Search results for: edge computing module

Authors: Ankur Chaudhuri, Sibani Sen Chakraborty

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In human, glutaminyl cyclase activity is highly abundant in neuronal and secretory tissues and is preferentially restricted to hypothalamus and pituitary. The N-terminal modification of β-amyloids (Aβs) peptides by the generation of a pyro-glutamyl (pGlu) modified Aβs (pE-Aβs) is an important process in the initiation of the formation of neurotoxic plaques in Alzheimer’s disease (AD). This process is catalyzed by glutaminyl cyclase (QC). The expression of QC is characteristically up-regulated in the early stage of AD, and the hallmark of the inhibition of QC is the prevention of the formation of pE-Aβs and plaques. A computer-aided drug design (CADD) process was employed to give an idea for the designing of potentially active compounds to understand the inhibitory potency against human glutaminyl cyclase (QC). This work elaborates the ligand-based and structure-based pharmacophore exploration of glutaminyl cyclase (QC) by using the known inhibitors. Three dimensional (3D) quantitative structure-activity relationship (QSAR) methods were applied to 154 compounds with known IC50 values. All the inhibitors were divided into two sets, training-set, and test-sets. Generally, training-set was used to build the quantitative pharmacophore model based on the principle of structural diversity, whereas the test-set was employed to evaluate the predictive ability of the pharmacophore hypotheses. A chemical feature-based pharmacophore model was generated from the known 92 training-set compounds by HypoGen module implemented in Discovery Studio 2017 R2 software package. The best hypothesis was selected (Hypo1) based upon the highest correlation coefficient (0.8906), lowest total cost (463.72), and the lowest root mean square deviation (2.24Å) values. The highest correlation coefficient value indicates greater predictive activity of the hypothesis, whereas the lower root mean square deviation signifies a small deviation of experimental activity from the predicted one. The best pharmacophore model (Hypo1) of the candidate inhibitors predicted comprised four features: two hydrogen bond acceptor, one hydrogen bond donor, and one hydrophobic feature. The Hypo1 was validated by several parameters such as test set activity prediction, cost analysis, Fischer's randomization test, leave-one-out method, and heat map of ligand profiler. The predicted features were then used for virtual screening of potential compounds from NCI, ASINEX, Maybridge and Chembridge databases. More than seven million compounds were used for this purpose. The hit compounds were filtered by drug-likeness and pharmacokinetics properties. The selective hits were docked to the high-resolution three-dimensional structure of the target protein glutaminyl cyclase (PDB ID: 2AFU/2AFW) to filter these hits further. To validate the molecular docking results, the most active compound from the dataset was selected as a reference molecule. From the density functional theory (DFT) study, ten molecules were selected based on their highest HOMO (highest occupied molecular orbitals) energy and the lowest bandgap values. Molecular dynamics simulations with explicit solvation systems of the final ten hit compounds revealed that a large number of non-covalent interactions were formed with the binding site of the human glutaminyl cyclase. It was suggested that the hit compounds reported in this study could help in future designing of potent inhibitors as leads against human glutaminyl cyclase.

Keywords: glutaminyl cyclase, hit lead, pharmacophore model, simulation

Procedia PDF Downloads 131

Authors: Daniele Lerede, Gianvito Colucci, Matteo Nicoli, Laura Savoldi

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The European Green Deal is the first internationally agreed set of measures to contrast climate change and environmental degradation. Besides the main target of reducing emissions by at least 55% by 2030, it sets the target of accompanying European countries through an energy transition to make the European Union into a modern, resource-efficient, and competitive net-zero emissions economy by 2050, decoupling growth from the use of resources and ensuring a fair adaptation of all social categories to the transformation process. While the general purpose to allow the realization of the purposes of the Green Deal already dates back to 2019, strategies and policies keep being developed coping with recent circumstances and achievements. However, general long-term measures like the Circular Economy Action Plan, the proposals to shift from fossil natural gas to renewable and low-carbon gases, in particular biomethane and hydrogen, and to end the sale of gasoline and diesel cars by 2035, will all have significant effects on energy supply and demand evolution across the next decades. The interactions between energy supply and demand over long-term time frames are usually assessed via energy system models to derive useful insights for policymaking and to address technological choices and research and development. TEMOA-Europe is a newly developed energy system optimization model instance based on the minimization of the total cost of the system under analysis, adopting a technologically integrated, detailed, and explicit formulation and considering the evolution of the system in partial equilibrium in competitive markets with perfect foresight. TEMOA-Europe is developed on the TEMOA platform, an open-source modeling framework totally implemented in Python, therefore ensuring third-party verification even on large and complex models. TEMOA-Europe is based on a single-region representation of the European Union and EFTA countries on a time scale between 2005 and 2100, relying on a set of assumptions for socio-economic developments based on projections by the International Energy Outlook and a large technological dataset including 7 sectors: the upstream and power sectors for the production of all energy commodities and the end-use sectors, including industry, transport, residential, commercial and agriculture. TEMOA-Europe also includes an updated hydrogen module considering its production, storage, transportation, and utilization. Besides, it can rely on a wide set of innovative technologies, ranging from nuclear fusion and electricity plants equipped with CCS in the power sector to electrolysis-based steel production processes and steel in the industrial sector – with a techno-economic characterization based on public literature – to produce insightful energy scenarios and especially to cope with the very long analyzed time scale. The aim of this work is to examine in detail the scheme of measures and policies for the realization of the purposes of the Green Deal and to transform them into a set of constraints and new socio-economic development pathways. Based on them, TEMOA-Europe will be used to produce and comparatively analyze scenarios to assess the consequences of Green Deal-related measures on the future evolution of the energy mix over the whole energy system in an economic optimization environment.

Keywords: European Green Deal, energy system optimization modeling, scenario analysis, TEMOA-Europe

Procedia PDF Downloads 105

Authors: Yannick Willemin

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Carbon fibre-reinforced plastic (CFRP) offers outstanding value. However, like all materials, CFRP also has its challenges. Many forming processes are largely manual and hard to automate, making it challenging to control repeatability and reproducibility (R&R); they generate significant scrap and are too slow for high-series production; fibre costs are relatively high and subject to supply and cost fluctuations; the supply chain is fragmented; many forms of CFRP are not recyclable, and many materials have yet to be fully characterized for accurate simulation; shelf life and outlife limitations add cost; continuous-fibre forms have design limitations; many materials are brittle; and small and/or thick parts are costly to produce and difficult to automate. A majority of small structural parts are metal due to high CFRP fabrication costs for the small-size class. The fact that CFRP manufacturing processes that produce the highest performance parts also tend to be the slowest and least automated is another reason CFRP parts are generally higher in cost than comparably performing metal parts, which are easier to produce. Fortunately, business is in the midst of a major manufacturing evolution—Industry 4.0— one technology seeing rapid growth is additive manufacturing/3D printing, thanks to new processes and materials, plus an ability to harness Industry 4.0 tools. No longer limited to just prototype parts, metal-additive technologies are used to produce tooling and mold components for high-volume manufacturing, and polymer-additive technologies can incorporate fibres to produce true composites and be used to produce end-use parts with high aesthetics, unmatched complexity, mass customization opportunities, and high mechanical performance. A new hybrid manufacturing process combines the best capabilities of additive—high complexity, low energy usage and waste, 100% traceability, faster to market—and post-consolidation—tight tolerances, high R&R, established materials, and supply chains—technologies. The platform was developed by Zürich-based 9T Labs AG and is called Additive Fusion Technology (AFT). It consists of a design software offering the possibility to determine optimal fibre layup, then exports files back to check predicted performance—plus two pieces of equipment: a 3d-printer—which lays up (near)-net-shape preforms using neat thermoplastic filaments and slit, roll-formed unidirectional carbon fibre-reinforced thermoplastic tapes—and a post-consolidation module—which consolidates then shapes preforms into final parts using a compact compression press fitted with a heating unit and matched metal molds. Matrices—currently including PEKK, PEEK, PA12, and PPS, although nearly any high-quality commercial thermoplastic tapes and filaments can be used—are matched between filaments and tapes to assure excellent bonding. Since thermoplastics are used exclusively, larger assemblies can be produced by bonding or welding together smaller components, and end-of-life parts can be recycled. By combining compression molding with 3D printing, higher part quality with very-low voids and excellent surface finish on A and B sides can be produced. Tight tolerances (min. section thickness=1.5mm, min. section height=0.6mm, min. fibre radius=1.5mm) with high R&R can be cost-competitively held in production volumes of 100 to 10,000 parts/year on a single set of machines.

Keywords: additive manufacturing, composites, thermoplastic, hybrid manufacturing

Procedia PDF Downloads 96

Authors: Mari A. Virtanen, Elina Haavisto, Eeva Liikanen, Maria Kääriäinen

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Rapid technological development and digitalization has affected also on higher education. During last twenty years multiple of electronic and mobile learning (e-learning, m-learning) platforms have been developed and have become prevalent in many universities and in the all fields of education. Ubiquitous learning (u-learning) is not that widely known or used. Ubiquitous learning environments (ULE) are the new era of computer-assisted learning. They are based on ubiquitous technology and computing that fuses the learner seamlessly into learning process by using sensing technology as tags, badges or barcodes and smart devices like smartphones and tablets. ULE combines real-life learning situations into virtual aspects and can be flexible used in anytime and anyplace. The aim of this study was to assess the effects of ubiquitous 360 o learning environment on higher education students’ clinical histotechnology competence. A quasi-experimental study design was used. 57 students in biomedical laboratory science degree program was assigned voluntarily to experiment (n=29) and to control group (n=28). Experimental group studied via ubiquitous 360o learning environment and control group via traditional web-based learning environment (WLE) in a 8-week educational intervention. Ubiquitous 360o learning environment (ULE) combined authentic learning environment (histotechnology laboratory), digital environment (virtual laboratory), virtual microscope, multimedia learning content, interactive communication tools, electronic library and quick response barcodes placed into authentic laboratory. Web-based learning environment contained equal content and components with the exception of the use of mobile device, interactive communication tools and quick response barcodes. Competence of clinical histotechnology was assessed by using knowledge test and self-report developed for this study. Data was collected electronically before and after clinical histotechnology course and analysed by using descriptive statistics. Differences among groups were identified by using Wilcoxon test and differences between groups by using Mann-Whitney U-test. Statistically significant differences among groups were identified in both groups (p<0.001). Competence scores in post-test were higher in both groups, than in pre-test. Differences between groups were very small and not statistically significant. In this study the learning environment have developed based on 360o technology and successfully implemented into higher education context. And students’ competence increases when ubiquitous learning environment were used. In the future, ULE can be used as a learning management system for any learning situation in health sciences. More studies are needed to show differences between ULE and WLE.

Keywords: competence, higher education, histotechnology, ubiquitous learning, u-learning, 360o

Procedia PDF Downloads 286

Authors: A. Calabrese, V. F. Uricchio, D. di Noia, S. Favale, C. Caiati, G. P. Maggi, G. Donvito, D. Diacono, S. Tangaro, A. Italiano, E. Riezzo, M. Zippitelli, M. Toriello, E. Celiberti, D. Festa, A. Colaianni

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Climate change has a severe impact on human health. There is a vast literature demonstrating temperature increase is causally related to cardiovascular problem and represents a high risk for human health, but there are not study that improve a solution. In this work, it is studied how the clime influenced the human parameter through the analysis of climatic conditions in an area of the Apulia Region: Capurso Municipality. At the same time, medical personnel involved identified a set of variables useful to define an index describing health condition. These scientific studies are the base of an innovative alert system, IPPOCRATE, whose aim is to asses climate risk and share information to population at risk to support prevention and mitigation actions. IPPOCRATE is an e-health system, it is designed to provide technological support to analysis of health risk related to climate and provide tools for prevention and management of critical events. It is the first integrated system of prevention of human risk caused by climate change. IPPOCRATE calculates risk weighting meteorological data with the vulnerability of monitored subjects and uses mobile and cloud technologies to acquire and share information on different data channels. It is composed of four components: Multichannel Hub. Multichannel Hub is the ICT infrastructure used to feed IPPOCRATE cloud with a different type of data coming from remote monitoring devices, or imported from meteorological databases. Such data are ingested, transformed and elaborated in order to be dispatched towards mobile app and VoIP phone systems. IPPOCRATE Multichannel Hub uses open communication protocols to create a set of APIs useful to interface IPPOCRATE with 3rd party applications. Internally, it uses non-relational paradigm to create flexible and highly scalable database. WeHeart and Smart Application The wearable device WeHeart is equipped with sensors designed to measure following biometric variables: heart rate, systolic blood pressure and diastolic blood pressure, blood oxygen saturation, body temperature and blood glucose for diabetic subjects. WeHeart is designed to be easy of use and non-invasive. For data acquisition, users need only to wear it and connect it to Smart Application by Bluetooth protocol. Easy Box was designed to take advantage from new technologies related to e-health care. EasyBox allows user to fully exploit all IPPOCRATE features. Its name, Easy Box, reveals its purpose of container for various devices that may be included depending on user needs. Territorial Registry is the IPPOCRATE web module reserved to medical personnel for monitoring, research and analysis activities. Territorial Registry allows to access to all information gathered by IPPOCRATE using GIS system in order to execute spatial analysis combining geographical data (climatological information and monitored data) with information regarding the clinical history of users and their personal details. Territorial Registry was designed for different type of users: control rooms managed by wide area health facilities, single health care center or single doctor. Territorial registry manages such hierarchy diversifying the access to system functionalities. IPPOCRATE is the first e-Health system focused on climate risk prevention.

Keywords: climate change, health risk, new technological system

Procedia PDF Downloads 867

Authors: Jasmin Cowin, Amany Alkhayat

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This paper will concentrate on a comparative analysis of both the advantages and limitations of using digital learning resources (DLRs). DLRs covered will be Virtual Reality (VR), Mobile Learning (M-learning) and Computer-Assisted Language Learning (CALL) together with their subset, Mobile Assisted Language Learning (MALL) in language education. In addition, best practices for language teaching and the application of established language teaching methodologies such as Communicative Language Teaching (CLT), the audio-lingual method, or community language learning will be explored. Education has changed dramatically since the eruption of the pandemic. Traditional face-to-face education was disrupted on a global scale. The rise of distance learning brought new digital tools to the forefront, especially web conferencing tools, digital storytelling apps, test authoring tools, and VR platforms. Language educators raced to vet, learn, and implement multiple technology resources suited for language acquisition. Yet, questions remain on how to harness new technologies, digital tools, and their ubiquitous availability while using established methods and methodologies in language learning paired with best teaching practices. In M-learning language, learners employ portable computing devices such as smartphones or tablets. CALL is a language teaching approach using computers and other technologies through presenting, reinforcing, and assessing language materials to be learned or to create environments where teachers and learners can meaningfully interact. In VR, a computer-generated simulation enables learner interaction with a 3D environment via screen, smartphone, or a head mounted display. Research supports that VR for language learning is effective in terms of exploration, communication, engagement, and motivation. Students are able to relate through role play activities, interact with 3D objects and activities such as field trips. VR lends itself to group language exercises in the classroom with target language practice in an immersive, virtual environment. Students, teachers, schools, language institutes, and institutions benefit from specialized support to help them acquire second language proficiency and content knowledge that builds on their cultural and linguistic assets. Through the purposeful application of different language methodologies and teaching approaches, language learners can not only make cultural and linguistic connections in DLRs but also practice grammar drills, play memory games or flourish in authentic settings.

Keywords: language teaching methodologies, computer-assisted language learning, mobile learning, virtual reality

Procedia PDF Downloads 238

Authors: I Putu Deny Arthawan Sugih Prabowo, Eko Nugroho, Rudy Hartanto

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Known, Green IT is a concept about how to use the technology (IT) wisely, efficiently, and environmentally. However, it exists as the consequence of the rapid-growth of the technology (especially IT) currently. Not only for the environments, the usage of Green IT applications, e.g. Cloud Computing (Cloud Storage) and E-Money (E-Cash), also gives its benefits for the organizational business strategy (especially the organizational product/service strategy) in order to gain the organizational competitive advantage (to be the market leader). This paper takes the case at E-Money as a Value-Added Services (VAS) of a telecommunication firm (company) in Indonesia which it also competes with the competitors’ similar product (service). Although it has been a popular telecommunication firm’s product/service, but its strategic values for the organization (firm) is still unknown, and therefore, the aim of this paper is for analyzing its strategic values for gaining the organizational competitive advantage. However, in this paper, its strategic value analysis is viewed by how to assess (consider) its strategic benefits and also manage the challenges or risks of its implementation at the organization as an organizational product/service. Then the paper uses a research model for investigating the influences of both perceived risks and the organizational cultures to the usage of Green IT Application at the organization and also both the usage of Green IT Application at the organization and the threats-challenges of the organizational products/services to the competitive advantage of the organizational products/services. However, the paper uses the quantitative research method (collecting the information from the field respondents by using the research questionnaires) and then, the primary data is analyzed by both descriptive and inferential statistics. Also in this paper, SmartPLS is used for analyzing the primary data by the quantitative research method. Besides using the quantitative research method, the paper also uses the qualitative research method, such as interviewing the field respondent and/or directly field observation, for deeply confirming the quantitative research method’s analysis results at the certain domain, e.g. both organizational cultures and internal processes that support the usage of Green IT applications for the organizational product/service (E-Money in this paper case). However, the paper is still at an infant stage of in-progress research. Then the paper’s results may be used as a reference for the organization (firm or company) in developing the organizational business strategies, especially about the organizational product/service that relates to Green IT applications. Besides it, the paper may also be the future study, e.g. the influence of knowledge transfer about E-Money and/or other Green IT application-based products/services to the organizational service performance that relates to the product (service) in order to gain the competitive advantage.

Keywords: Green IT, competitive advantage, strategic value, organization (firm or company), organizational product (service)

Procedia PDF Downloads 305

Authors: Wiseman Emmanuel Nkhomah, Chiyembekezo Kachimanga, Moses Banda Aron, Julia Higgins, Manuel Mulwafu, Kondwani Mpinga, Mwayi Chunga, Grace Momba, Enock Ndarama, Dickson Sumphi, Atupere Phiri, Fabien Munyaneza

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Background: Data is key to supporting health service delivery as stakeholders, including NGOs rely on it for effective service delivery, decision-making, and system strengthening. Several studies generated debate on data quality from national health management information systems (HMIS) in sub-Saharan Africa. This limits the utilization of data in resource-limited settings, which already struggle to meet standards set by the World Health Organization (WHO). We aimed to evaluate data quality improvement of Neno district HMIS over a 4-year period (2018 – 2021) following quarterly data reviews introduced in January 2020 by the district health management team and Partners In Health. Methods: Exploratory Mixed Research was used to examine report rates, followed by in-depth interviews using Key Informant Interviews (KIIs) and Focus Group Discussions (FGDs). We used the WHO module desk review to assess the quality of HMIS data in the Neno district captured from 2018 to 2021. The metrics assessed included the completeness and timeliness of 34 reports. Completeness was measured as a percentage of non-missing reports. Timeliness was measured as the span between data inputs and expected outputs meeting needs. We computed T-Test and recorded P-values, summaries, and percentage changes using R and Excel 2016. We analyzed demographics for key informant interviews in Power BI. We developed themes from 7 FGDs and 11 KIIs using Dedoose software, from which we picked perceptions of healthcare workers, interventions implemented, and improvement suggestions. The study was reviewed and approved by Malawi National Health Science Research Committee (IRB: 22/02/2866). Results: Overall, the average reporting completeness rate was 83.4% (before) and 98.1% (after), while timeliness was 68.1% and 76.4 respectively. Completeness of reports increased over time: 2018, 78.8%; 2019, 88%; 2020, 96.3% and 2021, 99.9% (p< 0.004). The trend for timeliness has been declining except in 2021, where it improved: 2018, 68.4%; 2019, 68.3%; 2020, 67.1% and 2021, 81% (p< 0.279). Comparing 2021 reporting rates to the mean of three preceding years, both completeness increased from 88% to 99% (in 2021), while timeliness increased from 68% to 81%. Sixty-five percent of reports have maintained meeting a national standard of 90%+ in completeness while only 24% in timeliness. Thirty-two percent of reports met the national standard. Only 9% improved on both completeness and timeliness, and these are; cervical cancer, nutrition care support and treatment, and youth-friendly health services reports. 50% of reports did not improve to standard in timeliness, and only one did not in completeness. On the other hand, factors associated with improvement included improved communications and reminders using internal communication, data quality assessments, checks, and reviews. Decentralizing data entry at the facility level was suggested to improve timeliness. Conclusion: Findings suggest that data quality in HMIS for the district has improved following collaborative efforts. We recommend maintaining such initiatives to identify remaining quality gaps and that results be shared publicly to support increased use of data. These results can inform Ministry of Health and its partners on some interventions and advise initiatives for improving its quality.

Keywords: data quality, data utilization, HMIS, collaboration, completeness, timeliness, decision-making

Procedia PDF Downloads 84

Authors: Neil Bar, Andrew Heweston

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Probability of failure (PF) often appears alongside factor of safety (FS) in design acceptance criteria for rock slope, underground excavation and open pit mine designs. However, the design acceptance criteria generally provide no guidance relating to how PF should be calculated for homogeneous and heterogeneous rock masses, or what qualifies a ‘reasonable’ PF assessment for a given slope design. Observational and kinematic methods were widely used in the 1990s until advances in computing permitted the routine use of numerical modelling. In the 2000s and early 2010s, PF in numerical models was generally calculated using the point estimate method. More recently, some limit equilibrium analysis software offer statistical parameter inputs along with Monte-Carlo or Latin-Hypercube sampling methods to automatically calculate PF. Factors including rock type and density, weathering and alteration, intact rock strength, rock mass quality and shear strength, the location and orientation of geologic structure, shear strength of geologic structure and groundwater pore pressure influence the stability of rock slopes. Significant engineering and geological judgment, interpretation and data interpolation is usually applied in determining these factors and amalgamating them into a geotechnical model which can then be analysed. Most factors are estimated ‘approximately’ or with allowances for some variability rather than ‘exactly’. When it comes to numerical modelling, some of these factors are then treated deterministically (i.e. as exact values), while others have probabilistic inputs based on the user’s discretion and understanding of the problem being analysed. This paper discusses the importance of understanding the key aspects of slope design for homogeneous and heterogeneous rock masses and how they can be translated into reasonable PF assessments where the data permits. A case study from a large open pit gold mine in a complex geological setting in Western Australia is presented to illustrate how PF can be calculated using different methods and obtain markedly different results. Ultimately sound engineering judgement and logic is often required to decipher the true meaning and significance (if any) of some PF results.

Keywords: probability of failure, point estimate method, Monte-Carlo simulations, sensitivity analysis, slope stability

Procedia PDF Downloads 208

Authors: Victor Mantilla, Romeu Vicente, António Figueiredo, Victor Ferreira, Sandra Sorte

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Integrating dynamic facades into contemporary building design is shaping a new era of energy efficiency and user comfort. These innovative facades, often constructed using lightweight construction systems and materials, offer an opportunity to have a responsive and adaptive nature to the dynamic behavior of the outdoor climate. Therefore, in regions characterized by high fluctuations in daily temperatures, the ability to adapt to environmental changes is of paramount importance and a challenge. This paper presents a thorough review of the state of the art on double-skin facades (DSF), focusing on lightweight solutions for the external envelope. Dynamic facades featuring elements like movable shading devices, phase change materials, and advanced control systems have revolutionized the built environment. They offer a promising path for reducing energy consumption while enhancing occupant well-being. Lightweight construction systems are increasingly becoming the choice for the constitution of these facade solutions, offering benefits such as reduced structural loads and reduced construction waste, improving overall sustainability. However, the performance of dynamic facades based on low thermal inertia solutions in climatic contexts with high thermal amplitude is still in need of research since their ability to adapt is traduced in variability/manipulation of the thermal transmittance coefficient (U-value). Emerging technologies can enable such a dynamic thermal behavior through innovative materials, changes in geometry and control to optimize the facade performance. These innovations will allow a facade system to respond to shifting outdoor temperature, relative humidity, wind, and solar radiation conditions, ensuring that energy efficiency and occupant comfort are both met/coupled. This review addresses the potential configuration of double-skin facades, particularly concerning their responsiveness to seasonal variations in temperature, with a specific focus on addressing the challenges posed by winter and summer conditions. Notably, the design of a dynamic facade is significantly shaped by several pivotal factors, including the choice of materials, geometric considerations, and the implementation of effective monitoring systems. Within the realm of double skin facades, various configurations are explored, encompassing exhaust air, supply air, and thermal buffering mechanisms. According to the review places a specific emphasis on the thermal dynamics at play, closely examining the impact of factors such as the color of the facade, the slat angle's dimensions, and the positioning and type of shading devices employed in these innovative architectural structures.This paper will synthesize the current research trends in this field, with the presentation of case studies and technological innovations with a comprehensive understanding of the cutting-edge solutions propelling the evolution of building envelopes in the face of climate change, namely focusing on double-skin lightweight solutions to create sustainable, adaptable, and responsive building envelopes. As indicated in the review, flexible and lightweight systems have broad applicability across all building sectors, and there is a growing recognition that retrofitting existing buildings may emerge as the predominant approach.

Keywords: adaptive, control systems, dynamic facades, energy efficiency, responsive, thermal comfort, thermal transmittance

Procedia PDF Downloads 80

Authors: Kumar Divyendra

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Underwater Sensor Networks (UWSNs) have wide applications in areas such as water quality monitoring, marine wildlife management etc. A typical UWSN system consists of a set of sensors deployed randomly underwater which communicate with each other using acoustic links. RF communication doesn't work underwater, and GPS too isn't available underwater. Additionally Automated Underwater Vehicles (AUVs) are deployed to collect data from some special nodes called Cluster Heads (CHs). These CHs aggregate data from their neighboring nodes and forward them to the AUVs using optical links when an AUV is in range. This helps reduce the number of hops covered by data packets and helps conserve energy. We consider the three-dimensional model of the UWSN. Nodes are initially deployed randomly underwater. They attach themselves to the surface using a rod and can only move upwards or downwards using a pump and bladder mechanism. We use graph theory concepts to maximize the coverage volume while every node maintaining connectivity with at least one surface node. We treat the surface nodes as landmarks and each node finds out its hop distance from every surface node. We treat these hop-distances as coordinates and use them for AUV navigation. An AUV intending to move closer to a node with given coordinates moves hop by hop through nodes that are closest to it in terms of these coordinates. In absence of GPS, multiple different approaches like Inertial Navigation System (INS), Doppler Velocity Log (DVL), computer vision-based navigation, etc., have been proposed. These systems have their own drawbacks. INS accumulates error with time, vision techniques require prior information about the environment. We propose a method that makes use of the earth's magnetic field values for navigation and combines it with other methods that simultaneously increase the coverage volume under the UWSN. The AUVs are fitted with magnetometers that measure the magnetic intensity (I), horizontal inclination (H), and Declination (D). The International Geomagnetic Reference Field (IGRF) is a mathematical model of the earth's magnetic field, which provides the field values for the geographical coordinateson earth. Researchers have developed an inverse deep learning model that takes the magnetic field values and predicts the location coordinates. We make use of this model within our work. We combine this with with the hop-by-hop movement described earlier so that the AUVs move in such a sequence that the deep learning predictor gets trained as quickly and precisely as possible We run simulations in MATLAB to prove the effectiveness of our model with respect to other methods described in the literature.

Keywords: clustering, deep learning, network backbone, parallel computing

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Authors: Peter Egri, Tamas Kis

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Scheduling problems have been studied by the algorithmic mechanism design research from the beginning. This paper is focusing on a practically important, but theoretically rather neglected field: the project scheduling problem where the jobs connected by precedence constraints compete for various nonrenewable resources, such as materials. Although the centralized problem can be solved in polynomial-time by applying the algorithm of Carlier and Rinnooy Kan from the Eighties, obtaining materials in a decentralized environment is usually far from optimal. It can be observed in practical production scheduling situations that project managers tend to cache the required materials as soon as possible in order to avoid later delays due to material shortages. This greedy practice usually leads both to excess stocks for some projects and materials, and simultaneously, to shortages for others. The aim of this study is to develop a model for the material allocation problem of a production plant, where a central decision maker—the inventory—should assign the resources arriving at different points in time to the jobs. Since the actual due dates are not known by the inventory, the mechanism design approach is applied with the projects as the self-interested agents. The goal of the mechanism is to elicit the required information and allocate the available materials such that it minimizes the maximal tardiness among the projects. It is assumed that except the due dates, the inventory is familiar with every other parameters of the problem. A further requirement is that due to practical considerations monetary transfer is not allowed. Therefore a mechanism without money is sought which excludes some widely applied solutions such as the Vickrey–Clarke–Groves scheme. In this work, a type of Serial Dictatorship Mechanism (SDM) is presented for the studied problem, including a polynomial-time algorithm for computing the material allocation. The resulted mechanism is both truthful and Pareto optimal. Thus the randomization over the possible priority orderings of the projects results in a universally truthful and Pareto optimal randomized mechanism. However, it is shown that in contrast to problems like the many-to-many matching market, not every Pareto optimal solution can be generated with an SDM. In addition, no performance guarantee can be given compared to the optimal solution, therefore this approximation characteristic is investigated with experimental study. All in all, the current work studies a practically relevant scheduling problem and presents a novel truthful material allocation mechanism which eliminates the potential benefit of the greedy behavior that negatively influences the outcome. The resulted allocation is also shown to be Pareto optimal, which is the most widely used criteria describing a necessary condition for a reasonable solution.

Keywords: material allocation, mechanism without money, polynomial-time mechanism, project scheduling

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Authors: James Forren

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This research paper details a recent demonstrator project in which digital form found textile structures were built by human craftspersons wearing augmented reality (AR) head-worn displays (HWDs). The project utilized a wet-state natural fiber / cementitious matrix composite to generate minimal bending shapes in tension which, when cured and rotated, performed as minimal-bending compression members. The significance of the project is that it synthesizes computational structural simulations with visually guided handcraft production. Computational and physical form-finding methods with textiles are well characterized in the development of architectural form. One difficulty, however, is physically building computer simulations: often requiring complicated digital fabrication workflows. However, AR HWDs have been used to build a complex digital form from bricks, wood, plastic, and steel without digital fabrication devices. These projects utilize, instead, the tacit knowledge motor schema of the human craftsperson. Computational simulations offer unprecedented speed and performance in solving complex structural problems. Human craftspersons possess highly efficient complex spatial reasoning motor schemas. And textiles offer efficient form-generating possibilities for individual structural members and overall structural forms. This project proposes that the synthesis of these three modalities of structural problem-solving – computational, human, and material - may not only develop efficient structural form but offer further creative potentialities when the respective intelligence of each modality is productively leveraged. The project methodology pertains to its three modalities of production: 1) computational, 2) human, and 3) material. A proprietary three-dimensional graphic statics simulator generated a three-legged arch as a wireframe model. This wireframe was discretized into nine modules, three modules per leg. Each module was modeled as a woven matrix of one-inch diameter chords. And each woven matrix was transmitted to a holographic engine running on HWDs. Craftspersons wearing the HWDs then wove wet cementitious chords within a simple falsework frame to match the minimal bending form displayed in front of them. Once the woven components cured, they were demounted from the frame. The components were then assembled into a full structure using the holographically displayed computational model as a guide. The assembled structure was approximately eighteen feet in diameter and ten feet in height and matched the holographic model to under an inch of tolerance. The construction validated the computational simulation of the minimal bending form as it was dimensionally stable for a ten-day period, after which it was disassembled. The demonstrator illustrated the facility with which computationally derived, a structurally stable form could be achieved by the holographically guided, complex three-dimensional motor schema of the human craftsperson. However, the workflow traveled unidirectionally from computer to human to material: failing to fully leverage the intelligence of each modality. Subsequent research – a workshop testing human interaction with a physics engine simulation of string networks; and research on the use of HWDs to capture hand gestures in weaving seeks to develop further interactivity with rope and chord towards a bi-directional workflow within full-scale building environments.

Keywords: augmented reality, cementitious composites, computational form finding, textile structures

Procedia PDF Downloads 175

Authors: Kaleem Kashif, Tayyaba Anam, Yizhi Wu

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This paper introduces a methodology for advancing Italian speech vowels landmark detection within the distinctive feature-based speech recognition domain. Leveraging the legacy tool 'xkl' by integrating combined convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the study presents a comprehensive enhancement to the 'xkl' legacy software. This integration incorporates re-assigned spectrogram methodologies, enabling meticulous acoustic analysis. Simultaneously, our proposed model, integrating combined CNNs and RNNs, demonstrates unprecedented precision and robustness in landmark detection. The augmentation of re-assigned spectrogram fusion within the 'xkl' software signifies a meticulous advancement, particularly enhancing precision related to vowel formant estimation. This augmentation catalyzes unparalleled accuracy in landmark detection, resulting in a substantial performance leap compared to conventional methods. The proposed model emerges as a state-of-the-art solution in the distinctive feature-based speech recognition systems domain. In the realm of deep learning, a synergistic integration of combined CNNs and RNNs is introduced, endowed with specialized temporal embeddings, harnessing self-attention mechanisms, and positional embeddings. The proposed model allows it to excel in capturing intricate dependencies within Italian speech vowels, rendering it highly adaptable and sophisticated in the distinctive feature domain. Furthermore, our advanced temporal modeling approach employs Bayesian temporal encoding, refining the measurement of inter-landmark intervals. Comparative analysis against state-of-the-art models reveals a substantial improvement in accuracy, highlighting the robustness and efficacy of the proposed methodology. Upon rigorous testing on a database (LaMIT) speech recorded in a silent room by four Italian native speakers, the landmark detector demonstrates exceptional performance, achieving a 95% true detection rate and a 10% false detection rate. A majority of missed landmarks were observed in proximity to reduced vowels. These promising results underscore the robust identifiability of landmarks within the speech waveform, establishing the feasibility of employing a landmark detector as a front end in a speech recognition system. The synergistic integration of re-assigned spectrogram fusion, CNNs, RNNs, and Bayesian temporal encoding not only signifies a significant advancement in Italian speech vowels landmark detection but also positions the proposed model as a leader in the field. The model offers distinct advantages, including unparalleled accuracy, adaptability, and sophistication, marking a milestone in the intersection of deep learning and distinctive feature-based speech recognition. This work contributes to the broader scientific community by presenting a methodologically rigorous framework for enhancing landmark detection accuracy in Italian speech vowels. The integration of cutting-edge techniques establishes a foundation for future advancements in speech signal processing, emphasizing the potential of the proposed model in practical applications across various domains requiring robust speech recognition systems.

Keywords: landmark detection, acoustic analysis, convolutional neural network, recurrent neural network

Procedia PDF Downloads 63

Authors: Michelle Eichinger, Upali Nanda

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Behavior choices during college years can establish the pattern of lifelong healthy living. Nearly 1/3rd of American college students are either overweight (25 < BMI < 30) or obese (BMI > 30). In addition, overweight/obesity contributes to depression, which is a rising epidemic among college students, affecting academic performance and college drop-out rates. Overweight and obesity result in an imbalance of energy consumption (diet) and energy expenditure (physical activity). Overweight/obesity is a significant contributor to heart disease, diabetes, stroke, physical disabilities and some cancers, which are the leading causes of death and disease in the US. There has been a significant increase in obesity and obesity-related disorders such as type 2 diabetes, hypertension, and dyslipidemia among people in their teens and 20s. Historically, the evidence-based interventions for obesity prevention focused on changing the health behavior at the individual level and aimed at increasing awareness and educating people about nutrition and physical activity. However, it became evident that the environmental context of where people live, work and learn was interdependent to healthy behavior change. As a result, a comprehensive approach was required to include altering the social and built environment to support healthy living. College campus provides opportunities to support lifestyle behavior and form a health-promoting culture based on some key point of decisions such as stairs/ elevator, walk/ bike/ car, high-caloric and fast foods/balanced and nutrient-rich foods etc. At each point of decision, design, can help/hinder the healthier choice. For example, stair well design and motivational signage support physical activity; grocery store/market proximity influence healthy eating etc. There is a need to collate the vast information that is in planning and public health domains on a range of successful point of decision prompts, and translate it into architectural guidelines that help define the edge condition for critical point of decision prompts. This research study aims to address healthy behaviors through the built environment with the questions, how can we make the healthy choice an easy choice through the design of critical point of decision prompts? Our hypothesis is that well-designed point of decision prompts in the built environment of college campuses can promote healthier choices by students, which can directly impact mental and physical health related to obesity. This presentation will introduce a combined health and architectural framework aimed to influence healthy behaviors through design applied for college campuses. The premise behind developing our concept, point-of-decision design (PODD), is healthy decision-making can be built into, or afforded by our physical environments. Using effective design intervention strategies at these 'points-of-decision' on college campuses to make the healthy decision the default decision can be instrumental in positively impacting health at the population level. With our model, we aim to advance health research by utilizing point-of-decision design to impact student health via core sectors of influences within college settings, such as campus facilities and transportation. We will demonstrate how these domains influence patterns/trends in healthy eating and active living behaviors among students. how these domains influence patterns/trends in healthy eating and active living behaviors among students.

Keywords: architecture and health promotion, college campus, design strategies, health in built environment

Procedia PDF Downloads 222

Authors: Jelena Vucicevic

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Reliability analysis represents a very important task in different areas of work. In any industry, this is crucial for maintenance, efficiency, safety and monetary costs. There are ways to calculate reliability, unreliability, failure density and failure rate. This paper will try to introduce another way of calculating reliability by using R statistical software. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. The R programming environment is a widely used open source system for statistical analysis and statistical programming. It includes thousands of functions for the implementation of both standard and new statistical methods. R does not limit user only to operation related only to these functions. This program has many benefits over other similar programs: it is free and, as an open source, constantly updated; it has built-in help system; the R language is easy to extend with user-written functions. The significance of the work is calculation of time to failure or reliability in a new way, using statistic. Another advantage of this calculation is that there is no need for technical details and it can be implemented in any part for which we need to know time to fail in order to have appropriate maintenance, but also to maximize usage and minimize costs. In this case, calculations have been made on diesel generator fans but the same principle can be applied to any other part. The data for this paper came from a field engineering study of the time to failure of diesel generator fans. The ultimate goal was to decide whether or not to replace the working fans with a higher quality fan to prevent future failures. Seventy generators were studied. For each one, the number of hours of running time from its first being put into service until fan failure or until the end of the study (whichever came first) was recorded. Dataset consists of two variables: hours and status. Hours show the time of each fan working and status shows the event: 1- failed, 0- censored data. Censored data represent cases when we cannot track the specific case, so it could fail or success. Gaining the result by using R was easy and quick. The program will take into consideration censored data and include this into the results. This is not so easy in hand calculation. For the purpose of the paper results from R program have been compared to hand calculations in two different cases: censored data taken as a failure and censored data taken as a success. In all three cases, results are significantly different. If user decides to use the R for further calculations, it will give more precise results with work on censored data than the hand calculation.

Keywords: censored data, R statistical software, reliability analysis, time to failure

Procedia PDF Downloads 401

Authors: Elena Petersen, Inna Kornienko, Svetlana Guryeva, Sergey Simakov

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In vitro organoid cultivation requires the simultaneous provision of necessary vascularization and nutrients perfusion of cells during organoid development. However, many aspects of this problem are still unsolved. The functionality of vascular network intergrowth is limited during early stages of organoid development since a function of the vascular network initiated on final stages of in vitro organoid cultivation. Therefore, a microchannel network should be created in early stages of organoid cultivation in hydrogel matrix aimed to conduct and maintain minimally required the level of nutrients perfusion for all cells in the expanding organoid. The network configuration should be designed properly in order to exclude hypoxic and necrotic zones in expanding organoid at all stages of its cultivation. In vitro vascularization is currently the main issue within the field of tissue engineering. As perfusion and oxygen transport have direct effects on cell viability and differentiation, researchers are currently limited only to tissues of few millimeters in thickness. These limitations are imposed by mass transfer and are defined by the balance between the metabolic demand of the cellular components in the system and the size of the scaffold. Current approaches include growth factor delivery, channeled scaffolds, perfusion bioreactors, microfluidics, cell co-cultures, cell functionalization, modular assembly, and in vivo systems. These approaches may improve cell viability or generate capillary-like structures within a tissue construct. Thus, there is a fundamental disconnect between defining the metabolic needs of tissue through quantitative measurements of oxygen and nutrient diffusion and the potential ease of integration into host vasculature for future in vivo implantation. A model is proposed for growth prognosis of the organoid perfusion based on joint simulations of general nutrient diffusion, nutrient diffusion to the hydrogel matrix through the contact surfaces and microchannels walls, nutrient consumption by the cells of expanding organoid, including biomatrix contraction during tissue development, which is associated with changed consumption rate of growing organoid cells. The model allows computing effective microchannel network design giving minimally required the level of nutrients concentration in all parts of growing organoid. It can be used for preliminary planning of microchannel network design and simulations of nutrients supply rate depending on the stage of organoid development.

Keywords: 3D model, consumption model, diffusion, spheroid, tissue organoid

Procedia PDF Downloads 308

Authors: Faranak Felfeliyan, Parvaneh Shokrani, Maryam Atarod

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Introduction Using electron beam is widespread in radiotherapy. The main criteria in radiation therapy is to irradiate the tumor volume with maximum prescribed dose and minimum dose to vital organs around it. Using abutting fields is common in radiotherapy. The main problem in using abutting fields is dose inhomogeneity in the junction region. Electron beam divergence and lateral scattering may lead to hot and cold spots in the junction region. One solution for this problem is using of a spoiler to broaden the penumbra and uniform dose in the junction region. The goal of this research was to compare titanium and aluminum effects as a spoiler for dose uniformity achievement in the junction region of oblique electron fields with Monte Carlo simulation. Dose uniformity in the junction region depends on density, scattering power, thickness of the spoiler and the angle between two fields. Materials and Methods In this study, Monte Carlo model of Siemens Primus linear accelerator was simulated for a 5 MeV nominal energy electron beam using manufacture provided specifications. BEAMnrc and EGSnrc user code were used to simulate the treatment head in electron mode (simulation of beam model). The resulting phase space file was used as a source for dose calculations for 10×10 cm2 field size at SSD=100 cm in a 30×30×45 cm3 water phantom using DOSXYZnrc user code (dose calculations). An automatic MP3-M water phantom tank, MEPHYSTO mc2 software platform and a Semi-Flex Chamber-31010 with sensitive vol­ume of 0.125 cm3 (PTW, Freiburg, Germany) were used for dose distribution measurements. Moreover, the electron field size was 10×10 cm2 and SSD=100 cm. Validation of devel­oped beam model was done by comparing the measured and calculated depth and lateral dose distributions (verification of electron beam model). Simulation of spoilers (using SLAB compo­nent module) placed at the end of the electron applicator, was done using previously vali­dated phase space file for a 5 MeV nominal energy and 10×10 cm2 field size (simulation of spoiler). An in-house routine was developed in order to calculate the combined isodose curves re­sulting from the two simulated abutting fields (calculation of dose distribution in abutting electron fields). Results Verification of the developed 5.9 MeV elec­tron beam model was done by comparing the calculated and measured dose distributions. The maximum percentage difference between calculated and measured PDD was 1%, except for the build-up region in which the difference was 2%. The difference between calculated and measured profile was 2% at the edges of the field and less than 1% in other regions. The effect of PMMA, aluminum, titanium and chromium in dose uniformity achievement in abutting normal electron fields with equivalent thicknesses to 5mm PMMA was evaluated. Comparing R90 and uniformity index of different materials, aluminum was chosen as the optimum spoiler. Titanium has the maximum surface dose. Thus, aluminum and titanium had been chosen to use for dose uniformity achievement in oblique electron fields. Using the optimum beam spoiler, junction dose decreased from 160% to 110% for 15 degrees, from 180% to 120% for 30 degrees, from 160% to 120% for 45 degrees and from 180% to 100% for 60 degrees oblique abutting fields. Using Titanium spoiler, junction dose decreased from 160% to 120% for 15 degrees, 180% to 120% for 30 degrees, 160% to 120% for 45 degrees and 180% to 110% for 60 degrees. In addition, penumbra width for 15 degrees, without spoiler in the surface was 10 mm and was increased to 15.5 mm with titanium spoiler. For 30 degrees, from 9 mm to 15 mm, for 45 degrees from 4 mm to 6 mm and for 60 degrees, from 5 mm to 8 mm. Conclusion Using spoilers, penumbra width at the surface increased, size and depth of hot spots was decreased and dose homogeneity improved at the junc­tion of abutting electron fields. Dose at the junction region of abutting oblique fields was improved significantly by using spoiler. Maximum dose at the junction region for 15⁰, 30⁰, 45⁰ and 60⁰ was decreased about 40%, 60%, 40% and 70% respectively for Titanium and about 50%, 60%, 40% and 80% for Aluminum. Considering significantly decrease in maximum dose using titanium spoiler, unfortunately, dose distribution in the junction region was not decreased less than 110%.

Keywords: abutting fields, electron beam, radiation therapy, spoilers

Procedia PDF Downloads 176

Authors: Michael Russelle S. Alvarez, Noel S. Quiming, Francisco M. Heralde

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This study aims to: a) obtain ethanolic (95% EtOH) and aqueous extracts of Selaginella elmeri, Christella dentata, Elatostema sinnatum, Curculigo capitulata, Euphorbia hirta, Murraya koenigii, Alpinia speciosa, Cymbopogon citratus, Eucalyptus globulus, Jatropha curcas, Psidium guajava, Gliricidia sepium, Ixora coccinea and Capsicum frutescens and screen them for larvicidal activities against Aedes aegypti (Linn.) and Aedes albopictus (Skuse) larvae; b) to fractionate the most active extract and determine the most active fraction; c) to determine the larvicidal properties of the most active extract and fraction against by computing their percentage mortality, LC50, and LC90 after 24 and 48 hours of exposure; and d) to determine the nature of the components of the active extracts and fractions using phytochemical screening. Ethanolic (95% EtOH) and aqueous extracts of the selected plants will be screened for potential larvicidal activity against Ae. aegypti and Ae. albopictus using standard procedures and 1% malathion and a Piper nigrum based ovicide-larvicide by the Department of Science and Technology as positive controls. The results were analyzed using One-Way ANOVA with Tukey’s and Dunnett’s test. The most active extract will be subjected to partial fractionation using normal-phase column chromatography, and the fractions subsequently screened to determine the most active fraction. The most active extract and fraction were subjected to dose-response assay and probit analysis to determine the LC50 and LC90 after 24 and 48 hours of exposure. The active extracts and fractions will be screened for phytochemical content. The ethanolic extracts of C. citratus, E. hirta, I. coccinea, G. sepium, M. koenigii, E globulus, J. curcas and C. frutescens exhibited significant larvicidal activity, with C. frutescens being the most active. After fractionation, the ethyl acetate fraction was found to be the most active. Phytochemical screening of the extracts revealed the presence of alkaloids, tannins, indoles and steroids. A formulation using talcum powder–300 mg fraction per 1 g talcum powder–was made and again tested for larvicidal activity. At 2 g/L, the formulation proved effective in killing all of the test larvae after 24 hours.

Keywords: larvicidal activity screening, partial purification, dose-response assay, capsicum frutescens

Procedia PDF Downloads 329

Authors: Mikhail Zimin, Svetlana Zimina, Maxim Zimin

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Without question, the basic method of prevention of human and material losses is the provision for adequate strength of constructions. At the same time, seismic load has a stochastic character. So, at all times, there is little danger of earthquake forces exceeding the selected design load. This risk is very low, but the consequences of such events may be extremely serious. Very dangerous are also occasional mistakes in seismic zoning, soil conditions changing before temblors, and failure to take into account hazardous natural phenomena caused by earthquakes. Besides, it is known that temblors detrimentally affect the environmental situation in regions where they occur, resulting in panic and worsening various disease courses. It may lead to mistakes of personnel of hazardous production facilities like the production and distribution of gas and oil, which may provoke severe accidents. In addition, gas and oil pipelines often have long mileage and cross many perilous zones by contrast with buildings. This situation increases the risk of heavy accidents. In such cases, complex monitoring of potential earthquake localities would be relevant. Even though the number of successful real-time forecasts of earthquakes is not great, it is well in excess, such as may be under random guessing. Experimental performed time-lapse study and analysis consist of searching seismic, biological, meteorological, and light earthquake precursors, processing such data with the help of fuzzy sets, collecting weather information, utilizing a database of terrain, and computing risk of slope processes under the temblor in a given setting. Works were done in a real-time environment and broadly acceptable results took place. Observations from already in-place seismic recording systems are used. Furthermore, a look back study of precursors of known earthquakes is done. Situations before Ashkhabad, Tashkent, and Haicheng seismic events are analyzed. Fairish findings are obtained. Results of earthquake forecasts can be used for predicting dangerous natural phenomena caused by temblors such as avalanches and mudslides. They may also be utilized for prophylaxis of some diseases and their complications. Relevant software is worked out too. It should be emphasized that such control does not require serious financial expenses and can be performed by a small group of professionals. Thus, complex monitoring of potential earthquake localities, including short-term earthquake forecasts and analysis of possible hazardous consequences of temblors, may further the safety of pipeline facilities.

Keywords: risk, earthquake, monitoring, forecast, precursor

Procedia PDF Downloads 22

Authors: Eyob Seife, Molla Alemayaehu, Tesfalem Teshome, Bereket Seyoum, Behailu Getachew

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Globally, immunization averts between 2 and 3 million deaths yearly, but Vaccine-Preventable Diseases still account for more in Sub-Saharan African countries and takes the majority of under-five deaths yearly, which indicates the need for consistent and on-time information to have evidence-based decision so as to save lives of these vulnerable groups. However, ensuring data of sufficient quality and promoting an information-use culture at the point of collection remains critical and challenging, especially in remote areas where the Ararso district is selected based on a hypothesis of there is a difference in reported and recounted immunization data consistency. Data quality is dependent on different factors where organizational, behavioral, technical and contextual factors are the mentioned ones. A cross-sectional quantitative study was conducted on September 2022 in the Ararso district. The study used the world health organization (WHO) recommended data quality self-assessment (DQS) tools. Immunization tally sheets, registers and reporting documents were reviewed at 4 health facilities (1 health center and 3 health posts) of primary health care units for one fiscal year (12 months) to determine the accuracy ratio, availability and timeliness of reports. The data was collected by trained DQS assessors to explore the quality of monitoring systems at health posts, health centers, and at the district health office. A quality index (QI), availability and timeliness of reports were assessed. Accuracy ratios formulated were: the first and third doses of pentavalent vaccines, fully immunized (FI), TT2+ and the first dose of measles-containing vaccines (MCV). In this study, facility-level results showed poor timeliness at all levels and both over-reporting and under-reporting were observed at all levels when computing the accuracy ratio of registration to health post reports found at health centers for almost all antigens verified. A quality index (QI) of all facilities also showed poor results. Most of the verified immunization data accuracy ratios were found to be relatively better than that of quality index and timeliness of reports. So attention should be given to improving the capacity of staff, timeliness of reports and quality of monitoring system components, namely recording, reporting, archiving, data analysis and using information for decisions at all levels, especially in remote and areas.

Keywords: accuracy ratio, ararso district, quality of monitoring system, regular immunization program, timeliness of reports, Somali region-Ethiopia

Procedia PDF Downloads 70

Authors: Victoria L. Chester, Usha Kuruganti

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The forest industry is a major economic sector of Canada and also one of the most dangerous industries for workers. The use of mechanized mobile forestry harvesting machines has successfully reduced the incidence of injuries in forest workers related to manual labor. However, these machines have also created additional concerns, including a high machine operation learning curve, increased the length of the workday, repetitive strain injury, cognitive load, physical and mental fatigue, and increased postural loads due to sitting in a confined space. It is critical to obtain objective performance data for employers to develop appropriate work practices for this industry, however ergonomic field studies of this industry are lacking mainly due to the difficulties in obtaining comprehensive data while operators are cutting trees in the woods. The purpose of this study was to establish a measurement and experimental protocol to examine the effects of worker skill level and movement training speed (joystick crane speed) on harvesting performance using a forestry simulator. A custom wrist angle measurement device was developed as part of the study to monitor Euler angles during operation of the simulator. The device of the system consisted of two accelerometers, a Bluetooth module, three 3V coin cells, a microcontroller, a voltage regulator and an application software. Harvesting performance and crane data was provided by the simulator software and included tree to frame collisions, crane to tree collisions, boom tip distance, number of trees cut, etc. A pilot study of 3 operators with various skill levels was tested to identify factors that distinguish highly skilled operators from novice or intermediate operators. Dependent variables such as reaction time, math skill, past work experience, training movement speed (e.g. joystick control speeds), harvesting experience level, muscle activity, and wrist biomechanics were measured and analyzed. A 10-channel wireless surface EMG system was used to monitor the amplitude and mean frequency of 10 upper extremity muscles during pre and postperformance on the forestry harvest stimulator. The results of the pilot study showed inconsistent changes in median frequency pre-and postoperation, but there was the increase in the activity of the flexor carpi radialis, anterior deltoid and upper trapezius of both arms. The wrist sensor results indicated that wrist supination and pronation occurred more than flexion and extension with radial-ulnar rotation demonstrating the least movement. Overall, wrist angular motion increased as the crane speed increased from slow to fast. Further data collection is needed and will help industry partners determine those factors that separate skill levels of operators, identify optimal training speeds, and determine the length of training required to bring new operators to an efficient skill level effectively. In addition to effective and employment training programs, results of this work will be used for selective employee recruitment strategies to improve employee retention after training. Further, improved training procedures and knowledge of the physical and mental demands on workers will lead to highly trained and efficient personnel, reduced risk of injury, and optimal work protocols.

Keywords: EMG, forestry, human factors, wrist biomechanics

Procedia PDF Downloads 145

Authors: Amani Tahat, Serdal Kirmizialtin

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Trajectory fragment methods such as Markov State Models (MSM), Milestoning (MS) and Transition Path sampling are the prime choice of extending the timescale of all atom Molecular Dynamics simulations. In these approaches, a set of structures that covers the accessible phase space has to be chosen a priori using cluster analysis. Structural clustering serves to partition the conformational state into natural subgroups based on their similarity, an essential statistical methodology that is used for analyzing numerous sets of empirical data produced by Molecular Dynamics (MD) simulations. Local transition kernel among these clusters later used to connect the metastable states using a Markovian kinetic model in MSM and a non-Markovian model in MS. The choice of clustering approach in constructing such kernel is crucial since the high dimensionality of the biomolecular structures might easily confuse the identification of clusters when using the traditional hierarchical clustering methodology. Of particular interest, in the case of MS where the milestones are very close to each other, accurate determination of the milestone identity of the trajectory becomes a challenging issue. Throughout this work we present two cluster analysis methods applied to the cis–trans isomerism of dinucleotide AA. The choice of nucleic acids to commonly used proteins to study the cluster analysis is two fold: i) the energy landscape is rugged; hence transitions are more complex, enabling a more realistic model to study conformational transitions, ii) Nucleic acids conformational space is high dimensional. A diverse set of internal coordinates is necessary to describe the metastable states in nucleic acids, posing a challenge in studying the conformational transitions. Herein, we need improved clustering methods that accurately identify the AA structure in its metastable states in a robust way for a wide range of confused data conditions. The single linkage approach of the hierarchical clustering available in GROMACS MD-package is the first clustering methodology applied to our data. Self Organizing Map (SOM) neural network, that also known as a Kohonen network, is the second data clustering methodology. The performance comparison of the neural network as well as hierarchical clustering method is studied by means of computing the mean first passage times for the cis-trans conformational rates. Our hope is that this study provides insight into the complexities and need in determining the appropriate clustering algorithm for kinetic analysis. Our results can improve the effectiveness of decisions based on clustering confused empirical data in studying conformational transitions in biomolecules.

Keywords: milestoning, self organizing map, single linkage, structure clustering

Procedia PDF Downloads 224

Authors: Stefan Papastefanou

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Artificial intelligence (AI) is an interdisciplinary field of computer science with the aim of creating intelligent machine behavior. Early approaches to AI have been configured to operate in very constrained environments where the behavior of the AI system was previously determined by formal rules. Knowledge was presented as a set of rules that allowed the AI system to determine the results for specific problems; as a structure of if-else rules that could be traversed to find a solution to a particular problem or question. However, such rule-based systems typically have not been able to generalize beyond the knowledge provided. All over the world and especially in IT-heavy industries such as the United States, the European Union, Singapore, and China, machine learning has developed to be an immense asset, and its applications are becoming more and more significant. It has to be examined how such products of machine learning models can and should be protected by IP law and for the purpose of this paper patent law specifically, since it is the IP law regime closest to technical inventions and computing methods in technical applications. Genetic breeding models are currently less popular than recursive neural network method and deep learning, but this approach can be more easily described by referring to the evolution of natural organisms, and with increasing computational power; the genetic breeding method as a subset of the evolutionary algorithms models is expected to be regaining popularity. The research method focuses on patentability (according to the world’s most significant patent law regimes such as China, Singapore, the European Union, and the United States) of AI inventions and machine learning. Questions of the technical nature of the problem to be solved, the inventive step as such, and the question of the state of the art and the associated obviousness of the solution arise in the current patenting processes. Most importantly, and the key focus of this paper is the problem of patenting inventions that themselves are developed through machine learning. The inventor of a patent application must be a natural person or a group of persons according to the current legal situation in most patent law regimes. In order to be considered an 'inventor', a person must actually have developed part of the inventive concept. The mere application of machine learning or an AI algorithm to a particular problem should not be construed as the algorithm that contributes to a part of the inventive concept. However, when machine learning or the AI algorithm has contributed to a part of the inventive concept, there is currently a lack of clarity regarding the ownership of artificially created inventions. Since not only all European patent law regimes but also the Chinese and Singaporean patent law approaches include identical terms, this paper ultimately offers a comparative analysis of the most relevant patent law regimes.

Keywords: algorithms, inventor, genetic breeding models, machine learning, patentability

Procedia PDF Downloads 108

Authors: Vincent Schlageter, Maroua Mestiri, Florian Denzinger, Hugo Raetzo, Michel Demierre

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Precise landslide monitoring with differential global navigation satellite system (GNSS) is well known, but technical or economic reasons limit its application by geotechnical companies. This study demonstrates the reliability and the usefulness of Geomon (Infrasurvey Sàrl, Switzerland), a stand-alone and cost-effective rover network. The system permits deploying up to 15 rovers, plus one reference station for differential GNSS. A dedicated radio communication links all the modules to a base station, where an embedded computer automatically provides all the relative positions (L1 phase, open-source RTKLib software) and populates an Internet server. Each measure also contains information from an internal inclinometer, battery level, and position quality indices. Contrary to standard GNSS survey systems, which suffer from a limited number of beacons that must be placed in areas with good GSM signal, Geomon offers greater flexibility and permits a real overview of the whole landslide with good spatial resolution. Each module is powered with solar panels, ensuring autonomous long-term recordings. In this study, we have tested the system on several sites in the Swiss mountains, setting up to 7 rovers per site, for an 18 month-long survey. The aim was to assess the robustness and the accuracy of the system in different environmental conditions. In one case, we ran forced blind tests (vertical movements of a given amplitude) and compared various session parameters (duration from 10 to 90 minutes). Then the other cases were a survey of real landslides sites using fixed optimized parameters. Sub centimetric-accuracy with few outliers was obtained using the best parameters (session duration of 60 minutes, baseline 1 km or less), with the noise level on the horizontal component half that of the vertical one. The performance (percent of aborting solutions, outliers) was reduced with sessions shorter than 30 minutes. The environment also had a strong influence on the percent of aborting solutions (ambiguity search problem), due to multiple reflections or satellites obstructed by trees and mountains. The length of the baseline (distance reference-rover, single baseline processing) reduced the accuracy above 1 km but had no significant effect below this limit. In critical weather conditions, the system’s robustness was limited: snow, avalanche, and frost-covered some rovers, including the antenna and vertically oriented solar panels, leading to data interruption; and strong wind damaged a reference station. The possibility of changing the sessions’ parameters remotely was very useful. In conclusion, the rover network tested provided the foreseen sub-centimetric-accuracy while providing a dense spatial resolution landslide survey. The ease of implementation and the fully automatic long-term survey were timesaving. Performance strongly depends on surrounding conditions, but short pre-measures should allow moving a rover to a better final placement. The system offers a promising hazard mitigation technique. Improvements could include data post-processing for alerts and automatic modification of the duration and numbers of sessions based on battery level and rover displacement velocity.

Keywords: GNSS, GSM, landslide, long-term, network, solar, spatial resolution, sub-centimeter.

Procedia PDF Downloads 111

Authors: Dmitriy Berillo, Areej K. A. Al-Jwaid, Jonathan L. Caplin, Andrew Cundy, Irina Savina

Abstract:

Chlorophenols (CPs) are used as a precursor in the production of higher CPs and dyestuffs, and as a preservative. Contamination by CPs of the ground water is located in the range from 0.15-100mg/L. The EU has set maximum concentration limits for pesticides and their degradation products of 0.1μg/L and 0.5μg/L, respectively. People working in industries which produce textiles, leather products, domestic preservatives, and petrochemicals are most heavily exposed to CPs. The International Agency for Research on Cancers categorized CPs as potential human carcinogens. Existing multistep water purification processes for CPs such as hydrogenation, ion exchange, liquid-liquid extraction, adsorption by activated carbon, forward and inverse osmosis, electrolysis, sonochemistry, UV irradiation, and chemical oxidation are not always cost effective and can cause the formation of even more toxic or mutagenic derivatives. Bioremediation of CPs derivatives utilizing microorganisms results in 60 to 100% decontamination efficiency and the process is more environmentally-friendly compared with existing physico-chemical methods. Microorganisms immobilized onto a substrate show many advantages over free bacteria systems, such as higher biomass density, higher metabolic activity, and resistance to toxic chemicals. They also enable continuous operation, avoiding the requirement for biomass-liquid separation. The immobilized bacteria can be reused several times, which opens the opportunity for developing cost-effective processes for wastewater treatment. In this study, we develop a bioremediation system for CPs based on macroporous materials, which can be efficiently used for wastewater treatment. Conditions for the preparation of the macroporous material from specific bacterial strains (Pseudomonas mendocina and Rhodococus koreensis) were optimized. The concentration of bacterial cells was kept constant; the difference was only the type of cross-linking agents used e.g. glutaraldehyde, novel polymers, which were utilized at concentrations of 0.5 to 1.5%. SEM images and rheology analysis of the material indicated a monolithic macroporous structure. Phenol was chosen as a model system to optimize the function of the cryogel material and to estimate its enzymatic activity, since it is relatively less toxic and harmful compared to CPs. Several types of macroporous systems comprising live bacteria were prepared. The viability of the cross-linked bacteria was checked using Live/Dead BacLight kit and Laser Scanning Confocal Microscopy, which revealed the presence of viable bacteria with the novel cross-linkers, whereas the control material cross-linked with glutaraldehyde(GA), contained mostly dead cells. The bioreactors based on bacteria were used for phenol degradation in batch mode at an initial concentration of 50mg/L, pH 7.5 and a temperature of 30°C. Bacterial strains cross-linked with GA showed insignificant ability to degrade phenol and for one week only, but a combination of cross-linking agents illustrated higher stability, viability and the possibility to be reused for at least five weeks. Furthermore, conditions for CPs degradation will be optimized, and the chlorophenol degradation rates will be compared to those for phenol. This is a cutting-edge bioremediation approach, which allows the purification of waste water from sustainable compounds without a separation step to remove free planktonic bacteria. Acknowledgments: Dr. Berillo D. A. is very grateful to Individual Fellowship Marie Curie Program for funding of the research.

Keywords: bioremediation, cross-linking agents, cross-linked microbial cell, chlorophenol degradation

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Authors: Alejandro Adorjan

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Universities are motivated to understand the factor contributing to low retention of engineering undergraduates. While precollege students for engineering increases, the number of engineering graduates continues to decrease and attrition rates for engineering undergraduates remains high. Calculus 1 (C1) is the entry point of most undergraduate Engineering Science and often a prerequisite for Computing Curricula courses. Mathematics continues to be a major hurdle for engineering students and many students who drop out from engineering cite specifically Calculus as one of the most influential factors in that decision. In this context, creating course activities that increase retention and motivate students to obtain better final results is a challenge. In order to develop several competencies in our students of Software Engineering courses, Calculus 1 at Universidad ORT Uruguay focuses on developing several competencies such as capacity of synthesis, abstraction, and problem solving (based on the ACM/AIS/IEEE). Every semester we try to reflect on our practice and try to answer the following research question: What kind of teaching approach in Calculus 1 can we design to retain students and obtain better results? Since 2010, Universidad ORT Uruguay offers a six-week summer noncompulsory bridge course of preparatory math (to bridge the math gap between high school and university). Last semester was the first time the Department of Mathematics offered the course while students were enrolled in C1. Traditional lectures in this bridge course lead to just transcribe notes from blackboard. Last semester we proposed a Hands On Lab course using Geogebra (interactive geometry and Computer Algebra System (CAS) software) as a Math Driven Development Tool. Students worked in a computer laboratory class and developed most of the tasks and topics in Geogebra. As a result of this approach, several pros and cons were found. It was an excessive amount of weekly hours of mathematics for students and, as the course was non-compulsory; the attendance decreased with time. Nevertheless, this activity succeeds in improving final test results and most students expressed the pleasure of working with this methodology. This teaching technology oriented approach strengthens student math competencies needed for Calculus 1 and improves student performance, engagement, and self-confidence. It is important as a teacher to reflect on our practice, including innovative proposals with the objective of engaging students, increasing retention and obtaining better results. The high degree of motivation and engagement of participants with this methodology exceeded our initial expectations, so we plan to experiment with more groups during the summer so as to validate preliminary results.

Keywords: calculus, engineering education, PreCalculus, Summer Program

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Authors: Benjamin Blat Belmonte, Stephan Rinderknecht

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The transition to electric vehicles (EVs) stands at the forefront of innovative strategies designed to address environmental concerns and reduce fossil fuel dependency. As the number of EVs on the roads increases, so too does the potential for their integration into energy markets. This research dives deep into the transformative possibilities of using electric vehicle fleets, specifically electric bus fleets, not just as consumers but as active participants in the energy market. This paper investigates the feasibility and grid effects of electric vehicle fleet operators in the energy market. Our objective centers around a comprehensive exploration of the sector coupling domain, with an emphasis on the economic potential in both electricity and balancing markets. Methodologically, our approach combines data mining techniques with thorough pre-processing, pulling from a rich repository of electricity and balancing market data. Our findings are grounded in the actual operational realities of the bus fleet operator in Darmstadt, Germany. We employ a Mixed Integer Linear Programming (MILP) approach, with the bulk of the computations being processed on the High-Performance Computing (HPC) platform ‘Lichtenbergcluster’. Our findings underscore the compelling economic potential of EV fleets in the energy market. With electric buses becoming more prevalent, the considerable size of these fleets, paired with their substantial battery capacity, opens up new horizons for energy market participation. Notably, our research reveals that economic viability is not the sole advantage. Participating actively in the energy market also translates into pronounced positive effects on grid stabilization. Essentially, EV fleet operators can serve a dual purpose: facilitating transport while simultaneously playing an instrumental role in enhancing grid reliability and resilience. This research highlights the symbiotic relationship between the growth of EV fleets and the stabilization of the energy grid. Such systems could lead to both commercial and ecological advantages, reinforcing the value of electric bus fleets in the broader landscape of sustainable energy solutions. In conclusion, the electrification of transport offers more than just a means to reduce local greenhouse gas emissions. By positioning electric vehicle fleet operators as active participants in the energy market, there lies a powerful opportunity to drive forward the energy transition. This study serves as a testament to the synergistic potential of EV fleets in bolstering both economic viability and grid stabilization, signaling a promising trajectory for future sector coupling endeavors.

Keywords: electric vehicle fleet, sector coupling, optimization, electricity market, balancing market

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Authors: James Gallagher, Phillip Benachour

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As location aware mobile technologies become ever more omnipresent, the prospect of exploiting their context awareness to enforce learning approaches thrives. Utilizing the growing acceptance of ubiquitous computing, and the steady progress both in accuracy and battery usage of pervasive devices, we present a case study of remote location viewing, how the application can be utilized to support mobile learning in situ using an existing scenario. Through the case study we introduce a new innovative application: Mobipeek based around a request/response protocol for the viewing of a remote location and explore how this can apply both as part of a teacher lead activity and informal learning situations. The system developed allows a user to select a point on a map, and send a request. Users can attach messages alongside time and distance constraints. Users within the bounds of the request can respond with an image, and accompanying message, providing context to the response. This application can be used alongside a structured learning activity such as the use of mobile phone cameras outdoors as part of an interactive lesson. An example of a learning activity would be to collect photos in the wild about plants, vegetation, and foliage as part of a geography or environmental science lesson. Another example could be to take photos of architectural buildings and monuments as part of an architecture course. These images can be uploaded then displayed back in the classroom for students to share their experiences and compare their findings with their peers. This can help to fosters students’ active participation while helping students to understand lessons in a more interesting and effective way. Mobipeek could augment the student learning experience by providing further interaction with other peers in a remote location. The activity can be part of a wider study between schools in different areas of the country enabling the sharing and interaction between more participants. Remote location viewing can be used to access images in a specific location. The choice of location will depend on the activity and lesson. For example architectural buildings of a specific period can be shared between two or more cities. The augmentation of the learning experience can be manifested in the different contextual and cultural influences as well as the sharing of images from different locations. In addition to the implementation of Mobipeek, we strive to analyse this application, and a subset of other possible and further solutions targeted towards making learning more engaging. Consideration is given to the benefits of such a system, privacy concerns, and feasibility of widespread usage. We also propose elements of “gamification”, in an attempt to further the engagement derived from such a tool and encourage usage. We conclude by identifying limitations, both from a technical, and a mobile learning perspective.

Keywords: context aware, location aware, mobile learning, remote viewing

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

Abstract:

Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, student engagement

Procedia PDF Downloads 94