Search results for: real time stress detection

Authors: Soner Cubuk, Mirgul Kosif, M. Vezir Kahraman, Ece Kok Yetimoglu

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Boron is an essential trace element for the completion of the life circle for organisms. Suitable methods for the determination of boron have been proposed, including acid - base titrimetric, inductively coupled plasma emission spectroscopy flame atomic absorption and spectrophotometric. However, the above methods have some disadvantages such as long analysis times, requirement of corrosive media such as concentrated sulphuric acid and multi-step sample preparation requirements and time-consuming procedures. In this study, a selective and reusable fluorescent sensor for boron based on glycosyloxyethyl methacrylate was prepared by photopolymerization. The response characteristics such as response time, pH, linear range, limit of detection were systematically investigated. The excitation/emission maxima of the membrane were at 378/423 nm, respectively. The approximate response time was measured as 50 sec. In addition, sensor had a very low limit of detection which was 0.3 ppb. The sensor was successfully used for the determination of boron in water samples with satisfactory results.

Keywords: boron, fluorescence, photopolymerization, polymeric sensor

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

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Traditional optimization methods like evolutionary algorithms are widely used in production processes to find an optimal or near-optimal solution of control parameters based on the simulated environment space of a process. These algorithms are computationally intensive and therefore do not provide the opportunity for real-time optimization. This paper utilizes the Deep Reinforcement Learning (DRL) framework to find an optimal or near-optimal solution for control parameters. A model based on maximum a posteriori policy optimization (Hybrid-MPO) that can handle both numerical and categorical parameters is used as a benchmark for comparison. A comparative study shows that DRL can find optimal solutions of similar quality as compared to evolutionary algorithms while requiring significantly less time making them preferable for real-time optimization. The results are confirmed in a large-scale validation study on datasets from production and other fields. A trained XGBoost model is used as a surrogate for process simulation. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, evolutionary algorithms, production process optimization, real-time optimization, hybrid-MPO

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Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

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Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

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Authors: Buntheng Chhorn, WooYoung Jung

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Large-scale swing has been used in entertainment and performance, especially in circus, for a very long time. To increase the safety of this type of structure, a thorough analysis for displacement and bearing stress was performed for an extreme condition where a full cycle swing occurs. Different masses, ranging from 40 kg to 220 kg, and velocities were applied on the swing. Then, based on the solution of differential dynamics equation, swing velocity response to harmonic force was obtained. Moreover, the resistance capacity was estimated based on ACI steel structure design guide. Subsequently, numerical analysis was performed in ABAQUS to obtain the stress on each frame of the swing. Finally, the analysis shows that the expansion of swing structure frame section was required for mass bigger than 150kg.

Keywords: swing structure, displacement, bearing stress, dynamic loads response, finite element analysis

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Authors: Farhat Nigar

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Social stress is stress which arises from one's relationships with others and from the social environment. When a person finds that they are not capable of coping with a situation, stress arises. Sanitation workers faces a lot of discrimination from the society which leads to stress and have severe impact on their mental health. Sanitation workers face lot of work pressure which sometimes leads to mental health problems, but there is lack of proper data of sanitation workers dealing with mental health problems which is a big obstacle before evolving policies for the welfare of sewage and septic tank workers which needs attention. The objective of the study is to find out the effect of social stress on the mental health of sanitation workers and to explore the scope of social work in coping with mental health problems of workers. This descriptive and analytical study was conducted on 100 sanitation workers of Aligarh city through convenience sampling. Data were collected from respondents by schedule and interview method. Most of the respondents said that they don’t enjoy equal status in society and at the workplace as well which leads to stress. Many of them said that social stress leads to poor performance in the workplace. Some of the workers feel depressed when their work is not appreciated and recognized in society. Majority of respondents has stress in financial and employment-related difficulties. Thus it can be said that social stress has several impacts on mental health which leads to poor performance, lack of confidence, and motivation which sometimes leads to depression. Social work can play a very important and challenging role in overcoming these difficulties by providing education, motivation and guiding them and by making them aware of their rights and duties.

Keywords: discrimination, health, stress, sanitation workers

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Authors: Aysegul Sarac

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The aim of this study is to analyze the impacts of integrating agile development principles and practices, in particular to reduce project lead time in a multi-project environment. We analyze Arçelik Washing Machine R&D Center in which multiple projects are conducted by shared resources. In the first part of the study, we illustrate the current waterfall model system by using a value stream map. We define all activities starting from the first idea of the project to the customer and measure process time and lead time of projects. In the second part of the study we estimate potential improvements and select a set of these improvements to integrate agile principles. We aim to develop a future state map and analyze the impacts of integrating lean principles on project lead time. The main contribution of this study is that we analyze and integrate agile product development principles in a real multi-project system.

Keywords: agile project management, multi project system, project lead time, product development

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Authors: Jun Su Park, Byung Kwan Oh, Jin Woo Hwang, Yousok Kim, Hyo Seon Park

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For the structural safety, the maximum stress calculated from the stress distribution of a structure is widely used. The stress distribution can be estimated by deformed shape of the structure obtained from measurement. Although the estimation of stress is strongly affected by the location and number of sensing points, most studies have conducted the stress estimation without reasonable basis on sensing plan such as the location and number of sensors. In this paper, an optimal sensing technique for estimating the stress distribution is proposed. This technique proposes the optimal location and number of sensing points for a 2-D frame structure while minimizing the error of stress distribution between analytical model and estimation by cubic smoothing splines using genetic algorithm. To verify the proposed method, the optimal sensor measurement technique is applied to simulation tests on 2-D steel frame structure. The simulation tests are performed under various loading scenarios. Through those tests, the optimal sensing plan for the structure is suggested and verified.

Keywords: genetic algorithm, optimal sensing, optimizing sensor placements, steel frame structure

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Authors: Teresa Smith, Farrah Jacquez

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Health-risk behaviors (e.g., smoking, poor diet) directly increase the risk for chronic disease and morbidity. There is substantial evidence of a negative association between socioeconomic status (SES) and engagement in health-risk behaviors. However, due to the complexity of SES, researchers have suggested looking beyond this factor to fully understand the mechanisms that underlie engagement in health-risk behaviors. Stress is one plausible mechanism through which SES impacts health-risk behaviors. Currently, it remains unclear how stress occurring across the life course might impact health behaviors and explain the association between SES and these behaviors. To address the gaps in the literature, 172 adults between the ages of 18-49 were surveyed about their lifetime stress exposure, sociodemographic variables, and health-risk behaviors via an online recruitment portal, Prolific. Five major findings emerged from the current study. First, SES was negatively associated with engagement in health-risk behaviors and lifetime stress above and beyond current stress and other relevant demographics. Second, lifetime stress was significantly associated with health-risk behaviors above and beyond current stress and relevant demographic variables. Third, lifetime stress fully mediated the association between SES and health-risk behaviors above and beyond current stress and other demographics. Fourth, the severity of stress experienced emerged as the most significant lifetime stress variable that explains the relation between SES and health-risk behaviors. Fifth and finally, lower SES and experiencing financial and legal/crime stressors increased the likelihood of engaging in health-risk behaviors. The current study results align with previous research and suggest that stress occurring over the lifespan impacts the relation between SES and health-risk behaviors, which are in turn known to impact health outcomes. However, our findings move the current literature forward by providing a more nuanced understanding of the specific aspects of stress that influence this association. Specifically, the severity of stress experienced across the entire lifespan was the most important aspect of stress when examining the association between SES and health-risk behaviors. Further, individuals most at risk for engaging in health-risk behaviors are those of the lowest SES and experience financial and legal/crime stressors. These findings have the potential to inform interventions and policies aimed at addressing health-risk behaviors by providing a more sophisticated understanding of the impact of stress.

Keywords: stress, health behaviors, socioeconomic status, health

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Authors: Esmael Makarian, Ayub Elyasi, Fatemeh Saberi, Olusegun Stanley Tomomewo

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Rock mechanical investigation is one of the most crucial activities in underground operations, especially in surveys related to hydrocarbon exploration and production, geothermal reservoirs, energy storage, mining, and geotechnics. There is a wide range of traditional methods for driving, collecting, and analyzing rock mechanics data. However, these approaches may not be suitable or work perfectly in some situations, such as fractured zones. Cutting-edge technologies have been provided to solve and optimize the mentioned issues. Internet of Things (IoT), Edge, and Cloud Computing technologies (ECt & CCt, respectively) are among the most widely used and new artificial intelligence methods employed for geomechanical studies. IoT devices act as sensors and cameras for real-time monitoring and mechanical-geological data collection of rocks, such as temperature, movement, pressure, or stress levels. Structural integrity, especially for cap rocks within hydrocarbon systems, and rock mass behavior assessment, to further activities such as enhanced oil recovery (EOR) and underground gas storage (UGS), or to improve safety risk management (SRM) and potential hazards identification (P.H.I), are other benefits from IoT technologies. EC techniques can process, aggregate, and analyze data immediately collected by IoT on a real-time scale, providing detailed insights into the behavior of rocks in various situations (e.g., stress, temperature, and pressure), establishing patterns quickly, and detecting trends. Therefore, this state-of-the-art and useful technology can adopt autonomous systems in rock mechanical surveys, such as drilling and production (in hydrocarbon wells) or excavation (in mining and geotechnics industries). Besides, ECt allows all rock-related operations to be controlled remotely and enables operators to apply changes or make adjustments. It must be mentioned that this feature is very important in environmental goals. More often than not, rock mechanical studies consist of different data, such as laboratory tests, field operations, and indirect information like seismic or well-logging data. CCt provides a useful platform for storing and managing a great deal of volume and different information, which can be very useful in fractured zones. Additionally, CCt supplies powerful tools for predicting, modeling, and simulating rock mechanical information, especially in fractured zones within vast areas. Also, it is a suitable source for sharing extensive information on rock mechanics, such as the direction and size of fractures in a large oil field or mine. The comprehensive review findings demonstrate that digital transformation through integrated IoT, Edge, and Cloud solutions is revolutionizing traditional rock mechanical investigation. These advanced technologies have empowered real-time monitoring, predictive analysis, and data-driven decision-making, culminating in noteworthy enhancements in safety, efficiency, and sustainability. Therefore, by employing IoT, CCt, and ECt, underground operations have experienced a significant boost, allowing for timely and informed actions using real-time data insights. The successful implementation of IoT, CCt, and ECt has led to optimized and safer operations, optimized processes, and environmentally conscious approaches in underground geological endeavors.

Keywords: rock mechanical studies, internet of things, edge computing, cloud computing, underground surveys, geological operations

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Authors: Mamman Jimoh Ahmadu, Sanusi Abubakar Sadiq, Eldah Ephraim Buba

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In modern societies, stress has become a widespread phenomenon and therefore an issue of major concern to employees, organizations, and the state. As senior management of an organization, executives are not immune to this problem because they carry out lots of activities while on duty. This paper is centered on the benefits of Tourism, Leisure and Recreation to the management of executive stress. Executives work has always been considered to be stressful. The key objective of the research is to gain a better understanding of the causes of stress among executives and to find out how tourism, leisure, and recreational activities could be used as a means to managing stress. Interview and observation data were analyzed using SPSS. The major finding revealed that that human system has specific limitations and nature cannot be cheated. It is recommended that executives should take regular and mandatory vacation of least forty days in a year. The only answer then is rest. The research recommends that a break tends to improves and relaxes, refreshes the mind and enhances performance.

Keywords: executive, recreation, stress, tourism

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Authors: Eryati Darwin, Arina Widya Murni, Adnil Edwin Nurdin

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Background: Functional dyspepsia (FD) is a highly prevalent and heterogeneous disorder. Most patients with FD complain of symptoms related to the intake of meals. Psychological stress may promote peptic ulcer and had an effect on ulcers associated Hp, and may also trigger worsen symptoms in inflammatory disorders of the gastrointestinal. Cells in mucosal gastric stimulate the production of several cytokines, which might associated with Helicobacter pylori infection. The cascade of biological events leading to stress-induced FD remains poorly understood. Aim of Study: To determine the prion-flammatory cytokine IL-6, and Helicobacter pylori activity on mucosal gastric of FD and their association with psychological stress. Methods: The subjects of this study were dyspeptic patients who visited M. Djamil General Hospital and in two Community Health Centers in Padang. On the basis of the stress index scale to identify psychological stress by using Depression Anxiety and Stress Scale (DASS 42), subjects were divided into two groups of 20 each, stress groups and non-stress groups. All diagnoses were confirmed by review of cortisol and esophagogastroduodenoscopy reports. Gastric biopsy samples and peripheral blood were taken during diagnostic procedures. Immunohistochemistry methods were used to determine the expression of IL-6 and Hp in gastric mucosal. The data were statistically analyzed by univariate and bivariate analysis. All procedures of this study were approved by Research Ethics Committee of Medical Faculty Andalas University. Results: In this study, we enrolled 40 FD patients (26 woman and 14 men) in range between 35-56 years old. Cortisol level of blood FD patients as parameter of stress hormone which taken in the morning was significantly higher in stress group than non-stress group. The expression of IL-6 in gastric mucosa was significantly higher in stress group in compared to non-stress group (p<0,05). Helicobacter pylori activity in gastric mucosal in stress group were significantly higher than non-stress group. Conclusion: The present study showed that psychological stress can induce gastric mucosal inflammation and increase of Helicobacter pylori activity.

Keywords: functional dyspepsia, Helicobacter pylori, interleukin-6, psychological stress

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Authors: Sergio Saponara, Luca Fanucci, Riccardo Cassettari, Ruggero Piernicola, Marco Righetto

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The paper presents an innovative networked radar system for detection of obstacles in a railway level crossing scenario. This Monitoring System (MS) is able to detect moving or still obstacles within the railway level crossing area automatically, avoiding the need of human presence for surveillance. The MS is also connected to the National Railway Information and Signaling System to communicate in real-time the level crossing status. The architecture is compliant with the highest Safety Integrity Level (SIL4) of the CENELEC standard. The number of radar sensors used is configurable at set-up time and depends on how large the level crossing area can be. At least two sensors are expected and up four can be used for larger areas. The whole processing chain that elaborates the output sensor signals, as well as the communication interface, is fully-digital, was designed in VHDL code and implemented onto a Xilinx Virtex 6.

Keywords: radar for safe mobility, railroad crossing, railway, transport safety

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Authors: Ardalan Sabamehr, Ashutosh Bagchi

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Recently, Structural Health Monitoring (SHM) based on the vibration of structures has attracted the attention of researchers in different fields such as: civil, aeronautical and mechanical engineering. Operational Modal Analysis (OMA) have been developed to identify modal properties of infrastructure such as bridge, building and so on. Frequency Domain Decomposition (FDD), Stochastic Subspace Identification (SSI) and Continuous Wavelet Transform (CWT) are the three most common methods in output only modal identification. FDD, SSI, and CWT operate based on the frequency domain, time domain, and time-frequency plane respectively. So, FDD and SSI are not able to display time and frequency at the same time. By the way, FDD and SSI have some difficulties in a noisy environment and finding the closed modes. CWT technique which is currently developed works on time-frequency plane and a reasonable performance in such condition. The other advantage of wavelet transform rather than other current techniques is that it can be applied for the non-stationary signal as well. The aim of this paper is to compare three most common modal identification techniques to find modal properties (such as natural frequency, mode shape, and damping ratio) of three story steel frame which was built in Concordia University Lab by use of ambient vibration. The frame has made of Galvanized steel with 60 cm length, 27 cm width and 133 cm height with no brace along the long span and short space. Three uniaxial wired accelerations (MicroStarin with 100mv/g accuracy) have been attached to the middle of each floor and gateway receives the data and send to the PC by use of Node Commander Software. The real-time monitoring has been performed for 20 seconds with 512 Hz sampling rate. The test is repeated for 5 times in each direction by hand shaking and impact hammer. CWT is able to detect instantaneous frequency by used of ridge detection method. In this paper, partial derivative ridge detection technique has been applied to the local maxima of time-frequency plane to detect the instantaneous frequency. The extracted result from all three methods have been compared, and it demonstrated that CWT has the better performance in term of its accuracy in noisy environment. The modal parameters such as natural frequency, damping ratio and mode shapes are identified from all three methods.

Keywords: ambient vibration, frequency domain decomposition, stochastic subspace identification, continuous wavelet transform

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Authors: Feng Yin

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Clouds are inevitable contaminants in optical satellite imagery, and prevent the satellite imaging systems from acquiring clear view of the earth surface. The presence of clouds in satellite imagery bring negative influences for remote sensing land resources investigation. As a consequence, detecting the locations of clouds in satellite imagery is an essential preprocessing step, and further remove the existing clouds is crucial for the application of imagery. In this paper, a multi-temporal based satellite imagery cloud detection and removal method is proposed, which will be used for large-scale land resource investigation. The proposed method is mainly composed of four steps. First, cloud masks are generated for cloud contaminated images by single temporal cloud detection based on multiple spectral features. Then, a cloud-free reference image of target areas is synthesized by weighted averaging time-series images in which cloud pixels are ignored. Thirdly, the refined cloud detection results are acquired by multi-temporal analysis based on the reference image. Finally, detected clouds are removed via multi-temporal linear regression. The results of a case application in Hubei province indicate that the proposed multi-temporal cloud detection and removal method is effective and promising for large-scale land resource investigation.

Keywords: cloud detection, cloud remove, multi-temporal imagery, land resources investigation

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Authors: Madushani Rodrigo, Banuka Athuraliya

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In today's world of medical diagnosis and prediction, machine learning stands out as a strong tool, transforming old ways of caring for health. This study analyzes the use of machine learning in the specialized domain of sports medicine, with a focus on the timely and accurate detection of bone fractures in cricket athletes. Failure to identify bone fractures in real time can result in malunion or non-union conditions. To ensure proper treatment and enhance the bone healing process, accurately identifying fracture locations and types is necessary. When interpreting X-ray images, it relies on the expertise and experience of medical professionals in the identification process. Sometimes, radiographic images are of low quality, leading to potential issues. Therefore, it is necessary to have a proper approach to accurately localize and classify fractures in real time. The research has revealed that the optimal approach needs to address the stated problem and employ appropriate radiographic image processing techniques and object detection algorithms. These algorithms should effectively localize and accurately classify all types of fractures with high precision and in a timely manner. In order to overcome the challenges of misidentifying fractures, a distinct model for fracture localization and classification has been implemented. The research also incorporates radiographic image enhancement and preprocessing techniques to overcome the limitations posed by low-quality images. A classification ensemble model has been implemented using ResNet18 and VGG16. In parallel, a fracture segmentation model has been implemented using the enhanced U-Net architecture. Combining the results of these two implemented models, the FracXpert system can accurately localize exact fracture locations along with fracture types from the available 12 different types of fracture patterns, which include avulsion, comminuted, compressed, dislocation, greenstick, hairline, impacted, intraarticular, longitudinal, oblique, pathological, and spiral. This system will generate a confidence score level indicating the degree of confidence in the predicted result. Using ResNet18 and VGG16 architectures, the implemented fracture segmentation model, based on the U-Net architecture, achieved a high accuracy level of 99.94%, demonstrating its precision in identifying fracture locations. Simultaneously, the classification ensemble model achieved an accuracy of 81.0%, showcasing its ability to categorize various fracture patterns, which is instrumental in the fracture treatment process. In conclusion, FracXpert has become a promising ML application in sports medicine, demonstrating its potential to revolutionize fracture detection processes. By leveraging the power of ML algorithms, this study contributes to the advancement of diagnostic capabilities in cricket athlete healthcare, ensuring timely and accurate identification of bone fractures for the best treatment outcomes.

Keywords: multiclass classification, object detection, ResNet18, U-Net, VGG16

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Authors: Kaveh Shahi, Helmi Z. M. Shafri, Ebrahim Taherzadeh

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In remote sensing, shadow causes problems in many applications such as change detection and classification. It is caused by objects which are elevated, thus can directly affect the accuracy of information. For these reasons, it is very important to detect shadows particularly in urban high spatial resolution imagery which created a significant problem. This paper focuses on automatic shadow detection based on a new spectral index for multispectral imagery known as Shadow Detection Index (SDI). The new spectral index was tested on different areas of World-View 2 images and the results demonstrated that the new spectral index has a massive potential to extract shadows effectively and automatically.

Keywords: spectral index, shadow detection, remote sensing images, World-View 2

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Authors: Z. Yousefniayejahr, S. Bolognini, A. Bonini, C. Campobasso, N. Poma, F. Vivaldi, M. Di Luca, A. Tavanti, F. Di Francesco

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Pathogenic bacteria represent a threat to healthcare systems and the food industry because their rapid detection remains challenging. Electrochemical biosensors are gaining prominence as a novel technology for the detection of pathogens due to intrinsic features such as low cost, rapid response time, and portability, which make them a valuable alternative to traditional methodologies. These sensors use biorecognition elements that are crucial for the identification of specific bacteria. In this context, bacteriophages are promising tools for their inherent high selectivity towards bacterial hosts, which is of fundamental importance when detecting bacterial pathogens in complex biological samples. In this study, we present the development of a low-cost and portable sensor based on the Zeno phage for the rapid detection of Staphylococcus aureus. Screen-printed gold electrodes functionalized with the Zeno phage were used, and electrochemical impedance spectroscopy was applied to evaluate the change of the charge transfer resistance (Rct) as a result of the interaction with S. aureus MRSA ATCC 43300. The phage-based biosensor showed a linear range from 101 to 104 CFU/mL with a 20-minute response time and a limit of detection (LOD) of 1.2 CFU/mL under physiological conditions. The biosensor’s ability to recognize various strains of staphylococci was also successfully demonstrated in the presence of clinical isolates collected from different geographic areas. Assays using S. epidermidis were also carried out to verify the species-specificity of the phage sensor. We only observed a remarkable change of the Rct in the presence of the target S. aureus bacteria, while no substantial binding to S. epidermidis occurred. This confirmed that the Zeno phage sensor only targets S. aureus species within the genus Staphylococcus. In addition, the biosensor's specificity with respect to other bacterial species, including gram-positive bacteria like Enterococcus faecium and the gram-negative bacterium Pseudomonas aeruginosa, was evaluated, and a non-significant impedimetric signal was observed. Notably, the biosensor successfully identified S. aureus bacterial cells in a complex matrix such as a nasal swab, opening the possibility of its use in a real-case scenario. We diluted different concentrations of S. aureus from 108 to 100 CFU/mL with a ratio of 1:10 in the nasal swap matrices collected from healthy donors. Three different sensors were applied to measure various concentrations of bacteria. Our sensor indicated high selectivity to detect S. aureus in biological matrices compared to time-consuming traditional methods, such as enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR), and radioimmunoassay (RIA), etc. With the aim to study the possibility to use this biosensor to address the challenge associated to pathogen detection, ongoing research is focused on the assessment of the biosensor’s analytical performances in different biological samples and the discovery of new phage bioreceptors.

Keywords: electrochemical impedance spectroscopy, bacteriophage, biosensor, Staphylococcus aureus

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Authors: Abdelkader Elhanaoui, Mhamed Hadji, Rachid Skouri, Said Agounad

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The purpose of this paper is to study the phenomenon of acoustic scattering by using a new method. The signal processing (Fast Fourier Transform FFT Inverse Fast Fourier Transform iFFT and BESSEL functions) is widely applied to obtain information with high precision accuracy. Signal processing has a wider implementation in general-purpose pro-cessors. Our interest was focused on the use of FPGAs (Field-Programmable Gate Ar-rays) in order to minimize the computational complexity in single processor architecture, then be accelerated on FPGA and meet real-time and energy efficiency requirements. Gen-eral-purpose processors are not efficient for signal processing. We implemented the acous-tic backscattered signal processing model on the Altera DE-SOC board and compared it to Odroid xu4. By comparison, the computing latency of Odroid xu4 and FPGA is 60 sec-onds and 3 seconds, respectively. The detailed SoC FPGA-based system has shown that acoustic spectra are performed up to 20 times faster than the Odroid xu4 implementation. FPGA-based system of processing algorithms is realized with an absolute error of about 10⁻³. This study underlines the increasing importance of embedded systems in underwater acoustics, especially in non-destructive testing. It is possible to obtain information related to the detection and characterization of submerged cells. So we have achieved good exper-imental results in real-time and energy efficiency.

Keywords: DE1 FPGA, acoustic scattering, form function, signal processing, non-destructive testing

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Authors: Jeng-Shin Sheu, Qi-Xun Zheng

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This paper mainly discusses a method of separating speech when using a microphone array without knowing the number and direction of sound sources. In recent years, there have been many studies on the method of separating signals by using masking, but most of the separation methods must be operated under the condition of a known number of sound sources. Such methods cannot be used for real-time applications. In our method, this paper uses Circular-Integrated-Cross-Spectrum to estimate the statistical histogram distribution of the direction of arrival (DOA) to obtain the number of sound sources and sound in the mixed-signal Source direction. In calculating the relevant parameters of the ring integrated cross-spectrum, the phase (Phase of the Cross-Power Spectrum) and phase rotation factors (Phase Rotation Factors) calculated by the cross power spectrum of each microphone pair are used. In the part of separating speech, it uses the DOA weighting and shielding separation method to calculate the sound source direction (DOA) according to each T-F unit (time-frequency point). The weight corresponding to each T-F unit can be used to strengthen the intensity of each sound source from the T-F unit and reduce the influence of the remaining sound sources, thereby achieving voice separation.

Keywords: real-time, spectrum analysis, sound source localization, sound source separation

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Authors: S. Silva

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Some professions had an increased risk for occupational stress and less quality of working life. Among several professions this risk is particularly preoccupant in teachers, namely high school teachers. This study aims to characterize the work stress in teachers and understand how the work stress influences their quality of working life. One hundred teachers, 60 women and 40 men with mean age of 43,2 years (SD=7,8), from North Portugal teaching in several high schools filled in the following questionnaires: Social-Demographic Questionnaire, Teacher Stress Questionnaire and the Survey of Professional Life, during January to March 2015. The results of our study show that high school teachers have several occupational stressors (M=5) and poor perceived quality of working life. They are unsatisfied with their current job and they refer to a considerable job frustration. 33% referred to no expectations about a better future in these profession and 40% have no career development. There is a strong negative correlation between stress and teacher quality of working life (r=-.775). Moderate levels of stress are related to more favorable quality of working life (r=.632). Stress, frequent in teachers, is a significant predictor of poor quality of working life. There are several stressors affecting the teachers’ performance. Career development is not considered among this professional class and it seems related to current job frustration. Considering the role of high school teacher in the development and learning of students, these results should be taken in consideration when planning the graduation and interventions with teachers.

Keywords: career, quality of working life, stress, teachers

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Authors: Alexander A. Karabutov, Natalia B. Podymova, Elena B. Cherepetskaya

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The theoretical analysis is carried out to get the relation between the ultrasonic wave velocity and the value of residual stresses. The laser-ultrasonic method is developed to evaluate the residual stresses and subsurface defects in metals. The method is based on the laser thermooptical excitation of longitudinal ultrasonic wave sand their detection by a broadband piezoelectric detector. A laser pulse with the time duration of 8 ns of the full width at half of maximum and with the energy of 300 µJ is absorbed in a thin layer of the special generator that is inclined relative to the object under study. The non-uniform heating of the generator causes the formation of a broadband powerful pulse of longitudinal ultrasonic waves. It is shown that the temporal profile of this pulse is the convolution of the temporal envelope of the laser pulse and the profile of the in-depth distribution of the heat sources. The ultrasonic waves reach the surface of the object through the prism that serves as an acoustic duct. At the interface ‚laser-ultrasonic transducer-object‘ the conversion of the most part of the longitudinal wave energy takes place into the shear, subsurface longitudinal and Rayleigh waves. They spread within the subsurface layer of the studied object and are detected by the piezoelectric detector. The electrical signal that corresponds to the detected acoustic signal is acquired by an analog-to-digital converter and when is mathematically processed and visualized with a personal computer. The distance between the generator and the piezodetector as well as the spread times of acoustic waves in the acoustic ducts are the characteristic parameters of the laser-ultrasonic transducer and are determined using the calibration samples. There lative precision of the measurement of the velocity of longitudinal ultrasonic waves is 0.05% that corresponds to approximately ±3 m/s for the steels of conventional quality. This precision allows one to determine the mechanical stress in the steel samples with the minimal detection threshold of approximately 22.7 MPa. The results are presented for the measured dependencies of the velocity of longitudinal ultrasonic waves in the samples on the values of the applied compression stress in the range of 20-100 MPa.

Keywords: laser-ultrasonic method, longitudinal ultrasonic waves, metals, residual stresses

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Authors: Parisa Alizadeh Oskuie, Shahram Baghban Ciruse

Abstract:

The influence of arbuscular mycorrhizal (AM) fungus(Glomus mossea) on plant growth and leaf-water potential of tomato (lycopersicum esculentum L.cv.super star) were studied in potted culture water stress stress period of 3 months in greenhouse conditions with the soil matric potential maintained at Fc1, Fc2, Fc3, and Fc4 respectively (0.8,0.7,0.6,0.5 Fc). Seven-day-old seedlings of tomato were transferred to pots containing Glomus mossea or non-AMF. AM colonization significantly stimulated shoot dry matter and leaf-water potential but water stress significantly decreased leaf area, shoot dry matter colonization and leaf-water potential.

Keywords: leaf-water potential, plant growth, tomato, VA mycorrhiza, water-stress

Procedia PDF Downloads 424

Authors: Rama Debbarma, Debanjan Das

Abstract:

The performance of multiple tuned mass dampers to mitigate the seismic vibration of structures considering real time history data is investigated in this paper. Three different real earthquake time history data like Kobe, Imperial Valley and Mammoth Lake are taken in the present study. The multiple tuned mass dampers (MTMD) are distributed at each storey. For comparative study, single tuned mass damper (STMD) is installed at top of the similar structure. This study is conducted for a fixed mass ratio (5%) and fixed damping ratio (5%) of structures. Numerical study is performed to evaluate the effectiveness of MTMDs and overall system performance. The displacement, acceleration, base shear and storey drift are obtained for both combined system (structure with MTMD and structure with STMD) for all earthquakes. The same responses are also obtained for structure without damper system. From obtained results, it is investigated that the MTMD configuration is more effective for controlling the seismic response of the primary system with compare to STMD configuration.

Keywords: Earthquake, multiple tuned mass dampers, single tuned mass damper, Time history.

Procedia PDF Downloads 270

Authors: Nam-Uk Kim, Sung-Hwan Kim, Tai-Myoung Chung

Abstract:

Typical security management systems are not suitable for detecting APT attack, because they cannot draw the big picture from trivial events of security solutions. Although SIEM solutions have security analysis engine for that, their security analysis mechanisms need to be verified in academic field. Although this paper proposes merely an architectural model for APT detection, we will keep studying on correlation analysis mechanism in the future.

Keywords: advanced persistent threat, anomaly detection, data mining

Procedia PDF Downloads 528

Authors: Vilma Zydziunaite, Ausra Rutkiene

Abstract:

In modern society, problems related to the teacher's personality, mental and physical health, teacher's emotions and competencies are becoming more and more relevant. In Lithuania, compared to other European countries, teachers experience specific difficulties at work: they have to work in conditions of constant reforms and changes and face growing competition due to the decrease in students and schools. Professional burnout, teacher’s self-efficacy and perceived stress are interrelated personally and/or organisationally. So, the relationship between teachers' professional burnout, self-efficacy, and perceived stress in the school environment seems to be a relatively underresearched area in Lithuania. The research aim was to reveal and characterize teacher burnout, self-efficacy, and perceived stress in the Lithuanian school context. The quantitative research design with a questioning survey was chosen for the study. The sample size consisted of 427 Lithuanian teachers. Research results revealed the highest scores for exhaustion and the lowest for cynicism; at a time when the teacher experiences professional burnout, cynicism is observed as the weakest characteristic; no significant differences were found according to educational level work experience; significant differences were identified according to age for exhaustion and overall burnout level among teachers; the most of teachers in Lithuanian sample perceive the moderate stress level in school environment; overall burnout has a significant correlation with self-efficacy and stress among Lithuanian teachers. This study has empirical and practical implications: it is relevant to study the problems of teacher's professional burnout, stress, and self-efficacy in connection with contextual qualitative variables and specify the interrelationships between variables in order to be able to identify specific problems and provide empirical evidence to practically solve them. From a practical point of view, the results show that the socio-emotional state of teachers should not be dismissed as an insignificant aspect. Therefore, the school administration must make efforts to develop a positive school climate that supports the socio-emotional state of the teacher. At the same time, school administration must pay great attention to the development of teachers' socio-emotional competencies without ignoring their importance in the teacher's professional life.

Keywords: Lithuania, perceived stress, professional burnout, self-efficacy, teacher

Procedia PDF Downloads 52

Authors: Yeongyu Choi, Ju H. Park, Ho-Youl Jung

Abstract:

In this paper, we propose labeling based RANSAC algorithm for lane detection. Advanced driver assistance systems (ADAS) have been widely researched to avoid unexpected accidents. Lane detection is a necessary system to assist keeping lane and lane departure prevention. The proposed vision based lane detection method applies Canny edge detection, inverse perspective mapping (IPM), K-means algorithm, mathematical morphology operations and 8 connected-component labeling. Next, random samples are selected from each labeling region for RANSAC. The sampling method selects the points of lane with a high probability. Finally, lane parameters of straight line or curve equations are estimated. Through the simulations tested on video recorded at daytime and nighttime, we show that the proposed method has better performance than the existing RANSAC algorithm in various environments.

Keywords: Canny edge detection, k-means algorithm, RANSAC, inverse perspective mapping

Procedia PDF Downloads 244

Authors: Paul B Stone, Subhashini Ganapathy, Mary E. Fendley, Layla Akilan

Abstract:

As notifications become more common through mobile devices, it is important to understand the impact of wearable devices on the improved user experience of man-machine interfaces. This study examined the use of a wearable device for a real-time system using a computer-simulated petrochemical system. The key research question was to determine how using the information provided by the wearable device can improve human performance through measures of situational awareness and decision making. Results indicate that there was a reduction in response time when using the watch, and there was no difference in situational awareness. Perception of using the watch was positive, with 83% of users finding value in using the watch and receiving haptic feedback.

Keywords: computer applications, haptic feedback, petrochemical systems, situational awareness, wearable technology

Procedia PDF Downloads 200

Authors: D. Srinivasacharya, G. Madhava Rao

Abstract:

In this article, the pulsatile flow of blood flow in bifurcated artery with mild stenosis is investigated. Blood is treated to be a micropolar fluid with constant density. The arteries forming bifurcation are assumed to be symmetric about its axes and straight cylinders of restricted length. As the geometry of the stenosed bifurcated artery is irregular, it is changed to regular geometry utilizing the appropriate transformations. The numerical solutions, using the finite difference method, are computed for the flow rate, the shear stress, and the impedance. The influence of time, coupling number, half of the bifurcated angle and Womersley number on shear stress, flow rate and impedance (resistance to the flow) on both sides of the flow divider is shown graphically. It has been observed that the shear stress and flow rate are increasing with increase in the values of Womersley number and bifurcation angle on both sides of the apex. The shear stress is increasing along the inner wall and decreasing along the outer wall of the daughter artery with an increase in the value of coupling number. Further, it has been noticed that the shear stress, flow rate, and impedance are perturbed largely near to the apex in the parent artery due to the presence of backflow near the apex.

Keywords: micropolar fluid, bifurcated artery, stenosis, back flow, secondary flow, pulsatile flow, Womersley number

Procedia PDF Downloads 193

Authors: Fadzai Munyuki

Abstract:

Parental divorce is one of youth's most stressful life events and is associated with long-lasting emotional and behavioral problems. Over the last few decades, research has consistently found strong associations between divorce and adverse health effects in adolescents. Parental divorce has been hypothesized to lead to psychosocial development problems, mental health challenges, internalizing and externalizing behavior problems, and low academic performance among adolescents. This is supported by the Positive youth development theory, which states that a family setup has a major role to play in adolescent development and well-being. So, the focus of this research will be to test this hypothesized process model among adolescents in five provinces in Zimbabwe. A cross-sectional study will be conducted to test this hypothesis, and 1840 (n = 1840) adolescents aged between 14 to 17 will be employed for this study. A Stress and Questionnaire scale, a Child behavior checklist scale, and an academic concept scale will be used for this study. Data analysis will be done using Structural Equations Modeling. This study has many limitations, including the lack of a 'real-time' study, a few cross-sectional studies, a lack of a thorough and validated population measure, and many studies that have been done that have focused on one variable in relation to parental divorce. Therefore, this study seeks to bridge this gap between past research and current literature by using a validated population measure, a real-time study, and combining three latent variables in this study.

Keywords: mental health, internalizing and externalizing behavior, divorce, academic achievements

Procedia PDF Downloads 77

Authors: U.B Mahadevswamy UBM, Siraj Ahmed Siraj

Abstract:

As the title of the paper indicates about brainwaves and its uses for various applications based on their frequencies and different parameters which can be implemented as real time application with the title a smart brain wave sensor system for paralyzed patients. Brain wave sensing is to detect a person's mental status. The purpose of brain wave sensing is to give exact treatment to paralyzed patients. The data or signal is obtained from the brainwaves sensing band. This data are converted as object files using Visual Basics. The processed data is further sent to Arduino which has the human's behavioral aspects like emotions, sensations, feelings, and desires. The proposed device can sense human brainwaves and detect the percentage of paralysis that the person is suffering. The advantage of this paper is to give a real-time smart sensor device for paralyzed patients with paralysis percentage for their exact treatment. Keywords:-Brainwave sensor, BMI, Brain scan, EEG, MCH.

Keywords: Keywords:-Brainwave sensor , BMI, Brain scan, EEG, MCH

Procedia PDF Downloads 154