Search results for: remote monitoring

Authors: Bhaargav Gupta P., Vignesh N., Nithish Kumar R., Rahul J., Nivetha Ruvah D.

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Delivering essential medical supplies to rural and underserved areas is challenging due to infrastructure limitations and logistical barriers, often resulting in inefficiencies and delays. Traditional delivery methods are hindered by poor road networks, long distances, and difficult terrains, compromising timely access to vital resources, especially in emergencies. This paper introduces an autonomous drone system engineered to optimize last-mile delivery. By utilizing advanced navigation and object-detection algorithms, such as region-based convolutional neural networks (R-CNN), our drones efficiently avoid obstacles, identify safe landing zones, and adapt dynamically to varying environments. Equipped with high-precision GPS and autonomous capabilities, the drones effectively navigate complex, remote areas with minimal dependence on established infrastructure. The system includes a dedicated mobile application for secure order placement and real-time tracking, and a secure payload box with OTP verification ensures tamper-resistant delivery to authorized recipients. This project demonstrates the potential of automated drone technology in healthcare logistics, offering a scalable and eco-friendly approach to enhance accessibility and service delivery in underserved regions. By addressing logistical gaps through advanced automation, this system represents a significant advancement toward sustainable, accessible healthcare in remote areas.

Keywords: region-based convolutional neural network, one time password, global positioning system, autonomous drones, healthcare logistics

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Authors: María-José Palacin-Sanchez, Filippo Di Pietro, Reyes Samaniego-Medina

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This article examines, in an institutional context such as Spanish one, the corporate board structure characteristics and determinants in entrepreneurial firms to go public. Specifically, it explores these issues through all the initial public offerings in the Spanish Alternative Equity Market (MAB), which is a market segment for smaller growing companies. The results show that: a) firm size, age of the company, and the reputation of the auditor and the nominated advisor and Corporate Governance Code favour a larger and more independent board structure that enhances its monitoring functions; and b) leverage, opportunities of growth, sector risk and ownership by executive directors all lead towards a smaller broad of directors where the role of entrepreneurship provided by executive directors remains crucial. This reflects the delicate balance of power between small-business entrepreneurs and financial equity market forces, which demand more transparency and monitoring in the companies.

Keywords: board composition, board size, corporate governance, IPO, SMEs

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Authors: Mohammad Arif, Soumen Chatterjee, Krishnendu Gupta

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The peri-urban interface is an area of transition where the urban and rural areas meet and interact. So the peri-urban areas, which is characterized by strong urban influence, easy access to markets, services and other inputs, are ready supplies of labour but distant from the land paucity and pollution related to urban growth. Hence, the present study is primarily aimed at quantifying the spatio-temporal pattern of land use/land cover change during the last three decades (i.e., 1987 to 2016) in the peri-urban area of Burdwan city. In the recent past, the morphology of the study region has rapid change due to high growth of population and establishment of industries. The change has predominantly taken place along the State and National Highway 2 (NH-2) and around the Burdwan Municipality for meeting both residential and commercial purposes. To ascertain the degree of change in land use and land cover, over the specified time, satellite imageries and topographical sheets are employed. The data is processed through appropriate software packages to arrive at a deduction that most of the land use changes have occurred by obliterating agricultural land & water bodies and substituting them by built area and industrial spaces. Geospatial analysis of study area showed that this area has experienced a steep increase (30%) of built-up areas and excessive decrease (15%) in croplands between 1987 and 2016. Increase in built-up areas is attributed to the increase of out-migration during this period from the core city. This study also examined social, economic and institutional factors that lead to this rapid land use change in peri-urban areas of the Burdwan city by carrying out a field survey of 250 households in peri-urban areas. The research concludes with an urgency for regulating land subdivisions in peri-urban areas to prevent haphazard land use development. It is expected that the findings of the study would go a long way in facilitating better policy making.

Keywords: growth, land use land cover, morphology, peri-urban, policy making

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Authors: Avoseh Funmilola, Osman Khalid, Wayne Nelson, Paramespri Naidoo, Deresh Ramjugernath

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The increasing global atmospheric temperature is of great concern and this has led to the development of technologies to reduce the emission of greenhouse gases into the atmosphere. Flue gas emissions from fossil fuel combustion are major sources of greenhouse gases. One of the ways to reduce the emission of CO₂ from flue gases is by post combustion capture process and this can be done by absorbing the gas into suitable chemical solvents before emitting the gas into the atmosphere. Alkanolamines are promising solvents for this capture process. Vapour liquid equilibrium of CO₂-alkanolamine systems is often represented by CO₂ loading and partial pressure of CO₂ without considering the liquid phase. The liquid phase of this system is a complex one comprising of 9 species. Online analysis of the process is important to monitor the concentrations of the liquid phase reacting and product species. Liquid phase analysis of CO₂-diethanolamine (DEA) solution was performed by attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy. A robust Calibration was performed for the CO₂-aqueous DEA system prior to an online monitoring experiment. The partial least square regression method was used for the analysis of the calibration spectra obtained. The models obtained were used for prediction of DEA and CO₂ concentrations in the online monitoring experiment. The experiment was performed with a newly built recirculating experimental set up in the laboratory. The set up consist of a 750 ml equilibrium cell and ATR-FTIR liquid flow cell. Measurements were performed at 400°C. The results obtained indicated that the FTIR spectroscopy combined with Partial least square method is an effective tool for online monitoring of speciation.

Keywords: ATR-FTIR, CO₂ capture, online analysis, PLS regression

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Authors: Anirudh Sharma

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Introduction: The Covid-19 pandemic necessitated a change in the manner outpatient fracture clinics are conducted due to the need to reduce footfall in hospital. While studies regarding virtual fracture clinics have shown these to be useful and effective, they focus exclusively on remote consultations. However, our service was bespoke to the patient – either a face-to-face or telephone consultation depending on patient need – a ‘hybrid virtual clinic (HVC).’ We report patient satisfaction and outcomes with this novel service. Methods: Patients booked onto our fracture clinics during the first 2 weeks of national lockdown were retrospectively contacted to assess the mode of consultations (virtual, face-to-face, or hybrid), patient experience, and outcome. Patient experience was assessed using the net promoter (NPS), customer effort (CES) and customer satisfaction scores (CSS), and their likelihood of using the HVC in the absence of a pandemic. Patient outcomes were assessed using the components of the EQ5D score. Results: Of 269 possible patients, 140 patients responded to the questionnaire. Of these, 66.4% had ‘hybrid’ consultations, 27.1% had only virtual consultations, and 6.4% had only face-to-face consultations. The mean overall NPS, CES, and CSS (on a scale of 1-10) were 7.27, 7.25, and 7.37, respectively. The mean likelihood of patients using the HVC in the absence of a pandemic was 6.5/10. Patients who had ‘hybrid’ consultations showed better effort scores and greater overall satisfaction than those with virtual consultations only and also reported superior EQ5D outcomes (mean 79.27 vs. 72.7). Patients who did not require surgery reported increased satisfaction (mean 7.51 vs. 7.08) and were more likely to use the HVC in the absence of a pandemic. Conclusion: Our study indicates that a bespoke HVC has good overall patient satisfaction and outcomes and is a better format of fracture clinic service than virtual consultations alone. It may be the preferred mode for fracture clinics in similar situations in the future. Further analysis needs to be conducted in order to explore the impact on resources and clinician experience of HVC in order to appreciate this new paradigm shift.

Keywords: hybrid virtual clinic, coronavirus, COVID-19, fracture clinic, remote consultation

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Authors: Yuxiang Pan, Yong Qiu, Chenlei Gu, Ping Wang

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In the past decade, three-dimensional (3D) tumor cell models have attracted increasing interest in the field of drug screening due to their great advantages in simulating more accurately the heterogeneous tumor behavior in vivo. Drug sensitivity testing based on 3D tumor cell models can provide more reliable in vivo efficacy prediction. The gold standard fluorescence staining is hard to achieve the real-time and label-free monitoring of the viability of 3D tumor cell models. In this study, micro-groove impedance sensor (MGIS) was specially developed for dynamic and non-invasive monitoring of 3D cell viability. 3D tumor cells were trapped in the micro-grooves with opposite gold electrodes for the in-situ impedance measurement. The change of live cell number would cause inversely proportional change to the impedance magnitude of the entire cell/matrigel to construct and reflect the proliferation and apoptosis of 3D cells. It was confirmed that 3D cell viability detected by the MGIS platform is highly consistent with the standard live/dead staining. Furthermore, the accuracy of MGIS platform was demonstrated quantitatively using 3D lung cancer model and sophisticated drug sensitivity testing. In addition, the parameters of micro-groove impedance chip processing and measurement experiments were optimized in details. The results demonstrated that the MGIS and 3D cell-based biosensor and would be a promising platform to improve the efficiency and accuracy of cell-based anti-cancer drug screening in vitro.

Keywords: micro-groove impedance sensor, 3D cell-based biosensors, 3D cell viability, micro-electromechanical systems

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Authors: Faizul Azly Abd Dzubir, Muhammad F. Othman

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Conventional structural health monitoring approach for mechanical equipment uses inspection data from Non-Destructive Testing (NDT) during plant shut down window and fitness for service evaluation to estimate the integrity of the equipment that is prone to crack damage. Yet, this forecast is fraught with uncertainty because it is often based on assumptions of future operational parameters, and the prediction is not continuous or online. Advanced Diagnostic and Prognostic Technology (ADaPT) uses Acoustic Emission (AE) technology and a stochastic prognostic model to provide real-time monitoring and prediction of mechanical defects or cracks. The forecast can help the plant authority handle their cracked equipment before it ruptures, causing an unscheduled shutdown of the facility. The ADaPT employs process historical data trending, finite element analysis, fitness for service, and probabilistic statistical analysis to develop a prediction model for crack initiation and growth due to mechanical damage. The prediction model is combined with live equipment operating data for real-time prediction of the remaining life span owing to fracture. ADaPT was devised at a hot combined feed exchanger (HCFE) that had suffered creep crack damage. The ADaPT tool predicts the initiation of a crack at the top weldment area by April 2019. During the shutdown window in April 2019, a crack was discovered and repaired. Furthermore, ADaPT successfully advised the plant owner to run at full capacity and improve output by up to 7% by April 2019. ADaPT was also used on a coke drum that had extensive fatigue cracking. The initial cracks are declared safe with ADaPT, with remaining crack lifetimes extended another five (5) months, just in time for another planned facility downtime to execute repair. The prediction model, when combined with plant information data, allows plant operators to continuously monitor crack propagation caused by mechanical damage for improved maintenance planning and to avoid costly shutdowns to repair immediately.

Keywords: mechanical damage, cracks, continuous monitoring tool, remaining life, acoustic emission, prognostic model

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Authors: Sang Hun Park, Moung Young Lee, Su Min Yu, Hyun Sang Jo, Ji Hyeon Kim, Chul Gyu Song

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A near-infrared light source used as a light source in the fluorescence imaging system is suitable for use in real-time during the operation since it has no interference in surgical vision. However, fluorescence images do not have depth information. In this paper, we configured the device with the research on molecular imaging systems for monitoring thrombus imaging using fluorescence and photoacoustic. Fluorescence imaging was performed using a phantom experiment in order to search the exact location, and the Photoacoustic image was in order to detect the depth. Fluorescence image obtained when evaluated through current phantom experiments when the concentration of the contrast agent is 25μg / ml, it was confirmed that it looked sharper. The phantom experiment is has shown the possibility with the fluorescence image and photoacoustic image using an indocyanine green contrast agent. For early diagnosis of cardiovascular diseases, more active research with the fusion of different molecular imaging devices is required.

Keywords: fluorescence, photoacoustic, indocyanine green, carotid artery

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Authors: Heramb Somthankar, Anujoy Chakraborty

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This paper presents a classification study on yield prediction in semiconductor production using machine learning approaches. A complicated semiconductor production process is generally monitored continuously by signals acquired from sensors and measurement sites. A monitoring system contains a variety of signals, all of which contain useful information, irrelevant information, and noise. In the case of each signal being considered a feature, "Feature Selection" is used to find the most relevant signals. The open-source UCI SECOM Dataset provides 1567 such samples, out of which 104 fail in quality assurance. Feature extraction and selection are performed on the dataset, and useful signals were considered for further study. Afterward, common machine learning algorithms were employed to predict whether the signal yields pass or fail. The most relevant algorithm is selected for prediction based on the accuracy and loss of the ML model.

Keywords: deep learning, feature extraction, feature selection, machine learning classification algorithms, semiconductor production monitoring, signal processing, time-series analysis

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Authors: Umberto Di Staso, Marco Soave, Alessio Giori, Federico Prandi, Raffaele De Amicis

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The increasing availability of information about earth surface elevation (Digital Elevation Models DEM) generated from different sources (remote sensing, Aerial Images, Lidar) poses the question about how to integrate and make available to the most than possible audience this huge amount of data. In order to exploit the potential of 3D elevation representation the quality of data management plays a fundamental role. Due to the high acquisition costs and the huge amount of generated data, highresolution terrain surveys tend to be small or medium sized and available on limited portion of earth. Here comes the need to merge large-scale height maps that typically are made available for free at worldwide level, with very specific high resolute datasets. One the other hand, the third dimension increases the user experience and the data representation quality, unlocking new possibilities in data analysis for civil protection, real estate, urban planning, environment monitoring, etc. The open-source 3D virtual globes, which are trending topics in Geovisual Analytics, aim at improving the visualization of geographical data provided by standard web services or with proprietary formats. Typically, 3D Virtual globes like do not offer an open-source tool that allows the generation of a terrain elevation data structure starting from heterogeneous-resolution terrain datasets. This paper describes a technological solution aimed to set up a so-called “Terrain Builder”. This tool is able to merge heterogeneous-resolution datasets, and to provide a multi-resolution worldwide terrain services fully compatible with CesiumJS and therefore accessible via web using traditional browser without any additional plug-in.

Keywords: Terrain Builder, WebGL, Virtual Globe, CesiumJS, Tiled Map Service, TMS, Height-Map, Regular Grid, Geovisual Analytics, DTM

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Authors: Michael D. Vinod, Hadi Khabbaz

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Construction of infrastructure above or adjacent to landfills is becoming more common to capitalize on the limited space available within urban areas. However, development above landfills is a challenging task due to large voids, the presence of organic matter, heterogeneous nature of waste and ambiguity surrounding landfill settlement prediction. Prior to construction of infrastructure above landfills, ground improvement techniques are being employed to improve the geotechnical properties of landfill material. Although the ground improvement techniques have little impact on long term biodegradation and creep related landfill settlement, they have shown some notable short term success with a variety of techniques, including methods for verifying the level of effectiveness of ground improvement techniques. This paper provides geotechnical and landfill engineers a guideline for selection of landfill ground improvement techniques and their suitability to project-specific sites. Ground improvement methods assessed and compared in this paper include concrete injected columns (CIC), dynamic compaction, rapid impact compaction (RIC), preloading, high energy impact compaction (HEIC), vibro compaction, vibro replacement, chemical stabilization and the inclusion of geosynthetics such as geocells. For each ground improvement technique a summary of the existing theory, benefits, limitations, suitable modern ground improvement monitoring methods, the applicability of ground improvement techniques for landfills and supporting case studies are provided. The authors highlight the importance of implementing cost-effective monitoring techniques to allow observation and necessary remediation of the subsidence effects associated with long term landfill settlement. These ground improvement techniques are primarily for the purpose of construction above closed landfills to cater for transport infrastructure loading.

Keywords: closed landfills, ground improvement, monitoring, settlement, transport infrastructure

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Authors: N. Ayob, N. C. Nkosi, R. P. Burger, S. J. Piketh, F. Letlaila, O. Maphosa

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South African higher tertiary institution have been faced with high dropout rates. About 50 to 60% of first years drop out to due to various reasons one being inadequate academic support. Geography 111 (GEOG 111) module is historically known for having below 50% pass rate, high dropout rate and identified as a first year risk module. For the first time GEOG 111 (2021) on the Mahikeng Campus admitted 150 students pursuing more than 6 different qualifications (BA and BSc) from the Humanities Faculty and FNAS. First year students had difficulties transitioning from secondary to tertiary institutions as we shifted to remote learning while we navigate through the Covid-19 pandemic. The traditional method of teaching does not encourage students to help each other. With remote learning we do not have control over what the students share and perhaps this can be a learning opportunity to embrace peer learning and change the manner in which we assess the students. The purpose of this pilot study was to assist GEOG111 students with academic challenges whilst improving their University experience. This was a qualitative study open to all GEOG111, repeaters, students who are not confident in their Geographical knowledge and never did Geography at high school level. The selected 9 Golden Key International Honour Society Geography mentors attended an academic mentor training program with module lecturers. About 17.6% of the mentees did not have a geography background however, 94% of the mentees passed, 1 mentee had a mark of 38%. 11 of the participants had a mark >60% with one student that excelled 70%. It is evident that mentorship helped students reach their academic potential. Peer learning and mentoring are associated with improved academic performance and allows the students to take charge of their learning and academic experience. Thus an important element as we transform pedagogies at higher learning institutions.

Keywords: geography, risk module, peer mentoring, peer learning

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Authors: Hongquan Zhang

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Inspired by endogenous protein motors, researchers have constructed various synthetic DNA motors based on the specificity and predictability of Watson-Crick base pairing. However, the application of DNA motors to signal amplification and biosensing is limited because of low mobility and difficulty in real-time monitoring of the walking process. The objective of our work was to construct a new type of DNA motor termed target-triggered DNA motors that can walk for hundreds of steps in response to a single target binding event. To improve the mobility and processivity of DNA motors, we used gold nanoparticles (AuNPs) as scaffolds to build high-density, three-dimensional tracks. Hundreds of track strands are conjugated to a single AuNP. To enable DNA motors to respond to specific protein and nucleic acid targets, we adapted the binding-induced DNA assembly into the design of the target-triggered DNA motors. In response to the binding of specific target molecules, DNA motors are activated to autonomously walk along AuNP, which is powered by a nicking endonuclease or DNAzyme-catalyzed cleavage of track strands. Each moving step restores the fluorescence of a dye molecule, enabling monitoring of the operation of DNA motors in real time. The motors can translate a single binding event into the generation of hundreds of oligonucleotides from a single nanoparticle. The motors have been applied to amplify the detection of proteins and nucleic acids in test tubes and live cells. The motors were able to detect low pM concentrations of specific protein and nucleic acid targets in homogeneous solutions without the need for separation. Target-triggered DNA motors are significant for broadening applications of DNA motors to molecular sensing, cell imagining, molecular interaction monitoring, and controlled delivery and release of therapeutics.

Keywords: biosensing, DNA motors, gold nanoparticles, signal amplification

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Authors: Raziel Mojica

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This study delves into the challenging working conditions faced by physical education (PE) teachers in public schools, particularly during the transition to remote teaching due to the impact of the COVID-19 pandemic. Using a qualitative, hermeneutic phenomenological approach, the research involves in-depth interviews with PE teachers to gain profound insight into their lived experiences and to answer the main question: What is the essence of the lived experience of physical education? The study explores the following sub-questions: (1) How do the participants describe their lived experience regarding their working conditions as physical education teachers in the new normal setup?; (2) What themes emerge from the testimonies of Physical Education Public School teachers from the Division of Calamba?; (3) Based on the consolidated findings and reflection, what material may be produced to inspire the physical education public school teachers? The study identifies emerging themes such as professional growth, personal life boundaries, accessible facilities and equipment, time management, and school leadership. Framed by Frederick Herzberg's Work-Motivation Theory, the study emphasizes motivator and hygienic factors that significantly impact job satisfaction and dissatisfaction. As a result of the study, the teachers vividly identified the challenges they face, including the lack of resources, training, and support from school leaders, which have made it difficult for them to adapt to the new teaching environment. These findings underscore the urgent need to revise teacher training curricula and for school leaders to provide strategic support to PE teachers in remote learning contexts. In conclusion, the study recommends targeted interventions to address these challenges and better equip PE teachers for the new normal in education.

Keywords: PE teachers, COVID-19 pandemic, hermeneutic phenomenological, physical education, new normal

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Authors: M. Sundaram, H. R. Sanath Kumar, A. Ramprakash

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Energy saving is a key sustainability focus area for the Indian telecom industry today. This is especially true in rural India where energy consumption contributes to 70 % of the total network operating cost. In urban areas, the energy cost for network operation ranges between 15-30 %. This expenditure on energy as a result of the lack of grid power availability highlights a potential barrier to telecom industry growth. As a result of this, telecom tower companies switch to diesel generators, making them the second largest consumer of diesel in India, consuming over 2.5 billion litres per annum. The growing cost of energy due to increasing diesel prices and concerns over rising greenhouse emissions have caused these companies to look at other renewable energy options. Even the TRAI (Telecom Regulation Authority of India) has issued a number of guidelines to implement Renewable Energy Technologies (RETs) in the telecom towers as part of its ‘Implementation of Green Technologies in Telecom Sector’ initiative. Our proposal suggests the implementation of a Programmable Logic Controller (PLC) based ‘optimisation controller’ that can not only efficiently utilize the energy from RETs but also help to conserve the power used in the telecom towers. When there are multiple RETs available to supply energy, this controller will pick the optimum amount of energy from each RET based on the availability and feasibility at that point of time, reducing the dependence on diesel generators. For effective maintenance of the towers, we are planing to implement a SCADA based monitoring system along with the ‘optimization controller’.

Keywords: operation costs, consumption of fuel and carbon footprint, implementation of a programmable logic controller (PLC) based ‘optimisation controller’, efficient SCADA based monitoring system

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Authors: Salihah Alghamdi, Surajit Ray

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Space-time data can be observed over irregularly shaped manifolds, which might have complex boundaries or interior gaps. Most of the existing methods do not consider the shape of the data, and as a result, it is difficult to model irregularly shaped data accommodating the complex domain. We used a method that can deal with space-time data that are distributed over non-planner shaped regions. The method is based on partial differential equations and finite element analysis. The model can be estimated using a penalized least squares approach with a regularization term that controls the over-fitting. The model is regularized using two roughness penalties, which consider the spatial and temporal regularities separately. The integrated square of the second derivative of the basis function is used as temporal penalty. While the spatial penalty consists of the integrated square of Laplace operator, which is integrated exclusively over the domain of interest that is determined using finite element technique. In this paper, we applied a spatio-temporal regression model with partial differential equations regularization (ST-PDE) approach to analyze a remote sensing data measuring the greenness of vegetation, measure by an index called enhanced vegetation index (EVI). The EVI data consist of measurements that take values between -1 and 1 reflecting the level of greenness of some region over a period of time. We applied (ST-PDE) approach to irregular shaped region of the EVI data. The approach efficiently accommodates the irregular shaped regions taking into account the complex boundaries rather than smoothing across the boundaries. Furthermore, the approach succeeds in capturing the temporal variation in the data.

Keywords: irregularly shaped domain, partial differential equations, finite element analysis, complex boundray

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Authors: Faiza Marrium, Masood Rabbani, Ali Ahmad Sheikh, Muhammad Yasin Tipu Javed Muhammad, Sohail Raza

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More than 75% diseases spreading in the past 10 years in human population globally are linked to veterinary sector. Veterinary diagnostic labs are the powerful ally for diagnosis, prevention and monitoring of animal diseases in any country. In order to avoid detrimental effects of errors in disease diagnostic and biorisk management, there is a dire need to establish quality control system. In current study, 3 private and 6 public sectors veterinary diagnostic labs were selected for survey. A questionnaire survey in biorisk management guidelines of CWA 15793 was designed to find quality control breaches in lab design, personal, equipment and consumable, quality control measures adopted in lab, waste management, environmental monitoring and customer care. The data was analyzed through frequency distribution statistically by using (SPSS) version 18.0. A non-significant difference was found in all parameters of lab design, personal, equipment and consumable, quality control measures adopted in lab, waste management, environmental monitoring and customer care with an average percentage of 46.6, 57.77, 52.7, 55.5, 54.44, 48.88 and 60, respectively. A non-significant difference among all nine labs were found, with highest average compliance percentage of all parameters are lab 2 (78.13), Lab 3 (70.56), Lab 5 (57.51), Lab 6 (56.37), Lab 4 (55.02), Lab 9 (49.58), Lab 7 (47.76), Lab 1 (41.01) and Lab 8 (36.09). This study shows that in Lahore district veterinary diagnostic labs are not giving proper attention to quality of their system and there is no significant difference between setups of private and public sector laboratories. These results show that most of parameters are between 50 and 80 percent, which needs some work and improvement as per WHO criteria.

Keywords: veterinary lab, quality management system, accreditation, regulatory body, disease identification

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Authors: Harkishen Singh, Gavin Mangeni

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This project aims at building an efficient and automatic power monitoring SCADA system, which is capable of monitoring the electrical parameters of high voltage powered devices in real time for example RMS voltage and current, frequency, energy consumed, power factor etc. The system uses RS-485 serial communication interface to transfer data over longer distances. Embedded C programming is the platform used to develop two hardware modules namely: RTU and Master Station modules, which both use the CC2540 BLE 4.0 microcontroller configured in slave / master mode. The Si8900 galvanic ally isolated microchip is used to perform ADC externally. The hardware communicates via UART port and sends data to the user PC using the USB port. Labview software is used to design a user interface to display current state of the power loads being monitored as well as logs data to excel spreadsheet file. An understanding of the Si8900’s auto baud rate process is key to successful implementation of this project.

Keywords: SCADA, RS485, CC2540, labview, Si8900

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Authors: Huan-Chieh Chiu, Hung-Shuo Wu, Chien-Hao Wang, Yu-Cheng Yang, Ching-Ya Tseng, Joe-Air Jiang

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Power failure resulting from tower collapse due to violent seismic events might bring enormous and inestimable losses. The Chi-Chi earthquake, for example, strongly struck Taiwan and caused huge damage to the power system on September 21, 1999. Nearly 10% of extra high voltage (EHV) transmission towers were damaged in the earthquake. Therefore, seismic hazards of EHV transmission towers should be monitored and evaluated. The ultimate goal of this study is to establish a damage level assessment model for EHV transmission towers. The data of earthquakes provided by Taiwan Central Weather Bureau serve as a reference and then lay the foundation for earthquake simulations and analyses afterward. Some parameters related to the damage level of each point of an EHV tower are simulated and analyzed by the data from monitoring stations once an earthquake occurs. Through the Fourier transform, the seismic wave is then analyzed and transformed into different wave frequencies, and the data would be shown through a response spectrum. With this method, the seismic frequency which damages EHV towers the most is clearly identified. An estimation model is built to determine the damage level caused by a future seismic event. Finally, instead of relying on visual observation done by inspectors, the proposed model can provide a power company with the damage information of a transmission tower. Using the model, manpower required by visual observation can be reduced, and the accuracy of the damage level estimation can be substantially improved. Such a model is greatly useful for health and construction monitoring because of the advantages of long-term evaluation of structural characteristics and long-term damage detection.

Keywords: damage level monitoring, drift ratio, fragility curve, smart grid, transmission tower

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Authors: Luisa Cabezas, Karol Leal, Harold Mendoza, Fabio Trochez, Angel Lozada

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According to the 2030 Agenda for Sustainable Development Goals (SDG) in which equal importance is given to economic, social, and environmental dimensions where the equality and dignity of each human person is placed at the center of discussion, changing the development concept for one with more responsibility with the environment. It can be found that the energy and food systems are deeply entangled, and they are transversal to the 17 proposed SDG. In this order of ideas, a research project is carried out at Unidad Central del Valle del Cauca (UCEVA) with these two systems in mind, on one hand the energy transition and, on the other hand the transformation of agri-food systems. This project it could be achieved by automation and control irrigation system of medicinal, aromatic, and condimentary plants (MACP) area within the UCEVA Agroecological Farm and located in rural area of Tulua municipality (Valle del Cauca Department, Colombia). This system have allowed to stablish a remote monitoring of MACP area, including MACP moisture measurement, and execute the required system actions. In addition, the electrical system of irrigation control system is powered by a scalable photovoltaic solar energy system based on its specifications. Thus, the developed system automates and control de irrigation system, which is energetically self-sustainable and allows to satisfy the MACP area requirements. Is important to highlight that at MACP area, several medicinal, aromatic, and condimentary plants species are preserved to become primary sources for the pharmaceutical industry and, in many occasions, the only medicines for many communities. Therefore, preserve medicinal plants area would generates medicinal security and preserve cultural heritage as these plants are part of ancestral knowledge that penetrate academic and research communities at UCEVA campus to other society sectors.

Keywords: ancestral knowledge, climate change, medicinal plants, solar energy

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Authors: Ivo Zution Goncalves, Christopher M. U. Neale, Hiran Medeiros, Everardo Mantovani, Natalia Souza

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Multispectral and thermal infrared imagery from satellite sensors coupled with climate and soil datasets were used to estimate evapotranspiration and biomass in center pivots planted to maize in Brazil during the 2016 season. The hybrid remote sensing based model named Spatial EvapoTranspiration Modelling Interface (SETMI) was applied using multispectral and thermal infrared imagery from the Landsat Thematic Mapper instrument. Field data collected by the IRRIGER center pivot management company included daily weather information such as maximum and minimum temperature, precipitation, relative humidity for estimating reference evapotranspiration. In addition, soil water content data were obtained every 0.20 m in the soil profile down to 0.60 m depth throughout the season. Early season soil samples were used to obtain water-holding capacity, wilting point, saturated hydraulic conductivity, initial volumetric soil water content, layer thickness, and saturated volumetric water content. Crop canopy development parameters and irrigation application depths were also inputs of the model. The modeling approach is based on the reflectance-based crop coefficient approach contained within the SETMI hybrid ET model using relationships developed in Nebraska. The model was applied to several fields located in Minas Gerais State in Brazil with approximate latitude: -16.630434 and longitude: -47.192876. The model provides estimates of real crop evapotranspiration (ET), crop irrigation requirements and all soil water balance outputs, including biomass estimation using multi-temporal satellite image inputs. An interpolation scheme based on the growing degree-day concept was used to model the periods between satellite inputs, filling the gaps between image dates and obtaining daily data. Actual and accumulated ET, accumulated cold temperature and water stress and crop water requirements estimated by the model were compared with data measured at the experimental fields. Results indicate that the SETMI modeling approach using data assimilation, showed reliable daily ET and crop water requirements for maize, interpolated between remote sensing observations, confirming the applicability of the SETMI model using new relationships developed in Nebraska for estimating mainly ET and water requirements in Brazil under tropical conditions.

Keywords: basal crop coefficient, irrigation, remote sensing, SETMI

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Authors: Shativel S., Chandana B. R., Kavya B. C., Obli B. Vikram, Suganthi J., Nagendra Rao G.

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Satellite communication plays a pivotal role in modern global communication networks, serving as a vital link between terrestrial infrastructure and remote regions. The demand for reliable satellite reception systems, especially in UHF (Ultra High Frequency) and VHF (Very High Frequency) bands, has grown significantly over the years. This research paper presents the design and optimization of a high-gain, dual-band crossed Yagi-Uda antenna in CST Studio Suite, specifically tailored for satellite reception. The proposed antenna system incorporates a circularly polarized (Right-Hand Circular Polarization - RHCP) design to reduce Faraday loss. Our aim was to use fewer elements and achieve gain, so the antenna is constructed using 6x2 elements arranged in cross dipole and supported with a boom. We have achieved 10.67dBi at 146MHz and 9.28dBi at 437.5MHz.The process includes parameter optimization and fine-tuning of the Yagi-Uda array’s elements, such as the length and spacing of directors and reflectors, to achieve high gain and desirable radiation patterns. Furthermore, the optimization process considers the requirements for UHF and VHF frequency bands, ensuring broad frequency coverage for satellite reception. The results of this research are anticipated to significantly contribute to the advancement of satellite reception systems, enhancing their capabilities to reliably connect remote and underserved areas to the global communication network. Through innovative antenna design and simulation techniques, this study seeks to provide a foundation for the development of next-generation satellite communication infrastructure.

Keywords: Yagi-Uda antenna, RHCP, gain, UHF antenna, VHF antenna, CST, radiation pattern.

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Authors: Sergio Celaschi, Henrique Canavarro de Alencar, Claaudecir Biazoli

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Effluent quality is of the highest priority for compliance with the permit limits of environmental protection agencies and ensures the protection of their local water system. Of the pollutants monitored, the biochemical oxygen demand (BOD) posed one of the greatest challenges. This work presents a solution for wastewater treatment plants - WWTP’s ability to react to different situations and meet treatment goals. Delayed BOD5 results from the lab take 7 to 8 analysis days, hindered the WWTP’s ability to react to different situations and meet treatment goals. Reducing BOD turnaround time from days to hours is our quest. Such a solution is based on a system of two BOD bioreactors associated with Digital Twin (DT) and Machine Learning (ML) methodologies via an Internet of Things (IoT) platform to monitor and control a WWTP to support decision making. DT is a virtual and dynamic replica of a production process. DT requires the ability to collect and store real-time sensor data related to the operating environment. Furthermore, it integrates and organizes the data on a digital platform and applies analytical models allowing a deeper understanding of the real process to catch sooner anomalies. In our system of continuous time monitoring of the BOD suppressed by the effluent treatment process, the DT algorithm for analyzing the data uses ML on a chemical kinetic parameterized model. The continuous BOD monitoring system, capable of providing results in a fraction of the time required by BOD5 analysis, is composed of two thermally isolated batch bioreactors. Each bioreactor contains input/output access to wastewater sample (influent and effluent), hydraulic conduction tubes, pumps, and valves for batch sample and dilution water, air supply for dissolved oxygen (DO) saturation, cooler/heater for sample thermal stability, optical ODO sensor based on fluorescence quenching, pH, ORP, temperature, and atmospheric pressure sensors, local PLC/CPU for TCP/IP data transmission interface. The dynamic BOD system monitoring range covers 2 mg/L < BOD < 2,000 mg/L. In addition to the BOD monitoring system, there are many other operational WWTP sensors. The CPU data is transmitted/received to/from the digital platform, which in turn performs analyses at periodic intervals, aiming to feed the learning process. BOD bulletins and their credibility intervals are made available in 12-hour intervals to web users. The chemical kinetics ML algorithm is composed of a coupled system of four first-order ordinary differential equations for the molar masses of DO, organic material present in the sample, biomass, and products (CO₂ and H₂O) of the reaction. This system is solved numerically linked to its initial conditions: DO (saturated) and initial products of the kinetic oxidation process; CO₂ = H₂0 = 0. The initial values for organic matter and biomass are estimated by the method of minimization of the mean square deviations. A real case of continuous monitoring of BOD wastewater effluent quality is being conducted by deploying an IoT application on a large wastewater purification system located in S. Paulo, Brazil.

Keywords: effluent treatment, biochemical oxygen demand, continuous monitoring, IoT, machine learning

Procedia PDF Downloads 73

Authors: Leila Jafari, Viliam Makis, G. B. Akram Khaleghei

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In this paper, we present a maintenance model of a two-unit series system with economic dependence. Unit#1, which is considered to be more expensive and more important, is subject to condition monitoring (CM) at equidistant, discrete time epochs and unit#2, which is not subject to CM, has a general lifetime distribution. The multivariate observation vectors obtained through condition monitoring carry partial information about the hidden state of unit#1, which can be in a healthy or a warning state while operating. Only the failure state is assumed to be observable for both units. The objective is to find an optimal opportunistic maintenance policy minimizing the long-run expected average cost per unit time. The problem is formulated and solved in the partially observable semi-Markov decision process framework. An effective computational algorithm for finding the optimal policy and the minimum average cost is developed and illustrated by a numerical example.

Keywords: condition-based maintenance, semi-Markov decision process, multivariate Bayesian control chart, partially observable system, two-unit system

Procedia PDF Downloads 459

Authors: Dingding Chen, Kazuo Arakawa, Masakazu Uchino, Changheng Xu

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Vacuum assisted resin transfer moulding (VARTM) is a promising manufacture process for making large and complex fiber reinforced composite structures. However, the complexity of the flow of the resin in the infusion stage usually leads to nonuniform property distribution of the produced composite part. In order to control the flow of the resin, the situation of flow should be mastered. For the safety of the usage of the produced composite in practice, the understanding of the property distribution is essential. In this paper, we did some trials on monitoring the resin infusion stage and evaluation for the fiber volume fraction distribution of the VARTM produced composite using the digital image correlation methods. The results show that 3D-DIC is valid on monitoring the resin infusion stage and it is possible to use 2D-DIC to estimate the distribution of the fiber volume fraction on a FRP plate.

Keywords: digital image correlation, VARTM, FRP, fiber volume fraction

Procedia PDF Downloads 342

Authors: Salman Al-Alyani

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In line with the Kingdom’s Vision 2030, the Saudi Green initiative was announced aimed at reducing carbon emissions by more than 4% of the global contribution. The initiative included plans to generate 50% of its energy from renewables by 2030. The geographical location of Saudi Arabia makes it among the best countries in terms of solar irradiation and has good wind resources in many areas across the Kingdom. Saudi Arabia is a wide country and has many remote locations where it is not economically feasible to connect those loads to the national grid. With the improvement of battery innovation and reduction in cost, different renewable technologies (primarily wind and solar) can be integrated to meet the need for energy in a more effective and cost-effective way. Saudi Arabia is famous for high solar irradiations in which solar power generation can extend up to six (6) hours per day (25% capacity factor) in some locations. However, the net present value (NPV) falls down to negative in some locations due to distance and high installation costs. Wind generation in Saudi Arabia is a promising technology. Hybrid renewable generation will increase the net present value and lower the payback time due to additional energy generated by wind. The infrastructure of the power system can be capitalized to contain solar generation and wind generation feeding the inverter, controller, and load. Storage systems can be added to support the hours that have an absence of wind or solar energy. Also, the smart controller that can help integrate various renewable technologies primarily wind and solar, to meet demand considering load characteristics. It could be scalable for grid or off-grid applications. The objective of this paper is to study the feasibility of introducing a hybrid renewable system in remote locations and the concept for the development of a smart controller.

Keywords: battery storage systems, hybrid power generation, solar energy, wind energy

Procedia PDF Downloads 178

Authors: Sam Weber, Deepak Mishra, Susan Wilde, Elizabeth Kramer

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The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing over time, with point and non-point source eutrophication and shifting climate paradigms being blamed as the primary culprits. Excessive nutrients, warm temperatures, quiescent water, and heavy and less regular rainfall create more conducive environments for CyanoHABs. CyanoHABs have the potential to produce a spectrum of toxins that cause gastrointestinal stress, organ failure, and even death in humans and animals. To promote enhanced, proactive CyanoHAB management, risk modeling using geospatial tools can act as predictive mechanisms to supplement current CyanoHAB monitoring, management and mitigation efforts. The risk maps would empower water managers to focus their efforts on high risk water bodies in an attempt to prevent CyanoHABs before they occur, and/or more diligently observe those waterbodies. For this research, exploratory spatial data analysis techniques were used to identify the strongest predicators for CyanoHAB blooms based on remote sensing-derived cyanobacteria cell density values for 771 waterbodies in the Georgia Piedmont and landscape characteristics of their watersheds. In-situ datasets for cyanobacteria cell density, nutrients, temperature, and rainfall patterns are not widely available, so free gridded geospatial datasets were used as proxy variables for assessing CyanoHAB risk. For example, the percent of a watershed that is agriculture was used as a proxy for nutrient loading, and the summer precipitation within a watershed was used as a proxy for water quiescence. Cyanobacteria cell density values were calculated using atmospherically corrected images from the European Space Agency’s Sentinel-2A satellite and multispectral instrument sensor at a 10-meter ground resolution. Seventeen explanatory variables were calculated for each watershed utilizing the multi-petabyte geospatial catalogs available within the Google Earth Engine cloud computing interface. The seventeen variables were then used in a multiple linear regression model, and the strongest predictors of cyanobacteria cell density were selected for the final regression model. The seventeen explanatory variables included land cover composition, winter and summer temperature and precipitation data, topographic derivatives, vegetation index anomalies, and soil characteristics. Watershed maximum summer temperature, percent agriculture, percent forest, percent impervious, and waterbody area emerged as the strongest predictors of cyanobacteria cell density with an adjusted R-squared value of 0.31 and a p-value ~ 0. The final regression equation was used to make a normalized cyanobacteria cell density index, and a Jenks Natural Break classification was used to assign waterbodies designations of low, medium, or high risk. Of the 771 waterbodies, 24.38% were low risk, 37.35% were medium risk, and 38.26% were high risk. This study showed that there are significant relationships between free geospatial datasets representing summer maximum temperatures, nutrient loading associated with land use and land cover, and the area of a waterbody with cyanobacteria cell density. This data analytics approach to CyanoHAB risk assessment corroborated the literature-established environmental triggers for CyanoHABs, and presents a novel approach for CyanoHAB risk mapping in waterbodies across the greater southeastern United States.

Keywords: cyanobacteria, land use/land cover, remote sensing, risk mapping

Procedia PDF Downloads 211

Authors: Sanjay Adhikesaven

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Workplace accidents are a critical problem that causes many deaths, injuries, and financial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively find unsafe environments where injuries could occur by detecting the use of personal protective equipment (PPE) and identifying unsafe activities. Thus, we propose an industrial workplace alerting and monitoring platform to detect PPE use and classify unsafe activity in group settings involving multiple humans and objects over a long period of time. Our proposed method is the first to analyze prolonged actions involving multiple people or objects. It benefits from combining pose estimation with PPE detection in one platform. Additionally, we propose the first open-source annotated data set with video data from industrial workplaces annotated with the action classifications and detected PPE. The proposed system can be implemented within the surveillance cameras already present in industrial settings, making it a practical and effective solution.

Keywords: computer vision, deep learning, workplace safety, automation

Procedia PDF Downloads 103

Authors: Wanderson Goncalves e Goncalves, José Alberto Silva de Sá

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Human actions are a threat to the balance and conservation of the Amazon forest. Therefore the environmental monitoring services play an important role as the preservation and maintenance of this environment. This study classified forest types using data from a forest inventory provided by the 'Florestal e da Biodiversidade do Estado do Pará' (IDEFLOR-BIO), located between the municipalities of Santarém, Juruti and Aveiro, in the state of Pará, Brazil, covering an area approximately of 600,000 hectares, Bands 3, 4 and 5 of the TM-Landsat satellite image, and Self - Organizing Maps. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training the neural network. The midpoints of each sample of forest inventory have been linked to images. Later the Digital Numbers of the pixels have been extracted, composing the database that fed the training process and testing of the classifier. The neural network was trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and the number of examples in the training data set was 400, 200 examples for each class (Dbe and Dbe + Abp), and the size of the test data set was 100, with 50 examples for each class (Dbe and Dbe + Abp). Therefore, total mass of data consisted of 500 examples. The classifier was compiled in Orange Data Mining 2.7 Software and was evaluated in terms of the confusion matrix indicators. The results of the classifier were considered satisfactory, and being obtained values of the global accuracy equal to 89% and Kappa coefficient equal to 78% and F1 score equal to 0,88. It evaluated also the efficiency of the classifier by the ROC plot (receiver operating characteristics), obtaining results close to ideal ratings, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon.

Keywords: artificial neural network, computational intelligence, pattern recognition, unsupervised learning

Procedia PDF Downloads 361

Authors: Mahesh Kumar Jat, Manisha Choudhary

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Rapid population and economic growth resulted in changes in large-scale land use land cover (LULC) changes. Changes in the biophysical properties of the Earth's surface and its impact on climate are of primary concern nowadays. Different approaches, ranging from location-based relationships or modelling earth surface - atmospheric interaction through modelling techniques like surface energy balance (SEB) have been used in the recent past to examine the relationship between changes in Earth surface land cover and climatic characteristics like temperature and precipitation. A remote sensing-based model i.e., Surface Energy Balance Algorithm for Land (SEBAL), has been used to estimate the surface heat fluxes over Mahi Bajaj Sagar catchment (India) from 2001 to 2020. Landsat ETM and OLI satellite data are used to model the SEB of the area. Changes in observed precipitation and temperature, obtained from India Meteorological Department (IMD) have been correlated with changes in surface heat fluxes to understand the relative contributions of LULC change in changing these climatic variables. Results indicate a noticeable impact of LULC changes on climatic variables, which are aligned with respective changes in SEB components. Results suggest that precipitation increases at a rate of 20 mm/year. The maximum and minimum temperature decreases and increases at 0.007 ℃ /year and 0.02 ℃ /year, respectively. The average temperature increases at 0.009 ℃ /year. Changes in latent heat flux and sensible heat flux positively correlate with precipitation and temperature, respectively. Variation in surface heat fluxes influences the climate parameters and is an adequate reason for climate change. So, SEB modelling is helpful to understand the LULC change and its impact on climate.

Keywords: remote sensing, GIS, object based, classification

Procedia PDF Downloads 130