Search results for: demand models

Authors: D. Muyama, M. Luyiga, P. Buyungo, D. Chemonges, M. Namukwaya, L. Ssekabembe, B. Lukwago, D. Kyamagwa

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Context: The study focuses on the sustainability of health interventions in Uganda, particularly in the private sector, beyond donor-funded project periods. The Population Services International (PSI) implemented the Women Health Project (WHP) to ensure continued access to quality family planning, cervical cancer screening, and post-abortion care services through private clinics. Research Aim: The aim of the study is to assess the continued access to quality family planning, cervical cancer screening, and post-abortion care services through the private sector after the closure or reduction in funding of the WHP. Methodology: PSI trained and mentored 83 clinics to establish functional systems in self-regulatory quality improvement, supply chain, referral, and demand creation. The clinics were also connected to the national reporting system and utilized Ministry of Health reporting tools. An assessment tool with six criteria was designed and used to evaluate the progress of the clinics. Clinics scoring 75% and above were considered independent and graduated from the program. Findings: Out of the 83 private clinics, 56 successfully met the graduation criteria and graduated from the program, while 25 lost interest and were gradually dropped. Two clinics failed to achieve the criteria due to leadership challenges. The 59 graduating clinics continued to provide high-quality family planning services, including IUD, implant, Depo-Provera, oral contraceptives, and post-abortion care. All graduating clinics were reassessed and found to still be capable of offering services, attributing their success to government stock availability and acquired skills through mentorships. The clinics expressed appreciation to PSI for the sustainable plan that allowed them to operate beyond the project period. Theoretical Importance: This study contributes to the understanding of sustainability planning and the importance of clinic owners' attitudes and buy-in for continued service provision. It emphasizes the implementation of sustainability plans through existing structures to leverage available resources and ensure continuity of care. Data Collection and Analysis Procedures: The study collected data through the assessment tool that evaluated the progress of clinics based on the established criteria. The tool was scored out of 100%, and clinics scoring above 75% were deemed independent. The findings were analyzed quantitatively to determine the success rate of clinics in meeting the graduation criteria. Questions Addressed: The study addresses the question of whether private clinics in Uganda can sustain the provision of family planning, cervical cancer screening, and post-abortion care services after the closure or reduction in funding of the WHP. Conclusion: The study concludes that the attitude and buy-in of clinic owners are essential for sustainability planning. Implementing sustainability plans through existing structures and leveraging available resources are crucial for the continuity of care after the end of a project or reduced funding. The findings highlight the importance of establishing sustainable plans to ensure continued access to essential health services beyond the project period. Contributions: This study contributes to the existing knowledge for programmers implementing or intending to implement donor-funded projects. It provides insights into designing sustainable plans that enable the independent operation of clinics even after the end of a project.

Keywords: graduation, family planning, systems strengthening, sustainability

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Authors: Hamed Hedayatnia, Nathan Van Den Bossche

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Understanding the ways in which climate change accelerates or slows down the process of material deterioration is the first step towards assessing adaptive approaches for the conservation of historical heritage. Analysis of the climate change effects on the degradation risk assessment parameters like freeze-thaw cycles and wind erosion is also a key parameter when considering mitigating actions. Due to the vulnerability of cultural heritage to climate change, the impact of this phenomenon on material degradation criteria with the focus on brick masonry walls in Timurid heritage, located in Iran, was studied. The Timurids were the final great dynasty to emerge from the Central Asian steppe. Through their patronage, the eastern Islamic world in northwestern of Iran, especially in Mashhad and Herat, became a prominent cultural center. Goharshad Mosque is a mosque in Mashhad of the Razavi Khorasan Province, Iran. It was built by order of Empress Goharshad, the wife of Shah Rukh of the Timurid dynasty in 1418 CE. Choosing an appropriate regional climate model was the first step. The outputs of two different climate model: the 'ALARO-0' and 'REMO,' were analyzed to find out which model is more adopted to the area. For validating the quality of the models, a comparison between model data and observations was done in 4 different climate zones in Iran for a period of 30 years. The impacts of the projected climate change were evaluated until 2100. To determine the material specification of Timurid bricks, standard brick samples from a Timurid mosque were studied. Determination of water absorption coefficient, defining the diffusion properties and determination of real density, and total porosity tests were performed to characterize the specifications of brick masonry walls, which is needed for running HAM-simulations. Results from the analysis showed that the threatening factors in each climate zone are almost different, but the most effective factor around Iran is the extreme temperature increase and erosion. In the north-western region of Iran, one of the key factors is wind erosion. In the north, rainfall erosion and mold growth risk are the key factors. In the north-eastern part, in which our case study is located, the important parameter is wind erosion.

Keywords: brick, climate change, degradation criteria, heritage, Timurid period

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Authors: Divyansh Goel, Varun Jain

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Classical economics works on the principle that people are rational and analytical in their decision making and their choices fall in line with the most suitable option according to the dominant strategy in a standard game theory model. This model has failed at many occasions in estimating the behavior and dealings of rational people, giving proof of some other underlying heuristics and cognitive biases at work. This paper probes into the study of these factors, which fall under the umbrella of behavioral economics and through their medium explore the solution to a problem which a lot of nations presently face. There has long been a wide disparity in the number of people holding favorable views on organ donation and the actual number of people signing up for the same. This paper, in its entirety, is an attempt to shape the public policy which leads to an increase the number of organ donations that take place and close the gap in the statistics of the people who believe in signing up for organ donation and the ones who actually do. The key assumption here is that in cases of cognitive dissonance, where people have an inconsistency due to conflicting views, people have a tendency to go with the default choice. This tendency is a well-documented cognitive bias known as the status quo bias. The research in this project involves an assay of mandated choice models of organ donation with two case studies. The first of an opt-in system of Germany (where people have to explicitly sign up for organ donation) and the second of an opt-out system of Austria (every citizen at the time of their birth is an organ donor and has to explicitly sign up for refusal). Additionally, there has also been presented a detailed analysis of the experiment performed by Eric J. Johnson and Daniel G. Goldstein. Their research as well as many other independent experiments such as that by Tsvetelina Yordanova of the University of Sofia, both of which yield similar results. The conclusion being that the general population has by and large no rigid stand on organ donation and are gullible to status quo bias, which in turn can determine whether a large majority of people will consent to organ donation or not. Thus, in our paper, we throw light on how governments can use status quo bias to drive positive social change by making policies in which everyone by default is marked an organ donor, which will, in turn, save the lives of people who succumb on organ transplantation waitlists and save the economy countless hours of economic productivity.

Keywords: behavioral economics, game theory, organ donation, status quo bias

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Authors: Javed Mohammed

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Right now, bio-medical infrastructure lags well behind the curve. Our healthcare system is dispersed and disjointed; medical records are a bit of a mess; and we do not yet have the capacity to store and process the crazy amounts of data coming our way from widespread whole-genome sequencing. And then there are privacy issues. Despite these infrastructure challenges, some researchers are plunging into bio medical Big Data now, in hopes of extracting new and actionable knowledge. They are doing delving into molecular-level data to discover bio markers that help classify patients based on their response to existing treatments; and pushing their results out to physicians in novel and creative ways. Computer scientists and bio medical researchers are able to transform data into models and simulations that will enable scientists for the first time to gain a profound under-standing of the deepest biological functions. Solving biological problems may require High-Performance Computing HPC due either to the massive parallel computation required to solve a particular problem or to algorithmic complexity that may range from difficult to intractable. Many problems involve seemingly well-behaved polynomial time algorithms (such as all-to-all comparisons) but have massive computational requirements due to the large data sets that must be analyzed. High-throughput techniques for DNA sequencing and analysis of gene expression have led to exponential growth in the amount of publicly available genomic data. With the increased availability of genomic data traditional database approaches are no longer sufficient for rapidly performing life science queries involving the fusion of data types. Computing systems are now so powerful it is possible for researchers to consider modeling the folding of a protein or even the simulation of an entire human body. This research paper emphasizes the computational biology's growing need for high-performance computing and Big Data. It illustrates this article’s indispensability in meeting the scientific and engineering challenges of the twenty-first century, and how Protein Folding (the structure and function of proteins) and Phylogeny Reconstruction (evolutionary history of a group of genes) can use HPC that provides sufficient capability for evaluating or solving more limited but meaningful instances. This article also indicates solutions to optimization problems, and benefits Big Data and Computational Biology. The article illustrates the Current State-of-the-Art and Future-Generation Biology of HPC Computing with Big Data.

Keywords: high performance, big data, parallel computation, molecular data, computational biology

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Authors: Yu-Wen Chen, Szu-Wei Huang, Chung-Hsiang Mu, Kelvin Cheng

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This paper developed a data-driven based model to deal with the causality between the Continuous Emission Monitoring System (CEMS, by Environmental Protection Administration, Taiwan) in industrial factories, and the air quality around environment. Compared to the heavy burden of traditional numerical models of regional weather and air pollution simulation, the lightweight burden of the proposed model can provide forecasting hourly with current observations of weather, air pollution and emissions from factories. The observation data are included wind speed, wind direction, relative humidity, temperature and others. The observations can be collected real time from Open APIs of civil IoT Taiwan, which are sourced from 439 weather stations, 10,193 qualitative air stations, 77 national quantitative stations and 140 CEMS quantitative industrial factories. This study completed a causal inference engine and gave an air pollution forecasting for the next 12 hours related to local industrial factories. The outcomes of the pollution forecasting are produced hourly with a grid resolution of 1km*1km on IIoTC (Industrial Internet of Things Cloud) and saved in netCDF4 format. The elaborated procedures to generate forecasts comprise data recalibrating, outlier elimination, Kriging Interpolation and particle tracking and random walk techniques for the mechanisms of diffusion and advection. The solution of these equations reveals the causality between factories emission and the associated air pollution. Further, with the aid of installed real-time flue emission (Total Suspension Emission, TSP) sensors and the mentioned forecasted air pollution map, this study also disclosed the converting mechanism between the TSP and PM2.5/PM10 for different region and industrial characteristics, according to the long-term data observation and calibration. These different time-series qualitative and quantitative data which successfully achieved a causal inference engine in cloud for factory management control in practicable. Once the forecasted air quality for a region is marked as harmful, the correlated factories are notified and asked to suppress its operation and reduces emission in advance.

Keywords: continuous emission monitoring system, total suspension particulates, causal inference, air pollution forecast, IoT

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Authors: Ugo Rovigatti

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Neuroblastoma (NBL) has epitomized for at least 40 years our understanding of cancer cellular and molecular biology and its potential applications to novel therapeutic strategies. This includes the discovery of the very first oncogene aberrations and tumorigenesis suppression by differentiation in the 80s; the potential role of suppressor genes in the 90s; the relevance of immunotherapy in the millennium first, and the discovery of additional mutations by NGS technology in the millennium second decade. Similar discoveries were achieved in the majority of human cancers, and similar therapeutic interventions were obtained subsequently to NBL discoveries. Unfortunately, targeted therapies suggested by specific mutations (such as MYCN amplification –MNA- present in ¼ or 1/5 of cases) have not elicited therapeutic successes in aggressive NBL, where the prognosis is still dismal. The reasons appear to be linked to Tumor Heterogeneity, which is particularly evident in NBL but also a clear hallmark of aggressive human cancers generally. The new avenue of cancer immunotherapy (CIT) provided new hopes for cancer patients, but we still ignore the cellular or molecular targets. CIT is emblematic of high-risk disease (HR-NBL) since the mentioned GD2 passive immunotherapy is still providing better survival. We recently critically reviewed and evaluated the literature depicting the genomic landscapes of HR-NBL, coming to the qualified conclusion that among hundreds of affected genes, potential targets, or chromosomal sites, none correlated with anti-GD2 sensitivity. A better explanation is provided by the Micro-Foci inducing Virus (MFV) model, which predicts that neuroblasts infection with the MFV, an RNA virus isolated from a cancer-cluster (space-time association) of HR-NBL cases, elicits the appearance of MNA and additional genomic aberrations with mechanisms resembling chromothripsis. Neuroblasts infected with low titers of MFV amplified MYCN up to 100 folds and became highly transformed and malignant, thus causing neuroblastoma in young rat pups of strains SD and Fisher-344 and larger tumor masses in nu/nu mice. An association was discovered with GD2 since this glycosphingolipid is also the receptor for the family of MFV virus (dsRNA viruses). It is concluded that a dsRNA virus, MFV, appears to provide better explicatory mechanisms for the genesis of i) specific genomic aberrations such as MNA; ii) extensive tumor heterogeneity and chromothripsis; iii) the effects of passive immunotherapy with anti-GD2 monoclonals and that this and similar models should be further investigated in both pediatric and adult cancers.

Keywords: neuroblastoma, MYCN, amplification, viruses, GD2

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Authors: Zan Chen, D. Kwok

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The SkillsFuture Singapore’s mission to develop a responsive and forward-looking Training and Adult Education (TAE) and workforce development system is undergirded by how successful TAE providers are in their business performance and strategies that strengthen their operational efficiency and processes. Therefore, understanding the factors that drive the business performance of TAE providers is critical to the success of SkillsFuture Singapore’s initiatives. This study aims to investigate how business strategy, work autonomy, work intensity and professional development support impact the business performance of private TAE providers. Specifically, the three research questions are: (1) Are there significant relationships between the above-mentioned four factors and TAE providers’ business performance?; (2) Are there significant differences on the four factors between low and high TAE providers’ business performance groups?; and (3) To what extent and in what manner do the four factors predict TAE providers’ business performance? This was part of the first national study on organizations and professionals working in the Training and Adult Education (TAE) sector. Data from 265 private TAE providers where respondents were Chief Executive Officers representatives from the Senior Management were analyzed. The results showed that business strategy (the extent that the organization leads the way in terms of developing new products and services; uses up-to-date learning technologies; customizes its products and services to the client’s needs), work autonomy (the extent that the staff personally have an influence on how hard they work; deciding what tasks they are to do; deciding how they are to do the tasks, and deciding the quality standards to which they work) and professional development support (both monetary and non-monetary support and incentives) had positive and significant relationships with business performance. However, no significant relationship is found between work intensity and business performance. A business strategy, work autonomy and professional development support were significantly higher in the high business performance group compared to the low-performance group among the TAE providers. Results of hierarchical regression analyses controlling for the size of the TAE providers showed significant impacts of business strategy, work autonomy and professional development support on TAE providers’ business performance. Overall, the model accounted for 27% of the variance in TAE providers’ business performance. This study provides policymakers with insights into improving existing policies, designing new initiatives and implementing targeting interventions to support TAE providers. The findings also have implications on how the TAE providers could better formulate their organizational strategies and business models. Finally, limitations of study, along with directions for future research will be discussed in the paper.

Keywords: adult education, business performance, business strategy, training, work autonomy

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Authors: Wenya Shu, Ilinca Stanciulescu

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Carbon nanotubes (CNTs) possess attractive properties, such as high stiffness and strength, and high thermal and electrical conductivities, making them promising filler in multifunctional nanocomposites. Although CNTs can be efficient reinforcements, the expected level of mechanical performance of CNT-polymers is not often reached in practice due to the poor mechanical behavior of the CNT-polymer interfaces. It is believed that the interactions of CNT and polymer mainly result from the Van der Waals force. The interface debonding is a fracture and delamination phenomenon. Thus, the cohesive zone modeling (CZM) is deemed to give good capture of the interface behavior. The detailed, cohesive zone modeling provides an option to consider the CNT-matrix interactions, but brings difficulties in mesh generation and also leads to high computational costs. Homogenized models that smear the fibers in the ground matrix and treat the material as homogeneous are studied in many researches to simplify simulations. But based on the perfect interface assumption, the traditional homogenized model obtained by mixing rules severely overestimates the stiffness of the composite, even comparing with the result of the CZM with artificially very strong interface. A mechanical model that can take into account the interface debonding and achieve comparable accuracy to the CZM is thus essential. The present study first investigates the CNT-matrix interactions by employing cohesive zone modeling. Three different coupled CZM laws, i.e., bilinear, exponential and polynomial, are considered. These studies indicate that the shapes of the CZM constitutive laws chosen do not influence significantly the simulations of interface debonding. Assuming a bilinear traction-separation relationship, the debonding process of single CNT in the matrix is divided into three phases and described by differential equations. The analytical solutions corresponding to these phases are derived. A homogenized model is then developed by introducing a parameter characterizing interface sliding into the mixing theory. The proposed mechanical model is implemented in FEAP8.5 as a user material. The accuracy and limitations of the model are discussed through several numerical examples. The CZM simulations in this study reveal important factors in the modeling of CNT-matrix interactions. The analytical solutions and proposed homogenized model provide alternative methods to efficiently investigate the mechanical behaviors of CNT/polymer composites.

Keywords: carbon nanotube, cohesive zone modeling, homogenized model, interface debonding

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Authors: Sangwoo Han, Saeed Khaleghi Rahimian, Ying Liu

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Vehicle electrification is gaining momentum, and many car manufacturers promise to deliver more electric vehicle (EV) models to consumers in the coming years. In controlling the battery pack, the battery management system (BMS) must maintain optimal battery performance while ensuring the safety of a battery pack. Tasks related to battery performance include determining state-of-charge (SOC), state-of-power (SOP), state-of-health (SOH), cell balancing, and battery charging. Safety related functions include making sure cells operate within specified, static and dynamic voltage window and temperature range, derating power, detecting faulty cells, and warning the user if necessary. The BMS often utilizes an RC circuit model to model a Li-ion cell because of its robustness and low computation cost among other benefits. Because an equivalent circuit model such as the RC model is not a physics-based model, it can never be a prognostic model to predict battery state-of-health and avoid any safety risk even before it occurs. A physics-based Li-ion cell model, on the other hand, is more capable at the expense of computation cost. To avoid the high computation cost associated with a full-order model, many researchers have demonstrated the use of a single particle model (SPM) for BMS applications. One drawback associated with the single particle modeling approach is that it forces to use the average current density in the calculation. The SPM would be appropriate for simulating drive cycles where there is insufficient time to develop a significant current distribution within an electrode. However, under a continuous or high-pulse electrical load, the model may fail to predict cell voltage or Li⁺ plating potential. To overcome this issue, a multi-particle reduced-order model is proposed here. The use of multiple particles combined with either linear or nonlinear charge-transfer reaction kinetics enables to capture current density distribution within an electrode under any type of electrical load. To maintain computational complexity like that of an SPM, governing equations are solved sequentially to minimize iterative solving processes. Furthermore, the model is validated against a full-order model implemented in COMSOL Multiphysics.

Keywords: battery management system, physics-based li-ion cell model, reduced-order model, single-particle and multi-particle model

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Authors: Ilaria Barletta, Mahesh Mani, Björn Johansson

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The concept of sustainability encompasses economic, environmental, social and institutional considerations. Sustainable manufacturing (SM) is, therefore, a multi-faceted concept. It broadly implies the development and implementation of technologies, projects and initiatives that are concerned with the life cycle of products and services, and are able to bring positive impacts to the environment, company stakeholders and profitability. Because of this, achieving SM-related goals requires a holistic, life-cycle-thinking approach from manufacturing companies. Further, such an approach must rely on a logic of continuous improvement and ease of implementation in order to be effective. Currently, there exists in the academic literature no comprehensively structured frameworks that support manufacturing companies in the identification of the issues and the capabilities that can either hinder or foster sustainability. This scarcity of support extends to difficulties in obtaining quantifiable measurements in order to objectively evaluate solutions and programs and identify improvement areas within SM for standards conformance. To bridge this gap, this paper proposes the concept of a framework for assessing and continuously improving the sustainability of manufacturing companies. The framework addresses strategies and projects for SM and operates in three sequential phases: analysis of the issues, design of solutions and continuous improvement. A set of interviews, observations and questionnaires are the research methods to be used for the implementation of the framework. Different decision-support methods - either already-existing or novel ones - can be 'plugged into' each of the phases. These methods can assess anything from business capabilities to process maturity. In particular, the authors are working on the development of a sustainable manufacturing maturity model (SMMM) as decision support within the phase of 'continuous improvement'. The SMMM, inspired by previous maturity models, is made up of four maturity levels stemming from 'non-existing' to 'thriving'. Aggregate findings from the use of the framework should ultimately reveal to managers and CEOs the roadmap for achieving SM goals and identify the maturity of their companies’ processes and capabilities. Two cases from two manufacturing companies in Australia are currently being employed to develop and test the framework. The use of this framework will bring two main benefits: enable visual, intuitive internal sustainability benchmarking and raise awareness of improvement areas that lead companies towards an increasingly developed SM.

Keywords: life cycle management, continuous improvement, maturity model, sustainable manufacturing

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Authors: Abdreheman Seid Abdella

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This study examined instructors willingness, self-efficacy beliefs, attitudes and knowledge about provisions of instructional accommodations for students with disabilities in universities. Major concepts used in this study operationally defined and some models of disability were reviewed. Questionnaires were distributed to a total of 181 instructors from four universities and quantitative data was generated. Then to analyze the data, appropriate methods of data analysis were employed. The result indicated that on average instructors had positive willingness, strong self-efficacy beliefs and positive attitudes towards providing instructional accommodations. In addition, the result showed that the majority of participants had moderate level of knowledge about provision of instructional accommodations. Concerning the relationship between instructors background variables and dependent variables, the result revealed that location of university and awareness raising training about Inclusive Education showed statistically significant relationship with all dependent variables (willingness, self-efficacy beliefs, attitudes and knowledge). On the other hand, gender and college/faculty did not show a statistically significant relationship. In addition, it was found that among the inter-correlation of dependent variables, the correlation between attitudes and willingness to provide accommodations was the strongest. Furthermore, using multiple linear regression analysis, this study also indicated that predictor variables like self-efficacy beliefs, attitudes, knowledge and teaching methodology training made statistically significant contribution to predicting the criterion willingness. Predictor variables like willingness and attitudes made statistically significant contribution to predicting self-efficacy beliefs. Predictor variables like willingness, Special Needs Education course and self-efficacy beliefs made statistically significant contribution to predict attitudes. Predictor variables like Special Needs Education courses, the location of university and willingness made statistically significant contribution to predicting knowledge. Finally, using exploratory factor analysis, this study showed that there were four components or factors each that represent the underlying constructs of willingness and self-efficacy beliefs to provide instructional accommodations items, five components for attitudes towards providing accommodations items and three components represent the underlying constructs for knowledge about provisions of instructional accommodations items. Based on the findings, recommendations were made for improving the situation of instructional accommodations in Ethiopian universities.

Keywords: willingness, self-efficacy belief, attitude, knowledge

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Authors: Ina Nasto, Denada Sota, Kerol Sacaj, Brunilda Veshaj, Hajdar Kicaj

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Rocky shores are often used as models to describe the dynamics of biodiversity around the world, making them one of the most studied marine habitats and their communities. The variability in the number of species and the abundance of hard-bottom benthic animal communities on the coast of Triporti, north of the Bay of Vlora, Albania is described in relation to environmental variables using multivariate analysis. The purpose of this study is to monitor the species composition, quantitative characteristics, and seasonal variations of the benthic macroinvertebrate populations of the shallow rocky shores of the Triportit-Vlora area, as well as the assessment of the ecological condition of these populations. The rocky coast of Triport, with a length of 7 km, was divided into three sampling stations, with three transects each of 50m. The monitoring of benthic macroinvertebrates in these areas was carried out in two seasons, spring and summer (June and August 2021). In each station and sampling season, estimates of the total and average density for each species, the presence constant, and the assessment of biodiversity were calculated using the Shannon–Wiener and the Simpson index. The species composition, the quantitative characteristics of the populations, and the indicators mentioned above were analyzed in a comparative way, both between the seasons within one station and between the three stations with each other. Statistical processing of the data was carried out to analyze the changes between the seasons and between the sampling stations for the species composition, population density, as well as correlation between them. A total of 105 benthic macroinvertebrate taxa were found, dominated by Molluscs, Annelids, and Arthropods. The small density of species and the low degree of stability of the macrozoobenthic community are indicators of the poor ecological condition and environmental impact in the studied areas. Algal cover, the diversity of coastal microhabitats, and the degree of coastal exposure to waves play an important role in the characteristics of macrozoobenthos populations in the studied areas. Also, the rocky shores are of special interest because, in the infralittoral of these areas, there are dense kelp forests with Gongolaria barbata, Ericaria crinita as well as fragmented areas with Posidonia oceanica that reach the coast, priority habitats of special conservation importance in the Mediterranean.

Keywords: Macrozoobenthic communities, Shannon–Wiener, Triporti, Vlore, rocky shore

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Authors: Abrehet Kahsay Mehari

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Aquatic plants provide the physical structure of wetlands and increase their habitat complexity and heterogeneity, and as such, have a profound influence on other biotas. In this study, we investigated how human disturbance activities influenced the species richness and community composition of aquatic plants in the wetlands of Lake Tana, Ethiopia. Twelve wetlands were selected: four lacustrine, four river mouths, and four riverine papyrus swamps. Data on aquatic plants, environmental variables, and human activities were collected during the dry and wet seasons of 2018. A linear mixed effect model and a distance-based Redundancy Analysis (db-RDA) were used to relate aquatic plant species richness and community composition, respectively, to human activities and environmental variables. A total of 113 aquatic plant species, belonging to 38 families, were identified across all wetlands during the dry and wet seasons. Emergent species had the maximum area covered at 73.45 % and attained the highest relative abundance, followed by amphibious and other forms. The mean taxonomic richness of aquatic plants was significantly lower in wetlands with high overall human disturbance scores compared to wetlands with low overall human disturbance scores. Moreover, taxonomic richness showed a negative correlation with livestock grazing, tree plantation, and sand mining. The community composition also varied across wetlands with varying levels of human disturbance and was primarily driven by turnover (i.e., replacement of species) rather than nestedness resultant(i.e., loss of species). Distance-based redundancy analysis revealed that livestock grazing, tree plantation, sand mining, waste dumping, and crop cultivation were significant predictors of variation in aquatic plant communities’ composition in the wetlands. Linear mixed effect models and distance-based redundancy analysis also revealed that water depth, turbidity, conductivity, pH, sediment depth, and temperature were important drivers of variations in aquatic plant species richness and community composition. Papyrus swamps had the highest species richness and supported different plant communities. Conservation efforts should therefore focus on these habitats and measures should be taken to restore the highly disturbed and species poor wetlands near the river mouths.

Keywords: species richness, community composition, aquatic plants, wetlands, Lake Tana, human disturbance activities

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Authors: Breno Barrreto Silva, Agnieszka Otwinowska, Katarzyna Kutylowska

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Research comparing lexical learning following the writing of sentences and longer texts with keywords is limited and contradictory. One possibility is that the recursivity of writing may enhance processing and increase lexical learning; another possibility is that the higher cognitive load of complex-text writing (e.g., essays), at least when timed, may hinder the learning of words. In our study, we selected 2 sets of 10 academic keywords matched for part of speech, length (number of characters), frequency (SUBTLEXus), and concreteness, and we asked 90 L1-Polish advanced-level English majors to use the keywords when writing sentences, timed (60 minutes) or untimed essays. First, all participants wrote a timed Control essay (60 minutes) without keywords. Then different groups produced Timed essays (60 minutes; n=33), Untimed essays (n=24), or Sentences (n=33) using the two sets of glossed keywords (counterbalanced). The comparability of the participants in the three groups was ensured by matching them for proficiency in English (LexTALE), and for few measures derived from the control essay: VocD (assessing productive lexical diversity), normed errors (assessing productive accuracy), words per minute (assessing productive written fluency), and holistic scores (assessing overall quality of production). We measured lexical learning (depth and breadth) via an adapted Vocabulary Knowledge Scale (VKS) and a free association test. Cognitive load was measured in the three essays (Control, Timed, Untimed) using normed number of errors and holistic scores (TOEFL criteria). The number of errors and essay scores were obtained from two raters (interrater reliability Pearson’s r=.78-91). Generalized linear mixed models showed no difference in the breadth and depth of keyword knowledge after writing Sentences, Timed essays, and Untimed essays. The task-based measurements found that Control and Timed essays had similar holistic scores, but that Untimed essay had better quality than Timed essay. Also, Untimed essay was the most accurate, and Timed essay the most error prone. Concluding, using keywords in Timed, but not Untimed, essays increased cognitive load, leading to more errors and lower quality. Still, writing sentences and essays yielded similar lexical learning, and differences in the cognitive load between Timed and Untimed essays did not affect lexical acquisition.

Keywords: learning academic words, writing essays, cognitive load, english as an L2

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Authors: R. L. Shirole, N. L. Shirole, R. B. Patil, M. N. Saraf

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The present investigation aimed to elucidate the probable mechanism of antiasthmatic action of essential oil of Pistacia integerrima J.L. Stewart ex Brandis galls (EOPI). EOPI was investigated for its potential antiasthmatic action using in vitro antiallergic assays mast cell degranulation and soyabean lipoxidase enzyme activit, and spasmolytic action using isolated guinea pig ileum preparation. In vivo studies included lipopolysaccharide-induced bronchial inflammation in rats and airway hyperresponsiveness in ovalbumin in sensitized guinea pigs using spirometry. Data was analysed by GraphPad Prism 5.01 and results were expressed as means ± SEM. P < 0.05 was considered to be significant. EOPI inhibits 5-lipoxidase enzyme activity, DPPH scavenging activity and erythropoietin- induced angiogenesis. It showed dose dependent anti-allergic activity by inhibiting compound 48/80 induced mast cell degranulation. The finding that essential oil induced inhibition of transient contraction of acetylcholine in calcium free medium, and relaxation of S-(-)-Bay 8644-precontracted isolated guinea pig ileum jointly suggest that suggesting that the L-subtype Cav channel is involved in spasmolytic action of EOPI. Treatment with EOPI dose dependently (7.5, 15 and 30 mg/kg i.p.) inhibited lipopolysaccharide- induced increased in total cell count, neutrophil count, nitrate-nitrite, total protein, albumin levels in bronchoalveolar fluid and myeloperoxidase levels in lung homogenates. Mild diffused lesions involving focal interalveolar septal, intraluminal infiltration of neutrophils were observed in EOPI (7.5 &15 mg/kg) pretreated while no abnormality was detected in EOPI (30 mg/kg) and roflumilast (1mg/kg) pretreated rats. Roflumilast was used as standard. EOPI reduced the respiratory flow due to gasping in ovalbumin sensitized guinea pigs. The study demonstrates the effectiveness of EOPI in bronchial asthma possibly related to its ability to inhibit L-subtype Cav channel, mast cell stabilization, antioxidant, angiostatic and through inhibition of 5-lipoxygenase enzyme.

Keywords: asthma, lipopolysaccharide, spirometry, Pistacia integerrima J.L. Stewart ex Brandis, essential oil

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Authors: Adela Mariana Vadastreanu, Adrian Bot, Andreea Maier, Dorin Maier

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This paper approaches the need for innovation management in the context of an existing integrated management system implemented in an organization. The road to success for companies in today’s economic environment is more demanding than ever and the capacity of adapting to the rapid changes is compensatory in order to resist on the market. The managers struggle, daily, with increasingly complex problems, caused by fierce competition in the market but also from the rising demands of customers. Innovation seems to be the solution for these problems. During the last decade almost all companies have been certificated according to various management systems, like quality management system, environmental management system, health and safety management system and others; furthermore many companies have implemented an integrated management system, by integrating two or more management systems. The problem rising today is how to integrate innovation in this integrated management systems. The challenge of the problem is that the development of an innovation management system is in the early phase. In this paper we have studied the possibility of integrating some of the innovation request in an existing management system, we have identify the innovation performance request and we proposed some recommendations regarding innovation management and its implementation as a part of an integrated management system. This paper lies down the bases for developing an model of integration management systems that include innovation as a main part of it. Organizations are becoming more aware of the importance of Integrated Management Systems (IMS). Integrating two or more management systems into an integrated management system can have much advantages.This paper examines various models of management systems integration in accordance with professional references ISO 9001, ISO 18001 and OHSAS 18001, highlighting strengths and weaknesses, creating a basis for future development of integrated management systems, and their involvement in various other processes within the organization, such as innovation management. The more and more demanding economic context emphasizes the awareness of the importance of innovation for organizations. This paper highlights the importance of the innovation for an organization and also gives some practical solution in order to improve the overall success of the business through a better approach of innovation. Various standards have been developed in order to certificate organizations that they respect the requirements. Applying an integrated standards model is shown to be a more effective way then applying the standards independently. The problem that arises is that in order to adopt the integrated version of standards there have to be made some changes at the organizational level. Every change that needs to be done has an effect on its activity, and in this sense the paper tries to deal with the changes needed for adopting an integrated management system and if those changes have an influence over the performance. After the analysis of the results, we can conclude that in order to improve the performance a necessary step is the implementation of innovation in the existing integrated management system.

Keywords: innovation, integrated management systems, innovation management, quality

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Authors: Soiduate Ogoye-Atanga

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During funeral ceremonies, it has become common for attendees to jostle for burial programs in some southern Nigerian towns. Beginning from ordinary typewritten text only sheets of paper in the 1980s to their current digitally formatted multicolor magazine style, burial programs continue to be collected and kept in homes where they remain as archival documents of family photo histories and as a veritable form of leveraging family status and visibility in a social economy through the inclusion of lots of choreographically arranged photographs and text. The biographical texts speak of idealized and often lofty and aestheticized accomplishments of deceased peoples, which are often corroborated by an accompanying section of tributes from first the immediate family members, and then from affiliations as well as organizations deceased people belonged, in the form of scanned letterheaded corporate tributes. Others speak of modest biographical texts when the deceased accomplished little. Usually, in majority of the cases, the display of photographs and text in these programs follow a trajectory of historical compartmentalization of the deceased, beginning from parentage to the period of youth, occupation, retirement, and old age as the case may be, which usually drives from black and white historical photographs to the color photography of today. This compartmentalization follows varied models but is designed to show the deceased in varying activities during his lifetime. The production of these programs ranges from the extremely expensive and luscious full colors of near fifty-eighty pages to bland and very simplified low-quality few-page editions in a single color and no photographs, except on the cover. Cost and quality, therefore, become determinants of varying family status and social visibility. By a critical selection of photographs and text, family members construct an idealized image of deceased people and themselves, concentrating on mutuality based on appropriate sartorial selections, socioeconomic grade, and social temperaments that are framed to corroborate the public’s perception of them. Burial magazines, therefore, serve purposes beyond their primary use; they symbolize an orchestrated social site for image-making and the validation of the social status of families, shaped by prior family histories.

Keywords: biographical texts, burial programs, compartmentalization, magazine, multicolor, photo-histories, social status

Procedia PDF Downloads 188

Authors: Emery Coppola Jr., Marwan Sadat, Il Kim, Diane Trube, Richard Kurisko

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Contamination sites like landfills often pose significant risks to receptors like surface water bodies. Surface water bodies are often a source of recreation, including fishing and swimming, which not only enhances their value but also serves as a direct exposure pathway to humans, increasing their need for protection from water quality degradation. In this paper, a case study presents the potential effects of leachate-impacted groundwater from a large closed sanitary landfill on the surface water quality of the nearby Raritan River, situated in New Jersey. The study, performed over a two year period, included in-depth field evaluation of both the groundwater and surface water systems, and was supplemented by computer modeling. The analysis required delineation of a representative average daily groundwater discharge from the Landfill shoreline into the large, highly tidal Raritan River, with a corresponding estimate of daily mass loading of potential contaminants of concern. The average daily groundwater discharge into the river was estimated from a high-resolution water level study and a 24-hour constant-rate aquifer pumping test. The significant tidal effects induced on groundwater levels during the aquifer pumping test were filtered out using an advanced algorithm, from which aquifer parameter values were estimated using conventional curve match techniques. The estimated hydraulic conductivity values obtained from individual observation wells closely agree with tidally-derived values for the same wells. Numerous models were developed and used to simulate groundwater contaminant transport and surface water quality impacts. MODFLOW with MT3DMS was used to simulate the transport of potential contaminants of concern from the down-gradient edge of the Landfill to the Raritan River shoreline. A surface water dispersion model based upon a bathymetric and flow study of the river was used to simulate the contaminant concentrations over space within the river. The modeling results helped demonstrate that because of natural attenuation, the Landfill does not have a measurable impact on the river, which was confirmed by an extensive surface water quality study.

Keywords: groundwater flow and contaminant transport modeling, groundwater/surface water interaction, landfill leachate, surface water quality modeling

Procedia PDF Downloads 261

Authors: Jiaxin Shi, Kaifeng Hao, Qingfan An, Zeng Peng

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Due to a lack of data sources and data processing techniques, it has always been difficult to quantify public space quality, which includes urban construction quality and how it is perceived by people, especially in large urban areas. This study proposes a quantitative research method based on the consideration of emotional health and physical health of the built environment. It highlights the low quality of public areas in Tianjin, China, where there are many negative elements. Deep learning technology is then used to measure how effectively people perceive urban areas. First, this work suggests a deep learning model that might simulate how people can perceive the quality of urban construction. Second, we perform semantic segmentation on street images to identify visual elements influencing scene perception. Finally, this study correlated the scene perception score with the proportion of visual elements to determine the surrounding environmental elements that influence scene perception. Using a small-scale labeled Tianjin street view data set based on transfer learning, this study trains five negative spatial discriminant models in order to explore the negative space distribution and quality improvement of urban streets. Then it uses all Tianjin street-level imagery to make predictions and calculate the proportion of negative space. Visualizing the spatial distribution of negative space along the Tianjin Inner Ring Road reveals that the negative elements are mainly found close to the five key districts. The map of Tianjin was combined with the experimental data to perform the visual analysis. Based on the emotional assessment, the distribution of negative materials, and the direction of street guidelines, we suggest guidance content and design strategy points of the negative phenomena in Tianjin street space in the two dimensions of perception and substance. This work demonstrates the utilization of deep learning techniques to understand how people appreciate high-quality urban construction, and it complements both theory and practice in urban planning. It illustrates the connection between human perception and the actual physical public space environment, allowing researchers to make urban interventions.

Keywords: human perception, public space quality, deep learning, negative elements, street images

Procedia PDF Downloads 115

Authors: Adan Valdez, Shawn Gallaher, James Morison, Jordan Aragon

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Global climate change has impacted atmospheric temperatures contributing to rising sea levels, decreasing sea ice, and increased freshening of high latitude oceans. This freshening has contributed to increased stratification inhibiting local mixing and nutrient transport and modifying regional circulations in polar oceans. In recent years, the Western Arctic has seen an increase in freshwater volume at an average rate of 397+-116 km3/year. The majority of the freshwater volume resides in the Beaufort Gyre surface lens driven by anticyclonic wind forcing, sea ice melt, and Arctic river runoff. The total climatological freshwater content is typically defined as water fresher than 34.8. The near-isothermal nature of Arctic seawater and non-linearities in the equation of state for near-freezing waters result in a salinity driven pycnocline as opposed to the temperature driven density structure seen in the lower latitudes. In this study, we investigate the relationship between freshwater content and remotely sensed dynamic ocean topography (DOT). In-situ measurements of freshwater content are useful in providing information on the freshening rate of the Beaufort Gyre; however, their collection is costly and time consuming. NASA’s Advanced Topographic Laser Altimeter System (ATLAS) derived dynamic ocean topography (DOT), and Air Expendable CTD (AXCTD) derived Freshwater Content are used to develop a linear regression model. In-situ data for the regression model is collected across the 150° West meridian, which typically defines the centerline of the Beaufort Gyre. Two freshwater content models are determined by integrating the freshwater volume between the surface and an isopycnal corresponding to reference salinities of 28.7 and 34.8. These salinities correspond to those of the winter pycnocline and total climatological freshwater content, respectively. Using each model, we determine the strength of the linear relationship between freshwater content and satellite derived DOT. The result of this modeling study could provide a future predictive capability of freshwater volume changes in the Beaufort-Chukchi Sea using non in-situ methods. Successful employment of the ICESat-2’s DOT approximation of freshwater content could potentially reduce reliance on field deployment platforms to characterize physical ocean properties.

Keywords: ICESat-2, dynamic ocean topography, freshwater content, beaufort gyre

Procedia PDF Downloads 87

Authors: Azeem Tariq, Stephane De Tourdonnet, Lars Stoumann Jensen, Reiner Wassmann, Bjoern Ole Sander, Quynh Duong Vu, Trinh Van Mai, Andreas De Neergaard

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Rice growing area is increasing to meet the food demand of increasing population. Mostly, rice is growing on lowland, small landholder fields in most part of the world, which is one of the major sources of greenhouse gases (GHG) emissions from agriculture fields. The strategies such as, altering water and residues (carbon) management practices are assumed to be essential to mitigate the GHG emissions from flooded rice system. The actual implementation and potential of these measures on small farmer fields is still challenging. A field study was conducted on red river delta in Northern Vietnam to identify the potential challenges and barriers to the small rice farmers for implementation of climate smart rice practices. The objective of this study was to develop and access the feasibility of climate smart rice prototypes under actual farmer conditions. Field and scientific oriented framework was used to meet our objective. The methodological framework composed of six steps: i) identification of stakeholders and possible options, ii) assessment of barrios, drawbacks/advantages of new technologies, iii) prototype design, iv) assessment of mitigation potential of each prototype, v) scenario building and vi) scenario assessment. A farm survey was conducted to identify the existing farm practices and major constraints of small rice farmers. We proposed the two water (pre transplant+midseason drainage and early+midseason drainage) and one straw (full residue incorporation) management option keeping in views the farmers constraints and barriers for implementation. To test new typologies with existing prototypes (midseason drainage, partial residue incorporation) at farmer local conditions, a participatory field experiment was conducted for two consecutive rice seasons at farmer fields. Following the results of each season a workshop was conducted with stakeholders (farmers, village leaders, cooperatives, irrigation staff, extensionists, agricultural officers) at local and district level to get feedbacks on new tested prototypes and to develop possible scenarios for climate smart rice production practices. The farm analysis survey showed that non-availability of cheap labor and lacks of alternatives for straw management influence the small farmers to burn the residues in the fields except to use for composting or other purposes. Our field results revealed that application of early season drainage significantly mitigates (40-60%) the methane emissions from residue incorporation. Early season drainage was more efficient and easy to control under cooperate manage system than individually managed water system, and it leads to both economic (9-11% high rice yield, low cost of production, reduced nutrient loses) and environmental (mitigate methane emissions) benefits. The participatory field study allows the assessment of adaptation potential and possible benefits of climate smart practices on small farmer fields. If farmers have no other residue management option, full residue incorporation with early plus midseason drainage is adaptable and beneficial (both environmentally and economically) management option for small rice farmers.

Keywords: adaptation, climate smart agriculture, constrainsts, smallholders

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Authors: Robin Devillers, Chantal van Dijk

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Children face ambiguities in everyday language use. Particularly ambiguity in pronoun resolution can be challenging, whereas adults can rapidly identify the antecedent of the mentioned pronoun. Two main factors underlie this process, namely the accessibility of the referent and the syntactic cues of the pronoun. After 200ms, adults have converged the accessibility and the syntactic constraints, while relieving cognitive effort by considering contextual cues. As children are still developing their cognitive capacity, they are not able yet to simultaneously assess and integrate accessibility, contextual cues and syntactic information. As such, they fail to identify the correct referent and possibly fixate more on the competitor in comparison to adults. In this study, Dutch while-clauses were used to investigate the interpretation of pronouns by children. The aim is to a) examine the extent to which 7-10 year old children are able to utilise discourse and syntactic information during online and offline sentence processing and b) analyse the contribution of individual factors, including age, working memory, condition and vocabulary. Adult and child participants are presented with filler-items and while-clauses, and the latter follows a particular structure: ‘Anna and Sophie are sitting in the library. While Anna is reading a book, she is taking a sip of water.’ This sentence illustrates the ambiguous situation, as it is unclear whether ‘she’ refers to Anna or Sophie. In the unambiguous situation, either Anna or Sophie would be substituted by a boy, such as ‘Peter’. The pronoun in the second sentence will unambiguously refer to one of the characters due to the syntactic constraints of the pronoun. Children’s and adults’ responses were measured by means of a visual world paradigm. This paradigm consisted of two characters, of which one was the referent (the target) and the other was the competitor. A sentence was presented and followed by a question, which required the participant to choose which character was the referent. Subsequently, this paradigm yields an online (fixations) and offline (accuracy) score. These findings will be analysed using Generalised Additive Mixed Models, which allow for a thorough estimation of the individual variables. These findings will contribute to the scientific literature in several ways; firstly, the use of while-clauses has not been studied much and it’s processing has not yet been identified. Moreover, online pronoun resolution has not been investigated much in both children and adults, and therefore, this study will contribute to adults and child’s pronoun resolution literature. Lastly, pronoun resolution has not been studied yet in Dutch and as such, this study adds to the languages

Keywords: pronouns, online language processing, Dutch, eye-tracking, first language acquisition, language development

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Authors: Simon Komesker, Achim Wagner, Martin Ruskowski

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In times of volatile markets with fluctuating demand and the uncertainty of global supply chains, flexible production systems are the key to an efficient implementation of a desired production program. In this publication, the authors present a holistic information concept taking into account various influencing factors for operating towards the global optimum. Therefore, a strategy for the implementation of multi-level planning for a flexible, reconfigurable production system with an alternative production concept in the automotive industry is developed. The main contribution of this work is a system structure mixing central and decentral planning and control evaluated in a simulation framework. The information system structure in current production systems in the automotive industry is rigidly hierarchically organized in monolithic systems. The production program is created rule-based with the premise of achieving uniform cycle time. This program then provides the information basis for execution in subsystems at the station and process execution level. In today's era of mixed-(car-)model factories, complex conditions and conflicts arise in achieving logistics, quality, and production goals. There is no provision for feedback loops of results from the process execution level (resources) and process supporting (quality and logistics) systems and reconsideration in the planning systems. To enable a robust production flow, the complexity of production system control is artificially reduced by the line structure and results, for example in material-intensive processes (buffers and safety stocks - two container principle also for different variants). The limited degrees of freedom of line production have produced the principle of progress figure control, which results in one-time sequencing, sequential order release, and relatively inflexible capacity control. As a result, modularly structured production systems such as modular production according to known approaches with more degrees of freedom are currently difficult to represent in terms of information technology. The remedy is an information concept that supports cross-system and cross-level information processing for centralized and decentralized decision-making. Through an architecture of hierarchically organized but decoupled subsystems, the paradigm of hybrid control is used, and a holonic manufacturing system is offered, which enables flexible information provisioning and processing support. In this way, the influences from quality, logistics, and production processes can be linked holistically with the advantages of mixed centralized and decentralized planning and control. Modular production systems also require modularly networked information systems with semi-autonomous optimization for a robust production flow. Dynamic prioritization of different key figures between subsystems should lead the production system to an overall optimum. The tasks and goals of quality, logistics, process, resource, and product areas in a cyber-physical production system are designed as an interconnected multi-agent-system. The result is an alternative system structure that executes centralized process planning and decentralized processing. An agent-based manufacturing control is used to enable different flexibility and reconfigurability states and manufacturing strategies in order to find optimal partial solutions of subsystems, that lead to a near global optimum for hybrid planning. This allows a robust near to plan execution with integrated quality control and intralogistics.

Keywords: holonic manufacturing system, modular production system, planning, and control, system structure

Procedia PDF Downloads 169

Authors: Lassi Lehto, Jaakko Kahkonen, Juha Oksanen, Tapani Sarjakoski

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The use of raster-formatted data sets in geospatial analysis is increasing rapidly. At the same time, geographic data are being introduced into disciplines outside the traditional domain of geoinformatics, like climate change, intelligent transport, and immigration studies. These developments call for better methods to deliver raster geodata in an efficient and easy-to-use manner. Data cube technologies have traditionally been used in the geospatial domain for managing Earth Observation data sets that have strict requirements for effective handling of time series. The same approach and methodologies can also be applied in managing other types of geospatial data sets. A cloud service-based geodata cube, called GeoCubes Finland, has been developed to support online delivery and analysis of most important geospatial data sets with national coverage. The main target group of the service is the academic research institutes in the country. The most significant aspects of the GeoCubes data repository include the use of multiple resolution levels, cloud-optimized file structure, and a customized, flexible content access API. Input data sets are pre-processed while being ingested into the repository to bring them into a harmonized form in aspects like georeferencing, sampling resolutions, spatial subdivision, and value encoding. All the resolution levels are created using an appropriate generalization method, selected depending on the nature of the source data set. Multiple pre-processed resolutions enable new kinds of online analysis approaches to be introduced. Analysis processes based on interactive visual exploration can be effectively carried out, as the level of resolution most close to the visual scale can always be used. In the same way, statistical analysis can be carried out on resolution levels that best reflect the scale of the phenomenon being studied. Access times remain close to constant, independent of the scale applied in the application. The cloud service-based approach, applied in the GeoCubes Finland repository, enables analysis operations to be performed on the server platform, thus making high-performance computing facilities easily accessible. The developed GeoCubes API supports this kind of approach for online analysis. The use of cloud-optimized file structures in data storage enables the fast extraction of subareas. The access API allows for the use of vector-formatted administrative areas and user-defined polygons as definitions of subareas for data retrieval. Administrative areas of the country in four levels are available readily from the GeoCubes platform. In addition to direct delivery of raster data, the service also supports the so-called virtual file format, in which only a small text file is first downloaded. The text file contains links to the raster content on the service platform. The actual raster data is downloaded on demand, from the spatial area and resolution level required in each stage of the application. By the geodata cube approach, pre-harmonized geospatial data sets are made accessible to new categories of inexperienced users in an easy-to-use manner. At the same time, the multiresolution nature of the GeoCubes repository facilitates expert users to introduce new kinds of interactive online analysis operations.

Keywords: cloud service, geodata cube, multiresolution, raster geodata

Procedia PDF Downloads 136

Authors: Theoktisti Makridou, Konstantinos Tsaprailis, George Arvanitakis, Charalampos Kontoes

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In the literature, the data-driven approaches for mosquito abundance prediction relaying on supervised machine learning models that get trained with historical in-situ measurements. The counterpart of this approach is once the model gets trained on pointlevel (specific x,y coordinates) measurements, the predictions of the model refer again to point-level. These point-level predictions reduce the applicability of those solutions once a lot of early warning and mitigation actions applications need predictions for an area level, such as a municipality, village, etc... In this study, we apply a data-driven predictive model, which relies on public-open satellite Earth Observation and geospatial data and gets trained with historical point-level in-Situ measurements of mosquito abundance. Then we propose a methodology to extract information from a point-level predictive model to a broader area-level prediction. Our methodology relies on the randomly spatial sampling of the area of interest (similar to the Poisson hardcore process), obtaining the EO and geomorphological information for each sample, doing the point-wise prediction for each sample, and aggregating the predictions to represent the average mosquito abundance of the area. We quantify the performance of the transformation from the pointlevel to the area-level predictions, and we analyze it in order to understand which parameters have a positive or negative impact on it. The goal of this study is to propose a methodology that predicts the mosquito abundance of a given area by relying on point-level prediction and to provide qualitative insights regarding the expected performance of the area-level prediction. We applied our methodology to historical data (of Culex pipiens) of two areas of interest (Veneto region of Italy and Central Macedonia of Greece). In both cases, the results were consistent. The mean mosquito abundance of a given area can be estimated with similar accuracy to the point-level predictor, sometimes even better. The density of the samples that we use to represent one area has a positive effect on the performance in contrast to the actual number of sampling points which is not informative at all regarding the performance without the size of the area. Additionally, we saw that the distance between the sampling points and the real in-situ measurements that were used for training did not strongly affect the performance.

Keywords: mosquito abundance, supervised machine learning, culex pipiens, spatial sampling, west nile virus, earth observation data

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Authors: Rahil Bahrami, Kaveh Ashenayi

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This paper presents a systematic classification and comparative analysis of non-isolated bi-directional DC-DC converters. The increasing demand for efficient energy conversion in diverse applications has spurred the development of various converter topologies. In this study, we categorize bi-directional converters into three distinct classes: Inverting, Non-Inverting, and Interleaved. Each category is characterized by its unique operational characteristics and benefits. Furthermore, a practical comparison is conducted by evaluating the results of simulation of each bi-directional converter. BDCs can be classified into isolated and non-isolated topologies. Non-isolated converters share a common ground between input and output, making them suitable for applications with minimal voltage change. They are easy to integrate, lightweight, and cost-effective but have limitations like limited voltage gain, switching losses, and no protection against high voltages. Isolated converters use transformers to separate input and output, offering safety benefits, high voltage gain, and noise reduction. They are larger and more costly but are essential for automotive designs where safety is crucial. The paper focuses on non-isolated systems.The paper discusses the classification of non-isolated bidirectional converters based on several criteria. Common factors used for classification include topology, voltage conversion, control strategy, power capacity, voltage range, and application. These factors serve as a foundation for categorizing converters, although the specific scheme might vary depending on contextual, application, or system-specific requirements. The paper presents a three-category classification for non-isolated bi-directional DC-DC converters: inverting, non-inverting, and interleaved. In the inverting category, converters produce an output voltage with reversed polarity compared to the input voltage, achieved through specific circuit configurations and control strategies. This is valuable in applications such as motor control and grid-tied solar systems. The non-inverting category consists of converters maintaining the same voltage polarity, useful in scenarios like battery equalization. Lastly, the interleaved category employs parallel converter stages to enhance power delivery and reduce current ripple. This classification framework enhances comprehension and analysis of non-isolated bi-directional DC-DC converters. The findings contribute to a deeper understanding of the trade-offs and merits associated with different converter types. As a result, this work aids researchers, practitioners, and engineers in selecting appropriate bi-directional converter solutions for specific energy conversion requirements. The proposed classification framework and experimental assessment collectively enhance the comprehension of non-isolated bi-directional DC-DC converters, fostering advancements in efficient power management and utilization.The simulation process involves the utilization of PSIM to model and simulate non-isolated bi-directional converter from both inverted and non-inverted category. The aim is to conduct a comprehensive comparative analysis of these converters, considering key performance indicators such as rise time, efficiency, ripple factor, and maximum error. This systematic evaluation provides valuable insights into the dynamic response, energy efficiency, output stability, and overall precision of the converters. The results of this comparison facilitate informed decision-making and potential optimizations, ensuring that the chosen converter configuration aligns effectively with the designated operational criteria and performance goals.

Keywords: bi-directional, DC-DC converter, non-isolated, energy conversion

Procedia PDF Downloads 100

Authors: Moustafa Osman Mohammed

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Construction industry, as one of the main contributor in depletion of natural resources, influences climate change. This paper discusses incremental and evolutionary development of the proposed models for optimization of a life-cycle analysis to explicit strategy for evaluation systems. The main categories are virtually irresistible for introducing uncertainties, uptake composite structure model (CSM) as environmental management systems (EMSs) in a practice science of evaluation small and medium-sized enterprises (SMEs). The model simplified complex systems to reflect nature systems’ input, output and outcomes mode influence “framework measures” and give a maximum likelihood estimation of how elements are simulated over the composite structure. The traditional knowledge of modeling is based on physical dynamic and static patterns regarding parameters influence environment. It unified methods to demonstrate how construction systems ecology interrelated from management prospective in procedure reflects the effect of the effects of engineering systems to ecology as ultimately unified technologies in extensive range beyond constructions impact so as, - energy systems. Sustainability broadens socioeconomic parameters to practice science that meets recovery performance, engineering reflects the generic control of protective systems. When the environmental model employed properly, management decision process in governments or corporations could address policy for accomplishment strategic plans precisely. The management and engineering limitation focuses on autocatalytic control as a close cellular system to naturally balance anthropogenic insertions or aggregation structure systems to pound equilibrium as steady stable conditions. Thereby, construction systems ecology incorporates engineering and management scheme, as a midpoint stage between biotic and abiotic components to predict constructions impact. The later outcomes’ theory of environmental obligation suggests either a procedures of method or technique that is achieved in sustainability impact of construction system ecology (SICSE), as a relative mitigation measure of deviation control, ultimately.

Keywords: sustainability, environmental impact assessment, environemtal management, construction ecology

Procedia PDF Downloads 393

Authors: Alberto Mínguez-Martínez, Jesús De Vicente Y Oliva

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It seems that we live in a world in which the trend in industrial environments is the miniaturization of systems and materials and the fabrication of parts at the micro-and nano-scale. The problem arises when manufacturers want to study the quality of their production. This characteristic is becoming crucial due to the evolution of the industry and the development of Industry 4.0. As Industry 4.0 is based on digital models of production and processes, having accurate measurements becomes capital. At this point, the metrology field plays an important role as it is a powerful tool to ensure more stable production to reduce scrap and the cost of non-conformities. The most extended measuring instruments that allow us to carry out accurate measurements at these scales are optical microscopes, whether they are traditional, confocal, focus variation microscopes, profile projectors, or any other similar measurement system. However, the accuracy of measurements is connected to the traceability of them to the SI unit of length (the meter). The fact of providing adequate traceability to 2D and 3D dimensional measurements at micro-and nano-scale in industrial environments is a problem that is being studied, and it does not have a unique answer. In addition, if commercial material standards for micro-and nano-scale are considered, we can find that there are two main problems. On the one hand, those material standards that could be considered complete and very interesting do not give traceability of dimensional measurements and, on the other hand, their calibration is very expensive. This situation implies that these kinds of standards will not succeed in industrial environments and, as a result, they will work in the absence of traceability. To solve this problem in industrial environments, it becomes necessary to have material standards that are easy to use, agile, adaptive to different forms, cheap to manufacture and, of course, traceable to the definition of meter with simple methods. By using these ‘customized standards’, it would be possible to adapt and design measuring procedures for each application and manufacturers will work with some traceability. It is important to note that, despite the fact that this traceability is clearly incomplete, this situation is preferable to working in the absence of it. Recently, it has been demonstrated the versatility and the utility of using laser technology and other AM technologies to manufacture customized material standards. In this paper, the authors propose to manufacture a customized material standard using an ultraviolet laser system and a method to calibrate it. To conclude, the results of the calibration carried out in an accredited dimensional metrology laboratory are presented.

Keywords: industrial environment, material standards, optical measuring instrument, traceability

Procedia PDF Downloads 122

Authors: Cristóbal Moënne-Loccoz, Rodrigo C. Vergara, Vladimir López, Domingo Mery, Diego Cosmelli

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Our daily interaction with computational interfaces is plagued of situations in which we go from inexperienced users to experts through self-motivated exploration of the same task. In many of these interactions, we must learn to find our way through a sequence of decisions and actions before obtaining the desired result. For instance, when drawing cash from an ATM machine, choices are presented in a step-by-step fashion so that a specific sequence of actions must be performed in order to produce the expected outcome. But, as they become experts in the use of such interfaces, do users adopt specific search and learning strategies? Moreover, if so, can we use this information to follow the process of expertise development and, eventually, predict future actions? This would be a critical step towards building truly adaptive interfaces that can facilitate interaction at different moments of the learning curve. Furthermore, it could provide a window into potential mechanisms underlying decision-making behavior in real world scenarios. Here we tackle this question using a simple game interface that instantiates a 4-level binary decision tree (BDT) sequential decision-making task. Participants have to explore the interface and discover an underlying concept-icon mapping in order to complete the game. We develop a Hidden Markov Model (HMM)-based approach whereby a set of stereotyped, hierarchically related search behaviors act as hidden states. Using this model, we are able to track the decision-making process as participants explore, learn and develop expertise in the use of the interface. Our results show that partitioning the problem space into such stereotyped strategies is sufficient to capture a host of exploratory and learning behaviors. Moreover, using the modular architecture of stereotyped strategies as a Mixture of Experts, we are able to simultaneously ask the experts about the user's most probable future actions. We show that for those participants that learn the task, it becomes possible to predict their next decision, above chance, approximately halfway through the game. Our long-term goal is, on the basis of a better understanding of real-world decision-making processes, to inform the construction of interfaces that can establish dynamic conversations with their users in order to facilitate the development of expertise.

Keywords: behavioral modeling, expertise acquisition, hidden markov models, sequential decision-making

Procedia PDF Downloads 252

Authors: Stamatis Zoras, Vasilis Evagelopoulos, Theodoros Staurakas

Abstract:

The association between elevated air pollution levels and extreme climate conditions (temperature, particulate matter, ozone levels, etc.) and mental consequences has been, recently, the focus of significant number of studies. It varies depending on the time of the year it occurs either during the hot period or cold periods but, specifically, when extreme air pollution and weather events are observed, e.g. air pollution episodes and persistent heatwaves. It also varies spatially due to different effects of air quality and climate extremes to human health when considering metropolitan or rural areas. An air pollutant concentration and a climate extreme are taking a different form of impact if the focus area is countryside or in the urban environment. In the built environment the climate extreme effects are driven through the formed microclimate which must be studied more efficiently. Variables such as biological, age groups etc may be implicated by different environmental factors such as increased air pollution/noise levels and overheating of buildings in comparison to rural areas. Gridded air quality and climate variables derived from the land surface observations network of West Macedonia in Greece will be analysed against mortality data in a spatial format in the region of West Macedonia. Artificial intelligence (AI) tools will be used for data correction and prediction of health deterioration with climatic conditions and air pollution at local scale. This would reveal the built environment implications against the countryside. The air pollution and climatic data have been collected from meteorological stations and span the period from 2000 to 2009. These will be projected against the mortality rates data in daily, monthly, seasonal and annual grids. The grids will be operated as AI-based warning models for decision makers in order to map the health conditions in rural and urban areas to ensure improved awareness of the healthcare system by taken into account the predicted changing climate conditions. Gridded data of climate conditions, air quality levels against mortality rates will be presented by AI-analysed gridded indicators of the implicated variables. An Al-based gridded warning platform at local scales is then developed for future system awareness platform for regional level.

Keywords: air quality, artificial inteligence, climatic conditions, mortality

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