Search results for: network monitoring

Authors: Marcos Silva Tavares, Jamile Raquel Regazzo, Edson José de Souza Sardinha, Murilo Mesquita Baesso

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Common beans are a widely cultivated and consumed legume globally, serving as a staple food for humans, especially in developing countries, due to their nutritional characteristics. Nitrogen (N) is the most limiting nutrient for productivity, and foliar analysis is crucial to ensure balanced nitrogen fertilization. Excessive N applications can cause, either isolated or cumulatively, soil and water contamination, plant toxicity, and increase their susceptibility to diseases and pests. However, the quantification of N using conventional methods is time-consuming and costly, demanding new technologies to optimize the adequate supply of N to plants. Thus, it becomes necessary to establish constant monitoring of the foliar content of this macronutrient in plants, mainly at the V4 stage, aiming at precision management of nitrogen fertilization. In this work, the objective was to evaluate the performance of a deep learning model, Resnet-50, in the classification of foliar nitrogen in common beans using RGB images. The BRS Estilo cultivar was sown in a greenhouse in a completely randomized design with four nitrogen doses (T1 = 0 kg N ha-1, T2 = 25 kg N ha-1, T3 = 75 kg N ha-1, and T4 = 100 kg N ha-1) and 12 replications. Pots with 5L capacity were used with a substrate composed of 43% soil (Neossolo Quartzarênico), 28.5% crushed sugarcane bagasse, and 28.5% cured bovine manure. The water supply of the plants was done with 5mm of water per day. The application of urea (45% N) and the acquisition of images occurred 14 and 32 days after sowing, respectively. A code developed in Matlab© R2022b was used to cut the original images into smaller blocks, originating an image bank composed of 4 folders representing the four classes and labeled as T1, T2, T3, and T4, each containing 500 images of 224x224 pixels obtained from plants cultivated under different N doses. The Matlab© R2022b software was used for the implementation and performance analysis of the model. The evaluation of the efficiency was done by a set of metrics, including accuracy (AC), F1-score (F1), specificity (SP), area under the curve (AUC), and precision (P). The ResNet-50 showed high performance in the classification of foliar N levels in common beans, with AC values of 85.6%. The F1 for classes T1, T2, T3, and T4 was 76, 72, 74, and 77%, respectively. This study revealed that the use of RGB images combined with deep learning can be a promising alternative to slow laboratory analyses, capable of optimizing the estimation of foliar N. This can allow rapid intervention by the producer to achieve higher productivity and less fertilizer waste. Future approaches are encouraged to develop mobile devices capable of handling images using deep learning for the classification of the nutritional status of plants in situ.

Keywords: convolutional neural network, residual network 50, nutritional status, artificial intelligence

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Authors: Jia-Jane Shuai

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Purpose of this research aims to improve understanding of the nature of online religion by examining the religious social websites. What motivates individual users to use the online religious social websites, and which factors affect those motivations. We survey various online religious social websites provided by different religions, especially the Taiwanese folk religion. Based on the theory of the Content Analysis and Social Network Analysis, religious social websites and religious web activities are examined. This research examined the folk religion websites’ presentation and contents that promote the religious use of the Internet in Taiwan. The difference among different religions and religious websites also be compared. First, this study used keywords to examine what types of messages gained the most clicks of “Like”, “Share” and comments on Facebook. Dividing the messages into four media types, namely, text, link, video, and photo, reveal which category receive more likes and comments than the others. Meanwhile, this study analyzed the five dialogic principles of religious websites accessed from mobile phones and also assessed their mobile readiness. Using the five principles of dialogic theory as a basis, do a general survey on the websites with elements of online religion. Second, the project analyzed the characteristics of Taiwanese participants for online religious activities. Grounded by social network analysis and text mining, this study comparatively explores the network structure, interaction pattern, and geographic distribution of users involved in communication networks of the folk religion in social websites and mobile sites. We studied the linkage preference of different religious groups. The difference among different religions and religious websites also be compared. We examined the reasons for the success of these websites, as well as reasons why young users accept new religious media. The outcome of the research will be useful for online religious service providers and non-profit organizations to manage social websites and internet marketing.

Keywords: content analysis, online religion, social network analysis, social websites

Procedia PDF Downloads 167

Authors: Mohammad Derakhshani, Saeed Reza Allahkarama, Michael Isakhani-Zakaria, Masoud Samadian, Hojjat Sharifi Rasaey

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In order to investigate the effects of stray current from DC traction systems (subway) on cathodically protected gas pipelines, the subway and the gas network maps in the city of Tehran were superimposed and a comprehensive map was prepared. 213 intersections and about 100150 meters of parallel sections of gas pipelines were found with respect to the railway right of way which was specified for field measurements. The potential measurements data were logged for one hour in each test point. 24-hour potential monitoring was carried out in selected test points as well. Results showed that dynamic stray current from subway on pipeline potential appears as fluctuations in its static potential that is visible in the diagrams during night periods. These fluctuations can cause the pipeline potential to exit the safe zone and lead to corrosion or overprotection. In this study, a maximum potential shift of 100 mv in the pipe-to-soil potential was considered as a criterion for dynamic stray current effective presence. Results showed that a potential fluctuation range between 100 mV to 3 V exists in measured points on pipelines which exceeds the proposed criterion and needs to be investigated. Corrosion rates influenced by stray currents were calculated using coupons. Results showed that coupon linked to the pipeline in one of the locations at region 1 of the city of Tehran has a corrosion rate of 4.2 mpy (with cathodic protection and under influence of stray currents) which is about 1.5 times more than free corrosion rate of 2.6 mpy.

Keywords: stray current, DC traction, subway, buried Pipelines, cathodic protection list

Procedia PDF Downloads 822

Authors: Azadeh Mohajer Milani

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Urbanization constitutes an irreversible global trend, presenting myriad challenges such as heightened energy consumption, pollution, congestion, and the depletion of natural resources. Today's urban landscapes have emerged as focal points for economic, social, and environmental challenges, underscoring the pressing need for sustainable development. This article delves into the realm of sustainable urban development, concentrating on the pivotal role played by integrated green spaces as an optimal solution to address environmental concerns within cities. The study utilizes Tehran as a case study. Our findings underscore the imperative of preserving and expanding green spaces in urban areas, coupled with the establishment of well-designed ecological networks, to enhance environmental quality and elevate the sustainability of cities. Notably, Tehran's urban green spaces exhibit a disjointed design, lacking a cohesive network to connect various patches and corridors, resulting in significant environmental impacts. The results emphasize the necessity of a balanced and proportional distribution of urban green spaces and the creation of a cohesive patch-corridor-matrix network tailored to the ecological and social needs of residents. This approach is crucial for fostering a more sustainable and livable urban environment for all species, with a specific focus on humans.

Keywords: ecology, sustainable urban development, sustainable landscape, urban green space network

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Authors: Huang Yong, Chen Qianting, Zhao Shurong

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In order to identify the role and effect of Characteristic Towns as an important means to promote urban-rural integration, this paper uses Flow Theory and complex network analysis methods to jointly construct the identification path of urban-rural integration capabilities of Characteristic Towns. Take the National Characteristic Towns of Yunnan Province as the empirical objects to identify their role laws. The study found that in the implementation of the National Characteristic Town Project in Yunnan Province, (1) the population is more susceptible to the impact of the Characteristic Town Project than the technical elements, but the stability is poor; (2) The flow capacity of urban and rural technical elements is weak, and the quality of the enterprise cooperation network in general; (3) Compared with the batch of Characteristic Towns in 2016, its ability to promote urban-rural integration is higher in 2017; (4) The role of the Characteristic Town Project on urban-rural integration focuses on the improvement of the number of urban and rural flow elements. This paper analyzes the mode of the role of Characteristic Towns on urban-rural integration from the perspective of ‘flow,’ establishes a research paradigm for evaluating the role of Characteristic Towns in urban-rural integration capabilities, and builds a path for the application of Characteristic Towns to support the realization of urban-rural integration goals.

Keywords: characteristic town, urban-rural integration, flow theory, complex network analysis

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Authors: Ana L. Martins, Rita S. Saraiva, João C. Ferreira

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Much effort has been placed on the assessment of perceived service quality. Several models can be found in literature, but these are mainly focused on business-to-consumer (B2C) relationships. Literature on how to assess perceived quality in business-to-business (B2B) contexts is scarce both conceptually and in terms of its application. This research aims at filling this gap in literature by applying INDSERV to a case study situation. Under this scope, this research aims at analyzing the adequacy of the proposed assessment tool to other context besides the one where it was developed and by doing so analyzing the perceive quality of the advertisement service provided by a specific television network to its B2B customers. The INDSERV scale was adopted and applied to a sample of 33 clients, via questionnaires adapted to interviews. Data was collected in person or phone. Both quantitative and qualitative data collection was performed. Qualitative data analysis followed content analysis protocol. Quantitative analysis used hypotheses testing. Findings allowed to conclude that the perceived quality of the television service provided by television network is very positive, being the Soft Process Quality the parameter that reveals the highest perceived quality of the service as opposed to Potential Quality. To this end, some comments and suggestions were made by the clients regarding each one of these service quality parameters. Based on the hypotheses testing, it was noticed that only advertisement clients that maintain a connection to the television network from 5 to 10 years do show a significant different perception of the TV advertisement service provided by the company in what the Hard Process Quality parameter is concerned. Through the collected data content analysis, it was possible to obtain the percentage of clients which share the same opinions and suggestions for improvement. Finally, based on one of the four service quality parameter in a B2B context, managerial suggestions were developed aiming at improving the television network advertisement perceived quality service.

Keywords: B2B, case study, INDSERV, perceived service quality

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Authors: K. Parandhama Gowd

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The aim of this research paper is to conceptualize, discuss, analyze and propose alternate design methodologies for futuristic Black Box for flight safety. The proposal also includes global networking concepts for real time surveillance and monitoring of flight performance parameters including GPS parameters. It is expected that this proposal will serve as a failsafe real time diagnostic tool for accident investigation and location of debris in real time. In this paper, an attempt is made to improve the existing methods of flight data recording techniques and improve upon design considerations for futuristic FDR to overcome the trauma of not able to locate the block box. Since modern day communications and information technologies with large bandwidth are available coupled with faster computer processing techniques, the attempt made in this paper to develop a failsafe recording technique is feasible. Further data fusion/data warehousing technologies are available for exploitation.

Keywords: flight data recorder (FDR), black box, diagnostic tool, global networking, cockpit voice and data recorder (CVDR), air traffic control (ATC), air traffic, telemetry, tracking and control centers ATTTCC)

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Authors: Isabelle Barrett

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Natural watercourses provide a range of vital ecosystem services, notably freshwater provision. They also offer highly heterogeneous habitat which supports an extreme diversity of aquatic life. Exploitation of rivers, changing land use and flood prevention measures have led to habitat degradation and subsequent biodiversity loss; indeed, freshwater species currently face a disproportionate rate of extinction compared to their terrestrial and marine counterparts. Large woody debris (LWD) encompasses the trees, large branches and logs which fall into watercourses, and is responsible for important habitat characteristics. Historically, natural LWD has been removed from streams under the assumption that it is not aesthetically pleasing and is thus ecologically unfavourable, despite extensive evidence contradicting this. Restoration efforts aim to replace lost LWD in order to reinstate habitat heterogeneity. This paper aims to assess the current state of such restoration schemes for improving fluvial ecological health in the UK. A detailed review of the scientific literature was conducted alongside a meta-analysis of 25 UK-based projects involving LWD restoration. Projects were chosen for which sufficient information was attainable for analysis, covering a broad range of budgets and scales. The most effective strategies for river restoration encompass ecological success, stakeholder engagement and scientific advancement, however few projects surveyed showed sensitivity to all three; for example, only 32% of projects stated biological aims. Focus tended to be on stakeholder engagement and public approval, since this is often a key funding driver. Consequently, there is a tendency to focus on the aesthetic outcomes of a project, however physical habitat restoration does not necessarily lead to direct biodiversity increases. This highlights the significance of rivers as highly heterogeneous environments with multiple interlinked processes, and emphasises a need for a stronger scientific presence in project planning. Poor scientific rigour means monitoring is often lacking, with varying, if any, definitions of success which are rarely pre-determined. A tendency to overlook negative or neutral results was apparent, with unjustified focus often put on qualitative results. The temporal scale of monitoring is typically inadequate to facilitate scientific conclusions, with only 20% of projects surveyed reporting any pre-restoration monitoring. Furthermore, monitoring is often limited to a few variables, with biotic monitoring often fish-focussed. Due to their longer life cycles and dispersal capability, fish are usually poor indicators of environmental change, making it difficult to attribute any changes in ecological health to restoration efforts. Although the potential impact of LWD restoration may be positive, this method of restoration could simply be making short-term, small-scale improvements; without addressing the underlying symptoms of degradation, for example water quality, the issue cannot be fully resolved. Promotion of standardised monitoring for LWD projects could help establish a deeper understanding of the ecology surrounding the practice, supporting movement towards adaptive management in which scientific evidence feeds back to practitioners, enabling the design of more efficient projects with greater ecological success. By highlighting LWD, this study hopes to address the difficulties faced within river management, and emphasise the need for a more holistic international and inter-institutional approach to tackling problems associated with degradation.

Keywords: biological monitoring, ecological health, large woody debris, river management, river restoration

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Authors: Angeliki Peponi, Paulo Morgado, Jorge Trindade

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Artificial Neural Networks (ANNs) and Geographic Information Systems (GIS) are applied as a robust tool for modeling and forecasting the erosion changes in Costa Caparica, Lisbon, Portugal, for 2021. ANNs present noteworthy advantages compared with other methods used for prediction and decision making in urban coastal areas. Multilayer perceptron type of ANNs was used. Sensitivity analysis was conducted on natural and social forces and dynamic relations in the dune-beach system of the study area. Variations in network’s parameters were performed in order to select the optimum topology of the network. The developed methodology appears fitted to reality; however further steps would make it better suited.

Keywords: artificial neural networks, backpropagation, coastal urban zones, erosion prediction

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Authors: H. Aoudia, Y. Touati, E. H. Teguig, A. Ali Cherif

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Conceiving and developing routing protocols for wireless sensor networks requires considerations on constraints such as network lifetime and energy consumption. In this paper, we propose a hybrid hierarchical routing protocol named HHRP combining both clustering mechanism and multipath optimization taking into account residual energy and RSSI measures. HHRP consists of classifying dynamically nodes into clusters where coordinators nodes with extra privileges are able to manipulate messages, aggregate data and ensure transmission between nodes according to TDMA and CDMA schedules. The reconfiguration of the network is carried out dynamically based on a threshold value which is associated with the number of nodes belonging to the smallest cluster. To show the effectiveness of the proposed approach HHRP, a comparative study with LEACH protocol is illustrated in simulations.

Keywords: routing protocol, optimization, clustering, WSN

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Authors: Akshit Singhal, Xuan Wang, Zhijun Wang, Hao Che, Hong Jiang

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Multiple Path Transmission Control Protocols (MPTCPs) allow flows to explore path diversity to improve the throughput, reliability and network resource utilization. However, the existing solutions may discourage users to adopt the solutions in the face of multipath scenario where different paths are charged based on different pricing structures, e.g., WiFi vs cellular connections, widely available for mobile phones. In this paper, we propose a Hybrid MPTCP (H-MPTCP) with a built-in mechanism to incentivize users to use multiple paths with different pricing structures. In the meantime, H-MPTCP preserves the nice properties enjoyed by the state-of-the-art MPTCP solutions. Extensive real Linux implementation results verify that H-MPTCP can indeed achieve the design objectives.

Keywords: network, TCP, WiFi, cellular, congestion control

Procedia PDF Downloads 718

Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao

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Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.

Keywords: artificial intelligence and office, NLP, deep learning, text classification

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Authors: Jing Zhao, Dayong Liu, Shihao Wang, Xinghua Zhu, Delong Li

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Uhumanned Underwater Vehicles generally operate in the deep sea, which has its own unique working conditions. Lithium-ion power batteries should have the necessary stability and endurance for use as an underwater vehicle’s power source. Therefore, it is essential to accurately forecast how long lithium-ion batteries will last in order to maintain the system’s reliability and safety. In order to model and forecast lithium battery Remaining Useful Life (RUL), this research suggests a model based on Complete Ensemble Empirical Mode Decomposition with Adaptive noise-Temporal Convolutional Net (CEEMDAN-TCN). In this study, two datasets, NASA and CALCE, which have a specific gap in capacity data fluctuation, are used to verify the model and examine the experimental results in order to demonstrate the generalizability of the concept. The experiments demonstrate the network structure’s strong universality and ability to achieve good fitting outcomes on the test set for various battery dataset types. The evaluation metrics reveal that the CEEMDAN-TCN prediction performance of TCN is 25% to 35% better than that of a single neural network, proving that feature expansion and modal decomposition can both enhance the model’s generalizability and be extremely useful in industrial settings.

Keywords: lithium-ion battery, remaining useful life, complete EEMD with adaptive noise, temporal convolutional net

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Authors: Meriem Mossaddek, El Mehdi Laadissi, El Mehdi Loualid, Chouaib Ennawaoui, Sohaib Bouzaid, Abdelowahed Hajjaji

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An electrochemical system is a subset of mechatronic systems that includes a wide variety of batteries and nickel-cadmium, lead-acid batteries, and lithium-ion. Those structures have several non-linear behaviors and uncertainties in their running range. This paper studies an effective technique for modeling Lithium-Ion (Li-Ion) batteries using a Nonlinear Auto-Regressive model with exogenous input (NARX). The Artificial Neural Network (ANN) is trained to employ the data collected from the battery testing process. The proposed model is implemented on a Li-Ion battery cell. Simulation of this model in MATLAB shows good accuracy of the proposed model.

Keywords: lithium-ion battery, neural network, energy storage, battery model, nonlinear models

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Authors: Wei Li, Tuo Yang

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In order to satisfy the real-time and accurate requirements of cigarette smoke detection in complex scenes, a cigarette smoke detection technology based on the combination of deep learning and color features was proposed. Firstly, based on the color features of cigarette smoke, the suspicious cigarette smoke area in the image is extracted. Secondly, combined with the efficiency of cigarette smoke detection and the problem of network overfitting, a network model for cigarette smoke detection was designed according to YOLOV3 algorithm to reduce the false detection rate. The experimental results show that the method is feasible and effective, and the accuracy of cigarette smoke detection is up to 99.13%, which satisfies the requirements of real-time cigarette smoke detection in complex scenes.

Keywords: deep learning, computer vision, cigarette smoke detection, YOLOV3, color feature extraction

Procedia PDF Downloads 87

Authors: Jie Zhang, Chaomin Zhang

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Based on the network theory, this paper selects three Emerging-Market Multinationals Enterprises (EMNEs) as the research object and takes the typical overseas market opportunities constructed by them as the analysis unit to research the internal mechanism of overseas market opportunity construction of the EMNEs. The results show that: (1) EMNEs overseas market opportunity construction is a complex process, through the continuous interaction between enterprises and entities in the internal and external networks to achieve opportunity prototype, opportunity creation, and opportunity optimization in overseas markets. (2) Governments, foreign institutions and industry associations in the institutional network and competitors, partners, and customers in the commercial networks are the important entities in the construction of overseas market opportunities. Through the interaction of entity perception, relationship construction, and utilization, enterprises can obtain the necessary information, resources, and political asylum in the process of opportunity construction. (3) Organizations, project teams, and organizational sub-units within the enterprise are important internal entities for the construction of overseas market opportunities. Through the connection between different entities, they can achieve the circulation of resources within the organization and promote the opportunity construction of overseas markets. The research conclusions expand the relevant research on international opportunities and have inspiring and guiding significance for the expansion of EMNEs overseas markets.

Keywords: international (overseas) opportunities, opportunity construction, network entities, interaction, resource circulation

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Authors: Yibeltal Chanie Manie, Zebider Asire Munyelet

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5G and 6G technology offers enhanced quality of service with high data transmission rates, which necessitates the implementation of the Internet of Things (IoT) in 5G/6G architecture. In this paper, we proposed the integration of free space optical communication (FSO) with fiber sensor networks for IoT applications. Recently, free-space optical communications (FSO) are gaining popularity as an effective alternative technology to the limited availability of radio frequency (RF) spectrum. FSO is gaining popularity due to flexibility, high achievable optical bandwidth, and low power consumption in several applications of communications, such as disaster recovery, last-mile connectivity, drones, surveillance, backhaul, and satellite communications. Hence, high-speed FSO is an optimal choice for wireless networks to satisfy the full potential of 5G/6G technology, offering 100 Gbit/s or more speed in IoT applications. Moreover, machine learning must be integrated into the design, planning, and optimization of future optical wireless communication networks in order to actualize this vision of intelligent processing and operation. In addition, fiber sensors are important to achieve real-time, accurate, and smart monitoring in IoT applications. Moreover, we proposed deep learning techniques to estimate the strain changes and peak wavelength of multiple Fiber Bragg grating (FBG) sensors using only the spectrum of FBGs obtained from the real experiment.

Keywords: optical sensor, artificial Intelligence, Internet of Things, free-space optics

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Authors: Muhammad Akbar Hussain, Muhammad Mehdi Hussain, Waqar Haider

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There are tens of thousands of undetected and uncatalogued pieces of space debris in the Low Earth Orbit (LEO). They are not only difficult to be detected and tracked, their sheer number puts active satellites and humans in orbit around Earth into danger. With the entry of more governments and private companies into harnessing the Earth’s orbit for communication, research and military purposes, there is an ever-increasing need for not only the detection and cataloguing of these pieces of space debris, it is time to take measures to take them out and clean up the space around Earth. Current optical and radar-based Space Situational Awareness initiatives are useful mostly in detecting and cataloguing larger pieces of debris mainly for avoidance measures. Smaller than 10 cm pieces are in a relatively dark zone, yet these are deadly and capable of destroying satellites and human missions. A network of mobile observatories, connected to each other in real time and working in unison as a single instrument, may be able to detect small pieces of debris and achieve effective triangulation to help create a comprehensive database of their trajectories and parameters to the highest level of precision. This data may enable ground-based laser systems to help deorbit individual debris. Such a network of observatories can join current efforts in detection and removal of space debris in Earth’s orbit.

Keywords: space debris, low earth orbit, mobile observatories, triangulation, seamless operability

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Authors: Colin Smith, Linsey S Passarella

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Due to the proliferation and decentralized nature of academic publication, no widely accepted scheme exists for organizing papers by their scientific field of study (FoS) to the author’s best knowledge. While many academic journals require author provided keywords for papers, these keywords range wildly in scope and are not consistent across papers, journals, or field domains, necessitating alternative approaches to paper classification. Past attempts to perform field-of-study (FoS) classification on scientific texts have largely used a-hierarchical FoS schemas or ignored the schema’s inherently hierarchical structure, e.g. by compressing the structure into a single layer for multi-label classification. In this paper, we introduce an application of a Layered Neural Network (LNN) to the problem of performing supervised hierarchical classification of scientific fields of study (FoS) on research papers. In this approach, paper embeddings from a pretrained language model are fed into a top-down LNN. Beginning with a single neural network (NN) for the highest layer of the class hierarchy, each node uses a separate local NN to classify the subsequent subfield child node(s) for an input embedding of concatenated paper titles and abstracts. We compare our LNN-FOS method to other recent machine learning methods using the Microsoft Academic Graph (MAG) FoS hierarchy and find that the LNN-FOS offers increased classification accuracy at each FoS hierarchical level.

Keywords: hierarchical classification, layer neural network, scientific field of study, scientific taxonomy

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Authors: Mayada Mazher, Ahmed Moustafa, Ahmed Abdellatif

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Background: Autophagy is a eukaryotic, highly conserved catabolic process implicated in many pathophysiologies such as wound healing. Autophagy-associated genes serve as a scaffolding platform for signal transduction of macrophage polarization during the inflammatory phase of wound healing and tissue repair process. In the current study, we report a model for the interplay between autophagy-associated genes and macrophages polarization associated genes. Methods: In silico analysis was performed on 249 autophagy-related genes retrieved from the public autophagy database and gene expression data retrieved from Gene Expression Omnibus (GEO); GSE81922 and GSE69607 microarray data macrophages polarization 199 DEGS. An integrated protein-protein interaction network was constructed for autophagy and macrophage gene sets. The gene sets were then used for GO terms pathway enrichment analysis. Common transcription factors for autophagy and macrophages' polarization were identified. Finally, microRNAs enriched in both autophagy and macrophages were predicated. Results: In silico prediction of common transcription factors in DEGs macrophages and autophagy gene sets revealed a new role for the transcription factors, HOMEZ, GABPA, ELK1 and REL, that commonly regulate macrophages associated genes: IL6,IL1M, IL1B, NOS1, SOC3 and autophagy-related genes: Atg12, Rictor, Rb1cc1, Gaparab1, Atg16l1. Conclusions: Autophagy and macrophages' polarization are interdependent cellular processes, and both autophagy-related proteins and macrophages' polarization related proteins coordinate in tissue remodelling via transcription factors and microRNAs regulatory network. The current work highlights a potential new role for transcription factors HOMEZ, GABPA, ELK1 and REL in wound healing.

Keywords: autophagy related proteins, integrated network analysis, macrophages polarization M1 and M2, tissue remodelling

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Authors: K. Hołda, D. Śliwa, K. Daniec, A. Nawrat

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This article was created as part of the developed master's thesis. It attempts to present a newly developed device, which will support the work of specialists dealing with broadly understood cybersecurity terms. The device is contrived to automate security tests. In addition, it simulates potential cyberattacks in the most realistic way possible, without causing permanent damage to the network, in order to maximize the quality of the subsequent corrections to the tested network systems. The proposed solution is a fully operational prototype created from commonly available electronic components and a single board computer. The focus of the following article is not only put on the hardware part of the device but also on the theoretical and applicatory way in which implemented cybersecurity tests operate and examples of their results.

Keywords: Raspberry Pi, ethernet, automated cybersecurity tests, ARP, DNS, backdoor, TCP, password sniffing

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Authors: Aulia Dwi Nastiti

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A number of studies and reports on terrorism have continuously addressed the role of internet and online activism to support terrorist and extremist groups. In particular, they stress on social media’s usage that generally serves for the terrorist’s propaganda as well as justification of their causes. While those analyses are important to understand how social media is a vital tool for global network terrorism, they are inadequate to explain the online communication patterns that enable terrorism acts. Beyond apparent online narratives, there is a deep cyber sphere where the very vein of terrorism movement lies. That is a hidden space in the internet called ‘darknet’. Recent investigations, particularly in Middle Eastern context, have shed some lights that this invisible space in the internet is fundamental to maintain the terrorist activities. Despite that, limited number of research examines darknet within the issue of terrorist movements in Indonesian context—where the country is considered quite a hotbed for extremist groups. Therefore, this paper attempts to provide an insight of darknet operation in Indonesian cases. By exploring how the darknet is used by the Indonesian-based extremist groups, this paper maps out communication patterns of terrorist groups on the internet which appear as an intermingled network. It shows the usage of internet is differentiated in different level of anonymity for distinctive purposes. This paper further argues that the emerging terrorist communication network calls for a more comprehensive counterterrorism strategy on the Internet.

Keywords: communication pattern, counterterrorism, darknet, extremist groups, terrorism

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Authors: Deborah Weidner, Melissa Morgera

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The model of care delivery in the Behavioral Health Network (BHN) is integrated across all five regions of Hartford Healthcare and thus spans the entirety of the state of Connecticut, with care provided in seven inpatient settings and over 30 ambulatory outpatient locations. While safety has been a core priority of the BHN in alignment with High Reliability practices, safety initiatives have historically been facilitated locally in each region or within each entity, with interventions implemented locally as opposed to throughout the network. To address this, the BHN introduced a network wide Safety Huddle during 2022. Launched in January, the BHN Safety Huddle brought together internal stakeholders, including medical and administrative leaders, along with executive institute leadership, quality, and risk management. By bringing leaders together and introducing a network-wide safety huddle into the way we work, the benefit has been an increase in awareness of safety events occurring in behavioral health areas as well as increased systemization of countermeasures to prevent future events. One significant discussion topic presented in huddles has pertained to environmental design and patient access to potentially dangerous items, addressing some of the most relevant factors resulting in harm to patients in inpatient and emergency settings for behavioral health patients. The safety huddle has improved visibility of potential environmental safety risks through the generation of over 15 safety alerts cascaded throughout the BHN and also spurred a rapid improvement project focused on standardization of patient belonging searches to reduce patient access to potentially dangerous items on inpatient units. Safety events pertaining to potentially dangerous items decreased by 31% as a result of standardized interventions implemented across the network and as a result of increased awareness. A second positive outcome originating from the BHN Safety Huddle was implementation of a recommendation to increase the emergency Narcan®(naloxone) supply on hand in ambulatory settings of the BHN after incidents involving accidental overdose resulted in higher doses of naloxone administration. By increasing the emergency supply of naloxone on hand in all ambulatory and residential settings, colleagues are better prepared to respond in an emergency situation should a patient experience an overdose while on site. Lastly, discussions in safety huddle spurred a new initiative within the BHN to improve responsiveness to assaultive incidents through a consultation service. This consult service, aligned with one of the network’s improvement priorities to reduce harm events related to assaultive incidents, was borne out of discussion in huddle in which it was identified that additional interventions may be needed in providing clinical care to patients who are experiencing multiple and/ or frequent safety events.

Keywords: quality, safety, behavioral health, risk management

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Authors: Rajeshwar S. Matche, Subhash V. Pawde, Suraj P, Sachin R. Chaudhari

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Time Temperature Indicator’s (TTI) are trending approach in a food packaging that will be insightful to have safe and hygienic food products. Currently, available TTI in the market are mostly a product specific and sometime even difficult to handle especially in supply chain as these are pre-activated and require specific storage conditions. In the present study, research focus is on the development of a cost-effective lactic acid based TTI that can work over a wide range of temperature and can be activated at time of packaging or on demand. The correlation between activation energies of colour change of the developed indicator and packed pasteurized milk spoilage with respect to time and temperature was established. Developed lactic acid based TTI strips have range of activation energy from 10.13 to 24.20 KJ/mol. We found that the developed TTI strip’s with activation energy 12.42, and 14.41KJ/mol can be correlated with spoilage activation energy of packed pasteurized milk which was 25.71 KJ/mol with factor of 2 at storage temperature 4°C. The implementation of these TTI on packed pasteurized milk allow us see visual colour change during the storage and can be fruitful to monitoring quality of the milk and understand its freshness especially in a cold supply chain, viz distributor and road vendor etc.

Keywords: pasteurised packed milk, time temperature indicator, spoilage, freshness

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Authors: H. El-Kamchouchi, Heba Gaber, Fatma Ahmed, Dalia H. El-Kamchouchi

Abstract:

A digital signature is an electronic signature form used by an original signer to sign a specific document. When the original signer is not in his office or when he/she travels outside, he/she delegates his signing capability to a proxy signer and then the proxy signer generates a signing message on behalf of the original signer. The two parties must be able to authenticate one another and agree on a secret encryption key, in order to communicate securely over an unreliable public network. Authenticated key agreement protocols have an important role in building a secure communications network between the two parties. In this paper, we present a secure proxy signature scheme over an efficient and secure authenticated key agreement protocol based on factoring and discrete logarithm problem.

Keywords: discrete logarithm, factoring, proxy signature, key agreement

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Authors: Salim Ouchtati, Jean Sequeira, Mouldi Bedda

Abstract:

In this work we present an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods: the parameters of distribution, the moments centered of the different projections and the Barr features. It should be noted that these methods are applied on segments gotten after the division of the binary image of the word in six segments. The classification is achieved by a multi layers perceptron. Detailed experiments are carried and satisfactory recognition results are reported.

Keywords: handwritten word recognition, neural networks, image processing, pattern recognition, features extraction

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Authors: S. K. Gupta, Richa Bansal

Abstract:

In a deregulated power system structure, power producers, and customers share a common transmission network for wheeling power from the point of generation to the point of consumption. All parties in this open access environment may try to purchase the energy from the cheaper source for greater profit margins, which may lead to overloading and congestion of certain corridors of the transmission network. This may result in violation of line flow, voltage and stability limits and thereby undermine the system security. Utilities therefore need to determine adequately their Available Transfer Capability (ATC) to ensure that system reliability is maintained while serving a wide range of bilateral and multilateral transactions. This paper presents power transfer distribution factor based on AC load flow for the determination and enhancement of ATC. The study has been carried out for IEEE 24 bus Reliability Test System.

Keywords: available transfer capability, FACTS devices, power transfer distribution factors, electric

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Authors: Muhammad Ali

Abstract:

Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Tayyab Hassan, Talha Rehman, Qasim Abdul Aziz, Ahmad Salman

Abstract:

The constant monitoring of blood glucose level is necessary for maintaining health of patients and to alert medical specialists to take preemptive measures before the onset of any complication as a result of diabetes. The current clinical monitoring of blood glucose uses invasive methods repeatedly which are uncomfortable and may result in infections in diabetic patients. Several attempts have been made to develop non-invasive techniques for blood glucose measurement. In this regard, the existing methods are not reliable and are less accurate. Other approaches claiming high accuracy have not been tested on extended dataset, and thus, results are not statistically significant. It is a well-known fact that acetone concentration in breath has a direct relation with blood glucose level. In this paper, we have developed the first of its kind, reliable and high accuracy breath analyzer for non-invasive blood glucose measurement. The acetone concentration in breath was measured using MQ 138 sensor in the samples collected from local hospitals in Pakistan involving one hundred patients. The blood glucose levels of these patients are determined using conventional invasive clinical method. We propose a linear regression classifier that is trained to map breath acetone level to the collected blood glucose level achieving high accuracy.

Keywords: blood glucose level, breath acetone concentration, diabetes, linear regression

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Authors: Muhamad Aris Burhanudin, Angga Firmansyas, Bagus Jaya Santosa

Abstract:

Earthquakes are natural hazard that can trigger the most dangerous hazard, tsunami. 26 December 2004, a giant earthquake occurred in north-west Andalas Island. It made giant tsunami which crushed Sumatra, Bangladesh, India, Sri Lanka, Malaysia and Singapore. More than twenty thousand people dead. The occurrence of earthquake and tsunami can not be avoided. But this hazard can be mitigated by earthquake forecasting. Early preparation is the key factor to reduce its damages and consequences. We aim to investigate quantitatively on pattern of earthquake. Then, we can know the trend. We study about earthquake which has happened in Andalas island, Indonesia one last decade. Andalas is island which has high seismicity, more than a thousand event occur in a year. It is because Andalas island is in tectonic subduction zone of Hindia sea plate and Eurasia plate. A tsunami forecasting is needed to mitigation action. Thus, a Tsunami Forecasting Method is presented in this work. Neutral Network has used widely in many research to estimate earthquake and it is convinced that by using Backpropagation Method, earthquake can be predicted. At first, ANN is trained to predict Tsunami 26 December 2004 by using earthquake data before it. Then after we get trained ANN, we apply to predict the next earthquake. Not all earthquake will trigger Tsunami, there are some characteristics of earthquake that can cause Tsunami. Wrong decision can cause other problem in the society. Then, we need a method to reduce possibility of wrong decision. Fuzzy TOPSIS is a statistical method that is widely used to be decision seconder referring to given parameters. Fuzzy TOPSIS method can make the best decision whether it cause Tsunami or not. This work combines earthquake prediction using neural network method and using Fuzzy TOPSIS to determine the decision that the earthquake triggers Tsunami wave or not. Neural Network model is capable to capture non-linear relationship and Fuzzy TOPSIS is capable to determine the best decision better than other statistical method in tsunami prediction.

Keywords: earthquake, fuzzy TOPSIS, neural network, tsunami

Procedia PDF Downloads 496