Search results for: storm surge prediction

Authors: Chen Chan Wei

Abstract:

The purpose of this study was to investigate the impact of swim 1500m, 10000m run, VO2 max, and body fat on Olympic distance triathlon competition performance. The subjects were thirteen college triathletes with endurance training, with an average age, height and weight of 20.61±1.04 years (mean ± SD), 171.76±8.54 cm and 65.32±8.14 kg respectively. All subjects were required to take the tests of swim 1500m, run 10000m, VO2 max, body fat, and participate in the Olympic distance triathlon competition. First, the swim 1500m test was taken in the standardized 50m pool, with a depth of 2m, and the 10000m run test on the standardized 400m track. After three days, VO2 max was tested with the MetaMax 3B and body fat was measured with the DEXA machine. After two weeks, all 13 subjects joined the Olympic distance triathlon competition at the 2016 New Taipei City Asian Cup. The relationships between swim 1500m, 10000m run, VO2 max, body fat test, and Olympic distance triathlon competition performance were evaluated using Pearson's product-moment correlation. The results show that 10000m run and body fat had a significant positive correlation with Olympic distance triathlon performance (r=.830, .768), but VO2 max has a significant negative correlation with Olympic distance triathlon performance (r=-.735). In conclusion, for improved non-draft Olympic distance triathlon performance, triathletes should focus on running than swimming training and can be measure VO2 max to prediction triathlon performance. Also, managing body fat can improve Olympic distance triathlon performance. In addition, swimming performance was not significantly correlated to Olympic distance triathlon performance, possibly because the 2016 New Taipei City Asian Cup age group was not a drafting competition. The swimming race is the shortest component of Olympic distance triathlons. Therefore, in a non-draft competition, swimming ability is not significantly correlated with overall performance.

Keywords: triathletes, olympic, non-drafting, correlation

Procedia PDF Downloads 248

Authors: Goudjil kamel, Boukhatem Ghania, Jlailia Djihene

Abstract:

This paper delves into the development of a sophisticated desktop application designed to calculate soil bearing capacity and predict limit pressure. Drawing from an extensive review of existing methodologies, the study meticulously examines various approaches employed in soil bearing capacity calculations, elucidating their theoretical foundations and practical applications. Furthermore, the study explores the burgeoning intersection of artificial intelligence (AI) and geotechnical engineering, underscoring the transformative potential of AI- driven solutions in enhancing predictive accuracy and efficiency.Central to the research is the utilization of cutting-edge machine learning techniques, including Artificial Neural Networks (ANN), XGBoost, and Random Forest, for predictive modeling. Through comprehensive experimentation and rigorous analysis, the efficacy and performance of each method are rigorously evaluated, with XGBoost emerging as the preeminent algorithm, showcasing superior predictive capabilities compared to its counterparts. The study culminates in a nuanced understanding of the intricate dynamics at play in geotechnical analysis, offering valuable insights into optimizing soil bearing capacity calculations and limit pressure predictions. By harnessing the power of advanced computational techniques and AI-driven algorithms, the paper presents a paradigm shift in the realm of geotechnical engineering, promising enhanced precision and reliability in civil engineering projects.

Keywords: limit pressure of soil, xgboost, random forest, bearing capacity

Procedia PDF Downloads 10

Authors: Moustafa Osman Mohammed

Abstract:

This paper will explore formation of HCl aerosol at atmospheric boundary layers and encourages the uptake of environmental modeling systems (EMSs) as a practice evaluation of gaseous emissions (“framework measures”) from small and medium-sized enterprises (SMEs). The conceptual model predicts greenhouse gas emissions to ecological points beyond landfill site operations. It focuses on incorporation traditional knowledge into baseline information for both measurement data and the mathematical results, regarding parameters influence model variable inputs. The paper has simplified parameters of aerosol processes based on the more complex aerosol process computations. The simple model can be implemented to both Gaussian and Eulerian rural dispersion models. Aerosol processes considered in this study were (i) the coagulation of particles, (ii) the condensation and evaporation of organic vapors, and (iii) dry deposition. The chemical transformation of gas-phase compounds is taken into account photochemical formulation with exposure effects according to HCl concentrations as starting point of risk assessment. The discussion set out distinctly aspect of sustainability in reflection inputs, outputs, and modes of impact on the environment. Thereby, models incorporate abiotic and biotic species to broaden the scope of integration for both quantification impact and assessment risks. The later environmental obligations suggest either a recommendation or a decision of what is a legislative should be achieved for mitigation measures of landfill gas (LFG) ultimately.

Keywords: air pollution, landfill emission, environmental management, monitoring/methods and impact assessment

Procedia PDF Downloads 316

Authors: Arfan Ali Nagra, Tariq Shahzad, Meshal Alharbi, Khalid Masood Khan, Muhammad Mugees Asif, Taher M. Ghazal, Khmaies Ouahada

Abstract:

Gene selection is an essential step for the classification of microarray cancer data. Gene expression cancer data (DNA microarray) facilitates computing the robust and concurrent expression of various genes. Particle swarm optimization (PSO) requires simple operators and less number of parameters for tuning the model in gene selection. The selection of a prognostic gene with small redundancy is a great challenge for the researcher as there are a few complications in PSO based selection method. In this research, a new variant of PSO (Self-inertia weight adaptive PSO) has been proposed. In the proposed algorithm, SIW-APSO-ELM is explored to achieve gene selection prediction accuracies. This new algorithm balances the exploration capabilities of the improved inertia weight adaptive particle swarm optimization and the exploitation. The self-inertia weight adaptive particle swarm optimization (SIW-APSO) is used to search the solution. The SIW-APSO is updated with an evolutionary process in such a way that each particle iteratively improves its velocities and positions. The extreme learning machine (ELM) has been designed for the selection procedure. The proposed method has been to identify a number of genes in the cancer dataset. The classification algorithm contains ELM, K- centroid nearest neighbor (KCNN), and support vector machine (SVM) to attain high forecast accuracy as compared to the start-of-the-art methods on microarray cancer datasets that show the effectiveness of the proposed method.

Keywords: microarray cancer, improved PSO, ELM, SVM, evolutionary algorithms

Procedia PDF Downloads 80

Authors: A. Chandra, M. Arthur, L. Mize, A. Pomeroy-Stevens

Abstract:

Background: Lower and middle-income countries (LMICs) in Asia face rapid urbanization resulting in both economic opportunities (the urban advantage) and emerging health challenges. Urban health risks are magnified in informal settlements and include infectious disease outbreaks, inadequate access to health services, and poor air quality. Over the coming years, urban spaces in Asia will face accelerating public health risks related to migration, climate change, and environmental health. These challenges are complex and require multi-sectoral and multi-stakeholder solutions. The Building Health Cities (BHC) program is funded by the United States Agency for International Development (USAID) to work with smart city initiatives in the Asia region. BHC approaches urban health challenges by addressing policies, planning, and services through a health equity lens, with a particular focus on informal settlements and migrant communities. The program works to develop data-driven decision-making, build inclusivity through stakeholder engagement, and facilitate the uptake of appropriate technology. Methodology: The BHC program has partnered with the smart city initiatives of Indore in India, Makassar in Indonesia, and Da Nang in Vietnam. Implementing partners support municipalities to improve health delivery and equity using two key approaches: political economy analysis and participatory systems mapping. Political economy analyses evaluate barriers to collective action, including corruption, security, accountability, and incentives. Systems mapping evaluates community health challenges using a cross-sectoral approach, analyzing the impact of economic, environmental, transport, security, health system, and built environment factors. The mapping exercise draws on the experience and expertise of a diverse cohort of stakeholders, including government officials, municipal service providers, and civil society organizations. Results: Systems mapping and political economy analyses identified significant barriers for health care in migrant populations. In Makassar, migrants are unable to obtain the necessary card that entitles them to subsidized health services. This finding is being used to engage with municipal governments to mitigate the barriers that limit migrant enrollment in the public social health insurance scheme. In Indore, the project identified poor drainage of storm and wastewater in migrant settlements as a cause of poor health. Unsafe and inadequate infrastructure placed residents of these settlements at risk for both waterborne diseases and injuries. The program also evaluated the capacity of urban primary health centers serving migrant communities, identifying challenges related to their hours of service and shortages of health workers. In Da Nang, the systems mapping process has only recently begun, with the formal partnership launched in December 2019. Conclusion: This paper explores lessons learned from BHC’s systems mapping, political economy analyses, and stakeholder engagement approaches. The paper shares progress related to the health of migrants in informal settlements. Case studies feature barriers identified and mitigating steps, including governance actions, taken by local stakeholders in partner cities. The paper includes an update on ongoing progress from Indore and Makassar and experience from the first six months of program implementation from Da Nang.

Keywords: informal settlements, migration, stakeholder engagement mapping, urban health

Procedia PDF Downloads 115

Authors: Ameer A. Jebur, William Atherton, Rafid M. Alkhaddar, Edward Loffill

Abstract:

Understanding the behaviour of the piles under the action of the independent lateral loads and the precise prediction of the capacity of piles subjected to different lateral loads are vital topics in foundation design and analysis. Moreover, the laterally loaded behaviour of deep foundations penetrated in cohesive and non-cohesive soils is basically analysed by the Winkler Model (beam on elastic foundation), in which the interaction between the pile embedded depth and contacted soil is simulated by nonlinear p–y curves. The presence of many approaches to interpret the behaviour of soil-pile interaction has resulted in numerous outputs and indicates that no general approach has yet been adopted. The current study presents the result of numerical modelling of the behaviour of steel tubular piles (25.4mm) outside diameter with various embedment depth-to-diameter ratios (L/d) embedded in a sand calibrated chamber of known relative density. The study revealed that the shear strength parameters of the sand specimens and the (L/d) ratios are the most significant factor influencing the response of the pile and its capacity while taking into consideration the complex interaction between the pile and soil. Good agreement has been achieved when comparing the application of this modelling approach with experimental physical modelling carried out by another researcher.

Keywords: deep foundations, slenderness ratio, soil-pile interaction, winkler model (beam on elastic foundation), non-cohesive soil

Procedia PDF Downloads 295

Authors: Y. Baba, A. Archibong-Eso, H. Yeung

Abstract:

Experimental study of high viscosity oil-gas flows in horizontal pipelines published in literature has indicated that hydrodynamic slug flow is the dominant flow pattern observed. Investigations have shown that hydrodynamic slugging brings about high instabilities in pressure that can damage production facilities thereby making it inherent to study high viscous slug flow regime so as to improve the understanding of its flow dynamics. Most slug flow models used in the petroleum industry for the design of pipelines together with their closure relationships were formulated based on observations of low viscosity liquid-gas flows. New experimental investigations and data are therefore required to validate these models. In cases where these models underperform, improving upon or building new predictive models and correlations will also depend on the new experimental dataset and further understanding of the flow dynamics in high viscous oil-gas flows. In this study conducted at the Flow laboratory, Oil and Gas Engineering Centre of Cranfield University, slug flow variables such as pressure gradient, mean liquid holdup, frequency and slug length for oil viscosity ranging from 1..0 – 5.5 Pa.s are experimentally investigated and analysed. The study was carried out in a 0.076m ID pipe, two fast sampling gamma densitometer and pressure transducers (differential and point) were used to obtain experimental measurements. Comparison of the measured slug flow parameters to the existing slug flow prediction models available in the literature showed disagreement with high viscosity experimental data thus highlighting the importance of building new predictive models and correlations.

Keywords: gamma densitometer, mean liquid holdup, pressure gradient, slug frequency and slug length

Procedia PDF Downloads 325

Authors: Zoe Goldberger, Priscilla S. Briquez, Jeffrey A. Hubbell

Abstract:

Tumor cells have mutations resulting from genetic instability that the immune system can actively recognize. Immune checkpoint immunotherapy (ICI) is commonly used in the clinic to re-activate immune reactions against mutated proteins, called neoantigens, resulting in tumor remission in cancer patients. However, only around 20% of patients show durable response to ICI. While tumor mutational burden (TMB) has been approved by the Food and Drug Administration (FDA) as a criterion for ICI therapy, the relevance of the subcellular localizations of the mutated proteins within the tumor cell has not been investigated. Here, we hypothesized that localization of mutations impacts the effect of immune responsiveness to ICI. We analyzed publicly available tumor mutation sequencing data of ICI treated patients from 3 independent datasets. We extracted the subcellular localization from the UniProtKB/Swiss-Prot database and quantified the proportion of membrane, cytoplasmic, nuclear, or secreted mutations per patient. We analyzed this information in relation to response to ICI treatment and overall survival of patients showing with 1722 ICI-treated patients that high mutational burden localized at the membrane (mTMB), correlate with ICI responsiveness, and improved overall survival in multiple cancer types. We anticipate that our results will ameliorate predictability of cancer patient response to ICI with potential implications in clinical guidelines to tailor ICI treatment. This would not only increase patient survival for those receiving ICI, but also patients’ quality of life by reducing the number of patients enduring non-effective ICI treatments.

Keywords: cancer, immunotherapy, membrane neoantigens, efficacy prediction, biomarkers

Procedia PDF Downloads 106

Authors: Myungjin Lee, Daegun Han, Jongsung Kim, Soojun Kim, Hung Soo Kim

Abstract:

Recently, the localized heavy rainfall and typhoons are frequently occurred due to the climate change and the damage is becoming bigger. Therefore, we may need a more accurate prediction of the rainfall and runoff. However, the gauge rainfall has the limited accuracy in space. Radar rainfall is better than gauge rainfall for the explanation of the spatial variability of rainfall but it is mostly underestimated with the uncertainty involved. Therefore, the ensemble of radar rainfall was simulated using error structure to overcome the uncertainty and gauge rainfall. The simulated ensemble was used as the input data of the rainfall-runoff models for obtaining the ensemble of runoff hydrographs. The previous studies discussed about the accuracy of the rainfall-runoff model. Even if the same input data such as rainfall is used for the runoff analysis using the models in the same basin, the models can have different results because of the uncertainty involved in the models. Therefore, we used two models of the SSARR model which is the lumped model, and the Vflo model which is a distributed model and tried to simulate the optimum runoff considering the uncertainty of each rainfall-runoff model. The study basin is located in Han river basin and we obtained one integrated runoff hydrograph which is an optimum runoff hydrograph using the blending methods such as Multi-Model Super Ensemble (MMSE), Simple Model Average (SMA), Mean Square Error (MSE). From this study, we could confirm the accuracy of rainfall and rainfall-runoff model using ensemble scenario and various rainfall-runoff model and we can use this result to study flood control measure due to climate change. Acknowledgements: This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 18AWMP-B083066-05).

Keywords: radar rainfall ensemble, rainfall-runoff models, blending method, optimum runoff hydrograph

Procedia PDF Downloads 272

Authors: Cheng-Yu Tsai, Wen-Te Liu, Shin-Mei Hsu, Yin-Tzu Lin, Chi Wu

Abstract:

Background: Obstructive Sleep apnea syndrome (OSAS) is a common respiratory disorder during sleep. In addition, Body parameters were identified high predictive importance for OSAS severity. However, the effects of body parameters on OSAS severity remain unclear. Objective: In this study, the objective is to establish a prediction model for OSAS by using body parameters and investigate the effects of body parameters in OSAS. Methodologies: Severity was quantified as the polysomnography and the mean hourly number of greater than 3% dips in oxygen saturation during examination in a hospital in New Taipei City (Taiwan). Four levels of OSAS severity were classified by the apnea and hypopnea index (AHI) with American Academy of Sleep Medicine (AASM) guideline. Body parameters, including neck circumference, waist size, and body mass index (BMI) were obtained from questionnaire. Next, dividing the collecting subjects into two groups: training and testing groups. The training group was used to establish the random forest (RF) to predicting, and test group was used to evaluated the accuracy of classification. Results: There were 3330 subjects recruited in this study, whom had been done polysomnography for evaluating severity for OSAS. A RF of 1000 trees achieved correctly classified 79.94 % of test cases. When further evaluated on the test cohort, RF showed the waist and BMI as the high import factors in OSAS. Conclusion It is possible to provide patient with prescreening by body parameters which can pre-evaluate the health risks.

Keywords: apnea and hypopnea index, Body parameters, obstructive sleep apnea syndrome, Random Forest

Procedia PDF Downloads 149

Authors: Isaiah Oluranti Olurinola, Michael Kayode Bolarinwa, Ebenezer Bowale, Ifeoluwa Ogunrinola

Abstract:

Abstract - While much literature exists on microfinancing and its impact on the development of micro, small and medium-scale enterprises (MSME), yet little is known in respect of the impact of different types of education of owner-managers on the performances as well as innovative possibilities of such enterprises. This paper aims at contributing to the understanding of the impact of different types of education (academic, technical, apprenticeship, etc) that influence the performance of micro, small and medium-sized enterprise (MSME). This study utilises a recent and larger data-set collected in six states and FCT Abuja, Nigeria in the year 2014. Furthermore, the study carries out a comparative analysis of business performance among the different geo-political zones in Nigeria, given the educational attainment of the owner-managers. The data set were enterprise-based and were collected by the Nigerian Institute for Social and Economic Research (NISER) in the year 2014. Six hundred and eighty eight enterprises were covered in the survey. The method of data analysis for this study is the use of basic descriptive statistics in addition to the Logistic Regression model used in the prediction of the log of odds of business performance in relation to any of the identified educational attainment of the owner-managers in the sampled enterprises. An OLS econometric technique is also used to determine the effects of owner-managers' different educational types on the performance of the sampled MSME. Policy measures that will further enhance the contributions of education to MSME performance will be put forward.

Keywords: Business Performance, Education, Microfinancing, Micro, Small and Medium Scale Enterprises

Procedia PDF Downloads 517

Authors: Lizhi Ma, Dan Liu

Abstract:

Pipeline parallelism has been widely used to accelerate distributed deep learning to alleviate GPU memory bottlenecks and to ensure that models can be trained and deployed smoothly under limited graphics memory conditions. However, in highly heterogeneous distributed clusters, traditional model partitioning methods are not able to achieve load balancing. The overlap of communication and computation is also a big challenge. In this paper, HePipe is proposed, an efficient pipeline parallel training method for highly heterogeneous clusters. According to the characteristics of the neural network model pipeline training task, oriented to the 2-level heterogeneous cluster computing topology, a training method based on the 2-level stage division of neural network modeling and partitioning is designed to improve the parallelism. Additionally, a multi-forward 1F1B scheduling strategy is designed to accelerate the training time of each stage by executing the computation units in advance to maximize the overlap between the forward propagation communication and backward propagation computation. Finally, a dynamic recomputation strategy based on task memory requirement prediction is proposed to improve the fitness ratio of task and memory, which improves the throughput of the cluster and solves the memory shortfall problem caused by memory differences in heterogeneous clusters. The empirical results show that HePipe improves the training speed by 1.6×−2.2× over the existing asynchronous pipeline baselines.

Keywords: pipeline parallelism, heterogeneous cluster, model training, 2-level stage partitioning

Procedia PDF Downloads 11

Authors: Junya Kouwa, Shinsuke Matsuno, Chihiro Inoue, Takehiro Himeno, Toshinori Watanabe

Abstract:

Bi-propellant thrusters use impinging jet atomization to atomize liquid fuel and oxidizer. Atomized propellants are mixed and combusted due to auto-ignitions. Therefore, it is important for a prediction of thruster’s performance to simulate the primary atomization phenomenon; especially, the local mixture ratio can be used as indicator of thrust performance, so it is useful to evaluate it from numerical simulations. In this research, we propose a numerical method for considering bi-liquid and the mixture and install it to CIP-LSM which is a two-phase flow simulation solver with level-set and MARS method as an interfacial tracking method and can predict local mixture ratio distribution downstream from an impingement point. A new parameter, beta, which is defined as the volume fraction of one liquid in the mixed liquid within a cell is introduced and the solver calculates the advection of beta, inflow and outflow flux of beta to a cell. By validating this solver, we conducted a simple experiment and the same simulation by using the solver. From the result, the solver can predict the penetrating length of a liquid jet correctly and it is confirmed that the solver can simulate the mixing of liquids. Then we apply this solver to the numerical simulation of impinging jet atomization. From the result, the inclination angle of fan after the impingement in the bi-liquid condition reasonably agrees with the theoretical value. Also, it is seen that the mixture of liquids can be simulated in this result. Furthermore, simulation results clarify that the injecting condition affects the atomization process and local mixture ratio distribution downstream drastically.

Keywords: bi-propellant thrusters, CIP-LSM, free-surface flow simulation, impinging jet atomization

Procedia PDF Downloads 277

Authors: Huajuan Shi

Abstract:

Background: The biopsy of the preimplantation embryo may increase the potential risk and concern of embryo viability. Clinically discarded spent embryo medium (SEM) has entered the view of researchers, sparking an interest in noninvasive embryo screening. However, one of the major restrictions is the extremelty low quantity of cf-RNA, which is difficult to efficiently and unbiased amplify cf-RNA using traditional methods. Hence, there is urgently need to an efficient and low bias amplification method which can comprehensively and accurately obtain cf-RNA information to truly reveal the state of SEM cf-RNA. Result: In this present study, we established an agarose PCR amplification system, and has significantly improved the amplification sensitivity and efficiency by ~90 fold and 9.29 %, respectively. We applied agarose to sequencing library preparation (named AG-seq) to quantify and characterize cf-RNA in SEM. The number of detected cf-RNAs (3533 vs 598) and coverage of 3' end were significantly increased, and the noise of low abundance gene detection was reduced. The increasing percentage 5' end adenine and alternative splicing (AS) events of short fragments (< 400 bp) were discovered by AG-seq. Further, the profiles and characterizations of cf-RNA in spent cleavage medium (SCM) and spent blastocyst medium (SBM) indicated that 4‐mer end motifs of cf-RNA fragments could remarkably differentiate different embryo development stages. Significance: This study established an efficient and low-cost SEM amplification and library preparation method. Not only that, we successfully described the characterizations of SEM cf-RNA of preimplantation embryo by using AG-seq, including abundance features fragment lengths. AG-seq facilitates the study of cf-RNA as a noninvasive embryo screening biomarker and opens up potential clinical utilities of trace samples.

Keywords: cell-free RNA, agarose, spent embryo medium, RNA sequencing, non-invasive detection

Procedia PDF Downloads 62

Authors: T. Nozu, K. Hibi, T. Nishiie

Abstract:

This paper discusses the applicability of the numerical model for a damage prediction method of the accidental hydrogen explosion occurring in a hydrogen facility. The numerical model was based on an unstructured finite volume method (FVM) code “NuFD/FrontFlowRed”. For simulating unsteady turbulent combustion of leaked hydrogen gas, a combination of Large Eddy Simulation (LES) and a combustion model were used. The combustion model was based on a two scalar flamelet approach, where a G-equation model and a conserved scalar model expressed a propagation of premixed flame surface and a diffusion combustion process, respectively. For validation of this numerical model, we have simulated the previous two types of hydrogen explosion tests. One is open-space explosion test, and the source was a prismatic 5.27 m3 volume with 30% of hydrogen-air mixture. A reinforced concrete wall was set 4 m away from the front surface of the source. The source was ignited at the bottom center by a spark. The other is vented enclosure explosion test, and the chamber was 4.6 m × 4.6 m × 3.0 m with a vent opening on one side. Vent area of 5.4 m2 was used. Test was performed with ignition at the center of the wall opposite the vent. Hydrogen-air mixtures with hydrogen concentrations close to 18% vol. were used in the tests. The results from the numerical simulations are compared with the previous experimental data for the accuracy of the numerical model, and we have verified that the simulated overpressures and flame time-of-arrival data were in good agreement with the results of the previous two explosion tests.

Keywords: deflagration, large eddy simulation, turbulent combustion, vented enclosure

Procedia PDF Downloads 237

Authors: Asawari Ajay Avhad

Abstract:

The Soil and Water Assessment Tool (SWAT) is a hydrological model designed to predict the complex interactions within natural and human-altered watersheds. This research applies the SWAT model to the Ulhas River basin, a small watershed undergoing urbanization and characterized by bowl-like topography. Three simulation scenarios (LC17, LC22, and LC27) are investigated, each representing different land use and land cover (LULC) configurations, to assess the impact of urbanization on runoff. The LULC for the year 2027 is generated using the MOLUSCE Plugin of QGIS, incorporating various spatial factors such as DEM, Distance from Road, Distance from River, Slope, and distance from settlements. Future climate data is simulated within the SWAT model using historical data spanning 30 years. A susceptibility map for runoff across the basin is created, classifying runoff into five susceptibility levels ranging from very low to very high. Sub-basins corresponding to major urban settlements are identified as highly susceptible to runoff. With consideration of future climate projections, a slight increase in runoff is forecasted. The reliability of the methodology was validated through the identification of sub-basins known for experiencing severe flood events, which were determined to be highly susceptible to runoff. The susceptibility map successfully pinpointed these sub-basins with a track record of extreme flood occurrences, thus reinforcing the credibility of the assessment methodology. This study suggests that the methodology employed could serve as a valuable tool in flood management planning.

Keywords: future land use impact, flood management, run off prediction, ArcSWAT

Procedia PDF Downloads 42

Authors: Ren Moses, Emmanuel Kidando, Eren Ozguven, Yassir Abdelrazig

Abstract:

This study applied traffic speed and occupancy to develop clustering models that identify different traffic conditions. Particularly, these models are based on the Dirichlet Process Mixture of Generalized Linear regression (DML) and change-point regression (CR). The model frameworks were implemented using 2015 historical traffic data aggregated at a 15-minute interval from an Interstate 295 freeway in Jacksonville, Florida. Using the deviance information criterion (DIC) to identify the appropriate number of mixture components, three traffic states were identified as free-flow, transitional, and congested condition. Results of the DML revealed that traffic occupancy is statistically significant in influencing the reduction of traffic speed in each of the identified states. Influence on the free-flow and the congested state was estimated to be higher than the transitional flow condition in both evening and morning peak periods. Estimation of the critical speed threshold using CR revealed that 47 mph and 48 mph are speed thresholds for congested and transitional traffic condition during the morning peak hours and evening peak hours, respectively. Free-flow speed thresholds for morning and evening peak hours were estimated at 64 mph and 66 mph, respectively. The proposed approaches will facilitate accurate detection and prediction of traffic congestion for developing effective countermeasures.

Keywords: traffic congestion, multistate speed distribution, traffic occupancy, Dirichlet process mixtures of generalized linear model, Bayesian change-point detection

Procedia PDF Downloads 291

Authors: Lolwa Alansari, Hanen Mrabet, Kholoud Khaled, Abdelhamid Azhaghdani, Sufia Athar, Aska Kaima, Zaineb Mhamdia, Zubaria Altaf, Almunzer Zakaria, Tamara Alshadafat

Abstract:

Introduction: The Obstetrics and Gynecology Emergency Department at Alwakra Hospital has faced significant challenges, which have been further worsened by the impact of the COVID-19 pandemic. These challenges involve issues such as overcrowding, extended wait times, and a notable surge in demand for emergency care services. Moreover, prolonged waiting times have emerged as a primary factor contributing to situations where patients leave without receiving attention, known as left without being seen (LWBS), and unexpectedly abscond. Addressing the issue of insufficient patient mobility in the obstetrics and gynecology emergency department has brought about substantial improvements in patient care, healthcare administration, and overall departmental efficiency. These changes have not only alleviated overcrowding but have also elevated the quality of emergency care, resulting in higher patient satisfaction, better outcomes, and operational rewards. Methodology: The COVID-19 pandemic has served as a catalyst for substantial transformations in the obstetrics and gynecology emergency, aligning seamlessly with the strategic direction of Hamad Medical Corporation (HMC). The fundamental aim of this initiative is to revolutionize the operational efficiency of the OB-GYN ED. To accomplish this mission, a range of transformations has been initiated, focusing on essential areas such as digitizing systems, optimizing resource allocation, enhancing budget efficiency, and reducing overall costs. The project utilized the Plan-Do-Study-Act (PDSA) model, involving a diverse team collecting baseline data and introducing throughput improvements. Post-implementation data and feedback were analysed, leading to the integration of effective interventions into standard procedures. These interventions included optimized space utilization, real-time communication, bedside registration, technology integration, pre-triage screening, enhanced communication and patient education, consultant presence, and a culture of continuous improvement. These strategies significantly reduced waiting times, enhancing both patient care and operational efficiency. Results: Results demonstrated a substantial reduction in overall average waiting time, dropping from 35 to approximately 14 minutes by August 2023. The wait times for priority 1 cases have been reduced from 22 to 0 minutes, and for priority 2 cases, the wait times have been reduced from 32 to approximately 13.6 minutes. The proportion of patients spending less than 8 hours in the OB ED observation beds rose from 74% in January 2022 to over 98% in 2023. Notably, there was a remarkable decrease in LWBS and absconded patient rates from 2020 to 2023. Conclusion: The project initiated a profound change in the department's operational environment. Efficiency became deeply embedded in the unit's culture, promoting teamwork among staff that went beyond the project's original focus and had a positive influence on operations in other departments. This effectiveness not only made processes more efficient but also resulted in significant cost reductions for the hospital. These cost savings were achieved by reducing wait times, which in turn led to fewer prolonged patient stays and reduced the need for additional treatments. These continuous improvement initiatives have now become an integral part of the Obstetrics and Gynecology Division's standard operating procedures, ensuring that the positive changes brought about by the project persist and evolve over time.

Keywords: overcrowding, waiting time, person centered care, quality initiatives

Procedia PDF Downloads 60

Authors: Habtamu Tkubet Ebuy

Abstract:

Purpose Conventional dynamic energy simulation tools go beyond the static dimension of simplified methods by providing better and more accurate prediction of building performance. However, their ability to forecast actual performance is undermined by a low representation of human interactions. The purpose of this study is to examine the potential benefits of incorporating information on occupant diversity into occupant behavior models used to simulate building performance. The co-simulation of the stochastic behavior of the occupants substantially increases the accuracy of the simulation. Design/methodology/approach In this article, probabilistic models of the "opening and closing" behavior of the window of inhabitants have been developed in a separate multi-agent platform, SimOcc, and implemented in the building simulation, TRNSYS, in such a way that the behavior of the window with the interconnectivity can be reflected in the simulation analysis of the building. Findings The results of the study prove that the application of complex behaviors is important to research in predicting actual building performance. The results aid in the identification of the gap between reality and existing simulation methods. We hope this study and its results will serve as a guide for researchers interested in investigating occupant behavior in the future. Research limitations/implications Further case studies involving multi-user behavior for complex commercial buildings need to more understand the impact of the occupant behavior on building performance. Originality/value This study is considered as a good opportunity to achieve the national strategy by showing a suitable tool to help stakeholders in the design phase of new or retrofitted buildings to improve the performance of office buildings.

Keywords: occupant behavior, co-simulation, energy consumption, thermal comfort

Procedia PDF Downloads 96

Authors: Victor Igwemezie, Ali Mehmanparast

Abstract:

The correct knowledge of corrosion fatigue mechanism in marine steel is very important. This is because it enables the design, selection, and use of steels for offshore applications. It also supports realistic corrosion fatigue life prediction of marine structures. A study has been conducted to increase the understanding of corrosion fatigue mechanism in marine steels. The materials investigated are normalized and advanced S355 Thermomechanical control process (TMCP) steels commonly used in the design of offshore wind turbine support structures. The experimental study was carried out by conducting corrosion fatigue tests under conditions pertinent to offshore wind turbine operations, using the state of the art facilities. A careful microstructural study of the crack growth path was conducted using metallurgical optical microscope (OM), scanning electron microscope (SEM) and Energy Dispersive X-Ray Spectroscopy (EDX). The test was conducted on three subgrades of S355 steel: S355J2+N, S355G8+M and S355G10+M and the data compared with similar studies in the literature. The result shows that the ferrite-pearlite morphology primarily controls the corrosion-fatigue crack growth path in marine steels. A corrosion fatigue mechanism which relies on the hydrogen embrittlement of the grain boundaries and pearlite phase is used to explain the crack propagation behaviour. The crack growth trend in the Paris region of the da/dN vs. ΔK curve is used to explain the dependency of the corrosion-fatigue crack growth rate on the ferrite-pearlite morphology.

Keywords: corrosion-fatigue mechanism, fatigue crack growth rate, ferritic-pearlitic steel, microstructure, phase morphology

Procedia PDF Downloads 153

Authors: Gulce Kanturer Yasar, Hayriye Esbah Tuncay

Abstract:

Cultivated food production systems have coexisted harmoniously with nature for thousands of years through ancient techniques. Based on this experience, experimentation, and discovery, these culturally embedded methods have evolved to sustain food production, restore ecosystems, and harmoniously adapt to nature. In this era, as we seek solutions to food security challenges, enhancing and repairing our food production systems is crucial, making them more resilient to future disasters without harming the ecosystem. Instead of unsustainable conventional systems with ongoing destructive effects, we must investigate innovative and restorative production systems that integrate ancient wisdom and technology. Whether we consider agricultural fields, pastures, forests, coastal wetland ecosystems, or lagoons, it is crucial to harness the potential of these natural resources in addressing future global challenges, fostering both socio-economic resilience and ecological sustainability through strategic organization for food production. When thoughtfully designed and managed, marine-based food production has the potential to function as a living infrastructure system that addresses social and environmental challenges despite its known adverse impacts on the environment and local economies. These areas are also stages of daily life, vibrant hubs where local culture is produced and shared, contributing to the distinctive rural character of coastal settlements and exhibiting numerous spatial expressions of public nature. When we consider the history of humanity, indigenous communities have engaged in these sustainable production practices that provide goods for food, trade, culture, and the environment for many ages. Ecosystem restoration and socio-economic resilience can be achieved by combining production techniques based on ecological knowledge developed by indigenous societies with modern technologies. Coastal lagoons are highly productive coastal features that provide various natural services and societal values. They are especially vulnerable to severe physical, ecological, and social impacts of changing, challenging global conditions because of their placement within the coastal landscape. Coastal lagoons are crucial in sustaining fisheries productivity, providing storm protection, supporting tourism, and offering other natural services that hold significant value for society. Although there is considerable literature on the physical and ecological dimensions of lagoons, much less literature focuses on their economic and social values. This study will discuss the possibilities of coastal lagoons to achieve both ecologically sustainable and socio-economically resilient while maintaining their productivity by combining local techniques and modern technologies. The case study will present Turkey’s traditional aquaculture method, "Dalyans," predominantly operated by small-scale farmers in coastal lagoons. Due to human, ecological, and economic factors, dalyans are losing their landscape characteristics and efficiency. These 1000-year-old ancient techniques, rooted in centuries of traditional and agroecological knowledge, are under threat of tourism, urbanization, and unsustainable agricultural practices. Thus, Dalyans have diminished from 29 to approximately 4-5 active Dalyans. To deal with the adverse socio-economic and ecological consequences on Turkey's coastal areas, conserving Dalyans by protecting their indigenous practices while incorporating contemporary methods is essential. This study seeks to generate scenarios that envision the potential ways protection and development can manifest within case study areas.

Keywords: coastal foodscape, lagoon aquaculture, regenerative food systems, watershed food networks

Procedia PDF Downloads 67

Authors: M. Aghili, S. Tabarestani, C. Freytes, M. Shojaie, M. Cabrerizo, A. Barreto, N. Rishe, R. E. Curiel, D. Loewenstein, R. Duara, M. Adjouadi

Abstract:

A major challenge in medical studies, especially those that are longitudinal, is the problem of missing measurements which hinders the effective application of many machine learning algorithms. Furthermore, recent Alzheimer's Disease studies have focused on the delineation of Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) from cognitively normal controls (CN) which is essential for developing effective and early treatment methods. To address the aforementioned challenges, this paper explores the potential of using the eXtreme Gradient Boosting (XGBoost) algorithm in handling missing values in multiclass classification. We seek a generalized classification scheme where all prodromal stages of the disease are considered simultaneously in the classification and decision-making processes. Given the large number of subjects (1631) included in this study and in the presence of almost 28% missing values, we investigated the performance of XGBoost on the classification of the four classes of AD, NC, EMCI, and LMCI. Using 10-fold cross validation technique, XGBoost is shown to outperform other state-of-the-art classification algorithms by 3% in terms of accuracy and F-score. Our model achieved an accuracy of 80.52%, a precision of 80.62% and recall of 80.51%, supporting the more natural and promising multiclass classification.

Keywords: eXtreme gradient boosting, missing data, Alzheimer disease, early mild cognitive impairment, late mild cognitive impair, multiclass classification, ADNI, support vector machine, random forest

Procedia PDF Downloads 185

Authors: Srinivas Peri, Siva Abhishek Sirivella, Tejaswini Kallakuri, Uzair Ahmad

Abstract:

Smart cities and intelligent transportation systems rely heavily on effective traffic management and infrastructure planning. This research tackles the data scarcity challenge by generating realistically synthetic traffic data from the PeMS-Bay dataset, enhancing predictive modeling accuracy and reliability. Advanced techniques like TimeGAN and GaussianCopula are utilized to create synthetic data that mimics the statistical and structural characteristics of real-world traffic. The future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is anticipated to capture both spatial and temporal correlations, further improving data quality and realism. Each synthetic data generation model's performance is evaluated against real-world data to identify the most effective models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are employed to model and predict complex temporal dependencies within traffic patterns. This holistic approach aims to identify areas with low vehicle counts, reveal underlying traffic issues, and guide targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study facilitates data-driven decision-making that improves urban mobility, safety, and the overall efficiency of city planning initiatives.

Keywords: GAN, long short-term memory (LSTM), synthetic data generation, traffic management

Procedia PDF Downloads 8

Authors: C. C. Egwuonwu, N. A. A. Okereke, K. O. Chilakpu, S. O. Ohanyere

Abstract:

In this study, initial soil strength indices (qu) and stress-strain characteristics, namely failure strain (ϵf), area under the stress-strain curve up to failure (Is) and stress-strain modulus between no load and failure (Es) were investigated as potential indicators for characterizing the erosion resistance of two compacted soils, namely sandy clay loam (SCL) and clay loam (CL) in some selected sites in Enugu State, Nigeria. The unconfined compressive strength (used in obtaining strength indices) and stress-strain measurements were obtained as a function of moisture content in percentage (mc %) and dry density (γd). Test were conducted over a range of 8% to 30% moisture content and 1.0 g/cm3 to 2.0 g/cm3 dry density at applied loads of 20, 40, 80, 160 and 320 kPa. Based on the results, it was found out that initial soil strength alone was not a good indicator of erosion resistance. For instance, in the comparison of exponents of mc% and γd for jet index or erosion resistance index (Ji) and the strength measurements, qu and Es agree in signs for mc%, but are opposite in signs for γd. Therefore, there is an inconsistency in exponents making it difficult to develop a relationship between the strength parameters and Ji for this data set. In contrast, the exponents of mc% and γd for Ji and ϵf and Is are opposite in signs, there is potential for an inverse relationship. The measured stress-strain characteristics, however, appeared to have potential in providing useful information on erosion resistance. The models developed for the prediction of the extent or the susceptibility of soils to erosion and subjected to sensitivity test on some selected sites achieved over 90% efficiency in their functions.

Keywords: characterization of erodibility, selected sites in Enugu state, soil strength, stress-strain indices

Procedia PDF Downloads 412

Authors: Roy Y. C. Huang, J. S. Kuang, Hamdolah Behnam

Abstract:

Beam-column connections play an important role in the reinforced concrete moment resisting frame (RCMRF), which is one of the most commonly used structural systems around the world. The premature failure of such connections would severely limit the seismic performance and increase the vulnerability of RCMRF. In the past decades, researchers primarily focused on investigating the structural behaviour and failure mechanisms of conventional beam-column joints, the beam width of which is either smaller than or equal to the column width, while studies in wide beam-column joints were scarce. This paper presents the preliminary experimental results of two full-scale exterior wide beam-column connections, which are mainly designed and detailed according to ACI 318-14 and ACI 352R-02, under reversed cyclic loading. The ratios of the design shear force to the nominal shear strength of these specimens are 1.0 and 1.7, respectively, so as to probe into differences of the joint shear strength between experimental results and predictions by design codes of practice. Flexural failure dominated in the specimen with ratio of 1.0 in which full-width plastic hinges were observed, while both beam hinges and post-peak joint shear failure occurred for the other specimen. No sign of premature joint shear failure was found which is inconsistent with ACI codes’ prediction. Finally, a modification of current codes of practice is provided to accurately predict the joint shear strength in wide beam-column joint.

Keywords: joint shear strength, reversed cyclic loading, seismic vulnerability, wide beam-column joints

Procedia PDF Downloads 321

Authors: Wajid Rehman, Sirajul Haq, Bakhtiar Muhammad, Syed Fahad Hassan, Amin Badshah, Muhammad Waseem, Fazal Rahim, Obaid-Ur-Rahman Abid, Farzana Latif Ansari, Umer Rashid

Abstract:

Five novel triorganotin (IV) compounds have been synthesized and characterized. The tin atom is penta-coordinated to assume trigonal-bipyramidal geometry. Using in silico derived parameters; the objective of our study is to design and synthesize promiscuous antibacterials potent enough to combat resistance. Among various synthesized organotin (IV) complexes, compound 5 was found as potent antibacterial agent against various bacterial strains. Further lead optimization of drug-like properties was evaluated through in silico predictions. Data mining and computational analysis were utilized to derive compound promiscuity phenomenon to avoid drug attrition rate in designing antibacterials. Xanthine oxidase and human glucose- 6-phosphatase were found as only true positive off-target hits by ChEMBL database and others utilizing similarity ensemble approach. Propensity towards a-3 receptor, human macrophage migration factor and thiazolidinedione were found as false positive off targets with E-value 1/4> 10^-4 for compound 1, 3, and 4. Further, displaying positive drug-drug interaction of compound 1 as uricosuric was validated by all databases and docked protein targets with sequence similarity and compositional matrix alignment via BLAST software. Promiscuity of the compound 5 was further confirmed by in silico binding to different antibacterial targets.

Keywords: antibacterial activity, drug promiscuity, ADMET prediction, metallo-pharmaceutical, antimicrobial resistance

Procedia PDF Downloads 500

Authors: Chi Wu, Dean Wu, Wen-Te Liu, Cheng-Yu Tsai, Shin-Mei Hsu, Yin-Tzu Lin, Ru-Yin Yang

Abstract:

Background: The results of previous studies compared the benefits and risks of receiving insomnia medication. However, the effects between hypnotic drugs used and enhancement of sleep quality were still unclear. Objective: The aim of this study is to establish a prediction model for hypnotic drugs' dosage used for insomnia subjects and associated the relationship between sleep stage ratio change and drug types. Methodologies: According to American Academy of Sleep Medicine (AASM) guideline, sleep stages were classified and transformed to hypnogram via the polysomnography (PSG) in a hospital in New Taipei City (Taiwan). The subjects with diagnosis for insomnia without receiving hypnotic drugs treatment were be set as the comparison group. Conversely, hypnotic drugs dosage within the past three months was obtained from the clinical registration for each subject. Furthermore, the collecting subjects were divided into two groups for training and testing. After training convolution neuron network (CNN) to predict types of hypnotics used and dosages are taken, the test group was used to evaluate the accuracy of classification. Results: We recruited 76 subjects in this study, who had been done PSG for transforming hypnogram from their sleep stages. The accuracy of dosages obtained from confusion matrix on the test group by CNN is 81.94%, and accuracy of hypnotic drug types used is 74.22%. Moreover, the subjects with high ratio of wake stage were correctly classified as requiring medical treatment. Conclusion: CNN with hypnogram was potentially used for adjusting the dosage of hypnotic drugs and providing subjects to pre-screening the types of hypnotic drugs taken.

Keywords: convolution neuron network, hypnotic drugs, insomnia, polysomnography

Procedia PDF Downloads 190

Authors: Aung Phyo Kyaw, Kuo Chieh Chao

Abstract:

Expansive soils are more complicated than normal soils, although the soil itself is not very complicated. When evaluating foundation performance on expansive soil, it is important to consider soil expansion. The primary focus of this study is on hydraulic cement and expansive soil mixtures, and the research aims to identify key parameters for controlling the swell of the expansive soil-hydraulic cement mixture. Treatment depths can be determined using hydraulic cement ratios of 4%, 8%, 12%, and 15% for treating expansive soil. To understand the effect of hydraulic cement percentages on the swelling of expansive soil-hydraulic admixture, performing the consolidation-swell test σ''ᶜˢ is crucial. This investigation primarily focuses on consolidation-swell tests σ''ᶜˢ, although the heave index Cₕ is also needed to determine total heave. The heave index can be measured using the percent swell in the specific inundation stress in both the consolidation-swell test and the constant-volume test swelling pressure. Obtaining the relationship between swelling pressure and σ''ᶜⱽ determined from the "constant volume test" is useful in predicting heave from a single oedometer test. The relationship between σ''ᶜˢ and σ''ᶜⱽ is based on experimental results of expansive soil behavior and facilitates heave prediction for each soil. In this method, the soil property "m" is used as a parameter, and common soil property tests include compaction, particle size distribution, and the Atterberg limit. The Electricity Generating Authority of Thailand (EGAT) provided the soil sample for this study, and all laboratory testing is performed according to American Society for Testing and Materials (ASTM) standards.

Keywords: expansive soil, swelling pressure, total heave, treatment depth

Procedia PDF Downloads 81

Authors: Helene Thieblemont, Fariborz Haghighat

Abstract:

Energy storage systems play a crucial role in decreasing building energy consumption during peak periods and expand the use of renewable energies in buildings. To provide a high building thermal performance, the energy storage system has to be properly controlled to insure a good energy performance while maintaining a satisfactory thermal comfort for building’s occupant. In the case of passive discharge storages, defining in advance the required amount of energy is required to avoid overheating in the building. Consequently, anticipatory supervisory control strategies have been developed forecasting future energy demand and production to coordinate systems. Anticipatory supervisory control strategies are based on some predictions, mainly of the weather forecast. However, if the forecasted hourly outdoor temperature may be found online with a high accuracy, solar radiations predictions are most of the time not available online. To estimate them, this paper proposes an advanced approach based on the forecast of weather conditions. Several methods to correlate hourly weather conditions forecast to real hourly solar radiations are compared. Results show that using weather conditions forecast allows estimating with an acceptable accuracy solar radiations of the next day. Moreover, this technique allows obtaining hourly data that may be used for building models. As a result, this solar radiation prediction model may help to implement model-based controller as Model Predictive Control.

Keywords: anticipatory control, model predictive control, solar radiation forecast, thermal storage

Procedia PDF Downloads 266

Authors: Vera Wilden, Benno Hoffmeister, Georgios Balaskas, Lukas Rauber, Burkhard Walter

Abstract:

Light-weight timber frame elements represent an efficient structural solution for wooden multistory buildings. The wall elements of such buildings – which act as shear diaphragms- provide lateral stiffness and resistance to wind and seismic loads. The tendency towards multi-story structures leads to challenges regarding the prediction of stiffness, strength and ductility of the buildings. Lightweight timber frame elements are built up of several structural parts (sheeting, fasteners, frame, support and anchorages); each of them contributing to the dynamic response of the structure. This contribution describes the experimental and numerical investigation and development of enhanced lightweight timber frame buildings. These developments comprise high-performance timber frame walls with the variable arrangements of sheathing planes and dissipative anchors at the base of the timber buildings, which reduce damages to the timber structure and can be exchanged after significant earthquakes. In order to prove the performance of the developed elements in the context of a real building a full-scale two-story building core was designed and erected in the laboratory and tested experimentally for its seismic performance. The results of the tests and a comparison of the test results to the predicted behavior are presented. Observation during the test also reveals some aspects of the design and details which need to consider in the application of the timber walls in the context of the complete building.

Keywords: dissipative anchoring, full scale test, push-over-test, wood shear walls

Procedia PDF Downloads 238