Search results for: mobile networks

Authors: H. Hammoudi, S. Bendenia, I. Batonneau-Gener, A. Khelifa

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Applying growing zeolites is due to their intrinsic physicochemical properties: a porous structure, regular, generating a large free volume, a high specific surface area, acidic properties of interest to the origin of their activity, selectivity energy and dimensional, leading to a screening phenomenon, hence the name of molecular sieves is generally attributed to them. Most of the special properties of zeolites have been valued as direct applications such as ion exchange, adsorption, separation and catalysis. Due to their crystalline structure stable, their large pore volume and their high content of cation X zeolites are widely used in the process of adsorption and separation. The acidic properties of zeolites X and interesting selectivity conferred on them their porous structure is also have potential catalysts. The study presented in this manuscript is devoted to the chemical modification of an X zeolite by cation exchange. Ion exchange of zeolite NaX by Zn 2 + cations and / or Cu 2 + is gradually conducted by following the evolution of some of its characteristics: crystallinity by XRD, micropore volume by nitrogen adsorption. Once characterized, the different samples will be used for the adsorption of propane and propylene. Particular attention is paid thereafter, on the modeling of adsorption isotherms. In this vein, various equations of adsorption isotherms and localized mobile, some taking into account the adsorbate-adsorbate interactions, are used to describe the experimental isotherms. We also used the Toth equation, a mathematical model with three parameters whose adjustment requires nonlinear regression. The last part is dedicated to the study of acid properties of Cu (x) X, Zn (x) X and CuZn (x) X, with the adsorption-desorption of pyridine followed by IR. The effect of substitution at different rates of Na + by Cu2 + cations and / or Zn 2 +, on the crystallinity and on the textural properties was treated. Some results on the morphology of the crystallites and the thermal effects during a temperature rise, obtained by scanning electron microscopy and DTA-TGA thermal analyzer, respectively, are also reported. The acidity of our different samples was also studied. Thus, the nature and strength of each type of acidity are estimated. The evaluation of these various features will provide a comparison between Cu (x) X, Zn (x) X and CuZn (x) X. One study on adsorption of C3H8 and C3H6 in NaX, Cu (x) X , Zn (x) x and CuZn (x) x has been undertaken.

Keywords: adsorption, acidity, ion exchange, zeolite

Procedia PDF Downloads 186

Authors: Libia Jiménez Chávez

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This is a study of freedom riders and their courageous journey for civil rights, but the year was not 1961. It was 2003. This paper chronicles the emergence of a new civil rights movement for immigrant rights through an oral history of the 2003 U.S. Immigrant Workers Freedom Ride (IWFR). During the height of the post-9/11 immigrant repression, a bloc of organizations inspired by the Civil Rights Movement of the 1960s mobilized 900 multinational immigrants and their allies in the fight for legal status, labor protections, family reunification, and civil rights. The activists visited over 100 U.S. cities, met with Congressional leaders in the nation’s capital, and led a rally of over 50,000 people in New York City. This unified effort set the groundwork for the national May Day immigration protests of 2006. Movements can be characterized in two distinct ways: physical movement and social movements. In the past, historians have considered immigrants both as people and as participants in social movements. In contrast, studies of recent migrants tend to say little about their involvement in immigrant political mobilizations. The dominant literature on immigration portrays immigrants as objects of exclusion, border enforcement, detention, and deportation instead of strategic political actors. This paper aims to change this perception. It considers the Freedom Riders both as immigrants who were literally on the move and as participants in a social movement. Through interviews with participants and archival video footage housed at the University of California Los Angeles, it is possible to study this mobile protest as a movement. This contemporary immigrant struggle is an opportunity to explore the makeup and development of a heterogenous immigrant coalition and consider the relationship between population movements and social justice. In addition to oral histories and archival research, the study will utilize social movement literature, U.S. immigration and labor history, and Undocumented Critical Theory to expand the historiography of immigrant social movements in America.

Keywords: civil rights, immigrant social movements, undocumented communities, undocumented critical theory

Procedia PDF Downloads 150

Authors: Feng Tao, Han Ye, Shaoyi Liao

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City constructions collapse after earthquake and people will be buried under ruins. Search and rescue should be conducted as soon as possible to save them. Therefore, according to the complicated environment, irregular aftershocks and rescue allow of no delay, a kind of target localization method based on RSSI (Received Signal Strength Indication) is proposed in this article. The target localization technology based on RSSI with the features of low cost and low complexity has been widely applied to nodes localization in WSN (Wireless Sensor Networks). Based on the theory of RSSI transmission and the environment impact to RSSI, this article conducts the experiments in five scenes, and multiple filtering algorithms are applied to original RSSI value in order to establish the signal propagation model with minimum test error respectively. Target location can be calculated from the distance, which can be estimated from signal propagation model, through improved centroid algorithm. Result shows that the localization technology based on RSSI is suitable for large-scale nodes localization. Among filtering algorithms, mixed filtering algorithm (average of average, median and Gaussian filtering) performs better than any other single filtering algorithm, and by using the signal propagation model, the minimum error of distance between known nodes and target node in the five scene is about 3.06m.

Keywords: signal propagation model, centroid algorithm, localization, mixed filtering, RSSI

Procedia PDF Downloads 287

Authors: Wardatutthoyyibah, Satyawan Pudyatmoko, Sena Adi Subrata, Muhammad Ali Imron

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The proboscis monkey is an endemic species to the island of Borneo with conservation status IUCN (The International Union for Conservation of Nature) of endangered. The population of the monkey has a specific habitat and sensitive to habitat disturbances. As a consequence of increasing rates of land-use change in the last four decades, its population was reported significantly decreased. We quantified the effect of land use change on the proboscis monkey’s habitat through the species distribution modeling (SDM) approach with Maxent Software. We collected presence data and environmental variables, i.e., land cover, topography, bioclimate, distance to the river, distance to the road, and distance to the anthropogenic disturbance to generate predictive distribution maps of the monkeys. We compared two prediction maps for 2000 and 2015 data to represent the current habitat of the monkey. We overlaid the monkey’s predictive distribution map with the existing protected areas to investigate whether the habitat of the monkey is protected under the protected areas networks. The results showed that almost 50% of the monkey’s habitat reduced as the effect of land use change. And only 9% of the current proboscis monkey’s habitat within protected areas. These results are important for the master plan of conservation of the endangered proboscis monkey and provide scientific guidance for the future development incorporating biodiversity issue.

Keywords: endemic species, land use change, maximum entropy, spatial distribution

Procedia PDF Downloads 137

Authors: Bruno Di Pace, Roxanne H. Padley

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Aims: The recent increase in remote video interaction has increased the number of requests for teleconsultations with plastic surgeons in private practice (70% in the UK and 64% in the USA). This study investigated the motivations for such an increase and the underlying psychological impact on patients. Method: An anonymous web-based poll of 8 questions was designed and distributed to patients seeking cosmetic surgery through social networks in both Italy and the UK. The questions gathered responses regarding 1. Reasons for pursuing cosmetic surgery; 2. The effects of delays caused by the SARS-COV-2 pandemic; 3. The effects on mood; 4. The influence of video conferencing on body-image perception. Results: 85 respondents completed the online poll. Overall, 68% of respondents stated that seeing themselves more frequently online had influenced their decision to seek cosmetic surgery. The types of surgeries indicated were predominantly to the upper body and face (82%). Delays and access to surgeons during the pandemic were perceived as negatively impacting patients' moods (95%). Body-image perception and self-esteem were lower than in the pre-pandemic, particularly during lockdown (72%). Patients were more inclined to undergo cosmetic surgery during the pandemic, both due to the wish to improve their “lockdown face” for video conferencing (77%) and also due to the benefits of home recovery while in smart working (58%). Conclusions: Overall, findings suggest that video conferencing has led to a significant increase in requests for cosmetic surgery and the so-called “Zoom Boom” effect.

Keywords: cosmetic surgery, remote communication, telehealth, zoom boom

Procedia PDF Downloads 165

Authors: Amirhossein Chambari

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This article proposes a bi-objective model for the facility location problem subject to congestion (overcrowding). Motivated by implementations to locate servers in internet mirror sites, communication networks, one-server-systems, so on. This model consider for situations in which immobile (or fixed) service facilities are congested (or queued) by stochastic demand to behave as M/M/1/K queues. We consider for this problem two simultaneous perspectives; (1) Customers (desire to limit times of accessing and waiting for service) and (2) Service provider (desire to limit average facility idle-time). A bi-objective model is setup for facility location problem with two objective functions; (1) Minimizing sum of expected total traveling and waiting time (customers) and (2) Minimizing the average facility idle-time percentage (service provider). The proposed model belongs to the class of mixed-integer nonlinear programming models and the class of NP-hard problems. In addition, to solve the model, controlled elitist non-dominated sorting genetic algorithms (Controlled NSGA-II) and controlled elitist non-dominated ranking genetic algorithms (NRGA-I) are proposed. Furthermore, the two proposed metaheuristics algorithms are evaluated by establishing standard multiobjective metrics. Finally, the results are analyzed and some conclusions are given.

Keywords: bi-objective, facility location, queueing, controlled NSGA-II, NRGA-I

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Authors: Grzegorz Kubinski

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The proposed paper is focused on forms of identity expression in people with disabilities (PWD) in the social networks like Instagram. Theoretical analysis widely proposes using the new media as an assistive tool for improving wellbeing and labour activities of PWD. This kind of use is definitely important and plays a key role in all social inclusion processes. However, Instagram is not a place where PWD only express their own problems, but in the opposite, allows them to construct a new definition of disability. In the paper, the problem how this different than a classical approach to disability is created by PWD will be discussed. This issue will be scrutinized mainly in two points. Firstly, the question of how disability is changed by other everyday activities, like fashion or sport, will be described. Secondly, and this could be seen as more important, the point how PWD redefining their bodies creating a different form of aesthetic will be presented. The paper is based on content analysis of Instagram accounts. About 20 accounts created by PWD were analyzed for 6 month period, taking into account elements like photos, comments and discussions. All those information were studied in relation to 'everyday life' category and 'aesthetic' category. Works by T. Siebers, L. J. Davis or R. McRuer were used as theoretical background. Conclusions and interpretations presented in the proposed paper show that the Internet can be used by PWD not only as prosthetic and assistive tools. PWD willingly use them as modes of expression their independence, agency and identity. The paper proposes that in further research this way of using the Internet communication by PWD should be taken into account as an important part of the understanding of disability.

Keywords: body, disability, identity, new media

Procedia PDF Downloads 124

Authors: A. Degale Desta, Cheng Jian

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Deep learning application in computer vision is rapidly advancing, giving it the ability to monitor the public and quickly identify potentially anomalous behaviour from crowd scenes. Therefore, the purpose of the current work is to improve the performance of safety of people in crowd events from panic behaviour through introducing the innovative idea of Aggregation of Ensembles (AOE), which makes use of the pre-trained ConvNets and a pool of classifiers to find anomalies in video data with packed scenes. According to the theory of algorithms that applied K-means, KNN, CNN, SVD, and Faster-CNN, YOLOv5 architectures learn different levels of semantic representation from crowd videos; the proposed approach leverages an ensemble of various fine-tuned convolutional neural networks (CNN), allowing for the extraction of enriched feature sets. In addition to the above algorithms, a long short-term memory neural network to forecast future feature values and a handmade feature that takes into consideration the peculiarities of the crowd to understand human behavior. On well-known datasets of panic situations, experiments are run to assess the effectiveness and precision of the suggested method. Results reveal that, compared to state-of-the-art methodologies, the system produces better and more promising results in terms of accuracy and processing speed.

Keywords: action recognition, computer vision, crowd detecting and tracking, deep learning

Procedia PDF Downloads 146

Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia Makedon, Glenn Wylie

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Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.

Keywords: fMRI, brain imaging, deep learning, self-supervised learning, contrastive learning, cognitive fatigue

Procedia PDF Downloads 175

Authors: Lauren B. Birney

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Summer STEM Institute for Middle and High School Students at Pace University The STEM Collaboratory NYC® Summer Fellows Institute takes place on Pace University’s New York City campus during July and provides the following key features for all participants: (i) individual meetings with Pace faculty to discuss and refine future educational goals; (ii) mentorship, guidance, and new friendships with program leaders; and (iii) guest lectures from professionals in STEM disciplines and businesses. The Summer STEM Institute allows middle school and high school students to work in teams to conceptualize, develop, and build native mobile applications that teach and reinforce skills in the sciences and mathematics. These workshops enhance students’STEM problem solving techniques and teach advanced methods of computer science and engineering. Topics include: big data and analytics at the Big Data lab at Seidenberg, Data Science focused on social and environmental advancement and betterment; Natural Disasters and their Societal Influences; Algal Blooms and Environmental Impacts; Green CitiesNYC; STEM jobs and growth opportunities for the future; renew able energy and sustainable infrastructure; and climate and the economy. In order to better align the existing Summer STEM, Institute with the CCERS model and expand the overall network, Pace is actively recruiting new content area specialists from STEM industries and private sector enterprises to participate in an enhanced summer institute in order to1) nurture student progress and connect summer learning to school year curriculum, 2) increase peer-to-peer collaboration amongst STEM professionals and private sector technologists, and 3) develop long term funding and sponsorship opportunities for corporate sector partners to support CCERS schools and programs directly.

Keywords: environmental restoration science, citizen science, data science, STEM

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Authors: Reza Mohammadzadeh

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The major challenges in geotechnical engineering in underground spaces arise from uncertainties and different probabilities. The collection, collation, and collaboration of existing data to incorporate them in analysis and design for given prospect evaluation would be a reliable, practical problem solving method under uncertainty. Machine learning (ML) is a subfield of artificial intelligence in statistical science which applies different techniques (e.g., Regression, neural networks, support vector machines, decision trees, random forests, genetic programming, etc.) on data to automatically learn and improve from them without being explicitly programmed and make decisions and predictions. In this paper, a conceptual database schema of geotechnical risks in underground coal mining based on a cloud system architecture has been designed. A new approach of risk assessment using a three-dimensional risk matrix supported by the level of knowledge (LoK) has been proposed in this model. Subsequently, the model workflow methodology stages have been described. In order to train data and LoK models deployment, an ML platform has been implemented. IBM Watson Studio, as a leading data science tool and data-driven cloud integration ML platform, is employed in this study. As a Use case, a data set of geotechnical hazards and risk assessment in underground coal mining were prepared to demonstrate the performance of the model, and accordingly, the results have been outlined.

Keywords: data model, geotechnical risks, machine learning, underground coal mining

Procedia PDF Downloads 259

Authors: T. Iakovleva

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Present paper suggests a theoretical model that describes the process of the development of responsible innovations on the firm level in health and welfare sectors. There is a need to develop new firm strategies in these sectors. This paper suggests to look on the concept of responsible innovation that was originally developed on the social level and to apply this new concept to the new area of firm strategy. The rapid global diffusion of information and communication technologies has greatly improved access to knowledge. At the same time, communication is cheap, information is a commodity, and global trade increases technological diffusion. As a result, firms and users, including those outside of industrialized nations, get early exposure to the latest technologies and information. General-purpose technologies such as mobile phones and 3D printers enable individuals to solve local needs and customize products. The combined effect of these changes is having a profound impact on the innovation landscape. Meanwhile, the healthcare sector is facing unprecedented challenges, which are magnified by budgetary constraints, an aging population and the desire to provide care for all. On the other hand, patients themselves are changing. They are savvier about their diseases, they expect their relation with the healthcare professionals to be open and interactive, but above all they want to be part of the decision process. All of this is a reflection of what is already happening in other industries where customers have access to large amount of information and became educated buyers. This article addresses the question of how ICT research and innovation may contribute to developing solutions to grand societal challenges in a responsible way. A broad definition of the concept of responsibility in the context of innovation is adopted in this paper. Responsibility is thus seen as a collective, uncertain and future-oriented activity. This opens the questions of how responsibilities are perceived and distributed and how innovation and science can be governed and stewarded towards socially desirable and acceptable ends. This article addresses a central question confronting politicians, business leaders, and regional planners.

Keywords: responsible innovation, ICT, healthcare, welfare sector

Procedia PDF Downloads 188

Authors: M. Dhana Lakshmi, S. Sakthivel Murugan

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As light passes from source to observer in the water medium, it is scattered by the suspended particulate matter. This scattering effect will plague the captured images with non-uniform illumination, blurring details, halo artefacts, weak edges, etc. To overcome this, pixel normalization with an Amended Unsharp Mask (AUM) filter is proposed to enhance the degraded image. To validate the robustness of the proposed technique irrespective of atmospheric light, the considered datasets are collected on dual locations. For those images, the maxima and minima pixel intensity value is computed and normalized; then the AUM filter is applied to strengthen the blurred edges. Finally, the enhanced image is obtained with good illumination and contrast. Thus, the proposed technique removes the effect of scattering called de-hazing and restores the perceptual information with enhanced edge detail. Both qualitative and quantitative analyses are done on considering the standard non-reference metric called underwater image sharpness measure (UISM), and underwater image quality measure (UIQM) is used to measure color, sharpness, and contrast for both of the location images. It is observed that the proposed technique has shown overwhelming performance compared to other deep-based enhancement networks and traditional techniques in an adaptive manner.

Keywords: underwater drone imagery, pixel normalization, thresholding, masking, unsharp mask filter

Procedia PDF Downloads 182

Authors: N. A. Parvin, E. B. Ferranti, L. A. Chapman, C. A. Pfrang

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This study intends to investigate the effects of the Urban Heat Island on public health in Birmingham. Birmingham is located at the center of the West Midlands and its weather is Highly variable due to geographical factors. Residential developments, road networks and infrastructure often replace open spaces and vegetation. This transformation causes the temperature of urban areas to increase and creates an "island" of higher temperatures in the urban landscape. Extreme heat in the urban area is influencing public health in the UK as well as in the world. Birmingham is a densely built-up area with skyscrapers and congested buildings in the city center, which is a barrier to air circulation. We will investigate the city regarding heat and cold-related human mortality and other impacts. We are using primary and secondary datasets to examine the effect of population shift and land-use change on the UHI in Birmingham. We will also use freely available weather data from the Birmingham Urban Observatory and will incorporate satellite data to determine urban spatial expansion and its effect on the UHI. We have produced a temperature map based on summer datasets of 2020, which has covered 25 weather stations in Birmingham to show the differences between diurnal and nocturnal summer and annual temperature trends. Some impacts of the UHI may be beneficial, such as the lengthening of the plant growing season, but most of them are highly negative. We are looking for various effects of urban heat which is impacting human health and investigating mitigation options.

Keywords: urban heat, public health, climate change

Procedia PDF Downloads 88

Authors: B. Lokuketagoda, M. Weerakoon, U. Madushan, A. N. Senaratne, K. Y. Abeywardena

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Ransomware has become a common threat in past few years and the recent threat reports show an increase of growth in Ransomware infections. Researchers have identified different variants of Ransomware families since 2015. Lack of knowledge of the user about the threat is a major concern. Ransomware detection methodologies are still growing through the industry. Email is the easiest method to send Ransomware to its victims. Uninformed users tend to click on links and attachments without much consideration assuming the emails are genuine. As a solution to this in this paper R-Killer Ransomware detection tool is introduced. Tool can be integrated with existing email services. The core detection Engine (CDE) discussed in the paper focuses on separating suspicious samples from emails and handling them until a decision is made regarding the suspicious mail. It has the capability of preventing execution of identified ransomware processes. On the other hand, Sandboxing and URL analyzing system has the capability of communication with public threat intelligence services to gather known threat intelligence. The R-Killer has its own mechanism developed in its Proactive Monitoring System (PMS) which can monitor the processes created by downloaded email attachments and identify potential Ransomware activities. R-killer is capable of gathering threat intelligence without exposing the user’s data to public threat intelligence services, hence protecting the confidentiality of user data.

Keywords: ransomware, deep learning, recurrent neural networks, email, core detection engine

Procedia PDF Downloads 198

Authors: Pallavi Bhogaram, Xiaolong Wu, Min He, Onyedikachi Okenwa

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A transportation network is a realization of a spatial network, describing a structure which permits either vehicular movement or flow of some commodity. Examples include road networks, railways, air routes, pipelines, and many more. The transportation network plays a vital role in maintaining the vigor of the nation’s economy. Hence, ensuring the network stays resilient all the time, especially in the face of challenges such as heavy traffic loads and large scale natural disasters, is of utmost importance. In this paper, we used the Neo4j application to develop the graph. Neo4j is the world's leading open-source, NoSQL, a native graph database that implements an ACID-compliant transactional backend to applications. The Southern California network model is developed using the Neo4j application and obtained the most critical and optimal nodes and paths in the network using centrality algorithms. The edge betweenness centrality algorithm calculates the critical or optimal paths using Yen's k-shortest paths algorithm, and the node betweenness centrality algorithm calculates the amount of influence a node has over the network. The preliminary study results confirm that the Neo4j application can be a suitable tool to study the important nodes and the critical paths for the major congested metropolitan area.

Keywords: critical path, transportation network, connectivity reliability, network model, Neo4j application, edge betweenness centrality index

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Authors: Misbah Fatma, Anam Saiyeda

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This analysis charts the introduction and expansion of research into the filter bubble phenomena over the last 10 years using a large dataset of academic publications. This bibliometric study demonstrates how interdisciplinary filter bubble research is. The identification of key authors and organizations leading the filter bubble study sheds information on collaborative networks and knowledge transfer. Relevant papers are organized based on themes including algorithmic bias, polarisation, social media, and ethical implications through a systematic examination of the literature. In order to shed light on how these patterns have changed over time, the study plots their historical history. The study also looks at how research is distributed globally, showing geographic patterns and discrepancies in scholarly output. The results of this bibliometric analysis let us fully comprehend the development and reach of filter bubble research. This study offers insights into the ongoing discussion surrounding information personalization and its implications for societal discourse, democratic participation, and the potential risks to an informed citizenry by exposing dominant themes, interdisciplinary collaborations, and geographic patterns. In order to solve the problems caused by filter bubbles and to advance a more diverse and inclusive information environment, this analysis is essential for scholars and researchers.

Keywords: bibliometric analysis, social media, social networking, algorithmic personalization, self-selection, content moderation policies and limited access to information, recommender system and polarization

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Authors: Md. Shafiullah

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This study delves into the evolving landscape of urban childcare in Bangladesh, focusing on the experiences and challenges faced by parents in Dhaka city. This paper argues that the traditional childcare arrangement of city families is inadequate to meet the development needs of children. The study aims to explore the childcare challenges faced by urban parents as they transition from traditional family-based childcare networks to alternative caregiving arrangements amidst urbanization, economic shifts, and social transformations. Utilizing a mixed-method research approach, combining quantitative surveys (n = 200) and four qualitative interviews, the research examines the parental viewpoints on childcare practices and the role of societal norms and values. The study finds childcare crises in both the family and daycare settings. In family care, caregiving suffers from the less availability of grandparents, a lack of skills of caregivers, and a lack of child interaction. As for the daycare, it is affected by the absence of appropriate policies, a lack of quality, health and safety concerns, affordability issues, and cultural concerns. Additionally, the study highlights inadequacies in childcare policies and regulatory frameworks, calling for comprehensive reforms to address the childcare vacuum in urban areas. By shifting the focus from developed to developing countries, this study contributes to the literature and suggests policy implications for Bangladesh and beyond.

Keywords: childcare, child development, childcare policy, daycare, Bangladesh

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Authors: Krishan P. Sharma, T. P. Sharma

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Wireless sensor nodes are resource constrained battery powered devices usually deployed in hostile and ill-disposed areas to cooperatively monitor physical or environmental conditions. Due to their limited power supply, the major challenge for researchers is to utilize their battery power for enhancing the lifetime of whole network. Communication and sensing are two major sources of energy consumption in sensor networks. In this paper, we propose a deployment strategy for enhancing the average lifetime of a sensor network by effectively utilizing communication and sensing energy to provide full coverage. The proposed scheme is based on the fact that due to heavy relaying load, sensor nodes near to the sink drain energy at much faster rate than other nodes in the network and consequently die much earlier. To cover this imbalance, proposed scheme finds optimal communication and sensing ranges according to effective load at each node and uses a non-uniform deployment strategy where there is a comparatively high density of nodes near to the sink. Probable relaying load factor at particular node is calculated and accordingly optimal communication distance and sensing range for each sensor node is adjusted. Thus, sensor nodes are placed at locations that optimize energy during network operation. Formal mathematical analysis for calculating optimized locations is reported in present work.

Keywords: load factor, network lifetime, non-uniform deployment, sensing range

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Authors: Mahmoud Zakeri Niri, Saber Moazami, Arman Abdollahipour, Hossein Ghalkhani

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The objective of this research is to hydrological evaluation of four widely-used satellite precipitation products named PERSIANN, TMPA-3B42V7, TMPA-3B42RT, and CMORPH over Zarinehrood basin in Iran. For this aim, at first, daily streamflow of Sarough-cahy river of Zarinehrood basin was simulated using IHACRES rainfall-runoff model with daily rain gauge and temperature as input data from 1988 to 2008. Then, the model was calibrated in two different periods through comparison the simulated discharge with the observed one at hydrometric stations. Moreover, in order to evaluate the performance of satellite precipitation products in streamflow simulation, the calibrated model was validated using daily satellite rainfall estimates from the period of 2003 to 2008. The obtained results indicated that TMPA-3B42V7 with CC of 0.69, RMSE of 5.93 mm/day, MAE of 4.76 mm/day, and RBias of -5.39% performs better simulation of streamflow than those PERSIANN and CMORPH over the study area. It is noteworthy that in Iran, the availability of ground measuring station data is very limited because of the sparse density of hydro-meteorological networks. On the other hand, large spatial and temporal variability of precipitations and lack of a reliable and extensive observing system are the most important challenges to rainfall analysis, flood prediction, and other hydrological applications in this country.

Keywords: hydrological evaluation, IHACRES, satellite precipitation product, streamflow simulation

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Authors: Amy L. Irvine, Gabriel Reygondeau, Derek P. Tittensor

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Marine protected areas (MPAs) within Canada’s exclusive economic zone help ensure the conservation and sustainability of marine ecosystems and the continued provision of ecosystem services to society (e.g., food, carbon sequestration). With ongoing and accelerating climate change, however, MPAs may become undermined in terms of their effectiveness at fulfilling these outcomes. Many populations of species, especially those at their thermal range limits, may shift to cooler waters or become extirpated due to climate change, resulting in new species compositions and ecological interactions within static MPA boundaries. While Canadian MPA management follows international guidelines for marine conservation, no consistent approach exists for adapting MPA networks to climate change and the resulting altered ecosystem conditions. To fill this gap, projected climate-driven shifts in species distributions on Canada’s east coast were analyzed to identify when native species emigrate and novel species immigrate within the network and how high mitigation and carbon emission scenarios influence these timelines. Indicators of the ecological changes caused by these species' shifts in the biological community were also developed. Overall, our research provides projections of climate change impacts and helps to guide adaptive management responses within the Canadian east coast MPA network.

Keywords: climate change, ecosystem modeling, marine protected areas, management

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Authors: Josiel Delgadillo, Johnson Kinyua, Charles Mutigwe

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The purpose of sentiment analysis is to determine the sentiment strength (e.g., positive, negative, neutral) from a textual source for good decision-making. Natural language processing in domains such as financial markets requires knowledge of domain ontology, and pre-trained language models, such as BERT, have made significant breakthroughs in various NLP tasks by training on large-scale un-labeled generic corpora such as Wikipedia. However, sentiment analysis is a strong domain-dependent task. The rapid growth of social media has given users a platform to share their experiences and views about products, services, and processes, including financial markets. StockTwits and Twitter are social networks that allow the public to express their sentiments in real time. Hence, leveraging the success of unsupervised pre-training and a large amount of financial text available on social media platforms could potentially benefit a wide range of financial applications. This work is focused on sentiment analysis using social media text on platforms such as StockTwits and Twitter. To meet this need, SkyBERT, a domain-specific language model pre-trained and fine-tuned on financial corpora, has been developed. The results show that SkyBERT outperforms current state-of-the-art models in financial sentiment analysis. Extensive experimental results demonstrate the effectiveness and robustness of SkyBERT.

Keywords: BERT, financial markets, Twitter, sentiment analysis

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Authors: Shadatul Hanom Rashid, Xiaorong Zhou

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Hybrid sol-gel coatings are the blend of both advantages of inorganic and organic networks have been reported as environmentally friendly anti-corrosion surface pre-treatment for several metals, including aluminum alloys. In this current study, Si-Zr hybrid sol-gel coatings were synthesized from (3-glycidoxypropyl)trimethoxysilane (GPTMS), tetraethyl orthosilicate (TEOS) and zirconium(IV) propoxide (TPOZ) precursors and applied on AA6111 aluminum alloy by dip coating technique. The hybrid sol-gel coatings doped with different concentrations of cerium nitrate (Ce(NO3)3) as a corrosion inhibitor were also prepared and the effect of Ce(NO3)3 concentrations on the morphology and corrosion resistance of the coatings were examined. The surface chemistry and morphology of the hybrid sol-gel coatings were analyzed by Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM). The corrosion behavior of the coated aluminum alloy samples was evaluated by electrochemical impedance spectroscopy (EIS). Results revealed that good corrosion resistance of hybrid sol-gel coatings were prepared from hydrolysis and condensation reactions of GPTMS, TEOS and TPOZ precursors deposited on AA6111 aluminum alloy. When the coating doped with cerium nitrate, the properties were improved significantly. The hybrid sol-gel coatings containing lower concentration of cerium nitrate offer the best inhibition performance. A proper doping concentration of Ce(NO3)3 can effectively improve the corrosion resistance of the alloy, while an excessive concentration of Ce(NO3)3 would reduce the corrosion protection properties, which is associated with defective morphology and instability of the sol-gel coatings.

Keywords: AA6111, Ce(NO3)3, corrosion, hybrid sol-gel coatings

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Authors: Amira Mabrouk, Chokri Abdennadher

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The reduction of road congestion which is inherent to the use of vehicles is an obvious priority to public authority. Therefore, assessing the willingness to pay of an individual in order to save trip-time is akin to estimating the change in price which was the result of setting up a new transport policy to increase the networks fluidity and improving the level of social welfare. This study holds an innovative perspective. In fact, it initiates an economic calculation that has the objective of giving an estimation of the monetized time value during the trips made in Sfax. This research is founded on a double-objective approach. The aim of this study is to i) give an estimation of the monetized value of time; an hour dedicated to trips, ii) determine whether or not the consumer considers the environmental variables to be significant, iii) analyze the impact of applying a public management of the congestion via imposing taxation of city tolls on urban dwellers. This article is built upon a rich field survey led in the city of Sfax. With the use of the contingent valuation method, we analyze the “declared time preferences” of 450 drivers during rush hours. Based on the fond consideration of attributed bias of the applied method, we bring to light the delicacy of this approach with regards to the revelation mode and the interrogative techniques by following the NOAA panel recommendations bearing the exception of the valorization point and other similar studies about the estimation of transportation externality.

Keywords: willingness to pay, contingent valuation, time value, city toll

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Authors: Lukumba Phiri, Simon Tembo, Kumbuso Joshua Nyoni

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In Zambia recent initiatives by various power operators like ZESCO, CEC, and consumers like the mines to upgrade power systems into smart grids target an even tighter integration with information technologies to enable the integration of renewable energy sources, local and bulk generation, and demand response. Thus, for the reliable operation of smart grids, its information infrastructure must be secure and reliable in the face of both failures and cyberattacks. Due to the nature of the systems, ICS/SCADA cybersecurity and governance face additional challenges compared to the corporate networks, and critical systems may be left exposed. There exist control frameworks internationally such as the NIST framework, however, there are generic and do not meet the domain-specific needs of the SCADA systems. Zambia is also lagging in cybersecurity awareness and adoption, therefore there is a concern about securing ICS controlling key infrastructure critical to the Zambian economy as there are few known facts about the true posture. In this paper, we introduce a stochastic Edged-based Anomaly Detection for SCADA systems (SEADS) framework for threat modeling and risk assessment. SEADS enables the calculation of steady-steady probabilities that are further applied to establish metrics like system availability, maintainability, and reliability.

Keywords: anomaly, availability, detection, edge, maintainability, reliability, stochastic

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Authors: Oyuna Weina

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This study examines how musicians use visual and zoological metaphors for singing technique and voice quality in a genre of traditional music called urtiin duu (‘long song’) in Alshaa, Inner Mongolia, China. Previous studies have discussed melodic contour in Mongol music, but little study of the intersection of singing technique, visual and zoological metaphors has yet been undertaken. The purpose of this study is to address this lack by analysing urtiin duu itself, traditional pedagogy and performances, all of which have been inspired and are assessed by reference to nature and mobile pastoral herding practices. This study investigates the visual and zoological metaphors related to urtiin duu especially colour, the shape of the circle and animals in the Mongol community. Urtiin duu singing is associated with certain colours in song texts, in selection of repertoire and in the status of singers. Musicians also use colour to describe timbre. These colours in turn reference worship of nature, religions, and daily practices of most Mongols in Alshaa. Moreover, voice quality and singing technique are often related to the animals not only in song text but also in the approach to breathing and to melodic contour. Additionally, the concept of boronhoi (‘the shape of circle’), not only is applied to the melodic contour but also to the voice quality and singing technique. These three factors illustrate the connections among nature, spiritual world and everyday herding life of Mongols. These different connections provide evidence of multi-layered meanings. In contemporary Alshaa, urtiin duu singers received Western musical training from the city and returned to their homelands to perform urtiin duu. In doing so, they are also trying to reconnect with the history, nature and spiritual world in order to achieve their ideal sound. Within a multicultural society, singers negotiate amongst themselves, and with ethnic groups, audiences and government officials. The power of the metaphor therefore assists and reconnects the strength of regional identity and ethnic identity in Alshaa.

Keywords: Alshaa, urtiin duu, visual, zoological metaphors

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Authors: Shikhar Shrivastava, Varun Khare

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Background: We are confronted by a society that is becoming more complex, more mobile, and more dysfunctional. Teen pregnancy, suicide, elopement, and the perusal of dangerous drugs have become commonplace. In addition, children do not settle their disputes as they once did. Guns and knives are quotidian. Therefore, it has been an exigent to have a "Juvenile Code" that would provide specific substantive and procedural rules for juveniles in the justice system. However, until the twentieth century, there was little difference between how the justice system treated adults and children. Age was considered only in terms of appropriate punishment and juveniles were eligible for the same punishment as adults. Findings: The increased prevalence and legislative support for specialized courts, Juvenile Justice Boards, including juvenile drug, mental health and truancy court programs, as well as diversion programs and evidence-based approaches into the fabric of juvenile justice are just a few examples of recent advances. In India, various measures were taken to prosecute young offenders who committed violent crimes as adults. But it was argued that equating juveniles with adult criminals was neither scientifically correct nor normatively defensible. It would defeat the very purpose of the justice system. Methodology and Conclusion: This paper attempts to bring forth the results of analytical and descriptive research that examined changing trends in juvenile justice legislation. It covers the investigative and inspective practices of police, the various administrative agencies who have roles in implementing the legislation, the courts, and the detention centers. In this paper we shall discuss about how the juvenile justice system is the dumping ground for many of a youths’ problem. The changing notions of justice, from retributive to restorative and rehabilitative shall be discussed. A comparative study of the Juvenile act in India and that of the U.S has been discussed. Specific social institutions and forces that explain juvenile delinquency are identified. In addition, various influences on juvenile delinquency are noted, such as families, schools, peer groups and communities. The text concludes by addressing socialization, deterrence, imprisonments, alternatives, restitution and preventions.

Keywords: juvenile, justice system, retributive, rehabilitative, delinquency

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Authors: El Miloudi Djelloul

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The computer network play an important position for practical implementations of the differently system. To implement a system into network above all is needed to know all the configurations, which is responsible to be a part of the system, and to give adequate information and solution in realtime. So if want to implement this system for example in the school or relevant institutions, the first step is to analyze the types of model which is needed to be configured and another important step is to organize the works in the context of devices, as a part of the general system. Often before configuration, as important point is descriptions and documentations from all the works into the respective process, and then to organize in the aspect of problem-solving. The computer network as critic infrastructure is very specific so the paper present the effectiveness solutions in the structured aspect viewed from one side, and another side is, than the paper reflect the positive aspect in the context of modeling and block schema presentations as an better alternative to solve the specific problem because of continually distortions of the system from the line of devices, programs and signals or packed collisions, which are in movement from one computer node to another nodes.

Keywords: local area networks, LANs, block schema presentations, computer network system, computer node, critical infrastructure packed collisions, structural control system representations, computer network, implementations, modeling structural representations, companies, computers, context, control systems, internet, software

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Authors: Ons Slimene, Aroua Taamallah, Maha Khemaja

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In recent years, human activity recognition (HAR) has been a key area of research due to its diverse applications. It has garnered increasing attention in the field of computer vision. HAR plays an important role in people’s daily lives as it has the ability to learn advanced knowledge about human activities from data. In HAR, activities are usually represented by exploiting different types of sensors, such as embedded sensors or visual sensors. However, these sensors have limitations, such as local obstacles, image-related obstacles, sensor unreliability, and consumer concerns. Recently, several deep learning-based approaches have been proposed for HAR and these approaches are classified into two categories based on the type of data used: vision-based approaches and sensor-based approaches. This research paper highlights the importance of multimodal data fusion from skeleton data obtained from videos and data generated by embedded sensors using deep neural networks for achieving HAR. We propose a deep multimodal fusion network based on a twostream architecture. These two streams use the Convolutional Neural Network combined with the Bidirectional LSTM (CNN BILSTM) to process skeleton data and data generated by embedded sensors and the fusion at the feature level is considered. The proposed model was evaluated on a public OPPORTUNITY++ dataset and produced a accuracy of 96.77%.

Keywords: human activity recognition, action recognition, sensors, vision, human-centric sensing, deep learning, context-awareness

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Authors: David Percy

Abstract:

Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

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