Search results for: asymptotic tail estimate

Authors: Nowrozy Jahan, Sharifah Syed Hassan, Daniel Reidpath

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In recent decades, Malaysia has become a dengue hyper-endemic country with the co-circulation of the four-dengue virus (DENV) serotypes. The number of symptomatic dengue cases is maintaining an increasing trend since 1995 and sharply increased in 2014. The four DENV serotypes have been co-circulating since 2000, and this pattern of cyclical dominance of sub-types contributed to the development of frequent major dengue epidemics in Malaysia. Since 2012, different Malaysian state was dominated by different serotypes. The study aims to estimate the burden of asymptomatic dengue in a healthy adult population which may act as a potential source of further symptomatic dengue infection. It also aims to identify the predominant DENV serotypes which are circulating at the community level. A longitudinal prospective community-based study was conducted in the Segamat district of Johor State, southern part of Malaysia where the number of reported dengue cases has steadily increased over the last three years (2013-2015). More specifically, the study was conducted in and around of Kampung Abdullah of Sungai Segamat sub-district which was identified as a hot spot area over the period of 2013-2015. This community-based study has been conducted by Southeast Asia Community Observatory (SEACO), an ISO-certified research platform in collaboration of the Ministry of Health Malaysia and Monash University Malaysia. It was conducted from May 2015 to May 2016. In this study, 277 apparently looking healthy respondents joined who were followed up as a cohort for four times during the one-year study period. Blood was collected to detect the serological marker of dengue at each round of follow-up. Among 277, 184 respondents (66%) joined all four rounds. Half of the study respondents were at the age-group of 45-64 years, slightly more than half of the respondents (59%) were female, and the most (69%) of them were Malay; only 35% lived in urban areas. During the baseline, the study found a very high prevalence of exposure to dengue virus; 89% of the study respondents had serological evidence of previous asymptomatic dengue infection; the majority of them did not know about it as they did not develop any symptom of dengue fever; only 13% knew as they developed symptoms. At the end of the one-year study period, 19% of respondents developed recent secondary dengue infection which was also identified by the serological marker as they did not develop any symptom (asymptomatic cases). The asymptomatic dengue incidence was higher during the rainy season compared to the dry season. All four dengue serotypes were identified in the serum of the infected respondents; among them, DENV-2 was the most prominent. Further genetic analysis is going on to identify the association of HLA-B*46 and HLA-DRB1*08 with dengue resistance. This study provides evidence for the policymakers to be aware of asymptomatic dengue infection, to develop a useful tool for raising awareness about asymptomatic dengue infection among the general population, to monitor the community participation to strengthen the individual and community level dengue prevention and control measures when neither there is vaccine nor particular treatment for dengue.

Keywords: asymptomatic, dengue, health adults, prospective study

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Authors: Peter Palasti, Eva Kerepeczki

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Extensive fish farming is one of the most traditional forms of aquaculture in Europe, usually practiced in large pond systems with earthen beds, where the growth of fish is based on natural feed and supplementary foraging. These farms have semi-natural environmental conditions, sustaining diverse wildlife communities that have complex effects on fish production and also provide a livelihood for many wetland related taxa. Based on their characteristics, these communities could be sources of various ecosystem services (ESs), that could also enhance the value and enable the multifunctional use of these artificially constructed and maintained production zones. To identify and estimate the whole range of wildlife’s contribution we have conducted an integrated assessment in an extensively managed pond system in Biharugra, Hungary, where we studied 14 previously revealed ESs: fish and reed production, water storage, water and air quality regulation, CO2 absorption, groundwater recharge, aesthetics, recreational activities, inspiration, education, scientific research, presence of semi-natural habitats and useful/protected species. ESs were collected through structured interviews with the local experts of all major stakeholder groups, where we have also gathered information about the known forms, levels (none, low, high) and orientations (positive, negative) of the contributions of the wildlife community. After that, a quantitative analysis was carried out: we calculated the total mean value of the services being used between 2014-16, then we estimated the value and percentage of contributions. For the quantification, we mainly used biophysical indicators with the available data and empirical knowledge of the local experts. During the interviews, 12 of the previously listed services (85%) were mentioned to be related to wildlife community, consisting of 5 fully (e.g., recreation, reed production) and seven partially dependent ESs (e.g., inspiration, CO2 absorption) from our list. The orientation of the contributions was said to be positive almost every time; however, in the case of fish production, the feeding habit of some wild species (Phalacrocorax carbo, Lutra lutra) caused significant losses in fish stocks in the study period. During the biophysical assessment, we calculated the total mean value of the services and quantified the aid of wildlife community at the following services: fish and reed production, recreation, CO2 absorption, and the presence of semi-natural habitats and wild species. The combined results of our interviews and biophysical evaluations showed that the presence of wildlife community not just greatly increased the productivity of the fish farms in Biharugra (with ~53% of natural yield generated by planktonic and benthic communities) but also enhanced the multifunctionality of the system through expanding the quality and number of its services. With these abilities, extensively managed fishponds could play an important role in the future as refugia for wetland related services and species threatened by the effects of global warming.

Keywords: ecosystem services, fishpond systems, integrated assessment, wildlife community

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Authors: Elissandro Voigt Beier, Cristiano Poleto

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The main objective of the research is to assess the loss through the quantification of material culture (archaeological fragments) in rural areas, sites explored economically by machining on seasonal crops, and also permanent, in a hydrographic subsystem Camaquã River in the state of Rio Grande do Sul, Brazil. The study area consists of different micro basins and differs in area, ranging between 1,000 m² and 10,000 m², respectively the largest and the smallest, all with a large number of occurrences and outcrop locations of archaeological material and high density in intense farm environment. In the first stage of the research aimed to identify the dispersion of points of archaeological material through field survey through plot points by the Global Positioning System (GPS), within each river basin, was made use of concise bibliography on the topic in the region, helping theoretically in understanding the old landscaping with preferences of occupation for reasons of ancient historical people through the settlements relating to the practice observed in the field. The mapping was followed by the cartographic development in the region through the development of cartographic products of the land elevation, consequently were created cartographic products were to contribute to the understanding of the distribution of the absolute materials; the definition and scope of the material dispersed; and as a result of human activities the development of revolving letter by mechanization of in situ material, it was also necessary for the preparation of materials found density maps, linking natural environments conducive to ancient historical occupation with the current human occupation. The third stage of the project it is for the systematic collection of archaeological material without alteration or interference in the subsurface of the indigenous settlements, thus, the material was prepared and treated in the laboratory to remove soil excesses, cleaning through previous communication methodology, measurement and quantification. Approximately 15,000 were identified archaeological fragments belonging to different periods of ancient history of the region, all collected outside of its environmental and historical context and it also has quite changed and modified. The material was identified and cataloged considering features such as object weight, size, type of material (lithic, ceramic, bone, Historical porcelain and their true association with the ancient history) and it was disregarded its principles as individual lithology of the object and functionality same. As observed preliminary results, we can point out the change of materials by heavy mechanization and consequent soil disturbance processes, and these processes generate loading of archaeological materials. Therefore, as a next step will be sought, an estimate of potential losses through a mathematical model. It is expected by this process, to reach a reliable model of high accuracy which can be applied to an archeological site of lower density without encountering a significant error.

Keywords: degradation of heritage, quantification in archaeology, watershed, use and occupation of land

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Authors: Ayesha Mushtaq

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Asthma is a pulmonary disease in which blockade of the airway takes place due to inflammation as a response to certain allergens. Breathing troubles, cough, and dyspnea are one of the few symptoms. Several studies have indicated a significant effect on asthma due to changes in dietary routines. Certain food items, such as oily foods and other materials, are known to cause an increase in the symptoms of asthma. Low dietary intake of fruits and vegetables may be important in relation to asthma prevalence. The objective of this study is to assess and compare the nutritional status of asthmatic and non-asthmatic patients. The significance of this study lies in the factor that it will help nutritionists to arrange a feasible dietary routine for asthmatic patients. This research was conducted at the Pulmonology Department of the Pakistan Institute of Medical Science Islamabad. About thirty hundred thirty-four million people are affected by asthma worldwide. Pakistan is on the verge of being an uplifted urban population and asthma cases are increasingly high these days. Several studies suggest an increase in the Asthmatic patient population due to improper diet. This is a cross-sectional study aimed at assessing the nutritious standing of Asthmatic and non-asthmatic patients. This research took place at the Pakistan Institute of Medical Sciences (PIMS), Islamabad, Pakistan. The research included asthmatic and non-asthmatic patients coming to the pulmonology department clinic at the Pakistan Institute of Medical Sciences (PIMS). These patients were aged between 20-60 years. A questionnaire was developed for these patients to estimate their dietary plans in these patients. The methodology included four sections. The first section was the Socio-Demographic profile, which included age, gender, monthly income and occupation. The next section was anthropometric measurements which included the weight, height and body mass index (BMI) of an individual. The next section, section three, was about the biochemical attributes, such as for biochemical profiling, pulmonary function testing (PFT) was performed. In the next section, Dietary habits were assessed by a food frequency questionnaire (FFQ) through food habits and consumption pattern was assessed. The next section life style data, in which the person's level of physical activity, sleep and smoking habits were assessed. The next section was statistical analysis. All the data obtained from the study were statistically analyzed and assessed. Most of the asthma Patients were females, with weight more than normal or even obese. Body Mass Index (BMI) was higher in asthma Patients than those in non-Asthmatic ones. When the nutritional Values were assessed, we came to know that these patients were low on certain nutrients and their diet included more junk and oily food than healthy vegetables and fruits. Beverages intake was also included in the same assessment. It is evident from this study that nutritional status has a contributory effect on asthma. So, patients on the verge of developing asthma or those who have developed asthma should focus on their diet, maintain good eating habits and take healthy diets, including fruits and vegetables rather than oily foods. Proper sleep may also contribute to the control of asthma.

Keywords: BMI, nutrition, PAL, diet

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Authors: Ch. Sridevi, S. P. Shailender Kumar, B. Gurudayal, A. Chalapathi Rao, K. Koteswara Rao, P. Srinivasulu

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System reliability and system availability evaluation plays a crucial role in ensuring the seamless operation of complex satellite data reception system with consistent performance for longer periods. This paper presents a novel approach for the same using a case study on one of the antenna systems at satellite data reception ground station in India. The methodology involves analyzing system's components, their failure rates, system's architecture, generation of logical reliability block diagram model and estimating the reliability of the system using the component level mean time between failures considering exponential distribution to derive a baseline estimate of the system's reliability. The model is then validated with collected system level field failure data from the operational satellite data reception systems that includes failure occurred, failure time, criticality of the failure and repair times by using statistical techniques like median rank, regression and Weibull analysis to extract meaningful insights regarding failure patterns and practical reliability of the system and to assess the accuracy of the developed reliability model. The study mainly focused on identification of critical units within the system, which are prone to failures and have a significant impact on overall performance and brought out a reliability model of the identified critical unit. This model takes into account the interdependencies among system components and their impact on overall system reliability and provides valuable insights into the performance of the system to understand the Improvement or degradation of the system over a period of time and will be the vital input to arrive at the optimized design for future development. It also provides a plug and play framework to understand the effect on performance of the system in case of any up gradations or new designs of the unit. It helps in effective planning and formulating contingency plans to address potential system failures, ensuring the continuity of operations. Furthermore, to instill confidence in system users, the duration for which the system can operate continuously with the desired level of 3 sigma reliability was estimated that turned out to be a vital input to maintenance plan. System availability and station availability was also assessed by considering scenarios of clash and non-clash to determine the overall system performance and potential bottlenecks. Overall, this paper establishes a comprehensive methodology for reliability and availability analysis of complex satellite data reception systems. The results derived from this approach facilitate effective planning contingency measures, and provide users with confidence in system performance and enables decision-makers to make informed choices about system maintenance, upgrades and replacements. It also aids in identifying critical units and assessing system availability in various scenarios and helps in minimizing downtime and optimizing resource allocation.

Keywords: exponential distribution, reliability modeling, reliability block diagram, satellite data reception system, system availability, weibull analysis

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Authors: Amirah Azzeri, Fatiha H. Shabaruddin, Scott A. McDonald, Rosmawati Mohamed, Maznah Dahlui

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Objective: A scaled-up treatment cascade with direct-acting antiviral (DAA) therapy is necessary to achieve global WHO targets for hepatitis C virus (HCV) elimination in Malaysia. Recently, limited access to Sofosbuvir/Daclatasvir (SOF/DAC) is available through compulsory licensing, with future access to Sofosbuvir/Velpatasvir (SOF/VEL) expected through voluntary licensing due to recent agreements. SOF/VEL has superior clinical outcomes, particularly for cirrhotic stages, but has higher drug acquisition costs compared to SOF/DAC. It has been proposed that a stratified treatment cascade might be the most cost-efficient approach for Malaysia whereby all HCV patients are treated with SOF/DAC except for patients with cirrhosis who are treated with SOF/VEL. This study aimed to conduct a five-year budget impact analysis from the provider perspective of the proposed stratified treatment cascade for HCV treatment in Malaysia. Method: A disease progression model that was developed based on model-predicted HCV epidemiology data in Malaysia was used for the analysis, where all HCV patients in scenario A were treated with SOF/DAC for all disease stages while in scenario B, SOF/DAC was used only for non-cirrhotic patients and SOF/VEL was used for the cirrhotic patients. The model projections estimated the annual numbers of patients in care and the numbers of patients to be initiated on DAA treatment nationally. Healthcare costs associated with DAA therapy and disease stage monitoring was included to estimate the downstream cost implications. For scenario B, the estimated treatment uptake of SOF/VEL for cirrhotic patients were 25%, 50%, 75%, 100% and 100% for 2018, 2019, 2020, 2021 and 2022 respectively. Healthcare costs were estimated based on standard clinical pathways for DAA treatment described in recent guidelines. All costs were reported in US dollars (conversion rate US$1=RM4.09, the price year 2018). Scenario analysis was conducted for 5% and 10% reduction of SOF/VEL acquisition cost anticipated from the competitive market pricing of generic DAA in Malaysia. Results: The stratified treatment cascade with SOF/VEL in Scenario B was found to be cost-saving compared to Scenario A. A substantial portion of the cost reduction was due to the costs associated with DAA therapy which resulted in USD 40 thousand (year 1) to USD 443 thousand (year 5) savings annually, with cumulative savings of USD 1.1 million after 5 years. Cost reductions for disease stage monitoring were seen in year three onwards which resulted in cumulative savings of USD 1.1 thousand. Scenario analysis estimated cumulative savings of USD 1.24 to USD 1.35 million when the acquisition cost of SOF/VEL was reduced. Conclusion: A stratified treatment cascade with SOF/VEL was expected to be cost-saving and can results in a budget impact reduction in overall healthcare expenditure in Malaysia compared to treatment with SOF/DAC. The better clinical efficacy with SOF/VEL is expected to halt patients’ HCV disease progression and may reduce downstream costs of treating advanced disease stages. The findings of this analysis may be useful to inform healthcare policies for HCV treatment in Malaysia.

Keywords: Malaysia, direct acting antiviral, compulsory licensing, voluntary licensing

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Authors: Massimo Ferrari, Maria Sole Pagliari

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Pollution is a classic example of economic externality: agents who produce it do not face direct costs from emissions. Therefore, there are no direct economic incentives for reducing pollution. One way to address this market failure would be directly taxing emissions. However, because emissions are global, governments might as well find it optimal to wait let foreign countries to tax emissions so that they can enjoy the benefits of lower pollution without facing its direct costs. In this paper, we first document the empirical relation between pollution and economic output with static and dynamic regression methods. We show that there is a negative relation between aggregate output and the stock of pollution (measured as the stock of CO₂ emissions). This relationship is also highly non-linear, increasing at an exponential rate. In the second part of the paper, we develop and estimate a two-country, two-sector model for the US and the euro area. With this model, we aim at analyzing how the public sector should respond to higher emissions and what are the direct costs that these policies might have. In the model, there are two types of firms, brown firms (which produce a polluting technology) and green firms. Brown firms also produce an externality, CO₂ emissions, which has detrimental effects on aggregate output. As brown firms do not face direct costs from polluting, they do not have incentives to reduce emissions. Notably, emissions in our model are global: the stock of CO₂ in the economy affects all countries, independently from where it is produced. This simplified economy captures the main trade-off between emissions and production, generating a classic market failure. According to our results, the current level of emission reduces output by between 0.4 and 0.75%. Notably, these estimates lay in the upper bound of the distribution of those delivered by studies in the early 2000s. To address market failure, governments should step in introducing taxes on emissions. With the tax, brown firms pay a cost for polluting hence facing the incentive to move to green technologies. Governments, however, might also adopt a beggar-thy-neighbour strategy. Reducing emissions is costly, as moves production away from the 'optimal' production mix of brown and green technology. Because emissions are global, a government could just wait for the other country to tackle climate change, ripping the benefits without facing any costs. We study how this strategic game unfolds and show three important results: first, cooperation is first-best optimal from a global prospective; second, countries face incentives to deviate from the cooperating equilibria; third, tariffs on imported brown goods (the only retaliation policy in case of deviation from the cooperation equilibrium) are ineffective because the exchange rate would move to compensate. We finally study monetary policy under when costs for climate change rise and show that the monetary authority should react stronger to deviations of inflation from its target.

Keywords: climate change, general equilibrium, optimal taxation, monetary policy

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Authors: L. Bidyalakshmi, R. Ananthan, T. Longvah

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Antioxidants are substances that can prevent or delay oxidative damage of lipids, proteins and nucleic acids by reactive oxygen species. These help in lowering incidence of degenerative diseases such as cancer, arthritis, atherosclerosis, heart disease, inflammation, brain dysfunction and acceleration of the ageing process. The north eastern part of India falls among the global hotspots of biodiversity. Over the years, the local communities in the region have developed ingenious uses of many wild plants within their environment as food sources. Many of these less familiar foods form an integral part of the diet of these communities, and some are traditionally valued for its therapeutic effects. So the study was carried to estimate the antioxidant activity of some of these indigenous foods. Twenty-eight indigenous plant foods were studied for their antioxidant activity. Antioxidant activities were determined by using DPPH (2, 2-diphenyl-1-picrylhydrazyl) assay, FRAP (Ferric Reducing Antioxidant Power) assay and SOSA (Super Oxide Scavenging Assay). Out of the twenty-eight plant foods, there were thirteen leafy vegetables, four fruits, five roots and tubers, four spices and two mushrooms. Water extract and methanol extract of the samples were used for the analysis. The leafy vegetable samples exhibited antioxidant capacity with IC50 ranging from 8-1414 mg/ml for lipid extract and 34-37878 mg/ml for aqueous extract in DPPH assay. Total FRAP value ranging from 58-1005 mmol FeSO4 Eq/100g of the sample, which is comparatively higher than the antioxidant capacity of some commonly consumed leafy vegetables. In SOSA, water extract of leafy vegetables show a range of 0.05-193.68 µmol ascorbic acid equivalent/g of the samples. While the methanol extract of the samples show 0.20-21.94 µmol Trolox equivalent/g of the samples. Polygonum barbatum, Wendlandia glabrata and Polygonum posumbu have higher antioxidant activity among the leafy vegetables analysed. Among the fruits, Rhus hookerii showed the highest antioxidant activities in both FRAP and SOSA methods while Spondias magnifera exhibited higher antioxidant activity in DPPH method. Alocasia cucullata exhibited higher antioxidant activity in DPPH and FRAP assays while Alpinia galanga showed higher antioxidant activity in SOSA assay when compared to the other samples of roots and tubers. Elsholtzia communis showed high antioxidant activity in all the three parameters among the spices. For the mushrooms, Pleurotus ostreatus exhibited higher antioxidant activity than Auricularia delicate in DPPH and SOSA. The samples analysed exhibited antioxidant activity at varying levels and some exhibited higher antioxidant activity than the commonly consumed foods. So consumption of these less familiar foods may play a role in preventing human disease in which free radicals are involved. Further studies on these food samples on phytonutrients and its contribution to the antioxidant activities are required.

Keywords: antioxidant activity, DPPH, FRAP, SOSA

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Authors: Ionut Vintu, Stefan Laible, Ruth Schulz

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Agricultural robotics has been developing steadily over recent years, with the goal of reducing and even eliminating pesticides used in crops and to increase productivity by taking over human labor. The majority of crops are arranged in rows. The first step towards autonomous robots, capable of driving in fields and performing crop-handling tasks, is for robots to robustly detect the rows of plants. Recent work done towards autonomous driving between plant rows offers big robotic platforms equipped with various expensive sensors as a solution to this problem. These platforms need to be driven over the rows of plants. This approach lacks flexibility and scalability when it comes to the height of plants or distance between rows. This paper proposes instead an algorithm that makes use of cheaper sensors and has a higher variability. The main application is in tree nurseries. Here, plant height can range from a few centimeters to a few meters. Moreover, trees are often removed, leading to gaps within the plant rows. The core idea is to combine row detection algorithms with graph-based localization methods as they are used in SLAM. Nodes in the graph represent the estimated pose of the robot, and the edges embed constraints between these poses or between the robot and certain landmarks. This setup aims to improve individual plant detection and deal with exception handling, like row gaps, which are falsely detected as an end of rows. Four methods were developed for detecting row structures in the fields, all using a point cloud acquired with a 3D LiDAR as an input. Comparing the field coverage and number of damaged plants, the method that uses a local map around the robot proved to perform the best, with 68% covered rows and 25% damaged plants. This method is further used and combined with a graph-based localization algorithm, which uses the local map features to estimate the robot’s position inside the greater field. Testing the upgraded algorithm in a variety of simulated fields shows that the additional information obtained from localization provides a boost in performance over methods that rely purely on perception to navigate. The final algorithm achieved a row coverage of 80% and an accuracy of 27% damaged plants. Future work would focus on achieving a perfect score of 100% covered rows and 0% damaged plants. The main challenges that the algorithm needs to overcome are fields where the height of the plants is too small for the plants to be detected and fields where it is hard to distinguish between individual plants when they are overlapping. The method was also tested on a real robot in a small field with artificial plants. The tests were performed using a small robot platform equipped with wheel encoders, an IMU and an FX10 3D LiDAR. Over ten runs, the system achieved 100% coverage and 0% damaged plants. The framework built within the scope of this work can be further used to integrate data from additional sensors, with the goal of achieving even better results.

Keywords: 3D LiDAR, agricultural robots, graph-based localization, row detection

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Authors: Rose Branch-Allen, John Jayachandran

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Background and purpose: Canada is a pluralistic, multicultural society with an ethno-cultural composition that has been shaped over time by immigrants and their descendants. Although Canada welcomes these immigrants, many will endure hardship and assimilation difficulties. Despite these life hurdles, surveys consistently disclose high life satisfaction for all Canadians. Most research studies on Life Satisfaction/ Subjective Wellbeing (SWB) have focused on one main determinant and a variety of social demographic variables to delineate the determinants of life satisfaction. However, very few research studies examine life satisfaction from a holistic approach. In addition, we need to understand the causal pathways leading to life satisfaction, and develop theories that explain why certain variables differentially influence the different components of SWB. The aim this study was to utilize a holistic approach to construct a causal model and identify major determinants of life satisfaction. Data and measures: This study utilized data from the General Social Survey, with a sample size of 19, 597. The exogenous concepts included age, gender, marital status, household size, socioeconomic status, ethnicity, location, immigration status, religiosity, and neighborhood. The intervening concepts included health, social contact, leisure, enjoyment, work-family balance, quality time, domestic labor, and sense of belonging. The endogenous concept life satisfaction was measured by multiple indicators (Cronbach’s alpha = .83). Analysis: Several multiple regression models were run sequentially to estimate path coefficients for the causal model. Results: Overall, above average satisfaction with life was reported for respondents with specific socio-economic, demographic and lifestyle characteristics. With regard to exogenous factors, respondents who were female, younger, married, from high socioeconomic status background, born in Canada, very religious, and demonstrated high level of neighborhood interaction had greater satisfaction with life. Similarly, intervening concepts suggested respondents had greater life satisfaction if they had better health, more social contact, less time on passive leisure activities and more time on active leisure activities, more time with family and friends, more enjoyment with volunteer activities, less time on domestic labor and a greater sense of belonging to the community. Conclusions and Implications: Our results suggest that a holistic approach is necessary for establishing determinants of life satisfaction, and that life satisfaction is not merely comprised of positive or negative affect rather understanding the causal process of life satisfaction. Even though, most of our findings are consistent with previous studies, a significant number of causal connections contradict some of the findings in literature today. We have provided possible explanation for these anomalies researchers encounter in studying life satisfaction and policy implications.

Keywords: causal model, holistic approach, life satisfaction, socio-demographic variables, subjective well-being

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Authors: Adriana Postal, Silvio C. Sampaio, Marcio A. Villas Boas, Josué P. Castro

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This study deals with the estimation of reference evapotranspiration (ET₀) in an agricultural context, focusing on efficient irrigation management to meet the growing interest in the sustainable management of water resources. Given the importance of water in agriculture and its scarcity in many regions, efficient use of this resource is essential to ensure food security and environmental sustainability. The methodology used involved the application of artificial intelligence techniques, specifically Multilayer Perceptron (MLP) Artificial Neural Networks (ANNs), to predict ET₀ in the state of Paraná, Brazil. The models were trained and validated with meteorological data from the Brazilian National Institute of Meteorology (INMET), together with data obtained from a producer's weather station in the western region of Paraná. Two optimizers (SGD and Adam) and different meteorological variables, such as temperature, humidity, solar radiation, and wind speed, were explored as inputs to the models. Nineteen configurations with different input variables were tested; amidst them, configuration 9, with 8 input variables, was identified as the most efficient of all. Configuration 10, with 4 input variables, was considered the most effective, considering the smallest number of variables. The main conclusions of this study show that MLP ANNs are capable of accurately estimating ET₀, providing a valuable tool for irrigation management in agriculture. Both configurations (9 and 10) showed promising performance in predicting ET₀. The validation of the models with cultivator data underlined the practical relevance of these tools and confirmed their generalization ability for different field conditions. The results of the statistical metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²), showed excellent agreement between the model predictions and the observed data, with MAE as low as 0.01 mm/day and 0.03 mm/day, respectively. In addition, the models achieved an R² between 0.99 and 1, indicating a satisfactory fit to the real data. This agreement was also confirmed by the Kolmogorov-Smirnov test, which evaluates the agreement of the predictions with the statistical behavior of the real data and yields values between 0.02 and 0.04 for the producer data. In addition, the results of this study suggest that the developed technique can be applied to other locations by using specific data from these sites to further improve ET₀ predictions and thus contribute to sustainable irrigation management in different agricultural regions. The study has some limitations, such as the use of a single ANN architecture and two optimizers, the validation with data from only one producer, and the possible underestimation of the influence of seasonality and local climate variability. An irrigation management application using the most efficient models from this study is already under development. Future research can explore different ANN architectures and optimization techniques, validate models with data from multiple producers and regions, and investigate the model's response to different seasonal and climatic conditions.

Keywords: agricultural technology, neural networks in agriculture, water efficiency, water use optimization

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Authors: Supriya Majumder, Pabitra Banik

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Fates of Arsenic (As) on the soil-plant environment belong to the critical emerging issue, which in turn to appraises the threatening implications of a human health risk — assessing the dynamics of As in soil solid components are likely to impose its potential availability towards plant uptake. In the present context, we introduced an improved Sequential Extraction Procedure (SEP) questioning to identify solid-phase speciation of As in paddy soil under variable soil environmental conditions during two consecutive seasons of rice cultivation practices. We coupled gradients of water management practices with the addition of fertilizer amendments to assess the changes in a partition of As through a field experimental study during monsoon and post-monsoon season using two rice cultivars. Water management regimes were varied based on the methods of cultivation of rice by Conventional (waterlogged) vis-a-vis System of Rice Intensification-SRI (saturated). Fertilizer amendment through the nutrient treatment of absolute control, NPK-RD, NPK-RD + Calcium silicate, NPK-RD + Ferrous sulfate, Farmyard manure (FYM), FYM + Calcium silicate, FYM + Ferrous sulfate, Vermicompost (VC), VC + Calcium silicate, VC + Ferrous sulfate were selected to construct the study. After harvest, soil samples were sequentially extracted to estimate partition of As among the different fractions such as: exchangeable (F1), specifically sorbed (F2), As bound to amorphous Fe oxides (F3), crystalline Fe oxides (F4), organic matter (F5) and residual phase (F6). Results showed that the major proportions of As were found in F3, F4 and F6, whereas F1 exhibited the lowest proportion of total soil As. Among the nutrient treatment mediated changes on As fractions, the application of organic manure and ferrous sulfate were significantly found to restrict the release of As from exchangeable phase. Meanwhile, conventional practice produced much higher release of As from F1 as compared to SRI, which may substantially increase the environmental risk. In contrast, SRI practice was found to retain a significantly higher proportion of As in F2, F3, and F4 phase resulting restricted mobilization of As. This was critically reflected towards rice grain As bioavailability where the reduction in grain As concentration of 33% and 55% in SRI concerning conventional treatment (p <0.05) during monsoon and post-monsoon season respectively. Also, prediction assay for rice grain As bioavailability based on the linear regression model was performed. Results demonstrated that rice grain As concentration was positively correlated with As concentration in F1 and negatively correlated with F2, F3, and F4 with a satisfactory level of variation being explained (p <0.001). Finally, we conclude that F1, F2, F3 and F4 are the major soil. As fractions critically may govern the potential availability of As in soil and suggest that rice cultivation with the SRI treatment is particularly at less risk of As availability in soil. Such exhaustive information may be useful for adopting certain management practices for rice grown in contaminated soil concerning to the environmental issues in particular.

Keywords: arsenic, fractionation, paddy soil, potential availability

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Authors: F. Infante, W. Kaal, S. Perfetto, S. Herold

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In the last decade the demand of wireless sensors and low-power electric devices for condition monitoring in mechanical structures has been strongly increased. Networks of wireless sensors can potentially be applied in a huge variety of applications. Due to the reduction of both size and power consumption of the electric components and the increasing complexity of mechanical systems, the interest of creating dense nodes sensor networks has become very salient. Nevertheless, with the development of large sensor networks with numerous nodes, the critical problem of powering them is drawing more and more attention. Batteries are not a valid alternative for consideration regarding lifetime, size and effort in replacing them. Between possible alternative solutions for durable power sources useable in mechanical components, vibrations represent a suitable source for the amount of power required to feed a wireless sensor network. For this purpose, energy harvesting from structural vibrations has received much attention in the past few years. Suitable vibrations can be found in numerous mechanical environments including automotive moving structures, household applications, but also civil engineering structures like buildings and bridges. Similarly, a dynamic vibration absorber (DVA) is one of the most used devices to mitigate unwanted vibration of structures. This device is used to transfer the primary structural vibration to the auxiliary system. Thus, the related energy is effectively localized in the secondary less sensitive structure. Then, the additional benefit of harvesting part of the energy can be obtained by implementing dedicated components. This paper describes the design process of an energy harvesting tuned vibration absorber (EHTVA) for rotating systems using piezoelectric elements. The energy of the vibration is converted into electricity rather than dissipated. The device proposed is indeed designed to mitigate torsional vibrations as with a conventional rotational TVA, while harvesting energy as a power source for immediate use or storage. The resultant rotational multi degree of freedom (MDOF) system is initially reduced in an equivalent single degree of freedom (SDOF) system. The Den Hartog’s theory is used for evaluating the optimal mechanical parameters of the initial DVA for the SDOF systems defined. The performance of the TVA is operationally assessed and the vibration reduction at the original resonance frequency is measured. Then, the design is modified for the integration of active piezoelectric patches without detuning the TVA. In order to estimate the real power generated, a complex storage circuit is implemented. A DC-DC step-down converter is connected to the device through a rectifier to return a fixed output voltage. Introducing a big capacitor, the energy stored is measured at different frequencies. Finally, the electromechanical prototype is tested and validated achieving simultaneously reduction and harvesting functions.

Keywords: energy harvesting, piezoelectricity, torsional vibration, vibration absorber

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Authors: Andrea Ghermandi

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Natural treatment systems such as constructed wetlands and waste stabilization ponds are increasingly used to treat water and wastewater from a variety of sources, including stormwater and polluted surface water. The provision of ancillary benefits in the form of cultural ecosystem services makes these systems unique among water and wastewater treatment technologies and greatly contributes to determine their potential role in promoting sustainable water management practices. A quantitative analysis of these benefits, however, has been lacking in the literature. Here, a critical assessment of the recreational and educational benefits in natural treatment systems is provided, which combines observed public use from a survey of managers and operators with estimated public use as obtained using geotagged photos from social media as a proxy for visitation rates. Geographic Information Systems (GIS) are used to characterize the spatial boundaries of 273 natural treatment systems worldwide. Such boundaries are used as input for the Application Program Interfaces (APIs) of two popular photo-sharing websites (Flickr and Panoramio) in order to derive the number of photo-user-days, i.e., the number of yearly visits by individual photo users in each site. The adequateness and predictive power of four univariate calibration models using the crowdsourced data as a proxy for visitation are evaluated. A high correlation is found between photo-user-days and observed annual visitors (Pearson's r = 0.811; p-value < 0.001; N = 62). Standardized Major Axis (SMA) regression is found to outperform Ordinary Least Squares regression and count data models in terms of predictive power insofar as standard verification statistics – such as the root mean square error of prediction (RMSEP), the mean absolute error of prediction (MAEP), the reduction of error (RE), and the coefficient of efficiency (CE) – are concerned. The SMA regression model is used to estimate the intensity of public use in all 273 natural treatment systems. System type, influent water quality, and area are found to statistically affect public use, consistently with a priori expectations. Publicly available information regarding the home location of the sampled visitors is derived from their social media profiles and used to infer the distance they are willing to travel to visit the natural treatment systems in the database. Such information is analyzed using the travel cost method to derive monetary estimates of the recreational benefits of the investigated natural treatment systems. Overall, the findings confirm the opportunities arising from an integrated design and management of natural treatment systems, which combines the objectives of water quality enhancement and provision of cultural ecosystem services through public use in a multi-functional approach and compatibly with the need to protect public health.

Keywords: constructed wetlands, cultural ecosystem services, ecological engineering, waste stabilization ponds

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

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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

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Authors: S. Sabatino, V. Calderaro, V. Galdi, G. Graber, L. Ippolito

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Climate change is a rapidly growing global threat caused mainly by increased emissions of carbon dioxide (CO₂) into the atmosphere. These emissions come from multiple sources, including industry, power generation, and the transport sector. The need to tackle climate change and reduce CO₂ emissions is indisputable. A crucial solution to achieving decarbonization in the transport sector is the adoption of electric vehicles (EVs). These vehicles use lithium (Li-Ion) batteries as an energy source, making them extremely efficient and with low direct emissions. However, Li-Ion batteries are not without problems, including the risk of overheating and performance degradation. To ensure its safety and longevity, it is essential to use a battery management system (BMS). The BMS constantly monitors battery status, adjusts temperature and cell balance, ensuring optimal performance and preventing dangerous situations. From the monitoring carried out, it is also able to optimally manage the battery to increase its life. Among the parameters monitored by the BMS, the main ones are State of Charge (SoC), State of Health (SoH), and State of Power (SoP). The evaluation of these parameters can be carried out in two ways: offline, using benchtop batteries tested in the laboratory, or online, using batteries installed in moving vehicles. Online estimation is the preferred approach, as it relies on capturing real-time data from batteries while operating in real-life situations, such as in everyday EV use. Actual battery usage conditions are highly variable. Moving vehicles are exposed to a wide range of factors, including temperature variations, different driving styles, and complex charge/discharge cycles. This variability is difficult to replicate in a controlled laboratory environment and can greatly affect performance and battery life. Online estimation captures this variety of conditions, providing a more accurate assessment of battery behavior in real-world situations. In this article, a hybrid approach based on a neural network and a statistical method for real-time estimation of SoC, SoH, and SoP parameters of interest is proposed. These parameters are estimated from the analysis of a one-day driving profile of an electric vehicle, assumed to be divided into the following four phases: (i) Partial discharge (SoC 100% - SoC 50%), (ii) Partial discharge (SoC 50% - SoC 80%), (iii) Deep Discharge (SoC 80% - SoC 30%) (iv) Full charge (SoC 30% - SoC 100%). The neural network predicts the values of ohmic resistance and incremental capacity, while the statistical method is used to estimate the parameters of interest. This reduces the complexity of the model and improves its prediction accuracy. The effectiveness of the proposed model is evaluated by analyzing its performance in terms of square mean error (RMSE) and percentage error (MAPE) and comparing it with the reference method found in the literature.

Keywords: electric vehicle, Li-Ion battery, BMS, state-of-charge, state-of-health, state-of-power, artificial neural networks

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Authors: Damian Graham, Ashleigh H. Hall, Damani R. Sulph, Michael A. James, Shawn B. Vassell

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This paper assesses the design and feasibility of a Materials Recovery Facility (MRF) in Jamaica as a possible green industry approach to the nation’s economic and solid waste management problems. Jamaica is a developing nation that is vulnerable to climate change that can affect its blue economy and tourism on which it is heavily reliant. Jamaica’s National Solid Waste Management Authority (NSWMA) collects only a fraction of all the solid waste produced annually which is then transported to dumpsites. The remainder is either burnt by the population or disposed of illegally. These practices negatively impact the environment, threaten the sustainability of economic growth from blue economy and tourism and its waste management system is predominantly a cost centre. The implementation of an MRF could boost the manufacturing sector, contribute to economic growth, and be a catalyst in creating a green industry with multiple downstream value chains with supply chain linkages. Globally, there is a trend to reuse and recycle that created an international market for recycled solid waste. MRFs enable the efficient sorting of solid waste into desired recoverable materials thus providing a gateway for entrance to the international trading of recycled waste. Research into the current state and effort to improve waste management in Jamaica in contrast with the similar and more advanced territories are outlined. The study explores the concept of green industrialization and its applicability to vulnerable small state economies like Jamaica. The study highlights the possible contributions and benefits derived from MRFs as a seeding factory that can anchor the reverse and forward logistics of other green industries as part of a logistic-cantered economy. Further, the study showcases an engineering economic analysis that assesses the viability of the implementation of an MRF in Jamaica. This research outlines the potential cost of constructing and operating an MRF and provides a realistic cash flow estimate to establish a baseline for profitability. The approach considers quantitative and qualitative data, assumptions, and modelling using industrial engineering tools and techniques that are outlined. Techniques of facility planning, system analysis and operations research with a focus on linear programming techniques are expressed. Approaches to overcome some implementation challenges including policy, technology and public education are detailed. The results of this study present a reasonable judgment of the prospects of incorporating an MRF to improve Jamaica’s solid waste management and contribute to socioeconomic and environmental benefits and an alternate pathway for economic sustainability.

Keywords: engineering-economic analysis, facility design, green industry, MRF, manufacturing, plant layout, solid-waste management, sustainability, waste disposal

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Authors: Casta Guillaume, Robert Jagers, Deborah Rivas-Drake

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Civically engaged youth are critical to maintaining and/or improving the functioning of local, national and global communities and their institutions. The present study investigated how school climate and academic beliefs (academic self-efficacy and school belonging) may inform emergent civic behaviors (emergent citizenship) among self-identified middle school youth of color (African American, Multiracial or Mixed, Latino, Asian American or Pacific Islander, Native American, and other). Study aims: 1) Understand whether and how school climate is associated with civic engagement behaviors, directly and indirectly, by fostering a positive sense of connection to the school and/or engendering feelings of self-efficacy in the academic domain. Accordingly, we examined 2) The association of youths’ sense of school connection and academic self-efficacy with their personally responsible and participatory civic behaviors in school and community contexts—both concurrently and longitudinally. Data from two subsamples of a larger study of social/emotional development among middle school students were used for longitudinal and cross sectional analysis. The cross-sectional sample included 324 6th-8th grade students, of which 43% identified as African American, 20% identified as Multiracial or Mixed, 18% identified as Latino, 12% identified as Asian American or Pacific Islander, 6% identified as Other, and 1% identified as Native American. The age of the sample ranged from 11 – 15 (M = 12.33, SD = .97). For the longitudinal test of our mediation model, we drew on data from the 6th and 7th grade cohorts only (n =232); the ethnic and racial diversity of this longitudinal subsample was virtually identical to that of the cross-sectional sample. For both the cross-sectional and longitudinal analyses, full information maximum likelihood was used to deal with missing data. Fit indices were inspected to determine if they met the recommended thresholds of RMSEA below .05 and CFI and TLI values of at least .90. To determine if particular mediation pathways were significant, the bias-corrected bootstrap confidence intervals for each indirect pathway were inspected. Fit indices for the latent variable mediation model using the cross-sectional data suggest that the hypothesized model fit the observed data well (CFI = .93; TLI =. 92; RMSEA = .05, 90% CI = [.04, .06]). In the model, students’ perceptions of school climate were significantly and positively associated with greater feelings of school connectedness, which were in turn significantly and positively associated with civic engagement. In addition, school climate was significantly and positively associated with greater academic self-efficacy, but academic self-efficacy was not significantly associated with civic engagement. Tests of mediation indicated there was one significant indirect pathway between school climate and civic engagement behavior. There was an indirect association between school climate and civic engagement via its association with sense of school connectedness, indirect association estimate = .17 [95% CI: .08, .32]. The aforementioned indirect association via school connectedness accounted for 50% (.17/.34) of the total effect. Partial support was found for the prediction that students’ perceptions of a positive school climate are linked to civic engagement in part through their role in students’ sense of connection to school.

Keywords: civic engagement, early adolescence, school climate, school belonging, developmental niche

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Authors: Rida Wanbha Nongbri, Sabuj Kumar Mandal

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The Sustainable Development Goals (SDGs) for 2016-2030 were adopted in response to Millennium Development Goals (MDGs) which focused on access to sustainable water and sanitations. For over a decade, water has been a significant subject that is explored in various facets of life. Our day-to-day life is significantly impacted by water poverty at the socio-economic level. Reducing water poverty is an important policy challenge, particularly in emerging economies like India, owing to its population growth, huge variation in topology and climatic factors. To design appropriate water policies and its effectiveness, a proper measurement of water poverty is essential. In this backdrop, this study uses the Alkire Foster (AF) methodology to estimate a multidimensional water poverty index for India at the household level. The methodology captures several attributes to understand the complex issues related to households’ water deprivation. The study employs two rounds of Indian Human Development Survey data (IHDS 2005 and 2012) which focuses on 4 dimensions of water poverty including water access, water quantity, water quality, and water capacity, and seven indicators capturing these four dimensions. In order to quantify water deprivation at the household level, an AF dual cut-off counting method is applied and Multidimensional Water Poverty Index (MWPI) is calculated as the product of Headcount Ratio (Incidence) and average share of weighted dimension (Intensity). The results identify deprivation across all dimensions at the country level and show that a large proportion of household in India is deprived of quality water and suffers from water access in both 2005 and 2012 survey rounds. The comparison between the rural and urban households shows that higher ratio of the rural households are multidimensionally water poor as compared to their urban counterparts. Among the four dimensions of water poverty, water quality is found to be the most significant one for both rural and urban households. In 2005 round, almost 99.3% of households are water poor for at least one of the four dimensions, and among the water poor households, the intensity of water poverty is 54.7%. These values do not change significantly in 2012 round, but we could observe significance differences across the dimensions. States like Bihar, Tamil Nadu, and Andhra Pradesh are ranked the most in terms of MWPI, whereas Sikkim, Arunachal Pradesh and Chandigarh are ranked the lowest in 2005 round. Similarly, in 2012 round, Bihar, Uttar Pradesh and Orissa rank the highest in terms of MWPI, whereas Goa, Nagaland and Arunachal Pradesh rank the lowest. The policy implications of this study can be multifaceted. It can urge the policy makers to focus either on the impoverished households with lower intensity levels of water poverty to minimize total number of water poor households or can focus on those household with high intensity of water poverty to achieve an overall reduction in MWPI.

Keywords: .alkire-foster (AF) methodology, deprivation, dual cut-off, multidimensional water poverty index (MWPI)

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Authors: Samia Elfekih, Wee Tek Tay, Karl Gordon, Paul De Barro

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Invasive species represent an increasing threat to food biosecurity, causing significant economic losses in agricultural systems. An example is the sweet potato whitefly, Bemisia tabaci, which is a complex of morphologically indistinguishable species causing average annual global damage estimated at US$2.4 billion. The Bemisia complex represents an interesting model for evolutionary studies because of their extensive distribution and potential for invasiveness and population expansion. Within this complex, two species, Middle East-Asia Minor 1 (MEAM1) and Mediterranean (MED) have invaded well beyond their home ranges whereas others, such as Indian Ocean (IO) and Australia (AUS), have not. In order to understand why some Bemisia species have become invasive, genome-wide sequence scans were used to estimate population dynamics over time and relate these to climate. The Bayesian Skyline Plot (BSP) method as implemented in BEAST was used to infer the historical effective population size. In order to overcome sampling bias, the populations were combined based on geographical origin. The datasets used for this particular analysis are genome-wide SNPs (single nucleotide polymorphisms) called separately in each of the following groups: Sub-Saharan Africa (Burkina Faso), Europe (Spain, France, Greece and Croatia), USA (Arizona), Mediterranean-Middle East (Israel, Italy), Middle East-Central Asia (Turkmenistan, Iran) and Reunion Island. The non-invasive ‘AUS’ species endemic to Australia was used as an outgroup. The main findings of this study show that the BSP for the Sub-Saharan African MED population is different from that observed in MED populations from the Mediterranean Basin, suggesting evolution under a different set of environmental conditions. For MED, the effective size of the African (Burkina Faso) population showed a rapid expansion ≈250,000-310,000 years ago (YA), preceded by a period of slower growth. The European MED populations (i.e., Spain, France, Croatia, and Greece) showed a single burst of expansion at ≈160,000-200,000 YA. The MEAM1 populations from Israel and Italy and the ones from Iran and Turkmenistan are similar as they both show the earlier expansion at ≈250,000-300,000 YA. The single IO population lacked the latter expansion but had the earlier one. This pattern is shared with the Sub-Saharan African (Burkina Faso) MED, suggesting IO also faced a similar history of environmental change, which seems plausible given their relatively close geographical distributions. In conclusion, populations within the invasive species MED and MEAM1 exhibited signatures of population expansion lacking in non-invasive species (IO and AUS) during the Pleistocene, a geological epoch marked by repeated climatic oscillations with cycles of glacial and interglacial periods. These expansions strongly suggested the potential of some Bemisia species’ genomes to affect their adaptability and invasiveness.

Keywords: whitefly, RADseq, invasive species, SNP, climate change

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Authors: Vitor M. Vilas Boas, Cleison D. Silva, Gustavo S. Mafra, Alexandre Trofino Neto

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Motor imagery (MI) based brain-computer interfaces (BCI) uses event-related (de)synchronization (ERS/ ERD), typically recorded using electroencephalography (EEG), to translate brain electrical activity into control commands. To mitigate undesirable artifacts and noise measurements on EEG signals, methods based on band-pass filters defined by a specific frequency band (i.e., 8 – 30Hz), such as the Infinity Impulse Response (IIR) filters, are typically used. Spatial techniques, such as Common Spatial Patterns (CSP), are also used to estimate the variations of the filtered signal and extract features that define the imagined motion. The CSP effectiveness depends on the subject's discriminative frequency, and approaches based on the decomposition of the band of interest into sub-bands with smaller frequency ranges (SBCSP) have been suggested to EEG signals classification. However, despite providing good results, the SBCSP approach generally increases the computational cost of the filtering step in IM-based BCI systems. This paper proposes the use of the Fast Fourier Transform (FFT) algorithm in the IM-based BCI filtering stage that implements SBCSP. The goal is to apply the FFT algorithm to reduce the computational cost of the processing step of these systems and to make them more efficient without compromising classification accuracy. The proposal is based on the representation of EEG signals in a matrix of coefficients resulting from the frequency decomposition performed by the FFT, which is then submitted to the SBCSP process. The structure of the SBCSP contemplates dividing the band of interest, initially defined between 0 and 40Hz, into a set of 33 sub-bands spanning specific frequency bands which are processed in parallel each by a CSP filter and an LDA classifier. A Bayesian meta-classifier is then used to represent the LDA outputs of each sub-band as scores and organize them into a single vector, and then used as a training vector of an SVM global classifier. Initially, the public EEG data set IIa of the BCI Competition IV is used to validate the approach. The first contribution of the proposed method is that, in addition to being more compact, because it has a 68% smaller dimension than the original signal, the resulting FFT matrix maintains the signal information relevant to class discrimination. In addition, the results showed an average reduction of 31.6% in the computational cost in relation to the application of filtering methods based on IIR filters, suggesting FFT efficiency when applied in the filtering step. Finally, the frequency decomposition approach improves the overall system classification rate significantly compared to the commonly used filtering, going from 73.7% using IIR to 84.2% using FFT. The accuracy improvement above 10% and the computational cost reduction denote the potential of FFT in EEG signal filtering applied to the context of IM-based BCI implementing SBCSP. Tests with other data sets are currently being performed to reinforce such conclusions.

Keywords: brain-computer interfaces, fast Fourier transform algorithm, motor imagery, sub-band common spatial patterns

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Authors: A. Sgobba, C. Meskell

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The feasibility of on-site electricity production from solar and wind and the resulting load management for a specific manufacturing plant in Ireland are assessed. The industry sector accounts directly and indirectly for a high percentage of electricity consumption and global greenhouse gas emissions; therefore, it will play a key role in emission reduction and control. Manufacturing plants, in particular, are often located in non-residential areas since they require open spaces for production machinery, parking facilities for the employees, appropriate routes for supply and delivery, special connections to the national grid and other environmental impacts. Since they have larger spaces compared to commercial sites in urban areas, they represent an appropriate case study for evaluating the technical and economic viability of energy system integration with low power density technologies, such as solar and wind, for on-site electricity generation. The available open space surrounding the analysed manufacturing plant can be efficiently used to produce a discrete quantity of energy, instantaneously and locally consumed. Therefore, transmission and distribution losses can be reduced. The usage of storage is not required due to the high and almost constant electricity consumption profile. The energy load of the plant is identified through the analysis of gas and electricity consumption, both internally monitored and reported on the bills. These data are not often recorded and available to third parties since manufacturing companies usually keep track only of the overall energy expenditures. The solar potential is modelled for a period of 21 years based on global horizontal irradiation data; the hourly direct and diffuse radiation and the energy produced by the system at the optimum pitch angle are calculated. The model is validated using PVWatts and SAM tools. Wind speed data are available for the same period within one-hour step at a height of 10m. Since the hub of a typical wind turbine reaches a higher altitude, complementary data for a different location at 50m have been compared, and a model for the estimate of wind speed at the required height in the right location is defined. Weibull Statistical Distribution is used to evaluate the wind energy potential of the site. The results show that solar and wind energy are, as expected, generally decoupled. Based on the real case study, the percentage of load covered every hour by on-site generation (Level of Autonomy LA) and the resulting electricity bought from the grid (Expected Energy Not Supplied EENS) are calculated. The economic viability of the project is assessed through Net Present Value, and the influence the main technical and economic parameters have on NPV is presented. Since the results show that the analysed renewable sources can not provide enough electricity, the integration with a cogeneration technology is studied. Finally, the benefit to energy system integration of wind, solar and a cogeneration technology is evaluated and discussed.

Keywords: demand, energy system integration, load, manufacturing, national grid, renewable energy sources

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Authors: Abderrhamane Jarou, Dominique Sauter, Christophe Aubrun

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Over the past decade, the growing demand for energy efficiency in buildings has attracted the attention of the control community. Failures in HVAC (heating, ventilation and air conditioning) systems in buildings can have a significant impact on the desired and expected energy performance of buildings and on the user's comfort as well. FTC is a recent technology area that studies the adaptation of control algorithms to faulty operating conditions of a system. The application of Fault-Tolerant Control (FTC) in HVAC systems has gained attention in the last two decades. The objective is to maintain the variations in system performance due to faults within an acceptable range with respect to the desired nominal behavior. This paper considers the so-called active approach, which is based on fault and identification scheme combined with a control reconfiguration algorithm that consists in determining a new set of control parameters so that the reconfigured performance is "as close as possible, "in some sense, to the nominal performance. Thermal models of buildings and their HVAC systems are described by non-linear (usually bi-linear) equations. Most of the works carried out so far in FDI (fault diagnosis and isolation) or FTC consider a linearized model of the studied system. However, this model is only valid in a reduced range of variation. This study presents a new fault diagnosis (FD) algorithm based on a bilinear observer for the detection and accurate estimation of the magnitude of the HVAC system failure. The main contribution of the proposed FD algorithm is that instead of using specific linearized models, the algorithm inherits the structure of the actual bilinear model of the building thermal dynamics. As an immediate consequence, the algorithm is applicable to a wide range of unpredictable operating conditions, i.e., weather dynamics, outdoor air temperature, zone occupancy profile. A bilinear fault detection observer is proposed for a bilinear system with unknown inputs. The residual vector in the observer design is decoupled from the unknown inputs and, under certain conditions, is made sensitive to all faults. Sufficient conditions are given for the existence of the observer and results are given for the explicit computation of observer design matrices. Dedicated observer schemes (DOS) are considered for sensor FDI while unknown input bilinear observers are considered for actuator or system components FDI. The proposed strategy for FTC works as follows: At a first level, FDI algorithms are implemented, making it also possible to estimate the magnitude of the fault. Once the fault is detected, the fault estimation is then used to feed the second level and reconfigure the control low so that that expected performances are recovered. This paper is organized as follows. A general structure for fault-tolerant control of buildings is first presented and the building model under consideration is introduced. Then, the observer-based design for Fault Diagnosis of bilinear systems is studied. The FTC approach is developed in Section IV. Finally, a simulation example is given in Section V to illustrate the proposed method.

Keywords: bilinear systems, fault diagnosis, fault-tolerant control, multi-zones building

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Authors: Jiaying Zhang, Xin Mu, Chang Ni, Jeff L. Zhang

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Magnetic resonance elastography (MRE) non-invasively assesses tissue elastic properties, such as shear modulus, by measuring tissue’s displacement in response to mechanical waves. The estimated metrics on tissue elasticity or stiffness have been shown to be valuable for monitoring physiologic or pathophysiologic status of tissue, such as a tumor or fatty liver. To quantify tissue shear modulus from MRE-acquired displacements (essentially an inverse problem), multiple approaches have been proposed, including Local Frequency Estimation (LFE) and Direct Inversion (DI). However, one common problem with these methods is that the estimates are severely noise-sensitive due to either the inverse-problem nature or noise propagation in the pixel-by-pixel process. With the advent of deep learning (DL) and its promise in solving inverse problems, a few groups in the field of MRE have explored the feasibility of using DL methods for quantifying shear modulus from MRE data. Most of the groups chose to use real MRE data for DL model training and to cut training images into smaller patches, which enriches feature characteristics of training data but inevitably increases computation time and results in outcomes with patched patterns. In this study, simulated wave images generated by Finite Differential Time Domain (FDTD) simulation are used for network training, and U-Net is used to extract features from each training image without cutting it into patches. The use of simulated data for model training has the flexibility of customizing training datasets to match specific applications. The proposed method aimed to estimate tissue shear modulus from MRE data with high robustness to noise and high model-training efficiency. Specifically, a set of 3000 maps of shear modulus (with a range of 1 kPa to 15 kPa) containing randomly positioned objects were simulated, and their corresponding wave images were generated. The two types of data were fed into the training of a U-Net model as its output and input, respectively. For an independently simulated set of 1000 images, the performance of the proposed method against DI and LFE was compared by the relative errors (root mean square error or RMSE divided by averaged shear modulus) between the true shear modulus map and the estimated ones. The results showed that the estimated shear modulus by the proposed method achieved a relative error of 4.91%±0.66%, substantially lower than 78.20%±1.11% by LFE. Using simulated data, the proposed method significantly outperformed LFE and DI in resilience to increasing noise levels and in resolving fine changes of shear modulus. The feasibility of the proposed method was also tested on MRE data acquired from phantoms and from human calf muscles, resulting in maps of shear modulus with low noise. In future work, the method’s performance on phantom and its repeatability on human data will be tested in a more quantitative manner. In conclusion, the proposed method showed much promise in quantifying tissue shear modulus from MRE with high robustness and efficiency.

Keywords: deep learning, magnetic resonance elastography, magnetic resonance imaging, shear modulus estimation

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Authors: Sukhvinder Kaur, Ashok Agarwal

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Background: For the first time in 2009, India was able to generate estimates of HIV incidence (the number of new HIV infections per year). Analysis of epidemic projections helped in revealing that the number of new annual HIV infections in India had declined by more than 50% during the last decade (GOI Ministry of Health and Family Welfare, 2010). Then, National AIDS Control Organisation (NACO) planned to scale up its efforts in generating projections through epidemiological analysis and modelling by taking recent available sources of evidence such as HIV Sentinel Surveillance (HSS), India Census data and other critical data sets. Recently, NACO generated current round of HIV estimates-2012 through globally recommended tool “Spectrum Software” and came out with the estimates for adult HIV prevalence, annual new infections, number of people living with HIV, AIDS-related deaths and treatment needs. State level prevalence and incidence projections produced were used to project consequences of the epidemic in spectrum. In presence of HIV estimates generated at state level in India by NACO, USIAD funded PIPPSE project under the leadership of NACO undertook the estimations and projections to district level using same Spectrum software. In 2011, adult HIV prevalence in one of the high prevalent States, Maharashtra was 0.42% ahead of the national average of 0.27%. Considering the heterogeneity of HIV epidemic between districts, two districts of Maharashtra – Thane and Mumbai were selected to estimate and project the number of People-Living-with-HIV/AIDS (PLHIV), HIV-prevalence among adults and annual new HIV infections till 2017. Methodology: Inputs in spectrum included demographic data from Census of India since 1980 and sample registration system, programmatic data on ‘Alive and on ART (adult and children)’,‘Mother-Baby pairs under PPTCT’ and ‘High Risk Group (HRG)-size mapping estimates’, surveillance data from various rounds of HSS, National Family Health Survey–III, Integrated Biological and Behavioural Assessment and Behavioural Sentinel Surveillance. Major Findings: Assuming current programmatic interventions in these districts, an estimated decrease of 12% points in Thane and 31% points in Mumbai among new infections in HRGs and migrants is observed from 2011 by 2017. Conclusions: Project also validated decrease in HIV new infection among one of the high risk groups-FSWs using program cohort data since 2012 to 2016. Though there is a decrease in HIV prevalence and new infections in Thane and Mumbai, further decrease is possible if appropriate programme response, strategies and interventions are envisaged for specific target groups based on this evidence. Moreover, evidence need to be validated by other estimation/modelling techniques; and evidence can be generated for other districts of the state, where HIV prevalence is high and reliable data sources are available, to understand the epidemic within the local context.

Keywords: HIV sentinel surveillance, high risk groups, projections, new infections

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Authors: Ani Wilson Uchenna, Ogbonna Chikodi

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This study examined the impact of artificial intelligence driven applications on banks’ performance in West Africa using Nigeria and Ghana as case studies. Specifically, the study examined the extent to which deployment of smart automated teller machine impacts the banks’ net worth within the reference period in Nigeria and Ghana. It ascertained the impact of point of sale on banks’ net worth within the reference period in Nigeria and Ghana. Thirdly, it verified the extent to which webpay services can influence banks’ performance in Nigeria and Ghana and finally, determined the impact of mobile pay services on banks’ performance in Nigeria and Ghana. The study used automated teller machine (ATM), Point of sale services (POS), Mobile pay services (MOP) and Web pay services (WBP) as proxies for explanatory variables while Bank net worth was used as explained variable for the study. The data for this study were sourced from central bank of Nigeria (CBN) Statistical Bulletin as well as Bank of Ghana (BoGH) Statistical Bulletin, Ghana payment systems oversight annual report and world development indicator (WDI). Furthermore, the mixed order of integration observed from the panel unit test result justified the use of autoregressive distributed lag (ARDL) approach to data analysis which the study adopted. While the cointegration test showed the existence of cointegration among the studied variables, bound test result justified the presence of long-run relationship among the series. Again, ARDL error correction estimate established satisfactory (13.92%) speed of adjustment from long run disequilibrium back to short run dynamic relationship. The study found that while Automated teller machine (ATM) had statistically significant impact on bank net worth (BNW) of Nigeria and Ghana, point of sale services application (POS) statistically and significantly impact on bank net worth within the study period, mobile pay services application was statistically significant in impacting the changes in the bank net worth of the countries of study while web pay services (WBP) had no statistically significant impact on bank net worth of the countries of reference. The study concluded that artificial intelligence driven application have significant an positive impact on bank performance with exception of web pay which had negative impact on bank net worth. The study recommended that management of banks both in Nigerian and Ghanaian should encourage more investments in AI-powered smart ATMs aimed towards delivering more secured banking services in order to increase revenue, discourage excessive queuing in the banking hall, reduced fraud and minimize error in processing transaction. Banks within the scope of this study should leverage on modern technologies to checkmate the excesses of the private operators POS in order to build more confidence on potential customers. Government should convert mobile pay services to a counter terrorism tool by ensuring that restrictions on over-the-counter withdrawals to a minimum amount is maintained and place sanctions on withdrawals above that limit.

Keywords: artificial intelligence (ai), bank performance, automated teller machines (atm), point of sale (pos)

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Authors: Matar Celine, Gaucel Sebastien, Gontard Nathalie, Guilbert Stephane, Guillard Valerie

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To increase the very short shelf life of fresh fruits and vegetable, Modified Atmosphere Packaging (MAP) allows an optimal atmosphere composition to be maintained around the product and thus prevent its decay. This technology relies on the modification of internal packaging atmosphere due to equilibrium between production/consumption of gases by the respiring product and gas permeation through the packaging material. While, to the best of our knowledge, benefit of MAP for fresh fruits and vegetable has been widely demonstrated in the literature, its effect on shelf life increase has never been quantified and formalized in a clear and simple manner leading difficult to anticipate its economic and environmental benefit, notably through the decrease of food losses. Mathematical modelling of mass transfers in the food/packaging system is the basis for a better design and dimensioning of the food packaging system. But up to now, existing models did not permit to estimate food quality nor shelf life gain reached by using MAP. However, shelf life prediction is an indispensable prerequisite for quantifying the effect of MAP on food losses reduction. The objective of this work is to propose an innovative approach to predict shelf life of MAP food product and then to link it to a reduction of food losses and wastes. In this purpose, a ‘Virtual MAP modeling tool’ was developed by coupling a new predictive deterioration model (based on visual surface prediction of deterioration encompassing colour, texture and spoilage development) with models of the literature for respiration and permeation. A major input of this modelling tool is the maximal percentage of deterioration (MAD) which was assessed from dedicated consumers’ studies. Strawberries of the variety Charlotte were selected as the model food for its high perishability, high respiration rate; 50-100 ml CO₂/h/kg produced at 20°C, allowing it to be a good representative of challenging post-harvest storage. A value of 13% was determined as a limit of acceptability for the consumers, permitting to define products’ shelf life. The ‘Virtual MAP modeling tool’ was validated in isothermal conditions (5, 10 and 20°C) and in dynamic temperature conditions mimicking commercial post-harvest storage of strawberries. RMSE values were systematically lower than 3% for respectively, O₂, CO₂ and deterioration profiles as a function of time confirming the goodness of model fitting. For the investigated temperature profile, a shelf life gain of 0.33 days was obtained in MAP compared to the conventional storage situation (no MAP condition). Shelf life gain of more than 1 day could be obtained for optimized post-harvest conditions as numerically investigated. Such shelf life gain permitted to anticipate a significant reduction of food losses at the distribution and consumer steps. This food losses' reduction as a function of shelf life gain has been quantified using a dedicated mathematical equation that has been developed for this purpose.

Keywords: food losses and wastes, modified atmosphere packaging, mathematical modeling, shelf life prediction

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Authors: Raja Umer Sajjad, Chang Hee Lee

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Stormwater runoff is the leading contributor to pollution of receiving waters. In response, an efficient stormwater monitoring program is required to quantify and eventually reduce stormwater pollution. The overall goals of stormwater monitoring programs primarily include the identification of high-risk dischargers and the development of total maximum daily loads (TMDLs). The challenge in developing better monitoring program is to reduce the variability in flux estimates due to sampling errors; however, the success of monitoring program mainly depends on the accuracy of the estimates. Apart from sampling errors, manpower and budgetary constraints also influence the quality of the estimates. This study attempted to develop optimum stormwater monitoring design considering both cost and the quality of the estimated pollutants flux. Three years stormwater monitoring data (2012 – 2014) from a mix land use located within Geumhak watershed South Korea was evaluated. The regional climate is humid and precipitation is usually well distributed through the year. The investigation of a large number of water quality parameters is time-consuming and resource intensive. In order to identify a suite of easy-to-measure parameters to act as a surrogate, Principal Component Analysis (PCA) was applied. Means, standard deviations, coefficient of variation (CV) and other simple statistics were performed using multivariate statistical analysis software SPSS 22.0. The implication of sampling time on monitoring results, number of samples required during the storm event and impact of seasonal first flush were also identified. Based on the observations derived from the PCA biplot and the correlation matrix, total suspended solids (TSS) was identified as a potential surrogate for turbidity, total phosphorus and for heavy metals like lead, chromium, and copper whereas, Chemical Oxygen Demand (COD) was identified as surrogate for organic matter. The CV among different monitored water quality parameters were found higher (ranged from 3.8 to 15.5). It suggests that use of grab sampling design to estimate the mass emission rates in the study area can lead to errors due to large variability. TSS discharge load calculation error was found only 2 % with two different sample size approaches; i.e. 17 samples per storm event and equally distributed 6 samples per storm event. Both seasonal first flush and event first flush phenomena for most water quality parameters were observed in the study area. Samples taken at the initial stage of storm event generally overestimate the mass emissions; however, it was found that collecting a grab sample after initial hour of storm event more closely approximates the mean concentration of the event. It was concluded that site and regional climate specific interventions can be made to optimize the stormwater monitoring program in order to make it more effective and economical.

Keywords: first flush, pollutant load, stormwater monitoring, surrogate parameters

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Authors: Antero J. S. Kivinen, Suvi-Sadetta Kaarakainen

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Digitalization touches every aspect of contemporary society, changing the way we live our everyday life. Contemporary society is sometimes described as knowledge society including unprecedented amount of information people face daily. The tools to manage this information flow are ICT-skills which are both technical skills and reflective skills needed to manage incoming information. Therefore schools are under constant pressure of revision. In the latest Programme for International Student Assessment (PISA) girls have been outperforming boys in all Organization for Economic Co-operation and Development (OECD) member countries and the gender gap between girls and boys is widest in Finland. This paper presents results of the Comprehensive Schools in the Digital Age project of RUSE, University of Turku. The project is in connection with Finnish Government Analysis, Assessment and Research Activities. First of all, this paper examines gender differences in ICT-skills of Finnish upper comprehensive school students. Secondly, it explores in which way differences are changing when students proceed to upper secondary and vocational education. ICT skills are measured using a performance-based ICT-skill test. Data is collected in 3 phases, January-March 2017 (upper comprehensive schools, n=5455), September-December 2017 (upper secondary and vocational schools, n~3500) and January-March 2018 (Upper comprehensive schools). The age of upper comprehensive school student’s is 15-16 and upper secondary and vocational school 16-18. The test is divided into 6 categories: basic operations, productivity software, social networking and communication, content creation and publishing, applications and requirements for the ICT study programs. Students have filled a survey about their ICT-usage and study materials they use in school and home. Cronbach's alpha was used to estimate the reliability of the ICT skill test. Statistical differences between genders were examined using two-tailed independent samples t-test. Results of first data from upper comprehensive schools show that there is no statistically significant difference in ICT-skill tests total scores between genders (boys 10.24 and girls 10.64, maximum being 36). Although, there were no gender difference in total test scores, there are differences in above mentioned six categories. Girls get better scores on school related and social networking test subjects while boys perform better on more technical oriented subjects. Test scores on basic operations are quite low for both groups. Perhaps these can partly be explained by the fact that the test was made on computers and majority of students ICT-usage consist of smartphones and tablets. Against this background it is important to analyze further the reasons for these differences. In a context of ongoing digitalization of everyday life and especially working life, the significant purpose of this analyses is to find answers how to guarantee the adequate ICT skills for all students.

Keywords: basic education, digitalization, gender differences, ICT-skills, upper comprehensive education, upper secondary education, vocational education

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Authors: Ezzi Olfa, Bouafia Nabiha, Ammar Asma, Ben Cheikh Asma, Mahjoub Mohamed, Bannour Wadiaa, Achour Bechir, Khelif Abderrahim, Njah Mansour

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Background: In hematology/oncology, health care improvement has allowed increasingly aggressive management in diagnostic and therapeutic procedures. Nevertheless, these intensified procedures have been associated with higher risk of healthcare associated infections (HAIs). We undertook this study to estimate the burden of HAIs in the cancer patients in an onco -hematology unit in a Tunisian university hospital. Materials/Methods: A prospective, observational study, based on active surveillance for a period of 06 months from Mars through September 2016, was undertaken in the department of onco-hematology in a university hospital in Tunisia. Patients, who stayed in the unit for ≥ 48 h, were followed until hospital discharge. The Centers for Disease Control and Prevention criteria (CDC) for site-specific infections were used as standard definitions for HAIs. Results: One hundred fifty patients were included in the study. The gender distribution was 33.3% for girls and 66.6% boys. They have a mean age of 23.12 years (SD = 18.36 years). The main patient’s diagnosis is: Acute Lymphoblastic Leukemia (ALL): 48.7 %( n=73). The mean length of stay was 21 days +/- 18 days. Almost 8% of patients had an implantable port (n= 12), 34.9 % (n=52) had a lumber puncture and 42.7 % (n= 64) had a medullary puncture. Chemotherapy was instituted in 88% of patients (n=132). Eighty (53.3%) patients had neutropenia at admission. The incidence rate of HAIs was 32.66 % per patient; the incidence density was 15.73 per 1000 patient-days in the unit. Mortality rate was 9.3% (n= 14), and 50% of cases of death were caused by HAIs. The most frequent episodes of infection were: infection of skin and superficial mucosa (5.3%), pulmonary aspergillosis (4.6%), Healthcare associated pneumonia (HAP) (4%), Central venous catheter associated infection (4%), digestive infection (5%), and primary bloodstream infection (2.6%). Finally, fever of unknown origin (FUO) incidence rate was 14%. In case of skin and superficial infection (n= 8), 4 episodes were documented, and organisms implicated were Escherichia.coli, Geotricum capitatum and Proteus mirabilis. For pulmonary aspergillosis, 6 cases were diagnosed clinically and radiologically, and one was proved by positive aspergillus antigen in bronchial aspiration. Only one patient died due this infection. In HAP (6 cases), four episodes were diagnosed clinically and radiologically. No bacterial etiology was established in these cases. Two patients died due to HAP. For primary bloodstream infection (4 cases), implicated germs were Enterobacter cloacae, Geotricum capitatum, klebsiella pneumoniae, and Streptococcus pneumoniae. Conclusion: This type of prospective study is an indispensable tool for internal quality control. It is necessary to evaluate preventive measures and design control guides and strategies aimed to reduce the HAI’s rate and the morbidity and mortality associated with infection in a hematology/oncology unit.

Keywords: cohort prospective studies, healthcare associated infections, hematology oncology department, incidence

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