Search results for: Wireless Sensor Network

Authors: Suteera Pradubwong, Darawan Augsornwan, Pornpen Pathumwiwathana, Benjamas Prathanee, Bowornsilp Chowchuen

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Background: Cleft lip and palate (CLP) congenital anomalies have a high prevalence in the Northeast of Thailand. A care team’s understand of treatment plan would help to guide the family of patients with CLP to achieve the treatment. Objectives: To examine the impact of the empowering volunteer project, established in the northeast Thailand. Materials and Methods: The Empowering Volunteer project was conducted in 2008 under the Tawanchai Royal Granted project. The patients and family’s general information, treatment, the group brainstorming, and satisfaction with the project were analyzed. Results: Participants were 12 children with CLP, their families and five volunteers with CLP; the participating patients were predominantly females and the mean, age was 12.2 years. The treatment comprised of speech training, dental hygiene care, bone graft and orthodontic treatment. Four issues were addressed including: problems in taking care of breast feeding; instructions’ needs for care at birth; difficulty in access information and society impact; and needs in having a network of volunteers. Conclusions: Empowering volunteer is important for holistic care of patients with CLP which provides easy access and multiple channels for patients and their families. It should be developed as part of the self-help and family support group, the development of community based team and comprehensive CLP care program.

Keywords: self-help and family support group, community based model, volunteer, cleft lip-cleft palate

Procedia PDF Downloads 278

Authors: Nahla H. Alaswadko, Rayya A. Hassan, Bayar N. Mohammed

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Empirical deterministic models have been developed to predict roughness progression of heavy duty spray sealed pavements for a dataset representing rural arterial roads. The dataset provides a good representation of the relevant network and covers a wide range of operating and environmental conditions. A sample with a large size of historical time series data for many pavement sections has been collected and prepared for use in multilevel regression analysis. The modelling parameters include road roughness as performance parameter and traffic loading, time, initial pavement strength, reactivity level of subgrade soil, climate condition, and condition of drainage system as predictor parameters. The purpose of this paper is to report the approaches adopted for models development and validation. The study presents multilevel models that can account for the correlation among time series data of the same section and to capture the effect of unobserved variables. Study results show that the models fit the data very well. The contribution and significance of relevant influencing factors in predicting roughness progression are presented and explained. The paper concludes that the analysis approach used for developing the models confirmed their accuracy and reliability by well-fitting to the validation data.

Keywords: roughness progression, empirical model, pavement performance, heavy duty pavement

Procedia PDF Downloads 168

Authors: Kari Björn

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Introduction of games has expanded the application area of computer-aided learning tools to wide variety of age groups of learners. Serious games engage the learners into a real-world -type of simulation and potentially enrich the learning experience. Institutional background of a Bachelor’s level engineering program in Information and Communication Technology is introduced, with detailed focus on one of its majors, Health Technology. As part of a Customer Oriented Software Application thematic semester, one particular course of “eHealth Business and Solutions” is described and reflected in a gamified framework. Learning a consistent view into vast literature of business management, strategies, marketing and finance in a very limited time enforces selection of topics relevant to the industry. Health Technology is a novel and growing industry with a growing sector in consumer wearable devices and homecare applications. The business sector is attracting new entrepreneurs and impatient investor funds. From engineering education point of view the sector is driven by miniaturizing electronics, sensors and wireless applications. However, the market is highly consumer-driven and usability, safety and data integrity requirements are extremely high. When the same technology is used in analysis or treatment of patients, very strict regulatory measures are enforced. The paper introduces a course structure using gamification as a tool to learn the most essential in a new market: customer value proposition design, followed by a market entry game. Students analyze the existing market size and pricing structure of eHealth web-service market and enter the market as a steering group of their company, competing against the legacy players and with each other. The market is growing but has its rules of demand and supply balance. New products can be developed with an R&D-investment, and targeted to market with unique quality- and price-combinations. Product cost structure can be improved by investing to enhanced production capacity. Investments can be funded optionally by foreign capital. Students make management decisions and face the dynamics of the market competition in form of income statement and balance sheet after each decision cycle. The focus of the learning outcome is to understand customer value creation to be the source of cash flow. The benefit of gamification is to enrich the learning experience on structure and meaning of financial statements. The paper describes the gamification approach and discusses outcomes after two course implementations. Along the case description of learning challenges, some unexpected misconceptions are noted. Improvements of the game or the semi-gamified teaching pedagogy are discussed. The case description serves as an additional support to new game coordinator, as well as helps to improve the method. Overall, the gamified approach has helped to engage engineering student to business studies in an energizing way.

Keywords: engineering education, integrated curriculum, learning experience, learning outcomes

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Authors: Raymond Xu, Cindy Jingru Wang

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Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

Procedia PDF Downloads 254

Authors: Natália Bruno Chaves, Richardes Caúla, Roosevelt do Vale, Daniela Toneti, Rafaela Carvalho, Renata Silva Lopes, Antônio Carlos Júnior, Adner Nobre, Viviane Santiago, Yara Alana Caldato, Estefania Rodriguez Urrego, André Buarque Lemos, Catarina Nucci Stetner, Marcos Mauro Barreto, Stefany Moreira Lima, Mara Cavalcante, Ticiane Ribeiro

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The Well Born (Nascer Bem – in Portuguese) Program was created in the Hapvida health network with the aim of improving access to safe and quality prenatal care for users. In addition to offering a line of prenatal care, the inclusion of obstetric nursing and the decentralization of childbirth, bring security that professionals did not indicate the route of delivery for professional convenience. The introduction of the nursing consultation came to reinforce the care to our users, strengthening their bond and reception. In 2021, the program maintained an average of 40% of normal births in the north, northeast and central-west regions of Brazil, an average above that observed in the rest of the country's private health systems, around 20%. In addition, the neonatal hospitalization rate of this population remained around 5.1%, a figure below the national average. With these data, the “Nascer Bem” program is affirmed as a safe and effective strategy for the promotion of safe normal birth.

Keywords: quality, safe, prenatal, obstetric nursing

Procedia PDF Downloads 119

Authors: Juliza Hidayati, Sukardi, Ani Suryani, Sugiharto, Anas M. Fauzi

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The idea of making North Sumatera as a barometer of national oil palm industry requires efforts commodities and agro-industry development of oil palm. One effort that can be done is by successful execution plantation revitalization. The plantation Revitalization is an effort to accelerate the development of smallholder plantations, through expansion and replanting by help of palm Estate Company as business partner and bank financed plantation revitalization fund. Business partner agreement obliged and bound to make at least the same smallholder plantation productivity with business partners, so that the refund rate to banks become larger and prosperous people as a plantation owner. Generally low productivity of smallholder plantations under normal potential caused a lot of old and damaged plants with plant material at random. The purpose of revitalizing oil palm plantations is which are to increase their competitiveness through increased farm productivity. The research aims to identify potential criteria in influencing plantation productivity improvement priorities to be observed and followed up in order to improve the competitiveness of destinations and make North Sumatera barometer of national palm oil can be achieved. Research conducted with Analytical Network Process (ANP), to find the effect of dependency relationships between factors or criteria with the knowledge of the experts in order to produce an objective opinion and relevant depict the actual situation.

Keywords: palm barometer, acceleration of plantation development, productivity, revitalization

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Authors: Hicham Gourgue, Ahmed Aharoune, Ahmed Ihlal

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We present in this work our model of road traffic emissions (line sources) and dispersion of these emissions, named DISPOLSPEM (Dispersion of Poly Sources and Pollutants Emission Model). In its emission part, this model was designed to keep the consistent bottom-up and top-down approaches. It also allows to generate emission inventories from reduced input parameters being adapted to existing conditions in Morocco and in the other developing countries. While several simplifications are made, all the performance of the model results are kept. A further important advantage of the model is that it allows the uncertainty calculation and emission rate uncertainty according to each of the input parameters. In the dispersion part of the model, an improved line source model has been developed, implemented and tested against a reference solution. It provides improvement in accuracy over previous formulas of line source Gaussian plume model, without being too demanding in terms of computational resources. In the case study presented here, the biggest errors were associated with the ends of line source sections; these errors will be canceled by adjacent sections of line sources during the simulation of a road network. In cases where the wind is parallel to the source line, the use of the combination discretized source and analytical line source formulas minimizes remarkably the error. Because this combination is applied only for a small number of wind directions, it should not excessively increase the calculation time.

Keywords: air pollution, dispersion, emissions, line sources, road traffic, urban transport

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Authors: Haytam Kasem

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The problem of quick and easy reversible attachment has become of great importance in different fields of technology. For the reason, during the last decade, a new emerging field of adhesion science has been developed. Essentially inspired by some animals and insects, which during their natural evolution have developed fantastic biological attachment systems allowing them to adhere and run on walls and ceilings of uneven surfaces. Potential applications of engineering bio-inspired solutions include climbing robots, handling systems for wafers in nanofabrication facilities, and mobile sensor platforms, to name a few. However, despite the efforts provided to apply bio-inspired patterned adhesive-surfaces to the biomedical field, they are still in the early stages compared with their conventional uses in other industries mentioned above. In fact, there are some critical issues that still need to be addressed for the wide usage of the bio-inspired patterned surfaces as advanced biomedical platforms. For example, surface durability and long-term stability of surfaces with high adhesive capacity should be improved, but also the friction and adhesion capacities of these bio-inspired microstructures when contacting rough surfaces. One of the well-known prototypes for bio-inspired attachment systems is biomimetic wall-shaped hierarchical microstructure for gecko-like attachments. Although physical background of these attachment systems is widely understood, the influence of counter-face roughness and its relationship with the friction force generated when sliding against wall-shaped hierarchical microstructure have yet to be fully analyzed and understood. To elucidate the effect of the counter-face roughness on the friction of biomimetic wall-shaped hierarchical microstructure we have replicated the isotropic topography of 12 different surfaces using replicas made of the same epoxy material. The different counter-faces were fully characterized under 3D optical profilometer to measure roughness parameters. The friction forces generated by spatula-shaped microstructure in contact with the tested counter-faces were measured on a home-made tribometer and compared with the friction forces generated by the spatulae in contact with a smooth reference. It was found that classical roughness parameters, such as average roughness Ra and others, could not be utilized to explain topography-related variation in friction force. This has led us to the development of an integrated roughness parameter obtained by combining different parameters which are the mean asperity radius of curvature (R), the asperity density (η), the deviation of asperities high (σ) and the mean asperities angle (SDQ). This new integrated parameter is capable of explaining the variation of results of friction measurements. Based on the experimental results, we developed and validated an analytical model to predict the variation of the friction force as a function of roughness parameters of the counter-face and the applied normal load, as well.

Keywords: friction, bio-mimetic micro-structure, counter-face roughness, analytical model

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Authors: Shogo Nagano, Yusuke Tanigaki, Hirokazu Fukuda

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Vegetables harvested early in the morning or late in the afternoon are valued in plant production, and so the time of harvest is important. The biological functions known as circadian clocks have a significant effect on this harvest timing. The purpose of this study was to non-destructively estimate the circadian clock and so construct a method for determining a suitable harvest time. We took eight samples of green busil (Perilla frutescens var. crispa) every 4 hours, six times for 1 day and analyzed all samples at the same time. A hyperspectral camera was used to collect spectrum intensities at 141 different wavelengths (350–1050 nm). Calculation of correlations between spectrum intensity of each wavelength and harvest time suggested the suitability of the hyperspectral camera for non-destructive estimation. However, even the highest correlated wavelength had a weak correlation, so we used machine learning to raise the accuracy of estimation and constructed a machine learning model to estimate the internal time of the circadian clock. Artificial neural networks (ANN) were used for machine learning because this is an effective analysis method for large amounts of data. Using the estimation model resulted in an error between estimated and real times of 3 min. The estimations were made in less than 2 hours. Thus, we successfully demonstrated this method of non-destructively estimating internal time.

Keywords: artificial neural network (ANN), circadian clock, green busil, hyperspectral camera, non-destructive evaluation

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: Anouschka Swart

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In 2023, sport faces a plethora of challenges including but not limited to match-fixing, corruption and doping to its integrity that, threatens both the commercial and public appeal. The continuous changes and commercialisation that has occurred within sport have led to a variety of consequences resulting in the need for ethics to be revived, as it used to be in the past to ensure sport is not in danger. In order to understand governance better, the Institute of Directors in Southern Africa, a global network of professional firms providing Audit, Tax and Advisory services, outlined a process explaining all elements with regards to corporate governance. This process illustrates a governing body’s responsibilities as strategy, policy, oversight and accountability. These responsibilities are further elucidated to 16 governing principles which are highlighted as essential for all organisations in order to achieve and deliver on effective governance outcomes. These outcomes are good ethical culture, good performance, effective control and legitimacy therefore, the aim of the study was to investigate the general state of governance within the clubs affiliated with a rugby club in South Africa by utilizing the King IV Code as the framework. The results indicated that the King Code IV principles are implemented by these rugby clubs to ensure they demonstrate commitment to corporate governance to both internal and external stakeholders. It is however evident that a similar report focused solely on sport is a necessity in the industry as this will provide more clarity on sport specific problems.

Keywords: South Africa, sport, King IV, responsibilities

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Authors: Mohamed Al-Badrashiny, Abdelghani Bellaachia

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This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set.

Keywords: classifier ensemble, breast cancer survivability, data mining, SEER

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Authors: Yosia Mulumba, Klaus Schmidt

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Financial Inclusion is considered a key pillar for development in most countries around the world. Access to affordable financial services in a country’s economy can be a driver to overcome poverty and reduce income inequalities, and thus increase economic growth. Nevertheless, the number of financially excluded populations in developing countries continues to be very high. This paper explores the role of Financial Technology (Fintech) as a key driver for achieving financial inclusion in developing countries post the COVID-19 pandemic with an emphasis on four East African countries: Kenya, Tanzania, Uganda, and Rwanda. The research paper is inspired by the positive disruption caused by the pandemic, which has compelled societies in East Africa to adapt and embrace the use of financial technology innovations, specifically Mobile Money Services (MMS), to access financial services. MMS has been further migrated and integrated with other financial technology innovations such as Mobile Banking, Micro Savings, and Loans, and Insurance, to mention but a few. These innovations have been adopted across key sectors such as commerce, health care, or agriculture. The research paper will highlight the Mobile Network Operators (MNOs) that are behind MMS, along with numerous innovative products and services being offered to the customers. It will also highlight the regulatory framework under which these innovations are being governed to ensure the safety of the customers' funds.

Keywords: financial inclusion, financial technology, regulatory framework, mobile money services

Procedia PDF Downloads 146

Authors: Tohid Rahimi, Yahya Naderi, Babak Yousefi, Seyed Hossein Hoseini

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Today’s modern interconnected power system is highly complex in nature. In this, one of the most important requirements during the operation of the electric power system is the reliability and security. Power and frequency oscillation damping mechanism improve the reliability. Because of power system stabilizer (PSS) low speed response against of major fault such as three phase short circuit, FACTs devise that can control the network condition in very fast time, are becoming popular. However, FACTs capability can be seen in a major fault present when nonlinear models of FACTs devise and power system equipment are applied. To realize this aim, the model of multi-machine power system with FACTs controller is developed in MATLAB/SIMULINK using Sim Power System (SPS) blockiest. Among the FACTs device, Static synchronous series compensator (SSSC) due to high speed changes its reactance characteristic inductive to capacitive, is effective power flow controller. Tuning process of controller parameter can be performed using different method. However, Genetic Algorithm (GA) ability tends to use it in controller parameter tuning process. In this paper, firstly POD controller is used to power oscillation damping. But in this station, frequency oscillation dos not has proper damping situation. Therefore, FOD controller that is tuned using GA is using that cause to damp out frequency oscillation properly and power oscillation damping has suitable situation.

Keywords: power oscillation damping (POD), frequency oscillation damping (FOD), Static synchronous series compensator (SSSC), Genetic Algorithm (GA)

Procedia PDF Downloads 476

Authors: Jessica Cecchinelli, Amer Ali

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The continuing growth in road traffic and the resultant impact on the level of pollution and safety especially in urban areas have led local and national authorities to reduce traffic speed and flow in major towns and cities. Various boroughs of London have recently reduced the in-city speed limit from 30mph to 20mph mainly to calm traffic, improve safety and reduce noise and vibration. This paper reports the detailed field measurements using noise sensor and analyser and the corresponding theoretical calculations and analysis of the noise levels on a number of roads in the central London Borough of Camden where speed limit was reduced from 30mph to 20mph in all roads except the major routes of the ‘Transport for London (TfL)’. The measurements, which included the key noise levels and scales at residential streets and main roads, were conducted during weekdays and weekends normal and rush hours. The theoretical calculations were done according to the UK procedure ‘Calculation of Road Traffic Noise 1988’ and with conversion to the European L-day, L-evening, L-night, and L-den and other important levels. The current study also includes comparable data and analysis from previously measured noise in the Borough of Camden and other boroughs of central London. Classified traffic flow and speed on the roads concerned were observed and used in the calculation part of the study. Relevant data and description of the weather condition are reported. The paper also reports a field survey in the form of face-to-face interview questionnaires, which was carried out in parallel with the field measurement of noise, in order to ascertain the opinions and views of local residents and workers in the reduced speed zones of 20mph. The main findings are that the reduction in speed had reduced the noise pollution on the studied zones and that the measured and calculated noise levels for each speed zone are closely matched. Among the other findings was that of the field survey of the opinions and views of the local residents and workers in the reduced speed 20mph zones who supported the scheme and felt that it had improved the quality of life in their areas giving a sense of calmness and safety particularly for families with children, the elderly, and encouraged pedestrians and cyclists. The key conclusions are that lowering the speed limit in built-up areas would not just reduce the number of serious accidents but it would also reduce the noise pollution and promote clean modes of transport particularly walking and cycling. The details of the site observations and the corresponding calculations together with critical comparative analysis and relevant conclusions will be reported in the full version of the paper.

Keywords: noise calculation, noise field measurement, road traffic noise, speed limit in london, survey of people satisfaction

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Authors: Esther Van Andel, Stefanie C. Lange, Maarten M. J. Smulders, Han Zuilhof

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False outcomes of diagnostic tests are a major concern in medical health care. To improve the reliability of surface-based diagnostic tests, it is of crucial importance to diminish background signals that arise from the non-specific binding of biomolecules, a process called fouling. The aim is to create surfaces that repel all biomolecules except the molecule of interest. This can be achieved by incorporating antifouling protein repellent coatings in between the sensor surface and it’s recognition elements (e.g. antibodies, sugars, aptamers). Zwitterionic polymer brushes are considered excellent antifouling materials, however, to be able to bind the molecule of interest, the polymer brushes have to be functionalized and so far this was only achieved at the expense of either antifouling or binding capacity. To overcome this limitation, we combined both features into one single monomer: a zwitterionic sulfobetaine, ensuring antifouling capabilities, equipped with a clickable azide moiety which allows for further functionalization. By copolymerizing this monomer together with a standard sulfobetaine, the number of azides (and with that the number of recognition elements) can be tuned depending on the application. First, the clickable azido-monomer was synthesized and characterized, followed by copolymerizing this monomer to yield functionalizable antifouling brushes. The brushes were fully characterized using surface characterization techniques like XPS, contact angle measurements, G-ATR-FTIR and XRR. As a proof of principle, the brushes were subsequently functionalized with biotin via strain-promoted alkyne azide click reactions, which yielded a fully zwitterionic biotin-containing 3D-functionalized coating. The sensing capacity was evaluated by reflectometry using avidin and fibrinogen containing protein solutions. The surfaces showed excellent antifouling properties as illustrated by the complete absence of non-specific fibrinogen binding, while at the same time clear responses were seen for the specific binding of avidin. A great increase in signal-to-noise ratio was observed, even when the amount of functional groups was lowered to 1%, compared to traditional modification of sulfobetaine brushes that rely on a 2D-approach in which only the top-layer can be functionalized. This study was performed on stoichiometric silicon nitride surfaces for future microring resonator based assays, however, this methodology can be transferred to other biosensor platforms which are currently being investigated. The approach presented herein enables a highly efficient strategy for selective binding with retained antifouling properties for improved signal-to-noise ratios in binding assays. The number of recognition units can be adjusted to a specific need, e.g. depending on the size of the analyte to be bound, widening the scope of these functionalizable surface coatings.

Keywords: antifouling, signal-to-noise ratio, surface functionalization, zwitterionic polymer brushes

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Authors: Andrew Anis Fakhrey Mosaad

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Objectives: Physiotherapy in the intensive care unit (ICU) improves patient outcomes. We aimed to determine the characteristics of physiotherapy practice and critical barriers to applying physiotherapy in ICUs. Materials and Methods: A 54-item survey for determining the characteristics physiotherapists and physiotherapy applications in the ICU was developed. The survey was electronically sent to potential participants through the Turkish Physiotherapy Association network. Sixty-five physiotherapists (47F and 18M; 23–52 years; ICU experience: 6.0±6.2 years) completed the survey. The data were analyzed using quantitative and qualitative methods. Results: The duration of ICU practice was 3.51±2.10 h/day. Positioning (90.8%), active exercises (90.8%), breathing exercises (89.2%), passive exercises (87.7%), and percussion (87.7%) were the most commonly used applications. The barriers were related to physiotherapists (low level of employment and practice, lack of shift); patients (unwillingness, instability, participation restriction); teamwork (lack of awareness and communication); equipment (inadequacy, non-priority to purchase); and legal (reimbursement, lack of direct physiotherapy access, non-recognition of autonomy) procedures. Conclusion: The most common interventions were positioning, active, passive, breathing exercises, and percussion. Critical barriers toward physiotherapy are multifactorial and related to physiotherapists, patients, teams, equipment, and legal procedures. Physiotherapist employment, service maintenance, and multidisciplinary teamwork should be considered for physiotherapy effectiveness in ICUs.

Keywords: intensive care units, physical therapy, physiotherapy, exercises

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Authors: Jo Rhodes, Bruce Bergstrom, Peter Lok, Vincent Cheng

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The aim of this study is to determine key factors and processes for multinationals (MNCs) to develop an effective stakeholder engagement and sustainable development framework. A qualitative multiple-case approach was used. A triangulation method was adopted (interviews, archival documents and observations) to collect data on three global firms (MNCs). 9 senior executives were interviewed for this study (3 from each firm). An initial literature review was conducted to explore possible practices and factors (the deductive approach) to sustainable development. Interview data were analysed using Nvivo to obtain appropriate nodes and themes for the framework. A comparison of findings from interview data and themes, factors developed from the literature review and cross cases comparison were used to develop the final conceptual framework (the inductive approach). The results suggested that stakeholder engagement is a key mediator between ‘stakeholder network’ (internal and external factors) and outcomes (corporate social responsibility, social capital, shared value and sustainable development). Key internal factors such as human capital/talent, technology, culture, leadership and processes such as collaboration, knowledge sharing and co-creation of value with stakeholders were identified. These internal factors and processes must be integrated and aligned with external factors such as social, political, cultural, environment and NGOs to achieve effective stakeholder engagement.

Keywords: stakeholder, engagement, sustainable development, shared value, corporate social responsibility

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Authors: M. Lehtihet, S. Rosado, C. Pradère, J. Leng

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Poly(3,4-ethylene dioxythiophene) polystyrene sulfonate (PEDOT: PSS), is a polymer mixture well-known for its semiconducting properties and is widely used in the coating industry for its visible transparency and high electronic conductivity (up to 4600 S/cm) as a transparent non-metallic electrode and in organic light-emitting diodes (OLED). It also possesses strong absorption properties in the Near Infra-Red (NIR) range (λ ranging between 900 nm to 2.5 µm). In the present work, we take advantage of this absorption to explore its potential use as a transparent light-to-heat converter. PEDOT: PSS aqueous dispersions are deposited onto a glass substrate using a blade-coating technique in order to produce uniform coatings with controlled thicknesses ranging in ≈ 400 nm to 2 µm. Blade-coating technique allows us good control of the deposit thickness and uniformity by the tuning of several experimental conditions (blade velocity, evaporation rate, temperature, etc…). This liquid coating technique is a well-known, non-expensive technique to realize thin film coatings on various substrates. For coatings on glass substrates destined to solar insulation applications, the ideal coating would be made of a material able to transmit all the visible range while reflecting the NIR range perfectly, but materials possessing similar properties still have unsatisfactory opacity in the visible too (for example, titanium dioxide nanoparticles). NIR absorbing thin films is a more realistic alternative for such an application. Under solar illumination, PEDOT: PSS thin films heat up due to absorption of NIR light and thus act as planar heaters while maintaining good transparency in the visible range. Whereas they screen some NIR radiation, they also generate heat which is then conducted into the substrate that re-emits this energy by thermal emission in every direction. In order to quantify the heating power of these coatings, a sample (coating on glass) is placed in a black enclosure and illuminated with a solar simulator, a lamp emitting a calibrated radiation very similar to the solar spectrum. The temperature of the rear face of the substrate is measured in real-time using thermocouples and a black-painted Peltier sensor measures the total entering flux (sum of transmitted and re-emitted fluxes). The heating power density of the thin films is estimated from a model of the thin film/glass substrate describing the system, and we estimate the Solar Heat Gain Coefficient (SHGC) to quantify the light-to-heat conversion efficiency of such systems. Eventually, the effect of additives such as dimethyl sulfoxide (DMSO) or optical scatterers (particles) on the performances are also studied, as the first one can alter the IR absorption properties of PEDOT: PSS drastically and the second one can increase the apparent optical path of light within the thin film material.

Keywords: PEDOT: PSS, blade-coating, heat, thin-film, Solar spectrum

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Authors: Yonggu Jang, Jisong Ryu, Woosik Lee

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The study aims to address the limitations of existing underground facility exploration equipment in terms of exploration depth range, relative depth measurement, data processing time, and human-centered ground penetrating radar image interpretation. The study proposed the use of 3D absolute positioning technology to develop a precision underground facility exploration system. The aim of this study is to establish a precise exploration system for underground facilities based on 3D absolute positioning technology, which can accurately survey up to a depth of 5m and measure the 3D absolute location of precise underground facilities. The study developed software and hardware technologies to build the precision exploration system. The software technologies developed include absolute positioning technology, ground surface location synchronization technology of GPR exploration equipment, GPR exploration image AI interpretation technology, and integrated underground space map-based composite data processing technology. The hardware systems developed include a vehicle-type exploration system and a cart-type exploration system. The data was collected using the developed exploration system, which employs 3D absolute positioning technology. The GPR exploration images were analyzed using AI technology, and the three-dimensional location information of the explored precise underground facilities was compared to the integrated underground space map. The study successfully developed a precision underground facility exploration system based on 3D absolute positioning technology. The developed exploration system can accurately survey up to a depth of 5m and measure the 3D absolute location of precise underground facilities. The system comprises software technologies that build a 3D precise DEM, synchronize the GPR sensor's ground surface 3D location coordinates, automatically analyze and detect underground facility information in GPR exploration images and improve accuracy through comparative analysis of the three-dimensional location information, and hardware systems, including a vehicle-type exploration system and a cart-type exploration system. The study's findings and technological advancements are essential for underground safety management in Korea. The proposed precision exploration system significantly contributes to establishing precise location information of underground facility information, which is crucial for underground safety management and improves the accuracy and efficiency of exploration. The study addressed the limitations of existing equipment in exploring underground facilities, proposed 3D absolute positioning technology-based precision exploration system, developed software and hardware systems for the exploration system, and contributed to underground safety management by providing precise location information. The developed precision underground facility exploration system based on 3D absolute positioning technology has the potential to provide accurate and efficient exploration of underground facilities up to a depth of 5m. The system's technological advancements contribute to the establishment of precise location information of underground facility information, which is essential for underground safety management in Korea.

Keywords: 3D absolute positioning, AI interpretation of GPR exploration images, complex data processing, integrated underground space maps, precision exploration system for underground facilities

Procedia PDF Downloads 62

Authors: Andres Mejia-Ortiz, Catalina Lozada, German R. Santos, Rafael Angulo-Jaramillo, Bernardo Caicedo

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The interaction between atmospheric variables and soil properties is a determining factor when evaluating the flow of water through the soil. This interaction situation directly determines the behavior of the soil and greatly influences the changes that occur in it. The atmospheric variations such as changes in the relative humidity, air temperature, wind velocity and precipitation, are the external variables that reflect a greater incidence in the changes that are generated in the subsoil, as a consequence of the water flow in descending and ascending conditions. These environmental variations have a major importance in the study of the soil because the conditions of humidity and temperature in the soil surface depend on them. In addition, these variations control the thickness of the unsaturated zone and the position of the water table with respect to the surface. However, understanding the relationship between the atmosphere and the soil is a somewhat complex aspect. This is mainly due to the difficulty involved in estimating the changes that occur in the soil from climate changes; since this is a coupled process where act processes of mass transfer and heat. In this research, an experimental field was implemented to study in-situ the interaction between the atmosphere and the soft soils of the city of Bogota, Colombia. The soil under study consists of a 60 cm layer composed of two silts of similar characteristics at the surface and a deep soft clay deposit located under the silky material. It should be noted that the vegetal layer and organic matter were removed to avoid the evapotranspiration phenomenon. Instrumentation was carried on in situ through a field disposal of many measuring devices such as soil moisture sensors, thermocouples, relative humidity sensors, wind velocity sensor, among others; which allow registering the variations of both the atmospheric variables and the properties of the soil. With the information collected through field monitoring, the water balances were made using the Hydrus-1D software to determine the flow conditions that developed in the soil during the study. Also, the moisture profile for different periods and time intervals was determined by the balance supplied by Hydrus 1D; this profile was validated by experimental measurements. As a boundary condition, the actual evaporation rate was included using the semi-empirical equations proposed by different authors. In this study, it was obtained for the rainy periods a descending flow that was governed by the infiltration capacity of the soil. On the other hand, during dry periods. An increase in the actual evaporation of the soil induces an upward flow of water, increasing suction due to the decrease in moisture content. Also, cracks were developed accelerating the evaporation process. This work concerns to the study of soil-atmosphere interaction through the experimental field and it is a very useful tool since it allows considering all the factors and parameters of the soil in its natural state and real values of the different environmental conditions.

Keywords: field monitoring, soil-atmosphere, soft soils, soil-water balance

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Authors: Jen-Te Pai, Jao-Heng Liu, Shin-En Pai

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The impact of climate change has intensified in recent years, causing Taiwan to face higher frequency and serious natural disasters. Therefore, it is imperative for industrial parks manufacturers to promote adaptation policies in response to climate change. On the other hand, with the rise of the international anti-terrorism situation, once a terrorist attack occurs, it will attract domestic and international media attention, especially the strategic and economic status of the science park. Thus, it is necessary to formulate adaptation and mitigation strategies under climate change and social economic disasters. After reviewed the literature about climate change, urban disaster prevention, vulnerability assessment, and risk communication, the study selected 62 industrial parks compiled by the Industrial Bureau of the Ministry of Economic Affairs of Taiwan as the research object. This study explored the vulnerability and disaster prevention and disaster relief functional assessment of these industrial parks facing of natural and socio-economic disasters. Furthermore, this study explored planned adaptation of industrial parks management section and autonomous adaptation of corporate institutions in the park. The conclusion of this study is that Taiwan industrial parks with a higher vulnerability to natural and socio-economic disasters should employ positive adaptive behaviours.

Keywords: adaptive behaviours, analytic network process, vulnerability, industrial parks

Procedia PDF Downloads 145

Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

Procedia PDF Downloads 75

Authors: Alper Taner, Yesim Benal Oztekin, Huseyin Duran

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In this study, an Artificial Neural Network (ANN) was developed in order to classify barley varieties. For this purpose, physical properties of barley varieties were determined and ANN techniques were used. The physical properties of 8 barley varieties grown in Turkey, namely thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters of grain, were determined and it was found that these properties were statistically significant with respect to varieties. As ANN model, three models, N-l, N-2 and N-3 were constructed. The performances of these models were compared. It was determined that the best-fit model was N-1. In the N-1 model, the structure of the model was designed to be 11 input layers, 2 hidden layers and 1 output layer. Thousand kernel weight, geometric mean diameter, sphericity, kernel volume, surface area, bulk density, true density, porosity and colour parameters of grain were used as input parameter; and varieties as output parameter. R2, Root Mean Square Error and Mean Error for the N-l model were found as 99.99%, 0.00074 and 0.009%, respectively. All results obtained by the N-l model were observed to have been quite consistent with real data. By this model, it would be possible to construct automation systems for classification and cleaning in flourmills.

Keywords: physical properties, artificial neural networks, barley, classification

Procedia PDF Downloads 178

Authors: Noora Al Muftah, Reda Rawi, Richard Thompson, Halima Bensmail

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Clinical responses to anticancer therapies are often restricted to a subset of patients. In some cases, mutated cancer genes are potent biomarkers of response to targeted agents. There is an urgent need to identify biomarkers that predict which patients with are most likely to respond to treatment. Systematic efforts to correlate tumor mutational data with biologic dependencies may facilitate the translation of somatic mutation catalogs into meaningful biomarkers for patient stratification. To identify genomic features associated with drug sensitivity and uncover new biomarkers of sensitivity and resistance to cancer therapeutics, we have screened and integrated a panel of several hundred cancer cell lines from different databases, mutation, DNA copy number, and gene expression data for hundreds of cell lines with their responses to targeted and cytotoxic therapies with drugs under clinical and preclinical investigation. We found mutated cancer genes were associated with cellular response to most currently available Glioma cancer drugs and some frequently mutated genes were associated with sensitivity to a broad range of therapeutic agents. By linking drug activity to the functional complexity of cancer genomes, systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies.

Keywords: cancer, gene network, Lasso, penalized regression, P-values, unbiased estimator

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Authors: Josif Figueroa, Kush Bubbar, Greg Young-Morris

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The reliability evaluation of composite generation and bulk transmission systems is crucial for ensuring a reliable supply of electrical energy to significant system load points. However, evaluating adequacy indices using probabilistic methods like sequential Monte Carlo Simulation can be computationally expensive. Despite this, it is necessary when time-varying and interdependent resources, such as renewables and energy storage systems, are involved. Recent advances in solving power network optimization problems and parallel computing have improved runtime performance while maintaining solution accuracy. This work introduces CompositeSystems, an open-source Composite System Reliability Evaluation tool developed in Julia™, to address the current deficiencies of commercial and non-commercial tools. This work introduces its design, validation, and effectiveness, which includes analyzing two different formulations of the Optimal Power Flow problem. The simulations demonstrate excellent agreement with existing published studies while improving replicability and reproducibility. Overall, the proposed tool can provide valuable insights into the performance of transmission systems, making it an important addition to the existing toolbox for power system planning.

Keywords: open-source software, composite system reliability, optimization methods, Monte Carlo methods, optimal power flow

Procedia PDF Downloads 73

Authors: Benhui Fan, Fahmi Bedoui, Jinbo Bai

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Incorporated carbon nanotube-BaTiO₃ hybrids (H-CNT-BT) with core-shell structure, a better dispersion of CNTs can be achieved in a semi-crystalline polymeric matrix, polyvinylidene fluoride (PVDF). Carried by BT particles, CNTs are easy to mutually connect which helps to obtain an extremely low percolation threshold (fc). After thermal treatments, the dielectric constants (ε’) of samples further increase which depends on the conditions of thermal treatments such as annealing temperatures, annealing durations and cooling ways. Thus, in order to study more comprehensively about the influence of thermal treatments on composite’s dielectric behaviors, in situ synchrotron X-ray is used to detect re-crystalline behavior of PVDF. Results of wide-angle X-ray diffraction (WAXD) and small-angle X-ray scattering (SAXS) show that after the thermal treatment, the content of β polymorph (the polymorph with the highest ε’ among all the polymorphs of PVDF’s crystalline structure) has increased nearly double times at the interfacial region of CNT-PVDF, and the thickness of amorphous layers (La) in PVDF’s long periods (Lp) has shrunk around 10 Å. The evolution of CNT’s network possibly occurs in the procedure of La shrinkage, where the strong interfacial polarization may be aroused and increases ε’ at low frequency. Moreover, an increase in the thickness of crystalline lamella may also arouse more orientational polarization and improve ε’ at high frequency.

Keywords: dielectric properties, thermal treatments, carbon nanotubes, crystalline structure

Procedia PDF Downloads 324

Authors: A. Degale Desta, Cheng Jian

Abstract:

Deep learning application in computer vision is rapidly advancing, giving it the ability to monitor the public and quickly identify potentially anomalous behaviour from crowd scenes. Therefore, the purpose of the current work is to improve the performance of safety of people in crowd events from panic behaviour through introducing the innovative idea of Aggregation of Ensembles (AOE), which makes use of the pre-trained ConvNets and a pool of classifiers to find anomalies in video data with packed scenes. According to the theory of algorithms that applied K-means, KNN, CNN, SVD, and Faster-CNN, YOLOv5 architectures learn different levels of semantic representation from crowd videos; the proposed approach leverages an ensemble of various fine-tuned convolutional neural networks (CNN), allowing for the extraction of enriched feature sets. In addition to the above algorithms, a long short-term memory neural network to forecast future feature values and a handmade feature that takes into consideration the peculiarities of the crowd to understand human behavior. On well-known datasets of panic situations, experiments are run to assess the effectiveness and precision of the suggested method. Results reveal that, compared to state-of-the-art methodologies, the system produces better and more promising results in terms of accuracy and processing speed.

Keywords: action recognition, computer vision, crowd detecting and tracking, deep learning

Procedia PDF Downloads 161

Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia Makedon, Glenn Wylie

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Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.

Keywords: fMRI, brain imaging, deep learning, self-supervised learning, contrastive learning, cognitive fatigue

Procedia PDF Downloads 189

Authors: Jake Gonzalez, Tommy Dang

Abstract:

This paper proposes a temporal graph approach to visualize and analyze the evolution of gene relationships and nomenclature over time. An interactive web-based tool implements this temporal graph, enabling researchers to traverse a timeline and observe coupled dynamics in network topology and naming conventions. Analysis of a real human genomic dataset reveals the emergence of densely interconnected functional modules over time, representing groups of genes involved in key biological processes. For example, the antimicrobial peptide DEFA1A3 shows increased connections to related alpha-defensins involved in infection response. Tracking degree and betweenness centrality shifts over timeline iterations also quantitatively highlight the reprioritization of certain genes’ topological importance as knowledge advances. Examination of the CNR1 gene encoding the cannabinoid receptor CB1 demonstrates changing synonymous relationships and consolidating naming patterns over time, reflecting its unique functional role discovery. The integrated framework interconnecting these topological and nomenclature dynamics provides richer contextual insights compared to isolated analysis methods. Overall, this temporal graph approach enables a more holistic study of knowledge evolution to elucidate complex biology.

Keywords: temporal graph, gene relationships, nomenclature evolution, interactive visualization, biological insights

Procedia PDF Downloads 61