Search results for: PieceWise Affine Auto Regression with eXogenous input

Authors: Abhisek Chakrabarty, Subhraprakash Mandal

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Flash flood is one of the catastrophic natural hazards in the mountainous region of India. The recent flood in the Mandakini River in Kedarnath (14-17th June, 2013) is a classic example of flash floods that devastated Uttarakhand by killing thousands of people.The disaster was an integrated effect of high intensityrainfall, sudden breach of Chorabari Lake and very steep topography. Every year in Himalayan Region flash flood occur due to intense rainfall over a short period of time, cloud burst, glacial lake outburst and collapse of artificial check dam that cause high flow of river water. In Sikkim-Derjeeling Himalaya one of the probable flash flood occurrence zone is Teesta Watershed. The Teesta River is a right tributary of the Brahmaputra with draining mountain area of approximately 8600 Sq. km. It originates in the Pauhunri massif (7127 m). The total length of the mountain section of the river amounts to 182 km. The Teesta is characterized by a complex hydrological regime. The river is fed not only by precipitation, but also by melting glaciers and snow as well as groundwater. The present study describes an attempt to model surface runoff in upper Teesta basin, which is directly related to catastrophic flood events, by creating a system based on GIS technology. The main object was to construct a direct unit hydrograph for an excess rainfall by estimating the stream flow response at the outlet of a watershed. Specifically, the methodology was based on the creation of a spatial database in GIS environment and on data editing. Moreover, rainfall time-series data collected from Indian Meteorological Department and they were processed in order to calculate flow time and the runoff volume. Apart from the meteorological data, background data such as topography, drainage network, land cover and geological data were also collected. Clipping the watershed from the entire area and the streamline generation for Teesta watershed were done and cross-sectional profiles plotted across the river at various locations from Aster DEM data using the ERDAS IMAGINE 9.0 and Arc GIS 10.0 software. The analysis of different hydraulic model to detect flash flood probability ware done using HEC-RAS, Flow-2D, HEC-HMS Software, which were of great importance in order to achieve the final result. With an input rainfall intensity above 400 mm per day for three days the flood runoff simulation models shows outbursts of lakes and check dam individually or in combination with run-off causing severe damage to the downstream settlements. Model output shows that 313 Sq. km area were found to be most vulnerable to flash flood includes Melli, Jourthang, Chungthang, and Lachung and 655sq. km. as moderately vulnerable includes Rangpo,Yathang, Dambung,Bardang, Singtam, Teesta Bazarand Thangu Valley. The model was validated by inserting the rain fall data of a flood event took place in August 1968, and 78% of the actual area flooded reflected in the output of the model. Lastly preventive and curative measures were suggested to reduce the losses by probable flash flood event.

Keywords: flash flood, GIS, runoff, simulation model, Teesta river basin

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Authors: Caroline Smith, Professor Debi Bhattacharya, Sion Scott

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Introduction: Oropharyngeal Dysphagia (OD) effects around 15% of older people, however it is often unrecognised and under diagnosed until they are hospitalised. There is a need for primary care healthcare practitioners (HCPs) to assume a proactive role in identifying and managing OD to prevent adverse outcomes such as aspiration pneumonia. Understanding the determinants of primary care HCPs undertaking this new behaviour provides the intervention targets for addressing. This realist review, underpinned by the Theoretical Domains Framework (TDF), aims to synthesise relevant literature and develop programme theories to understand what interventions work, how they work and under what circumstances to facilitate HCPs to prevent harm from OD. Combining realist methodology with behavioural science will permit conceptualisation of intervention components as theoretical behavioural constructs, thus informing the design of a future behaviour change intervention. Furthermore, through the TDF’s linkage to a taxonomy of behaviour change techniques, we will identify corresponding behaviour change techniques to include in this intervention. Methods & analysis: We are following the five steps for undertaking a realist review: 1) clarify the scope 2) Literature search 3) appraise and extract data 4) evidence synthesis 5) evaluation. We have searched Medline, Google scholar, PubMed, EMBASE, CINAHL, AMED, Scopus and PsycINFO databases. We are obtaining additional evidence through grey literature, snowball sampling, lateral searching and consulting the stakeholder group. Literature is being screened, evaluated and synthesised in Excel and Nvivo. We will appraise evidence in relation to its relevance and rigour. Data will be extracted and synthesised according to its relation to Initial programme theories (IPTs). IPTs were constructed after the preliminary literature search, informed by the TDF and with input from a stakeholder group of patient and public involvement advisors, general practitioners, speech and language therapists, geriatricians and pharmacists. We will follow the Realist and Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) quality and publication standards to report study results. Results: In this ongoing review our search has identified 1417 manuscripts with approximately 20% progressing to full text screening. We inductively generated 10 IPTs that hypothesise practitioners require: the knowledge to spot the signs and symptoms of OD; the skills to provide initial advice and support; and access to resources in their working environment to support them conducting these new behaviours. We mapped the 10 IPTs to 8 TDF domains and then generated a further 12 IPTs deductively using domain definitions to fulfil the remaining 6 TDF domains. Deductively generated IPTs broadened our thinking to consider domains such as ‘Emotion,’ ‘Optimism’ and ‘Social Influence’, e.g. If practitioners perceive that patients, carers and relatives expect initial advice and support, then they will be more likely to provide this, because they will feel obligated to do so. After prioritisation with stakeholders using a modified nominal group technique approach, a maximum of 10 IPTs will progress to test against the literature.

Keywords: behaviour change, deglutition disorders, primary healthcare, realist review

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Authors: Mario Tauzene Afonso Matangue, Ivan Andres Sanchez Ortiz

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The Metropolitan area of Maputo (Mozambique Capital City) is located in semi-arid zone (800 mm annual rainfall) with 1101170 million inhabitants. On the west side, there are the flatlands of Infulene where the Mulauze River flows towards to the Indian Ocean, receiving at this site, the storm water contaminated with sanitary sewage from Maputo, transported through a concrete open channel. In Infulene, local communities grow salads crops such as tomato, onion, garlic, lettuce, and cabbage, which are then commercialized and consumed in several markets in Maputo City. Lettuce is the most daily consumed salad crop in different meals, generally in fast-foods, breakfasts, lunches, and dinners. However, the risk of infection by several pathogens due to the consumption of lettuce, using the Quantitative Microbial Risk Assessment (QMRA) tools, is still unknown since there are few studies or publications concerning to this matter in Mozambique. This work is aimed at determining the annual risk arising from the consumption of lettuce grown in Infulene valley, in Maputo, using QMRA tools. The exposure model was constructed upon the volume of contaminated water remaining in the lettuce leaves, the empirical relations between the number of pathogens and the indicator of microorganisms (E. coli), the consumption of lettuce (g) and reduction of pathogens (days). The reference pathogens were Vibrio cholerae, Cryptosporidium, norovirus, and Ascaris. The water quality samples (E. coli) were collected in the storm water channel from January 2016 to December 2018, comprising 65 samples, and the urban lettuce consumption data were collected through inquiry in Maputo Metropolis covering 350 persons. A non-parametric bootstrap was performed involving 10,000 iterations over the collected dataset, namely, water quality (E. coli) and lettuce consumption. The dose-response models were: Exponential for Cryptosporidium, Kummer Confluent hypergeomtric function (1F1) for Vibrio and Ascaris Gaussian hypergeometric function (2F1-(a,b;c;z) for norovirus. The annual infection risk estimates were performed using R 3.6.0 (CoreTeam) software by Monte Carlo (Latin hypercubes), a sampling technique involving 10,000 iterations. The annual infection risks values expressed by Median and the 95th percentile, per person per year (pppy) arising from the consumption of lettuce are as follows: Vibrio cholerae (1.00, 1.00), Cryptosporidium (3.91x10⁻³, 9.72x 10⁻³), nororvirus (5.22x10⁻¹, 9.99x10⁻¹) and Ascaris (2.59x10⁻¹, 9.65x10⁻¹). Thus, the consumption of the lettuce would result in greater risks than the tolerable levels ( < 10⁻³ pppy or 10⁻⁶ DALY) for all pathogens, and the Vibrio cholerae is the most virulent pathogens, according to the hit-single models followed by the Ascaris lumbricoides and norovirus. The sensitivity analysis carried out in this work pointed out that in the whole QMRA, the most important input variable was the reduction of pathogens (Spearman rank value was 0.69) between harvest and consumption followed by water quality (Spearman rank value was 0.69). The decision-makers (Mozambique Government) must strengthen the prevention measures related to pathogens reduction in lettuce (i.e., washing) and engage in wastewater treatment engineering.

Keywords: annual infections risk, lettuce, non-parametric bootstrapping, quantitative microbial risk assessment tools

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Authors: Sergio Celaschi, Henrique Canavarro de Alencar, Claaudecir Biazoli

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Effluent quality is of the highest priority for compliance with the permit limits of environmental protection agencies and ensures the protection of their local water system. Of the pollutants monitored, the biochemical oxygen demand (BOD) posed one of the greatest challenges. This work presents a solution for wastewater treatment plants - WWTP’s ability to react to different situations and meet treatment goals. Delayed BOD5 results from the lab take 7 to 8 analysis days, hindered the WWTP’s ability to react to different situations and meet treatment goals. Reducing BOD turnaround time from days to hours is our quest. Such a solution is based on a system of two BOD bioreactors associated with Digital Twin (DT) and Machine Learning (ML) methodologies via an Internet of Things (IoT) platform to monitor and control a WWTP to support decision making. DT is a virtual and dynamic replica of a production process. DT requires the ability to collect and store real-time sensor data related to the operating environment. Furthermore, it integrates and organizes the data on a digital platform and applies analytical models allowing a deeper understanding of the real process to catch sooner anomalies. In our system of continuous time monitoring of the BOD suppressed by the effluent treatment process, the DT algorithm for analyzing the data uses ML on a chemical kinetic parameterized model. The continuous BOD monitoring system, capable of providing results in a fraction of the time required by BOD5 analysis, is composed of two thermally isolated batch bioreactors. Each bioreactor contains input/output access to wastewater sample (influent and effluent), hydraulic conduction tubes, pumps, and valves for batch sample and dilution water, air supply for dissolved oxygen (DO) saturation, cooler/heater for sample thermal stability, optical ODO sensor based on fluorescence quenching, pH, ORP, temperature, and atmospheric pressure sensors, local PLC/CPU for TCP/IP data transmission interface. The dynamic BOD system monitoring range covers 2 mg/L < BOD < 2,000 mg/L. In addition to the BOD monitoring system, there are many other operational WWTP sensors. The CPU data is transmitted/received to/from the digital platform, which in turn performs analyses at periodic intervals, aiming to feed the learning process. BOD bulletins and their credibility intervals are made available in 12-hour intervals to web users. The chemical kinetics ML algorithm is composed of a coupled system of four first-order ordinary differential equations for the molar masses of DO, organic material present in the sample, biomass, and products (CO₂ and H₂O) of the reaction. This system is solved numerically linked to its initial conditions: DO (saturated) and initial products of the kinetic oxidation process; CO₂ = H₂0 = 0. The initial values for organic matter and biomass are estimated by the method of minimization of the mean square deviations. A real case of continuous monitoring of BOD wastewater effluent quality is being conducted by deploying an IoT application on a large wastewater purification system located in S. Paulo, Brazil.

Keywords: effluent treatment, biochemical oxygen demand, continuous monitoring, IoT, machine learning

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Authors: K. Bencherif, M. Bellifa

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The expected climatic changes in North Africa are the increase of both intensity and frequencies of the summer droughts and a reduction in water availability during growing season. The exiting coppices and forest formations in the national park of Tlemcen are dominated by holm oak, zen oak and cork oak. These opened-fragmented structures don’t seem enough strong so to hope durable protection against climate change. According to the observed climatic tendency, the objective is to analyze the climatic context and its evolution taking into account the eventual behaving of the oak species during the next 20-30 years on one side and the landscaped context in relation with the most adequate sylvicultural models to choose and especially in relation with human activities on another side. The study methodology is based on Climatic synthesis and Floristic and spatial analysis. Meteorological data of the decade 1989-2009 are used to characterize the current climate. An another approach, based on dendrochronological analysis of a 120 years sample Aleppo pine stem growing in the park, is used so to analyze the climate evolution during one century. Results on the climate evolution during the 50 years obtained through climatic predictive models are exploited so to predict the climate tendency in the park. Spatially, in each forest unit of the Park, stratified sampling is achieved so to reduce the degree of heterogeneity and to easily delineate different stands using the GPS. Results from precedent study are used to analyze the anthropogenic factor considering the forecasts for the period 2025-2100, the number of warm days with a temperature over 25°C would increase from 30 to 70. The monthly mean temperatures of the maxima’s (M) and the minima’s (m) would pass respectively from 30.5°C to 33°C and from 2.3°C to 4.8°C. With an average drop of 25%, precipitations will be reduced to 411.37 mm. These new data highlight the importance of the risk fire and the water stress witch would affect the vegetation and the regeneration process. Spatial analysis highlights the forest and the agricultural dimensions of the park compared to the urban habitat and bare soils. Maps show both fragmentation state and forest surface regression (50% of total surface). At the level of the park, fires affected already all types of covers creating low structures with various densities. On the silvi cultural plan, Zen oak form in some places pure stands and this invasion must be considered as a natural tendency where Zen oak becomes the structuring specie. Climate-related changes have nothing to do with the real impact that South-Mediterranean forests are undergoing because human constraints they support. Nevertheless, hardwoods stand of oak in the national park of Tlemcen will face up to unexpected climate changes such as changing rainfall regime associated with a lengthening of the period of water stress, to heavy rainfall and/or to sudden cold snaps. Faced with these new conditions, management based on mixed uneven aged high forest method promoting the more dynamic specie could be an appropriate measure.

Keywords: global warming, mediterranean forest, oak shrub-lands, Tlemcen

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Authors: Ousmane Sarr

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The Senegalese coasts have high productivity of fishery resources due to the frequency of intense up-welling system that occurs along its coast, caused by the maritime trade winds making its waters nutrients rich. Fishing plays a primordial role in Senegal's socioeconomic plans and food security. However, a global diagnosis of the Senegalese maritime fishing sector has highlighted the challenges this sector encounters. Among these concerns, some significant stocks, a priority target for artisanal fishing, need further assessment. If no efforts are made in this direction, most stock will be overexploited or even in decline. It is in this context that this research was initiated. This investigation aimed to apply a multi-modal approach (LBB, Catch-only-based CMSY model and its most recent version (CMSY++); JABBA, and JABBA-Select) to assess the stock of Atlantic bonito, Sarda sarda (Bloch, 1793) in the Senegalese Exclusive Economic Zone (SEEZ). Available catch, effort, and size data from Atlantic bonito over 15 years (2004-2018) were used to calculate the nominal and standardized CPUE, size-frequency distribution, and length at retentions (50 % and 95 % selectivity) of the species. These relevant results were employed as input parameters for stock assessment models mentioned above to define the stock status of this species in this region of the Atlantic Ocean. The LBB model indicated an Atlantic bonito healthy stock status with B/BMSY values ranging from 1.3 to 1.6 and B/B0 values varying from 0.47 to 0.61 of the main scenarios performed (BON_AFG_CL, BON_GN_Length, and BON_PS_Length). The results estimated by LBB are consistent with those obtained by CMSY. The CMSY model results demonstrate that the SEEZ Atlantic bonito stock is in a sound condition in the final year of the main scenarios analyzed (BON, BON-bt, BON-GN-bt, and BON-PS-bt) with sustainable relative stock biomass (B2018/BMSY = 1.13 to 1.3) and fishing pressure levels (F2018/FMSY= 0.52 to 1.43). The B/BMSY and F/FMSY results for the JABBA model ranged between 2.01 to 2.14 and 0.47 to 0.33, respectively. In contrast, The estimated B/BMSY and F/FMSY for JABBA-Select ranged from 1.91 to 1.92 and 0.52 to 0.54. The Kobe plots results of the base case scenarios ranged from 75% to 89% probability in the green area, indicating sustainable fishing pressure and an Atlantic bonito healthy stock size capable of producing high yields close to the MSY. Based on the stock assessment results, this study highlighted scientific advice for temporary management measures. This study suggests an improvement of the selectivity parameters of longlines and purse seines and a temporary prohibition of the use of sleeping nets in the fishery for the Atlantic bonito stock in the SEEZ based on the results of the length-base models. Although these actions are temporary, they can be essential to reduce or avoid intense pressure on the Atlantic bonito stock in the SEEZ. However, it is necessary to establish harvest control rules to provide coherent and solid scientific information that leads to appropriate decision-making for rational and sustainable exploitation of Atlantic bonito in the SEEZ and the Eastern Atlantic Ocean.

Keywords: multi-model approach, stock assessment, atlantic bonito, healthy stock, sustainable, SEEZ, temporary management measures

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Authors: Laura E. Hall, Joel Monárrez-Espino, Luz María Tejada Tayabas

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College students are at risk of weight gain and poor dietary habits, and health behaviours established during this period have been shown to track into midlife. They may therefore be an important target group for health promotion strategies, yet there is a lack of literature regarding dietary intakes and associated factors in this group, particularly in middle-income countries such as Mexico. The aim of this exploratory research was to describe and compare reported dietary intakes among nursing and nutrition college students at two public universities in Mexico, and to explore the relationship between demographic, behavioural and other health-related factors and the risk of low diet quality. Mexican college students (n=444) majoring in nutrition or nursing at two urban universities completed questionnaires regarding dietary and health-related behaviours and risks. Dietary intake was assessed via 24-hour recall. Weight, height and abdominal circumference were measured. Descriptive statistics were reported and nutrient intakes were compared between colleges and study tracks using Student’s t tests, odds ratios and Pearson chi square tests. Two dietary quality scores were constructed to explore the relationship between demographic, behavioural and other health-related factors and the diet quality scores using binary logistic regression. Analysis was performed using SPSS statistics, with differences considered statistically significant at p<0.05. The response rate to the survey was 91%. When macronutrients were considered as a percentage of total energy, the majority of students had protein intakes within recommended ranges, however one quarter of students had carbohydrate and fat intakes exceeding recommended levels. Three quarters had fibre intakes that were below recommendations. More than half of the students reported intakes of magnesium, zinc, vitamin A, folate and vitamin E that were below estimated average requirements. Students studying nutrition reported macronutrient and micronutrient intakes that were more compliant with recommendations compared to nursing students, and students studying in central-north Mexico were more compliant than those studying in southeast Mexico. Breakfast skipping (Adjusted Odds Ratio (OR) = 5.3; 95% Confidence Interval (CI) = 1.2-22.7), risk of anxiety (OR = 2.3; CI = 1.3-4.4), and university location (OR = 1.6; CI = 1.03-2.6) were associated with a greater risk of having a low macronutrient score. Caloric intakes <1800kcal (OR = 5.8; CI = 3.5-9.7), breakfast skipping (OR = 3.7; CI = 1.4-10.3), vigorous exercise ≤1h/week (OR = 2.6; CI = 1.3-5.2), soda consumption >250mls/day (OR = 2.0; CI = 1.2-3.3), unhealthy diet perception (OR = 1.9; CI = 1.2-3.0), and university location (OR = 1.8; CI = 1.1-2.8) were significantly associated with greater odds of having a low micronutrient score. College students studying nursing and nutrition did not report ideal diets, and these students should not be overlooked in public health interventions. Differences in dietary intakes between universities and study tracks were evident, with more favourable profiles evident in nutrition compared to nursing, and North-central compared to Southeast students. Further, demographic, behavioural and other health-related factors were associated with diet quality scores, warranting further research.

Keywords: college student, diet quality, nutrient intake, young adult

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Authors: Elizaveta S. Prokofyeva, Svetlana V. Maltseva, Roman D. Zaitsev

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Rapidly evolving modern data analysis technologies in healthcare play a large role in understanding the operation of the system and its characteristics. Nowadays, one of the key tasks in urban healthcare is to optimize the resource allocation. Thus, the application of data analysis in medical institutions to solve optimization problems determines the significance of this study. The purpose of this research was to establish the dependence between the indicators of the effectiveness of the medical institution and its resources. Hospital discharges by diagnosis; hospital days of in-patients and in-patient average length of stay were selected as the performance indicators and the demand of the medical facility. The hospital beds by type of care, medical technology (magnetic resonance tomography, gamma cameras, angiographic complexes and lithotripters) and physicians characterized the resource provision of medical institutions for the developed models. The data source for the research was an open database of the statistical service Eurostat. The choice of the source is due to the fact that the databases contain complete and open information necessary for research tasks in the field of public health. In addition, the statistical database has a user-friendly interface that allows you to quickly build analytical reports. The study provides information on 28 European for the period from 2007 to 2016. For all countries included in the study, with the most accurate and complete data for the period under review, predictive models were developed based on historical panel data. An attempt to improve the quality and the interpretation of the models was made by cluster analysis of the investigated set of countries. The main idea was to assess the similarity of the joint behavior of the variables throughout the time period under consideration to identify groups of similar countries and to construct the separate regression models for them. Therefore, the original time series were used as the objects of clustering. The hierarchical agglomerate algorithm k-medoids was used. The sampled objects were used as the centers of the clusters obtained, since determining the centroid when working with time series involves additional difficulties. The number of clusters used the silhouette coefficient. After the cluster analysis it was possible to significantly improve the predictive power of the models: for example, in the one of the clusters, MAPE error was only 0,82%, which makes it possible to conclude that this forecast is highly reliable in the short term. The obtained predicted values of the developed models have a relatively low level of error and can be used to make decisions on the resource provision of the hospital by medical personnel. The research displays the strong dependencies between the demand for the medical services and the modern medical equipment variable, which highlights the importance of the technological component for the successful development of the medical facility. Currently, data analysis has a huge potential, which allows to significantly improving health services. Medical institutions that are the first to introduce these technologies will certainly have a competitive advantage.

Keywords: data analysis, demand modeling, healthcare, medical facilities

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Authors: K. Ghosh, S. N. Tripathi, Manish Joshi, Y. S. Mayya, Arshad Khan, B. K. Sapra

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We have developed a CFD coupled aerosol microphysics model in the context of aerosol generation from a glowing wire. The governing equations can be solved implicitly for mass, momentum, energy transfer along with aerosol dynamics. The computationally efficient framework can simulate temporal behavior of total number concentration and number size distribution. This formulation uniquely couples standard K-Epsilon scheme with boundary layer model with detailed aerosol dynamics through residence time. This model uses measured temperatures (wire surface and axial/radial surroundings) and wire compositional data apart from other usual inputs for simulations. The model predictions show that bulk fluid motion and local heat distribution can significantly affect the aerosol behavior when the buoyancy effect in momentum transfer is considered. Buoyancy generated turbulence was found to be affecting parameters related to aerosol dynamics and transport as well. The model was validated by comparing simulated predictions with results obtained from six controlled experiments performed with a laboratory-made hot wire nanoparticle generator. Condensation particle counter (CPC) and scanning mobility particle sizer (SMPS) were used for measurement of total number concentration and number size distribution at the outlet of reactor cell during these experiments. Our model-predicted results were found to be in reasonable agreement with observed values. The developed model is fast (fully implicit) and numerically stable. It can be used specifically for applications in the context of the behavior of aerosol particles generated from glowing wire technique and in general for other similar large scale domains. Incorporation of CFD in aerosol microphysics framework provides a realistic platform to study natural convection driven systems/ applications. Aerosol dynamics sub-modules (nucleation, coagulation, wall deposition) have been coupled with Navier Stokes equations modified to include buoyancy coupled K-Epsilon turbulence model. Coupled flow-aerosol dynamics equation was solved numerically and in the implicit scheme. Wire composition and temperature (wire surface and cell domain) were obtained/measured, to be used as input for the model simulations. Model simulations showed a significant effect of fluid properties on the dynamics of aerosol particles. The role of buoyancy was highlighted by observation and interpretation of nucleation zones in the planes above the wire axis. The model was validated against measured temporal evolution, total number concentration and size distribution at the outlet of hot wire generator cell. Experimentally averaged and simulated total number concentrations were found to match closely, barring values at initial times. Steady-state number size distribution matched very well for sub 10 nm particle diameters while reasonable differences were noticed for higher size ranges. Although tuned specifically for the present context (i.e., aerosol generation from hotwire generator), the model can also be used for diverse applications, e.g., emission of particles from hot zones (chimneys, exhaust), fires and atmospheric cloud dynamics.

Keywords: nanoparticles, k-epsilon model, buoyancy, CFD, hot wire generator, aerosol dynamics

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Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Anupama Singh, Papia Raj

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Though Municipal Solid Waste (MSW) is a worldwide problem, yet its implications are enormous in developing countries, as they are unable to provide proper Municipal Solid Waste Management (MSWM) for the large volume of MSW. As a result, the collected wastes are dumped in open dumping at landfilling sites while the uncollected wastes remain strewn on the roadside, many-a-time clogging drainage. Such unsafe and inadequate management of MSW causes various public health hazards. For example, MSW directly on contact or by leachate contaminate the soil, surface water, and ground water; open burning causes air pollution; anaerobic digestion between the piles of MSW enhance the greenhouse gases i.e., carbon dioxide and methane (CO2 and CH4) into the atmosphere. Moreover, open dumping can cause spread of vector borne disease like cholera, typhoid, dysentery, and so on. Patna, the capital city of Bihar, one of the most underdeveloped provinces in India, is a unique representation of this situation. Patna has been identified as the ‘garbage city’. Over the last decade there has been an exponential increase in the quantity of MSW generation in Patna. Though a large proportion of such MSW is recyclable in nature, only a negligible portion is recycled. Plastic constitutes the major chunk of the recyclable waste. The chemical composition of plastic is versatile consisting of toxic compounds, such as, plasticizers, like adipates and phthalates. Pigmented plastic is highly toxic and it contains harmful metals such as copper, lead, chromium, cobalt, selenium, and cadmium. Human population becomes vulnerable to an array of health problems as they are exposed to these toxic chemicals multiple times a day through air, water, dust, and food. Based on analysis of health data it can be emphasized that in Patna there has been an increase in the incidence of specific diseases, such as, diarrhoea, dysentry, acute respiratory infection (ARI), asthma, and other chronic respiratory diseases (CRD). This trend can be attributed to improper MSWM. The results were reiterated through a survey (N=127) conducted during 2014-15 in selected areas of Patna. Random sampling method of data collection was used to better understand the relationship between different variables affecting public health due to exposure to MSW and lack of MSWM. The results derived through bivariate and logistic regression analysis of the survey data indicate that segregation of wastes at source, segregation behavior, collection bins in the area, distance of collection bins from residential area, and transportation of MSW are the major determinants of public health issues. Sustainable recycling is a robust method for MSWM with its pioneer concerns being environment, society, and economy. It thus ensures minimal threat to environment and ecology consequently improving public health conditions. Hence, this paper concludes that sustainable recycling would be the most viable approach to manage MSW in Patna and would eventually reduce public health hazards.

Keywords: municipal solid waste, Patna, public health, sustainable recycling

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Authors: M. A. C. S. Sampath Fernando, James M. Curran, Renate Meyer

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Model assessment, in the Bayesian context, involves evaluation of the goodness-of-fit and the comparison of several alternative candidate models for predictive accuracy and improvements. In posterior predictive checks, the data simulated under the fitted model is compared with the actual data. Predictive model accuracy is estimated using information criteria such as the Akaike information criterion (AIC), the Bayesian information criterion (BIC), the Deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). The goal of an information criterion is to obtain an unbiased measure of out-of-sample prediction error. Since posterior checks use the data twice; once for model estimation and once for testing, a bias correction which penalises the model complexity is incorporated in these criteria. Cross-validation (CV) is another method used for examining out-of-sample prediction accuracy. Leave-one-out cross-validation (LOO-CV) is the most computationally expensive variant among the other CV methods, as it fits as many models as the number of observations. Importance sampling (IS), truncated importance sampling (TIS) and Pareto-smoothed importance sampling (PSIS) are generally used as approximations to the exact LOO-CV and utilise the existing MCMC results avoiding expensive computational issues. The reciprocals of the predictive densities calculated over posterior draws for each observation are treated as the raw importance weights. These are in turn used to calculate the approximate LOO-CV of the observation as a weighted average of posterior densities. In IS-LOO, the raw weights are directly used. In contrast, the larger weights are replaced by their modified truncated weights in calculating TIS-LOO and PSIS-LOO. Although, information criteria and LOO-CV are unable to reflect the goodness-of-fit in absolute sense, the differences can be used to measure the relative performance of the models of interest. However, the use of these measures is only valid under specific circumstances. This study has developed 11 models using normal, log-normal, gamma, and student’s t distributions to improve the PCR stutter prediction with forensic data. These models are comprised of four with profile-wide variances, four with locus specific variances, and three which are two-component mixture models. The mean stutter ratio in each model is modeled as a locus specific simple linear regression against a feature of the alleles under study known as the longest uninterrupted sequence (LUS). The use of AIC, BIC, DIC, and WAIC in model comparison has some practical limitations. Even though, IS-LOO, TIS-LOO, and PSIS-LOO are considered to be approximations of the exact LOO-CV, the study observed some drastic deviations in the results. However, there are some interesting relationships among the logarithms of pointwise predictive densities (lppd) calculated under WAIC and the LOO approximation methods. The estimated overall lppd is a relative measure that reflects the overall goodness-of-fit of the model. Parallel log-likelihood profiles for the models conditional on equal posterior variances in lppds were observed. This study illustrates the limitations of the information criteria in practical model comparison problems. In addition, the relationships among LOO-CV approximation methods and WAIC with their limitations are discussed. Finally, useful recommendations that may help in practical model comparisons with these methods are provided.

Keywords: cross-validation, importance sampling, information criteria, predictive accuracy

Procedia PDF Downloads 392

Authors: S. B. R. Slagmulder

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Monitoring inspiratory effort and dynamic lung stress in patients on pressure support ventilation in the ICU is important for protecting against self inflicted lung injury (P-SILI) and diaphragm dysfunction. Strategies to address the detrimental effects of respiratory drive and effort can lead to improved patient outcomes. Two non-invasive estimation methods, occlusion pressure (Pocc) and P0.1, have been proposed for achieving lung and diaphragm protective ventilation. However, their relationship and interpretation in neuro ICU patients is not well understood. P0.1 is the airway pressure measured during a 100-millisecond occlusion of the inspiratory port. It reflects the neural drive from the respiratory centers to the diaphragm and respiratory muscles, indicating the patient's respiratory drive during the initiation of each breath. Occlusion pressure, measured during a brief inspiratory pause against a closed airway, provides information about the inspiratory muscles' strength and the system's total resistance and compliance. Research Objective: Understanding the relationship between Pocc and P0.1 in brain-injured patients can provide insights into the interpretation of these values in pressure support ventilation. This knowledge can contribute to determining extubation readiness and optimizing ventilation strategies to improve patient outcomes. The central goal is to asses a study protocol for determining the relationship between Pocc and P0.1 in brain-injured patients on pressure support ventilation and their ability to predict successful extubation. Additionally, comparing these values between brain-damaged and non-brain-damaged patients may provide valuable insights. Key Areas of Inquiry: 1. How do Pocc and P0.1 values correlate within brain injury patients undergoing pressure support ventilation? 2. To what extent can Pocc and P0.1 values serve as predictive indicators for successful extubation in patients with brain injuries? 3. What differentiates the Pocc and P0.1 values between patients with brain injuries and those without? Methodology: P0.1 and occlusion pressures are standard measurements for pressure support ventilation patients, taken by attending doctors as per protocol. We utilize electronic patient records for existing data. Unpaired T-test will be conducted to compare P0.1 and Pocc values between both study groups. Associations between P0.1 and Pocc and other study variables, such as extubation, will be explored with simple regression and correlation analysis. Depending on how the data evolve, subgroup analysis will be performed for patients with and without extubation failure. Results: While it is anticipated that neuro patients may exhibit high respiratory drive, the linkage between such elevation, quantified by P0.1, and successful extubation remains unknown The analysis will focus on determining the ability of these values to predict successful extubation and their potential impact on ventilation strategies. Conclusion: Further research is pending to fully understand the potential of these indices and their impact on mechanical ventilation in different patient populations and clinical scenarios. Understanding these relationships can aid in determining extubation readiness and tailoring ventilation strategies to improve patient outcomes in this specific patient population. Additionally, it is vital to account for the influence of sedatives, neurological scores, and BMI on respiratory drive and occlusion pressures to ensure a comprehensive analysis.

Keywords: brain damage, diaphragm dysfunction, occlusion pressure, p0.1, respiratory drive

Procedia PDF Downloads 68

Authors: Elaine Yee Sing Wong, Jonine Jancey, Andy H. Lee, Anthony P. James

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Singapore has a rapidly aging population, where the majority of older women aged 50 years and above, are physically inactive and have unhealthy dietary habits, placing them at ‘high risk’ of non-communicable diseases. Given the multiplicity of less than optimal dietary habits and high levels of physical inactivity among Singaporean women, it is imperative to develop appropriate lifestyle interventions at recreational centres to enhance both their physical and nutritional knowledge, as well as provide them with the opportunity to develop skills to support behaviour change. To the best of our knowledge, this proposed study is the first physical activity and nutrition cluster randomised controlled trial conducted in Singapore for older women. Findings from this study may provide insights and recommendations for policy makers and key stakeholders to create new healthy living, recreational centres with supportive environments. This 6-month community-based cluster randomised controlled trial will involve the implementation and evaluation of physical activity and nutrition program for community dwelling Singaporean women, who currently attend recreational centres to promote social leisure activities in their local neighbourhood. The intervention will include dietary education and counselling sessions, physical activity classes, and telephone contact by certified fitness instructors and qualified nutritionists. Social Cognitive Theory with Motivational Interviewing will inform the development of strategies to support health behaviour change. Sixty recreational centres located in Singapore will be randomly selected from five major geographical districts and randomly allocated to the intervention (n=30) or control (n=30) cluster. A sample of 600 (intervention n=300; control n=300) women aged 50 years and above will then be recruited from these recreational centres. The control clusters will only undergo pre and post data collection and will not receive the intervention. It is hypothesised that by the end of the intervention, the intervention group participants (n = 300) compared to the control group (n = 300), will show significant improvements in the following variables: lipid profile, body mass index, physical activity and dietary behaviour, anthropometry, mental and physical health. Data collection will be examined and compared via the Statistical Package for the Social Science version 23. Descriptive and summary statistics will be used to quantify participants’ characteristics and outcome variables. Multi-variable mixed regression analyses will be used to confirm the effects of the proposed health intervention, taking into account the repeated measures and the clustering of the observations. The research protocol was approved by the Curtin University Human Research Ethics Committee (approval number: HRE2016-0366). The study has been registered with the Australian and New Zealand Clinical Trial Registry (12617001022358).

Keywords: community based, healthy aging, intervention, nutrition, older women, physical activity

Procedia PDF Downloads 178

Authors: Helena Pera, Arcadi Sanmartin, Albert Falques, Rafael Rebolo, Xavier Ametller, Heiko Zillgen, Cecile Prum, Boris Even, Eric Kapornyai

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Sheet piles system can be an interesting solution when dealing with harbors or quays designs. However, current design methods lead to conservative approaches due to the lack of specific basis of design. For instance, some design features still deal with pseudo-static approaches, although being a dynamic problem. Under this concern, the study particularly focuses on hydrodynamic water pressure definition and stability analysis of sheet pile system under seismic loads. During a seismic event, seawater produces hydrodynamic pressures on structures. Currently, design methods introduce hydrodynamic forces by means of Westergaard formulation and Eurocodes recommendations. They apply constant hydrodynamic pressure on the front sheet pile during the entire earthquake. As a result, the hydrodynamic load may represent 20% of the total forces produced on the sheet pile. Nonetheless, some studies question that approach. Hence, this study assesses the soil-structure-fluid interaction of sheet piles under seismic action in order to evaluate if current design strategies overestimate hydrodynamic pressures. For that purpose, this study performs various simulations by Plaxis 2D, a well-known geotechnical software, and CFD models, which treat fluid dynamic behaviours. Knowing that neither Plaxis nor CFD can resolve a soil-fluid coupled problem, the investigation imposes sheet pile displacements from Plaxis as input data for the CFD model. Then, it provides hydrodynamic pressures under seismic action, which fit theoretical Westergaard pressures if calculated using the acceleration at each moment of the earthquake. Thus, hydrodynamic pressures fluctuate during seismic action instead of remaining constant, as design recommendations propose. Additionally, these findings detect that hydrodynamic pressure contributes a 5% to the total load applied on sheet pile due to its instantaneous nature. These results are in line with other studies that use added masses methods for hydrodynamic pressures. Another important feature in sheet pile design is the assessment of the geotechnical overall stability. It uses pseudo-static analysis since the dynamic analysis cannot provide a safety calculation. Consequently, it estimates the seismic action. One of its relevant factors is the selection of the seismic reduction factor. A huge amount of studies discusses the importance of it but also about all its uncertainties. Moreover, current European standards do not propose a clear statement on that, and they recommend using a reduction factor equal to 1. This leads to conservative requirements when compared with more advanced methods. Under this situation, the study calibrates seismic reduction factor by fitting results from pseudo-static to dynamic analysis. The investigation concludes that pseudo-static analyses could reduce seismic action by 40-50%. These results are in line with some studies from Japanese and European working groups. In addition, it seems suitable to account for the flexibility of the sheet pile-soil system. Nevertheless, the calibrated reduction factor is subjected to particular conditions of each design case. Further research would contribute to specifying recommendations for selecting reduction factor values in the early stages of the design. In conclusion, sheet pile design still has chances for improving its design methodologies and approaches. Consequently, design could propose better seismic solutions thanks to advanced methods such as findings of this study.

Keywords: computational fluid dynamics, hydrodynamic pressures, pseudo-static analysis, quays, seismic design, steel sheet pile

Procedia PDF Downloads 142

Authors: Soheila Sadeghi

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Infrastructure projects often encounter contract variations that can significantly deviate from the original tender estimates, leading to cost overruns, schedule delays, and financial implications. This research aims to quantitatively assess the impact of changes in contract variations on project performance by conducting an in-depth analysis of a comprehensive dataset from the Regional Airport Car Park project. The dataset includes tender budget, contract quantities, rates, claims, and revenue data, providing a unique opportunity to investigate the effects of variations on project outcomes. The study focuses on 21 specific variations identified in the dataset, which represent changes or additions to the project scope. The research methodology involves establishing a baseline for the project's planned cost and scope by examining the tender budget and contract quantities. Each variation is then analyzed in detail, comparing the actual quantities and rates against the tender estimates to determine their impact on project cost and schedule. The claims data is utilized to track the progress of work and identify deviations from the planned schedule. The study employs statistical analysis using R to examine the dataset, including tender budget, contract quantities, rates, claims, and revenue data. Time series analysis is applied to the claims data to track progress and detect variations from the planned schedule. Regression analysis is utilized to investigate the relationship between variations and project performance indicators, such as cost overruns and schedule delays. The research findings highlight the significance of effective variation management in construction projects. The analysis reveals that variations can have a substantial impact on project cost, schedule, and financial outcomes. The study identifies specific variations that had the most significant influence on the Regional Airport Car Park project's performance, such as PV03 (additional fill, road base gravel, spray seal, and asphalt), PV06 (extension to the commercial car park), and PV07 (additional box out and general fill). These variations contributed to increased costs, schedule delays, and changes in the project's revenue profile. The study also examines the effectiveness of project management practices in managing variations and mitigating their impact. The research suggests that proactive risk management, thorough scope definition, and effective communication among project stakeholders can help minimize the negative consequences of variations. The findings emphasize the importance of establishing clear procedures for identifying, assessing, and managing variations throughout the project lifecycle. The outcomes of this research contribute to the body of knowledge in construction project management by demonstrating the value of analyzing tender, contract, claims, and revenue data in variation impact assessment. However, the research acknowledges the limitations imposed by the dataset, particularly the absence of detailed contract and tender documents. This constraint restricts the depth of analysis possible in investigating the root causes and full extent of variations' impact on the project. Future research could build upon this study by incorporating more comprehensive data sources to further explore the dynamics of variations in construction projects.

Keywords: contract variation impact, quantitative analysis, project performance, claims analysis

Procedia PDF Downloads 40

Authors: M. Shahab Uddin, Pennung Warnitchai

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Currently more than half of the world’s populations are living in cities, and the number and sizes of cities are growing faster than ever. Cities rely on the effective functioning of complex and interdependent critical infrastructures networks to provide public services, enhance the quality of life, and save the community from hazards and disasters. In contrast, complex connectivity and interdependency among the urban critical infrastructures bring management challenges and make the urban system prone to the domino effect. Unplanned rapid growth, increased connectivity, and interdependency among the infrastructures, resource scarcity, and many other socio-political factors are affecting the typical state of an urban system and making it susceptible to numerous sorts of diversion. In addition to internal vulnerabilities, urban systems are consistently facing external threats from natural and manmade hazards. Cities are not just complex, interdependent system, but also makeup hubs of the economy, politics, culture, education, etc. For survival and sustainability, complex urban systems in the current world need to manage their vulnerabilities and hazardous incidents more wisely and more interactively. Coordinated management in such systems makes for huge potential when it comes to absorbing negative effects in case some of its components were to function improperly. On the other hand, ineffective management during a similar situation of overall disorder from hazards devastation may make the system more fragile and push the system to an ultimate collapse. Following the quantum, the current research hypothesizes that a hazardous event starts its journey as an emergency, and the system’s internal vulnerability and response capacity determine its destination. Connectivity and interdependency among the urban critical infrastructures during this stage may transform its vulnerabilities into dynamic damaging force. An emergency may turn into a disaster in the absence of effective management; similarly, mismanagement or lack of management may lead the situation towards a catastrophe. Situation awareness and factual decision-making is the key to win a battle. The current research proposed a contextual decision support system for an urban critical infrastructure system while integrating three different models: 1) Damage cascade model which demonstrates damage propagation among the infrastructures through their connectivity and interdependency, 2) Restoration model, a dynamic restoration process of individual infrastructure, which is based on facility damage state and overall disruptions in surrounding support environment, and 3) Optimization model that ensures optimized utilization and distribution of available resources in and among the facilities. All three models are tightly connected, mutually interdependent, and together can assess the situation and forecast the dynamic outputs of every input. Moreover, this integrated model will hold disaster managers and decision makers responsible when it comes to checking all the alternative decision before any implementation, and support to produce maximum possible outputs from the available limited inputs. This proposed model will not only support to reduce the extent of damage cascade but will ensure priority restoration and optimize resource utilization through adaptive and collaborative management. Complex systems predictably fail but in unpredictable ways. System understanding, situation awareness, and factual decisions may significantly help urban system to survive and sustain.

Keywords: disaster prevention, decision support system, emergency response, urban critical infrastructure system

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Authors: Sahar Nasr, Lin Li, Edwin Wang

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Bladder cancer (BLCA) is known as the 13th cause of death among cancer patients worldwide, and ~575,000 new BLCA cases are diagnosed each year. Urothelial carcinoma (UC) is the most prevalent subtype among BLCA patients, which can be categorized into muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC). Currently, various therapeutic options are available for UC patients, including (1) transurethral resection followed by intravesical instillation of chemotherapeutics or Bacillus Calmette-Guérin for NMIBC patients, (2) neoadjuvant platinum-based chemotherapy (NAC) plus radical cystectomy is the standard of care for localized MIBC patients, and (3) systematic chemotherapy for metastatic UC. However, conventional treatments may lead to several challenges for treating patients. As an illustration, some patients may suffer from recurrence of the disease after the first line of treatment. Recently, immune checkpoint therapy (ICT) has been introduced as an alternative treatment strategy for the first or second line of treatment in advanced or metastatic BLCA patients. Although ICT showed lucrative results for a fraction of BLCA patients, ~80% of patients were not responsive to it. Therefore, novel treatment methods are required to augment the ICI response rate within BLCA patients. It has been shown that the infiltration of T-cells into the tumor microenvironment (TME) is positively correlated with the response to ICT within cancerous patients. Therefore, the goal of this study is to enhance the infiltration of cytotoxic T-cells into TME through the identification of target genes within the tumor that are responsible for the non-T-cell inflamed TME and their inhibition. BLCA bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA) and immune score for TCGA samples were used to determine the Pearson correlation score between the expression of different genes and immune score for each sample. The genes with strong negative correlations were selected (r < -0.2). Thereafter, the correlation between the expression of each gene and survival in BLCA patients was calculated using the TCGA data and Cox regression method. The genes that are common in both selected gene lists were chosen for further analysis. Afterward, BLCA bulk and single-cell RNA-sequencing data were ranked based on the expression of each selected gene and the top and bottom 25% samples were used for pathway enrichment analysis. If the pathways related to the T-cell infiltration (e.g., antigen presentation, interferon, or chemokine pathways) were enriched within the low-expression group, the gene was included for downstream analysis. Finally, the selected genes will be used to calculate the correlation between their expression and the infiltration rate of the activated CD+8 T-cells, natural killer cells and the activated dendric cells. A list of potential target genes has been identified and ranked based on the above-mentioned analysis and criteria. SUN-1 got the highest score within the gene list and other identified genes in the literature as benchmarks. In conclusion, inhibition of SUN1 may increase the tumor-infiltrating lymphocytes and the efficacy of ICI in BLCA patients. BLCA tumor cells with and without SUN-1 CRISPR/Cas9 knockout will be injected into the syngeneic mouse model to validate the predicted SUN-1 effect on increasing tumor-infiltrating lymphocytes.

Keywords: data analysis, gene expression analysis, gene identification, immunoinformatic, functional genomics, transcriptomics

Procedia PDF Downloads 156

Authors: Nitza Davidovitch, Yael Yossel-Eisenbach

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The purpose of the study was to inquire whether there was a change in the shaping of undergraduate students’ digitally-oriented study pattern in the pre-Covid (2016-2017) versus post-Covid period (2022-2023), as affected by three factors: social background characteristics, high school, and academic background characteristics. These two-time points were cauterized by dramatic changes in teaching and learning at institutions of higher education. The data were collected via cross-sectional surveys at two-time points, in the 2016-2017 academic school year (N=443) and in the 2022-2023 school year (N=326). The questionnaire was distributed on social media and it includes questions on demographic background characteristics, previous studies in high school and present academic studies, and questions on learning and reading habits. Method of analysis: A. Statistical descriptive analysis, B. Mean comparison tests were conducted to analyze the variations in the mean score for the digitally-oriented learning pattern variable at two-time points (pre- and post-Covid) in relation to each of the independent variables. C. Analysis of variance was performed to test the main effects and the interactions. D. Applying linear regression, the research aimed to examine the combined effect of the independent variables on shaping students' digitally-oriented learning habits. The analysis includes four models. In all four models, the dependent variable is students’ perception of digitally oriented learning. The first model included social background variables; the second model included scholastic background as well. In the third model, the academic background variables were added, and the fourth model includes all the independent variables together with the variable of period (pre- and post-COVID). E. Factor analysis confirms using the principal component method with varimax rotation; the variables were constructed by a weighted mean of all the relevant statements merged to form a single variable denoting a shared content world. The research findings indicate a significant rise in students’ perceptions of digitally-oriented learning in the post-COVID period. From a gender perspective, the impact of COVID on shaping a digital learning pattern was much more significant for female students. The socioeconomic status perspective is eliminated when controlling for the period, and the student’s job is affected - more than all other variables. It may be assumed that the student’s work pattern mediates effects related to the convenience offered by digital learning regarding distance and time. The significant effect of scholastic background on shaping students’ digital learning patterns remained stable, even when controlling for all explanatory variables. The advantage that universities had over colleges in shaping a digital learning pattern in the pre-COVID period dissipated. Therefore, it can be said that after COVID, there was a change in how colleges shape students’ digital learning patterns in such a way that no institutional differences are evident with regard to shaping the digital learning pattern. The study shows that period has a significant independent effect on shaping students’ digital learning patterns when controlling for the explanatory variables.

Keywords: learning pattern, COVID, socioeconomic status, digital learning

Procedia PDF Downloads 63

Authors: Banafsheh Nikmehr, Mohsen Bahrami, Yueqiang Song, Anuradha Koduru, Ayse K. Vuruskan, Hongkun Lu, Mallory Pitts, Tolga B. Mesen, Tamer M. Yalcinkaya

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The number of people who receive in vitro fertilization (IVF) treatment has increased on a startling trajectory over the past two decades. Despite advances in this field, particularly the introduction of intracytoplasmic sperm injection (ICSI) and the preimplantation genetic screening (PGS), the IVF success remains low. A major factor contributing to IVF failure is embryonic aneuploidy (abnormal chromosome content), which often results in miscarriage and birth defects. Although PGS is often used as the standard diagnostic tool to identify aneuploid embryos, it is an invasive approach that could affect the embryo development, and yet inaccessible to many patients due its high costs. As such, there is a clear need for a non-invasive cost-effective approach to identify euploid embryos for single embryo transfer (SET). The reported differences between morphokinetic behaviors of aneuploid and euploid embryos has shown promise to address this need. However, current literature is inconclusive and further research is urgently needed to translate current findings into clinical diagnostics. In this ongoing study, we found significant differences between morphokinetic behaviors of euploid and aneuploid embryos that provides important insights and reaffirms the promise of such behaviors for developing non-invasive methodologies. Methodology—A total of 242 embryos (euploid: 149, aneuploid: 93) from 74 patients who underwent IVF treatment in Carolinas Fertility Clinics in Winston-Salem, NC, were analyzed. All embryos were incubated in an EmbryoScope incubator. The patients were randomly selected from January 2019 to June 2021 with most patients having both euploid and aneuploid embryos. All embryos reached the blastocyst stage and had known PGS outcomes. The ploidy assessment was done by a third-party testing laboratory on day 5-7 embryo biopsies. The morphokinetic variables of each embryo were measured by the EmbryoViewer software (Uniesense FertiliTech) on time-lapse images using 7 focal depths. We compared the time to: pronuclei fading (tPNf), division to 2,3,…,9 cells (t2, t3,…,t9), start of embryo compaction (tSC), Morula formation (tM), start of blastocyst formation (tSC), blastocyst formation (tB), and blastocyst expansion (tEB), as well as intervals between them (e.g., c23 = t3 – t2). We used a mixed regression method for our statistical analyses to account for the correlation between multiple embryos per patient. Major Findings— The average age of the patients was 35.04 yrs. The average patient age associated with euploid and aneuploid embryos was not different (P = 0.6454). We found a significant difference in c45 = t5-t4 (P = 0.0298). Our results indicated this interval on average lasts significantly longer for aneuploid embryos - c45(aneuploid) = 11.93hr vs c45(euploid) = 7.97hr. In a separate analysis limited to embryos from the same patients (patients = 47, total embryos=200, euploid=112, aneuploid=88), we obtained the same results (P = 0.0316). The statistical power for this analysis exceeded 87%. No other variable was different between the two groups. Conclusion— Our results demonstrate the importance of morphokinetic variables as potential biomarkers that could aid in non-invasively characterizing euploid and aneuploid embryos. We seek to study a larger population of embryos and incorporate the embryo quality in future studies.

Keywords: IVF, embryo, euploidy, aneuploidy, morphokinteic

Procedia PDF Downloads 88

Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

Procedia PDF Downloads 73

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

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

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

Procedia PDF Downloads 136

Authors: Utkarsh A. Mishra, Ankit Bansal

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At high temperature, radiative heat transfer is the dominant mode of heat transfer. It is governed by various phenomena such as photon emission, absorption, and scattering. The solution of the governing integrodifferential equation of radiative transfer is a complex process, more when the effect of participating medium and wavelength properties are taken into consideration. Although a generic formulation of such radiative transport problem can be modeled for a wide variety of problems with non-gray, non-diffusive surfaces, there is always a trade-off between simplicity and accuracy of the problem. Recently, solutions of complicated mathematical problems with statistical methods based on randomization of naturally occurring phenomena have gained significant importance. Photon bundles with discrete energy can be replicated with random numbers describing the emission, absorption, and scattering processes. Photon Monte Carlo (PMC) is a simple, yet powerful technique, to solve radiative transfer problems in complicated geometries with arbitrary participating medium. The method, on the one hand, increases the accuracy of estimation, and on the other hand, increases the computational cost. The participating media -generally a gas, such as CO₂, CO, and H₂O- present complex emission and absorption spectra. To model the emission/absorption accurately with random numbers requires a weighted sampling as different sections of the spectrum carries different importance. Importance sampling (IS) was implemented to sample random photon of arbitrary wavelength, and the sampled data provided unbiased training of MC estimators for better results. A better replacement to uniform random numbers is using deterministic, quasi-random sequences. Halton, Sobol, and Faure Low-Discrepancy Sequences are used in this study. They possess better space-filling performance than the uniform random number generator and gives rise to a low variance, stable Quasi-Monte Carlo (QMC) estimators with faster convergence. An optimal supervised learning scheme was further considered to reduce the computation costs of the PMC simulation. A one-dimensional plane-parallel slab problem with participating media was formulated. The history of some randomly sampled photon bundles is recorded to train an Artificial Neural Network (ANN), back-propagation model. The flux was calculated using the standard quasi PMC and was considered to be the training target. Results obtained with the proposed model for the one-dimensional problem are compared with the exact analytical and PMC model with the Line by Line (LBL) spectral model. The approximate variance obtained was around 3.14%. Results were analyzed with respect to time and the total flux in both cases. A significant reduction in variance as well a faster rate of convergence was observed in the case of the QMC method over the standard PMC method. However, the results obtained with the ANN method resulted in greater variance (around 25-28%) as compared to the other cases. There is a great scope of machine learning models to help in further reduction of computation cost once trained successfully. Multiple ways of selecting the input data as well as various architectures will be tried such that the concerned environment can be fully addressed to the ANN model. Better results can be achieved in this unexplored domain.

Keywords: radiative heat transfer, Monte Carlo Method, pseudo-random numbers, low discrepancy sequences, artificial neural networks

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Authors: Justin Byrne, Jose Ramon Cobo

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The growing importance of cross- or intercultural competencies (used here interchangeably) for the business and management professionals is now a commonplace in both academic and professional literature. This reflects two parallel developments. On the one hand, it is a consequence of the increased attention paid to a whole range of 'soft skills', now seen as fundamental in both individuals' and corporate success. On the other hand, and more specifically, the increasing demand for interculturally competent professionals is a corollary of ongoing processes of globalization, which multiply and intensify encounters between individuals and companies from different cultural backgrounds. Business schools have, for some decades, responded to the needs of the job market and their own students by providing students with training in intercultural skills, as they are encouraged to do so by the major accreditation agencies on both sides of the Atlantic. Adapting Early and Ang's (2003) formulation of Cultural Intelligence (CQ), this paper aims to help fill the lagunae in the current literature on intercultural training in three main ways. First, it offers an in-depth analysis of the CQ of a little studied group: contemporary Millenial and 'Generation Z' Business School students. The level of analysis distinguishes between the four different dimensions of CQ, cognition, metacognition, motivation and behaviour, and thereby provides a detailed picture of the strengths and weaknesses in CQ of the group as a whole, as well as of different sub-groups and profiles of students. Secondly, by crossing these individual-level findings with respondents' socio-cultural and educational data, this paper also proposes and tests hypotheses regarding the relative impact and importance of four possible antecedents of intercultural skills identified in the literature: prior international experience; intercultural training, foreign language proficiency, and experience of cultural diversity in habitual country of residence. Third, we use this analysis to suggest data-based intercultural training priorities for today's management students. These conclusions are based on the statistical analysis of individual responses of some 300 Bachelor or Masters students in a major European Business School provided to two on-line surveys: Ang, Van Dyne, et al's (2007) standard 20-question self-reporting CQ Scale, and an original questionnaire designed by the authors to collate information on respondent's socio-demographic and educational profile relevant to our four hypotheses and explanatory variables. The data from both instruments was crossed in both descriptive statistical analysis and regression analysis. This research shows that there is no statistically significant and positive relationship between the four antecedents analyzed and overall CQ level. The exception in this respect is the statistically significant correlation between international experience, and the cognitive dimension of CQ. In contrast, the results show that the combination of international experience and foreign language skills acting together, does have a strong overall impact on CQ levels. These results suggest that selecting and/or training students with strong foreign language skills and providing them with international experience (through multinational programmes, academic exchanges or international internships) constitutes one effective way of training culturally intelligent managers of tomorrow.

Keywords: business school, cultural intelligence, millennial, training

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Authors: Welder A. Baldassini, Jon J. Ramsey, Marcos R. Chiaratti, Amália S. Chaves, Renata H. Branco, Sarah F. M. Bonilha, Dante P. D. Lanna

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With increased production costs there is a need for animals that are more efficient in terms of meat production. In this context, the role of mitochondrial DNA (mtDNA) on physiological processes in liver, muscle and adipose tissues may account for inter-animal variation in energy expenditures and heat production. The purpose this study was to investigate if the amounts of mtDNA in liver, muscle and adipose tissue (subcutaneous and visceral depots) of Nellore bulls are associated with residual feed intake (RFI) and estimated heat production (EHP). Eighteen animals were individually fed in a feedlot for 90 days. RFI values were obtained by regression of dry matter intake (DMI) in relation to average daily gain (ADG) and mid-test metabolic body weight (BW). The animals were classified into low (more efficient) and high (less efficient) RFI groups. The bulls were then randomly distributed in individual pens where they were given excess feed twice daily to result in 5 to 10% orts for 90 d with diet containing 15% crude protein and 2.7 Mcal ME/kg DM. The heart rate (HR) of bulls was monitored for 4 consecutive days and used for calculation of EHP. Electrodes were fitted to bulls with stretch belts (POLAR RS400; Kempele, Finland). To calculate oxygen pulse (O2P), oxygen consumption was obtained using a facemask connected to the gas analyzer (EXHALYZER, ECOMedics, Zurich, Switzerland) and HR were simultaneously measured for 15 minutes period. Daily oxygen (O2) consumption was calculated by multiplying the volume of O2 per beat by total daily beats. EHP was calculated multiplying O2P by the average HR obtained during the 4 days, assuming 4.89 kcal/L of O2 to measure daily EHP that was expressed in kilocalories/day/kilogram metabolic BW (kcal/day/kg BW0.75). Blood samples were collected between days 45 and 90th after the beginning of the trial period in order to measure the concentration of hemoglobin and hematocrit. The bulls were slaughtered in an experimental slaughter house in accordance with current guidelines. Immediately after slaughter, a section of liver, a portion of longissimus thoracis (LT) muscle, plus a portion of subcutaneous fat (surrounding LT muscle) and portions of visceral fat (kidney, pelvis and inguinal fat) were collected. Samples of liver, muscle and adipose tissues were used to quantify mtDNA copy number per cell. The number of mtDNA copies was determined by normalization of mtDNA amount against a single copy nuclear gene (B2M). Mean of EHP, hemoglobin and hematocrit of high and low RFI bulls were compared using two-sample t-tests. Additionally, the one-way ANOVA was used to compare mtDNA quantification considering the mains effects of RFI groups. We found lower EHP (83.047 vs. 97.590 kcal/day/kgBW0.75; P < 0.10), hemoglobin concentration (13.533 vs. 15.108 g/dL; P < 0.10) and hematocrit percentage (39.3 vs. 43.6 %; P < 0.05) in low compared to high RFI bulls, respectively, which may be useful traits to identify efficient animals. However, no differences were observed between the mtDNA content in liver, muscle and adipose tissue of Nellore bulls with high and low RFI.

Keywords: bioenergetics, Bos indicus, feed efficiency, mitochondria

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Authors: Saule Balmagambetova, Zhenisgul Tlegenova, Saule Madinova

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Cardiotoxicity associated with anticancer treatment, now defined as cancer therapy-related cardiac dysfunction (CTRCD), accompanies cancer patients and negatively impacts their survivorship. Currently, a cardio-oncological service is being created in Kazakhstan based on the provisions of the European Society of Cardio-oncology (ESC) Guidelines. In the frames of a pilot project, a cohort study on CTRCD conditions was initiated at the Aktobe Cancer center. One hundred twenty-eight newly diagnosed breast cancer patients started on doxorubicin and/or trastuzumab were recruited. Echocardiography with global longitudinal strain (GLS) assessment, biomarkers panel (cardiac troponin (cTnI), brain natriuretic peptide (BNP), myeloperoxidase (MPO), galectin-3 (Gal-3), D-dimers, C-reactive protein (CRP)), and other tests were performed at baseline and every three months. Patients were stratified by the cardiovascular risks according to the ESC recommendations and allocated into the risk groups during the pre-treatment visit. Of them, 10 (7.8%) patients were assigned to the high-risk group, 48 (37.5%) to the medium-risk group, and 70 (54.7%) to the low-risk group, respectively. High-risk patients have been receiving their cardioprotective treatment from the outset. Patients were also divided by treatment - in the anthracycline-based 83 (64.8%), in trastuzumab- only 13 (10.2%), and in the mixed anthracycline/trastuzumab group 32 individuals (25%), respectively. Mild symptomatic CTRCD was revealed and treated in 2 (1.6%) participants, and a mild asymptomatic variant in 26 (20.5%). Mild asymptomatic conditions are defined as left ventricular ejection fraction (LVEF) ≥50% and further relative reduction in GLS by >15% from baseline and/or a further rise in cardiac biomarkers. The listed biomarkers were assessed longitudinally in repeated-measures linear regression models during 12 months of observation. The associations between changes in biomarkers and CTRCD and between changes in biomarkers and LVEF were evaluated. Analysis by risk groups revealed statistically significant differences in baseline LVEF scores (p 0.001), BNP (p 0.0075), and Gal-3 (p 0.0073). Treatment groups found no statistically significant differences at baseline. After 12 months of follow-up, only LVEF values showed a statistically significant difference by risk groups (p 0.0011). When assessing the temporal changes in the studied parameters for all treatment groups, there were statistically significant changes from visit to visit for LVEF (p 0.003); GLS (p 0.0001); BNP (p<0.00001); MPO (p<0.0001); and Gal-3 (p<0.0001). No moderate or strong correlations were found between the biomarkers values and LVEF, between biomarkers and GLS. Between the biomarkers themselves, a moderate, close to strong correlation was established between cTnI and D-dimer (r 0.65, p<0.05). The dose-dependent effect of anthracyclines has been confirmed: the summary dose has a moderate negative impact on GLS values: -r 0.31 for all treatment groups (p<0.05). The present study found myeloperoxidase as a promising biomarker of cardiac dysfunction in the mixed anthracycline/trastuzumab treatment group. The hazard of CTRCD increased by 24% (HR 1.21; 95% CI 1.01;1.73) per doubling in baseline MPO value (p 0.041). Increases in BNP were also associated with CTRCD (HR per doubling, 1.22; 95% CI 1.12;1.69). No cases of chemotherapy discontinuation due to cardiotoxic complications have been recorded. Further observations are needed to gain insight into the ability of biomarkers to predict CTRCD onset.

Keywords: breast cancer, chemotherapy, cardiotoxicity, Kazakhstan

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Authors: Dominik Kowitzke

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Housing constructions have direct and indirect environmental impacts especially caused by soil sealing and gray energy consumption related to the use of construction materials. Accordingly, the German government introduced regulations limiting additional annual soil sealing. At the same time, in many regions like metropolitan areas the demand for further housing is high and of current concern in the media and politics. It is argued that meeting this demand by making better use of the existing housing supply is more sustainable than the construction of new housing units. In this context, targeting the phenomenon of so-called over the housing of empty nest households seems worthwhile to investigate for its potential to free living space and thus, reduce the need for new housing constructions and related environmental harm. Over housing occurs if no space adjustment takes place in household lifecycle stages when children move out from home and the space formerly created for the offspring is from then on under-utilized. Although in some cases the housing space consumption might actually meet households’ equilibrium preferences, frequently space-wise adjustments to the living situation doesn’t take place due to transaction or information costs, habit formation, or government intervention leading to increasing costs of relocations like real estate transfer taxes or tenant protection laws keeping tenure rents below the market price. Moreover, many detached houses are not long-term designed in a way that freed up space could be rent out. Findings of this research based on socio-economic survey data, indeed, show a significant difference between the living space of empty nest and a comparison group of households which never had children. The approach used to estimate the average difference in living space is a linear regression model regressing the response variable living space on a two-dimensional categorical variable distinguishing the two groups of household types and further controls. This difference is assumed to be the under-utilized space and is extrapolated to the total amount of empty nests in the population. Supporting this result, it is found that households that move, despite market frictions impairing the relocation, after children left their home tend to decrease the living space. In the next step, only for areas with tight housing markets in Germany and high construction activity, the total under-utilized space in empty nests is estimated. Under the assumption of full substitutability of housing space in empty nests and space in new dwellings in these locations, it is argued that in a perfect market with empty nest households consuming their equilibrium demand quantity of housing space, dwelling constructions in the amount of the excess consumption of living space could be saved. This, on the other hand, would prevent environmental harm quantified in carbon dioxide equivalence units related to average constructions of detached or multi-family houses. This study would thus provide information on the amount of under-utilized space inside dwellings which is missing in public data and further estimates the external effect of over housing in environmental terms.

Keywords: empty nests, environment, Germany, households, over housing

Procedia PDF Downloads 171

Authors: Andreas Rudisch, Ralf Lampert, Andreas Kolbitsch

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It is a well-known fact among the engineering community that earthquakes with comparatively low magnitudes can cause serious damage to nonstructural components (NSCs) of buildings, even when the supporting structure performs relatively well. Past research works focused mainly on NSCs of nuclear power plants and industrial plants. Particular attention should also be given to architectural façade elements of old masonry buildings (e.g. ornamental figures, balustrades, vases), which are very vulnerable under seismic excitation. Large numbers of these historical nonstructural components (HiNSCs) can be found in highly frequented historical city centers and in the event of failure, they pose a significant danger to persons. In order to estimate the vulnerability of acceleration sensitive HiNSCs, the peak horizontal floor acceleration (PHFA) is used. The PHFA depends on the dynamic characteristics of the building, the ground excitation, and induced nonlinearities. Consequently, the PHFA can not be generalized as a simple function of height. In the present research work, an extensive case study was conducted to investigate the influence of induced nonlinearity on the PHFA for old masonry buildings. Probabilistic nonlinear FE time-history analyses considering three different hazard levels were performed. A set of eighteen synthetically generated ground motions was used as input to the structure models. An elastoplastic macro-model (multiPlas) for nonlinear homogenized continuum FE-calculation was calibrated to multiple scales and applied, taking specific failure mechanisms of masonry into account. The macro-model was calibrated according to the results of specific laboratory and cyclic in situ shear tests. The nonlinear macro-model is based on the concept of multi-surface rate-independent plasticity. Material damage or crack formation are detected by reducing the initial strength after failure due to shear or tensile stress. As a result, shear forces can only be transmitted to a limited extent by friction when the cracking begins. The tensile strength is reduced to zero. The first goal of the calibration was the consistency of the load-displacement curves between experiment and simulation. The calibrated macro-model matches well with regard to the initial stiffness and the maximum horizontal load. Another goal was the correct reproduction of the observed crack image and the plastic strain activities. Again the macro-model proved to work well in this case and shows very good correlation. The results of the case study show that there is significant scatter in the absolute distribution of the PHFA between the applied ground excitations. An absolute distribution along the normalized building height was determined in the framework of probability theory. It can be observed that the extent of nonlinear behavior varies for the three hazard levels. Due to the detailed scope of the present research work, a robust comparison with code-recommendations and simplified PHFA distributions are possible. The chosen methodology offers a chance to determine the distribution of PHFA along the building height of old masonry structures. This permits a proper hazard assessment of HiNSCs under seismic loads.

Keywords: nonlinear macro-model, nonstructural components, time-history analysis, unreinforced masonry

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Authors: Claudio Loconsole, Daniele Leonardis, Antonio Brunetti, Gianpaolo Francesco Trotta, Nicholas Caporusso, Vitoantonio Bevilacqua

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Unfortunately, many people still do not have access to communication, with specific regard to reading and writing. Among them, people who are blind or visually impaired, have several difficulties in getting access to the world, compared to the sighted. Indeed, despite technology advancement and cost reduction, nowadays assistive devices are still expensive such as Braille-based input/output systems which enable reading and writing texts (e.g., personal notes, documents). As a consequence, assistive technology affordability is fundamental in supporting the visually impaired in communication, learning, and social inclusion. This, in turn, has serious consequences in terms of equal access to opportunities, freedom of expression, and actual and independent participation to a society designed for the sighted. Moreover, the visually impaired experience difficulties in recognizing objects and interacting with devices in any activities of daily living. It is not a case that Braille indications are commonly reported only on medicine boxes and elevator keypads. Several software applications for the automatic translation of written text into speech (e.g., Text-To-Speech - TTS) enable reading pieces of documents. However, apart from simple tasks, in many circumstances TTS software is not suitable for understanding very complicated pieces of text requiring to dwell more on specific portions (e.g., mathematical formulas or Greek text). In addition, the experience of reading\writing text is completely different both in terms of engagement, and from an educational perspective. Statistics on the employment rate of blind people show that learning to read and write provides the visually impaired with up to 80% more opportunities of finding a job. Especially in higher educational levels, where the ability to digest very complex text is key, accessibility and availability of Braille plays a fundamental role in reducing drop-out rate of the visually impaired, thus affecting the effectiveness of the constitutional right to get access to education. In this context, the Braille Lab project aims at overcoming these social needs by including affordability in designing and developing assistive tools for visually impaired people. In detail, our awarded project focuses on a technology innovation of the operation principle of existing assistive tools for the visually impaired leaving the Human-Machine Interface unchanged. This can result in a significant reduction of the production costs and consequently of tool selling prices, thus representing an important opportunity for social entrepreneurship. The first two assistive tools designed within the Braille Lab project following the proposed approach aims to provide the possibility to personally print documents and handouts and to read texts written in Braille using refreshable Braille display, respectively. The former, named ‘Braille Cartridge’, represents an alternative solution for printing in Braille and consists in the realization of an electronic-controlled dispenser printing (cartridge) which can be integrated within traditional ink-jet printers, in order to leverage the efficiency and cost of the device mechanical structure which are already being used. The latter, named ‘Braille Cursor’, is an innovative Braille display featuring a substantial technology innovation by means of a unique cursor virtualizing Braille cells, thus limiting the number of active pins needed for Braille characters.

Keywords: Human rights, social challenges and technology innovations, visually impaired, affordability, assistive tools

Procedia PDF Downloads 274

Authors: Raptis Sotirios

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Health and social care (HSc) services planning and scheduling are facing unprecedented challenges due to the pandemic pressure and also suffer from unplanned spending that is negatively impacted by the global financial crisis. Data-driven can help to improve policies, plan and design services provision schedules using algorithms assist healthcare managers’ to face unexpected demands using fewer resources. The paper discusses services packing using statistical significance tests and machine learning (ML) to evaluate demands similarity and coupling. This is achieved by predicting the range of the demand (class) using ML methods such as CART, random forests (RF), and logistic regression (LGR). The significance tests Chi-Squared test and Student test are used on data over a 39 years span for which HSc services data exist for services delivered in Scotland. The demands are probabilistically associated through statistical hypotheses that assume that the target service’s demands are statistically dependent on other demands as a NULL hypothesis. This linkage can be confirmed or not by the data. Complementarily, ML methods are used to linearly predict the above target demands from the statistically found associations and extend the linear dependence of the target’s demand to independent demands forming, thus groups of services. Statistical tests confirm ML couplings making the prediction also statistically meaningful and prove that a target service can be matched reliably to other services, and ML shows these indicated relationships can also be linear ones. Zero paddings were used for missing years records and illustrated better such relationships both for limited years and in the entire span offering long term data visualizations while limited years groups explained how well patients numbers can be related in short periods or can change over time as opposed to behaviors across more years. The prediction performance of the associations is measured using Receiver Operating Characteristic(ROC) AUC and ACC metrics as well as the statistical tests, Chi-Squared and Student. Co-plots and comparison tables for RF, CART, and LGR as well as p-values and Information Exchange(IE), are provided showing the specific behavior of the ML and of the statistical tests and the behavior using different learning ratios. The impact of k-NN and cross-correlation and C-Means first groupings is also studied over limited years and the entire span. It was found that CART was generally behind RF and LGR, but in some interesting cases, LGR reached an AUC=0 falling below CART, while the ACC was as high as 0.912, showing that ML methods can be confused padding or by data irregularities or outliers. On average, 3 linear predictors were sufficient, LGR was found competing RF well, and CART followed with the same performance at higher learning ratios. Services were packed only if when significance level(p-value) of their association coefficient was more than 0.05. Social factors relationships were observed between home care services and treatment of old people, birth weights, alcoholism, drug abuse, and emergency admissions. The work found that different HSc services can be well packed as plans of limited years, across various services sectors, learning configurations, as confirmed using statistical hypotheses.

Keywords: class, cohorts, data frames, grouping, prediction, prob-ability, services

Procedia PDF Downloads 235