Search results for: monitoring networks

Authors: Vedant Vardhan Rathour, Anant Agrawal

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The project aims to provide a touchless, intuitive interface for human-computer interaction, enabling users to control their computer using hand gestures and voice commands. The system leverages advanced computer vision techniques using the MediaPipe framework and OpenCV to detect and interpret real time hand gestures, transforming them into mouse actions such as clicking, dragging, and scrolling. Additionally, the integration of a voice assistant powered by the Speech Recognition library allows for seamless execution of tasks like web searches, location navigation and gesture control on the system through voice commands.

Keywords: gesture recognition, hand tracking, machine learning, convolutional neural networks

Procedia PDF Downloads 12

Authors: Boris M. Fedorov, Liudmila P. Goncharenko, Sergey A. Sybachin, Natalia A. Mamedova, Ekaterina V. Makarenkova, Saule Rakhimova

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In modern economic conditions, the question of the possibility of correct recognition of a signature on digital documents in order to verify the expression of will or confirm a certain operation is relevant. The additional complexity of processing lies in the dynamic variability of the signature for each individual, as well as in the way information is processed because the signature refers to biometric data. The article discusses the issues of using artificial intelligence models in order to improve the quality of signature confirmation in document recognition. The analysis of several possible options for using the model is carried out. The results of the study are given, in which it is possible to correctly determine the authenticity of the signature on small samples.

Keywords: signature recognition, biometric data, artificial intelligence, neural networks

Procedia PDF Downloads 148

Authors: Mera-Martínez Daniela

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Dendropsophus minutus, is a species distributed in South America including the slopes of the Andes, the Amazon basin, forests of southeastern Brazil and in Guyana where tropical forests are characteristic. The relationship of amphibians found in this locality is evidenced by molecular markers, with the objective of analyzing if the geographic distance is influencing the structure of the populations of D. minutus in Guyana; we analyzed 65 sequences from the 3 localities of Guyana where haplotype networks, Mantel Test and phylogeny were realized to know the influence. It was evidenced that there is a haplotypic difference in the locality of Guyana compared to Suriname and French Guyana, but this does not have a correlation with the geographic distance, but this one can be influenced by the conditions of the places.

Keywords: phylogeography, Dendropsophus, geographic distance, molecular markers

Procedia PDF Downloads 211

Authors: Daisy Owomugasho Ndikuno

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The paper explores the extent to which mindset change has been critical in the community rural development in Uganda. It is descriptive research with The Parish Development Model as a case study. The results show that rural community development is possible and its success largely depends on harnessing local resources and knowledge; leveraging education, empowerment and awareness; creating sustainable livelihoods and encouraging entrepreneurship and innovation; access to financial resources; and building collaborative networks and partnerships. In all these, the role of mindset change is critical. By instilling a positive, collaborative and innovative mindset, rural communities can overcome challenges and chat a path towards sustainable development.

Keywords: community, development, mindset, change

Procedia PDF Downloads 93

Authors: Manoj Gupta, Nirmendra Singh Bhadauria

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Most of the applications in image processing require high spatial and high spectral resolution in a single image. For example satellite image system, the traffic monitoring system, and long range sensor fusion system all use image processing. However, most of the available equipment is not capable of providing this type of data. The sensor in the surveillance system can only cover the view of a small area for a particular focus, yet the demanding application of this system requires a view with a high coverage of the field. Image fusion provides the possibility of combining different sources of information. In this paper, we have decomposed the image using DTCWT and then fused using average and hybrid of (maxima and average) pixel level techniques and then compared quality of both the images using PSNR.

Keywords: image fusion, DWT, DT-CWT, PSNR, average image fusion, hybrid image fusion

Procedia PDF Downloads 606

Authors: Emmanuel U. Oleka, Anil Khanal, Gary L. Lebby, Ali R. Osareh

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Synchrophasor technology is fast being deployed in electric power grids all over the world and is fast changing the way the grids are managed. This trend is to continue until the entire power grids are fully connected so they can be monitored and controlled in real-time. Much achievement has been made in the synchrophasor technology development and deployment, and much more are yet to be achieved. Real-time power grid control and protection potentials of synchrophasor are yet to be explored. It is of necessity that researchers keep in view the various challenges that still need to be overcome in expanding the frontiers of synchrophasor technology. This paper outlines the major challenges that should be dealt with in order to achieve the goal of total power grid visualization, monitoring and control using synchrophasor technology.

Keywords: electric power grid, grid visualization, phasor measurement unit, synchrophasor

Procedia PDF Downloads 558

Authors: Virany Diana, Martono Tri Utomo, Risa Etika

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Background: Shock is one severe condition that can be a major cause of morbidity and mortality in the Neonatal Intensive Care Unit. Preterm infants are very susceptible to shock caused by many complications such as asphyxia, patent ductus arteriosus, intra ventricle haemorrhage, necrotizing enterocolitis, persistent pulmonal hypertension of the newborn, and septicaemia. Limited hemodynamic monitoring for early detection of shock causes delayed intervention and comprises the outcomes. Clinical parameters still used in neonatal shock detection, such as Capillary Refill Time, heart rate, cold extremity, and urine production. Blood pressure is most frequently used to evaluate preterm's circulation, but hypotension indicates uncompensated shock. Near-infrared spectroscopy (NIRS) is known as a noninvasive tool for monitoring and detecting the state of inadequate tissue perfusion. Muscle oxygen saturation shows decreased cardiac output earlier than systemic parameters of tissue oxygenation when cerebral regional oxygen saturation is still stabilized by autoregulation. However, to our best knowledge, until now, no study has analyzed the decrease of muscle oxygen regional saturation (mRSO₂) and the ratio of muscle and cerebral oxygen regional saturation (mRSO₂/cRSO₂) by NIRS in preterm with shock. Purpose: The purpose of this study is to analyze the decrease of mRSO₂ and ratio of muscle to cerebral oxygen regional saturation (mRSO₂/cRSO₂) by NIRS in preterm with shock. Patients and Methods: This cross-sectional study was conducted on preterm infants with 28-34 weeks gestational age, admitted to the NICU of Dr. Soetomo Hospital from November to January 2022. Patients were classified into two groups: shock and non-shock. The diagnosis of shock is based on clinical criteria (tachycardia, prolonged CRT, cold extremity, decreased urine production, and MAP Blood Pressure less than GA in weeks). Measurement of mRSO₂ and cRSO₂ by NIRS was performed by the doctor in charge when the patient came to NICU. Results: We enrolled 40 preterm infants. The initial conventional hemodynamic parameter as the basic diagnosis of shock showed significant differences in all variables. Preterm with shock had higher mean HR (186.45±1.5), lower MAP (29.8±2.1), and lower SBP (45.1±4.28) than non-shock children, and most had a prolonged CRT. The patients’ outcome was not a significant difference between shock and non-shock patients. The mean mRSO₂ in the shock and non-shock groups were 33,65 ± 11,32 vs. 69,15 ± 3,96 (p=0.001), and the mean ratio mRSO₂/cRSO₂ 0,45 ± 0,12 vs. 0,84 ± 0,43 (p=0,001), were significantly different. The mean cRSO₂ in the shock and non-shock groups were 71,60 ± 4,90 vs. 81,85 ± 7,85 (p 0.082), not significantly different. Conclusion: The decrease of mRSO₂ and ratio of mRSO₂/cRSO₂ can differentiate between shock and non-shock in the preterm infant when cRSO₂ is still normal.

Keywords: preterm infant, regional muscle oxygen saturation, regional cerebral oxygen saturation, NIRS, shock

Procedia PDF Downloads 91

Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: F. Benhamida, A. Graa, L. Benameur, I. Ziane

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The state estimation of the electrical power system operation state is very important for supervising task. With the nonlinearity of the AC power flow model, the state estimation problem (SEP) is a nonlinear mathematical problem with many local optima. This paper treat the mathematical model for the SEP and the monitoring of the nonlinear systems of great dimensions with an application on power electrical system, the modelling, the analysis and state estimation synthesis in order to supervise the power system behavior. in fact, it is very difficult, to see impossible, (for reasons of accessibility, techniques and/or of cost) to measure the excessive number of the variables of state in a large-sized system. It is thus important to develop software sensors being able to produce a reliable estimate of the variables necessary for the diagnosis and also for the control.

Keywords: power system, state estimation, robustness, observability

Procedia PDF Downloads 523

Authors: Hassan M. Al-Ahmadi, Hamad Bader Almobayedh

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Planning for urban transportation is essential for developing effective and sustainable transportation networks that meet the needs of various communities. Advanced modeling software is required for effective transportation planning, management, and optimization. This paper compares PTV VISUM, Aimsun, TransCAD, and Emme, four industry-leading software tools for transportation planning and modeling. Each software has strengths and limitations, and the project's needs, financial constraints, and level of technical expertise influence the choice of software. Transportation experts can design and improve urban transportation systems that are effective, sustainable, and meet the changing needs of their communities by utilizing these software tools.

Keywords: PTV VISUM, Aimsun, TransCAD, transportation modeling software

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Authors: Mahesh Chandra Paliwal, Ajay Kumar Jain

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Supervisors working in the industries must have the knowledge and skills for performing their job for environmental protection and sustainable development. A survey of thirty industries was conducted to know the roles of supervisors related to environmental protection and sustainable development. A questionnaire was prepared based on the discussion with the environmental experts. The findings of the study show that supervisors must be aware of practices followed for good housekeeping, water management, waste management, maintenance of effluent treatment plants, monitoring pollution control level to perform their job to save the environment. These aspects must be incorporated in diploma curriculum so that the diploma pass outs may use this knowledge and skills in the industries.

Keywords: environmental protection, sustainable development, water management, waste management, curriculum

Procedia PDF Downloads 330

Authors: Stephen Gerome

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This study examines the translation and use of metaphors in the realm of public safety discourse and intends to shed light on a continuing problem in cross-cultural communication. Metaphors can cause problems not only within languages but also in interlingual communication. The use and misuse of metaphors may hinder the ability to adequately communicate prevention efforts and, in some cases, facilitate and allow financial crime to go undetected. The use of lexicalized metaphors in communications by political entities, journalists, and legal agents in communications regarding law, policy making, compliance monitoring and enforcement as well as in adjudication can have negative consequences if misconstrued. This study provides examples of metaphor usage in published documents in a corpus linguistic study that compares the use of lexicalized metaphors in this discourse to shed light on possible unexpected consequences as well as counterproductive ones.

Keywords: translation, legal, corpus linguistics, financial

Procedia PDF Downloads 119

Authors: Sedat Bilgili, Alper K. Demir

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The current Internet has become the dominant use with continuing growth in the home, medical, health, smart cities and industrial automation applications. Internet of Things (IoT) is an emerging technology to enable such applications in our lives. Moreover, Named Data Networking (NDN) is also emerging as a Future Internet architecture where it fits the communication needs of IoT networks. The aim of this study is to provide an NDN protocol stack implementation running on the Contiki operating system (OS). Contiki OS is an OS that is developed for constrained IoT devices. In this study, an NDN protocol stack that can work on top of IEEE 802.15.4 link and physical layers have been developed and presented.

Keywords: internet of things (IoT), named-data, named data networking (NDN), operating system

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Authors: Samia Dardouri, Ridha Bouallegue

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Scheduling is the process of dynamically allocating physical resources to User Equipment (UE) based on scheduling algorithms implemented at the LTE base station. Various algorithms have been proposed by network researchers as the implementation of scheduling algorithm which represents an open issue in Long Term Evolution (LTE) standard. This paper makes an attempt to study and compare the performance of PF, MLWDF and EXP/PF scheduling algorithms. The evaluation is considered for a single cell with interference scenario for different flows such as Best effort, Video and VoIP in a pedestrian and vehicular environment using the LTE-Sim network simulator. The comparative study is conducted in terms of system throughput, fairness index, delay, packet loss ratio (PLR) and total cell spectral efficiency.

Keywords: LTE, multimedia flows, scheduling algorithms, mobile computing

Procedia PDF Downloads 383

Authors: Suratno

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Metacognition thinking skills should be developed early on in learning. The aim of research builds metacognition thinking skills through collaborative learning with self-evaluation. Approach to action research study involving 32 middle school students in Jember Indonesia. Indicators metacognition skills consist of planning, information management strategies, comprehension monitoring, and debugging strategies. Data were analyzed by t test and analysis of instructional videos. Results of the study here were significant differences in metacognition skills before and after the implementation of collaborative learning with self-evaluation. Analysis instructional video showing the difference artifacts of student learning activities to learning before and after implementation of collaborative learning with self-evaluation. Self-evaluation makes students familiar practice thinking skills metacognition.

Keywords: metacognition, collaborative, evaluation, thinking skills

Procedia PDF Downloads 361

Authors: Romel N. Georgees, Rayya A Hassan, Robert P. Evans, Piratheepan Jegatheesan

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Among other traditional and non-traditional additives, polymers have shown an efficient performance in the field and improved sustainability. Polyacrylamide (PAM) is one such additive that has demonstrated many advantages including a reduction in permeability, an increase in durability and the provision of strength characteristics. However, information about its effect on the improved geotechnical characteristics is very limited to the field performance monitoring. Therefore, a laboratory investigation was carried out to examine the basic and engineering behaviors of three types of soils treated with a PAM additive. The results showed an increase in dry density and unconfined compressive strength for all the soils. The results further demonstrated an increase in unsoaked CBR and a reduction in permeability for all stabilized samples.

Keywords: CBR, hydraulic conductivity, PAM, unconfined compressive strength

Procedia PDF Downloads 375

Authors: Q. Raza

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Getting old is inevitable, tasks which were once simple are now a daily struggle. This paper is a study of how older users interact with web application based upon a series of experiments. The experiments conducted involved 12 participants and the experiments were split into two parts. The first set gives the users a feel of current social networks and the second set take into considerations from the participants and the results of the two are compared. This paper goes in detail on the psychological aspects such as social exclusion, Metacognition memory and Therapeutic memories and how this relates to users becoming isolated from society, social networking can be the roof on a foundation of successful computer interaction. The purpose of this paper is to carry out a study and to propose new ideas to help users to be able to use social networking sites easily and efficiently.

Keywords: cognitive psychology, special memory, social networking and human computer interaction

Procedia PDF Downloads 445

Authors: Abdelaziz Moujane, Abedelali Boulli, Abdellah Ouigmane

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The present work focuses on the assessment of forestlandscape changes in the region of ZaouitAhansal, usingmultitemporal satellite images at high spatial resolution.Severalremotesensingmethodswereappliednamely: The supervised classification algorithm and NDVI whichwerecombined in a GIS environment to quantify the extent and change in density of forest stands (holmoak, juniper, thya, Aleppo pine, crops, and others).The resultsobtainedshowedthat the forest of ZaouitAhansal has undergonesignificantdegradationresulting in a decrease in the area of juniper, cedar, and zeenoak, as well as an increase in the area of baresoil and agricultural land. The remotesensing data providedsatisfactoryresults for identifying and quantifying changes in forestcover. In addition, thisstudycould serve as a reference for the development of management strategies and restoration programs.

Keywords: remote sensing, GIS, satellite image, NDVI, deforestation, zaouit ahansal

Procedia PDF Downloads 153

Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

Procedia PDF Downloads 125

Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Le Zhao, Alain Nogaret

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We have built universal Central Pattern Generator (CPG) hardware by interconnecting Hodgkin-Huxley neurons with reciprocally inhibitory synapses. We investigate the dynamics of neuron oscillations as a function of the time delay between current steps applied to individual neurons. We demonstrate stimulus dependent switching between spiking polyrhythms and map the phase portraits of the neuron oscillations to reveal the basins of attraction of the system. We experimentally study the dependence of the attraction basins on the network parameters: the neuron response time and the strength of inhibitory connections.

Keywords: central pattern generator, winnerless competition principle, artificial neural networks, synapses

Procedia PDF Downloads 475

Authors: Anna Romanova, Morteza A. Alani

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Corrosion of concrete sewer pipes induced by sulfuric acid is an acknowledged problem and a ticking time-bomb to sewer operators. Whilst the chemical reaction of the corrosion process is well-understood, the indirect roles of other parameters in the corrosion process which are found in sewer environment are not highly reflected on. This paper reports on a field studies undertaken in Austria and United Kingdom, where the parameters of temperature, pH, H2S and CO2 were monitored over a period of time. The study establishes that (i) effluent temperature and pH have similar daily pattern and peak times, When examined in minutes scale, (ii) H2S and CO2 have an identical hourly pattern, (iii) H2S instant or shifted relation to effluent temperature is governed by the root mean square value of CO2.

Keywords: concrete corrosion, carbon dioxide, hydrogen sulphide, sewer pipe, sulfuric acid

Procedia PDF Downloads 307

Authors: Aishrith Rao

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COVID 19 is a pandemic which has resulted in thousands of deaths around the world and a huge impact on the global economy. Testing is a huge issue as the test kits have limited availability and are expensive to manufacture. Using deep learning methods on radiology images in the detection of the coronavirus as these images contain information about the spread of the virus in the lungs is extremely economical and time-saving as it can be used in areas with a lack of testing facilities. This paper focuses on binary classification and multi-class classification of COVID 19 and other diseases such as pneumonia, tuberculosis, etc. Different deep learning methods such as VGG-19, COVID-Net, ResNET+ SVM, Deep CNN, DarkCovidnet, etc., have been used, and their accuracy has been compared using the Chest X-Ray dataset.

Keywords: deep learning, computer vision, radiology, COVID-19, ResNet, VGG-19, deep neural networks

Procedia PDF Downloads 160

Authors: Jaroslav Masek, Juraj Camaj, Eva Nedeliakova

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The paper is focused on the operational model for transport the single wagon consignments on railway network by using two different models of train formation. The paper gives an overview of possibilities of improving the quality of transport services. Paper deals with two models used in problematic of train formatting - time continuously and time discrete. By applying these models in practice, the transport company can guarantee a higher quality of service and expect increasing of transport performance. The models are also applicable into others transport networks. The models supplement a theoretical problem of train formation by new ways of looking to affecting the organization of wagon flows.

Keywords: train formation, wagon flows, marshalling yard, railway technology

Procedia PDF Downloads 438

Authors: Wallia Majeed

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Humidity sensors based on doped ZnO-rGO composites have shown promise due to their sensitivity to humidity changes. Here, it report on the hydrothermal synthesis of ZnO-rGO and doped ZnO-rGO nanocomposites, incorporating cobalt and iron dopants at 2% concentration. X-ray diffraction confirmed successful doping, while scanning electron microscopy revealed the composite's layered structure with embedded ZnO rods. To evaluate their performance, humidity sensors were fabricated by depositing aluminum electrodes on silicon substrates coated with the composites. The Fe-doped ZnO-rGO sensor exhibited rapid response (27 s) and recovery times (24 s) across a wide humidity range (11% to 97% RH), surpassing ZnO-rGO and Co-doped ZnO-rGO variants in sensitivity (2.2k at 100 Hz). These findings highlight Fe-doped ZnO-rGO composites as ideal candidates for humidity sensing applications, offering enhanced performance crucial for environmental monitoring and industrial processes.

Keywords: humidity sensors, nanocomposites, hydrothermal synthesis, sensitivity

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Authors: Kais Manai

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The main part of this work focuses on the study of pion/muon separation at low energy using a nuclear Emulsion Cloud Chamber (ECC) made of lead and nuclear emulsion films. The work consists of two parts: particle reconstruction algorithm and a Neural Network that assigns to each reconstructed particle the probability to be a muon or a pion. The pion/muon separation algorithm has been optimized by using a detailed Monte Carlo simulation of the ECC and tested on real data. The algorithm allows to achieve a 60% muon identification efficiency with a pion misidentification smaller than 3%.

Keywords: nuclear emulsion, particle identification, tracking, neural network

Procedia PDF Downloads 507

Authors: Sabina Hublova, Kristyna Hrabova, Petr Cikrle

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Sustainable development and preservation of existing buildings are becoming increasingly important worldwide. In order to reduce the amount of CO2 emissions in the air and to reduce the amount of waste from building structures, we can predict an increasing demand for maintenance of some existing buildings in the future. The use of modern diagnostic methods, which allow detailed determination of the properties of structures, the identification of critical points, could be the great importance for the better assessment of existing structures. Non-destructive methods could be one of the options. From these methods, ultrasonic appears to be a highly perspective method, thanks to which we are able to identify critical points of an element or a structure. The experiment will focus on the use of electroacoustic methods for defectoscopy in reinforced concrete columns.

Keywords: sustainability, defectoscopy, ultrasonic method, non-destructive methods, electroacoustic methods

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Authors: Marc Bachelet, Abhijit Kumar Chatterjee, José Manuel Avila

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Data quality is a key component of any data-driven organization. Without data quality, organizations cannot effectively make data-driven decisions, which often leads to poor business performance. Therefore, it is important for an organization to ensure that the data they use is of high quality. This is where the concept of data mesh comes in. Data mesh is an organizational and architectural decentralized approach to data management that can help organizations improve the quality of data. The concept of data mesh was first introduced in 2020. Its purpose is to decentralize data ownership, making it easier for domain experts to manage the data. This can help organizations improve data quality by reducing the reliance on centralized data teams and allowing domain experts to take charge of their data. This paper intends to discuss how a set of elements, including data mesh, are tools capable of increasing data quality. One of the key benefits of data mesh is improved metadata management. In a traditional data architecture, metadata management is typically centralized, which can lead to data silos and poor data quality. With data mesh, metadata is managed in a decentralized manner, ensuring accurate and up-to-date metadata, thereby improving data quality. Another benefit of data mesh is the clarification of roles and responsibilities. In a traditional data architecture, data teams are responsible for managing all aspects of data, which can lead to confusion and ambiguity in responsibilities. With data mesh, domain experts are responsible for managing their own data, which can help provide clarity in roles and responsibilities and improve data quality. Additionally, data mesh can also contribute to a new form of organization that is more agile and adaptable. By decentralizing data ownership, organizations can respond more quickly to changes in their business environment, which in turn can help improve overall performance by allowing better insights into business as an effect of better reports and visualization tools. Monitoring and analytics are also important aspects of data quality. With data mesh, monitoring, and analytics are decentralized, allowing domain experts to monitor and analyze their own data. This will help in identifying and addressing data quality problems in quick time, leading to improved data quality. Data culture is another major aspect of data quality. With data mesh, domain experts are encouraged to take ownership of their data, which can help create a data-driven culture within the organization. This can lead to improved data quality and better business outcomes. Finally, the paper explores the contribution of AI in the coming years. AI can help enhance data quality by automating many data-related tasks, like data cleaning and data validation. By integrating AI into data mesh, organizations can further enhance the quality of their data. The concepts mentioned above are illustrated by AEKIDEN experience feedback. AEKIDEN is an international data-driven consultancy that has successfully implemented a data mesh approach. By sharing their experience, AEKIDEN can help other organizations understand the benefits and challenges of implementing data mesh and improving data quality.

Keywords: data culture, data-driven organization, data mesh, data quality for business success

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Authors: D. Augutis, M. Balalaikins, D. Bastyte, R. Ferenca, A. Gintaras, R. Karpuska, G. Svitra, U. Valainis

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Osmoderma species complex (consisting of Osmoderma eremita, O. barnabita, O. lassallei and O. cristinae) is a scarab beetle serving as indicator species in nature conservation. Osmoderma inhabits cavities containing sufficient volume of wood mould usually caused by brown rot in veteran deciduous trees. As the species, having high demands for the habitat quality, they indicate the suitability of the habitat for a number of other specialized saproxylic species. Since typical habitat needed for Osmoderma and other species associated with hollow veteran trees is rapidly declining, the species complex is protected under various legislation, such as Bern Convention, EU Habitats Directive and the Red Lists of many European states. Natura 2000 sites are the main tool for conservation of O. barnabita in Lithuania, currently 17 Natura 2000 sites are designated for the species, where monitoring is implemented once in 3 years according to the approved methodologies. Despite these monitoring efforts in species reports, provided to EU according to the Article 17 of the Habitats Directive, it is defined on the national level, that overall assessment of O. barnabita is inadequate and future prospects are poor. Therefore, research on the distribution and habitat status of O. barnabita was launched on the national level in 2016, which was complemented by preparatory actions of LIFE OSMODERMA project. The research was implemented in the areas equally distributed in the whole area of Lithuania, where O. barnabita was previously not observed, or not observed in the last 10 years. 90 areas, such as Habitats of European importance (9070 Fennoscandian wooded pastures, 9180 Tilio-Acerion forests of slopes, screes, and ravines), Woodland key habitats (B1 broad-leaved forest, K1 single giant tree) and old manor parks, were chosen for the research after review of habitat data from the existing national databases. The first part of field inventory of the habitats was carried out in 2016 and 2017 autumn and winter seasons, when relative abundance of O. barnabita was estimated according to larval faecal pellets in the tree cavities or around the trees. The state of habitats was evaluated according to the density of suitable and potential trees, percentage of not overshadowed trees and amount of undergrowth. The second part of the field inventory was carried out in the summer with pheromone traps baited with (R)-(+)-γ –decalactone. Results of the research show not only occurrence and habitat status of O. barnabita, but also help to clarify O. barnabita habitat requirements in Lithuania, define habitat size, its structure and distribution. Also, it compares habitat needs between the regions in Lithuania and inside and outside Natura 2000 areas designated for the species.

Keywords: habitat status, insect conservation, Osmoderma barnabita, veteran trees

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Authors: R. F. Mesquita Brandão

Abstract:

Act Local, Think Global is the name of a campaign implemented at Superior Institute of Engineering of Porto (ISEP), one of schools of Polytechnic of Porto, with the main objective of increase the sustainability of the campus. ISEP has a campus with 52.000 m2 and more than 7.000 students. The campaign started in 2019 and the results are very clear. In 2019 only 16% of the waste created in the campus was correctly separate for recycling and now almost 50% of waste goes to the correct waste container. Actions to reduce the energy consumption were implemented with significantly results. One of the major problems in the campus are the water leaks. To solve this problem was implemented a methodology for water monitoring during the night, a period of time where consumptions are normally low. If water consumption in the period is higher than a determinate value it may mean a water leak and an alarm is created to the maintenance teams. In terms of energy savings, some measurements were implemented to create savings in energy consumption and in equivalent CO₂ produced. In order to reduce the use of plastics in the campus, was implemented the prohibition of selling 33 cl plastic water bottles and in collaboration with the students association all meals served in the restaurants changed the water plastic bottle for a glass that can be refilled with water in the water dispensers. This measures created a reduction of use of more than 75.000 plastic bottles per year. In parallel was implemented the ISEP water glass bottle to be used in all scientific meetings and events. Has a way of involving all community in sustainability issues was developed and implemented a vertical garden in aquaponic system. In 2019, the first vertical garden without soil was installed inside a large campus building. The system occupies the entire exterior façade (3 floors) of the entrance to ISEP's G building. On each of these floors there is a planter with 42 positions available for plants. Lettuces, strawberries, peppers are examples of some vegetable produced that can be collected by the entire community. Associated to the vertical garden was developed a monitoring system were some parameters of the system are monitored. This project is under development because it will work in a stand-alone energy feeding, with the use of photovoltaic panels for production of energy necessities. All the system was, and still is, developed by students and teachers and is used in class projects of some ISEP courses. These and others measures implemented in the campus, will be more developed in the full paper, as well as all the results obtained, allowed ISEP to be the first Portuguese high school to obtain the certification “Coração Verde” (Green Heart), awarded by LIPOR, a Portuguese company with the mission of transform waste into new resources through the implementation of innovative and circular practices, generating and sharing value.

Keywords: aquaponics, energy efficiency, recycling, sustainability, waste separation

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