known plant traditionally used as food and medicine. Many pharmacological effects of Trigonella foenum- graecum seeds extract (TF extract) were evaluated such as anti-diabetic, anti-tumor and anti-dementia effects using in vivo models. Regarding the anti-dementia effects of TF extract, diabetic rats, aluminum chloride-induced amnesia rats and scopolamine-injected mice were used previously for evaluation, which are not well established as Alzheimer’s disease models. In addition, those previous studies, active constituents in TF extract for memory function were not identified. Method: This study aimed to clarify the effect of TF extract on Alzheimer’s disease model, 5XFAD mouse that overexpresses mutated APP and PS1 genes and determine the major active constituent in the brain after oral intake of TF extract. Results: Trigonelline was detected in the cerebral cortex of 5XFAD mice after 24 hours of oral administration of TF extract by LC-MS/MS. Oral administration of TF extract for 17 days improved object location memory in 5XFAD mice. Conclusion: These results suggest that TF extract and its active constituents could be an expected therapeutic candidate for Alzheimer’s disease.

Keywords: Alzheimer's disease, LC-MS/MS, memory recovery, Trigonella foenum-graecum Seeds, 5XFAD mice

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Authors: Rajendra Roul

Abstract:

The main objective of the project is to bring attention towards the performance of an aerofoil when exposed to the fluid medium inside the wind tunnel. This project aims at involvement of civil as well as mechanical engineering thereby making itself as a multidisciplinary project. The airfoil of desired size is taken into consideration for the project to carry out effectively. An aerofoil is the shape of the wing or blade of propeller, rotor or turbine. Lot of experiment have been carried out through wind-tunnel keeping aerofoil as a reference object to make a future forecast regarding the design of turbine blade, car and aircraft. Lift and drag now become the major identification factor for any design industry which shows that wind tunnel testing along with software analysis (ANSYS) becomes the mandatory task for any researchers to forecast an aerodynamics design. This project is an initiative towards the mitigation of drag, better lift and analysis of wake surface profile by investigating the surface pressure distribution. The readings has been taken on airfoil model in Wind Tunnel Testing Machine (WTTM) at different air velocity 20m/sec, 25m/sec, 30m/sec and different angle of attack 00,50,100,150,200. Air velocity and pressures are measured in several ways in wind tunnel testing machine by use to measuring instruments like Anemometer and Multi tube manometer. Moreover to make the analysis more accurate Ansys fluent contribution become substantial and subsequently the CFD simulation results. Analysis on an Aerofoil have a wide spectrum of application other than aerodynamics including wind loads in the design of buildings and bridges for structural engineers.

Keywords: wind-tunnel, aerofoil, Ansys, multitube manometer

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Authors: Bárbara Durão, Pedro Almeida, David Ramilo, André Meneses, Rute Canejo-Teixeira

Abstract:

The importance of quality of life (QoL) assessment in veterinary medicine is an integral part of patient care. This is especially true in cases of chronic diseases, such as chronic kidney disease (CKD), where the ever more advanced treatment options prolong the patient’s life. Whether this prolongment of life comes with an acceptable quality of life remains has been called into question. The aim of this study was to evaluate the relationship between CKD disease biomarkers and QoL in cats. Thirty-seven cats diagnosed with CKD and with no known concurrent illness were enrolled in an observational study. Through the course of several evaluations, renal biomarkers were assessed in blood and urine samples, and owners retrospectively described their cat’s quality of life using a validated instrument for this disease. Correlations between QoL scores (AWIS) and the biomarkers were assessed using Spearman’s rank test. Statistical significance was set at p-value < 0.05, and every serial sample was considered independent. Thirty-seven cats met the inclusion criteria, and all owners completed the questionnaire every time their pet was evaluated, giving a total of eighty-four questionnaires, and the average-weighted-impact-score was –0.5. Results showed there was a statistically significant correlation between the quality of life and most of 17 the studied biomarkers and confirmed that CKD has a negative impact on QoL in cats especially due to the management of the disease and secondary appetite disorders. To our knowledge, this is the attempt to assess the correlation between renal biomarkers and QoL in cats. Our results reveal a strong potential of this type of approach in clinical management, mainly in situations where it is not possible to measure biomarkers. Whilst health-related QoL is a reliable predictor of mortality and morbidity in humans; our findings can help improve the clinical practice in cats with CKD.

Keywords: chronic kidney disease, biomarkers, quality of life, feline

Procedia PDF Downloads 151

Authors: Asrat M.Belachew, Tiago Pereira, Institute of Mathematics, Computer Sciences, Avenida Trabalhador São Carlense, 400, São Carlos, 13566-590, Brazil

Abstract:

Infectious diseases are among the most prominent threats to human beings. They cause morbidity and mortality to an individual and collapse the social, economic, and political systems of the whole world collectively. Mathematical models are fundamental tools and provide a comprehensive understanding of how infectious diseases spread and designing the control strategy to mitigate infectious diseases from the host population. Modeling the spread of infectious diseases using a compartmental model of inhomogeneous populations is good in terms of complexity. However, in the real world, there is a situation that accounts for heterogeneity, such as ages, locations, and contact patterns of the population which are ignored in a homogeneous setting. In this work, we study how classical an SEIR infectious disease spreading of the compartmental model can be extended by incorporating the mobility of population between heterogeneous cities during an outbreak of infectious disease. We have formulated an SEIR multi-cities epidemic spreading model using a system of 4k ordinary differential equations to describe the disease transmission dynamics in k-cities during the day and night. We have shownthat the model is epidemiologically (i.e., variables have biological interpretation) and mathematically (i.e., a unique bounded solution exists all the time) well-posed. We constructed the next-generation matrix (NGM) for the model and calculated the basic reproduction number R0for SEIR-epidemic spreading model with cities mobility. R0of the disease depends on the spectral radius mobility operator, and it is a threshold between asymptotic stability of the disease-free equilibrium and disease persistence. Using the eigenvalue perturbation theorem, we showed that sending a fraction of the population between cities decreases the reproduction number of diseases in interconnected cities. As a result, disease transmissiondecreases in the population.

Keywords: SEIR-model, mathematical model, city mobility, epidemic spreading

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Authors: Tarnai Tena, Strinić Dean

Abstract:

Chronic non-communicable diseases are today the leading cause of mortality, morbidity and mortality disability at the world level and in Croatia. Among them are the most represented precisely cardiovascular diseases (CVD), so today we are talking about their global card epidemic. From 2018 to 2021, cardiovascular diseases are the leading cause of death for both women and men in the Dubrovnik- Neretva County. With regard to the COVID-19 pandemic, which has taken over, without forgetting how much these patients are additionally affected, we are still talking about the primary cause of sickness and death in the population of this county and region. In this record, we present collected data processed according to gender and disease classification. We also bring a kind of overview because, for years, we have been following how the population of one of the origins of the Mediterranean diet has been struggling with cardiovascular diseases.

Keywords: cardiovascular disease, burden, COVID-19, epidemiology, ishemic heart disease, cardiovascular medicine

Procedia PDF Downloads 55

Authors: Hadi O. Tohme

Abstract:

This study is a review on the effectiveness of new physiotherapy techniques with external sensory stimulus compared to standard physiotherapy in the daily activities of patients with Parkinson's disease. Twenty studies from 1996 to 2015 were analyzed and discussed in this review, using the rehabilitation strategy with external sensory stimulus evaluating walking, freezing episodes, balance, transfers, and daily activities of parkinsonian patients. The study highlights the effectiveness of the variety of rehabilitation with cueing strategy used in the treatment of Parkinson's disease. Based on the literature review completed, there is a need for more specific trials with better treatment strategies to support the most appropriate choice of physiotherapy intervention using external sensory stimulus to the type and frequency of this stimulus. In addition, no trials examined the long-term benefits of the physiotherapy intervention with the external sensory stimulus. In order to determine if, or how long the improvements due to the external sensory stimulus physiotherapy intervention can last, long-term follow-up should be performed.

Keywords: cueing strategy, external sensory stimulus, parkinson disease, rehabilitation for parkinson, sensory attention focused exercises, sensory strategy reeducation

Procedia PDF Downloads 231

Authors: Man-Yun Liu, Emily Chia-Yu Su

Abstract:

Alzheimer's disease (AD) is the public health crisis of the 21st century. AD is a degenerative brain disease and the most common cause of dementia, a costly disease on the healthcare system. Unfortunately, the cause of AD is poorly understood, furthermore; the treatments of AD so far can only alleviate symptoms rather cure or stop the progress of the disease. Currently, there are several ways to diagnose AD; medical imaging can be used to distinguish between AD, other dementias, and early onset AD, and cerebrospinal fluid (CSF). Compared with other diagnostic tools, blood (plasma) test has advantages as an approach to population-based disease screening because it is simpler, less invasive also cost effective. In our study, we used blood biomarkers dataset of The Alzheimer’s disease Neuroimaging Initiative (ADNI) which was funded by National Institutes of Health (NIH) to do data analysis and develop a prediction model. We used independent analysis of datasets to identify plasma protein biomarkers predicting early onset AD. Firstly, to compare the basic demographic statistics between the cohorts, we used SAS Enterprise Guide to do data preprocessing and statistical analysis. Secondly, we used logistic regression, neural network, decision tree to validate biomarkers by SAS Enterprise Miner. This study generated data from ADNI, contained 146 blood biomarkers from 566 participants. Participants include cognitive normal (healthy), mild cognitive impairment (MCI), and patient suffered Alzheimer’s disease (AD). Participants’ samples were separated into two groups, healthy and MCI, healthy and AD, respectively. We used the two groups to compare important biomarkers of AD and MCI. In preprocessing, we used a t-test to filter 41/47 features between the two groups (healthy and AD, healthy and MCI) before using machine learning algorithms. Then we have built model with 4 machine learning methods, the best AUC of two groups separately are 0.991/0.709. We want to stress the importance that the simple, less invasive, common blood (plasma) test may also early diagnose AD. As our opinion, the result will provide evidence that blood-based biomarkers might be an alternative diagnostics tool before further examination with CSF and medical imaging. A comprehensive study on the differences in blood-based biomarkers between AD patients and healthy subjects is warranted. Early detection of AD progression will allow physicians the opportunity for early intervention and treatment.

Keywords: Alzheimer's disease, blood-based biomarkers, diagnostics, early detection, machine learning

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Authors: Elena Collino, Dario A. Ronzio, Goffredo Decimi, Maurizio Riva

Abstract:

The rapid increase of renewable energy in Italy is led by wind and solar installations. The 2017 Italian energy strategy foresees a further development of these sustainable technologies, especially solar. This fact has resulted in new opportunities, challenges, and different problems to deal with. The growth of renewables allows to meet the European requirements regarding energy and environmental policy, but these types of sources are difficult to manage because they are intermittent and non-programmable. Operationally, these characteristics can lead to instability on the voltage profile and increasing uncertainty on energy reserve scheduling. The increasing renewable production must be considered with more and more attention especially by the Transmission System Operator (TSO). The TSO, in fact, every day provides orders on energy dispatch, once the market outcome has been determined, on extended areas, defined mainly on the basis of power transmission limitations. In Italy, six market zone are defined: Northern-Italy, Central-Northern Italy, Central-Southern Italy, Southern Italy, Sardinia, and Sicily. An accurate hourly renewable power forecasting for the day-ahead on these extended areas brings an improvement both in terms of dispatching and reserve management. In this study, an operational forecasting tool of the hourly solar output for the six Italian market zones is presented, and the performance is analysed. The implementation is carried out by means of a numerical weather prediction model, coupled with a statistical post-processing in order to derive the power forecast on the basis of the meteorological projection. The weather forecast is obtained from the limited area model RAMS on the Italian territory, initialized with IFS-ECMWF boundary conditions. The post-processing calculates the solar power production with the Analog Ensemble technique (AN). This statistical approach forecasts the production using a probability distribution of the measured production registered in the past when the weather scenario looked very similar to the forecasted one. The similarity is evaluated for the components of the solar radiation: global (GHI), diffuse (DIF) and direct normal (DNI) irradiation, together with the corresponding azimuth and zenith solar angles. These are, in fact, the main factors that affect the solar production. Considering that the AN performance is strictly related to the length and quality of the historical data a training period of more than one year has been used. The training set is made by historical Numerical Weather Prediction (NWP) forecasts at 12 UTC for the GHI, DIF and DNI variables over the Italian territory together with corresponding hourly measured production for each of the six zones. The AN technique makes it possible to estimate the aggregate solar production in the area, without information about the technologic characteristics of the all solar parks present in each area. Besides, this information is often only partially available. Every day, the hourly solar power forecast for the six Italian market zones is made publicly available through a website.

Keywords: analog ensemble, electricity market, PV forecast, solar energy

Procedia PDF Downloads 127