Search results for: transient surge analysis

Authors: Maryam Jabeen

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In recent times, there has been a notable surge in attention towards diverse literacy concepts such as media literacy, information literacy, and digital literacy. These concepts have garnered escalating interest, spurring the emergence of novel approaches, particularly in the aftermath of the Covid-19 crisis. However, amidst discussions of crises, the domain of crisis literacy remains largely uncharted within academic exploration. Crisis literacy, also referred to as disaster literacy, denotes an individual's aptitude to not only comprehend but also effectively apply information, enabling well-informed decision-making and adherence to instructions about disaster mitigation, preparedness, response, and recovery. This theoretical and descriptive study seeks to transcend foundational literacy concepts, underscoring the urgency for an in-depth exploration of crisis literacy and its interplay with the realm of media communication. Given the profound impact of the pandemic experience and the looming uncertainty of potential future crises, there arises a pressing need to elevate crisis literacy, or disaster literacy, towards heightened autonomy and active involvement within the spheres of critical disaster preparedness, recovery initiatives, and media communication domains. This research paper is part of my ongoing Ph.D. research study, which explores on a broader level the Encoding and decoding of media communications in relation to crisis literacy. The primary objective of this research paper is to expound upon a descriptive, theoretical research endeavor delving into this domain. The emphasis lies in highlighting the paramount significance of media communications in literacy of crisis, coupled with an accentuated focus on its role in providing information to marginalized populations amidst crises. In conclusion, this research bridges the gap in crisis literacy correlation to media communications exploration, advocating for a comprehensive understanding of its dynamics and its symbiotic relationship with media communications. It intends to foster a heightened sense of crisis literacy, particularly within marginalized communities, catalyzing proactive participation in disaster preparedness, recovery processes, and adept media interactions.

Keywords: covid-19, crisis literacy, crisis, marginalized, media and communications, pandemic, vulnerable people

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Authors: D. Beziakina, E. Bulgakova

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This study continues the research on Tuvinian intonation and presents a general cross-dialect analysis of intonation of Tuvinian declarative utterances, specifically the character of the tone movement in order to test the hypothesis about the prevalence of level tone in some Tuvinian dialects. The results of the analysis of basic pitch characteristics of Tuvinian speech (in general and in comparison with two other Turkic languages - Uzbek and Azerbaijani) are also given in this paper. The goal of our work was to obtain the ranges of pitch parameter values typical for Tuvinian speech. Such language-specific values can be used in speaker identification systems in order to get more accurate results of ethnic speech analysis. We also present the results of a cross-dialect analysis of declarative intonation in the poorly studied Tuvinian language.

Keywords: speech analysis, statistical analysis, speaker recognition, identification of person

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Authors: Wadha Alqahtani

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In recent times, polymers have seen a great surge in interest in the field of medicine, particularly chemotherapeutics. One recent innovation is the conversion of polymeric materials into “polymeric nanoparticles”. These nanoparticles can be designed and modified to encapsulate and transport drugs selectively to cancer cells, minimizing collateral damage to surrounding healthy tissues, and improve patient quality of life. In this study, we have synthesized pseudo-branched polyester polymers from bio-based small molecules, including sorbitol, glutaric acid and a propargylic acid derivative to further modify the polymer to make it “click-able" with an azide-modified target ligand. Melt polymerization technique was used for this polymerization reaction, using lipase enzyme catalyst NOVO 435. This reaction was conducted between 90- 95 °C for 72 hours. The polymer samples were collected in 24-hour increments for characterization and to monitor reaction progress. The resulting polymer was purified with the help of methanol dissolving and filtering with filter paper then characterized via NMR, GPC, FTIR, DSC, TGA and MALDI-TOF. Following characterization, these polymers were converted to a polymeric nanoparticle drug delivery system using solvent diffusion method, wherein DiI optical dye and chemotherapeutic drug Taxol can be encapsulated simultaneously. The efficacy of the nanoparticle’s apoptotic effects were analyzed in-vitro by incubation with prostate cancer (LNCaP) and healthy (CHO) cells. MTT assays and fluorescence microscopy were used to assess the cellular uptake and viability of the cells after 24 hours at 37 °C and 5% CO2 atmosphere. Results of the assays and fluorescence imaging confirmed that the nanoparticles were successful in both selectively targeting and inducing apoptosis in 80% of the LNCaP cells within 24 hours without affecting the viability of the CHO cells. These results show the potential of using biodegradable polymers as a vehicle for receptor-specific drug delivery and a potential alternative for traditional systemic chemotherapy. Detailed experimental results will be discussed in the e-poster.

Keywords: chemotherapeutic drug, click chemistry, nanoparticle, prostat cancer

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Authors: Pingping Lin, Xudong Luo, Yifan Fan

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Sentiment analysis is a very active research topic. Every day, Facebook, Twitter, Weibo, and other social media, as well as significant e-commerce websites, generate a massive amount of comments, which can be used to analyse peoples opinions or emotions. The existing methods for sentiment analysis are based mainly on sentiment dictionaries, machine learning, and deep learning. The first two kinds of methods rely on heavily sentiment dictionaries or large amounts of labelled data. The third one overcomes these two problems. So, in this paper, we focus on the third one. Specifically, we survey various sentiment analysis methods based on convolutional neural network, recurrent neural network, long short-term memory, deep neural network, deep belief network, and memory network. We compare their futures, advantages, and disadvantages. Also, we point out the main problems of these methods, which may be worthy of careful studies in the future. Finally, we also examine the application of deep learning in multimodal sentiment analysis and aspect-level sentiment analysis.

Keywords: document analysis, deep learning, multimodal sentiment analysis, natural language processing

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Authors: Farshid Fathinia

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Numerical computation of unsteady laminar three-dimensional natural convection and entropy generation in an inclined cubical trapezoidal air-filled cavity is performed for the first time in this work. The vertical right and left sidewalls of the cavity are maintained at constant cold temperatures. The lower wall is subjected to a constant hot temperature, while the upper one is considered insulated. Computations are performed for Rayleigh numbers varied as 103 ≤ Ra ≤ 105, while the trapezoidal cavity inclination angle is varied as 0° ≤ ϕ ≤ 180°. Prandtl number is considered constant at Pr = 0.71. The second law of thermodynamics is applied to obtain thermodynamic losses inside the cavity due to both heat transfer and fluid friction irreversibilities. The variation of local and average Nusselt numbers are presented and discussed.While, streamlines, isotherms and entropy contours are presented in both two and three-dimensional pattern. The results show that when the Rayleigh number increases, the flow patterns are changed especially in three-dimensional results and the flow circulation increases. Also, the inclination angle effect on the total entropy generation becomes insignificant when the Rayleigh number is low.Moreover, when the Rayleigh number increases the average Nusselt number increases.

Keywords: transient natural convection, trapezoidal cavity, three-dimensional flow, entropy generation, second law

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Authors: Mamoon Masud, Suleman Mazhar

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Indus river basin’s irrigation system in Pakistan is extremely complex, spanning over 50,000 km. Maintenance and monitoring of this demands enormous resources. This paper describes the development of a streamlined and low-cost autonomous underwater vehicle (AUV) for the monitoring of irrigation canals including water quality monitoring and water theft detection. The vehicle is a hovering-type AUV, designed mainly for monitoring irrigation canals, with fully documented design and open source code. It has a length of 17 inches, and a radius of 3.5 inches with a depth rating of 5m. Multiple sensors are present onboard the AUV for monitoring water quality parameters including pH, turbidity, total dissolved solids (TDS) and dissolved oxygen. A 9-DOF Inertial Measurement Unit (IMU), GY-85, is used, which incorporates an Accelerometer (ADXL345), a Gyroscope (ITG-3200) and a Magnetometer (HMC5883L). The readings from these sensors are fused together using directional cosine matrix (DCM) algorithm, providing the AUV with the heading angle, while a pressure sensor gives the depth of the AUV. 2 sonar-based range sensors are used for obstacle detection, enabling the vehicle to align itself with the irrigation canals edges. 4 thrusters control the vehicle’s surge, heading and heave, providing 3 DOF. The thrusters are controlled using a proportional-integral-derivative (PID) feedback control system, with heading angle and depth being the controller’s input and the thruster motor speed as the output. A flow sensor has been incorporated to monitor canal water level to detect water-theft event in the irrigation system. In addition to water theft detection, the vehicle also provides information on water quality, providing us with the ability to identify the source(s) of water contamination. Detection of such events can provide useful policy inputs for improving irrigation efficiency and reducing water contamination. The AUV being low cost, small sized and suitable for autonomous maneuvering, water level and quality monitoring in the irrigation canals, can be used for irrigation network monitoring at a large scale.

Keywords: the autonomous underwater vehicle, irrigation canal monitoring, water quality monitoring, underwater line tracking

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Authors: Dong Wook Lee, Adrian Mistreanu

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The authors have studied a method for analyzing containment and penetration using an explicit nonlinear Finite Element Analysis. This method may be used in the stage of concept design for the protection of external configurations or components of aircraft engines and nuclear power plant structures. This paper consists of the modeling method, the results obtained from the method and the comparison of the results with those calculated from simple analytical method. It shows that the containment capability obtained by proposed method matches well with analytically calculated containment capability.

Keywords: computer aided engineering, containment analysis, finite element analysis, impact analysis, penetration analysis

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Authors: Kristin Thooft

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— Increasing pressure to manage healthcare costs has resulted in shifting care towards ambulatory settings and is driving a focus on cost transparency. There are few nurse staffing to workload models developed for ambulatory settings, less for multi-specialty clinics. Of the existing models, few have been evaluated against outcomes to understand any impact. This evaluation took place after the AWARD model for nurse staffing to workload was implemented in a multi-specialty clinic at a regional healthcare system in the Midwest. The multi-specialty clinic houses 26 medical and surgical specialty practices. The AWARD model was implemented in two specialty practices in October 2020. Donabedian’s Structure-Process-Outcome (SPO) model was used to evaluate outcomes based on changes to the structure and processes of care provided. The AWARD model defined and quantified the processes, recommended changes in the structure of day-to-day nurse staffing. Cost of care per patient visit, total visits, a total nurse performed visits used as structural and process measures, influencing the outcomes of cost of care and access to care. Independent t-tests were used to compare the difference in variables pre-and post-implementation. The SPO model was useful as an evaluation tool, providing a simple framework that is understood by a diverse care team. No statistically significant changes in the cost of care, total visits, or nurse visits were observed, but there were differences. Cost of care increased and access to care decreased. Two weeks into the post-implementation period, the multi-specialty clinic paused all non-critical patient visits due to a second surge of the COVID-19 pandemic. Clinic nursing staff was re-allocated to support the inpatient areas. This negatively impacted the ability of the Nurse Manager to utilize the AWARD model to plan daily staffing fully. The SPO framework could be used for the ongoing assessment of nurse staffing performance. Additional variables could be measured, giving a complete picture of the impact of nurse staffing. Going forward, there must be a continued focus on the outcomes of care and the value of nursing

Keywords: ambulatory, clinic, evaluation, outcomes, staffing, staffing model, staffing to workload

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Authors: Bao Liu, Maarten Vanierschot, Frank Buysschaert

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The present work examines the wake dynamics of a rim-driven thruster (RDT) with Computational Fluid Dynamics (CFD). Unsteady Reynolds-averaged Navier-Stokes (URANS) equations were solved in the commercial solver ANSYS Fluent in combination with the SST k-ω turbulence model. The application of the moving reference frame (MRF) and sliding mesh (SM) approach to handling the rotational movement of the propeller were compared in the transient simulations. Validation and verification of the numerical model was performed to ensure numerical accuracy. Two representative scenarios were considered, i.e., the bollard condition (J=0) and a very light loading condition(J=0.7), respectively. From the results, it’s confirmed that compared to the SM method, the MRF method is not suitable for resolving the unsteady flow features as it only gives the general mean flow but smooths out lots of characteristic details in the flow field. By evaluating the simulation results with the SM technique, the instantaneous wake flow field under both conditions is presented and analyzed, most notably the helical vortex structure. It’s observed from the results that the tip vortices, blade shed vortices, and hub vortices are present in the wake flow field and convect downstream in a highly non-linear way. The shear layer vortices shedding from the duct displayed a strong interaction with the distorted tip vortices in an irregularmanner.

Keywords: computational fluid dynamics, rim-driven thruster, sliding mesh, wake dynamics

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Authors: Anuta Mukherjee, Saswati Mukherjee

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Data is growing everyday. Social networking sites such as Twitter are becoming an integral part of our daily lives, contributing a large increase in the growth of data. It is a rich source especially for sentiment detection or mining since people often express honest opinion through tweets. However, although sentiment analysis is a well-researched topic in text, this analysis using Twitter data poses additional challenges since these are unstructured data with abbreviations and without a strict grammatical correctness. We have employed collision theory to achieve sentiment analysis in Twitter data. We have also incorporated discourse analysis in the collision theory based model to detect accurate sentiment from tweets. We have also used the retweet field to assign weights to certain tweets and obtained the overall weightage of a topic provided in the form of a query. Hadoop has been exploited for speed. Our experiments show effective results.

Keywords: sentiment analysis, twitter, collision theory, discourse analysis

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Authors: Prachi Abhishek Dave, Abhishek Dave

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Purpose: The purpose of this study is to elucidate the role of intravitreal injection Aflibercept in managing complex cases of Wet Age Related Macular Degeneration (ARMD) and the gratifying visual recovery experienced with a minimally invasive procedure. Methods: A 73-year-old gentleman presented with a drop in vision in the left eye for 25 days. On examination, his best corrected visual acuity (BCVA) in the Right eye (OD) was 6/60, and finger counting close to face in the Left eye (OS). On multimodal imaging, he was diagnosed to have a scarred Wet ARMD in OD and an active Wet ARMD with a large subretinal bleed secondary to Wet ARMD in OS. Treatment management options included monotherapy with an Injection Aflibercept or an intravitreal gas injection with tPA followed by Injection Aflibercept. Considering his one-eyed status, the patient decided to go for Aflibercept monotherapy. Results: After 3 monthly injections of injection Aflibercept, the subretinal bleed reduced, the subretinal fluid resolved, and his vision in OS improved to 6/9. He is on a regular follow-up and has not needed any further injections in OS and he maintains 6/9 vision. Conclusions: Conventional treatment guidelines for a large subretinal bleed dictate the use of gas followed by intravitreal Injection Aflibercept. However, gas has its own limitations of causing a rise in intraocular pressure and a transient loss of vision, which is particularly troublesome in one-eyed patients. Injection Aflibercept offers a much safer, less invasive, and elegant treatment option for such patients with equally good or even better visual outcomes.

Keywords: wet ARMD, subretinal bleed, intravitreal injections, aflibercept, EYELEA, intravitreal gas

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Authors: J. R. Wang, H. C. Chen, C. Shih, S. W. Chen, J. H. Yang, Y. Chiang

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In this study, we focus on the establishment of the analysis model for Maanshan PWR nuclear power plant (NPP) by using RADTRAD and SNAP codes with the FSAR, manuals, and other data. In order to evaluate the cumulative dose at the Exclusion Area Boundary (EAB) and Low Population Zone (LPZ) outer boundary, Maanshan NPP RADTRAD/SNAP model was used to perform the analysis of the DBA LOCA case. The analysis results of RADTRAD were similar to FSAR data. These analysis results were lower than the failure criteria of 10 CFR 100.11 (a total radiation dose to the whole body, 250 mSv; a total radiation dose to the thyroid from iodine exposure, 3000 mSv).

Keywords: RADionuclide, transport, removal, and dose estimation (RADTRAD), symbolic nuclear analysis package (SNAP), dose, PWR

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Authors: Michael Gidey Gebru

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Most of Data Envelopment Analysis models operate in a static environment with input and output parameters that are chosen by deterministic data. However, due to ambiguity brought on shifting market conditions, input and output data are not always precisely gathered in real-world scenarios. Fuzzy numbers can be used to address this kind of ambiguity in input and output data. Therefore, this work aims to expand crisp Data Envelopment Analysis into Data Envelopment Analysis with fuzzy environment. In this study, the input and output data are regarded as fuzzy triangular numbers. Then, the Data Envelopment Analysis model with fuzzy environment is solved using a multi-objective method to gauge the Decision Making Units' efficiency. Finally, the developed Data Envelopment Analysis model is illustrated with an application on real data 50 educational institutions.

Keywords: efficiency, Data Envelopment Analysis, fuzzy, higher education, input, output

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Authors: Abdelghani Qadem

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Urbanization in Morocco has ushered in profound shifts in hydrological dynamics, presenting a spectrum of challenges and avenues for sustainable water management. This abstract delves into the nuances of urban hydrology in Morocco, spotlighting the ramifications of rapid urban expansion, the imprint of climate change, and the imperative for cohesive water management strategies. The swift urban sprawl across Morocco has engendered a surge in impermeable surfaces, reshaping the natural hydrological cycle and amplifying quandaries such as urban inundations and water scarcity. Moreover, the specter of climate change looms large, heralding alterations in precipitation regimes and a heightened frequency of extreme meteorological events, thus compounding the hydrological conundrum. However, amidst these challenges, urban hydrology in Morocco also unfolds vistas of innovation and sustainability. The integration of green infrastructure, encompassing solutions like permeable pavements and vegetated roofs, emerges as a linchpin in ameliorating the hydrological imbalances wrought by urbanization, fostering infiltration, and curbing surface runoff. Additionally, embracing the tenets of water-sensitive urban design promises to fortify water efficiency and resilience in urban landscapes. Effectively navigating urban hydrology in Morocco mandates a cross-disciplinary approach that interweaves urban planning, water resource governance, and climate resilience strategies. A collaborative ethos, bridging governmental entities, academic institutions, and grassroots communities, assumes paramount importance in crafting and executing comprehensive solutions that grapple with the intricate interplay of urbanization, hydrology, and climate dynamics. In summation, confronting the labyrinthine challenges of urban hydrology in Morocco necessitates proactive strides toward fostering sustainable urban growth and bolstering resilience to climate vagaries. By embracing cutting-edge technologies and embracing an ethos of integrated water management, Morocco can forge a path toward a more water-secure and resilient urban future.

Keywords: urban hydrology, Morocco, urbanization, climate change, water management, green infrastructure, sustainable development

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Authors: Laidi Babouri

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The remarkable properties of steel, such as hardness and impact resistance, motivate their use in the automotive manufacturing industry. However, due to the very vulnerable environmental conditions of use, the steel that makes up the car body can corrode. This situation is motivating more and more automobile manufacturers to develop research to develop processes minimizing the rate of degradation of the physicomechanical properties of these materials. The present work falls within this perspective; it presents the results of a research study focused on the use of synthetic wax for the protection of steel, type XES (DC04), against corrosion in aggressive environments. The media used in this study are an acid medium with a pH=5.6, a 3% chloride medium, and a dry medium. Evaluation of the protective power of synthetic wax in different environments was carried out using mass loss techniques (immersion), completed by electrochemical techniques (stationary and transient). The results of the immersion of the steel samples, with a surface area of (1.44 cm²), in the various media, for a period of 30 days, using the immersion technique, showed high protective efficiency of synthetic wax in acidic and saline environments, with a lesser degree in a dry environment. Moreover, the study of the protective power, using electrochemical techniques, confirmed the results obtained in static mode (loss of mass), the protective efficiency of synthetic wax, against the corrosion of steel, in different environments, which reaches a maximum rate of 99.87% in a saline environment.

Keywords: corrosion, steel, industrial wax, environment, mass loss, electrochemical techniques

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Authors: H. Mansor, S.B. Mohd-Noor, N. I. Othman, N. Tazali, R. I. Boby

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This paper presents an improved robust Proportional Derivative controller for a 3-Degree-of-Freedom (3-DOF) bench-top helicopter by using adaptive methodology. Bench-top helicopter is a laboratory scale helicopter used for experimental purposes which is widely used in teaching laboratory and research. Proportional Derivative controller has been developed for a 3-DOF bench-top helicopter by Quanser. Experiments showed that the transient response of designed PD controller has very large steady state error i.e., 50%, which is very serious. The objective of this research is to improve the performance of existing pitch angle control of PD controller on the bench-top helicopter by integration of PD controller with adaptive controller. Usually standard adaptive controller will produce zero steady state error; however response time to reach desired set point is large. Therefore, this paper proposed an adaptive with deadbeat algorithm to overcome the limitations. The output response that is fast, robust and updated online is expected. Performance comparisons have been performed between the proposed self-tuning deadbeat PD controller and standard PD controller. The efficiency of the self-tuning dead beat controller has been proven from the tests results in terms of faster settling time, zero steady state error and capability of the controller to be updated online.

Keywords: adaptive control, deadbeat control, bench-top helicopter, self-tuning control

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Authors: Lancer Scott

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Purpose: High quality clinical disaster medicine requires providers working collaboratively to care for multiple patients in chaotic environments; however, many providers lack adequate training. To address this deficit, we created a competency-based, 5-hour Emergency Preparedness Training (EPT) curriculum using didactics, small-group discussion, and kinetic learning. The goal was to evaluate the effect of a short course on improving provider knowledge, confidence and skills in disaster scenarios. Methods: Diverse groups of medical university students, health care professionals, and community members were enrolled between 2011 and 2014. The course consisted of didactic lectures, small group exercises, and two live, multi-patient mass casualty incident (MCI) scenarios. The outcome measures were based on core competencies and performance objectives developed by a curriculum task force and assessed via trained facilitator observation, pre- and post-testing, and a course evaluation. Results: 708 participants completed were trained between November 2011 and August 2014, including 49.9% physicians, 31.9% medical students, 7.2% nurses, and 11% various other healthcare professions. 100% of participants completed the pre-test and 71.9% completed the post-test, with average correct answers increasing from 39% to 60%. Following didactics, trainees met 73% and 96% of performance objectives for the two small group exercises and 68.5% and 61.1% of performance objectives for the two MCI scenarios. Average trainee self-assessment of both overall knowledge and skill with clinical disasters improved from 33/100 to 74/100 (overall knowledge) and 33/100 to 77/100 (overall skill). The course assessment was completed by 34.3% participants, of whom 91.5% highly recommended the course. Conclusion: A relatively short, intensive EPT course can improve the ability of a diverse group of disaster care providers to respond effectively to mass casualty scenarios.

Keywords: clinical disaster medicine, training, hospital preparedness, surge capacity, education, curriculum, research, performance, training, student, physicians, nurses, health care providers, health care

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Authors: Komon Paisal

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The purposes of this research were (1) to create a learning activity for constructivism, (2) study the Mathematical Analysis courses learning achievement, and (3) study students’ attitude toward the learning activity for constructivism. The samples in this study were divided into 2 parts including 3 Mathematical Analysis courses instructors of Suan Sunandha Rajabhat University who provided basic information and attended the seminar and 17 Mathematical Analysis courses students who were studying in the academic and engaging in the learning activity for constructivism. The research instruments were lesson plans constructivism, subjective Mathematical Analysis courses achievement test with reliability index of 0.8119, and an attitude test concerning the students’ attitude toward the Mathematical Analysis courses learning activity for constructivism. The result of the research show that the efficiency of the Mathematical Analysis courses learning activity for constructivism is 73.05/72.16, which is more than expected criteria of 70/70. The research additionally find that the average score of learning achievement of students who engaged in the learning activities for constructivism are equal to 70% and the students’ attitude toward the learning activity for constructivism are at the medium level.

Keywords: constructivism, learning management, mathematics analysis courses, learning activity

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Authors: Felipe Magalhaes Lemos

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Functional behavior analysis is a procedure that has been studied for several decades by behavior analysts. In Brazil, we still have few studies in the area, so it was decided to carry out a systematic review of the articles published in the area by Brazilians. A search was done on the following scientific article registration sites: PsycINFO, ERIC, ISI Web of Science, Virtual Health Library. The research includes (a) peer-reviewed studies that (b) have been carried out in Brazil containing (c) functional assessment as a pre-treatment through (d) experimental procedures, direct or indirect observation and measurement of behavior problems (e) demonstrating a relationship between environmental events and behavior. During the review, 234 papers were found; however, only 9 were included in the final analysis. Of the 9 articles extracted, only 2 presented functional analysis procedures with manipulation of environmental variables, while the other 7 presented different procedures for a descriptive behavior assessment. Only the two studies using "functional analysis" used graphs to demonstrate the prevalent function of the behavior. Other studies described procedures and did not make clear the causal relationship between environment and behavior. There is still confusion in Brazil regarding the terms "functional analysis", "descriptive assessment" and "contingency analysis," which are generally treated in the same way. This study shows that few articles are published with a focus on functional analysis in Brazil.

Keywords: behavior, contingency, descriptive assessment, functional analysis

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Authors: Claudia Aguilar, Amen Farooq

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Among other cities in the United States, the city of Atlanta is experiencing levels of growth that surpass anything we have witnessed in the last century. With the surge of population influx, the available housing is practically bursting at the seams. Supply is low, and demand is high. In effect, the average one-bedroom apartment runs for 1,800 dollars per month. The city is desperately seeking new opportunities to provide affordable housing at an expeditious rate. This has been made evident by the recent updates to the city’s zoning. With the recent influx in the housing market, young professionals, in particular millennials, are desperately looking for alternatives to stay within the city. To remedy Atlanta’s affordable housing crisis, the city of Atlanta is planning to introduce 40 thousand of new affordable housing units by 2026. To achieve the urgent need for more affordable housing, the architectural response needs to adapt to overcome this goal. A method that has proven successful in modern housing is to practice modular means of development. A method that has been constrained to the dimensions of the max load for an eighteen-wheeler. This approach has diluted the architect’s ability to produce site-specific, informed design and rather contributes to the “cookie cutter” stigma that the method has been labeled with. This thesis explores the design methodology for modular housing by revisiting its constructability and adaptability. This research focuses on a modular housing type that could break away from the constraints of transport and deliver adaptive reconfigurable assemblies. The adaptive assemblies represent an integrated design strategy for assembling the future of affordable dwelling units. The goal is to take advantage of a component-based system and explore a scalable solution to modular housing. This proposal aims specifically to design a kit of parts that are made to be easily transported and assembled but also gives the ability to customize the use of components to benefit all unique conditions. The benefits of this concept could include decreased construction time, cost, on-site labor, and disruption while providing quality housing with affordable and flexible options.

Keywords: adaptive assemblies, modular architecture, adaptability, constructibility, kit of parts

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Authors: Muhammad Waqar Aziz, Syed Abdul Baqi Shah

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The execution time analysis is fundamental to the successful design and execution of real-time embedded software. In such analysis, the Worst-Case Execution Time (WCET) of a program is a key measure, on the basis of which system tasks are scheduled. The WCET analysis of embedded software is also needed for system understanding and to guarantee its behavior. WCET analysis can be performed statically (without executing the program) or dynamically (through measurement). Traditionally, research on the WCET analysis assumes sequential code running on single-core platforms. However, as computation is steadily moving towards using a combination of parallel programs and multi-core hardware, new challenges in WCET analysis need to be addressed. In this article, we report our experiences of performing the WCET analysis of Parallel Embedded Software (PES) running on multi-core platform. The primary purpose was to investigate how WCET estimates of PES can be computed statically, and how they can be derived dynamically. Our experiences, as reported in this article, include the challenges we faced, possible suggestions to these challenges and the workarounds that were developed. This article also provides observations on the benefits and drawbacks of deriving the WCET estimates using the said methods and provides useful recommendations for further research in this area.

Keywords: embedded software, worst-case execution-time analysis, static flow analysis, measurement-based analysis, parallel computing

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Authors: N. P. Patidar, J. Earnest, Laxmikant Nagar, Akshay Sharma

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This paper describes the diligence of a new input signal based fuzzy power system stabilizer in multi-machine power system. Instead of conventional input pairs like speed deviation (∆ω) and derivative of speed deviation i.e. acceleration (∆ω ̇) or speed deviation and accelerating power deviation of each machine, in this paper, deviation of active power through the tie line colligating two areas is used as one of the inputs to the fuzzy logic controller in concurrence with the speed deviation. Fuzzy Logic has the features of simple concept, easy effectuation, and computationally efficient. The advantage of this input is that, the same signal can be fed to each of the fuzzy logic controller connected with each machine. The simulated system comprises of two fully symmetrical areas coupled together by two 230 kV lines. Each area is equipped with two superposable generators rated 20 kV/900MVA and area-1 is exporting 413 MW to area-2. The effectiveness of the proposed control scheme has been assessed by performing small signal stability assessment and transient stability assessment. The proposed control scheme has been compared with a conventional PSS. Digital simulation is used to demonstrate the performance of fuzzy logic controller.

Keywords: Power System Stabilizer (PSS), small signal stability, inter-area oscillation, fuzzy logic controller, membership function, rule base

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Authors: J. Naalamle Amissah, Dorcas Osei‐Safo, C. M. Asare, Benjamin Missah‐Assihene, Eric. Y. Danquah, Ivan Addae‐Mensah

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Medicinal plants serve as a reservoir of active ingredients for the treatment of common ailments such as cancer, malaria and diabetes. With the recent wave of health consciousness and reliance on plant based medicines, the demand for medicinal plants has increased considerably. This surge in medicinal plant use has raised great concern amongst key players (herbalist, collectors, conservationist and researchers) along the value chain about the sustainability of the raw material. The over reliance on wild crafting as a means to obtain the raw material spells doom for several of Africa’s native medicinal plant species. In this study domestication protocols for the cultivation of Cryptolepis sanguinolenta (CS), a medicinal plant used in the treatment of malaria were developed. Initial surveys were conducted, using questionnaires comprising of open and close ended questions, to gather information that would inform the domestication and cultivation of the species. A field study was then conducted to determine the plant’s cropping cycle and the effect of staking and plant age on the active ingredient (cryptolepine) concentration in its roots. Results of the survey confirmed the demand for the raw material and threw more light on the harvesting methods and intensity of CS collection from the wild. Cryptolepine concentration was found to be highest (~1.84 mg/100 mg of root material) at 289 days after planting (DAP) which coincided with the peak of root dry weight (52.8 g), signifying the best time for root harvest. Staking was found to be important for seed production. The first 105 DAP were characterized by low yields of root dry weight (13.5 g), followed by a period of rapid growth in which the root dry weight increased almost linearly until 289 DAP. Although dry matter partitioned to the vines increased towards the end of the experimental period (60%), dry matter partitioned to the roots remained fairly constant (30%) throughout the experimental period. Cryptolepine was found to increase as the plant aged and the practice of staking CS promoted pod formation. A suitable cropping cycle for the cultivation of CS was also developed.

Keywords: domestication, staking, conservation, wild harvesting

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Authors: Ameur Abdelkader, Abed Bouarfa Hafida

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Since its inception, predictive analysis has revolutionized the IT industry through its robustness and decision-making facilities. It involves the application of a set of data processing techniques and algorithms in order to create predictive models. Its principle is based on finding relationships between explanatory variables and the predicted variables. Past occurrences are exploited to predict and to derive the unknown outcome. With the advent of big data, many studies have suggested the use of predictive analytics in order to process and analyze big data. Nevertheless, they have been curbed by the limits of classical methods of predictive analysis in case of a large amount of data. In fact, because of their volumes, their nature (semi or unstructured) and their variety, it is impossible to analyze efficiently big data via classical methods of predictive analysis. The authors attribute this weakness to the fact that predictive analysis algorithms do not allow the parallelization and distribution of calculation. In this paper, we propose to extend the predictive analysis algorithm, Classification And Regression Trees (CART), in order to adapt it for big data analysis. The major changes of this algorithm are presented and then a version of the extended algorithm is defined in order to make it applicable for a huge quantity of data.

Keywords: predictive analysis, big data, predictive analysis algorithms, CART algorithm

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Authors: Rahman Davtalab

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Florida is one of the most vulnerable states to natural disasters among the 50 states of the USA. The state exposed by tropical storms, hurricanes, storm surge, landslide, etc. Besides, the mentioned natural phenomena, global warming, sea-level rise, and other anthropogenic environmental changes make a very complicated and unpredictable system for decision-makers. In this study, we tried to highlight the effects of climate change and sea-level rise on surface water and groundwater systems for three different geographical locations in Florida; Main Canal of Jacksonville Beach (in the northeast of Florida adjacent to the Atlantic Ocean), Grace Lake in central Florida, far away from surrounded coastal line, and Mc Dill in Florida and adjacent to Tampa Bay and Mexican Gulf. An integrated hydrologic and hydraulic model was developed and simulated for all three cases, including surface water, groundwater, or a combination of both. For the case study of Main Canal-Jacksonville Beach, the investigation showed that a 76 cm sea-level rise in time horizon 2060 could increase the flow velocity of the tide cycle for the main canal's outlet and headwater. This case also revealed how the sea level rise could change the tide duration, potentially affecting the coastal ecosystem. As expected, sea-level rise can raise the groundwater level. Therefore, for the Mc Dill case, the effect of groundwater rise on soil storage and the performance of stormwater retention ponds is investigated. The study showed that sea-level rise increased the pond’s seasonal high water up to 40 cm by time horizon 2060. The reliability of the retention pond is dropped from 99% for the current condition to 54% for the future. The results also proved that the retention pond could not retain and infiltrate the designed treatment volume within 72 hours, which is a significant indication of increasing pollutants in the future. Grace Lake case study investigates the effects of climate change on groundwater recharge. This study showed that using the dynamically downscaled data of the groundwater recharge can decline up to 24% by the mid-21st century.

Keywords: groundwater, surface water, Florida, retention pond, tide, sea level rise

Procedia PDF Downloads 179

Authors: Ya Lv

Abstract:

This article presents the application of the semi-analytic method (SAM) in the thermal management solution (TMS) of the energy storage system (ESS). The TMS studied in this work is fluid cooling. In fluid cooling, both effective heat conduction and heat convection are indispensable due to the heat transfer from solid to fluid. Correspondingly, an efficient TMS requires a design investigation of the following parameters: fluid inlet temperature, ESS initial temperature, fluid flow rate, working c rate, continuous working time, and materials properties. Their variation induces a change of thermal performance in the battery module, which is usually evaluated by numerical simulation. Compared to complicated computation resources and long computation time in simulation, the SAM is developed in this article to predict the thermal influence within a few seconds. In SAM, a fast prediction model is reckoned by combining numerical simulation with theoretical/empirical equations. The SAM can explore the thermal effect of boundary parameters in both steady-state and transient heat transfer scenarios within a short time. Therefore, the SAM developed in this work can simplify the design cycle of TMS and inspire more possibilities in TMS design.

Keywords: semi-analytic method, fast prediction model, thermal influence of boundary parameters, energy storage system

Procedia PDF Downloads 145

Authors: Ursula Das, Manavjit Singh Dhindsa, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

Abstract:

Wildland fires, also known as forest fires or wildfires, are exhibiting an alarming surge in frequency in recent times, further adding to its perennial global concern. Forest fires often lead to devastating consequences ranging from loss of healthy forest foliage and wildlife to substantial economic losses and the tragic loss of human lives. Despite the existence of substantial literature on the detection of active forest fires, numerous potential research avenues in forest fire management, such as preventative measures and ancillary effects of forest fires, remain largely underexplored. This paper undertakes a systematic review of these underexplored areas in forest fire research, meticulously categorizing them into distinct phases, namely pre-fire, during-fire, and post-fire stages. The pre-fire phase encompasses the assessment of fire risk, analysis of fuel properties, and other activities aimed at preventing or reducing the risk of forest fires. The during-fire phase includes activities aimed at reducing the impact of active forest fires, such as the detection and localization of active fires, optimization of wildfire suppression methods, and prediction of the behavior of active fires. The post-fire phase involves analyzing the impact of forest fires on various aspects, such as the extent of damage in forest areas, post-fire regeneration of forests, impact on wildlife, economic losses, and health impacts from byproducts produced during burning. A comprehensive understanding of the three stages is imperative for effective forest fire management and mitigation of the impact of forest fires on both ecological systems and human well-being. Artificial intelligence and machine learning (AI/ML) methods have garnered much attention in the cyber-physical systems domain in recent times leading to their adoption in decision-making in diverse applications including disaster management. This paper explores the current state of AI/ML applications for managing the activities in the aforementioned phases of forest fire. While conventional machine learning and deep learning methods have been extensively explored for the prevention, detection, and management of forest fires, a systematic classification of these methods into distinct AI research domains is conspicuously absent. This paper gives a comprehensive overview of the state of forest fire research across more recent and prominent AI/ML disciplines, including big data, classical machine learning, computer vision, explainable AI, generative AI, natural language processing, optimization algorithms, and time series forecasting. By providing a detailed overview of the potential areas of research and identifying the diverse ways AI/ML can be employed in forest fire research, this paper aims to serve as a roadmap for future investigations in this domain.

Keywords: artificial intelligence, computer vision, deep learning, during-fire activities, forest fire management, machine learning, pre-fire activities, post-fire activities

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Authors: Joanne Danker, Iva Strnadová, Therese Cumming

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As a consequence of the increased evidence of the bi-directional relationship between student well-being and positive educational outcomes, there has been a surge in the number of research studies dedicated to understanding the notion of student well-being and the ways to enhance it. In spite of these efforts, the concept of student well-being remains elusive. Additionally, studies on student well-being mainly consulted adults' perspectives and failed to take into account students' views, which if considered, could contribute to a clearer understanding of the complex concept of student well-being. Furthermore, there is a lack of studies focusing on the well-being of students with autism spectrum disorder (ASD), and these students continue to fare worse in post-school outcomes as compared to students without disabilities, indicating a significant gap in the current research literature. Findings from research conducted on students without disabilities may not be applicable to students with ASD as their educational experiences may differ due to the characteristics associated with ASD. Thus, the purpose of this study was to explore how students with ASD, their parents, and teachers conceptualise student well-being. It also aims to identify the barriers and assets of the well-being of these students. To collect data, 19 teachers and 11 parents participated in interviews while 16 high school students with ASD were involved in a photovoice project regarding their well-being in school. Grounded theory approaches such as open and axial coding, memo-writing, diagramming, and making constant comparisons were adopted to analyse the data. All three groups of participants conceptualised student well-being as a multidimensional construct consisting of several domains. These domains were relationships, engagement, positive/negative emotions, and accomplishment. Three categories of barriers were identified. These were environmental, attitudes and behaviours of others, and impact of characteristics associated with ASD. The identified internal assets that could contribute to student well-being were acceptance, resilience, self-regulation, and ability to work with others. External assets were knowledgeable and inclusive school community, and having access to various school programs and resources. It is crucial that schools and policymakers provide ample resources and programs to adequately support the development of each identified domain of student well-being. This could in turn enhance student well-being and lead to more successful educational outcomes for students with ASD.

Keywords: autism spectrum disorder, grounded theory approach, school experiences, student well-being

Procedia PDF Downloads 282

Authors: Boo-Sung Koh, Seung-Eock Kim

Abstract:

In this paper, a direct design using a nonlinear inelastic analysis is suggested. Also, this paper compares the load carrying capacity obtained by a nonlinear inelastic analysis with experiment results to verify the accuracy of the results. The allowable stress design results of a railroad through a plate girder bridge and the safety factor of the nonlinear inelastic analysis were compared to examine the safety performance. As a result, the load safety factor for the nonlinear inelastic analysis was twice as high as the required safety factor under the allowable stress design standard specified in the civil engineering structure design standards for urban magnetic levitation railways, which further verified the advantages of the proposed direct design method.

Keywords: direct design, nonlinear inelastic analysis, residual stress, initial geometric imperfection

Procedia PDF Downloads 525

Authors: Maria Fernanda Ordoñez Martinez, Alvaro Mauricio Montenegro

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This work presents a statistical methodology for measuring and founding constructs in Latent Semantic Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations present on Item Response Theory. More precisely, we propose initially reducing dimensionality with specific use of Principal Component Analysis for the linguistic data and then, producing axes of groups made from a clustering analysis of the semantic data. This approach allows the user to give meaning to previous clusters and found the real latent structure presented by data. The methodology is applied in a set of real semantic data presenting impressive results for the coherence, speed and precision.

Keywords: semantic analysis, factorial analysis, dimension reduction, penalized logistic regression

Procedia PDF Downloads 436