Search results for: data center

Authors: Dina L. DiSantis

Abstract:

Strengthening a sense of responsibility while relating global sustainability concepts such as water quality and pollution to a local water system can be achieved by teaching students to conduct and interpret water quality monitoring tests. When students conduct their own research, they become better stewards of the environment. Providing outdoor learning and place-based opportunities for students helps connect them to the natural world. By conducting stream studies and collecting data, students are able to better understand how the natural environment is a place where everything is connected. Students have been collecting physical, chemical and biological data along the West and East Branches of the Brandywine River, in Pennsylvania for over ten years. The stream studies are part of the advanced placement environmental science and aquatic science courses that are offered as electives to juniors and seniors at the Downingtown High School West Campus in Downingtown, Pennsylvania. Physical data collected includes: temperature, turbidity, width, depth, velocity, and volume of flow or discharge. The chemical tests conducted are: dissolved oxygen, carbon dioxide, pH, nitrates, alkalinity and phosphates. Macroinvertebrates are collected with a kick net, identified and then released. Students collect the data from several locations while traveling by canoe. In the classroom, students prepare a water quality data analysis and interpretation report based on their collected data. The summary of the results from longitudinal water quality data collection by students, as well as the strengths and weaknesses of student data collection will be presented.

Keywords: place-based, student data collection, sustainability, water quality monitoring

Procedia PDF Downloads 156

Authors: James P. Takona

Abstract:

Most of the global population has never lived through a pandemic, and thus there is so much that remains unknown about students' capacity for resiliency under such environments and circumstances and what a timeline for full recovery will look like. The session will guide participants to focus on misinformation and disinformation in the context of recent crisis events, with specific reference to how information flows across. Particular focus will be given to the flow of information in mediated technology and platforms with particular reference to K-12 and teacher preparation program environments. The paper will draw on theories and responses from the sociology of disaster, the social psychology of rumoring, and published studies on disinformation and misinformation. Applications will be identified and applied in the context of online information-sharing during crisis events. The session will offer the application of the Center for Contagious Diseases' Crisis and Emergency Risk Communication model to understand the themes and evolution of misinformation and disinformation. The paper will invite session participants to suggest and interact with raised challenges on the impact of dis-and misinformation.

Keywords: Sociology of disaster, misinformation, dis-information, Social Psychology of rumors

Procedia PDF Downloads 89

Authors: Ye Wang, Ickjai Lee

Abstract:

Due to the widespread of mobile sensing, there is a strong need to handle trails of moving objects, trajectories. This paper proposes three visual analytic approaches for higher order information of trajectory data sets based on the higher order Voronoi diagram data structure. Proposed approaches reveal geometrical information, topological, and directional information. Experimental results demonstrate the applicability and usefulness of proposed three approaches.

Keywords: visual analytics, higher order information, trajectory datasets, spatio-temporal data

Procedia PDF Downloads 402

Authors: Win-Jin Chang, Haw-Long Lee, Yu-Ching Yang

Abstract:

Atomic finite element simulation is applied to investigate the vibration frequency of a single-layer gamma graphyne with an attached mass for the CCCC, SSSS, CFCF, SFSF boundary conditions using the commercial code ANSYS. The fundamental frequencies of the graphyne sheet are compared with the results of the previous study. The results of the comparison are very good in all considered cases. The attached mass causes a shift in the resonant frequency of the graphyne. The frequencies of the single-layer gamma graphyne with an attached mass for different boundary conditions are obtained, and the order based on the boundary condition is CCCC >SSSS > CFCF> SFSF. The highest frequency shift is obtained when the attached mass is located at the center of the graphyne sheet. This is useful for the design of a highly sensitive graphyne-based mass sensor.

Keywords: graphyne, finite element analysis, vibration analysis, frequency shift

Procedia PDF Downloads 212

Authors: Sean Paulsen, Michael Casey

Abstract:

In this work we present a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data.

Keywords: transfer learning, fMRI, self-supervised, brain decoding, transformer, multitask training

Procedia PDF Downloads 90

Authors: Angelo Corallo, Maria Elena Latino, Marta Menegoli

Abstract:

The link between man and food plays, in the social and economic system, a central role where cultural and multidisciplinary aspects intertwine: food is not only nutrition, but also communication, culture, politics, environment, science, ethics, fashion. This multi-dimensionality has many implications in the food economy. In recent years, the consumer became more conscious about his food choices, involving a consistent change in consumption models. This change concerns several aspects: awareness of food system issues, employment of socially and environmentally conscious decision-making, food choices based on different characteristics than nutritional ones i.e. origin of food, how it’s produced, and who’s producing it. In this frame the ‘consumption choices’ and the ‘interests of the citizen’ become one part of the others. The figure of the ‘Citizen Consumer’ is born, a responsible and ethically motivated individual to change his lifestyle, achieving the goal of sustainable consumption. Simultaneously the branding, that before was guarantee of the product quality, today is questioned. In order to meet these needs, Agri-Food companies are developing specific product lines that follow two main philosophies: ‘Back to basics’ and ‘Less is more’. However, the issue of ethical behavior does not seem to find an adequate on market offer. Most likely due to a lack of attention on the communication strategy used, very often based on market logic and rarely on ethical one. The label in its classic concept of ‘clean labeling’ can no longer be the only instrument through which to convey product information and its evolution towards a concept of ‘clear label’ is necessary to embrace ethical and transparent concepts in progress the process of democratization of the Food System. The implementation of a voluntary traceability path, relying on the technological models of the Internet of Things or Industry 4.0, would enable the Agri-Food Supply Chain to collect data that, if properly treated, could satisfy the information need of consumers. A change of approach is therefore proposed towards Agri-Food traceability that is no longer intended as a tool to be used to respond to the legislator, but rather as a promotional tool useful to tell the company in a transparent manner and then reach the slice of the market of food citizens. The use of mobile technology can also facilitate this information transfer. However, in order to guarantee maximum efficiency, an appropriate communication model based on the ethical communication principles should be used, which aims to overcome the pipeline communication model, to offer the listener a new way of telling the food product, based on real data collected through processes traceability. The Citizen Consumer is therefore placed at the center of the new model of communication in which he has the opportunity to choose what to know and how. The new label creates a virtual access point capable of telling the product according to different point of views, following the personal interests and offering the possibility to give several content modalities to support different situations and usability.

Keywords: agri food traceability, agri-food transparency, clear label, food system, internet of things

Procedia PDF Downloads 158

Authors: Huda Abdelrhman Osman Ahmed, Paul Feyi Waboso

Abstract:

Introduction: Placenta accreta spectrum (PAS) disorders present a significant challenge in obstetric management due to the high risk of hemorrhage and potential complications at delivery. This case describes a 27+3 weeks gestation in a patient with placenta accreta managed with classical cesarean section and peripartum hysterectomy. Case Description: AGravida 4P3 patient presented at 27+3 weeks gestation with painless, unprovoked vaginal bleeding and an estimated blood loss (EBL) of 300 mL. At the 20+5 week anomaly scan, a placenta previa was identified anterior, covering the os anterior uterus and containing lacunae with signs of myometrial thinning. At a 24+1 week scan conducted at a tertiary center, further imaging indicated placenta increta with invasion into the myometrium and potential areas of placenta percreta. The patient’s past obstetric history included three previous cesarean sections, with no significant medical or surgical history. Social history revealed heavy smoking but no alcohol use. No drug allergies were reported. Given the risks associated with PAS, a management plan was formulated, including an MRI at a later stage and cesarean delivery with a possible hysterectomy between 34-36 weeks. However, at 27+3 weeks, the patient experienced another episode of vaginal bleeding EBL 500 ml, necessitating immediate intervention. Management: As the patient was unstable, she was not transferred to the tertiary center. Completed and informed consent was obtained. MDT planning-group and cross-matching 4 units, uterotonics. Tranexamic acid blood products, cryo, cell salvage, 2 obstetric consultants and an anesthetic consultant, blood bank aware and hematologist. HDU bed and ITU availability. This study assisted in performing a classical Caesarean section, Where the urologist inserted JJ ureteric stents. Following this, we also assisted in a total abdominal hysterectomy with the conservation of ovaries. 4 units RBC and 1 unit FFP were transfused. The total blood loss was 2.3 L. Outcome: The procedure successfully achieved hemostasis, and the neonate was delivered with subsequent transfer to a neonatal intensive care unit for management. The patient’s postoperative course was monitored closely with no immediate complications. Discussion: This case highlights the complexity and urgency in managing placenta accreta spectrum disorders, particularly with the added challenges posed by remote location and limited tertiary support. The need for rapid decision-making and interdisciplinary coordination is emphasized in such high-risk obstetric cases. The case also underscores the potential for surgical intervention and the importance of family involvement in emergent care decisions. Conclusion: Placenta accreta spectrum disorders demand meticulous planning and timely intervention. This case contributes to understanding PAS management at earlier gestational ages and provides insights into the challenges posed by access to tertiary care, especially in urgent situations.

Keywords: Accreta, Hysterectomy, 3MDT, prematurity

Procedia PDF Downloads 11

Authors: Vitali Kremez

Abstract:

The researcher monitored an organized ransomware campaign in order to gain significant visibility into the tactics, techniques, and procedures employed by a campaign boss operating a ransomware scheme out of Russia. As the Russian hacking community lowered the access requirements for unsophisticated Russian cybercriminals to engage in ransomware campaigns, corporations and individuals face a commensurately greater challenge of effectively protecting their data and operations from being held ransom. This report discusses two notorious ransomware campaigns. Though the loss of data can be devastating, the findings demonstrate that sending ransom payments does not always help obtain data. Key learnings: 1. From the ransomware affiliate perspective, such campaigns have significantly lowered the barriers for entry for low-tier cybercriminals. 2. Ransomware revenue amounts are not as glamorous and fruitful as they are often publicly reported. Average ransomware crime bosses make only $90K per year on average. 3. Data gathered indicates that sending ransom payments does not always help obtain data. 4. The talk provides the complete payout structure and Bitcoin laundering operation related to the ransomware-as-a-service campaign.

Keywords: bitcoin, cybercrime, ransomware, Russia

Procedia PDF Downloads 195

Authors: Nurzaki Ikhsan, Ahmad Saifizul Abdullah, Rahizar Ramli

Abstract:

One of the functions of the commercial heavy vehicle is to safely and efficiently transport goods and people. Due to its size and carrying capacity, it is important to study the vehicle dynamic stability during cornering. Study has shown that there are a number of overloaded heavy vehicles or permissible gross vehicle weight (GVW) violations recorded at selected areas in Malaysia assigned by its type and category. Thus, the objective of this study is to investigate the correlation and effect of the GVW on heavy vehicle stability during cornering event using simulation. Various selected heavy vehicle types and category are simulated using IPG/Truck Maker® with different GVW and road condition (coefficient of friction of road surface), while the speed, driver characteristic, center of gravity of load and road geometry are constant. Based on the analysis, the relationship between GVW and lateral acceleration were established. As expected, on the same value of coefficient of friction, the maximum lateral acceleration would be increased as the GVW increases.

Keywords: heavy vehicle, road safety, vehicle stability, lateral acceleration, gross vehicle weight

Procedia PDF Downloads 532

Authors: Ilma Robo, Saimir Heta, Nedja Hysi, Vera Ostreni

Abstract:

Introduction: Marginal gingivitis is a disease with considerable frequency among patients who present routinely for periodontal control and treatment. In fact, this disease may not have alarming symptoms in patients and may go unnoticed by themselves when personal hygiene conditions are optimal. The aim of this study was to collect retrograde data on the prevalence of marginal gingiva in the respective group of patients, evaluated according to specific periodontal diagnostic tools. Materials and methods: The study was conducted in two patient groups. The first group was with 34 patients, during December 2019-January 2020, and the second group was with 64 patients during 2010-2018 (each year in the mentioned monthly period). Bacterial plaque index, hemorrhage index, amount of gingival fluid, presence of xerostomia and candidiasis were recorded in patients. Results: Analysis of the collected data showed that susceptibility to marginal gingivitis shows higher values according to retrograde data, compared to cross-sectional ones. Susceptibility to candidiasis and the occurrence of xerostomia, even in the combination of both pathologies, as risk factors for the occurrence of marginal gingivitis, show higher values ​​according to retrograde data. The female are presented with a reduced bacterial plaque index than the males, but more importantly, this index in the females is also associated with a reduced index of gingival hemorrhage, in contrast to the males. Conclusions: Cross-sectional data show that the prevalence of marginal gingivitis is more reduced, compared to retrograde data, based on the hemorrhage index and the bacterial plaque index together. Changes in production in the amount of gingival fluid show a higher prevalence of marginal gingivitis in cross-sectional data than in retrograde data; this is based on the sophistication of the way data are recorded, which evolves over time and also based on professional sensitivity to this phenomenon.

Keywords: marginal gingivitis, cross-sectional, retrograde, prevalence

Procedia PDF Downloads 161

Authors: Mustafa Aydın, Ali Murat Sunbul

Abstract:

Hierarchical or nested data structures usually are seen in many research areas. Especially, in the field of education, if we examine most of the studies, we can see the nested structures. Students in classes, classes in schools, schools in cities and cities in regions are similar nested structures. In a hierarchical structure, students being in the same class, sharing the same physical conditions and similar experiences and learning from the same teachers, they demonstrate similar behaviors between them rather than the students in other classes.

Keywords: hierarchical linear modeling, nested data, hierarchical structure, data structure

Procedia PDF Downloads 652

Authors: Lauren Zammerilla Westcott, Ronald C. Jones, James W. Fleshman

Abstract:

The surgical treatment of breast cancer has rapidly evolved over the past 50 years, progressing from Halsted’s radical mastectomy to a public campaign of surgical options, aesthetic reconstruction, and patient empowerment. This article examines the happenings that led to the transition of breast surgery as a subset of general surgery to its own specialized field. Sparked by the research of Dr. Bernard Fisher and the first National Surgical Adjuvant Breast and Bowel Project trial in 1971, the field of breast surgery underwent significant growth over the next several decades, enabling general surgeons to limit their practices to the breast. High surgical volumes eventually led to the development of the first formal breast surgical oncology fellowship in a large community-based hospital at Baylor University Medical Center in 1982. The establishment of the American Society of Breast Surgeons, as well several landmark clinical trials and public campaign efforts, further contributed to the advancement of breast surgery, making it the specialized field of the current era.

Keywords: breast cancer, breast fellowship, breast surgery, surgical history

Procedia PDF Downloads 133

Authors: Lorna Goulden, Kai M. Hermsen, Jari Isohanni, Mirko Ross, Jef Vanbockryck

Abstract:

This article introduces disposable identities, with reference use cases and explores possible technical approaches. The proposed approach, when fully developed as an open-source toolkit, enables developers of mobile or web apps to employ a self-sovereign identity and data privacy framework, in order to rebuild trust in digital services by providing greater transparency, decentralized control, and GDPR compliance. With a user interface for the management of self-sovereign identity, digital authorizations, and associated data-driven transactions, the advantage of Disposable Identities is that they may also contain verifiable data such as the owner’s photograph, official or even biometric identifiers for more proactive prevention of identity abuse. These Disposable Identities designed for decentralized privacy management can also be time, purpose and context-bound through a secure digital contract; with verification functionalities based on tamper-proof technology.

Keywords: dentity, trust, self-sovereign, disposable identity, privacy toolkit, decentralised identity, verifiable credential, cybersecurity, data driven business, PETs, GDPRdentity, trust, self-sovereign, disposable identity, privacy toolkit, decentralised identity, verifiable credential, cybersecurity, data driven business, PETs, GDPRI

Procedia PDF Downloads 218

Authors: John Tolentino, Yong Seok Park

Abstract:

There are currently many people living with limb loss in the USA. The main causes for amputation can range from vascular disease, to trauma, or cancer. This number is expected increase over the next decade. Many patients have a single prosthetic for the first year but end up getting a second one to accommodate their changing physique. Afterwards, the prosthesis gets replaced every three to five years depending on how often it is used. This could cost the patient up to $500,000 throughout their lifetime. Complications do not end there, however. Due to the absence of nerves, it becomes more difficult to traverse terrain with a prosthetic. Moving on an incline or decline becomes difficult, thus curbs and stairs can be a challenge. Certain physical activities, such as cycling, could be even more strenuous. It will need to be relearned to accommodate for the change in weight, center of gravity, and transfer of energy from the leg to the pedal. The purpose of this research project is to develop a new, alternate below-knee cycling prosthetic using Dieter & Schmidt’s design process approach. It will be subjected to fatigue analysis under dynamic loading to observe the limitations as well as the strengths and weaknesses of the prosthetic. Benchmark comparisons will be made between existing prosthetics and the proposed one, examining the benefits and disadvantages. The resulting prosthetic will be 3D printed using acrylonitrile butadiene styrene (ABS) or polycarbonate (PC) plastic.

Keywords: 3D Printing, Cycling, Prosthetic design, Synthetic design.

Procedia PDF Downloads 142

Authors: Ayhan Kucukmanisa, Orhan Akbulut, Oguzhan Urhan

Abstract:

Nowadays, advanced driver assistance systems (ADAS) have become popular, since they enable safe driving. Lane detection is a vital step for ADAS. The performance of the lane detection process is critical to obtain a high accuracy lane departure warning system (LDWS). Challenging factors such as road cracks, erosion of lane markings, weather conditions might affect the performance of a lane detection system. In this paper, 1-D weighted filter based on row filtering to detect lane marking is proposed. 2-D input image is filtered by 1-D weighted filter considering four-pixel values located symmetrically around the center of candidate pixel. Performance evaluation is carried out by two metrics which are true positive rate (TPR) and false positive rate (FPR). Experimental results demonstrate that the proposed approach provides better lane marking detection accuracy compared to the previous methods while providing real-time processing performance.

Keywords: lane marking filter, lane detection, ADAS, LDWS

Procedia PDF Downloads 194

Authors: Lin Wenli, Fu Linchun, Zhang Yi, Wu Ming

Abstract:

To meet the demands of long-life and high-intelligence for satellites, the electrical power system should be provided with self-health condition evaluation capability. Any over-stress events in operations should be recorded. Based on Linear stresses accumulation damage theory, accumulative damage analysis was performed on thermal-mechanical-electrical united stresses for three components including the solar array, the batteries and the power conditioning unit. Then an overall health percentage evaluation model for satellite electrical power system was built. To obtain the accurate quantity for system health percentage, an automatic feedback closed-loop correction method for all coefficients in the evaluation model was present. The evaluation outputs could be referred as taking earlier fault-forecast and interventions for Ground Control Center or Satellites self.

Keywords: satellite electrical power system, health percentage, linear stresses accumulation damage, evaluation model

Procedia PDF Downloads 412

Authors: Lethola Tshikose, Munyaradzi Katurura

Abstract:

Information and Communication Technology (ICT) plays a critical role in improving daily healthcare processes. The South African healthcare organizations have adopted Information Systems to integrate their patient records. This has made it much easier for healthcare organizations because patient information can now be accessible at any time. The primary purpose of this research study was to investigate the best practices that can be applied to enhance patient security and confidentiality when using e-health systems in South Africa. Security and confidentiality are critical in healthcare organizations as they ensure safety in EHRs. The research study used an inductive research approach that included a thorough literature review; therefore, no data was collected. The research paper’s scope included patient data and possible security threats associated with healthcare systems. According to the study, South African healthcare organizations discovered various patient data security and confidentiality issues. The study also revealed that when it comes to handling patient data, health professionals sometimes make mistakes. Some may not be computer literate, which posed issues and caused data to be tempered with. The research paper recommends that healthcare organizations ensure that security measures are adequately supported and promoted by their IT department. This will ensure that adequate resources are distributed to keep patient data secure and confidential. Healthcare organizations must correctly use standards set up by IT specialists to solve patient data security and confidentiality issues. Healthcare organizations must make sure that their organizational structures are adaptable to improve security and confidentiality.

Keywords: E-health, EHR, security, confidentiality, healthcare

Procedia PDF Downloads 58

Authors: Akriti Kafle Baral, Ruixing Zhang, Dzifa K Lalit, Manthar M Alli

Abstract:

Introduction: Hemodialysis is the most common type of dialysis globally approximately million are reported to receive this type of dialysis. Psychosocial issues in hemodialysis are the psychological and socioeconomic burdens emanating from the initiation and course of treatment and have the potential for gross deterioration in the quality of life and general well-being of patients. Understanding the psychosocial issues and needs of patients undergoing hemodialysis could pave the way for comprehensive support and therapies designed to reduce stress, improve social support, and foster mental resilience. Objectives: The aim of this study was to explore the psychosocial issues and support needs of patients undergoing hemodialysis at a tertiary care center in Nepal. Methods: A qualitative descriptive study was conducted among 20 purposefully selected patients attending hemodialysis treatment at Pokhara Academy of Health Sciences, Nepal. Data was analyzed via thematic analysis. Results: The study resulted in three major themes which included Emotional, psychological, and spiritual struggles, Social and economic impacts, and Support and information needs. Moreover, 16 sub-themes emerged which are Frustration with daily life, Constant fear of death, Thoughts of self-harm, Perceived Burden on Family, Sense of Divine Punishment, Sense of Unfairness, Fear about future uncertainties, Social avoidance, Social stigmatization, Loss of employment, Financial strain, Transportation challenges, Need for early, clear and comprehensive information, Need for support and reassurance from family, Support through peer connections, and Reassurance from healthcare providers. Conclusion: The findings of this study indicate that patients undergoing hemodialysis in Nepal experience numerous hardships and multifaceted struggles that require support from different dimensions. Establishing robust support systems that include family involvement, peer networks, and effective communication from healthcare professionals can significantly mitigate feelings of anxiety and isolation.

Keywords: hemodialysis, psychosocial issues, support needs, chronic kidney disease, end stage renal disease, Nepal

Procedia PDF Downloads 9

Authors: Richard Byrne, Emma Mulliner

Abstract:

Smart cities are a vision for the future that is increasingly becoming a reality. While a key concept of the smart city is the ability to capture, communicate, and process data that has long been produced through day-to-day activities of the city, much of the assessment models in place neglect this fact to focus on ‘smartness’ concepts. Although it is true technology often provides the opportunity to capture and communicate data in more effective ways, there are also human processes involved that are just as important. The growing importance with regards to the use and ownership of data in society can be seen by all with companies such as Facebook and Google increasingly coming under the microscope, however, why is the same scrutiny not applied to cities? The research area is therefore of great importance to the future of our cities here and now, while the findings will be of just as great importance to our children in the future. This research aims to understand the influence data is having on organisations operating throughout the smart cities sector and employs a mixed-method research approach in order to best answer the following question: Would a data-based evaluation model for smart cities be more appropriate than a smart-based model in assessing the development of the smart city? A fully comprehensive literature review concluded that there was a requirement for a data-driven assessment model for smart cities. This was followed by a documentary analysis to understand the root source of data integration to the smart city. A content analysis of city data platforms enquired as to the alternative approaches employed by cities throughout the UK and draws on best practice from New York to compare and contrast. Grounded in theory, the research findings to this point formulated a qualitative analysis framework comprised of: the changing environment influenced by data, the value of data in the smart city, the data ecosystem of the smart city and organisational response to the data orientated environment. The framework was applied to analyse primary data collected through the form of interviews with both public and private organisations operating throughout the smart cities sector. The work to date represents the first stage of data collection that will be built upon by a quantitative research investigation into the feasibility of data network effects in the smart city. An analysis into the benefits of data interoperability supporting services to the smart city in the areas of health and transport will conclude the research to achieve the aim of inductively forming a framework that can be applied to future smart city policy. To conclude, the research recognises the influence of technological perspectives in the development of smart cities to date and highlights this as a challenge to introduce theory applied with a planning dimension. The primary researcher has utilised their experience working in the public sector throughout the investigation to reflect upon what is perceived as a gap in practice of where we are today, to where we need to be tomorrow.

Keywords: data, planning, policy development, smart cities

Procedia PDF Downloads 310

Authors: Anurag Sharma, Dinesh Kumar, Rahul Malhotra, Manoj Kumar

Abstract:

Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

Procedia PDF Downloads 734

Authors: Nouha Khediri, Mohammed Ben Ammar, Monji Kherallah

Abstract:

Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. Accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER, benefiting from deep learning, especially CNN and VGG16. First, the data is pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work.

Keywords: CNN, deep-learning, facial emotion recognition, machine learning

Procedia PDF Downloads 95

Authors: Vivian Isaac, A. T. Joteeshwaran, Craig McLachlan

Abstract:

The University of New South Wales (UNSW) Rural Clinical School has a strategic collaborative focus on chronic disease and public health. Our objectives are to understand rural environmental and biological interactions in vulnerable community populations. The UNSW Rural Clinical School translational model is a spoke and hub network. This spoke and hub model connects rural data and biological specimens with city based collaborative public health research networks. Similar spoke and hub models are prevalent across research centers in India. The Australia-India Council grant was awarded so we could establish sustainable public health and community research collaborations. As part of the collaborative network we are developing strategies around data and biological sharing platforms between Indian Institute of Public Health, Public Health Foundation of India (PHFI), Hyderabad and Rural Clinical School UNSW. The key objective is to understand how research collaborations are conducted in India and also how data can shared and tracked with external collaborators such as ourselves. A framework to improve data sharing for research collaborations, including DNA was proposed as a project outcome. The complexities of sharing biological data has been investigated via a visit to India. A flagship sustainable project between Rural Clinical School UNSW and PHFI would illustrate a model of data sharing platforms.

Keywords: data sharing, collaboration, public health research, chronic disease

Procedia PDF Downloads 450

Authors: Iman Abu-Rub

Abstract:

This research paper examines if Artificial Intelligence is, in fact, racist or not. Different studies from all around the world, and covering different communities were analyzed to further understand AI’s true implications over different communities. The black community, Asian community, and Muslim community were all analyzed and discussed in the paper to figure out if AI is biased or unbiased towards these specific communities. It was found that the biggest problem AI faces is the biased distribution of data collection. Most of the data inserted and coded into AI are of a white male, which significantly affects the other communities in terms of reliable cultural, political, or medical research. Nonetheless, there are various research was done that help increase awareness of this issue, but also solve it completely if done correctly. Governments and big corporations are able to implement different strategies into their AI inventions to avoid any racist results, which could cause hatred culturally but also unreliable data, medically, for example. Overall, Artificial Intelligence is not racist per se, but the data implementation and current racist culture online manipulate AI to become racist.

Keywords: social media, artificial intelligence, racism, discrimination

Procedia PDF Downloads 116

Authors: Chico Horacio Jose Sambo

Abstract:

Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.

Keywords: neural network, permeability, multilayer perceptron, well log

Procedia PDF Downloads 403

Authors: Usman Qamar, Younus Javed

Abstract:

Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and rough-sets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%.

Keywords: rough-sets, classification, feature selection, entropy, outliers, frequent itemset mining

Procedia PDF Downloads 437

Authors: Ramiz M. Shubbar, Dong In Lee, Hatem A. Gzar, Arthur S. Rood

Abstract:

Air pollution is one of the biggest environmental problems in Baghdad, Iraq. The Daura refinery located nearest the center of Baghdad, represents the largest industrial area, which transmits enormous amounts of pollutants, therefore study the gaseous pollutants and particulate matter are very important to the environment and the health of the workers in refinery and the people whom leaving in areas around the refinery. Actually, some studies investigated the studied area before, but it depended on the basic Gaussian equation in a simple computer programs, however, that kind of work at that time is very useful and important, but during the last two decades new largest production units were added to the Daura refinery such as, PU_3 (Power unit_3 (Boiler 11&12)), CDU_1 (Crude Distillation unit_70000 barrel_1), and CDU_2 (Crude Distillation unit_70000 barrel_2). Therefore, it is necessary to use new advanced model to study air pollution at the region for the new current years, and calculation the monthly emission rate of pollutants through actual amounts of fuel which consumed in production unit, this may be lead to accurate concentration values of pollutants and the behavior of dispersion or transport in study area. In this study to the best of author’s knowledge CALPUFF model was used and examined for first time in Iraq. CALPUFF is an advanced non-steady-state meteorological and air quality modeling system, was applied to investigate the pollutants concentration of SO2, NO2, CO, and PM1-10μm, at areas adjacent to Daura refinery which located in the center of Baghdad in Iraq. The CALPUFF modeling system includes three main components: CALMET is a diagnostic 3-dimensional meteorological model, CALPUFF (an air quality dispersion model), CALPOST is a post processing package, and an extensive set of preprocessing programs produced to interface the model to standard routinely available meteorological and geophysical datasets. The targets of this work are modeling and simulation the four pollutants (SO2, NO2, CO, and PM1-10μm) which emitted from Daura refinery within one year. Emission rates of these pollutants were calculated for twelve units includes thirty plants, and 35 stacks by using monthly average of the fuel amount consumption at this production units. Assess the performance of CALPUFF model in this study and detect if it is appropriate and get out predictions of good accuracy compared with available pollutants observation. CALPUFF model was investigated at three stability classes (stable, neutral, and unstable) to indicate the dispersion of the pollutants within deferent meteorological conditions. The simulation of the CALPUFF model showed the deferent kind of dispersion of these pollutants in this region depends on the stability conditions and the environment of the study area, monthly, and annual averages of pollutants were applied to view the dispersion of pollutants in the contour maps. High values of pollutants were noticed in this area, therefore this study recommends to more investigate and analyze of the pollutants, reducing the emission rate of pollutants by using modern techniques and natural gas, increasing the stack height of units, and increasing the exit gas velocity from stacks.

Keywords: CALPUFF, daura refinery, Iraq, pollutants

Procedia PDF Downloads 198

Authors: Salihah Alghamdi, Surajit Ray

Abstract:

Space-time data can be observed over irregularly shaped manifolds, which might have complex boundaries or interior gaps. Most of the existing methods do not consider the shape of the data, and as a result, it is difficult to model irregularly shaped data accommodating the complex domain. We used a method that can deal with space-time data that are distributed over non-planner shaped regions. The method is based on partial differential equations and finite element analysis. The model can be estimated using a penalized least squares approach with a regularization term that controls the over-fitting. The model is regularized using two roughness penalties, which consider the spatial and temporal regularities separately. The integrated square of the second derivative of the basis function is used as temporal penalty. While the spatial penalty consists of the integrated square of Laplace operator, which is integrated exclusively over the domain of interest that is determined using finite element technique. In this paper, we applied a spatio-temporal regression model with partial differential equations regularization (ST-PDE) approach to analyze a remote sensing data measuring the greenness of vegetation, measure by an index called enhanced vegetation index (EVI). The EVI data consist of measurements that take values between -1 and 1 reflecting the level of greenness of some region over a period of time. We applied (ST-PDE) approach to irregular shaped region of the EVI data. The approach efficiently accommodates the irregular shaped regions taking into account the complex boundaries rather than smoothing across the boundaries. Furthermore, the approach succeeds in capturing the temporal variation in the data.

Keywords: irregularly shaped domain, partial differential equations, finite element analysis, complex boundray

Procedia PDF Downloads 141

Authors: P. Daniels, T. Adonis, P. September-Brown, R. Comalie

Abstract:

The community engagement unit at the University of the Western Cape was tasked with establishing a community engagement database. The database would store information of all community engagement projects related to the university. The wealth of knowledge obtained from the various disciplines would be used to facilitate interdisciplinary collaboration within the university, as well as facilitating community university partnership opportunities. The purpose of this qualitative study was to explore electronic data collection through the development of a database. Two types of electronic data collection platforms were used, namely online questionnaire and email. The semi structured questionnaire was used to collect data related to community engagement projects from different faculties and departments at the university. There are many benefits for using an electronic data collection platform, such as reduction of costs and time, ease in reaching large numbers of potential respondents, and the possibility of providing anonymity to participants. Despite all the advantages of using the electronic platform, there were as many challenges, as depicted in our findings. The findings suggest that certain barriers existed by using an electronic platform for data collection, even though it was in an academic environment, where knowledge and resources were in abundance. One of the challenges experienced in this process was the lack of dissemination of information via email to staff within faculties. The actual online software used for the questionnaire had its own limitations, such as only being able to access the questionnaire from the same electronic device. In a few cases, academics only completed the questionnaire after a telephonic prompt or face to face meeting about "Is higher education in South Africa ready to embrace electronic platform in data collection?"

Keywords: community engagement, database, data collection, electronic platform, electronic tools, knowledge sharing, university

Procedia PDF Downloads 264

Authors: Divya Juneja, Sukhjeet Kaur Matharu

Abstract:

Purpose: The paper is aimed at identifying the challenging factors experienced by the women entrepreneurs in India in operating their enterprises amidst the challenges posed by the COVID-19 pandemic. Methodology: The sample for the study comprised 396 women entrepreneurs from different regions of India. A purposive sampling technique was adopted for data collection. Data was collected through a self-administered questionnaire. Analysis was performed using the SPSS package for quantitative data analysis. Findings: The results of the study state that entrepreneurial characteristics, resourcefulness, networking, adaptability, and continuity have a positive influence on the resiliency of women entrepreneurs when faced with a crisis situation. Practical Implications: The findings of the study have some important implications for women entrepreneurs, organizations, government, and other institutions extending support to entrepreneurs.

Keywords: women entrepreneurs, analysis, data analysis, positive influence, resiliency

Procedia PDF Downloads 114

Authors: Mohammed Abdullah Alshahrani

Abstract:

The research focuses on investigating the use of partial least squares (PLS) methodology for addressing challenges associated with high-dimensional correlated data. Recent technological advancements have led to experiments producing data characterized by a large number of variables compared to observations, with substantial inter-variable correlations. Such data patterns are common in chemometrics, where near-infrared (NIR) spectrometer calibrations record chemical absorbance levels across hundreds of wavelengths, and in genomics, where thousands of genomic regions' copy number alterations (CNA) are recorded from cancer patients. PLS serves as a widely used method for analyzing high-dimensional data, functioning as a regression tool in chemometrics and a classification method in genomics. It handles data complexity by creating latent variables (components) from original variables. However, applying PLS can present challenges. The study investigates key areas to address these challenges, including unifying interpretations across three main PLS algorithms and exploring unusual negative shrinkage factors encountered during model fitting. The research presents an alternative approach to addressing the interpretation challenge of predictor weights associated with PLS. Sparse estimation of predictor weights is employed using a penalty function combining a lasso penalty for sparsity and a Cauchy distribution-based penalty to account for variable dependencies. The results demonstrate sparse and grouped weight estimates, aiding interpretation and prediction tasks in genomic data analysis. High-dimensional data scenarios, where predictors outnumber observations, are common in regression analysis applications. Ordinary least squares regression (OLS), the standard method, performs inadequately with high-dimensional and highly correlated data. Copy number alterations (CNA) in key genes have been linked to disease phenotypes, highlighting the importance of accurate classification of gene expression data in bioinformatics and biology using regularized methods like PLS for regression and classification.

Keywords: partial least square regression, genetics data, negative filter factors, high dimensional data, high correlated data

Procedia PDF Downloads 49