Search results for: user datagram protocol

Authors: Othman Maklouf, Matouk Elamari, M. Rgeai, Fateh Alej

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In modern avionics the main fundamental component is the flight management system (FMS). An FMS is a specialized computer system that automates a wide variety of in-flight tasks, reducing the workload on the flight crew to the point that modern civilian aircraft no longer carry flight engineers or navigators. The main function of the FMS is in-flight management of the flight plan using various sensors such as Global Positioning System (GPS) and Inertial Navigation System (INS) to determine the aircraft's position and guide the aircraft along the flight plan. GPS which is satellite based navigation system, and INS which generally consists of inertial sensors (accelerometers and gyroscopes). GPS is used to locate positions anywhere on earth, it consists of satellites, control stations, and receivers. GPS receivers take information transmitted from the satellites and uses triangulation to calculate a user’s exact location. The basic principle of an INS is based on the integration of accelerations observed by the accelerometers on board the moving platform, the system will accomplish this task through appropriate processing of the data obtained from the specific force and angular velocity measurements. Thus, an appropriately initialized inertial navigation system is capable of continuous determination of vehicle position, velocity and attitude without the use of the external information. The main objective of article is to introduce a comparative study between the two systems under different conditions and scenarios using MATLAB with SIMULINK software.

Keywords: flight management system, GPS, IMU, inertial navigation system

Procedia PDF Downloads 301

Authors: Marrone Silverio Melo Dantas Pedro Henrique Dreyer, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Ricardo Souza, Silvia Lins, Judith Kelner, Djamel Fawzi Hadj Sadok

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The creation of a dataset is time-consuming and often discourages researchers from pursuing their goals. To overcome this problem, we present and discuss two solutions adopted for the automation of this process. Both optimize valuable user time and resources and support video object segmentation with object tracking and 3D projection. In our scenario, we acquire images from a moving robotic arm and, for each approach, generate distinct annotated datasets. We evaluated the precision of the annotations by comparing these with a manually annotated dataset, as well as the efficiency in the context of detection and classification problems. For detection support, we used YOLO and obtained for the projection dataset an F1-Score, accuracy, and mAP values of 0.846, 0.924, and 0.875, respectively. Concerning the tracking dataset, we achieved an F1-Score of 0.861, an accuracy of 0.932, whereas mAP reached 0.894. In order to evaluate the quality of the annotated images used for classification problems, we employed deep learning architectures. We adopted metrics accuracy and F1-Score, for VGG, DenseNet, MobileNet, Inception, and ResNet. The VGG architecture outperformed the others for both projection and tracking datasets. It reached an accuracy and F1-score of 0.997 and 0.993, respectively. Similarly, for the tracking dataset, it achieved an accuracy of 0.991 and an F1-Score of 0.981.

Keywords: RJ45, automatic annotation, object tracking, 3D projection

Procedia PDF Downloads 170

Authors: Shiuh-Jer Huang, Chen-Zon Yan, C. K. Huang, Chun-Chien Ting

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Since the vision system application in industrial environment for autonomous purposes is required intensely, the image recognition technique becomes an important research topic. Here, deep learning algorithm is employed in image system to recognize the industrial object and integrate with a 7A6 Series Manipulator for object automatic gripping task. PC and Graphic Processing Unit (GPU) are chosen to construct the 3D Vision Recognition System. Depth Camera (Intel RealSense SR300) is employed to extract the image for object recognition and coordinate derivation. The YOLOv2 scheme is adopted in Convolution neural network (CNN) structure for object classification and center point prediction. Additionally, image processing strategy is used to find the object contour for calculating the object orientation angle. Then, the specified object location and orientation information are sent to robotic controller. Finally, a six-axis manipulator can grasp the specific object in a random environment based on the user command and the extracted image information. The experimental results show that YOLOv2 has been successfully employed to detect the object location and category with confidence near 0.9 and 3D position error less than 0.4 mm. It is useful for future intelligent robotic application in industrial 4.0 environment.

Keywords: deep learning, image processing, convolution neural network, YOLOv2, 7A6 series manipulator

Procedia PDF Downloads 251

Authors: Vladimir Kulyukin, Tanwir Zaman, Sarat Kiran Andhavarapu

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Research on nutrition label use identifies four factors that impede comprehension and retention of nutrition information by consumers: label’s location on the package, presentation of information within the label, label’s surface size, and surrounding visual clutter. In this paper, a system is presented that makes nutrition label use more effective for nutrition information comprehension and retention. The system’s front end is a smartphone application. The system’s back end is a four node Linux cluster for image recognition and data storage. Image frames captured on the smartphone are sent to the back end for skewed or aligned barcode recognition. When barcodes are recognized, corresponding nutrition labels are retrieved from a cloud database and presented to the user on the smartphone’s touchscreen. Each displayed nutrition label is positioned centrally on the touchscreen with no surrounding visual clutter. Wikipedia links to important nutrition terms are embedded to improve comprehension and retention of nutrition information. Standard touch gestures (e.g., zoom in/out) available on mainstream smartphones are used to manipulate the label’s surface size. The nutrition label database currently includes 200,000 nutrition labels compiled from public web sites by a custom crawler. Stress test experiments with the node cluster are presented. Implications for proactive nutrition management and food policy are discussed.

Keywords: mobile computing, cloud computing, nutrition label use, nutrition management, barcode scanning

Procedia PDF Downloads 375

Authors: Joseph Amponsah

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This research proposes an equation to predict and determine the momentum source equation term after factoring in the radial friction between the fluid and the blades and the impeller's propulsive power. This research aims to look at how CFD software can be used to predict the effect of flows in nuclear reactor stirred tanks through a momentum source equation and the concentration distribution of tracers that have been introduced in reactor tanks. The estimated findings, including the dimensionless concentration curves, power, and pumping numbers, dimensionless velocity profiles, and mixing times 4, were contrasted with results from tests in stirred containers. The investigation was carried out in Part I for vessels that were agitated by one impeller on a central shaft. The two types of impellers employed were an ordinary Rushton turbine and a 6-bladed 45° pitched blade turbine. The simulations made use of numerous reference frame techniques and the common k-e turbulence model. The impact of the grid type was also examined; unstructured, structured, and unique user-defined grids were looked at. The CFD model was used to simulate the flow field within the Rushton turbine nuclear reactor stirred tank. This method was validated using experimental data that were available close to the impeller tip and in the bulk area. Additionally, analyses of the computational efficiency and time using MRF and SM were done.

Keywords: Ansys fluent, momentum equation, CFD, prediction

Procedia PDF Downloads 80

Authors: Vathsala Kaluarachchi, Oshani Wimalarathna, Charith Vandebona, Gayani Chandrarathna, Lakmal Rupasinghe, Windhya Rankothge

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It is the fundamental function of a monitoring system to allow users to collect and process data. A worldwide threat, the corona outbreak has wreaked havoc in Sri Lanka, and the situation has gotten out of hand. Since the epidemic, the Sri Lankan government has been unable to establish a systematic system for monitoring corona patients and providing emergency care in the event of an outbreak. Most patients have been held at home because of the high number of patients reported in the nation, but they do not yet have access to a functioning medical system. It has resulted in an increase in the number of patients who have been left untreated because of a lack of medical care. The absence of competent medical monitoring is the biggest cause of mortality for many people nowadays, according to our survey. As a result, a smartphone app for analyzing the patient's state and determining whether they should be hospitalized will be developed. Using the data supplied, we are aiming to send an alarm letter or SMS to the hospital once the system recognizes them. Since we know what those patients need and when they need it, we will put up a desktop program at the hospital to monitor their progress. Deep learning, image processing and application development, natural language processing, and blockchain management are some of the components of the research solution. The purpose of this research paper is to introduce a mechanism to connect hospitals and patients even when they are physically apart. Further data security and user-friendliness are enhanced through blockchain and NLP.

Keywords: blockchain, deep learning, NLP, monitoring system

Procedia PDF Downloads 134

Authors: Misbah Javaid, Vladimir Estivill-Castro, Rene Hexel

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The field of robotics is advancing rapidly to the point where robots have become an integral part of the modern society. These robots collaborate and contribute productively with humans and compensate some shortcomings from human abilities and complement them with their skills. Effective teamwork of humans and robots demands to investigate the critical issue of trust. The field of human-computer interaction (HCI) has already examined trust humans place in technical systems mostly on issues like reliability and accuracy of performance. Early work in the area of expert systems suggested that automatic generation of explanations improved trust and acceptability of these systems. In this work, we augmented a robot with the user-invoked explanation generation proficiency. To measure explanations effect on human’s level of trust, we collected subjective survey measures and behavioral data in a human-robot team task into an interactive, adversarial and partial information environment. The results showed that with the explanation capability humans not only understand and recognize robot as an expert team partner. But, it was also observed that human's learning and human-robot team performance also significantly improved because of the meaningful interaction with the robot in the human-robot team. Moreover, by observing distinctive outcomes, we expect our research outcomes will also provide insights into further improvement of human-robot trustworthy relationships.

Keywords: explanation interface, adversaries, partial observability, trust building

Procedia PDF Downloads 203

Authors: Van Trieu, Shouhuai Xu, Yusheng Feng

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Tracking attack trajectories can be difficult, with limited information about the nature of the attack. Even more difficult as attack information is collected by Intrusion Detection Systems (IDSs) due to the current IDSs having some limitations in identifying malicious and anomalous traffic. Moreover, IDSs only point out the suspicious events but do not show how the events relate to each other or which event possibly cause the other event to happen. Because of this, it is important to investigate new methods capable of performing the tracking of attack trajectories task quickly with less attack information and dependency on IDSs, in order to prioritize actions during incident responses. This paper proposes a two-level graph causality framework for tracking attack trajectories in internet networks by leveraging observable malicious behaviors to detect what is the most probable attack events that can cause another event to occur in the system. Technically, given the time series of malicious events, the framework extracts events with useful features, such as attack time and port number, to apply to the conditional independent tests to detect the relationship between attack events. Using the academic datasets collected by IDSs, experimental results show that the framework can quickly detect the causal pairs that offer meaningful insights into the nature of the internet network, given only reasonable restrictions on network size and structure. Without the framework’s guidance, these insights would not be able to discover by the existing tools, such as IDSs. It would cost expert human analysts a significant time if possible. The computational results from the proposed two-level graph network model reveal the obvious pattern and trends. In fact, more than 85% of causal pairs have the average time difference between the causal and effect events in both computed and observed data within 5 minutes. This result can be used as a preventive measure against future attacks. Although the forecast may be short, from 0.24 seconds to 5 minutes, it is long enough to be used to design a prevention protocol to block those attacks.

Keywords: causality, multilevel graph, cyber-attacks, prediction

Procedia PDF Downloads 157

Authors: Mariana Floria, Elena-Diana Năfureanu, Diana-Mihaela Gălăţanu, Anca-Ecaterina Grumeza, Cristina Gorea-Bocîncă, Diana-Elena Iov, Aurelian-Corneliu Moraru, Dragoș-Marian Popescu

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SARS-CoV-2 is the most deadly and devastating virus of the last one hundred years, being more highly contagious than EBOLA, HIV, Swine Influenza, Severe Acute Respiratory Syndrome, or Middle Eastern Respiratory Syndrome. After two years of pandemic, planning and budgeting for use of healthcare resources and services is very important. The aim of this study was to analyze the costs for hospital stay in patients with predominantly moderate forms of COVID-19 in a support military hospital located in Nord-East of Romania. Inpatient COVID-19 hospitalizations costs, regardless of ICD-10 procedure codes (DRG payment), in a Covid-19 support military hospital were analyzed. From August 2020 through June 2021, 241 patientswere hospitalized. Our national protocol for the treatment of Covid-19 infection was applied. The main COVID-19 manifestations were: 69% respiratory (18% with severe pneumonia, 2.9% with pulmonary embolism, diagnosed by angio-computed tomography), 3.3% cardiac, 28% digestive, and 33% psychiatric (most common anxiety) manifestations. According to COVID-19 severity, most of the patients had moderate (104 patients – 43%) and severe (50 patients - 21%) forms. Seven patients with severe form died because of multiple comorbidities, and 30 patients were transferred in hospitals with COVID-19 intensive care units.Only two patients have had procalcitonin>10 ng/mL (high probability of severe sepsis or septic shock), and 1 patient had moderate risk for septic shock (0.5 - 2 ng/mL). The average estimated costs were about 3000€/patient, without significantly differences depending on disease severity. Equipment costs were 2 times higher than for drugs and 4 times than for laboratory tests. In a Covid-19 support military hospital that took care for predominantly moderate forms of COVID-19, the costs for equipment were much higher than that for treatment. Therefore, new criteria for hospitalization of these forms of COVID-19 deserve to be analyzed to avoid useless costs.

Keywords: Covid-19, costs, hospital stay, military hospital

Procedia PDF Downloads 179

Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

Procedia PDF Downloads 99

Authors: Hanan M. Alghamdi, Kholud E. Alsalami, Manal I. Alshaikhi, Nouf M. Alsalami, Sara A. Awad, Ruqaya A. Alrabei

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Tourism in Saudi Arabia is one of the emerging sectors with rapid growth. The Kingdom of Saudi Arabia is characterized by its wonderful and historical areas, which constitute important cultural and tourist landmarks. These landmarks attract the attention of the government of Saudi Arabia; hence the improvement of the tourism sector becomes one of the important axes of Saudi Arabia's vision 2030. There is a need to enhance the tourist experience by facilitating the tourism process for visitors to the Kingdom of Saudi Arabia. This project aims to design an application to serve domestic tourists and visitors from outside the Kingdom of Saudi Arabia. This application will contain an automated tourist generate plan service by sentiment analysis of comments in Google Map using Lexicon for method Rule-based approach. There are thirteen regions in the kingdom of Saudi Arabia. The regions supported in this application will be Makkah and Asir regions. According to the output of the sentiment analysis, the application will recommend restaurants and cafes, activities (parks, museums) and shopping (shopping centers) in the generated plan. After that, the system will show the user a drop-down list of “Mega-events in Saudi Arabia” containing a link to the site of events in the Kingdom of Saudi Arabia. and “important information for you” public decency regulations.

Keywords: tourist automated plan, sentiment analysis, comments in google map, tourism in Saudi Arabia

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Authors: Martin Leroux, Sylvain Brisebois

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Eating a meal is among the Activities of Daily Living, but it takes a lot of time and effort for people with physical or functional limitations. Dedicated technologies are cumbersome and not portable, while general-purpose assistive robots such as wheelchair-based manipulators are too hard to control for elaborate continuous motion like eating. Eating with such devices has not previously been automated, since there existed no description of a feeding motion for uncontrolled environments. In this paper, we introduce a feeding mode for assistive manipulators, including a mathematical description of trajectories for motions that are difficult to perform manually such as gathering and scooping food at a defined/desired pace. We implement these trajectories in a sequence of movements for a semi-automated feeding mode which can be controlled with a very simple 3-button interface, allowing the user to have control over the feeding pace. Finally, we demonstrate the feeding mode with a JACO robotic arm and compare the eating speed, measured in bites per minute of three eating methods: a healthy person eating unaided, a person with upper limb limitations or disability using JACO with manual control, and a person with limitations using JACO with the feeding mode. We found that the feeding mode allows eating about 5 bites per minute, which should be sufficient to eat a meal under 30min.

Keywords: assistive robotics, automated feeding, elderly care, trajectory design, human-robot interaction

Procedia PDF Downloads 163

Authors: Hui Shi

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This study investigates a newly emerged usage of Chinese numeral classifier mei (枚) in the cyberspace. In modern Chinese grammar, mei as a classifier should occupy the pre-nominal position, and its valid accompanying nouns are restricted to small, flat, fragile inanimate objects rather than humans. To examine the semantic change of mei, two types of data from Weibo.com were collected. First, 500 mei-included Weibo posts constructed a corpus for analyzing this classifier's word order distribution (post-nominal or pre-nominal) as well as its accompanying nouns' semantics (inanimate or human). Second, considering that mei accompanies a remarkable number of human nouns in the first corpus, the second corpus is composed of mei-involved Weibo IDs from users located in first and third-tier cities (n=8 respectively). The findings show that in the cyber community, mei frequently classifies human-related neologisms at the archaic post-normal position. Besides, the 23 to 29-year-old females as well as Weibo users from third-tier cities are the major populations who adopt mei in their user IDs for self-description and identity expression. This paper argues that the creative usage of mei gains popularity in the Chinese internet due to a humor effect. The marked word order switch and semantic misapplication combined to trigger incongruity and jocularity. This study has significance for research on Chinese cyber neologism. It may also lay a foundation for further studies on Chinese classifier change and Chinese internet communication.

Keywords: Chinese classifier, humor, neologism, semantic change

Procedia PDF Downloads 255

Authors: Robyn Moloney, HuiLing Xu

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Many countries wish to produce Asia-literate citizens, through language education. International contexts of Chinese language education are seeking pedagogical innovation to meet local contextual factors frequently holding back learner success. In multicultural Australia, innovative pedagogy is urgently needed to support motivation in sustained study, with greater strategic integration of technology. This research took a qualitative approach to identify need and solutions. The paper analyses strategies that three secondary school teachers are adopting to meet specific challenges in the Australian context. The data include teacher interviews, classroom observations and student interviews. We highlight the use of task-based learning and differentiated teaching for multilevel classes, and the role which digital technologies play in facilitating both areas. The strategy examples are analysed in reference both to a research-based framework for describing quality teaching, and to current understandings of motivation in language learning. The analysis of data identifies learning featuring deep knowledge, higher-order thinking, engagement, social support, utilisation of background knowledge, and connectedness, all of which work towards the learners having a sense of autonomy and an imagination of becoming an adult Chinese language user.

Keywords: Chinese pedagogy, digital technologies, motivation, secondary school

Procedia PDF Downloads 269

Authors: James Patrick A. Diaz, Raul E. Ramboyong

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Background: Hypertensive response during maximal exercise test provides important information on the level of blood pressure control and evaluation of treatment. Method: A single center retrospective descriptive study was conducted among 117 young (aged 20 to 40) and middle age (aged 40 to 65) hypertensive patients, who underwent treadmill stress test. Currently on maintenance frontline medication either monotherapy (Angiotensin-converting enzyme inhibitor/Angiotensin receptor blocker [ACEi/ARB], Calcium channel blocker [CCB], Diuretic - Hydrochlorthiazide [HCTZ]) or combination therapy (ARB+CCB, ARB+HCTZ), who attained a maximal exercise on treadmill stress test (TMST) with hypertensive response (systolic blood pressure: male >210 mm Hg, female >190 mm Hg, diastolic blood pressure >100 mmHg, or increase of >10 mm Hg at any time during the test), on Bruce and Modified Bruce protocol. Exaggerated blood pressure response during exercise (systolic [SBP] and diastolic [DBP]), peak exercise blood pressure (SBP and DBP), recovery period (SBP and DBP) and test for ischemia and their antihypertensive medication/s were investigated. Analysis of variance and chi-square test were used for statistical analysis. Results: Hypertensive responses on maximal exercise test were seen mostly among female population (P < 0.000) and middle age (P < 0.000) patients. Exaggerated diastolic blood pressure responses were significantly lower in patients who were taking CCB (P < 0.004). A longer recovery period that showed a delayed decline in SBP was observed in patients taking ARB+HCTZ (P < 0.036). There were no significant differences in the level of exaggerated systolic blood pressure response and during peak exercise (both systolic and diastolic) in patients using either monotherapy or combination antihypertensives. Conclusion: Calcium channel blockers provided lower exaggerated diastolic BP response during maximal exercise test in hypertensive middle age patients. Patients on combination therapy using ARB+HCTZ exhibited a longer recovery period of systolic blood pressure.

Keywords: antihypertensive, exercise test, hypertension, hyperytensive response

Procedia PDF Downloads 286

Authors: Almutasim Billa A. Alanazi, Hal S. Tharp

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Safe performance and efficient energy consumption are essential factors for designing a control system. This paper presents a reinforcement learning (RL) model that can be applied to control applications to improve safety and reduce energy consumption. As hardware constraints and environmental disturbances are imprecise and unpredictable, conventional control methods may not always be effective in optimizing control designs. However, RL has demonstrated its value in several artificial intelligence (AI) applications, especially in the field of control systems. The proposed model intelligently monitors a system's success by observing the rewards from the environment, with positive rewards counting as a success when the controlled reference is within the desired operating zone. Thus, the model can determine whether the system is safe to continue operating based on the designer/user specifications, which can be adjusted as needed. Additionally, the controller keeps track of energy consumption to improve energy efficiency by enabling the idle mode when the controlled reference is within the desired operating zone, thus reducing the system energy consumption during the controlling operation. Water temperature control for a hydroponic system is taken as a case study for the RL model, adjusting the variance of disturbances to show the model’s robustness and efficiency. On average, the model showed safety improvement by up to 15% and energy efficiency improvements by 35%- 40% compared to a traditional RL model.

Keywords: control system, hydroponics, machine learning, reinforcement learning

Procedia PDF Downloads 187

Authors: Sara Alzahrani, Samia Bokari, Patan Khan, Muneera Alshareef, Rania Safwat, Mohammed Galal, Hamdi Alqadi, Ameerah Alzahrani, Rehab Alboraie

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Introduction & Background: Diabetes in COVID-19 patients is individual risk factor and documented in worldwide studies to contribute to disease severity, increased length of stay and higher mortality. Aggressive management of blood sugars and acute diabetic complications reduce the length of stay and mortality. Methods: Randomly selected 200 patients admitted with diabetes and COVID-19 studied. The unified treatment protocol applied for all patients and blood sugars monitored closely and optimized .Data collected on bimonthly basis and analyzed. Patients’ characteristics taken from data extraction tool (Oasis) of hospital. Median values for length of stay and post discharge FBS and RBS were calculated via Microsoft Excel tool. Mortality rates were calculated by percentages. The results monitored in the post discharge clinic was 130 mg/dl and 170 mg/dl respectively. The results compared with the standard international studies. Discussion: Diabetes in COVID-19 patients posed great challenge as increased severity and mortalities reported compared to non-diabetic. Taking a pre-emptive strategy to combat this problem by aggressively manage diabetes help in reducing length of stay and morbidity. The length of stay in studded population was 3 days as compared to 13 days in a major international study. Financial saving come from rapid turnover of beds. The mortality was 2.5 % compared to reported 7.3% in a major study, reflecting the implications of aggressive management of diabetes. Regular follow-up and support by running post-discharge clinic definitely help reducing readmissions and acute complications of uncontrolled diabetes. Conclusion: Aggressive management of diabetes in COVID-19 patients by tailored treatment protocols and dedicated teams will help to decrease the morbidity and mortality.

Keywords: diabetes, covid-19, management, mortality

Procedia PDF Downloads 178

Authors: Ime R. Udotong, Justina I. R. Udotong, Ofonime U. M. John

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Brass terminal, one of the several crude oil and petroleum products storage/handling facilities in the Niger Delta was built in the 1980s. Activities at this site, over the years, released crude oil into this 3 m-deep, 1500 m-long canal lying adjacent to the terminal with oil floating on it and its sediment heavily polluted. To ensure effective clean-up, three major activities were planned: Site characterization, bioremediation pilot plant construction and testing and full-scale bioremediation of contaminated sediment/bank soil by land farming. The canal was delineated into 12 lots and each characterized, with reference to the floating oily phase, contaminated sediment and canal bank soil. As a result of site characterization, a pilot plant for on-site bioremediation was designed and a treatment basin constructed for carrying out pilot bioremediation test. Following a designed sampling protocol, samples from this pilot plant were collected for analysis at two laboratories as a quality assurance/quality control check. Results showed that Brass Canal upstream is contaminated with dark, thick and viscous oily film with characteristic hydrocarbon smell while downstream, thin oily film interspersed with water were observed. Sediments were observed to be dark with mixture of brownish sandy soil with TPH ranging from 17,800 mg/kg in Lot 1 to 88,500 mg/kg in Lot 12 samples. Brass Canal bank soil was observed to be sandy from ground surface to 3m, below ground surface (bgs) it was silty-sandy and brownish while subsurface soil (4-10m bgs) was sandy-clayey and whitish/grayish with typical hydrocarbon smell. Preliminary results obtained so far have been very promising but were proprietary. This project is considered, to the best of technical literature knowledge, the first large-scale on-site bioremediation project in the Niger Delta region, Nigeria.

Keywords: bioremediation, contaminated sediment, land farming, oily sludge, oil terminal

Procedia PDF Downloads 456

Authors: M. Peña Aguilar Juan, Ríos Hernández Ezequiel, R. Valencia Luis

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For certification, certain goods of public interest, such as medicines and food, it is required the preparation and delivery of a dossier. For its elaboration, legal and administrative knowledge must be taken, as well as organization of the documents of the process, and an order that allows the file verification. Therefore, a virtual platform was developed to support the process of management and elaboration of the dossier, providing accessibility to the information and interfaces that allow the user to know the status of projects. The development of dossier system on the cloud allows the inclusion of the technical requirements for the software management, including the validation and the manufacturing in the field industry. The platform guides and facilitates the dossier elaboration (report, file or history), considering Mexican legislation and regulations, it also has auxiliary tools for its management. This technological alternative provides organization support for documents and accessibility to the information required to specify the successful development of a dossier. The platform divides into the following modules: System control, catalog, dossier and enterprise management. The modules are designed per the structure required in a dossier in those areas. However, the structure allows for flexibility, as its goal is to become a tool that facilitates and does not obstruct processes. The architecture and development of the software allows flexibility for future work expansion to other fields, this would imply feeding the system with new regulations.

Keywords: electronic dossier, cloud management software, pharmaceutical industry, sanitary registration

Procedia PDF Downloads 299

Authors: Akram Farhadi, Mahshid Froughan, Farahnaz Mohammadi, Maryam Rassouli, Maryam Noroozian, Leila Sadeghmoghaddam

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Background: The caregivers’ assessment of their own caregiving is considered the most important concept in exploring their experiences and has a major role in care outcomes. The rising number of people with dementia and their need for care makes family caregiving really important matter to consider and evaluate. Objectives: This study was conducted with the aim to naturalize and validate the Persian version of the Revised Caregiving Appraisal Scale (RCAS) in family caregivers of older adults with dementia. Patients and Method: In this cross-sectional methodological study, the Revised Caregiving Appraisal Scale (RCAS) was translated using International Quality of Life Assessment (IQOLA) protocol, and then a panel of experts examined its face and content validities. To ensure construct validity, the translated Revised Caregiving Appraisal Scale (RCAS) was completed by 236 family caregivers, and factor construct of the scale was assessed with 5 initial factors using confirmatory factor analysis. Internal consistency was found using Cronbach's alpha, and test-retest using intraclass correlation coefficient. Confirmatory factor analysis was performed in LISREL-8.8 software in Windows®. Results: Participating caregivers' mean age was 53.5±13.13 years. Content and face validities of the scale were confirmed according to the views expressed by family caregivers and panel of experts. The confirmatory factor analysis (CFA) results showed appropriate values for all fitness indices (RMSEA=0.046, df/X2=2.428, CFI=0.98, AGFI=0.84, GFI=0.9), and the 5-factor model was confirmed with 27 items. Overall Cronbach's alpha was reported 0.894, and test retest showed overall ICC=0.94. Conclusion: The Persian version of RCAS is a valid and reliable tool for family caregivers' assessment of their caregiving of older adults with dementia, and can be useful in assessing family caregiving interventions.

Keywords: psychometric, family caregivers, reliability and validity, elderly, dementia, self-appraisal

Procedia PDF Downloads 235

Authors: Jeana Cadby, Kae Miyazawa

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With rapidly expanding worldwide adoption of mobile technologies, Information and Communication Technology (ITC) is a major energy user and a contributor to global carbon emissions, due to infrastructure and operational energy consumption. The agricultural sector is also significantly responsible for contributing to global carbon emissions. However, ICT cloud computing using mobile technology can directly reduce environmental impacts in the agricultural sector through applications and mobile connectivity, such as precision fertilizer and pesticide applications, or access to weather data, for example. While direct impacts are easily calculated, indirect environmental impacts from ICT cloud computing usage have not been thoroughly investigated. For example, while women may be more poorly equipped for adaptation to environmentally sustainable agricultural practices due to resource constraints, this research concludes that indirect environmental benefits can be achieved by improving rural access to mobile technology for women. Women in advanced roles and secure land tenure are more likely to invest in long-term agricultural conservation strategies, which protect against environmental degradation. This study examines how ICT using mobile technology advances the role of women in rural agricultural systems and indirectly reduces environmental impacts from agricultural production, through literature examination from secondary sources. Increasing access for women to ICT mobile technology provides indirect environmental and social benefits in the rural agricultural sector.

Keywords: cloud computing, environmental benefits, mobile technology, women

Procedia PDF Downloads 171

Authors: Shital Suresh Borse, Vijayalaxmi Kadroli

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E-commerce industries nowadays implement the latest AI, ML Techniques to improve their own performance and prediction accuracy. This helps to gain a huge profit from the online market. Ant Colony Optimization, Genetic algorithm, Particle Swarm Optimization, Neural Network & GWO help many e-commerce industries for up-gradation of their predictive performance. These algorithms are providing optimum results in various applications, such as stock price prediction, prediction of drug-target interaction & user ratings of similar products in e-commerce sites, etc. In this study, customer reviews will play an important role in prediction analysis. People showing much interest in buying a lot of services& products suggested by other customers. This ultimately increases net profit. In this work, a convolution neural network (CNN) is proposed which further is useful to optimize the prediction accuracy of an e-commerce website. This method shows that CNN is used to optimize hyperparameters of GWO algorithm using an appropriate coding scheme. Accurate model results are verified by comparing them to PSO results whose hyperparameters have been optimized by CNN in Amazon's customer review dataset. Here, experimental outcome proves that this proposed system using the GWO algorithm achieves superior execution in terms of accuracy, precision, recovery, etc. in prediction analysis compared to the existing systems.

Keywords: prediction analysis, e-commerce, machine learning, grey wolf optimization, particle swarm optimization, CNN

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Authors: Kishan Bharadwaj Shridhar

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Cricket is a data-rich sport, generating vast amounts of information, much of which is captured as textual commentary. Leading cricket data providers, such as ESPN Cricinfo include valuable Decision Review System (DRS) statistics within these commentaries, often as footnotes. Despite the significance of this data, accessing and analyzing it efficiently remains a challenge. This paper presents the development of a sophisticated chatbot designed to answer queries specifically about DRS in cricket. It supports up to seven distinct query types, including individual player statistics, umpire performance, player vs umpire dynamics, comparisons between batter and bowler, a player’s record at specific venues and more. Additionally, it enables stateful conversations, allowing a user to seamlessly build upon previous queries for a fluid and interactive experience. Leveraging advanced text-to-SQL methodologies and open-source frameworks such as Langgraph, it ensures low latency and robust performance. A distinct prompt engineering module enables the system to accurately interpret query intent, dynamically transitioning to an assisted text-to-SQL approach or a rule-based engine, as needed. This solution is the one of its kind in cricket analytics, offering unparalleled insights in cricket through an intuitive interface. It can be extended to other facets of cricket data and beyond, to other sports that generate textual data.

Keywords: conversational AI, cricket data analytics, text to SQL, large language models, stateful conversations.

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Authors: Akram Sadat Jafari Roodbandi, Alireza Choobineh, Nazanin Hosseini, Vafa Feyzi

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Introduction: Physical fitness and optimum functional movement are essential for efficiently performing job tasks without fatigue and injury. Functional Movement Screening (FMS) tests are used in screening of athletes and military forces. Nurses and emergency medical staff are obliged to perform many physical activities such as transporting patients, CPR operations, etc. due to the nature of their jobs. This study aimed to assess relationship between FMS test score and the prevalence of musculoskeletal disorders (MSDs) in emergency nurses and emergency medical services (EMS) staff. Methods: 134 male and female emergency nurses and EMS technicians participated in this cross-sectional, descriptive-analytical study. After video tutorial and practical training of how to do FMS test, the participants carried out the test while they were wearing comfortable clothes. The final score of the FMS test ranges from 0 to 21. The score of 14 is considered weak in the functional movement base on FMS test protocol. In addition to the demographic data questionnaire, the Nordic musculoskeletal questionnaire was also completed for each participant. SPSS software was used for statistical analysis with a significance level of 0.05. Results: Totally, 49.3% (n=66) of the subjects were female. The mean age and work experience of the subjects were 35.3 ± 8.7 and 11.4 ± 7.7, respectively. The highest prevalence of MSDs was observed at the knee and lower back with 32.8% (n=44) and 23.1% (n=31), respectively. 26 (19.4%) health worker had FMS test score of 14 and less. The results of the Spearman correlation test showed that the FMS test score was significantly associated with MSDs (r=-0.419, p < 0.0001). It meant that MSDs increased with the decrease of the FMS test score. Age, sex, and MSDs were the remaining significant factors in linear regression logistic model with dependent variable of FMS test score. Conclusion: FMS test seems to be a usable screening tool in pre-employment and periodic medical tests for occupations that require physical fitness and optimum functional movements.

Keywords: functional movement, musculoskeletal disorders, health care worker, screening test

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Authors: Basak Ozkendirci

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The discovery of chemical dyes, the development of regenerated fibers, and warp knitting technology have enormous effects on the fashion world. The trends created by the information obtained in the context of various studies today shape the fashion world. Trend analysts must follow scientific developments as well as sociological events, political developments and artwork to obtain healthy data on trends. Digital printing technologies have changed the dynamics of textile printing production and also the style of printed designs. Fashion designers already have started design 3D printed accessories and garments. The research fields like the internet of things, artificial intelligence, hologram technologies, mechatronics, energy storage systems, nanotechnology are seen as the technologies that will change the social life and economy of the future. It is clear that research carried out in these areas will affect the textiles of the future and whereat the trends of fashion. The article aims to create a future vision for trend researchers and designers by giving clues about the changes to be experienced in the fashion world. In the first part of the article, information about the scientific studies that are thought to shape the future is given, and the forecasting about how the inventions that can be obtained from these studies can be adapted at the textile are presented. In the second part of the article, examples of how the new generation of innovative textiles will affect the daily life experience of the user are given.

Keywords: biotextiles, fashion trends, nanotextiles, new materials, smart textiles, techno textiles

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Authors: Hameed Rukayat Opeyemi, Pericles Pilidis, Pagone Emanuele

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An infinitesimal increase in performance can have immense reduction in operating and capital expenses in a power generation system. Therefore, constant studies are being carried out to improve both conventional and novel power cycles. Globally, power producers are constantly researching on ways to minimize emission and to collectively downsize the total cost rate of power plants. A substantial spurt of developmental technologies of low carbon cycles have been suggested and studied, however they all have their limitations and financial implication. In the area of carbon abatement in power plants, three major objectives conflict: The cost rate of the plant, Power output and Environmental impact. Since, an increase in one of this parameter directly affects the other. This poses a multi-objective problem. It is paramount to be able to discern the point where improving one objective affects the other. Hence, the need for a Pareto-based optimization algorithm. Pareto-based optimization algorithm helps to find those points where improving one objective influences another objective negatively and stops there. The application of Pareto-based optimization algorithm helps the user/operator/designer make an informed decision. This paper sheds more light on areas that multi-objective optimization has been applied in carbon abatement technologies in the last five years, developments and prospects.

Keywords: gas turbine, low carbon technology, pareto optimal, multi-objective optimization

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Authors: Houda Najeh, Mahendra Pratap Singh, Stéphane Ploix, Antoine Caucheteux, Karim Chabir, Mohamed Naceur Abdelkrim

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Building system is highly vulnerable to different kinds of faults and human misbehaviors. Energy efficiency and user comfort are directly targeted due to abnormalities in building operation. The available fault diagnosis tools and methodologies particularly rely on rules or pure model-based approaches. It is assumed that model or rule-based test could be applied to any situation without taking into account actual testing contexts. Contextual tests with validity domain could reduce a lot of the design of detection tests. The main objective of this paper is to consider fault validity when validate the test model considering the non-modeled events such as occupancy, weather conditions, door and window openings and the integration of the knowledge of the expert on the state of the system. The concept of heterogeneous tests is combined with test validity to generate fault diagnoses. A combination of rules, range and model-based tests known as heterogeneous tests are proposed to reduce the modeling complexity. Calculation of logical diagnoses coming from artificial intelligence provides a global explanation consistent with the test result. An application example shows the efficiency of the proposed technique: an office setting at Grenoble Institute of Technology.

Keywords: heterogeneous tests, validity, building system, sensor grids, sensor fault, diagnosis, fault detection and isolation

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Authors: Naveen Kumar B. T., Anuj Tyagi, Prabjeet Singh, Shanthanagouda A. H., Sumeet Rai

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Inland saline water resources are gaining the importance in expanding the aquaculture activities to mitigate the nutritional and food security issues of the world. For profitable and sustainable aquaculture practices, scientific farming, biosecurity measure, and best fish health management should be the integral part of developmental activities. Keeping in line with global awareness and trends, the Indian government has taken an innovative step to conduct disease surveillance and awareness programme for aquatic disease through network project. This ‘National Surveillance Programme for Aquatic Animal Diseases (NSPAAD)’ is being implemented in collaboration of national institutes and state agriculture universities with funding support from National Fisheries Development Board (NFDB), Govt. of India. Guru Angad Dev Veterinary and Animal Sciences University (GADVASU), Ludhiana, an NSPAAD collaborator, has been actively engaged in disease surveillance in the Indian state of Punjab. Shrimp farming in inland saline areas of Punjab is expanding at a tremendous pace under the guidance of GADVASU along with the support of State Fisheries Department. Under this national disease surveillance programme, we reported Enterocytozoon hepatopenaei (EHP) infection in the Litopenaeus vannamei cultured in the inland saline waters. Polymerase chain reaction (PCR) based diagnosis was carried out using the OIE (World Organisation for Animal Health) protocol. It was observed that out of 20 shrimp farms, two farms were 1st step PCR positive and two more farms were nested PCR positive. All the EHP positive ponds had shown the white faeces along with mortalities at very low rate. Therefore, implementation of biosecurity and continuous surveillance and monitoring program for finfish and shellfish aquaculture are in need of the hour to prevent and control the large-scale disease outbreaks and subsequent economic losses.

Keywords: disease, EHP, inland saline water, shrimp culture

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Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

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Authors: Marc Gärtner, Monika Huesmann, Katharina Schiederig

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While workplace flexibility provides opportunities to better balance work and family care, careers in management are still predominantly based on physical presence, blurred boundaries and a culture of availability at the workplace. Thus, carers (mostly women) still experience disadvantages and stalled careers. In a multi-case study, funded by the German Federal Ministry of Education and Research, success factors and barriers of flexible work arrangements in five big organizations, including three of the largest German companies, have been identified. Using qualitative interview methods, the working models of 10 female and male users of flexible work arrangements like part time, home office and job sharing have been studied. The study group applied a 360-degree approach with focus groups, covering the users’ themselves, their superiors, colleagues and staff as well as in-house human resource managers. The group interviews reveal that success of flexible models is mainly built on three factors: (a) the inclusiveness of the organizational culture, (b) the commitment of leaders and especially the supervisors, and (c) the fitting of the model and the user(s). Flexibilization of time and space can indeed contribute to a better work-life balance. This is, however, not a necessary outcome, as the interviews suggest, but depends on the right implementation of the right model in the particular work environment. Beyond the actual study results, the presentation will also assess the methodological approach.

Keywords: flexible work, leadership, organizational culture, work-life balance

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