Search results for: real time pest tracking

Authors: Yi-Fan Lin, Peter P. Howley, Frank A. Tuyl

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Quality measurement and reporting systems are used in healthcare internationally. In Australia, the Australian Council on Healthcare Standards records and reports hundreds of clinical indicators (CIs) nationally across the healthcare system. These CIs are measures of performance in the clinical setting, and are used as a screening tool to help assess whether a standard of care is being met. Existing analysis and reporting of these CIs incorporate Bayesian methods to address sampling variation; however, such assessments are retrospective in nature, reporting upon the previous six or twelve months of data. The use of Bayesian methods within statistical process control for monitoring systems is an important pursuit to support more timely decision-making. Our research has developed and assessed a new graphical monitoring tool, similar to a control chart, based on the beta-binomial posterior predictive (BBPP) distribution to facilitate the real-time assessment of health care organizational performance via CIs. The BBPP charts have been compared with the traditional Bernoulli CUSUM (BC) chart by simulation. The more traditional “central” and “highest posterior density” (HPD) interval approaches were each considered to define the limits, and the multiple charts were compared via in-control and out-of-control average run lengths (ARLs), assuming that the parameter representing the underlying CI rate (proportion of cases with an event of interest) required estimation. Preliminary results have identified that the BBPP chart with HPD-based control limits provides better out-of-control run length performance than the central interval-based and BC charts. Further, the BC chart’s performance may be improved by using Bayesian parameter estimation of the underlying CI rate.

Keywords: average run length (ARL), bernoulli cusum (BC) chart, beta binomial posterior predictive (BBPP) distribution, clinical indicator (CI), healthcare organization (HCO), highest posterior density (HPD) interval

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Authors: Lancelot Boulet, Pierre Benard, Ghislain Lartigue, Vincent Moureau, Nicolas Chauvet, Sheddia Didorally

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International aeronautic standards demand a fire certification for engines that demonstrate their resistance. This demonstration relies on tests performed with prototype engines in the late stages of the development. Hardest tests require to place a kerosene standardized flame in front of the engine casing during a given time with imposed temperature and heat flux. The purpose of this work is to provide a better characterization of a kerosene/air certification burner in order to minimize the risks of test failure. A first Large-Eddy Simulation (LES) study of the certification burner permitted to model and simulate this burner, including both adiabatic and Conjugate Heat Transfer (CHT) computations. Carried out on unstructured grids with 40 million tetrahedral cells, using the finite-volume YALES2 code, spray combustion, forced convection on walls and conduction in the solid parts of the burner were coupled to achieve a detailed description of heat transfer. It highlighted the fact that conduction inside the solid has a real impact on the flame topology and the combustion regime. However, in the absence of radiative heat transfer, unrealistic temperature of the equipment was obtained. The aim of the present study is to include the radiative heat transfer in order to reach the same temperature given by experimental measurements. First, various test-cases are conducted to validate the coupling between the different heat solvers. Then, adiabatic case, CHT case, as well as CHT including radiative transfer are studied and compared. The LES model is finally applied to investigate the heat transfer in a flame impaction configuration. The aim is to progress on fire test modeling so as to reach a good confidence level as far as success of the certification test is concerned.

Keywords: conjugate heat transfer, fire resistance test, large-eddy simulation, radiative transfer, turbulent combustion

Procedia PDF Downloads 223

Authors: Yosep Chong, Yejin Kim, Jingyun Choi, Hwanjo Yu, Eun Jung Lee, Chang Suk Kang

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For the past decades, immunohistochemistry (IHC) has been playing an important role in the diagnosis of human neoplasms, by helping pathologists to make a clearer decision on differential diagnosis, subtyping, personalized treatment plan, and finally prognosis prediction. However, the IHC performed in various tumors of daily practice often shows conflicting and very challenging results to interpret. Even comprehensive diagnosis synthesizing clinical, histologic and immunohistochemical findings can be helpless in some twisted cases. Another important issue is that the IHC data is increasing exponentially and more and more information have to be taken into account. For this reason, we reached an idea to develop an expert supporting system to help pathologists to make a better decision in diagnosing human neoplasms with IHC results. We gave probabilistic decision tree algorithm and tested the algorithm with real case data of lymphoid neoplasms, in which the IHC profile is more important to make a proper diagnosis than other human neoplasms. We designed probabilistic decision tree based on Bayesian theorem, program computational process using MATLAB (The MathWorks, Inc., USA) and prepared IHC profile database (about 104 disease category and 88 IHC antibodies) based on WHO classification by reviewing the literature. The initial probability of each neoplasm was set with the epidemiologic data of lymphoid neoplasm in Korea. With the IHC results of 131 patients sequentially selected, top three presumptive diagnoses for each case were made and compared with the original diagnoses. After the review of the data, 124 out of 131 were used for final analysis. As a result, the presumptive diagnoses were concordant with the original diagnoses in 118 cases (93.7%). The major reason of discordant cases was that the similarity of the IHC profile between two or three different neoplasms. The expert supporting system algorithm presented in this study is in its elementary stage and need more optimization using more advanced technology such as deep-learning with data of real cases, especially in differentiating T-cell lymphomas. Although it needs more refinement, it may be used to aid pathological decision making in future. A further application to determine IHC antibodies for a certain subset of differential diagnoses might be possible in near future.

Keywords: database, expert supporting system, immunohistochemistry, probabilistic decision tree

Procedia PDF Downloads 224

Authors: Ibrahim Khan, Waqas Khalid

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The Healthcare sector is known to consume a higher proportion of total energy consumption in the HVAC market owing to an excessive cooling and heating requirement in maintaining human thermal comfort in indoor conditions, catering to patients undergoing treatment in hospital wards, rooms, and intensive care units. The indoor thermal comfort conditions in selected hospitals of Islamabad, Pakistan, were measured on a real-time basis with the collection of first-hand experimental data using calibrated sensors measuring Ambient Temperature, Wet Bulb Globe Temperature, Relative Humidity, Air Velocity, Light Intensity and CO2 levels. The Experimental data recorded was analyzed in conjunction with the Thermal Comfort Questionnaire Surveys, where the participants, including patients, doctors, nurses, and hospital staff, were assessed based on their thermal sensation, acceptability, preference, and comfort responses. The Recorded Dataset, including experimental and survey-based responses, was further analyzed in the development of a correlation between operative temperature, operative relative humidity, and other measured operative parameters with the predicted mean vote and adaptive predicted mean vote, with the adaptive temperature and adaptive relative humidity estimated using the seasonal data set gathered for both summer – hot and dry, and hot and humid as well as winter – cold and dry, and cold and humid climate conditions. The Machine Learning Logistic Regression Algorithm was incorporated to train the operative experimental data parameters and develop a correlation between patient sensations and the thermal environmental parameters for which a new ML-based adaptive thermal comfort model was proposed and developed in our study. Finally, the accuracy of our model was determined using the K-fold cross-validation.

Keywords: predicted mean vote, thermal comfort, energy management, logistic regression, machine learning

Procedia PDF Downloads 63

Authors: Mikael Soini, Kari Björn

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Metropolia University of Applied Sciences (MUAS) Information and Communication Technology (ICT) Degree Programme provides full-time Bachelor-level undergraduate studies. ICT Degree Programme has seven different major options; this paper focuses on Health Technology. In Health Technology, a significant curriculum change in 2014 enabled transition from fragmented curriculum including dozens of courses to a new integrated curriculum built around three 30 ECTS themes. This paper focuses especially on the second theme called Customer Oriented Software Applications. From students’ point of view, the goal of this theme is to get familiar with existing health related ICT solutions and systems, understand business around health technology, recognize social and healthcare operating principles and services, and identify customers and users and their special needs and perspectives. This also acts as a background for health related web application development. Built web application is tested, developed and evaluated with real users utilizing versatile user centred development methods. This paper presents experiences obtained from the first implementation of Customer Oriented Software Applications theme. Student feedback was gathered with two questionnaires, one in the middle of the theme and other at the end of the theme. Questionnaires had qualitative and quantitative parts. Similar questionnaire was implemented in the first theme; this paper evaluates how the theme-based integrated curriculum has progressed in Health Technology major by comparing results between theme 1 and 2. In general, students were satisfied for the implementation, timing and synchronization of the courses, and the amount of work. However there is still room for development. Student feedback and teachers’ observations have been and will be used to develop the content and operating principles of the themes and whole curriculum.

Keywords: engineering education, integrated curriculum, learning and teaching methods, learning experience

Procedia PDF Downloads 321

Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The increasing use of deep learning applications in production is becoming a competitive advantage. Predictive quality enables the assurance of product quality by using data-driven forecasts via machine learning models as a basis for decisions on test results. The use of real Bosch production data along the value chain of hydraulic valves is a promising approach to classifying the leakage of directional valves.

Keywords: artificial neural networks, classification, hydraulics, predictive quality, deep learning

Procedia PDF Downloads 244

Authors: Mohammad Alipour, Ekin Esen, Riza Kizilel

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Lithium-ion batteries are a commonly used type of rechargeable batteries because of their high specific energy and specific power. With the growing popularity of electric vehicles and hybrid electric vehicles, increasing attentions have been paid to rechargeable Lithium-ion batteries. However, safety problems, high cost and poor performance in low ambient temperatures and high current rates, are big obstacles for commercial utilization of these batteries. By proper thermal management, most of the mentioned limitations could be eliminated. Temperature profile of the Li-ion cells has a significant role in the performance, safety, and cycle life of the battery. That is why little temperature gradient can lead to great loss in the performances of the battery packs. In recent years, numerous researchers are working on new techniques to imply a better thermal management on Li-ion batteries. Keeping the battery cells within an optimum range is the main objective of battery thermal management. Commercial Li-ion cells are composed of several electrochemical layers each consisting negative-current collector, negative electrode, separator, positive electrode, and positive current collector. However, many researchers have adopted a single-layer cell to save in computing time. Their hypothesis is that thermal conductivity of the layer elements is so high and heat transfer rate is so fast. Therefore, instead of several thin layers, they model the cell as one thick layer unit. In previous work, we showed that single-layer model is insufficient to simulate the thermal behavior and temperature nonuniformity of the high-capacity Li-ion cells. We also studied the effects of the number of layers on thermal behavior of the Li-ion batteries. In this work, first thermal and electrochemical behavior of the LiFePO₄ battery is modeled with 3D multilayer cell. The model is validated with the experimental measurements at different current rates and ambient temperatures. Real time heat generation rate is also studied at different discharge rates. Results showed non-uniform temperature distribution along the cell which requires thermal management system. Therefore, aluminum plates with mini-channel system were designed to control the temperature uniformity. Design parameters such as channel number and widths, inlet flow rate, and cooling fluids are optimized. As cooling fluids, water and air are compared. Pressure drop and velocity profiles inside the channels are illustrated. Both surface and internal temperature profiles of single cell and battery packs are investigated with and without cooling systems. Our results show that using optimized Mini-channel cooling plates effectively controls the temperature rise and uniformity of the single cells and battery packs. With increasing the inlet flow rate, cooling efficiency could be reached up to 60%.

Keywords: lithium ion battery, 3D multilayer model, mini-channel cooling plates, thermal management

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Authors: Xiyue Wang, Shaoning Yan

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Cities in China today is faced with an increasingly serious river ecological crisis accompanying with the development of urbanization: waterlogging on account of the fragmented urban natural hydrological system; the limited ecological function of the hydrological system caused by a destruction of water system and waterfront ecological environment. Additionally, the eco-hydrological processes of rivers are affected by various environmental factors, which are more complex in the context of urban environment. Therefore, efficient hydrological monitoring and analysis tools, accurate and visual hydrological and hydraulic models are becoming more important basis for decision-makers and an important way for landscape architects to solve urban hydrological problems, formulating sustainable and forward-looking schemes. The study mainly introduces the river and flood analysis model based on the platform of Autodesk Civil 3D. Taking the Luanhe River in Qian'an City of Hebei Province as an example, the 3D models of the landform, river, embankment, shoal, pond, underground stream and other land features were initially built, with which the water transfer simulation analysis, river floodplain analysis, and river ecology analysis were carried out, ultimately the real-time visualized simulation and analysis of rivers in various hypothetical scenarios were realized. Through the establishment of digital hydrological and hydraulic model, the hydraulic data can be accurately and intuitively simulated, which provides basis for rational water system and benign urban ecological system design. Though, the hydrological and hydraulic model based on Autodesk Civil3D own its boundedness: the interaction between the model and other data and software is unfavorable; the huge amount of 3D data and the lack of basic data restrict the accuracy and application range. The hydrological and hydraulic model based on Autodesk Civil3D platform provides more possibility to access convenient and intelligent tool for urban planning and monitoring, a solid basis for further urban research and design.

Keywords: visualization, hydrological and hydraulic model, Autodesk Civil 3D, urban river

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Authors: Ching-Yu Liao, Ju-Joan Wong

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Recent years have shown a trend of younger stroke patients and an increase in ischemic strokes with the rise in stroke incidence. This has led to a growing demand for telemedicine, particularly during the COVID-19 pandemic, which has made the need for telemedicine even more urgent. This shift in healthcare is also closely related to advancements in Internet of Things (IoT) technology. Stroke-induced hemiparesis is a significant issue for patients. The medical community believes that if intervention occurs within three to six months of stroke onset, 80% of the residual effects can be restored to normal, a period known as the stroke golden period. During this time, patients undergo treatment and rehabilitation, and neural plasticity is at its best. Lower limb rehabilitation for stroke generally includes exercises such as support standing and walking posture, typically involving the healthy limb to guide the affected limb to achieve rehabilitation goals. Existing gait training aids in hospitals usually involve balance gait, sitting posture training, and precise muscle control, effectively addressing issues of poor gait, insufficient muscle activity, and inability to train independently during recovery. However, home training aids, such as braced and wheeled devices, often rely on the healthy limb to pull the affected limb, leading to lower usage of the affected limb, worsening circular walking, and compensatory movement issues. IoT technology connects devices via the internet to record, receive data, provide feedback, and adjust equipment for intelligent effects. Therefore, this study aims to explore how IoT can be integrated into existing gait training aids to monitor and sensor home rehabilitation movements, improve gait training compensatory issues through real-time feedback, and enable healthcare professionals to quickly understand patient conditions and enhance medical communication. To understand the needs of hemiparetic patients, a review of relevant literature from the past decade will be conducted. From the perspective of user experience, participant observation will be used to explore the use of home training aids by stroke patients and therapists, and interviews with physical therapists will be conducted to obtain professional opinions and practical experiences. Design specifications for home training aids for hemiparetic patients will be summarized. Applying IoT technology to lower limb training aids for stroke hemiparesis can help promote walking function recovery in hemiparetic patients, reduce muscle atrophy, and allow healthcare professionals to immediately grasp patient conditions and adjust gait training plans based on collected and analyzed information. Exploring these potential development directions provides a valuable reference for the further application of IoT technology in the field of medical rehabilitation.

Keywords: stroke, hemiplegia, rehabilitation, gait training, internet of things technology

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Authors: G. C. Jadeja, M. A. Desai, D. R. Bhatt, J. K. Parikh

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Green extraction techniques are fast paving ways into various industrial sectors due to the stringent governmental regulations leading to the banning of toxic chemicals’ usage and also due to the increasing health/environmental awareness. The present work describes the ionic liquids based sonication method for selectively extracting patchoulol from the leaves of patchouli. 1-Butyl-3-methylimidazolium tetrafluoroborate ([Bmim]BF4) and N,N,N,N’,N’,N’-Hexaethyl-butane-1,4-diammonium dibromide (dicationic ionic liquid - DIL) were selected for extraction. Ultrasound assisted ionic liquid extraction was employed considering concentration of ionic liquid (4–8 %, w/w), ultrasound power (50–150 W for [Bmim]BF4 and 20–80 W for DIL), temperature (30–50 oC) and extraction time (30–50 min) as major parameters influencing the yield of patchoulol. Using the Taguchi method, the parameters were optimized and analysis of variance (ANOVA) was performed to find the most influential factor in the selected extraction method. In case of [Bmim]BF4, the optimum conditions were found to be: 4 % (w/w) ionic liquid concentration, 50 W power, 30 oC temperature and extraction time of 30 min. The yield obtained under the optimum conditions was 3.99 mg/g. In case of DIL, the optimum conditions were obtained as 6 % (w/w) ionic liquid concentration, 80 W power, 30 oC temperature and extraction time of 40 min, for which the yield obtained was 4.03 mg/g. Temperature was found to be the most significant factor in both the cases. Extraction time was the insignificant parameter while extracting the product using [Bmim]BF4 and in case of DIL, power was found to be the least significant factor affecting the process. Thus, a green method of recovering patchoulol is proposed.

Keywords: green extraction, ultrasound, patchoulol, ionic liquids

Procedia PDF Downloads 362

Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

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Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

Procedia PDF Downloads 169

Authors: B. Kekezoglu, C. Kocatepe, O. Arikan, Y. Hacialiefendioglu, G. Ucar

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One of the significant problems of energy systems is to supply economic and efficient energy to consumers. Therefore studies has been continued to reduce technical losses in the network. In this paper, the technical losses analyzed for a portion of European side of Istanbul MV distribution network for different compensation scenarios by considering real system and load data and results are presented. Investigated system is modeled with CYME Power Engineering Software and optimal capacity placement has been proposed to minimize losses.

Keywords: distribution system, optimal capacitor placement, reactive power compensation, technical losses

Procedia PDF Downloads 674

Authors: Drishti Sharma, Mona Duggal

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Background: WhatsApp can serve as a cost-effective, scalable, convenient, and popular medium for public health management related communication in the developing world where the existing system of communication is top-down and slow. The product supports sending and receiving a variety of media: text, photos, videos, documents, and location, as well as voice/video calls. With growing number of users of smartphones and improving access and penetration of internet, the scope of information technology remains immense in resolving the hurdles faced by traditional public health system. Poor infrastructure, gap in digital literacy, faulty documentation, strict organizational hierarchy and slow movement of information across desks and offices- all these, make WhatsApp an efficient prospect to complement the existing system for communication, feedback and leadership for public health system in India. Objective: This study investigates the benefits, challenges and limitations associated with WhatsApp usage as a public health management tool. Methods: The study was conducted within the Chandigarh Union Territory. We used a qualitative approach and conducted individual semi-structured interviews and group interviews (n = 10). Participants included medical officers (n 20), Program managers (n = 4), academicians (n=2) and administrators (n=2). Thematic and content qualitative analyses were conducted. Message log of the WhatsApp group of one of the health program was assessed. Results: Medical Officers said that WhatsApp helped them remain in touch with the program officer. They could easily give feedback and highlight those challenges which needed immediate intervention from the program managers, hence they felt supported. Also, the application helped them share pictures of their activities (meetings and field activities) with the group which they thought inspired others and gave themselves immense satisfaction. Also, it helped build stronger relationships and better coordination among themselves, the same being important in team events. For program managers, it had become a portal for coordinating large scale campaigns. Its reach and the fact that the feedback is real-time make WhatsApp ideal for district level events. Though the easy informal connectivity made them answerable to their staff but it also provided them with flexibility in operations. It turned out to be an important portal for sharing outcome and goals related feedback (both positive and negative) to the team. To be sure, using WhatsApp for the purpose of public health program presents considerable challenges, including technological barriers, organizational challenges, gender issues, confidentiality concerns and unplanned aftereffects. Nevertheless, its advantages in a low-cost setting make it an efficient alternative. Conclusion: WhatsApp has become an integral part of our lives. Use of this app for public health program management within closed groups looks promising and useful. At the same time, addressing the challenges involved would make its usage safer.

Keywords: communication, mobile technology, public health management, WhatsApp

Procedia PDF Downloads 177

Authors: Suveen Kumar

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Oral cancer (OC) is the sixth most death causing cancer in world which includes tumour of lips, floor of the mouth, tongue, palate, cheeks, sinuses, throat, etc. Conventionally, the techniques used for OC detection are toluidine blue staining, biopsy, liquid-based cytology, visual attachments, etc., however these are limited by their highly invasive nature, low sensitivity, time consumption, sophisticated instrument handling, sample processing and high cost. Therefore, we developed biosensing chips for non-invasive detection of OC via CYFRA-21-1 biomarker. CYFRA-21-1 (molecular weight: 40 kDa) is secreted in saliva of OC patients which is a non-invasive biological fluid with a cut-off value of 3.8 ng mL-1, above which the subjects will be suffering from oral cancer. Therefore, in first work, 3-aminopropyl triethoxy silane (APTES) functionalized zirconia (ZrO2) nanoparticles (APTES/nZrO2) were used to successfully detect CYFRA-21-1 in a linear detection range (LDR) of 2-16 ng mL-1 with sensitivity of 2.2 µA mL ng-1. Successively, APTES/nZrO2-RGO was employed to prevent agglomeration of ZrO2 by providing high surface area reduced graphene oxide (RGO) support and much wider LDR (2-22 ng mL-1) was obtained with remarkable limit of detection (LOD) as 0.12 ng mL-1. Further, APTES/nY2O3/ITO platform was used for oral cancer bioseneor development. The developed biosensor (BSA/anti-CYFRA-21-1/APTES/nY2O3/ITO) have wider LDR (0.01-50 ng mL-1) with remarkable limit of detection (LOD) as 0.01 ng mL-1. To improve the sensitivity of the biosensing platform, nanocomposite of yattria stabilized nanostructured zirconia-reduced graphene oxide (nYZR) based biosensor has been developed. The developed biosensing chip having ability to detect CYFRA-21-1 biomolecules in the range of 0.01-50 ng mL-1, LOD of 7.2 pg mL-1 with sensitivity of 200 µA mL ng-1. Further, the applicability of the fabricated biosensing chips were also checked through real sample (saliva) analysis of OC patients and the obtained results showed good correlation with the standard protein detection enzyme linked immunosorbent assay (ELISA) technique.

Keywords: non-invasive, oral cancer, nanomaterials, biosensor, biochip

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Authors: Francesco Berald, Yunhui Zhao

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The COVID-19 pandemic further extended the multi-year housing boom in advanced economies and emerging markets alike against massive monetary easing during the pandemic. In this paper, we analyze the pricing-out phenomenon in the U.S. residential housing market due to higher house prices associated with monetary easing. We first set up a stylized general equilibrium model and show that although monetary easing decreases the mortgage payment burden, it would raise house prices and lower housing affordability for first-time homebuyers (through the initial housing wealth channel and the liquidity constraint channel that increases repeat buyers’ housing demand), and increase housing wealth inequality between first-time and repeat homebuyers. We then use the U.S. household-level data to quantify the effect of the house price change on housing affordability relative to that of the interest rate change. We find evidence of the pricing-out effect for all homebuyers; moreover, we find that the pricing-out effect is stronger for first-time homebuyers than for repeat homebuyers. The paper highlights the importance of accounting for general equilibrium effects and distributional implications of monetary policy while assessing housing affordability. It also calls for complementing monetary easing with well-targeted policy measures that can boost housing affordability, particularly for first-time and lower-income households. Such measures are also needed during aggressive monetary tightening, given that the fall in house prices may be insufficient or too slow to fully offset the immediate adverse impact of higher rates on housing affordability.

Keywords: pricing-out, U.S. housing market, housing affordability, distributional effects, monetary policy

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Authors: Amanda Aliende da Matta, Maria del Pilar Fogueiras Bertomeu, Valeria de Ormaechea Otalora, Maria Paz Sandin Esteban, Miriam Comet Donoso

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This communication presents proposed questions about the existential in research on the political participation of young women. The study follows a qualitative methodology, in particular, the applied hermeneutic phenomenological (AHP) method, and the general objective of the research is to give an account of the experience of political participation as a young woman. The study participants are women aged 18 to 35 who have experience in political participation. The techniques of data collection are the descriptive story and the phenomenological interview. Hermeneutic phenomenology as a research approach is based on phenomenological philosophy and applied hermeneutics. The ultimate objective of HP is to gain access to the meaning structures of lived experience by appropriating them, clarifying them, and reflectively making them explicit. Human experiences are always lived through existential: fundamental themes that are useful in exploring meaningful aspects of our life worlds. Everyone experiences the world through the existential of lived relationships, the lived body, lived space, lived time, and lived things. The phenomenological research, then, also tacitly asks about the existential. Existentials are universal themes useful for exploring significant aspects of our life world and of the particular phenomena under study. Four main existentials prove especially helpful as guides for reflection in the research process: relationship, body, space, and time. For example, in our case, we may ask ourselves how can the existentials of relationship, body, space, and time guide us in exploring the structures of meaning in the lived experience of political participation as a woman and a young person. The study is still not finished, as we are currently conducting phenomenological thematic analysis on the collected stories of lived experience. Yet, we have already identified some fragments of texts that show the existential in their experiences, which we will transcribe below. 1) Relationality - The experienced I-Other. It regards how relationships are experienced in our narratives about political participation as young women. One example would be: “As we had known each other for a long time, we understood each other with our eyes; we were all a little bit on the same page, thinking the same thing.” 2) Corporeality - The lived body. It regards how the lived body is experienced in activities of political participation as a young woman. One example would be: “My blood was boiling, but it was not the time to throw anything in their face, we had to look for solutions.”; “I had a lump in my throat and I wanted to cry.”. 3) Spatiality - The lived space. It regards how one experiences the lived space in political participation activities as a young woman. One example would be: “And the feeling I got when I saw [it] it's like watching everybody going into a mousetrap.” 4) Temporality - Lived time. It regards how one experiences the lived time in political participation activities as a young woman. One example would be: “Then, there were also meetings that went on forever…”

Keywords: applied hermeneutic phenomenology, existentials, hermeneutics, phenomenology, political participation

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Authors: Mouna Zitouni, Mariem Tounsi

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This research introduces an application of the Riesz mixture model for medical image segmentation for accurate diagnosis and treatment of brain tumors. We propose a pixel classification technique based on the Riesz distribution, derived from an extended Bartlett decomposition. To our knowledge, this is the first study addressing this approach. The Expectation-Maximization algorithm is implemented for parameter estimation. A comparative analysis, using both synthetic and real brain images, demonstrates the superiority of the Riesz model over a recent method based on the Wishart distribution.

Keywords: EM algorithm, segmentation, Riesz probability distribution, Wishart probability distribution

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Authors: Kasra Ghaemi, Syeda Tasnim, Shohel Mahmud

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One of the main issues affecting the quality and shelf life of food products is temperature fluctuation during transportation and storage. Packaging plays an important role in protecting food from environmental conditions, especially thermal variations. In this study, the performance of using microencapsulated Phase Change Material (PCM) as a promising thermal buffer layer in smart food packaging is investigated. The considered insulation layer is evaluated for different thicknesses and the absorbed heat from the environment. The results are presented in terms of the melting time of PCM or provided thermal protection period.

Keywords: food packaging, phase change material, thermal buffer, protection time

Procedia PDF Downloads 95

Authors: Taha Benarbia

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The advent of automated and connected vehicles has revolutionized the transportation industry, presenting new opportunities for enhancing the efficiency, safety, and sustainability of our transportation networks. This paper explores the integration of automated and connected vehicles into a smart transportation framework, leveraging the power of deep learning techniques to optimize the overall network performance. The first aspect addressed in this paper is the deployment of automated vehicles (AVs) within the transportation system. AVs offer numerous advantages, such as reduced congestion, improved fuel efficiency, and increased safety through advanced sensing and decisionmaking capabilities. The paper delves into the technical aspects of AVs, including their perception, planning, and control systems, highlighting the role of deep learning algorithms in enabling intelligent and reliable AV operations. Furthermore, the paper investigates the potential of connected vehicles (CVs) in creating a seamless communication network between vehicles, infrastructure, and traffic management systems. By harnessing real-time data exchange, CVs enable proactive traffic management, adaptive signal control, and effective route planning. Deep learning techniques play a pivotal role in extracting meaningful insights from the vast amount of data generated by CVs, empowering transportation authorities to make informed decisions for optimizing network performance. The integration of deep learning with automated and connected vehicles paves the way for advanced transportation network optimization. Deep learning algorithms can analyze complex transportation data, including traffic patterns, demand forecasting, and dynamic congestion scenarios, to optimize routing, reduce travel times, and enhance overall system efficiency. The paper presents case studies and simulations demonstrating the effectiveness of deep learning-based approaches in achieving significant improvements in network performance metrics

Keywords: automated vehicles, connected vehicles, deep learning, smart transportation network

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Authors: Ghayah Alsaleem

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This research project focussed on specific compounds, whereas a literature review was completed on the broader subject of long-lasting phosphorescence. For the review and subsequent laboratory work, long lasting phosphorescence compounds were defined as materials that have an afterglow decay time greater than a few minutes. The decay time is defined as the time between the end of excitation and the moment the light intensity drops below 0.32mcd/m2. This definition is widely used in industry and in most research studies. The experimental work focused on known long-lasting phosphorescence compounds – strontium aluminate (SrAl2O4: Eu2+, Dy3+). At first, preparation was similar to literary methods. Temperature, dopant levels and mixing methods were then varied in order to expose their effects on long-lasting phosphorescence. The effect of temperature was investigated for SrAl2O4: Eu2+, Dy3+, and resulted in the discovery that 1350°C was the only temperature that the compound could be heated to in the Differential scanning calorimetry (DSC) in order to achieve any phosphorescence. However, no temperatures above 1350°C were investigated. The variation of mixing method and co-dopant level in the strontium aluminate compounds resulted in the finding that the dry mixing method using a Turbula mixer resulted in the longest afterglow. It was also found that an increase of europium inclusion, from 1mol% to 2mol% in these compounds, increased the brightest of the phosphorescence. As this increased batch was mixed using sonication, the phosphorescent time was actually reduced which produced green long-lasting phosphorescence for up to 20 minutes following 30 minutes excitation and 50 minutes when the europium content was doubled and mixed using sonication.

Keywords: long lasting, phosphorescence, excitation, europium

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Authors: Mais Haj Qasem, Maen M. Al Assaf, Ali Rodan

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Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%.

Keywords: hybrid storage system, data mining, recurrent neural network, support vector machine

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Authors: Ruth Diego, Luis M. Romeo, Antonio Morán

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In the present work, a study of the coupling of a Bioelectrochemical System together with an oxy-combustion boiler is carried out; specifically, it proposes to connect the combustion gas outlet of a boiler with a microbial electrolysis cell (MEC) where the CO2 from the gases are transformed into methane in the cathode chamber, and the oxygen produced in the anode chamber is recirculated to the oxy-combustion boiler. The MEC mainly consists of two electrodes (anode and cathode) immersed in an aqueous electrolyte; these electrodes are separated by a proton exchange membrane (PEM). In this case, the anode is abiotic (where oxygen is produced), and it is at the cathode that an electroactive biofilm is formed with microorganisms that catalyze the CO2 reduction reactions. Real data from an oxy-combustion process in a boiler of around 20 thermal MW have been used for this study and are combined with data obtained on a smaller scale (laboratory-pilot scale) to determine the yields that could be obtained considering the system as environmentally sustainable energy storage. In this way, an attempt is made to integrate a relatively conventional energy production system (oxy-combustion) with a biological system (microbial electrolysis cell), which is a challenge to be addressed in this type of new hybrid scheme. In this way, a novel concept is presented with the basic dimensioning of the necessary equipment and the efficiency of the global process. In this work, it has been calculated that the efficiency of this power-to-gas system based on MEC cells when coupled to industrial processes is of the same order of magnitude as the most promising equivalent routes. The proposed process has two main limitations, the overpotentials in the electrodes that penalize the overall efficiency and the need for storage tanks for the process gases. The results of the calculations carried out in this work show that certain real potentials achieve an acceptable performance. Regarding the tanks, with adequate dimensioning, it is possible to achieve complete autonomy. The proposed system called OxyMES provides energy storage without energetically penalizing the process when compared to an oxy-combustion plant with conventional CO2 capture. According to the results obtained, this system can be applied as a measure to decarbonize an industry, changing the original fuel of the oxy-combustion boiler to the biogas generated in the MEC cell. It could also be used to neutralize CO2 emissions from industry by converting it to methane and then injecting it into the natural gas grid.

Keywords: microbial electrolysis cells, oxy-combustion, co2, power-to-gas

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Authors: Elmo Thiago Lins Cöuras Ford, Valentina Alessandra Carvalho do Vale, José Ubiragi de Lima Mendes

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In elapsing of the years it elaborates automobile it is developing automobiles more and more modern that, every year, the vehicles recently of the assembly lines, practically they push for the past produced models there is very little time. Those innovations didn't also pass unperceived in 0respect the safety of the vehicles. It is in this development apprenticeship the brakes systems equipped more and more with resources sophisticated. In that way, before of that context, this research tried to project a brake system for a vehicle off-road and to analyze your acting as the brakes efficiency: distances traveled and time, concluding with possible improvements in the system.

Keywords: brakes system, off-road, vehicle acting, automotive and mechanical engineering

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Authors: Ho Yin Wickson Cheung, Liora Malki-Epshtein

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Outdoor air pollution presents a significant challenge for public health globally, especially in urban areas, with road traffic acting as the primary contributor to air pollution. Several studies have documented the antagonistic relation between traffic-related air pollution (TRAP) and the impact on health, especially to the vulnerable group of population, particularly young pupils. Generally, TRAP could cause damage to their brain, restricting the ability of children to learn and, more importantly, causing detrimental respiratory issues in later life. Butlittle is known about the specific exposure of children at school during the school day and the impact this may have on their overall exposure to pollution at a crucial time in their development. This project has set out to examine the air quality across primary schools in South-East London and assesses the variability of data found based on their geographic location and surroundings. Nitrogen dioxide, PM contaminants, and carbon dioxide were collected with diffusion tubes and portable monitoring equipment for eight schools across three local areas, that are Greenwich, Lewisham, and Tower Hamlets. This study first examines the geographical features of the schools surrounding (E.g., coverage of urban road structure and green infrastructure), then utilize three different methods to capture pollutants data. Moreover, comparing the obtained results with existing data from monitoring stations to understand the differences in air quality before and during the pandemic. Furthermore, most studies in this field have unfortunately neglected human exposure to pollutants and calculated based on values from fixed monitoring stations. Therefore, this paper introduces an alternative approach by calculating human exposure to air pollution from real-time data obtained when commuting within related areas (Driving routes and field walking). It is found that schools located highly close to motorways are generally not suffering from the most air pollution contaminants. Instead, one with the worst traffic congested routes nearby might also result in poor air quality. Monitored results also indicate that the annual air pollution values have slightly decreased during the pandemic. However, the majority of the data is currently still exceeding the WHO guidelines. Finally, the total human exposures for NO2 during commuting in the two selected routes were calculated. Results illustrated the total exposure for route 1 were 21,730 μm/m3 and 28,378.32 μm/m3, and for route 2 were 30,672 μm/m3 and 16,473 μm/m3. The variance that occurred might be due to the difference in traffic volume that requires further research. Exposure for NO2 during commuting was plotted with detailed timesteps that have shown their peak usually occurred while commuting. These have consolidated the initial assumption to the extremeness of TRAP. To conclude, this paper has yielded significant benefits to understanding air quality across schools in London with the new approach of capturing human exposure (Driving routes). Confirming the severity of air pollution and promoting the necessity of considering environmental sustainability for policymakers during decision making to protect society's future pillars.

Keywords: air pollution, schools, pupils, congestion

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Authors: Xiaoyu Shen, Fang Fang, Chujun Qiu

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Herewith we present a Fourier method for credit risk quantification and allocation in the factor-copula model framework. The key insight is that, compared to directly computing the cumulative distribution function of the portfolio loss via Monte Carlo simulation, it is, in fact, more efficient to calculate the transformation of the distribution function in the Fourier domain instead and inverting back to the real domain can be done in just one step and semi-analytically, thanks to the popular COS method (with some adjustments). We also show that the Euler risk allocation problem can be solved in the same way since it can be transformed into the problem of evaluating a conditional cumulative distribution function. Once the conditional or unconditional cumulative distribution function is known, one can easily calculate various risk metrics. The proposed method not only fills the niche in literature, to the best of our knowledge, of accurate numerical methods for risk allocation but may also serve as a much faster alternative to the Monte Carlo simulation method for risk quantification in general. It can cope with various factor-copula model choices, which we demonstrate via examples of a two-factor Gaussian copula and a two-factor Gaussian-t hybrid copula. The fast error convergence is proved mathematically and then verified by numerical experiments, in which Value-at-Risk, Expected Shortfall, and conditional Expected Shortfall are taken as examples of commonly used risk metrics. The calculation speed and accuracy are tested to be significantly superior to the MC simulation for real-sized portfolios. The computational complexity is, by design, primarily driven by the number of factors instead of the number of obligors, as in the case of Monte Carlo simulation. The limitation of this method lies in the "curse of dimension" that is intrinsic to multi-dimensional numerical integration, which, however, can be relaxed with the help of dimension reduction techniques and/or parallel computing, as we will demonstrate in a separate paper. The potential application of this method has a wide range: from credit derivatives pricing to economic capital calculation of the banking book, default risk charge and incremental risk charge computation of the trading book, and even to other risk types than credit risk.

Keywords: credit portfolio, risk allocation, factor copula model, the COS method, Fourier method

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Authors: Anselm Ego Onyimonyi, Maduako Johnpaul O.

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The study was designed to investigate into the socio-ecological impact of migration of Fulani herders in Anambra state of Nigeria, through a climate justice lens. Nigeria is one of the world’s most densely populated countries with a population of over 284 million people, half of which are considered to be in abject poverty. There is no doubt that livestock production provides sustainable contributions to food security and poverty reduction to Nigeria economy, but not without some environmental implications like any other economic activities. Nigeria is recognized as being vulnerable to climate change. Climate change and global warming if left unchecked will cause adverse effects on livelihoods in Nigeria, such as livestock production, crop production, fisheries, forestry and post-harvest activities, because the rainfall regimes and patterns will be altered, floods which devastate farmlands would occur, increase in temperature and humidity which increases pest and disease would occur and other natural disasters like desertification, drought, floods, ocean and storm surges, which not only damage Nigerians’ livelihood but also cause harm to life and property, would occur. This and other climatic issue as it affects Fulani herdsmen was what this study investigated. In carrying out this research, a survey research design was adopted. A simple sampling technique was used. One local government area (LGA) was selected purposively from each of the four agricultural zone in the state based on its predominance of Fulani herders. For appropriate sampling, 25 respondents from each of the four Agricultural zones in the state were randomly selected making up the 100 respondent being sampled. Primary data were generated by using a set of structured 5-likert scale questionnaire. Data generated were analyzed using SPSS and the result presented using descriptive statistics. From the data analyzed, the study indentified; Unpredicted rainfall (mean = 3.56), Forest fire (mean = 4.63), Drying Water Source (mean = 3.99), Dwindling Grazing (mean 4.43), Desertification (mean = 4.44), Fertile land scarcity (mean = 3.42) as major factor predisposing Fulani herders to migrate southward while rejecting Natural inclination to migrate (mean = 2.38) and migration to cause trouble as a factor. On the reason why Fulani herders are trying to establish a permanent camp in Anambra state; Moderate temperature (mean= 3.60), Avoiding overgrazing (4.42), Search for fodder and water (mean = 4.81) and (mean = 4.70) respectively, Need for market (4.28), Favorable environment (mean = 3.99) and Access to fertile land (3.96) were identified. It was concluded that changing climatic variables necessitated the migration of herders from Northern Nigeria to areas in the South were the variables are most favorable to the herders and their animals.

Keywords: socio-ecological, migration, fulani, climate, justice, lens

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Authors: Hsiang-Yu Yuan, Marc Baguelin, Kin O. Kwok, Nimalan Arinaminpathy, Edwin Leeuwen, Steven Riley

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Traditional syndromic surveillance for influenza has substantial public health value in characterizing epidemics. Because the relationship between syndromic incidence and the true infection events can vary from one population to another and from one year to another, recent studies rely on combining serological test results with syndromic data from traditional surveillance into epidemic models to make inference on epidemiological processes of influenza. However, despite the widespread availability of serological data, epidemic models have thus far not explicitly represented antibody titre levels and their correspondence with immunity. Most studies use dichotomized data with a threshold (Typically, a titre of 1:40 was used) to define individuals as likely recently infected and likely immune and further estimate the cumulative incidence. Underestimation of Influenza attack rate could be resulted from the dichotomized data. In order to improve the use of serosurveillance data, here, a refinement of the concept of the stratified immunity within an epidemic model for influenza transmission was proposed, such that all individual antibody titre levels were enumerated explicitly and mapped onto a variable scale of susceptibility in different age groups. Haemagglutination inhibition titres from 523 individuals and 465 individuals during pre- and post-pandemic phase of the 2009 pandemic in Hong Kong were collected. The model was fitted to serological data in age-structured population using Bayesian framework and was able to reproduce key features of the epidemics. The effects of age-specific antibody boosting and protection were explored in greater detail. RB was defined to be the effective reproductive number in the presence of stratified immunity and its temporal dynamics was compared to the traditional epidemic model using use dichotomized seropositivity data. Deviance Information Criterion (DIC) was used to measure the fitness of the model to serological data with different mechanisms of the serological response. The results demonstrated that the differential antibody response with age was present (ΔDIC = -7.0). The age-specific mixing patterns with children specific transmissibility, rather than pre-existing immunity, was most likely to explain the high serological attack rates in children and low serological attack rates in elderly (ΔDIC = -38.5). Our results suggested that the disease dynamics and herd immunity of a population could be described more accurately for influenza when the distribution of immunity was explicitly represented, rather than relying only on the dichotomous states 'susceptible' and 'immune' defined by the threshold titre (1:40) (ΔDIC = -11.5). During the outbreak, RB declined slowly from 1.22[1.16-1.28] in the first four months after 1st May. RB dropped rapidly below to 1 during September and October, which was consistent to the observed epidemic peak time in the late September. One of the most important challenges for infectious disease control is to monitor disease transmissibility in real time with statistics such as the effective reproduction number. Once early estimates of antibody boosting and protection are obtained, disease dynamics can be reconstructed, which are valuable for infectious disease prevention and control.

Keywords: effective reproductive number, epidemic model, influenza epidemic dynamics, stratified immunity

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Authors: Khulood Miliany

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This study examines the media habits of young people in Saudi Arabia, in particular their use of the Internet and television in the domestic sphere, and how use of the Internet impacts upon other activities. In order to address the research questions, focus group interviews were conducted with Saudi university students. The study found that television has become a central part of social life within the household where television represents a main source for family time, particularly in Ramadan while the Internet is a solitary activity where it is used in more private spaces. Furthermore, Saudi females were also more likely to have their Internet access monitored and circumscribed by family members, with parents controlling the location and the amount of time spent using the Internet.

Keywords: domestication of technology, internet, social context, television, young people

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Authors: S. Adhirai Subramaniyam, Paramjit Singh

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This paper presents major issues in cloud computing. The paper describes different cloud computing deployment models and cloud service models available in the field of cloud computing. The paper then concentrates on various issues in the field. The issues such as cloud compatibility, compliance of the cloud, standardizing cloud technology, monitoring while on the cloud and cloud security are described. The paper suggests solutions for these issues and concludes that hybrid cloud infrastructure is a real boon for organizations.

Keywords: cloud, cloud computing, mobile cloud computing, private cloud, public cloud, hybrid cloud, SAAS, PAAS, IAAS, cloud security

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Authors: Sarwat Sultan, Muhammad Saleem, Frasat Kanwal

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Adolescence is a transition period of life that brings physical and psychological changes and always results in several challenges for an adolescent. An adolescent must learn life skills for a healthy transition from adolescence period to adulthood. Therefore this study was planned to examine the effects of life skill education on adolescents' mental health and academic benefits. A random sample of 720 school students aged between 13-17 years was categorized into two groups; experimental (n=360) and control (n=360). Life skill education was given to the students of the intervention group with repeated assessments of mental health and academic benefits at pre-intervention (T1) and post-intervention (T2) for both groups. Both groups were compared on scores of mental health and academic benefits across two times T1 and T2 by employing a mixed between-within-subjects analysis of variance. Findings showed the main effect of time suggesting the largest changes in mental health and academic benefits over time. Interaction effects between time and both groups were also found significant indicating the largest changes across time between both groups. Results of between-group comparisons showed significant values for Wilks’ Lambda and partial eta squared for students of the intervention group who scored higher on mental health and academic benefits after receiving life skills training than the students of the control group. Results of the present study determined the efficacy of life skill education and have implications for both teachers and psychotherapists to improve the students’ mental health and academic performance.

Keywords: academic benefits, life skills, mental health, adolescents

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