Search results for: wireless mesh networks

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: Mpai Mokoena, Nsenda Lukumwena

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South Africa is one of the driest countries in the world and is facing a water crisis. In addition to inadequate infrastructure and poor planning, the country is experiencing high rates of water wastage due to pipe leaks. This study outlines the level of water wastage and develops a smart solution to efficiently manage and reduce the effects of pipe leaks, while monitoring the situation before and after fixing the pipe leaks. To understand the issue in depth, a literature review of journal papers and government reports was conducted. A questionnaire was designed and distributed to the general public. Additionally, the municipality office was contacted from a managerial perspective. The analysis from the study indicated that the majority of the citizens are aware of the water crisis and are willing to participate positively to decrease the level of water wasted. Furthermore, the response from the municipality acknowledged that more practical solutions are needed to reduce water wastage, and resources to attend to pipe leaks swiftly. Therefore, this paper proposes a specific solution for municipalities, local plumbers and citizens to minimize the effects of pipe leaks. The solution provides web and mobile application platforms to report and manage leaks swiftly. The solution is beneficial to the country in achieving water security and would promote a culture of responsibility toward water usage.

Keywords: urban distribution networks, leak management, mobile application, responsible citizens, water crisis, water security

Procedia PDF Downloads 138

Authors: S. Candamano, A. Iorfida, L. Pagnotta, F. Crea

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Basalt fabric reinforced cementitious composites (FRCM) have attracted great attention because they result in being effective in structural strengthening and eco-efficient. In this study, authors investigate their mechanical behavior when an alkali-activated binder, with tuned properties and containing high amounts of industrial by-products, such as ground granulated blast furnace slag, is used. Reinforcement is made up of a balanced, coated bidirectional fabric made out of basalt fibres and stainless steel micro-wire, with a mesh size of 8x8 mm and an equivalent design thickness equal to 0.064 mm. Mortars mixes have been prepared by maintaining constant the water/(reactive powders) and sand/(reactive powders) ratios at 0.53 and 2.7 respectively. Tensile tests were carried out on composite specimens of nominal dimensions equal to 500 mm x 50 mm x 10 mm, with 6 embedded rovings in the loading direction. Direct shear tests (DST), aimed to the stress-transfer mechanism and failure modes of basalt-FRCM composites, were carried out on brickwork substrate using an externally bonded basalt-FRCM composite strip 10 mm thick, 50 mm wide and a bonded length of 300 mm. Mortars exhibit, after 28 days of curing, a compressive strength of 32 MPa and a flexural strength of 5.5 MPa. Main hydration product is a poorly crystalline CASH gel. The constitutive behavior of the composite has been identified by means of direct tensile tests, with response curves showing a tri-linear behavior. The first linear phase represents the uncracked (I) stage, the second (II) is identified by crack development and the third (III) corresponds to cracked stage, completely developed up to failure. All specimens exhibit a crack pattern throughout the gauge length and failure occurred as a result of sequential tensile failure of the fibre bundles, after reaching the ultimate tensile strength. The behavior is mainly governed by cracks development (II) and widening (III) up to failure. The main average values related to the stages are σI= 173 MPa and εI= 0.026% that are the stress and strain of the transition point between stages I and II, corresponding to the first mortar cracking; σu = 456 MPa and εu= 2.20% that are the ultimate tensile strength and strain, respectively. The tensile modulus of elasticity in stage III is EIII= 41 GPa. All single-lap shear test specimens failed due to composite debonding. It occurred at the internal fabric-to-matrix interface, and it was the result of fracture of the matrix between the fibre bundles. For all specimens, transversal cracks were visible on the external surface of the composite and involved only the external matrix layer. This cracking appears when the interfacial shear stresses increase and slippage of the fabric at the internal matrix layer interface occurs. Since the external matrix layer is bonded to the reinforcement fabric, it translates with the slipped fabric. Average peak load around 945 N, peak stress around 308 MPa, and global slip around 6 mm were measured. The preliminary test results allow affirming that Alkali Activated Binders can be considered a potentially valid alternative to traditional mortars in designing FRCM composites.

Keywords: alkali activated binders, basalt-FRCM composites, direct shear tests, structural strengthening

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Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

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Authors: S. Candamano, A. Iorfida, F. Crea, A. Macario

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Recently, basalt fabric reinforced cementitious composites (FRCM) have attracted great attention because they result to be effective in structural strengthening and cost/environment efficient. In this study, authors investigate their mechanical behavior when an inorganic matrix, belonging to the family of alkali-activated binders, is used. In particular, the matrix has been designed to contain high amounts of industrial by-products and waste, such as Ground Granulated Blast Furnace Slag (GGBFS) and Fly Ash. Fresh state properties, such as workability, mechanical properties and shrinkage behavior of the matrix have been measured, while microstructures and reaction products were analyzed by Scanning Electron Microscopy and X-Ray Diffractometry. Reinforcement is made up of a balanced, coated bidirectional fabric made out of basalt fibres and stainless steel micro-wire, with a mesh size of 8x8 mm and an equivalent design thickness equal to 0.064 mm. Mortars mixes have been prepared by maintaining constant the water/(reactive powders) and sand/(reactive powders) ratios at 0.53 and 2.7 respectively. An appropriate experimental campaign based on direct tensile tests on composite specimens and single-lap shear bond test on brickwork substrate has been thus carried out to investigate their mechanical behavior under tension, the stress-transfer mechanism and failure modes. Tensile tests were carried out on composite specimens of nominal dimensions equal to 500 mm x 50 mm x 10 mm, with 6 embedded rovings in the loading direction. Direct shear tests (DST) were carried out on brickwork substrate using an externally bonded basalt-FRCM composite strip 10 mm thick, 50 mm wide and a bonded length of 300 mm. Mortars exhibit, after 28 days of curing, an average compressive strength of 32 MPa and flexural strength of 5.5 MPa. Main hydration product is a poorly crystalline aluminium-modified calcium silicate hydrate (C-A-S-H) gel. The constitutive behavior of the composite has been identified by means of direct tensile tests, with response curves showing a tri-linear behavior. Test results indicate that the behavior is mainly governed by cracks development (II) and widening (III) up to failure. The ultimate tensile strength and strain were respectively σᵤ = 456 MPa and ɛᵤ= 2.20%. The tensile modulus of elasticity in stage III was EIII= 41 GPa. All single-lap shear test specimens failed due to composite debonding. It occurred at the internal fabric-to-matrix interface, and it was the result of a fracture of the matrix between the fibre bundles. For all specimens, transversal cracks were visible on the external surface of the composite and involved only the external matrix layer. This cracking appears when the interfacial shear stresses increase and slippage of the fabric at the internal matrix layer interface occurs. Since the external matrix layer is bonded to the reinforcement fabric, it translates with the slipped fabric. Average peak load around 945 N, peak stress around 308 MPa and global slip around 6 mm were measured. The preliminary test results allow affirming that Alkali-Activated Materials can be considered a potentially valid alternative to traditional mortars in designing FRCM composites.

Keywords: Alkali-activated binders, Basalt-FRCM composites, direct shear tests, structural strengthening

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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: Renata Mekovec, Dijana Peras, Ruben Picek

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The advent of the Internet, mobile devices and social media is revolutionizing the experience of retail customers by linking multiple sources through various channels. Omnichannel retailing is a retailing that combines multiple channels to allow customers to seamlessly leverage all the distribution information online and offline while shopping. Therefore, today data are an asset more critical than ever for all organizations. Nonetheless, because of its heterogeneity through platforms, developers are currently facing difficulties in dealing with personal data. Considering the possibilities of omnichannel communication, this paper presents channel categorization that could enhance the customer experience of omnichannel center called hyper center. The purpose of this paper is fundamentally to describe the connection between the omnichannel hyper center and the customer, with particular attention to privacy protection. The first phase was finding the most appropriate channels of communication for hyper center. Consequently, a selection of widely used communication channels has been identified and analyzed with regard to the effect requirements for optimizing user experience. The evaluation criteria are divided into 3 groups: general, user profile and channel options. For each criterion the weight of importance for omnichannel communication was defined. The most important thing was to consider how the hyper center can make user identification while respecting the privacy protection requirements. The study carried out also shows what customer experience across digital networks would look like, based on an omnichannel approach owing to privacy protection principles.

Keywords: personal data, privacy protection, omnichannel communication, retail

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Authors: Igor Peraza Curiel, Suzanne Strum

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Today, when many foreign architects are receiving commissions in the United Arab Emirates, it is essential to analyze how their designs are influenced by the region's culture, environment, and building traditions. This study examines the approach to siting, geometry, construction methods, and material choices in two private homes for a family in Dubai, a project being constructed on adjacent sites by the acclaimed Spanish team of RCR Architects. Their third project in Dubai, the houses mark a turning point in their design approach to the desert. The Pritzker Prize-winning architects of RCR gained renown for building works deeply responsive to the history, landscape, and customs of their hometown in a volcanic area of the Catalonia region of Spain. Key formative projects and their entry to practice in UAE will be analyzed according to the concepts of place identity, the poetics of construction, and material imagination. The poetics of construction, a theoretical position with a long practical tradition, was revived by the British critic Kenneth Frampton. The idea of architecture as a constructional craft is related to the concepts of material imagination and place identity--phenomenological concerns with the creative engagement with local matter and topography that are at the very essence of RCR's way of designing, detailing, and making. Our study situates RCR within the challenges of building in the region, where western forms and means have largely replaced the ingenious responsiveness of indigenous architecture to the climate and material scarcity. The dwellings, iterations of the same steel and concrete vaulting system, highlight the conceptual framework of RCR's design approach to offer a study in contemporary critical regionalism. The Kama House evokes Bedouin tents, while the Alwah House takes the form of desert dunes in response to the temporality of the winds. Metal mesh screens designed to capture the shifting sands will complete the forms. The original research draws on interviews with the architects and unique documentation provided by them and collected by the authors during on-site visits. By examining the two houses in-depth, this paper foregrounds a series of timely questions: 1) What is the impact of the local climatic, cultural, and material conditions on their project in the UAE? 2) How does this work further their experiences in the region? 3) How has RCR adapted their construction techniques as their work expands beyond familiar settings? The investigation seeks to understand how the design methodology developed for more than 20 years and enmeshed in the regional milieu of their hometown can transform as the architects encounter unique characteristics and values in the Middle East. By focusing on the contemporary interpretation of Arabic geometry and elements, the houses reveal the role of geometry, tectonics, and material specificity in the realization from conceptual sketches to built form. In emphasizing the importance of regional responsiveness, the dynamics of international construction practice, and detailing this study highlights essential issues for professionals and students looking to practice in an increasingly global market.

Keywords: material imagination, regional responsiveness, place identity, poetics of construction

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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: Jose A. Diaz-Olivares, Nafise Mahdavian, Farhad Abtahi, Kaj Lindecrantz, Abdelakram Hafid, Fernando Seoane

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Despite new recommendations and guidelines for the evaluation of occupational risk assessments and their prevention, work-related musculoskeletal disorders are still one of the biggest causes of work activity disruption, productivity loss, sick leave and chronic work disability. It affects millions of workers throughout Europe, with a large-scale economic and social burden. These specific efforts have failed to produce significant results yet, probably due to the limited availability and high costs of occupational risk assessment at work, especially when the methods are complex, consume excessive resources or depend on self-evaluations and observations of poor accuracy. To overcome these limitations, a pervasive system of risk assessment tools in real time has been developed, which has the characteristics of a systematic approach, with good precision, usability and resource efficiency, essential to facilitate the prevention of musculoskeletal disorders in the long term. The system allows the combination of different wearable sensors, placed on different limbs, to be used for data collection and evaluation by a software solution, according to the needs and requirements in each individual working environment. This is done in a non-disruptive manner for both the occupational health expert and the workers. The creation of this solution allows us to attend different research activities that require, as an essential starting point, the recording of data with ergonomic value of very diverse origin, especially in real work environments. The software platform is here presented with a complimentary smart clothing system for data acquisition, comprised of a T-shirt containing inertial measurement units (IMU), a vest sensorized with textile electronics, a wireless electrocardiogram (ECG) and thoracic electrical bio-impedance (TEB) recorder and a glove sensorized with variable resistors, dependent on the angular position of the wrist. The collected data is processed in real-time through a mobile application software solution, implemented in commercially available Android-based smartphones and tablet platforms. Based on the collection of this information and its analysis, real-time risk assessment and feedback about postural improvement is possible, adapted to different contexts. The result is a tool which provides added value to ergonomists and occupational health agents, as in situ analysis of postural behavior can assist in a quantitative manner in the evaluation of work techniques and the occupational environment.

Keywords: ergonomics, mobile technologies, risk assessment, smart textiles

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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

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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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Authors: Brandtner Patrick, Staberhofer Franz

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Logistics and Supply Chain Management are of crucial importance for organisational success. In the era of Digitalization, several implications and improvement potentials for these domains arise, which at the same time could lead to decreased competitiveness and could endanger long-term company success if ignored or neglected. However, empirical research on the issue of Digitalization and benefits purported to it by practitioners is scarce and mainly focused on single technologies or separate, isolated Supply Chain blocks as e.g. distribution logistics or procurement only. The current paper applies a holistic focus group approach to elaborate practitioner use cases at the nexus of the concepts of Supply Chain Management (SCM) and Digitalization. In the course of three focus group workshops with over 45 participants from more than 20 organisations, a comprehensive set of benefit entitlements and areas for improvement in terms of applying digitalization to SCM is developed. The main results of the paper indicate the relevance of Digitalization being realized in practice. In the form of seventeen concrete research action fields, the benefit entitlements are aggregated and transformed into potential starting points for future research projects in this area. The main contribution of this paper is an empirically grounded basis for future research projects and an overview of actual research action fields from practitioners’ point of view.

Keywords: digital supply chain, digital transformation, supply chain management, value networks

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: Jingting Luo, Yong Wang, Ying Wang

Abstract:

Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from the VOC data set and train with the INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2, and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.

Keywords: pedestrian detection, feature detection, convolutional neural network, real-time detection, YOLOv3

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Authors: Okanta Andrew, Richmond Kweku Frempong

Abstract:

As mobile networks are upgraded with technologies like WAP, GPRS and UMTS to deliver next-generation multimedia services, so are the banks and other financial institutions also getting ready to unleash the financial products on the mobile platform to meet growing demand for mobile based application services. Hence, the onset of Unstructured Supplementary Services (USSD) Banking which would make banking services available at anywhere, anytime through a string of interactive SMS sessions between a mobile device and an application server of a service provider. The aim of this studies was to find out whether the public will accept the sim banking service when it is implemented. Our target group includes: Working class. E. g. Businessmen/women, office workers, fishermen, market women, teachers etc. Nonworking class. E. g. Students (Tertiary, Senior High School), housewives. etc. The survey was in the form of a questionnaire and a verbal interview (video) which was to investigate their idea about the current banking system and the yet to be introduced sim banking concept. Meanwhile, some challenges accompanied the progression of data gathering because some populace showed reluctance in freeing their information. One other suggestion was that government should put measures against foremost challenges obstructing sim banking in Ghana counter to computers hackers. Government and individual have a key role to undertake to give suitable support to facelift the sim banking industry in the country. It was also suggested that Government put strong regulations on the use of sim banking products and services to streamline all the activities and also create awareness of the need for sim banking and emphasize its relevance in the aspect of national GDP.

Keywords: banking, mobile banking, SIM banking, mobile banking in Ghana

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Authors: África Vicario, Fernando J. Álvarez, Felipe Parralejo, Fernando Aranda

Abstract:

The irrigation systems of traditional agriculture, such as gravity-fed irrigation, produce a great waste of water because, generally, there is no control over the amount of water supplied in relation to the water needed. The AIRON Project tries to solve this problem by implementing an IoT-based system to sensor the irrigation plots so that the state of the crops and the amount of water used for irrigation can be known remotely. The IoT system consists of a sensor network that measures the humidity of the soil, the weather conditions (temperature, relative humidity, wind and solar radiation) and the irrigation water flow. The communication between this network and a central gateway is conducted by means of long-range wireless communication that depends on the characteristics of the irrigation plot. The main objective of the AIRON project is to deploy an IoT sensor network in two different plots of the irrigation community of Aranjuez in the Spanish region of Madrid. The first plot is 2 km away from the central gateway, so LoRa has been used as the base communication technology. The problem with this plot is the absence of mains electric power, so devices with energy-saving modes have had to be used to maximize the external batteries' use time. An ESP32 SOC board with a LoRa module is employed in this case to gather data from the sensor network and send them to a gateway consisting of a Raspberry Pi with a LoRa hat. The second plot is located 18 km away from the gateway, a range that hampers the use of LoRa technology. In order to establish reliable communication in this case, the long-term evolution (LTE) standard is used, which makes it possible to reach much greater distances by using the cellular network. As mains electric power is available in this plot, a Raspberry Pi has been used instead of the ESP32 board to collect sensor data. All data received from the two plots are stored on a proprietary server located at the irrigation management company's headquarters. The analysis of these data by means of machine learning algorithms that are currently under development should allow a short-term prediction of the irrigation water demand that would significantly reduce the waste of this increasingly valuable natural resource. The major finding of this work is the real possibility of deploying a remote sensing system for irrigated plots by using Commercial-Off-The-Shelf (COTS) devices, easily scalable and adaptable to design requirements such as the distance to the control center or the availability of mains electrical power at the site.

Keywords: internet of things, irrigation water control, LoRa, LTE, smart farming

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Authors: Sule Yucelbas, Gulay Tezel, Cuneyt Yucelbas, Seral Ozsen

Abstract:

In this study, it was tried to identify some heart rhythm disorders by electrocardiography (ECG) data that is taken from MIT-BIH arrhythmia database by subtracting the required features, presenting to artificial neural networks (ANN), artificial immune systems (AIS), artificial neural network based on artificial immune system (AIS-ANN) and particle swarm optimization based artificial neural network (PSO-NN) classifier systems. The main purpose of this study is to evaluate the performance of hybrid AIS-ANN and PSO-ANN classifiers with regard to the ANN and AIS. For this purpose, the normal sinus rhythm (NSR), atrial premature contraction (APC), sinus arrhythmia (SA), ventricular trigeminy (VTI), ventricular tachycardia (VTK) and atrial fibrillation (AF) data for each of the RR intervals were found. Then these data in the form of pairs (NSR-APC, NSR-SA, NSR-VTI, NSR-VTK and NSR-AF) is created by combining discrete wavelet transform which is applied to each of these two groups of data and two different data sets with 9 and 27 features were obtained from each of them after data reduction. Afterwards, the data randomly was firstly mixed within themselves, and then 4-fold cross validation method was applied to create the training and testing data. The training and testing accuracy rates and training time are compared with each other. As a result, performances of the hybrid classification systems, AIS-ANN and PSO-ANN were seen to be close to the performance of the ANN system. Also, the results of the hybrid systems were much better than AIS, too. However, ANN had much shorter period of training time than other systems. In terms of training times, ANN was followed by PSO-ANN, AIS-ANN and AIS systems respectively. Also, the features that extracted from the data affected the classification results significantly.

Keywords: AIS, ANN, ECG, hybrid classifiers, PSO

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Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour

Abstract:

We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.

Keywords: health trajectory, clustering, deep learning, DBN

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Authors: Elizabeth Spink

Abstract:

The United Nations' Sustainable Development Goal 6 sets a target for safe and affordable drinking water for all. Developing country governments aiming to achieve this goal often face significant challenges when trying to service last mile customers, particularly those in peri-urban and rural areas. Expansion of water networks often requires high connection fees from households, and demand for connections may be low if there are cheaper substitute sources of water available. This research studies the effect of the availability of substitute sources of water on demand for individual water connections in Livingstone, Zambia, using an event study analysis of metering campaigns. Metering campaigns reduce the share of a household's neighbors that can provide free water to the household if their water connection becomes disconnected due to nonpayment. The results show that household payments in newly metered regions increase by 10 percentage points in the months following metering events, with a decrease in disconnections of 6 percentage points for low-income households. To isolate the effect of changes in a household's substitution possibilities, a similar analysis is conducted among households that neighbor the metered region. These results show mixed evidence of the impact of substitutes on payment behavior and disconnections. The results suggest that metering may be effective in increasing household demand for individual water connections primarily through a lower monthly cost burden for newly metered households.

Keywords: piped-water access, water demand, water utilities, water sharing

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Authors: Yeliz Karaca, Carlo Cattani

Abstract:

This study focuses on the local Holder exponent as a measure of the function regularity for time series related to finance data. In this study, the attributes of the finance dataset belonging to 13 countries (India, China, Japan, Sweden, France, Germany, Italy, Australia, Mexico, United Kingdom, Argentina, Brazil, USA) located in 5 different continents (Asia, Europe, Australia, North America and South America) have been examined.These countries are the ones mostly affected by the attributes with regard to financial development, covering a period from 2012 to 2017. Our study is concerned with the most important attributes that have impact on the development of finance for the countries identified. Our method is comprised of the following stages: (a) among the multi fractal methods and Brownian motion Holder regularity functions (polynomial, exponential), significant and self-similar attributes have been identified (b) The significant and self-similar attributes have been applied to the Artificial Neuronal Network (ANN) algorithms (Feed Forward Back Propagation (FFBP) and Cascade Forward Back Propagation (CFBP)) (c) the outcomes of classification accuracy have been compared concerning the attributes that have impact on the attributes which affect the countries’ financial development. This study has enabled to reveal, through the application of ANN algorithms, how the most significant attributes are identified within the relevant dataset via the Holder functions (polynomial and exponential function).

Keywords: artificial neural networks, finance data, Holder regularity, multifractals

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Authors: Panagiotis Karadimos, Leonidas Anthopoulos

Abstract:

Predicting the actual cost and duration in construction projects concern a continuous and existing problem for the construction sector. This paper addresses this problem with modern methods and data available from past public construction projects. 39 bridge projects, constructed in Greece, with a similar type of available data were examined. Considering each project’s attributes with the actual cost and the actual duration, correlation analysis is performed and the most appropriate predictive project variables are defined. Additionally, the most efficient subgroup of variables is selected with the use of the WEKA application, through its attribute selection function. The selected variables are used as input neurons for neural network models through correlation analysis. For constructing neural network models, the application FANN Tool is used. The optimum neural network model, for predicting the actual cost, produced a mean squared error with a value of 3.84886e-05 and it was based on the budgeted cost and the quantity of deck concrete. The optimum neural network model, for predicting the actual duration, produced a mean squared error with a value of 5.89463e-05 and it also was based on the budgeted cost and the amount of deck concrete.

Keywords: actual cost and duration, attribute selection, bridge construction, neural networks, predicting models, FANN TOOL, WEKA

Procedia PDF Downloads 128

Authors: Yasaman Sanayei, Alireza Bahiraie

Abstract:

This paper presents a systematic methodology based on the application of artificial neural networks for sequencing batch reactor (SBR). The SBR is a fill-and-draw biological wastewater technology, which is specially suited for nutrient removal. Employing reactive dye by Sphingomonas paucimobilis bacteria at sequence batch reactor is a novel approach of dye removal. The influent COD, MLVSS, and reaction time were selected as the process inputs and the effluent COD and BOD as the process outputs. The best possible result for the discrete pole parameter was a= 0.44. In orderto adjust the parameters of ANN, the Levenberg-Marquardt (LM) algorithm was employed. The results predicted by the model were compared to the experimental data and showed a high correlation with R2> 0.99 and a low mean absolute error (MAE). The results from this study reveal that the developed model is accurate and efficacious in predicting COD and BOD parameters of the dye-containing wastewater treated by SBR. The proposed modeling approach can be applied to other industrial wastewater treatment systems to predict effluent characteristics. Note that SBR are normally operated with constant predefined duration of the stages, thus, resulting in low efficient operation. Data obtained from the on-line electronic sensors installed in the SBR and from the control quality laboratory analysis have been used to develop the optimal architecture of two different ANN. The results have shown that the developed models can be used as efficient and cost-effective predictive tools for the system analysed.

Keywords: artificial neural network, COD removal, SBR, Sphingomonas paucimobilis

Procedia PDF Downloads 407