Search results for: warning labels

Authors: Mohamed Chabi

Abstract:

The project of helping students at risk started at the Math department in the new foundation program at Qatar University in the fall 2012 semester. The purpose was to find ways to help students who were struggling with their math courses Elementary algebra or Precalculus course due to many factors. Department had formed the Committee “students at Risk” at the start of 12-13 to assist struggling students in our math courses to get their studies on track. A mechanism was developed to support students who are at risk using a developed E-Monitoring system. E-Monitoring system was developed to manage automatically all transactions relevant to the students’ attendance, Students ‘‘warning Students’’ grading, etc. E-Monitoring System produce various statistics such as, Overall course statistics, Performance, Students at Risk… to help department to develop a higher quality of education in the Foundation Program at Math department. The mechanism was studies and evaluated. Whatever the cause, the sooner we identify students who are not performing well academically, the sooner we can provide, or direct them to the resources that are available to them. In this paper, we outline the mechanism and its effect on students’ performance. The collected data from various exams shows that students had benefited from the mechanism.

Keywords: students at risk, e-monitoring system, warning students, performance

Procedia PDF Downloads 488

Authors: Ahmet Parlak, Celalettin Bilgen

Abstract:

In order to create an effective early warning system, Identification of the risks, preparation and carrying out risk modeling of risk scenarios, taking into account the shortcomings of the old disaster scenarios should be used to improve the system. In the light of this, the importance of risk modeling in creating an effective early warning system is understood. In the scope of TAFRISK project risk modeling trend analysis report on risk modeling developed and a demonstration was conducted for Risk Modeling for flood and mass movements. For risk modeling R&D, studies have been conducted to determine the information, and source of the information, to be gathered, to develop algorithms and to adapt the current algorithms to Turkey’s conditions for determining the risk score in the high disaster risk areas. For each type of the disaster; Disaster Deficit Index (DDI), Local Disaster Index (LDI), Prevalent Vulnerability Index (PVI), Risk Management Index (RMI) have been developed as disaster indices taking danger, sensitivity, fragility, and vulnerability, the physical and economic damage into account in the appropriate scale of the respective type.

Keywords: disaster, hazard, risk modeling, sensor

Procedia PDF Downloads 428

Authors: Miguel Contreras, David Long, Will Bachman

Abstract:

Background: Microscopy plays a central role in cell and developmental biology. In particular, fluorescence microscopy can be used to visualize specific cellular components and subsequently quantify their morphology through development of virtual-cell models for study of effects of mechanical forces on cells. However, there are challenges with these imaging experiments, which can make it difficult to quantify cell morphology: inconsistent results, time-consuming and potentially costly protocols, and limitation on number of labels due to spectral overlap. To address these challenges, the objective of this project is to develop an automatic computational machine learning pipeline to predict cellular components morphology for virtual-cell generation based on fluorescence cell membrane confocal z-stacks. Methods: Registered confocal z-stacks of nuclei and cell membrane of endothelial cells, consisting of 20 images each, were obtained from fluorescence confocal microscopy and normalized through software pipeline for each image to have a mean pixel intensity value of 0.5. An open source machine learning algorithm, originally developed to predict fluorescence labels on unlabeled transmitted light microscopy cell images, was trained using this set of normalized z-stacks on a single CPU machine. Through transfer learning, the algorithm used knowledge acquired from its previous training sessions to learn the new task. Once trained, the algorithm was used to predict morphology of nuclei using normalized cell membrane fluorescence images as input. Predictions were compared to the ground truth fluorescence nuclei images. Results: After one week of training, using one cell membrane z-stack (20 images) and corresponding nuclei label, results showed qualitatively good predictions on training set. The algorithm was able to accurately predict nuclei locations as well as shape when fed only fluorescence membrane images. Similar training sessions with improved membrane image quality, including clear lining and shape of the membrane, clearly showing the boundaries of each cell, proportionally improved nuclei predictions, reducing errors relative to ground truth. Discussion: These results show the potential of pre-trained machine learning algorithms to predict cell morphology using relatively small amounts of data and training time, eliminating the need of using multiple labels in immunofluorescence experiments. With further training, the algorithm is expected to predict different labels (e.g., focal-adhesion sites, cytoskeleton), which can be added to the automatic machine learning pipeline for direct input into Principal Component Analysis (PCA) for generation of virtual-cell mechanical models.

Keywords: cell morphology prediction, computational machine learning, fluorescence microscopy, virtual-cell models

Procedia PDF Downloads 205

Authors: Jacques Lamarche, Juhani Dickinson

Abstract:

This paper introduces a computational model of a Grammar as Logic of Labeling (GLL), where the lexical primitives of morphosyntax are phonological matrixes, the form of words, understood as labels that apply to realities (or targets) assumed to be outside of grammar altogether. The hypothesis is that even though a lexical label relates to its target arbitrarily, this label in a complex (constituent) label is part of a labeling pattern which, depending on its value (i.e., N, V, Adj, etc.), imposes language-specific restrictions on what it targets outside of grammar (in the world/semantics or in cognitive knowledge). Lexical forms categorized as nouns, verbs, adjectives, etc., are effectively targets of labeling patterns in use. The paper illustrates GLL through a computer model of basic patterns in English NPs. A constituent label is a binary object that encodes: i) alignment of input forms so that labels occurring at different points in time are understood as applying at once; ii) endocentric structuring - every grammatical constituent has a head label that determines the target of the constituent, and a limiter label (the non-head) that restricts this target. The N or A values are restricted to limiter label, the two differing in terms of alignment with a head. Consider the head initial DP ‘the dog’: the label ‘dog’ gets an N value because it is a limiter that is evenly aligned with the head ‘the’, restricting application of the DP. Adapting a traditional analysis of ‘the’ to GLL – apply label to something familiar – the DP targets and identifies one reality familiar to participants by applying to it the label ‘dog’ (singular). Consider next the DP ‘the large dog’: ‘large dog’ is nominal by even alignment with ‘the’, as before, and since ‘dog’ is the head of (head final) ‘large dog’, it is also nominal. The label ‘large’, however, is adjectival by narrow alignment with the head ‘dog’: it doesn’t target the head but targets a property of what dog applies to (a property or value of attribute). In other words, the internal composition of constituents determines that a form targets a property or a reality: ‘large’ and ‘dog’ happen to be valid targets to realize this constituent. In the presentation, the computer model of the analysis derives the 8 possible sequences of grammatical values with three labels after the determiner (the x y z): 1- D [ N [ N N ]]; 2- D [ A [ N N ] ]; 3- D [ N [ A N ] ]; 4- D [ A [ A N ] ]; 5- D [ [ N N ] N ]; 5- D [ [ A N ] N ]; 6- D [ [ N A ] N ] 7- [ [ N A ] N ] 8- D [ [ Adv A ] N ]. This approach that suggests that a computer model of these grammatical patterns could be used to construct ontologies/knowledge using speakers’ judgments about the validity of lexical meaning in grammatical patterns.

Keywords: syntactic theory, computational linguistics, logic and grammar, semantics, knowledge and grammar

Procedia PDF Downloads 38

Authors: Vito Telesca, Giuseppina A. Giorgio, M. Ragosta

Abstract:

The effects of extreme heat events are increasing in recent years. Humans are forced to adjust themselves to adverse climatic conditions. The impact of weather on human health has become public health significance, especially in light of climate change and rising frequency of devasting weather events (e.g., heat waves and floods). The interest of scientific community is widely known. In particular, the associations between temperature and mortality are well studied. Weather conditions are natural factors that affect the human organism. Recent works show that the temperature threshold at which an impact is seen varies by geographic area and season. These results suggest heat warning criteria should consider local thresholds to account for acclimation to local climatology as well as the seasonal timing of a forecasted heat wave. Therefore, it is very important the problem called ‘local warming’. This is preventable with adequate warning tools and effective emergency planning. Since climate change has the potential to increase the frequency of these types of events, improved heat warning systems are urgently needed. This would require a better knowledge of the full impact of extreme heat on morbidity and mortality. The majority of researchers who analyze the associations between human health and weather variables, investigate the effect of air temperature and bioclimatic indices. These indices combine air temperature, relative humidity, and wind speed and are very important to determine the human thermal comfort. Health impact studies of weather events showed that the prevention is an essential element to dramatically reduce the impact of heat waves. The summer Italian of 2012 was characterized with high average temperatures (con un +2.3°C in reference to the period 1971-2000), enough to be considered as the second hottest summer since 1800. Italy was the first among countries in Europe which adopted tools for to predict these phenomena with 72 hours in advance (Heat Health Watch Warning System - HHWWS). Furthermore, in Italy heat alert criteria relies on the different Indexes, for example Apparent temperature, Scharlau index, Thermohygrometric Index, etc. This study examines the importance of developing public health policies that protect the most vulnerable people (such as the elderly) to extreme temperatures, highlighting the factors that confer susceptibility.

Keywords: heat waves, Italy, local warming, temperature

Procedia PDF Downloads 243

Authors: Nditsheni Ramakuela, Sonto Maputle, Base Khoza, Augustine Tugli

Abstract:

Inadequate health literacy can cause difficulties in understanding and compliance to treatment plan. With diabetic condition, self-care activities include behaviours of following a diet plan, avoiding high fat foods, increased exercise, self-glucose monitoring, and foot care. Patients with poor health literacy have difficulty interpreting medication warning labels, following directions on a prescription label and identifying their medications. Difficulties in understanding and performing self-care and health-related activities may ultimately lead to poor health outcomes. The study explored and described factors affecting health literacy and self-care to diabetic regimen by female patients at selected hospital in Limpopo Province of South Africa. Qualitative and explorative research design was used. Female patients who were admitted and diagnosed with diabetes in female medical ward constituted the study population. Non-probability, purposive sampling was used to select 20 female patients diagnosed with diabetes, who were above 18 years and admitted during April–November 2014. An in-depth face-to-face, unstructured interview was used to collect data. Data were analysed using open coding method. Measures to ensure trustworthiness and ethical considerations were adhered to. Findings revealed factors affecting health literacy for diabetic self-care activities amongst patients were; patient, family, disease and facility related. Proposed recommendations were; to strengthen diabetes education and patient-provider partnership. This is important and must be transferred to strengthen self-care activities to fully benefit the patient.

Keywords: compliance, diabetes mellitus, diabetic regimen, health literacy, self activities

Procedia PDF Downloads 287

Authors: Al Romano, Jeryl C. Hije, Mechaela Marie O. Tabiolo

Abstract:

The decline in water quality is a major factor in aquatic disease outbreaks and can lead to significant mortality among aquatic organisms. Understanding the relationship between dissolved oxygen (DO) and water temperature is crucial, as these variables directly impact the health, behavior, and survival of fish populations. This study investigated how DO levels, water temperature, and atmospheric temperature interact in Sampaloc Lake to assess the risk of fish mortality. By employing a combination of linear regression models and machine learning techniques, researchers developed predictive models to forecast DO concentrations at various depths. The results indicate that while DO levels generally decrease with depth, the predicted concentrations are sufficient to support the survival of common fish species in Sampaloc Lake during March, April, and May 2025.

Keywords: aquaculture, dissolved oxygen, water temperature, regression analysis, machine learning, fish mass mortality, early warning system

Procedia PDF Downloads 35

Authors: Yogesh Kumar Singh, T. S. Murugesh Prabhu, Upasana Dutta, Girishchandra Yendargaye, Rahul Yadav, Rohini Gopinath Kale, Binay Kumar, Manoj Khare

Abstract:

The Indian subcontinent is severely affected by floods that cause intense irreversible devastation to crops and livelihoods. With increased incidences of floods and their related catastrophes, an Early Warning System for Flood Prediction and an efficient Flood Management System for the river basins of India is a must. Accurately modeled hydrological conditions and a web-based early warning system may significantly reduce economic losses incurred due to floods and enable end users to issue advisories with better lead time. This study describes the design and development of an EWS-FP using advanced computational tools/methods, viz. High-Performance Computing (HPC), Remote Sensing, GIS technologies, and open-source tools for the Mahanadi River Basin of India. The flood prediction is based on a robust 2D hydrodynamic model, which solves shallow water equations using the finite volume method. Considering the complexity of the hydrological modeling and the size of the basins in India, it is always a tug of war between better forecast lead time and optimal resolution at which the simulations are to be run. High-performance computing technology provides a good computational means to overcome this issue for the construction of national-level or basin-level flash flood warning systems having a high resolution at local-level warning analysis with a better lead time. High-performance computers with capacities at the order of teraflops and petaflops prove useful while running simulations on such big areas at optimum resolutions. In this study, a free and open-source, HPC-based 2-D hydrodynamic model, with the capability to simulate rainfall run-off, river routing, and tidal forcing, is used. The model was tested for a part of the Mahanadi River Basin (Mahanadi Delta) with actual and predicted discharge, rainfall, and tide data. The simulation time was reduced from 8 hrs to 3 hrs by increasing CPU nodes from 45 to 135, which shows good scalability and performance enhancement. The simulated flood inundation spread and stage were compared with SAR data and CWC Observed Gauge data, respectively. The system shows good accuracy and better lead time suitable for flood forecasting in near-real-time. To disseminate warning to the end user, a network-enabled solution is developed using open-source software. The system has query-based flood damage assessment modules with outputs in the form of spatial maps and statistical databases. System effectively facilitates the management of post-disaster activities caused due to floods, like displaying spatial maps of the area affected, inundated roads, etc., and maintains a steady flow of information at all levels with different access rights depending upon the criticality of the information. It is designed to facilitate users in managing information related to flooding during critical flood seasons and analyzing the extent of the damage.

Keywords: flood, modeling, HPC, FOSS

Procedia PDF Downloads 89

Authors: Mohammadhossein Moghimi, Robert Stone, Pia Rotshtein

Abstract:

Recording psychological and physiological correlates of human performance within virtual environments and interpreting their impacts on human engagement, ‘immersion’ and related emotional or ‘effective’ states is both academically and technologically challenging. By exposing participants to an effective, real-time (game-like) virtual environment, designed and evaluated in an earlier study, a psychophysiological database containing the EEG, GSR and Heart Rate of 30 male and female gamers, exposed to 10 games, was constructed. Some 174 features were subsequently identified and extracted from a number of windows, with 28 different timing lengths (e.g. 2, 3, 5, etc. seconds). After reducing the number of features to 30, using a feature selection technique, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) methods were subsequently employed for the classification process. The classifiers categorised the psychophysiological database into four effective clusters (defined based on a 3-dimensional space – valence, arousal and dominance) and eight emotion labels (relaxed, content, happy, excited, angry, afraid, sad, and bored). The KNN and SVM classifiers achieved average cross-validation accuracies of 97.01% (±1.3%) and 92.84% (±3.67%), respectively. However, no significant differences were found in the classification process based on effective clusters or emotion labels.

Keywords: virtual reality, effective computing, effective VR, emotion-based effective physiological database

Procedia PDF Downloads 233

Authors: Vladimir Kulyukin, Tanwir Zaman, Sarat Kiran Andhavarapu

Abstract:

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 373

Authors: Michael Bellas, George Voulgaridis

Abstract:

This paper refers to the world known mega - landslide induced at the community of Ropoto, belonging to the Municipality of Trikala, in the Central part of Greece. The landslide affected the debris as well as the colluvium mantle of the flysch, and makes up a special case of study in engineering geology and geotechnical engineering not only because of the size of the domain affected by the landslide (approximately 750m long), but also because of the geostructure’s global behavior. Due to the landslide, the whole community’s infrastructure massively collapsed and human lives were put in danger. After the complete simulation of the coupled Seepage - Deformation phenomenon due to the extreme rainfall, and by closely examining the slope’s global behavior, both the mitigation of the landslide, as well as, an advanced surveillance method (Fiber Bragg Grating) using fiber optics were further studied, in order both to retain the geostructure and to monitor its health by creating an early warning system, which would serve as a complete safety net for saving both the community’s infrastructure as well as the lives of its habitats.

Keywords: landslide, remediation measures, the finite element method (FEM), Fiber Bragg Grating (FBG) sensing method

Procedia PDF Downloads 329

Authors: Christian Arcos, Marley Vellasco, Abraham Alcaim

Abstract:

In this paper, we present a wavelet coefficients masking based on Local Binary Patterns (WLBP) approach to enhance the temporal spectra of the wavelet coefficients for speech enhancement. This technique exploits the wavelet denoising scheme, which splits the degraded speech into pyramidal subband components and extracts frequency information without losing temporal information. Speech enhancement in each high-frequency subband is performed by binary labels through the local binary pattern masking that encodes the ratio between the original value of each coefficient and the values of the neighbour coefficients. This approach enhances the high-frequency spectra of the wavelet transform instead of eliminating them through a threshold. A comparative analysis is carried out with conventional speech enhancement algorithms, demonstrating that the proposed technique achieves significant improvements in terms of PESQ, an international recommendation of objective measure for estimating subjective speech quality. Informal listening tests also show that the proposed method in an acoustic context improves the quality of speech, avoiding the annoying musical noise present in other speech enhancement techniques. Experimental results obtained with a DNN based speech recognizer in noisy environments corroborate the superiority of the proposed scheme in the robust speech recognition scenario.

Keywords: binary labels, local binary patterns, mask, wavelet coefficients, speech enhancement, speech recognition

Procedia PDF Downloads 229

Authors: Hamidou Diallo, Marianne Guille

Abstract:

This paper identifies leading triggers of deficient episodes in terms of GDP growth based on a sample of countries at different stages of development over 1994-2017. Using logit models, we build early warning systems (EWS), and our results show important differences between developing countries (DCs) and advanced economies (AEs). For AEs, the main predictors of the probability of entering in a GDP growth deficient episode are the deterioration of external imbalances and the vulnerability of fiscal position while DCs face different challenges that need to be considered. The key indicators for them are first, the low ability to pay their debts, and second, their belonging or not to a common currency area. We also build homogeneous pools of countries inside AEs and DCs. The evolution of the proportion of AE countries in the riskiest pool is marked first, by three distinct peaks just after the high-tech bubble burst, the global financial crisis, and the European sovereign debt crisis, and second by a very low minimum level in 2006 and 2007. In contrast, the situation of DCs is characterized first by the relative stability of this proportion and then by an upward trend from 2006, that can be explained by a more unfavorable socio-political environment leading to shortcomings in the fiscal consolidation.

Keywords: currency area, early warning system, external imbalances, fiscal vulnerability, GDP growth, public debt

Procedia PDF Downloads 126

Authors: Adrian Rifqi, Febe Napitupulu, Erdi Hermawan, Edwin Putra, Yang Leprilian

Abstract:

This article focuses on the implementation of the production improvement process of the Indonesian land certificate product that printed in Perum Peruri as the state-owned enterprises. Based on the data obtained, there are several complaints from customers of the 2019 land certificate production. The complaints become a negative value to loyal customers of Perum Peruri. Almost all the complaints are referring to ‘defective printouts and the difference between products in packaging and packaging labels both in terms of type and quantity’. To overcome this problem, we intend to make an improvement to the production process that focuses on complaints ‘there is a difference between products in packaging with packaging labels’. Improvements in the land certificate production process are relying on the technology of the scales and QR code on the packaging label. In addition, using the QR code on the packaging label will facilitate the process of tracking product data. With this method, we hope to reduce the error rate between products in packaging with the packaging label both in terms of quantity, type, and product number on the land certificate and error rate of sending land certificates, which will be sent to many places to 0%. With this solution, we also hope to get precise data and real-time reports on the production of land certificates in the near future, so track and trace implementation can be done as the solution of the land certificate production.

Keywords: land certificates, QR code, track and trace, packaging

Procedia PDF Downloads 160

Authors: Tapan Upadhyay, Aqib Siddiqui, Sameer Khan

Abstract:

Railway crossing system in India is a manually operated level crossing system, either manned or unmanned. The main aim is to protect pedestrians and vehicles from colliding with trains, which pass at regular intervals, as India has the largest and busiest railway network. But because of human error and negligence, every year thousands of lives are lost due to accidents at railway crossings. To avoid this, we suggest a solution, by using Programmable Logical Controller (PLC) based automatic system, which will automatically control the barrier as well as roadblocks to stop people from crossing while security warning is given. Often people avoid security warning, and pass two-wheelers from beneath the barrier, while the train is at a distance away. This paper aims at reducing the fatality and accident rate by controlling barrier and roadblocks using sensors which sense the incoming train and vehicles and sends a signal to PLC. The PLC in return sends a signal to barrier and roadblocks. Once the train passes, the barrier and roadblocks retrieve back, and the passage is clear for vehicles and pedestrians to cross. PLC’s are used because they are very flexible, cost effective, space efficient, reduces complexity and minimises errors. Supervisory Control And Data Acquisition (SCADA) is used to monitor the functioning.

Keywords: level crossing, PLC, sensors, SCADA

Procedia PDF Downloads 427

Authors: Shanaka Gunasekara, Maleen Jayasuriya, Nalin Harischandra, Lilantha Samaranayake, Gamini Dissanayake

Abstract:

One serious facet of the worsening Human-Elephant conflict (HEC) in nations such as Sri Lanka involves elephant-train collisions. Endangered Asian elephants are maimed or killed during such accidents, which also often result in orphaned or disabled elephants, contributing to the phenomenon of lone elephants. These lone elephants are found to be more likely to attack villages and showcase aggressive behaviour, which further exacerbates the overall HEC. Furthermore, Railway Services incur significant financial losses and disruptions to services annually due to such accidents. Most elephant-train collisions occur due to a lack of adequate reaction time. This is due to the significant stopping distance requirements of trains, as the full braking force needs to be avoided to minimise the risk of derailment. Thus, poor driver visibility at sharp turns, nighttime operation, and poor weather conditions are often contributing factors to this problem. Initial investigations also indicate that most collisions occur in localised “hotspots” where elephant pathways/corridors intersect with railway tracks that border grazing land and watering holes. Taking these factors into consideration, this work proposes the leveraging of recent developments in Convolutional Neural Network (CNN) technology to detect elephants using an RGB/infrared capable camera around known hotspots along the railway track. The CNN was trained using a curated dataset of elephants collected on field visits to elephant sanctuaries and wildlife parks in Sri Lanka. With this vision-based detection system at its core, a prototype unit of an early warning system was designed and tested. This weatherised and waterproofed unit consists of a Reolink security camera which provides a wide field of view and range, an Nvidia Jetson Xavier computing unit, a rechargeable battery, and a solar panel for self-sufficient functioning. The prototype unit was designed to be a low-cost, low-power and small footprint device that can be mounted on infrastructures such as poles or trees. If an elephant is detected, an early warning message is communicated to the train driver using the GSM network. A mobile app for this purpose was also designed to ensure that the warning is clearly communicated. A centralized control station manages and communicates all information through the train station network to ensure coordination among important stakeholders. Initial results indicate that detection accuracy is sufficient under varying lighting situations, provided comprehensive training datasets that represent a wide range of challenging conditions are available. The overall hardware prototype was shown to be robust and reliable. We envision a network of such units may help contribute to reducing the problem of elephant-train collisions and has the potential to act as an important surveillance mechanism in dealing with the broader issue of human-elephant conflicts.

Keywords: computer vision, deep learning, human-elephant conflict, wildlife early warning technology

Procedia PDF Downloads 226

Authors: Yulan Wu

Abstract:

The spread of fake news on social media has posed significant societal harm to the public and the nation, with its threats spanning various domains, including politics, economics, health, and more. News on social media often covers multiple domains, and existing models studied by researchers and relevant organizations often perform well on datasets from a single domain. However, when these methods are applied to social platforms with news spanning multiple domains, their performance significantly deteriorates. Existing research has attempted to enhance the detection performance of multi-domain datasets by adding single-domain labels to the data. However, these methods overlook the fact that a news article typically belongs to multiple domains, leading to the loss of domain knowledge information contained within the news text. To address this issue, research has found that news records in different domains often use different vocabularies to describe their content. In this paper, we propose a fake news detection framework that combines domain knowledge and expert knowledge. Firstly, it utilizes an unsupervised domain discovery module to generate a low-dimensional vector for each news article, representing domain embeddings, which can retain multi-domain knowledge of the news content. Then, a feature extraction module uses the domain embeddings discovered through unsupervised domain knowledge to guide multiple experts in extracting news knowledge for the total feature representation. Finally, a classifier is used to determine whether the news is fake or not. Experiments show that this approach can improve multi-domain fake news detection performance while reducing the cost of manually labeling domain labels.

Keywords: fake news, deep learning, natural language processing, multiple domains

Procedia PDF Downloads 73

Authors: Ammar Ayad Issa Al-Rifaie, Zuhreya Muazu, Maysam Ali Abdulwahid, Dermot Gleeson

Abstract:

The aim of this audit was to examine the efficiency of alcohol history documentation and screening for hazardous drinkers at the Medical Admission Unit (MAU) of Northern General Hospital (NGH), Sheffield, to identify any potential for enhancing clinical practice. Data were collected from medical clerking sheets, ICE system and directly from 82 patients by three junior medical doctors using both CAGE questionnaire and AUDIT-C tool for newly admitted patients to MAU in NGH, in the period between January and March 2015. Alcohol consumption was documented in around two-third of the patient sample and this was documented fairly accurately by health care professionals. Some used subjective words such as 'social drinking' in the alcohol units’ section of the history. CAGE questionnaire was applied to only four patients and none of the patients had documented advice, education or referral to an alcohol liaison team. AUDIT-C tool had identified 30.4%, while CAGE 10.9%, of patients admitted to the NGH MAU as hazardous drinkers. The amount of alcohol the patient consumes positively correlated with the score of AUDIT-C (Pearson correlation 0.83). Re-audit is planned to be carried out after integrating AUDIT-C tool as labels in the notes and presenting a brief teaching session to junior doctors. Alcohol misuse screening is not adequately undertaken and no appropriate action is being offered to hazardous drinkers. CAGE questionnaire is poorly applied to patients and when satisfactory and adequately used has low sensitivity to detect hazardous drinkers in comparison with AUDIT-C tool. Re-audit of alcohol screening practice after introducing AUDIT-C tool in clerking sheets (as labels) is required to compare the findings and conclude the audit cycle.

Keywords: alcohol screening, AUDIT-C, CAGE, hazardous drinking

Procedia PDF Downloads 409

Authors: Mladen Milicevic

Abstract:

This presentation will address how the statistical analysis of digitized popular music influences the music creation and emotionally manipulates consumers.Furthermore, it will deal with semiological aspect of uniformization of musical taste in order to predict the potential revenues generated by popular music sales. In the USA, we live in an age where most of the popular music (i.e. music that generates substantial revenue) has been digitized. It is safe to say that almost everything that was produced in last 10 years is already digitized (either available on iTunes, Spotify, YouTube, or some other platform). Depending on marketing viability and its potential to generate additional revenue most of the “older” music is still being digitized. Once the music gets turned into a digital audio file,it can be computer-analyzed in all kinds of respects, and the similar goes for the lyrics because they also exist as a digital text file, to which any kin of N Capture-kind of analysis may be applied. So, by employing statistical examination of different popular music metrics such as tempo, form, pronouns, introduction length, song length, archetypes, subject matter,and repetition of title, the commercial result may be predicted. Polyphonic HMI (Human Media Interface) introduced the concept of the hit song science computer program in 2003.The company asserted that machine learning could create a music profile to predict hit songs from its audio features Thus,it has been established that a successful pop song must include: 100 bpm or more;an 8 second intro;use the pronoun 'you' within 20 seconds of the start of the song; hit the bridge middle 8 between 2 minutes and 2 minutes 30 seconds; average 7 repetitions of the title; create some expectations and fill that expectation in the title. For the country song: 100 bpm or less for a male artist; 14-second intro; uses the pronoun 'you' within the first 20 seconds of the intro; has a bridge middle 8 between 2 minutes and 2 minutes 30 seconds; has 7 repetitions of title; creates an expectation,fulfills it in 60 seconds.This approach to commercial popular music minimizes the human influence when it comes to which “artist” a record label is going to sign and market. Twenty years ago,music experts in the A&R (Artists and Repertoire) departments of the record labels were making personal aesthetic judgments based on their extensive experience in the music industry. Now, the computer music analyzing programs, are replacing them in an attempt to minimize investment risk of the panicking record labels, in an environment where nobody can predict the future of the recording industry.The impact on the consumers taste through the narrow bottleneck of the above mentioned music selection by the record labels,created some very peculiar effects not only on the taste of popular music consumers, but also the creative chops of the music artists as well. What is the meaning of this semiological shift is the main focus of this research and paper presentation.

Keywords: music, semiology, commercial, taste

Procedia PDF Downloads 393

Authors: Yulan Wu

Abstract:

With the rapid development of social media, the issue of fake news has gained considerable prominence, drawing the attention of both the public and governments. The widespread dissemination of false information poses a tangible threat across multiple domains of society, including politics, economy, and health. However, much research has concentrated on supervised training models within specific domains, their effectiveness diminishes when applied to identify fake news across multiple domains. To solve this problem, some approaches based on domain labels have been proposed. By segmenting news to their specific area in advance, judges in the corresponding field may be more accurate on fake news. However, these approaches disregard the fact that news records can pertain to multiple domains, resulting in a significant loss of valuable information. In addition, the datasets used for training must all be domain-labeled, which creates unnecessary complexity. To solve these problems, an unsupervised domain knowledge discovery framework for fake news detection is proposed. Firstly, to effectively retain the multidomain knowledge of the text, a low-dimensional vector for each news text to capture domain embeddings is generated. Subsequently, a feature extraction module utilizing the unsupervisedly discovered domain embeddings is used to extract the comprehensive features of news. Finally, a classifier is employed to determine the authenticity of the news. To verify the proposed framework, a test is conducted on the existing widely used datasets, and the experimental results demonstrate that this method is able to improve the detection performance for fake news across multiple domains. Moreover, even in datasets that lack domain labels, this method can still effectively transfer domain knowledge, which can educe the time consumed by tagging without sacrificing the detection accuracy.

Keywords: fake news, deep learning, natural language processing, multiple domains

Procedia PDF Downloads 96

Authors: David De Smet, Myriam Vanneste

Abstract:

There is a constant need for the improvement of materials applied in textile industries. Nowadays there is a tendency for “bio, eco, natural and environmental friendly” consciousness of the consumer resulting in various textile labels. Materials, totally based on CO2-neutral renewable resources (biopolymers), respond very well to this tendency. Proteins and PLA were evaluated as binders for textile coatings. Much attention is paid to the functionalization of textiles, therefore bio-additves are examined to introduce abrasion resistance, antimicrobial and flame retardant properties.

Keywords: biomaterial, textile, coating, finishing

Procedia PDF Downloads 712

Authors: J. C. Valenzuela-Tripodoro, P. Álvarez-Ruíz, M. A. Mateos-Camacho, M. Cera

Abstract:

In 2004, it was introduced the concept of Roman domination in graphs. This concept was initially inspired and related to the defensive strategy of the Roman Empire. An undefended place is a city so that no legions are established on it, whereas a strong place is a city in which two legions are deployed. This situation may be modeled by labeling the vertices of a finite simple graph with labels {0, 1, 2}, satisfying the condition that any 0-vertex must be adjacent to, at least, a 2-vertex. Roman domination in graphs is a variant of classic domination. Clearly, the main aim is to obtain such labeling of the vertices of the graph with minimum cost, that is to say, having minimum weight (sum of all vertex labels). Formally, a function f: V (G) → {0, 1, 2} is a Roman dominating function (RDF) in the graph G = (V, E) if f(u) = 0 implies that f(v) = 2 for, at least, a vertex v which is adjacent to u. The weight of an RDF is the positive integer w(f)= ∑_(v∈V)▒〖f(v)〗. The Roman domination number, γ_R (G), is the minimum weight among all the Roman dominating functions? Obviously, the set of vertices with a positive label under an RDF f is a dominating set in the graph, and hence γ(G)≤γ_R (G). In this work, we start the study of a generalization of RDF in which we consider that any undefended place should be defended from a sudden attack by, at least, k legions. These legions can be deployed in the city or in any of its neighbours. A function f: V → {0, 1, . . . , k + 1} such that f(N[u]) ≥ k + |AN(u)| for all vertex u with f(u) < k, where AN(u) represents the set of active neighbours (i.e., with a positive label) of vertex u, is called a [k]-multiple Roman dominating functions and it is denoted by [k]-MRDF. The minimum weight of a [k]-MRDF in the graph G is the [k]-multiple Roman domination number ([k]-MRDN) of G, denoted by γ_[kR] (G). First, we prove that the [k]-multiple Roman domination decision problem is NP-complete even when restricted to bipartite and chordal graphs. A problem that had been resolved for other variants and wanted to be generalized. We know the difficulty of calculating the exact value of the [k]-MRD number, even for families of particular graphs. Here, we present several upper and lower bounds for the [k]-MRD number that permits us to estimate it with as much precision as possible. Finally, some graphs with the exact value of this parameter are characterized.

Keywords: multiple roman domination function, decision problem np-complete, bounds, exact values

Procedia PDF Downloads 108

Authors: Hj Mohd Sidek bin Hj Mohd Yusof

Abstract:

More than 1.5 billion people around the world are adversely affected by floods. Floods account for about a third of all natural catastrophes, cause more than half of all fatalities and are responsible for a third of overall economic loss around the world. Giving advanced warning of impending disasters can reduce or even avoid the number of deaths, social and economic hardships that are so commonly reported after the event. Integrated catchment management recognizes that it is not practical or viable to provide structural measures that will keep floodwater away from the community and their property. Non-structural measures are therefore required to assist the community to cope when flooding occurs which exceeds the capacity of the structural measures. Non-structural measures may need to be used to influence the way land is used or buildings are constructed, or they may be used to improve the community’s preparedness and response to flooding. The development and implementation of non-structural measures may be guided and encouraged by policy and legislation, or through voluntary action by the community based on knowledge gained from public education programs. There is a range of non-structural measures that can be used for flood hazard mitigation which can be the use measures includes policies and rules applied by government to regulate the kinds of activities that are carried out in various flood-prone areas, including minimum floor levels and the type of development approved. Voluntary actions taken by the authorities and by the community living and working on the flood plain to lessen flooding effects on themselves and their properties including monitoring land use changes, monitoring and investigating the effects of bush / forest clearing in the catchment and providing relevant flood related information to the community. Response modification measures may include: flood warning system, flood education, community awareness and readiness, evacuation arrangements and recovery plan. A Civil Defense Emergency Management needs to be established for Brunei Darussalam in order to plan, co-ordinate and undertake flood emergency management. This responsibility may be taken by the Ministry of Home Affairs, Brunei Darussalam who is already responsible for Fire Fighting and Rescue services. Several pieces of legislation and planning instruments are in place to assist flood management, particularly: flood warning system, flood education Community awareness and readiness, evacuation arrangements and recovery plan.

Keywords: RTB, radio television brunei, DDMC, district disaster management center, FIR, flood incidence report, PWD, public works department

Procedia PDF Downloads 256

Authors: Rita Chattopadhyay, Vivek Anand Thoutam

Abstract:

Speech is the most acceptable means of communication where we can quickly exchange our feelings and thoughts. Quite often, people can communicate orally but cannot interact or work with computers or devices. It’s easy and quick to give speech commands than typing commands to computers. In the same way, it’s easy listening to audio played from a device than extract output from computers or devices. Especially with Robotics being an emerging market with applications in warehouses, the hospitality industry, consumer electronics, assistive technology, etc., speech-based human-machine interaction is emerging as a lucrative feature for robot manufacturers. Considering this factor, the objective of this paper is to design the “Audio-Visual Co-Data Processing Pipeline.” This pipeline is an integrated version of Automatic speech recognition, a Natural language model for text understanding, object detection, and text-to-speech modules. There are many Deep Learning models for each type of the modules mentioned above, but OpenVINO Model Zoo models are used because the OpenVINO toolkit covers both computer vision and non-computer vision workloads across Intel hardware and maximizes performance, and accelerates application development. A speech command is given as input that has information about target objects to be detected and start and end times to extract the required interval from the video. Speech is converted to text using the Automatic speech recognition QuartzNet model. The summary is extracted from text using a natural language model Generative Pre-Trained Transformer-3 (GPT-3). Based on the summary, essential frames from the video are extracted, and the You Only Look Once (YOLO) object detection model detects You Only Look Once (YOLO) objects on these extracted frames. Frame numbers that have target objects (specified objects in the speech command) are saved as text. Finally, this text (frame numbers) is converted to speech using text to speech model and will be played from the device. This project is developed for 80 You Only Look Once (YOLO) labels, and the user can extract frames based on only one or two target labels. This pipeline can be extended for more than two target labels easily by making appropriate changes in the object detection module. This project is developed for four different speech command formats by including sample examples in the prompt used by Generative Pre-Trained Transformer-3 (GPT-3) model. Based on user preference, one can come up with a new speech command format by including some examples of the respective format in the prompt used by the Generative Pre-Trained Transformer-3 (GPT-3) model. This pipeline can be used in many projects like human-machine interface, human-robot interaction, and surveillance through speech commands. All object detection projects can be upgraded using this pipeline so that one can give speech commands and output is played from the device.

Keywords: OpenVINO, automatic speech recognition, natural language processing, object detection, text to speech

Procedia PDF Downloads 80

Authors: Traci A. Hefner

Abstract:

Limited research exists on the readiness of emergency departments to respond to human trafficking (HT). The purpose of this qualitative case study was to improve the readiness of a Department of Emergency Medicine (ED), located in the southeast region of the United States, in identifying, assessing, and responding to trafficked individuals. The research objectives were to 1) provide an organizing framework to understand the ED’s readiness to respond to HT, using the Transtheoretical Model’s stages of change construct, 2) explain the readiness of the ED through a three-pronged contextual approach that included policies and procedures, patient data collection processes, and clinical practice methods, and 3) develop recommendations to respond to HT. Content analysis was used for document reviews and on-site observations, while thematic analysis identified themes of staff perceptions of the ED’s readiness in interviews of over 30 clinical and non-clinical healthcare professionals. Results demonstrated low levels of readiness to identify HT through the ED’s policies and procedures, data collection processes, and clinical practice methods. Clinical practice-related factors consisted of limited awareness of HT warning signs and low-levels of knowledge about community resources for possible HT referrals. Policy and practice recommendations to increase the ED’s readiness to respond to HT included: developing staff trainings across the ED system to enhance awareness of HT warning signs, incorporating HT into current policies and procedures for vulnerable patient populations as well as creating a HT protocol that addresses policies and procedures, screening tools, and community referrals.

Keywords: emergency medicine, human trafficking, organizational assessment, stages of change

Procedia PDF Downloads 147

Authors: Khadija Al-Hosni, Moon Fai Chan, Mohammed Al-Azri

Abstract:

Background: Several studies suggest that most school-age adolescents are poorly informed on cancer warning signs and risk factors. Providing adolescents with sufficient knowledge would increase their awareness in adulthood and improve seeking behaviors later. Significant: The results will provide a clear vision in assisting key decision-makers in formulating policies on the students' awareness programs towards cancer. So, the likelihood of avoiding cancer in the future will be increased or even promote early diagnosis. Objectives: to evaluate the effectiveness of an education program designed to increase awareness of cancer signs and symptoms risk factors, improve the behavior of seeking help among school students in Oman, and address the barriers to obtaining medical help. Methods: A randomized controlled trial with two groups was conducted in Oman. A total of 1716 students (n=886/control, n= 830/education), aged 15-17 years, at 10th and 11th grade from 12 governmental schools 3 in governorates from 20-February-2022 to 12-May-2022. Basic demographic data were collected, and the Cancer Awareness Measure (CAM) was used as the primary outcome. Data were collected at baseline (T0) and 4 weeks after (T1). The intervention group received an education program about cancer's cause and its signs and symptoms. In contrast, the control group did not receive any education related to this issue during the study period. Non-parametric tests were used to compare the outcomes between groups. Results: At T0, the lamp was the most recognized cancer warning sign in control (55.0%) and intervention (55.2%) groups. However, there were no significant changes at T1 for all signs in the control group. In contrast, all sign outcomes were improved significantly (p<0.001) in the intervention group, the highest response was unexplained pain (93.3%). Smoking was the most recognized risk factor in both groups: (82.8% for control; 84.1% for intervention) at T0. However, there was no significant change in T1 for the control group, but there was for the intervention group (p<0.001), the highest identification was smoking cigarettes (96.5%). Too scared was the largest barrier to seeking medical help by students in the control group at T0 (63.0%) and T1 (62.8%). However, there were no significant changes in all barriers in this group. Otherwise, being too embarrassed (60.2%) was the largest barrier to seeking medical help for students in the intervention group at T0 and too scared (58.6%) at T1. Although there were reductions in all barriers, significant differences were found in six of ten only (p<0.001). Conclusion: The intervention was effective in improving students' awareness of cancer symptoms, warning signs (p<0.001), and risk factors (p<0.001 reduced the most addressed barriers to seeking medical help (p<0.001) in comparison to the control group. The Ministry of Education in Oman could integrate awareness of cancer within the curriculum, and more interventions are needed on the sociological part to overcome the barriers that interfere with seeking medical help.

Keywords: adolescents, awareness, cancer, education, intervention, student

Procedia PDF Downloads 86

Authors: Khadija Al-Hosni, Moon Fai Chan, Mohammed Al-Azri

Abstract:

Background: Several studies suggest that most school-age adolescents are poorly informed on cancer warning signs and risk factors. Providing adolescents with sufficient knowledge would increase their awareness in adulthood and improve seeking behaviors later. Significant: The results will provide a clear vision in assisting key decision-makers in formulating policies on the students' awareness programs towards cancer. So, the likelihood of avoiding cancer in the future will be increased or even promote early diagnosis. Objectives: to evaluate the effectiveness of an education program designed to increase awareness of cancer signs and symptoms risk factors, improve the behavior of seeking help among school students in Oman, and address the barriers to obtaining medical help. Methods: A randomized controlled trial with two groups was conducted in Oman. A total of 1716 students (n=886/control, n= 830/education), aged 15-17 years, at 10th and 11th grade from 12 governmental schools 3 in governorates from 20-February-2022 to 12-May-2022. Basic demographic data were collected, and the Cancer Awareness Measure (CAM) was used as the primary outcome. Data were collected at baseline (T0) and 4 weeks after (T1). The intervention group received an education program about cancer's cause and its signs and symptoms. In contrast, the control group did not receive any education related to this issue during the study period. Non-parametric tests were used to compare the outcomes between groups. Results: At T0, the lamp was the most recognized cancer warning sign in the control (55.0%) and intervention (55.2%) groups. However, there were no significant changes at T1 for all signs in the control group. In contrast, all sign outcomes were improved significantly (p<0.001) in the intervention group, and the highest response was unexplained pain (93.3%). Smoking was the most recognized risk factor in both groups: (82.8% for control; 84.1% for intervention) at T0. However, there was no significant change in T1 for the control group, but there was for the intervention group (p<0.001), the highest identification was smoking cigarettes (96.5%). Too scared was the largest barrier to seeking medical help by students in the control group at T0 (63.0%) and T1 (62.8%). However, there were no significant changes in all barriers in this group. Otherwise, being too embarrassed (60.2%) was the largest barrier to seeking medical help for students in the intervention group at T0 and too scared (58.6%) at T1. Although there were reductions in all barriers, significant differences were found in six of ten only (p<0.001). Conclusion: The intervention was effective in improving students' awareness of cancer symptoms, warning signs (p<0.001), and risk factors (p<0.001 reduced the most addressed barriers to seeking medical help (p<0.001) in comparison to the control group. The Ministry of Education in Oman could integrate awareness of cancer within the curriculum, and more interventions are needed on the sociological part to overcome the barriers that interfere with seeking medical help.

Keywords: adolescents, awareness, cancer, education, intervention, student

Procedia PDF Downloads 118

Authors: M. Salvador, J. C. Martinez-Garcia, A. Moyano, M. C. Blanco-Lopez, M. Rivas

Abstract:

Biosensors play a crucial role in the detection of molecules nowadays due to their advantages of user-friendliness, high selectivity, the analysis in real time and in-situ applications. Among them, Lateral Flow Immunoassays (LFIAs) are presented among technologies for point-of-care bioassays with outstanding characteristics such as affordability, portability and low-cost. They have been widely used for the detection of a vast range of biomarkers, which do not only include proteins but also nucleic acids and even whole cells. Although the LFIA has traditionally been a positive/negative test, tremendous efforts are being done to add to the method the quantifying capability based on the combination of suitable labels and a proper sensor. One of the most successful approaches involves the use of magnetic sensors for detection of magnetic labels. Bringing together the required characteristics mentioned before, our research group has developed a biosensor to detect biomolecules. Superparamagnetic nanoparticles (SPNPs) together with LFIAs play the fundamental roles. SPMNPs are detected by their interaction with a high-frequency current flowing on a printed micro track. By means of the instant and proportional variation of the impedance of this track provoked by the presence of the SPNPs, quantitative and rapid measurement of the number of particles can be obtained. This way of detection requires no external magnetic field application, which reduces the device complexity. On the other hand, the major limitations of LFIAs are that they are only qualitative or semiquantitative when traditional gold or latex nanoparticles are used as color labels. Moreover, the necessity of always-constant ambient conditions to get reproducible results, the exclusive detection of the nanoparticles on the surface of the membrane, and the short durability of the signal are drawbacks that can be advantageously overcome with the design of magnetically labeled LFIAs. The approach followed was to coat the SPIONs with a specific monoclonal antibody which targets the protein under consideration by chemical bonds. Then, a sandwich-type immunoassay was prepared by printing onto the nitrocellulose membrane strip a second antibody against a different epitope of the protein (test line) and an IgG antibody (control line). When the sample flows along the strip, the SPION-labeled proteins are immobilized at the test line, which provides magnetic signal as described before. Preliminary results using this practical combination for the detection and quantification of the Prostatic-Specific Antigen (PSA) shows the validity and consistency of the technique in the clinical range, where a PSA level of 4.0 ng/mL is the established upper normal limit. Moreover, a LOD of 0.25 ng/mL was calculated with a confident level of 3 according to the IUPAC Gold Book definition. Its versatility has also been proved with the detection of other biomolecules such as troponin I (cardiac injury biomarker) or histamine.

Keywords: biosensor, lateral flow immunoassays, point-of-care devices, superparamagnetic nanoparticles

Procedia PDF Downloads 231

Authors: Chaiyaporn Yuksen, Tapanawat Chaiwan

Abstract:

Introduction: Emergency medical service (EMS) care for trauma patients must be provided on-scene assessment and essential treatment and have appropriate transporting to the trauma center. The shock index (SI), reverse shock index Glasgow Coma Scale (rSIG), and National Early Warning Score (NEWS) triage tools are easy to use in a prehospital setting. There is no standardized on-scene triage protocol in prehospital care. The primary objective was to determine the accuracy of SI, rSIG, and NEWS to predict the severity of trauma patients in the emergency department (ED). Methods: This was a retrospective cross-sectional and diagnostic research conducted on trauma patients transported by EMS to the ED of Ramathibodi Hospital, a university-affiliated super tertiary care hospital in Bangkok, Thailand, from January 2015 to September 2022. We included the injured patients receiving prehospital care and transport to the ED of Ramathibodi Hospital by the EMS team from January 2015 to September 2022. We compared the on-scene parameter (SI, rSIG, and NEWS) and ED (Emergency Severity Index) with the area under ROC. Results: 218 patients were traumatic patients transported by EMS to the ED. 161 was ESI level 1-2, and 57 was level 3-5. NEWS was a more accurate triage tool to discriminate the severity of trauma patients than rSIG and SI. The area under the ROC was 0.743 (95%CI 0.70-0.79), 0.649 (95%CI 0.59-0.70), and 0.582 (95%CI 0.52-0.65), respectively (P-value <0.001). The cut point of NEWS to discriminate was 6 points. Conclusions: The NEWs was the most accurate triage tool in prehospital seeing in trauma patients.

Keywords: on-scene triage, trauma patient, ED triage, accuracy, NEWS

Procedia PDF Downloads 126

Authors: Chu-Tzu Chen, Angela S. Huang, Yu-Min Chou, Chin-Hui Yang

Abstract:

Dengue is a major public health concern throughout tropical and sub-tropical regions. Taiwan is located in the Pacific Ocean and overlying the tropical and subtropical zones. The island remains humid throughout the year and receives abundant rainfall, and the temperature is very hot in summer at southern Taiwan. It is ideal for the growth of dengue vectors and would be increasing the risk on dengue outbreaks. During the first half of the 20th century, there were three island-wide dengue outbreaks (1915, 1931, and 1942). After almost forty years of dormancy, a DEN-2 outbreak occurred in Liuchiu Township, Pingtung County in 1981. Thereafter, more dengue outbreaks occurred with different scales in southern Taiwan. However, there were more than ten thousands of dengue cases in 2014 and in 2015. It did not only affect human health, but also caused widespread social disruption and economic losses. The study would like to reveal the epidemiology of dengue on Taiwan, especially the severe outbreak in 2015, and try to find the effective interventions in dengue control including dengue vaccine development for the elderly. Methods: The study applied the Notifiable Diseases Surveillance System database of the Taiwan Centers for Disease Control as data source. All cases were reported with the uniform case definition and confirmed by NS1 rapid diagnosis/laboratory diagnosis. Results: In 2014, Taiwan experienced a serious DEN-1 outbreak with 15,492 locally-acquired cases, including 136 cases of dengue hemorrhagic fever (DHF) which caused 21 deaths. However, a more serious DEN-2 outbreak occurred with 43,419 locally-acquired cases in 2015. The epidemic occurred mainly at Tainan City (22,760 cases) and Kaohsiung City (19,723 cases) in southern Taiwan. The age distribution for the cases were mainly adults. There were 228 deaths due to dengue infection, and the case fatality rate was 5.25 ‰. The average age of them was 73.66 years (range 29-96) and 86.84% of them were older than 60 years. Most of them were comorbidities. To review the clinical manifestations of the 228 death cases, 38.16% (N=87) of them were reported with warning signs, while 51.75% (N=118) were reported without warning signs. Among the 87 death cases reported to dengue with warning signs, 89.53% were diagnosed sever dengue and 84% needed the intensive care. Conclusion: The year 2015 was characterized by large dengue outbreaks worldwide. The risk of serious dengue outbreak may increase significantly in the future, and the elderly is the vulnerable group in Taiwan. However, a dengue vaccine has been licensed for use in people 9-45 years of age living in endemic settings at the end of 2015. In addition to carry out the research to find out new interventions in dengue control, developing the dengue vaccine for the elderly is very important to prevent severe dengue and deaths.

Keywords: case fatality rate, dengue, dengue vaccine, the elderly

Procedia PDF Downloads 281