Search results for: health monitoring systems

Authors: Nuria Rubio, María Campo, Joana Ruiz, Paola Vecino

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Renewable energies have been proposed for some years as a solution to the ecological crisis caused by traditional fuels. The installation of solar parks for electricity production is therefore necessary for a transition to cleaner energy. Additionally, spaces occupied by solar parks can be ideal places for biodiversity promotion consisting in controlled areas allowing free transit of numerous animal species in absence of phytosanitary products or other substances commonly used in rural areas. The main objective of this project is increasing local biodiversity. Secondary objectives include the installation of beehives with Apis mellifera iberiensis swarms (native honeybee species), the monitoring and periodic evaluation of the state of health and demographic progression of these swarms and study of biodiversity increase in these areas, mainly due to the presence of Apis mellifera iberiensis. Prior to bee-hives installation, a preliminary study of the area is carried out to quantify floral load, biocenosis and geo-climatological characteristics of the area of study for determining the optimal number of hives for the benefit of the local ecosystem. Once beehives set up, the bee-swarms health status is monitored and evaluated quarterly using monitoring systems. Parameters studies are weight, humidity inside the hive, external and internal temperature, and sound inside the hive. Furthermore, a biodiversity study of the area was conducted by direct observation and quantification of species (S) in the area of bee-foraging (1 km around the beehives). A great diversity of species has been detected in the area of study. Therefore, the population of Apis mellifera iberiensis is not displacing other pollinators in the area, on the contrary, results show that it is contributing to the pollination of the different plant species enhancing wild bees’ biodiversity.

Keywords: biodiversity, honeybee, pollination, solar park

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Authors: Charawee Butbumrung

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This research aim to study the financial service of the Thailand financial Institution, second is to identify "best practices" offered by four financial institutions, namely, Kasikornthai Bank, Bangkok Bank, Siam Commercial Bank, and Thanachart Bank. In-depth interviews with managers of financial institution and borrowers reveal best practices from each financial institution. Close monitoring of and a close relationship with borrowers appear to be important for early detection of any problem. Another aspect that may be important is building up loyalty and developing reliability among members. A close and informal relationship with borrowers may also help in monitoring and early detection of problems that may arise in non-repayment of loans. Other factors that may be considered important to the success of a financial service scheme are cooperation and coordination among various agencies that provide additional support to borrowers. Indirectly, these support systems contribute to the success of a SME in Thailand.

Keywords: best practices, financial service, financial institution, SME in Thailand

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Authors: Qasim Khan, Furkan Ahmad, Asfar A. Khan, M. Saad Alam, Faiz Ahmad

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In this paper, partial discharge analysis is performed in cavities artificially created in insulation. The setup is according with Cigre-II Method. Circular Samples created from Perspex Sheet with different configuration with changing number of cavities. Assessment of insulation health can be performed by Partial Discharge measurement as this has been found to be important means of condition monitoring. The experiments are done using MPD 540, which is a modern partial discharge measurement system. By analyzing the PD activity obtained for various voids/cavities, it is observed that the PD voltages show variation for cavity’s diameter, depth even for its ratios. This can be employed for scrutiny of insulation system.

Keywords: partial discharges, condition monitoring, insulation defects, degradation and corrosion, PMMA

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Authors: Ahmed Noor Al-Qayyim

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During the past decades, the bridge structures are considered very important portions of transportation networks, due to the fast urban sprawling. With the failure of bridges that under operating conditions lead to focus on updating the default bridge inspection methodology. The structures health monitoring (SHM) using the vibration response appeared as a promising method to evaluate the condition of structures. The rapid development in the sensors technology and the condition assessment techniques based on the vibration-based damage detection made the SHM an efficient and economical ways to assess the bridges. SHM is set to assess state and expects probable failures of designated bridges. In this paper, a presentation for Frequency Response function method that uses the captured vibration test information of structures to evaluate the structure condition. Furthermore, the main steps of the assessment of bridge using the vibration information are presented. The Frequency Response function method is applied to the experimental data of a full-scale bridge.

Keywords: bridge assessment, health monitoring, damage detection, frequency response function (FRF), signal processing, structure identification

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Authors: Yihua Wang, Yunru Lai

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Statistical process control has been successfully applied in a variety of industries. In some applications, the quality of a process or product is better characterized and summarized by a functional relationship between a response variable and one or more explanatory variables. A collection of this type of data is called a profile. Profile monitoring is used to understand and check the stability of this relationship or curve over time. The independent assumption for the error term is commonly used in the existing profile monitoring studies. However, in many applications, the profile data show correlations over time. Therefore, we focus on a general linear regression model with a first-order autocorrelation between profiles in this study. We propose an exponentially weighted moving average charting scheme to monitor this type of profile. The simulation study shows that our proposed methods outperform the existing schemes based on the average run length criterion.

Keywords: autocorrelation, EWMA control chart, general linear regression model, profile monitoring

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Authors: Ofoegbu Ositadinma Edward

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This research paper discusses a web based monitoring system that enables effective monitoring of fuel pump output and sales volume from distributed fuel stations under the domain of a single company/organization. The traditional method of operation by these organizations in Nigeria is non-automated and accounting for dispensed product is usually approximated and manual as there is little or no technology implemented to presently provide information relating to the state of affairs in the station both to on-ground staff and to supervisory staff that are not physically present in the station. This results in unaccountable losses in product and revenue as well as slow decision making. Remote monitoring technology as a vast research field with numerous application areas incorporating various data collation techniques and sensor networks can be applied to provide information relating to fuel pump status in distributed fuel stations reliably. Thus, the proposed system relies upon a microcontroller, keypad and pump to demonstrate the traditional fuel dispenser. A web-enabled PC with an accompanying graphic user interface (GUI) was designed using virtual basic which is connected to the microcontroller via the serial port which is to provide the web implementation.

Keywords: fuel pump, microcontroller, GUI, web

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Authors: Terry Teo, Sun Woh Lye, Yufei Li, Zainuddin Zakaria

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Wearable devices such as Electroencephalography (EEG) hold immense potential in the monitoring and assessment of a person’s task engagement. This is especially so in remote or online sites. Research into its use in measuring an individual's cognitive state while performing task activities is therefore expected to increase. Despite the growing number of EEG research into brain functioning activities of a person, key challenges remain in adopting EEG for real-time operations. These include limited portability, long preparation time, high number of channel dimensionality, intrusiveness, as well as level of accuracy in acquiring neurological data. This paper proposes an approach using a 4-6 EEG channels to determine the cognitive states of a subject when undertaking a set of passive and active monitoring tasks of a subject. Air traffic controller (ATC) dynamic-tasks are used as a proxy. The work found that when using the channel reduction and identifier algorithm, good trend adherence of 89.1% can be obtained between a commercially available BCI 14 channel Emotiv EPOC+ EEG headset and that of a carefully selected set of reduced 4-6 channels. The approach can also identify different levels of engagement activities ranging from general monitoring ad hoc and repeated active monitoring activities involving information search, extraction, and memory activities.

Keywords: assessment, neurophysiology, monitoring, EEG

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Authors: C. J. Rossouw, T. I. van Niekerk

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The global manufacturing industry is utilizing the internet and cloud-based services to further explore the anatomy and optimize manufacturing processes in support of the movement into the Fourth Industrial Revolution (4IR). The 4IR from a third world and African perspective is hindered by the fact that many manufacturing systems that were developed in the third industrial revolution are not inherently equipped to utilize the internet and services of the 4IR, hindering the progression of third world manufacturing industries into the 4IR. This research focuses on the development of a non-invasive and cost-effective cyber-physical IoT system that will exploit a machine’s vibration to expose semantic characteristics in the manufacturing process and utilize these results through a real-time cloud-based machine condition monitoring system with the intention to optimize the system. A microcontroller-based IoT sensor was designed to acquire a machine’s mechanical vibration data, process it in real-time, and transmit it to a cloud-based platform via Wi-Fi and the internet. Time-frequency Fourier analysis was applied to the vibration data to form an image representation of the machine’s behaviour. This data was used to train a Convolutional Neural Network (CNN) to learn semantic characteristics in the machine’s behaviour and relate them to a state of operation. The same data was also used to train a Convolutional Autoencoder (CAE) to detect anomalies in the data. Real-time edge-based artificial intelligence was achieved by deploying the CNN and CAE on the sensor to analyse the vibration. A cloud platform was deployed to visualize the vibration data and the results of the CNN and CAE in real-time. The cyber-physical IoT system was deployed on a semi-automated metal granulation machine with a set of trained machine learning models. Using a single sensor, the system was able to accurately visualize three states of the machine’s operation in real-time. The system was also able to detect a variance in the material being granulated. The research demonstrates how non-IoT manufacturing systems can be equipped with edge-based artificial intelligence to establish a remote machine condition monitoring system.

Keywords: IoT, cyber-physical systems, artificial intelligence, manufacturing, vibration analytics, continuous machine condition monitoring

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Authors: Mamoon Masud, Suleman Mazhar

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Indus river basin’s irrigation system in Pakistan is extremely complex, spanning over 50,000 km. Maintenance and monitoring of this demands enormous resources. This paper describes the development of a streamlined and low-cost autonomous underwater vehicle (AUV) for the monitoring of irrigation canals including water quality monitoring and water theft detection. The vehicle is a hovering-type AUV, designed mainly for monitoring irrigation canals, with fully documented design and open source code. It has a length of 17 inches, and a radius of 3.5 inches with a depth rating of 5m. Multiple sensors are present onboard the AUV for monitoring water quality parameters including pH, turbidity, total dissolved solids (TDS) and dissolved oxygen. A 9-DOF Inertial Measurement Unit (IMU), GY-85, is used, which incorporates an Accelerometer (ADXL345), a Gyroscope (ITG-3200) and a Magnetometer (HMC5883L). The readings from these sensors are fused together using directional cosine matrix (DCM) algorithm, providing the AUV with the heading angle, while a pressure sensor gives the depth of the AUV. 2 sonar-based range sensors are used for obstacle detection, enabling the vehicle to align itself with the irrigation canals edges. 4 thrusters control the vehicle’s surge, heading and heave, providing 3 DOF. The thrusters are controlled using a proportional-integral-derivative (PID) feedback control system, with heading angle and depth being the controller’s input and the thruster motor speed as the output. A flow sensor has been incorporated to monitor canal water level to detect water-theft event in the irrigation system. In addition to water theft detection, the vehicle also provides information on water quality, providing us with the ability to identify the source(s) of water contamination. Detection of such events can provide useful policy inputs for improving irrigation efficiency and reducing water contamination. The AUV being low cost, small sized and suitable for autonomous maneuvering, water level and quality monitoring in the irrigation canals, can be used for irrigation network monitoring at a large scale.

Keywords: the autonomous underwater vehicle, irrigation canal monitoring, water quality monitoring, underwater line tracking

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Authors: Ricky Leung

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Artificial intelligence (AI) and machine learning (ML) have revolutionized the way health organizations approach social media. The sheer volume of data generated through social media can be overwhelming, but AI and ML can help organizations effectively manage this information to improve the health and well-being of individuals and communities. One way AI can be used to enhance social media in health organizations is through sentiment analysis. This involves analyzing the emotions expressed in social media posts to better understand public opinion and respond accordingly. This can help organizations gauge the impact of their campaigns, track the spread of misinformation, and improve communication with the public. While social media is a useful tool, researchers and practitioners have expressed fear that it will be used for the spread of misinformation, which can have serious consequences for public health. Health organizations must work to ensure that AI systems are transparent, trustworthy, and unbiased so they can help minimize the spread of misinformation. In conclusion, AI and ML have the potential to greatly enhance the use of social media in health organizations. These technologies can help organizations effectively manage large amounts of data and understand stakeholders' sentiments. However, it is important to carefully consider the potential consequences and ensure that these systems are carefully designed to minimize the spread of misinformation.

Keywords: AI, ML, social media, health organizations

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Authors: Vistorina Amputu, Katja Tielboerger, Nichola Knox

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Primary productivity in dry savannahs is constraint by moisture availability and under increasing anthropogenic pressure. Thus, considering climate change and the unprecedented pace and scale of rangeland deterioration, methods for assessing the status of such rangelands should be easy to apply, yield reliable and repeatable results that can be applied over large spatial scales. Global and local scale monitoring of rangelands through satellite data and labor-intensive field measurements respectively, are limited in accurately assessing the spatiotemporal heterogeneity of vegetation dynamics to provide crucial information that detects degradation in its early stages. Fortunately, newly emerging techniques such as unmanned aerial vehicles (UAVs), associated miniaturized sensors and improving digital photogrammetric software provide an opportunity to transcend these limitations. Yet, they have not been extensively calibrated in natural systems to encompass their complexities if they are to be integrated for long-term monitoring. Limited research using drone technology has been conducted in arid savannas, for example to assess the health status of this dynamic two-layer vegetation ecosystem. In our study, we fill this gap by testing the relationship between UAV-estimated cover of rangeland functional attributes and field data collected in discrete sample plots in a Namibian dryland savannah along a degradation gradient. The first results are based on a supervised classification performed on the ultra-high resolution multispectral imagery to distinguish between rangeland functional attributes (bare, non-woody, and woody), with a relatively good match to the field observations. Integrating UAV-based observations to improve rangeland monitoring could greatly assist in climate-adapted rangeland management.

Keywords: arid savannah, degradation gradient, field observations, narrow-band sensor, supervised classification

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Authors: Lakhoua Najeh

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Introduction: In most situations, an industrial system already existing, conditioned by its history, its culture and its context are in difficulty facing the necessity to restructure itself in an organizational and technological environment in perpetual evolution. This is why all operations of restructuring first of all require a diagnosis based on a functional analysis. After a presentation of the functionality of a supervisory system for complex processes, we present the concepts of industrial automation and supervisory control and data acquisition (SCADA). Methods: This global analysis exploits the various available documents on the one hand and takes on the other hand in consideration the various testimonies through investigations, the interviews or the collective workshops; otherwise, it also takes observations through visits as a basis and even of the specific operations. The exploitation of this diagnosis enables us to elaborate the project of restructuring thereafter. Leaving from the system analysis for the restructuring of industrial systems, and after a technical diagnosis based on visits, an analysis of the various technical documents and management as well as on targeted interviews, a focusing retailing the various levels of analysis has been done according a general methodology. Results: The methodology adopted in order to contribute to the restructuring of industrial systems by its participative and systemic character and leaning on a large consultation a lot of human resources that of the documentary resources, various innovating actions has been proposed. These actions appear in the setting of the TQM gait requiring applicable parameter quantification and a treatment valorising some information. The new management environment will enable us to institute an information and communication system possibility of migration toward an ERP system. Conclusion: Technological advancements in process monitoring, control and industrial automation over the past decades have contributed greatly to improve the productivity of virtually all industrial systems throughout the world. This paper tries to identify the principles characteristics of a process monitoring, control and industrial automation in order to provide tools to help in the decision-making process.

Keywords: automation, supervision, SCADA, TQM

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Authors: Jinfeng Zhang, Chun-Sing Lee

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The use of different nanocarriers for delivering hydrophobic pharmaceutical agents to tumor sites has garnered major attention. Despite the merits of these nanocarriers, further studies are needed for improving their drug loading capacities (typically less than 10%) and reducing their potential systemic toxicity. So development of alternative self-carried nanodrug delivery strategies without using any inert carriers is highly desirable. In this study, we developed a self-carried theranostic curcumin (Cur) nanodrug for highly effective cancer therapy in vitro and in vivo with real-time monitoring of drug release. With a biocompatible C18PMH-PEG functionalization, the Cur nanoparticles (NPs) showed excellent dispersibility and outstanding stability in physiological environment, with drug loading capacity higher than 78 wt.%. Both confocal microscopy and flow cytometry confirmed the cellular fluorescent “OFF-ON” activation and real-time monitoring of Cur molecule release, showing its potential for cancer diagnosis. In vitro and in vivo experiments clearly show that therapeutic efficacy of the PEGylated Cur NPs is much better than that of free Cur. This self-carried theranostic strategy with real-time monitoring of drug release may open a new way for simultaneous cancer therapy and diagnosis.

Keywords: drug delivery, in vitro and in vivo cancer therapy, real-time monitoring, self-carried

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Authors: Sarah Odofin, Zhiwei Gao, Sun Kai

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Operations, maintenance and reliability of wind turbines have received much attention over the years due to rapid expansion of wind farms. This paper explores early fault diagnosis scale technique based on a unique scheme of a 5MW wind turbine system that is optimized by genetic algorithm to be very sensitive to faults and resilient to disturbances. A quantitative model based analysis is pragmatic for primary fault diagnosis monitoring assessment to minimize downtime mostly caused by components breakdown and exploit productivity consistency. Simulation results are computed validating the wind turbine model which demonstrates system performance in a practical application of fault type examples. The results show the satisfactory effectiveness of the applied performance investigated in a Matlab/Simulink/Gatool environment.

Keywords: disturbance robustness, fault monitoring and detection, genetic algorithm, observer technique

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Authors: Reza Behboodian

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Monitoring the structural health and diagnosing their damage in the early stages has always been one of the topics of concern. Nowadays, research on structural damage detection methods based on vibration analysis is very extensive. Moreover, these methods can be used as methods of permanent and timely inspection of structures and prevent further damage to structures. Non-destructive methods are the low-cost and economical methods for determining the damage of structures. In this research, a non-destructive method for detecting and identifying the failure location in structures based on dynamic responses resulting from time history analysis is proposed. When the structure is damaged due to the reduction of stiffness, and due to the applied loads, the displacements in different parts of the structure were increased. In the proposed method, the damage position is determined based on the calculation of the strain energy difference in each member of the damaged structure and the healthy structure at any time. Defective members of the structure are indicated by the amount of strain energy relative to the healthy state. The results indicated that the proper accuracy and performance of the proposed method for identifying failure in structures.

Keywords: failure, time history analysis, dynamic response, strain energy

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Authors: Hatim Laalej, Salvador Sumohano-Verdeja, Thomas McLeay

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Monitoring of tool wear in milling operations is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Although there are numerous statistical models and artificial intelligence techniques available for monitoring the wear of cutting tools, these techniques cannot pin point which cutting edge of the tool, or which insert in the case of indexable tooling, is worn or broken. Currently, the task of monitoring the wear on the tool cutting edges is carried out by the operator who performs a manual inspection, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from lost productivity. The present study is concerned with the development of a flute tracking system to segment signals related to each physical flute of a cutter with three flutes used in an end milling operation. The purpose of the system is to monitor the cutting condition for individual flutes separately in order to determine their progressive wear rates and to predict imminent tool failure. The results of this study clearly show that signals associated with each flute can be effectively segmented using the proposed flute tracking system. Furthermore, the results illustrate that by segmenting the sensor signal by flutes it is possible to investigate the wear in each physical cutting edge of the cutting tool. These findings are significant in that they facilitate the online condition monitoring of a cutting tool for each specific flute without the need for operators/engineers to perform manual inspections of the tool.

Keywords: machining, milling operation, tool condition monitoring, tool wear prediction

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Authors: Andrey V. Timofeev

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The paper presents new results concerning selection of optimal information fusion formula for ensembles of C-OTDR channels. The goal of information fusion is to create an integral classificator designed for effective classification of seismoacoustic target events. The LPBoost (LP-β and LP-B variants), the Multiple Kernel Learning, and Weighing of Inversely as Lipschitz Constants (WILC) approaches were compared. The WILC is a brand new approach to optimal fusion of Lipschitz Classifiers Ensembles. Results of practical usage are presented.

Keywords: Lipschitz Classifier, classifiers ensembles, LPBoost, C-OTDR systems

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Authors: Arun Kumar, Neelesh Haulder

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This paper identifies and resolves some key issues relating to a specific aspect within the supply chain logistics of the public health care industry in the Republic of Mauritius. The analysis and the proposed solution are performed using soft systems methodology (SSM). Through the application of this relevant systematic approach at problem solving, the aim is to obtain an in-depth analysis of the problem, incorporating every possible world view of the problem and consequently to obtain a well explored solution aimed at implementing relevant changes within the current supply chain logistics of the health care industry, with the purpose of tackling the key identified issues.

Keywords: soft systems methodology, CATWOE, healthcare, logistics

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Authors: Cheng Yang Kwa, Yoke Rung Wong

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Laminated composite materials are widely employed in automotive, aerospace, and other industries. These materials provide distinct benefits due to their high specific strength, high specific modulus, and ability to be customized for a specific function. However, delamination of laminated composite materials is one of the main defects which can occur during manufacturing, regular operations, or maintenance. Delamination can bring about considerable internal damage, unobservable by visual check, that causes significant loss in strength and stability, leading to composite structure catastrophic failure. Structural health monitoring (SHM) is known to be the automated method for monitoring and evaluating the condition of a monitored object. There are several ways to conduct SHM in aerospace. One of the effective methods is to monitor the natural frequency shift of structure due to the presence of defect. This study investigated the mechanical resonance frequency shift of a multi-layer composite cantilever beam due to interlaminar delamination. ANSYS Workbench® was used to create a 4-plies laminated composite cantilever finite element model with [90/0]s fiber setting. Epoxy Carbon UD (230GPA) Prepreg was chosen, and the thickness was 2.5mm for each ply. The natural frequencies of the finite element model with various degree of delamination were simulated based on modal analysis and then validated by using literature. It was shown that the model without delamination had natural frequency of 40.412 Hz, which was 1.55% different from the calculated result (41.050 Hz). Thereafter, the various degree of delamination was mimicked by changing the frictional conditions at the middle ply-to-ply interface. The results suggested that delamination in the laminated composite cantilever induced a change in its stiffness which alters its mechanical resonance frequency.

Keywords: structural health monitoring, NDT, cantilever, laminate

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Authors: Muhammad Bilal Qadir, Danish Umer, Amir Shahzad

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The use of conductive materials in smart and interactive textiles is gaining significant importance for creating value addition, innovation, and functional product development. These products find their potential applications in health monitoring, military, protection, communication, sensing, monitoring, actuation, fashion, and lifestyles. The materials which are most commonly employed in such type of interactive textile include intrinsically conducting polymers, conductive inks, and metallic coating on textile fabrics and inherently conducting metallic fibre yarns. In this study, silver coated polyester filament yarn is explored for the development of multifunctional interactive gloves. The composite yarn was developed by covering the silver coated polyester filament around the polyester spun yarn using hollow spindle technique. The electrical and tensile properties of the yarn were studied. This novel yarn was used to manufacture a smart glove to explore the antibacterial, functional, and interactive properties of the yarn. The change in electrical resistance due to finger movement at different bending positions and antimicrobial properties were studied. This glove was also found useful as an interactive tool to operate the commonly used touch screen devices due to its conductive nature. The yarn can also be used to develop the sensing elements like stretch, strain, and piezoresistive sensors. Such sensor can be effectively used in medical and sports textile for performance monitoring, vital signs monitoring and development of antibacterial textile for healthcare and hygiene.

Keywords: conductive yarn, interactive textiles, piezoresistive sensors, smart gloves

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Authors: Hyun-Woo Cho

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The monitoring of industrial production quality has to be implemented to alarm early warning for unusual operating conditions. Furthermore, identification of their assignable causes is necessary for a quality control purpose. For such tasks many multivariate statistical techniques have been applied and shown to be quite effective tools. This work presents a process data-based monitoring scheme for production processes. For more reliable results some additional steps of noise filtering and preprocessing are considered. It may lead to enhanced performance by eliminating unwanted variation of the data. The performance evaluation is executed using data sets from test processes. The proposed method is shown to provide reliable quality control results, and thus is more effective in quality monitoring in the example. For practical implementation of the method, an on-line data system must be available to gather historical and on-line data. Recently large amounts of data are collected on-line in most processes and implementation of the current scheme is feasible and does not give additional burdens to users.

Keywords: detection, filtering, monitoring, process data

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Authors: Shunsuke Fujiwara, Takashi Kaburagi, Kazuyuki Kobayashi, Kajiro Watanabe, Yosuke Kurihara

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This paper describes a novel sensor device, a pressure pulse wave meter, which uses a bidirectional condenser microphone. The microphone work as a microphone as well as a sensor with high gain over a wide frequency range; they are also highly reliable and economical. Currently aging is becoming a serious social issue in Japan causing increased medical expenses in the country. Hence, it is important for elderly citizens to check health condition at home, and to care the health conditions through daily monitoring. Given this circumstances, we developed a novel pressure pulse wave meter based on a bidirectional condenser microphone. This novel pressure pulse wave meter device is used as a measuring instrument of health conditions.

Keywords: bidirectional microphone, pressure pulse wave meter, health condition, novel sensor device

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Authors: Kevin Montanez, Patricia Garcia

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The confinement and restrictions adopted to avoid an exponential spread of the COVID-19 have negatively impacted the Peruvian economy. In this context, Industries offering essential products could continue operating, but they have to follow safety protocols and implement strategies to ensure employee health. In view of the increasing internet access and mobile phone ownership, “Alerta Temprana”, a mobile app, was developed to self-report COVID-19 symptoms in the work context. In this study, the usability of the mobile app “Alerta Temprana” was evaluated from the perspective of health monitors and workers. In addition to reporting the metrics related to the usability of the application, the utility of the system is also evaluated from the monitors' perspective. In this descriptive study, the participants used the mobile app for two months. Afterwards, System Usability Scale (SUS) questionnaire was answered by the workers and monitors. A Usefulness questionnaire with open questions was also used for the monitors. The data related to the use of the application was collected during one month. Furthermore, descriptive statistics and bivariate analysis were used. The workers rated the application as good (70.39). In the case of the monitors, usability was excellent (83.0). The most important feature for the monitors were the emails generated by the application. The average interaction per user was 30 seconds and a total of 6172 self-reports were sent. Finally, a statistically significant association was found between the acceptability scale and the work area. The results of this study suggest that Alerta Temprana has the potential to be used for surveillance and health monitoring in any context of face-to-face modality. Participants reported a high degree of ease of use. However, from the perspective of workers, SUS cannot diagnose usability issues and we suggest we use another standard usability questionnaire to improve "Alerta Temprana" for future use.

Keywords: public health in informatics, mobile app, usability, self-report

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Authors: Wenhao Li, Shijun Guo

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Carbon fibre reinforced composites are progressively replacing metal structures in modern civil aircraft. This is because composite materials have large potential of weight saving compared with metal. However, the achievement to date of weight saving in composite structure is far less than the theoretical potential due to many uncertainties in structural integrity and safety concern. Unlike the conventional metallic structure, composite components are bonded together along the joints where structural integrity is a major concern. To ensure the safety, metal fasteners are used to reinforce the composite bonded joints. One of the solutions for a significant weight saving of composite structure is to develop an effective technology of on-board Structural Health Monitoring (SHM) System. By monitoring the real-life stress status of composite structures during service, the safety margin set in the structure design can be reduced with confidence. It provides a means of safeguard to minimize the need for programmed inspections and allow for maintenance to be need-driven, rather than usage-driven. The aim of this paper is to develop smart composite joint. The key technology is a multifunctional thermoplastic composite fastener (MTCF). The MTCF will replace some of the existing metallic fasteners in the most concerned locations distributed over the aircraft composite structures to reinforce the joints and form an on-board SHM network system. Each of the MTCFs will work as a unit of the AU and AE technology. The proposed MTCF technology has been patented and developed by Prof. Guo in Cranfield University, UK in the past a few years. The manufactured MTCF has been successfully employed in the composite SLJ (Single-Lap Joint). In terms of the structure integrity, the hybrid SLJ reinforced by MTCF achieves 19.1% improvement in the ultimate failure strength in comparison to the bonded SLJ. By increasing the diameter or rearranging the lay-up sequence of MTCF, the hybrid SLJ reinforced by MTCF is able to achieve the equivalent ultimate strength as that reinforced by titanium fastener. The predicted ultimate strength in simulation is in good agreement with the test results. In terms of the structural health monitoring, a signal from the MTCF was measured well before the load of mechanical failure. This signal provides a warning of initial crack in the joint which could not be detected by the strain gauge until the final failure.

Keywords: composite single-lap joint, crack propagation, multifunctional composite fastener, structural health monitoring

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Authors: Boris Kolev, Matt Kokan, Mohammad Deriszadeh, Farshid Bateni

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Due to the increasing demand for real estate and the need for efficient land utilization in Greater Vancouver, developers have been increasingly considering the construction of high-rise structures with multiple below-grade parking. The temporary excavations required to allow for the construction of underground levels have recently reached up to 40 meters in depth. One of the challenges with deep excavations is the prediction of wall displacements and ground settlements due to their effect on the integrity of City utilities, infrastructure, and adjacent buildings. A large database of survey monitoring data has been collected for deep excavations in various soil conditions and shoring systems. The majority of the data collected is for tie-back anchors and shotcrete lagging systems. The data were categorized, analyzed and the results were evaluated to find a relationship between the most dominant parameters controlling the displacement, such as depth of excavation, soil properties, and the tie-back anchor loading and arrangement. For a select number of deep excavations, finite element modeling was considered for analyses. The lateral displacements from the simulation results were compared to the recorded survey monitoring data. The study concludes with a discussion and comparison of the available empirical and numerical modeling methodologies for evaluating lateral displacements in deep excavations.

Keywords: deep excavations, lateral displacements, numerical modeling, shoring walls, tieback anchors

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Authors: Yasser Sanad, Thu Trang Dao

Abstract:

Introduction: Implementing the One Health (OH) approach requires an integrated, interdisciplinary, and cross-sectoral methodology. OH is a key tool for developing and implementing programs and projects and includes developing ambitious policies that consider the common needs and benefits of human, animal, plant, and ecosystem health. OH helps humanity readjust its path to environmentally friendly and impartial sustainability. As co-leader of the Global Health Security Agenda’s Zoonotic Disease Action Package, Vietnam pioneered a strong OH approach to effectively address early waves of the COVID-19 outbreak in-country. Context and Aim: The repeated surges in COVID-19 in Vietnam challenged the capabilities of the national system and disclosed the gaps in multi-sectoral coordination and resilience. To address this, FHI 360 advocated for the standardization of the OH platform by government actors to increase the resiliency of the system during and post COVID-19. Methods: FHI 360 coordinated technical resources to develop and implement evidence-based OH policies, promoting high-level policy dialogue between the Ministries of Health, Agriculture, and the Environment, and policy research to inform developed policies and frameworks. Through discussions, an OH-building Partnership (OHP) was formed, linking climate change, the environment, and human and animal health. Findings: The OHP Framework created a favorable policy environment within and between sectors, as well as between governments and international health security partners. It also promoted strategic dialogue, resource mobilization, policy advocacy, and integration of international systems with National Steering Committees to ensure accountability and emphasize national ownership. Innovative contribution to policy, practice and/or research: OHP was an effective evidence-based research-to-policy platform linking to the National One Health Strategic Plan (2021-2025). Collectively they serve as a national framework for the implementation and monitoring of OH activities. Through the adoption of policies and plans, the risk of zoonotic pathogens, environmental agent spillover, and antimicrobial resistance can be minimized through strengthening multi-sectoral OH collaboration for health security.

Keywords: one health, national policies, health security, COVID-19, Vietnam

Procedia PDF Downloads 105

Authors: Jose Flores, Nadia Gamboa

Abstract:

A proper water quality management requires the establishment of a monitoring network. Therefore, evaluation of the efficiency of water quality monitoring networks is needed to ensure high-quality data collection of critical quality chemical parameters. Unfortunately, in some Latin American countries water quality monitoring programs are not sustainable in terms of recording historical data or environmentally representative sites wasting time, money and valuable information. In this study, multivariate statistical techniques, such as principal components analysis (PCA) and hierarchical cluster analysis (HCA), are applied for identifying the most significant monitoring sites as well as critical water quality parameters in the monitoring network of the Jequetepeque River basin, in northern Peru. The Jequetepeque River basin, like others in Peru, shows socio-environmental conflicts due to economical activities developed in this area. Water pollution by trace elements in the upper part of the basin is mainly related with mining activity, and agricultural land lost due to salinization is caused by the extensive use of groundwater in the lower part of the basin. Since the 1980s, the water quality in the basin has been non-continuously assessed by public and private organizations, and recently the National Water Authority had established permanent water quality networks in 45 basins in Peru. Despite many countries use multivariate statistical techniques for assessing water quality monitoring networks, those instruments have never been applied for that purpose in Peru. For this reason, the main contribution of this study is to demonstrate that application of the multivariate statistical techniques could serve as an instrument that allows the optimization of monitoring networks using least number of monitoring sites as well as the most significant water quality parameters, which would reduce costs concerns and improve the water quality management in Peru. Main socio-economical activities developed and the principal stakeholders related to the water management in the basin are also identified. Finally, water quality management programs will also be discussed in terms of their efficiency and sustainability.

Keywords: PCA, HCA, Jequetepeque, multivariate statistical

Procedia PDF Downloads 355

Authors: Tewelde Gebreslassie Gebreanenia, Frie Ayalew Yimam, Seada Hussen Adem

Abstract:

Patient safety and quality of care are essential goals of health care delivery, but they are often compromised by human errors, system failures, or resource constraints. In a variety of healthcare contexts, artificial intelligence (AI), a quickly developing field, can provide fresh approaches to enhancing patient safety and treatment quality. Artificial Intelligence (AI) has the potential to decrease errors and enhance patient outcomes by carrying out tasks that would typically require human intelligence. These tasks include the detection and prevention of adverse events, monitoring and warning patients and clinicians about changes in vital signs, symptoms, or risks, offering individualized and evidence-based recommendations for diagnosis, treatment, or prevention, and assessing and enhancing the effectiveness of health care systems and services. This study examines the state-of-the-art and potential future applications of AI-based technologies for enhancing patient safety and care quality, as well as the opportunities and problems they present for patients, policymakers, researchers, and healthcare providers. In order to ensure the safe, efficient, and responsible application of AI in healthcare, the paper also addresses the ethical, legal, social, and technical challenges that must be addressed and regulated.

Keywords: artificial intelligence, health care, human intelligence, patient safty, quality of care

Procedia PDF Downloads 78

Authors: Ahmed Noor Al-qayyim, Barlas Özden Çağlayan

Abstract:

Structural failure is caused mainly by damage that often occurs on structures. Many researchers focus on obtaining very efficient tools to detect the damage in structures in the early state. In the past decades, a subject that has received considerable attention in literature is the damage detection as determined by variations in the dynamic characteristics or response of structures. This study presents a new damage identification technique. The technique detects the damage location for the incomplete structure system using output data only. The method indicates the damage based on the free vibration test data by using “Two Points - Condensation (TPC) technique”. This method creates a set of matrices by reducing the structural system to two degrees of freedom systems. The current stiffness matrices are obtained from optimization of the equation of motion using the measured test data. The current stiffness matrices are compared with original (undamaged) stiffness matrices. High percentage changes in matrices’ coefficients lead to the location of the damage. TPC technique is applied to the experimental data of a simply supported steel beam model structure after inducing thickness change in one element. Where two cases are considered, the method detects the damage and determines its location accurately in both cases. In addition, the results illustrate that these changes in stiffness matrix can be a useful tool for continuous monitoring of structural safety using ambient vibration data. Furthermore, its efficiency proves that this technique can also be used for big structures.

Keywords: damage detection, optimization, signals processing, structural health monitoring, two points–condensation

Procedia PDF Downloads 365

Authors: Hyun-Woo Cho

Abstract:

Continuous monitoring of industrial plants is one of necessary tasks when it comes to ensuring high-quality final products. In terms of monitoring and diagnosis, it is quite critical and important to detect some incipient abnormal events of manufacturing processes in order to improve safety and reliability of operations involved and to reduce related losses. In this work a new multivariate statistical online diagnostic method is presented using a case study. For building some reference models an empirical discriminant model is constructed based on various past operation runs. When a fault is detected on-line, an on-line diagnostic module is initiated. Finally, the status of the current operating conditions is compared with the reference model to make a diagnostic decision. The performance of the presented framework is evaluated using a dataset from complex industrial processes. It has been shown that the proposed diagnostic method outperforms other techniques especially in terms of incipient detection of any faults occurred.

Keywords: data mining, empirical model, on-line diagnostics, process fault, process monitoring

Procedia PDF Downloads 401