Search results for: cloud radio access network

Authors: Amir Reza Asnafi, Ehsan Tajabadi, Mohsen Hajizeinolabedini

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The purpose of this study is to identify the concept of knowledge flow in central libraries of Tehran universities in by the pattern of American Productivity & Quality Center (APQC). The present study is an applied and descriptive survey in terms of its purpose and the methodology used. In this study, APQC framework was used for data collection. The study population is managers and supervisors of central libraries’ departments of public universities of Tehran belonging to the Ministry of Science, Research and Technology. These libraries include: Central Libraries of Al-Zahra University, Amir Kabir, Tarbiat Modarres, Tehran, Khajeh Nasir Toosi University of Technology, Shahed, Sharif, Shahid Beheshti, Allameh Tabataba'i University, Iran University of Science and Technology. Due to the limited number of members of the community, sampling was not performed and the census was conducted instead. The study of knowledge flow in central libraries of public universities in Tehran showed that in seven dimensions of knowledge flow of APQC, these libraries are far from desirable level and to achieve the ideal point, many activities in the field of knowledge flow need to be made, therefore suggestions were made in this study to reach the desired level. One Sample t Test in this research showed that these libraries are at a poor level in terms of these factors: in the dimensions of creation, identification and use of knowledge at a medium level and in the aspects of knowledge acquisition, review, sharing and access and also Manova test or Multivariable Analyze of Variance proved that there was no significant difference between the dimensions of knowledge flow between these libraries and the status of the knowledge flow in these libraries is at the same level as well. Except for the knowledge creation aspect that is slightly different in this regard that was mentioned before.

Keywords: knowledge flow, knowledge management, APQC, Tehran’s academic university libraries

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Authors: Shriya Shukla, Lachin Fernando

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Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

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Authors: Myriam Rousseau, Marie-Hélène Poulin, Suzie McKinnon, Jacinthe Bourassa

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Context: There is a growing demand for effective training programs for parents of children with autism spectrum disorders. While traditional in-person training is effective, it can be difficult for some parents to participate due to distance, time, and cost. Telepractice, a form of distance education, could be a viable alternative to address these challenges. Research objective: The objective of this study is to explore the experiences of parents of children with autism who participated in a training program offered by telepractice in order to document: 1) the experience of parents who participated in a program telepractice training program for autistic children, 2) parental satisfaction with the telepractice modality, and 3) potential benefits of using telepractice to deliver training programs to parents of autistic children. Method: This study followed a qualitative research design, and Braun and Clarke's six-step procedure was used for the thematic analysis of the comments provided by parents. Data were collected through individual interviews with parents who participated in the project. The analysis focused on identifying patterns and themes in the comments in order to better understand parents' experiences with the telepractice modality. Results: The study revealed that parents were generally satisfied with the telepractice modality, as it was easy to use and enabled a better balance between work and family. This modality also enabled parents to share and receive mutual support. Despite the positive results, it is still relevant to offer training in different modalities to meet the different needs of parents. Conclusion: The study shows that parents of children with autism are generally satisfied with telepractice as a training modality. The results suggest that telepractice can be an effective alternative to traditional face-to-face training. The study highlights the importance of taking parents' needs and preferences into account when designing and implementing training programs.

Keywords: parents, children, training, telepractice

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Authors: Jay L. Fu

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Patients with Alzheimer's disease progressively lose their memory and thinking skills and, eventually, the ability to carry out simple daily tasks. The disease is irreversible, but early detection and treatment can slow down the disease progression. In this research, publicly available MRI data and demographic data from 373 MRI imaging sessions were utilized to build models to predict dementia. Various machine learning models, including logistic regression, k-nearest neighbor, support vector machine, random forest, and neural network, were developed. Data were divided into training and testing sets, where training sets were used to build the predictive model, and testing sets were used to assess the accuracy of prediction. Key risk factors were identified, and various models were compared to come forward with the best prediction model. Among these models, the random forest model appeared to be the best model with an accuracy of 90.34%. MMSE, nWBV, and gender were the three most important contributing factors to the detection of Alzheimer’s. Among all the models used, the percent in which at least 4 of the 5 models shared the same diagnosis for a testing input was 90.42%. These machine learning models allow early detection of Alzheimer’s with good accuracy, which ultimately leads to early treatment of these patients.

Keywords: Alzheimer's disease, clinical diagnosis, magnetic resonance imaging, machine learning prediction

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Authors: Izabel Cristina Zattar, Gilberto Passos Lima, Guilherme Schünemann de Oliveira

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In Brazil, there are several institutions committed to the development of new businesses based on product innovation. Among them are business incubators, universities and science institutes. Business incubators can be defined as nurseries for new companies, which may be in the technology segment, discussed in this article. Business incubators provide services related to infrastructure, such as physical space and meeting rooms. Besides these services, incubators also offer assistance in the form of information and communication, access to finance, relationship networks and business monitoring and mentoring processes. Business incubators support not all technology companies. One of the business incubators tasks is to assess the nature and feasibility of new business proposals. To assist this goal, this paper proposes a methodology for evaluating new business using the Analytic Hierarchy Process (AHP). This paper presents the concepts used in the assessing methodology application for new business, concepts that have been tested with positive results in practice. This study counts on three main steps: first, a hierarchy was built, based on new business manuals used by the business incubators. These books and manuals relate business selection requirements, such as the innovation status and other technological aspects. Then, a questionnaire was generated, in order to guide incubator experts in the parity comparisons at all hierarchy levels. The weights of each requirement are calculated from information obtained from the questionnaire responses. Finally, the proposed method was applied to evaluate five new business proposals, which were applying to be part of a company incubator. The main result is the classification of these new businesses, which helped the incubator experts to decide what companies were more eligible to work with. This classification may also be helpful to the decision-making process of business incubators in future selection processes.

Keywords: Analytic Hierarchy Process (AHP), Brazilian companies incubators, technology companies, incubator

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Authors: Amin Nazifi, Amir Raki, Doga Istanbulluoglu

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The incorporation of technological advancements into financial services, commonly referred to as Fintech, is primarily aimed at promoting services that are accessible, convenient, and inclusive, thereby benefiting both consumers and businesses. Fintech services employ a variety of technologies, including Artificial Intelligence (AI), blockchain, and big data, to enhance the efficiency and productivity of traditional services. Cryptocurrency, a component of Fintech, is projected to be a trillion-dollar industry, with over 320 million consumers globally investing in various forms of cryptocurrencies. However, these potentially transformative services can also lead to adverse outcomes. For instance, recent Fintech innovations have been increasingly linked to misconduct and disservice, resulting in serious implications for consumer well-being. This could be attributed to the ease of access to Fintech, which enables adults to trade cryptocurrencies, shares, and stocks via mobile applications. However, there is little known about the darker aspects of technological advancements, such as Fintech. Hence, this study aims to generate scholarly insights into the design of robust and resilient Fintech services that can add value to businesses and enhance consumer well-being. Using a mixed-method approach, the study will investigate the personal and contextual factors influencing consumers’ adoption and usage of technology innovations and their impacts on consumer well-being. First, semi-structured interviews will be conducted with a sample of Fintech users until theoretical saturation is achieved. Subsequently, based on the findings of the first study, a quantitative study will be conducted to develop and empirically test the impacts of these factors on consumers’ well-being using an online survey with a sample of 300 participants experienced in using Fintech services. This study will contribute to the growing Transformative Service Research (TSR) literature by addressing the latest priorities in service research and shedding light on the impact of fintech services on consumer well-being.

Keywords: consumer well-being, financial literacy, Fintech, service innovation

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Authors: Vibeke Sorensen, Nagaraju Thummanapalli, J. Stephen Lansing

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Inspired by the indigenous people of Borneo, Indonesia and their traditional bark cloth, artist and professor Vibeke Sorensen executed a “digital unwrapping” of several trees in Southeast Asia using a digital panorama camera and digitally “stitched” them together for printing onto sustainable silk and fashioning into the “Tree Dress”. This dress is a symbolic “un-wrapping” and “re-wrapping” of the tree’s bark onto a person as a second skin. The “digital bark” is directly responsive to the real tree through embedded and networked electronics that connect in real-time to sensors at the physical site of the living tree. LEDs and circuits inserted into the dress display the continuous measurement of the O2 / CO2, temperature, humidity, and light conditions at the tree. It is an “Internet of Living Things” (IOLT) textile that can be worn to track and interact with it. The computer system connecting the dress and the tree converts the gas emission data at the site of the real tree into sound and music as sonification. This communicates not only the scientific data but also translates it into a poetic representation. The wearer of the garment can symbolically identify with the tree, or “become one” with it by adorning its “skin.” In this way, the wearer also becomes a human agent for the tree, bringing its actual condition to direct perception of the wearer and others who may engage it. This project is an attempt to bring greater awareness to issues of deforestation by providing a direct access to living things separated by physical distance, and hopefully, to increase empathy for them by providing a way to sense individual trees and their daily existential condition through remote monitoring of data. Further extensions to this project and related issues of sustainability include the use of recycled and alternative plant materials such as bamboo and air plants, among others.

Keywords: IOLT, sonification, sustainability, tree, wearable technology

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Authors: Hassan Hussin Zwida

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The liquified LPG (Liquefied Petroleum Gas) drying system at the Complex is designed to remove water and mercaptans from the LPG stream. Upon saturation of the desiccant beds, a regeneration cycle becomes necessary. The original design routed the regeneration gas, produced during the LPG dryer heating cycle, to the sulfur recovery unit to the incineration. However, concerns regarding high temperatures and potential unit disruptions led to a modification where the gas is currently vented to the acid flare for the initial hour before being diverted to the LP network fuel gas system. While this addresses the temperature concerns, it generates significant smoke due to the presence of liquid hydrocarbons. This paper proposes an approach to recover the regeneration gas and redirect it back to the gas plant's (off-gas compressors) instead of sending it to the AC (Acid Flare), by utilizing the existing pipe 6” and connected to off gas compressor KO (Knock-Out ) Drums . This option is simple to operate, flexible, environment-friendly solution as long-term solution, lower in capital expenditure and increase the company's profitability. The feasibility of this proposal is supported by dynamic simulations. The simulations suggest the possibility of operating two out of the three off-gas compressors and LPG (Liquefied petroleum gas) as a liquid phase, is foreseen to be carried over and gathered at the bottom level of the KO (Knock-Out) Drum.

Keywords: thermal incinerator, off-gas compressors, environment, knock-out drums, acid flare

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Authors: Felix Boehnisch, Alexander Lutz

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Based on the number of users and the amount of content posted daily, Facebook is considered the largest social network in the world. This content includes images or text posts from companies but also private persons, which are also commented on by other users. However, it can sometimes be difficult for companies to keep track of all the posts and the reactions to them, especially when there are several posts a day that contain hundreds to thousands of comments. To facilitate this, the following paper deals with the possible applications of sentiment analysis to social media comments in order to be able to support the work in social media marketing. In a first step, post comments were divided into positive and negative by a subjective rating, then the same comments were checked for their polarity value by the two german python libraries TextBlobDE and SentiWS and also grouped into positive, negative, or even neutral. As a control, the subjective classifications were compared with the machine-generated ones by a confusion matrix, and relevant quality criteria were determined. The accuracy of both libraries was not really meaningful, with 60% to 66%. However, many words or sentences were not evaluated at all, so there seems to be room for optimization to possibly get more accurate results. In future studies, the use of these specific German libraries can be optimized to gain better insights by either applying them to stricter cleaned data or by adding a sentiment value to emojis, which have been removed from the comments in advance, as they are not contained in the libraries.

Keywords: Facebook, German libraries, polarity, sentiment analysis, social media comments

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Authors: Soofiyan Atar, Adil Shaikh, Sahil Rajpurkar, Pragnesh Bhalala, Aniket Desai, Irfan Siddavatam

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Deep reinforcement learning (deep RL) algorithms leverage the symbolic power of complex controllers by automating it by mapping sensory inputs to low-level actions. Deep RL eliminates the complex robot dynamics with minimal engineering. Deep RL provides high-risk involvement by directly implementing it in real-world scenarios and also high sensitivity towards hyperparameters. Tuning of hyperparameters on a pneumatic quadruped robot becomes very expensive through trial-and-error learning. This paper presents an automated learning control for a pneumatic quadruped robot using sample efficient deep Q learning, enabling minimal tuning and very few trials to learn the neural network. Long training hours may degrade the pneumatic cylinder due to jerk actions originated through stochastic weights. We applied this method to the pneumatic quadruped robot, which resulted in a hopping gait. In our process, we eliminated the use of a simulator and acquired a stable gait. This approach evolves so that the resultant gait matures more sturdy towards any stochastic changes in the environment. We further show that our algorithm performed very well as compared to programmed gait using robot dynamics.

Keywords: model-based reinforcement learning, gait stability, supervised learning, pneumatic quadruped

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Authors: Dhiraj Neupane

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Decentralized policy and practices have largely concentrated on the transformation of decision-making authorities from central to local institutions (or people) in the developing world. Such policy and practices always aimed for the equitable and efficient management of resources in the line of poverty reduction. The transformation of forest decision-making autonomy has also glorified as the best forest management alternatives to maximize the forest benefits and improve the livelihood of local people living nearby the forests. However, social heterogeneity and poor decision-making capacity of local institutions (or people) pose a nexus while managing the resources and sharing the forest benefits among the user households despite the policy objectives. The situation is severe in the lowland of Nepal, where forest resources have higher economic potential and user households have heterogeneous socio-economic conditions. The study discovered that utilizing the power of decision-making autonomy, user households were putting low values of timber considering the equitable access of timber to all user households as it is the most valuable product of community forest. Being the society is heterogeneous by socio-economic conditions, households of better economic conditions were always taking higher amount of forest benefits. The low valuation of timber has negative consequences on equitable benefit sharing and poor support to livelihood improvement of user households. Moreover, low valuation has possibility to increase the local demands of timber and increase the human pressure on forests.

Keywords: decentralized forest policy, Nepal, poverty, social heterogeneity, Terai

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Authors: Debra L. Stanley, Gabriela Wasileski

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Offenders face many challenges as they transition from being incarcerated to the community, ranging from housing and employment needs to long standing problems with addictions and mental health issues. A lack of appropriate behavioral health services in the more remote parts of the United States has led to a significant illegal substance abuse problem, housing instability, and unaddressed mental health and trauma issues. High rates of poverty and unemployment exacerbate the growing behavioral health issues, drug overdoses, co-occurring disorders, and crime that are so prevalent across rural communities. This study examines the challenges of rural jail reentry faced by offenders in a treatment capacity. The client-centered evidence-based program is uniquely designed to provide continuity of care that focuses on issues which affect rural communities. Prior to release from jail, individuals go through comprehensive assessment screenings to measure mental health and substance use disorder as well as trauma and prior crime victimization histories; the assessments help to target client-specific services. The quasi-experimental research design tracks clients throughout their recovery and reintegration into the community. Individuals in a rural program often do not have the benefit of easy access or peer mentoring that is so often found in urban recovery programs. Therefore, much of the support is provided through telehealth and e-services. The goal of this study is to explore the nature of rural reentry programs and measures of recidivism, drug overdoses, and other behavioral health needs and successful reentry to include stable housing and employment.

Keywords: jail reentry, rehabilitation, behavioral health, drug abuse, recidivism

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Authors: Tess Hartland, Moitree Banerjee, Sue Churchill, Antonina Pereira, Ian Tyndall, Ruth Lowry

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Background: Sexual health services and policies for middle-aged and older adults are underdeveloped, while global sexually transmitted infections in this age group are on the rise. The Interreg cross-Europe Sexual Health In Over 45s (SHIFT) project aims to increase participation in sexual health services and improve sexual health and wellbeing in people aged over 45, with an additional focus on disadvantaged groups. Methods: A two-pronged mixed-methodology is being used to develop a model for good service provision in sexual health for over 45s. (1) Following PRISMA-ScR guidelines, a scoping review is being conducted, using the databases PsychINFO, Web of Science, ERIC and PubMed. A key search strategy using terms around sexual health, good practice, over 45s and disadvantaged groups. The initial search for literature yielded 7914 results. (2) Surveys (n=1000) based on the Theory of Planned Behaviour are being administered across the UK, Belgium and Netherlands to explore current sexual health knowledge, awareness and attitudes. Expected results: It is expected that sexual health needs and potential gaps in service provision will be identified in order to inform good practice for sexual health services for the target population. Results of the scoping review are being analysed, while focus group and survey data is being gathered. Preliminary analysis of the survey data highlights barriers to access such as limited risk awareness and stigma. All data analysis will be completed by the time of the conference. Discussion: Findings will inform the development of a model to improve sexual health and wellbeing for among over 45s, a population which is often missed in sexual health policy improvement.

Keywords: adult health, disease prevention, health promotion, over 45s, sexual health

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Authors: Yan Wang; Mathis Stock

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The World Heritage Site of Honghe Hani rice terrace, also a marginal rural region in Southern China, is undergoing rapid change because of urbanization and heritage tourism. Influenced by out-migration and changing ways of living in the urbanization process, the place sees a tendency of losing its rice terrace landscape, traditional housings and other forms of cultural traditions. However, heritage tourism tends to keep the past, valorize them for tourism purposes and diversifies rural livelihood strategies. The place stands at this development trajectories, where the same resources are subjected to different uses by different actors. The research seeks to answer the questions of how the site is transformed and co-constructed by different institutions, practices and actors, and the how heritage tourism affects local livelihood. The research aims to describe the transformation of villages, rice terraces, and cultural traditions, analyze the place-making process, and assess the role of heritage tourism in local livelihood transition. The research uses a mixed of methods including direct observation, participant observation, interviews; collects various data of images, words, narratives, and statistics, and analyze them qualitatively and qualitatively. Theoretically, it is hoped that the research would reexamine the concept of heritage, the world heritage practice from UNESCO, reveal the conflicts it entails in development and brings more thoughts from a functional perspective on heritage in relation to rural development. Practically, it is also anticipated that the research could access the linkage between heritage tourism and local livelihood, and generate concrete suggestions on how tourism could engage locals and improve their livelihood.

Keywords: cultural landscape, Hani rice terraces, heritage tourism, livelihood strategy, place making, rural development, transformation

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Authors: Lan Wu

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The declared transformation towards a ‘new electricity system dominated by renewable energy’ by China requires a cleaner electricity consumption mix with high shares of renewable energy sourced-electricity (RES-E). Unfortunately, integration of RES-E into Chinese electricity markets remains a problem pending more robust legal support, evidenced by the curtailment of wind and solar power as a consequence of integration constraints. The upcoming energy law of the PRC (energy law) is expected to provide such long-awaiting support and coordinate the existing diverse sector-specific laws to deal with the weak implementation that dampening the delivery of their desired regulatory effects. However, in the shadow of the COVID-19 crisis, it remains uncertain how this new energy law brings synergies to RES-E integration, mindful of the significant impacts of the pandemic. Through the theoretical lens of the interplay between China’s electricity reform and legislative development, the present paper investigates whether there is a paradigm shift in energy law regarding renewable energy integration compared with the existing sector-specific energy laws. It examines the 2020 draft for comments on the energy law and analyses its relationship with sector-specific energy laws focusing on RES-E integration. The comparison is drawn upon five key aspects of the RES-E integration issue, including the status of renewables, marketisation, incentive schemes, consumption mechanisms, access to power grids, and dispatching. The analysis shows that it is reasonable to expect a more open and well-organized electricity market enabling absorption of high shares of RES-E. The present paper concludes that a period of prosperous development of RES-E in the post-COVID-19 era can be anticipated with the legal support by the upcoming energy law. It contributes to understanding the signals China is sending regarding the transition towards a cleaner energy future.

Keywords: energy law, energy transition, electricity market reform, renewable energy integration

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Authors: Bandar Alahmadi, Lethia Jackson

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Machine Learning model is used today in many real-life applications. The safety and security of such model is important, so the results of the model are as accurate as possible. One challenge of machine learning model security is the adversarial examples attack. Adversarial examples are designed by the attacker to cause the machine learning model to misclassify the input. We propose a method to generate adversarial examples to attack image classifiers. We are modifying the successfully classified images, so a classifier misclassifies them after the modification. In our method, we do not update the whole image, but instead we detect the important region, modify it, place it back to the original image, and then run it through a classifier. The algorithm modifies the detected region using two methods. First, it will add abstract image matrix on back of the detected image matrix. Then, it will perform a rotation attack to rotate the detected region around its axes, and embed the trace of image in image background. Finally, the attacked region is placed in its original position, from where it was removed, and a smoothing filter is applied to smooth the background with foreground. We test our method in cascade classifier, and the algorithm is efficient, the classifier confident has dropped to almost zero. We also try it in CNN (Convolutional neural network) with higher setting and the algorithm was successfully worked.

Keywords: adversarial examples, attack, computer vision, image processing

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Authors: Vanja Skoric

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The paper examines whether the impacts of artificial intelligence (AI) can be meaningfully addressed through the rights-based approach to AI design, investigating in particular how the inclusive, participatory process of assessing the AI impact would make this viable. There is a significant gap between envisioning rights-based AI systems and their practical application. Plausibly, internalizing human rights approach within AI design process might be achieved through identifying and assessing implications of AI features human rights, especially considering the case of vulnerable individuals and communities. However, there is no clarity or consensus on how such an instrument should be operationalised to usefully identify the impact, mitigate harms and meaningfully ensure relevant stakeholders’ participation. In practice, ensuring the meaningful inclusion of those individuals, groups, or entire communities who are affected by the use of the AI system is a prerequisite for a process seeking to assess human rights impacts and risks. Engagement in the entire process of the impact assessment should enable those affected and interested to access information and better understand the technology, product, or service and resulting impacts, but also to learn about their rights and the respective obligations and responsibilities of developers and deployers to protect and/or respect these rights. This paper will provide an overview of the study and practice of the participatory design process for AI, including inclusive impact assessment, its main elements, propose a framework, and discuss the lessons learned from the existing theory. In addition, it will explore pathways for enhancing and promoting individual and group rights through such engagement by discussing when, how, and whom to include, at which stage of the process, and what are the pre-requisites for meaningful and engaging. The overall aim is to ensure using the technology that works for the benefit of society, individuals, and particular (historically marginalised) groups.

Keywords: rights-based design, AI impact assessment, inclusion, harm mitigation

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Authors: Hamidreza Ghazvinian, Khosro Ghazvinian, Touba Khodaiean

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The importance of evaporation estimation in water resources and agricultural studies is undeniable. Pan evaporation are used as an indicator to determine the evaporation of lakes and reservoirs around the world due to the ease of interpreting its data. In this research, intelligent models were investigated in estimating pan evaporation on a daily basis. Shahroud and Mayamey were considered as the studied cities. These two cities are located in Semnan province in Iran. The mentioned cities have dry weather conditions that are susceptible to high evaporation potential. Meteorological data of 11 years of synoptic stations of Shahrood and Mayamey cities were used. The intelligent models used in this study are Artificial Neural Network (ANN), Least Squares Support Vector Machine (LSSVM), and M5 tree models. Meteorological parameters of minimum and maximum air temperature (Tmax, Tmin), wind speed (WS), sunshine hours (SH), air pressure (PA), relative humidity (RH) as selected input data and evaporation data from pan (EP) to The output data was considered. 70% of data is used at the education level, and 30 % of the data is used at the test level. Models used with explanation coefficient evaluation (R2) Root of Mean Squares Error (RMSE) and Mean Absolute Error (MAE). The results for the two Shahroud and Mayamey stations showed that the above three models' operations are rather appropriate.

Keywords: pan evaporation, intelligent methods, shahroud, mayamey

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Authors: Emelia Noronha, Milind Malshe

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The phatic function of communication is a vital element of any conversation. This research paper looks into this function with respect to personal blogs maintained by Indian bloggers. This paper is a study into the phenomenon of phatic communication maintained by bloggers through their blogs. Based on a linguistic analysis of the posts of twenty eight Indian bloggers, writing in English, studied over a period of three years, the study indicates that though the blogging phenomenon is not conversational in the same manner as face-to-face communication, it does make ample provision for feedback that is conversational in nature. Ordinary day to day offline conversations use conventionalized phatic utterances; those on the social media are in a perpetual mode of innovation and experimentation in order to sustain contact with its readers. These innovative methods and means are the focus of this study. Though the personal blogger aims to chronicle his/her personal life through the blog, the socializing function is crucial to these bloggers. In comparison to the western personal blogs which focus on the presentation of the ‘bounded individual self’, we find Indian personal bloggers engage in the presentation of their ‘social selves’. These bloggers yearn to reach out to the readers on the internet and the phatic function serves to initiate, sustain and renew social ties on the blogosphere thereby consolidating the social network of readers and bloggers.

Keywords: personal blogs, phatic, social-selves, blog readers

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Authors: Judith Klostermann, Miguel Segura, Wilma Jans, Dragana Bojovic, Isadora Christel Jimenez, Francisco Doblas-Reyees, Judit Snethlage

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The Climate Data Store (CDS), developed by the Copernicus Climate Change Service (C3S) implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Union, is intended to become a key instrument for exploring climate data. The CDS contains both raw and processed data to provide information to the users about the past, present and future climate of the earth. It allows for easy and free access to climate data and indicators, presenting an important asset for scientists and stakeholders on the path for achieving a more sustainable future. The C3S Evaluation and Quality Control (EQC) is assessing the quality of the CDS by undertaking a comprehensive user requirement assessment to measure the users’ satisfaction. Recommendations will be developed for the improvement and expansion of the CDS datasets and products. User requirements will be identified on the fitness of the datasets, the toolbox, and the overall CDS service. The EQC function of the CDS will help C3S to make the service more robust: integrated by validated data that follows high-quality standards while being user-friendly. This function will be closely developed with the users of the service. Through their feedback, suggestions, and contributions, the CDS can become more accessible and meet the requirements for a diverse range of users. Stakeholders and their active engagement are thus an important aspect of CDS development. This will be achieved with direct interactions with users such as meetings, interviews or workshops as well as different feedback mechanisms like surveys or helpdesk services at the CDS. The results provided by the users will be categorized as a function of CDS products so that their specific interests will be monitored and linked to the right product. Through this procedure, we will identify the requirements and criteria for data and products in order to build the correspondent recommendations for the improvement and expansion of the CDS datasets and products.

Keywords: climate data store, Copernicus, quality, user engagement

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Authors: Olivia Borgue, Massimo Panarotto, Ola Isaksson

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This article presents a neural networks-based strategy for reducing the need for support structures when designing for additive manufacturing (AM). Additive manufacturing is a relatively new and immature industrial technology, and the information to make confident decisions when designing for AM is limited. This lack of information impacts especially the early stages of engineering design, for instance, it is difficult to actively consider the support structures needed for manufacturing a part. This difficulty is related to the challenge of designing a product geometry accounting for customer requirements, manufacturing constraints and minimization of support structure. The approach presented in this article proposes an automatized geometry modification technique for reducing the use of the support structures while designing for AM. This strategy starts with a neural network-based strategy for shape recognition to achieve product classification, using an STL file of the product as input. Based on the classification, an automatic part geometry modification based on MATLAB© is implemented. At the end of the process, the strategy presents different geometry modification alternatives depending on the type of product to be designed. The geometry alternatives are then evaluated adopting a QFD-like decision support tool.

Keywords: additive manufacturing, engineering design, geometry modification optimization, neural networks

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Authors: Maria Albuquerque, Miguel Matias, Ângelo Sá, Juliana Sousa, Maria Manuel Mouzinho

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Obesity is a frequent disease in Portugal. The surgical treatment is very effective and has an indication when there is a failure of the medical treatment. Although massive weight loss is associated with considerable health gains, these patients are characterized by a variable degree of dermolipodistrophy. In some cases, there is even the development of physical symptoms such as intertriginous, and some degree of psychological distress is present. In almost all cases, a desire for a better body contour, which inhibits some aspects of social life, is a fact. A prospective study was made to access the impact of body contouring surgery in the quality of life of patients who underwent a massive weight lost correction surgical procedure at Centro Hospitalar de Lisboa Central between January 2020 and December 2021. The patients were submitted to the Body Q subjective questionnaire adapted for the Portuguese language and accessed for the following categories: Anguish with Appearance, Contempt with Body Image, Satisfaction with the Abdomen, and Overall Satisfaction with the Body. The questionnaire was repeated at the 6 months mark. A total of 80 patients were sampled. The sex distribution was 79 female and 1 male. The median BMI index before surgery was inferior to 28%. The pre operatory questionnaire showed high scores for Anguish with Appearance and low scores for the body image self-evaluation. Overall, there was an improvement of at least 50% in all the evaluated scores. Additionally, a correlation was found between abdominoplasty and the contempt with body image and satisfaction with the abdomen (p-value <0.05). Massive weight loss is associated with important body deformities that have a significant impact on the patient’s personal and social life. Body contouring surgery is then vital for these patients as it implicates major aesthetic and functional benefits.

Keywords: abdominoplasty, cruroplasty, obesity, massive weight loss

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Authors: Melodi Mbuyi Mata, Blessing Olamide Taiwo, Afolabi Ayodele David

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The purpose of this research is to create empirical models for assessing the profitability of granite aggregate production in Akure, Ondo state aggregate quarries. In addition, an artificial neural network (ANN) model and multivariate predicting models for granite profitability were developed in the study. A formal survey questionnaire was used to collect data for the study. The data extracted from the case study mine for this study includes granite marketing operations, royalty, production costs, and mine production information. The following methods were used to achieve the goal of this study: descriptive statistics, MATLAB 2017, and SPSS16.0 software in analyzing and modeling the data collected from granite traders in the study areas. The ANN and Multi Variant Regression models' prediction accuracy was compared using a coefficient of determination (R²), Root mean square error (RMSE), and mean square error (MSE). Due to the high prediction error, the model evaluation indices revealed that the ANN model was suitable for predicting generated profit in a typical quarry. More quarries in Nigeria's southwest region and other geopolitical zones should be considered to improve ANN prediction accuracy.

Keywords: national development, granite, profitability assessment, ANN models

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Authors: Caiwei Chen, Jianhao Li, Jiasong Zhu

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The reform and opening-up stimulated economic and technological take-off in China while resulting in massive urbanization and motorization. Wujiaochang area was set as a secondary business district in Shanghai to meet the growing demand, with the reconstruction of Wujiaochang Plaza in 2005 being a milestone of this intended urban renewal. Wujiaochang is now an economically dynamic area providing much larger traffic and transit capacity transportation-wise. However, this rebuilding has completely changed the face of the district. It is, therefore, appropriate to evaluate its impact on neighborhoods and communities while assessing the overall sustainability of such an operation. In this study, via an online questionnaire survey among local residents and daily visitors, we assess the perceptions and the estimated impact of Wujiaochang Plaza's reconstruction. We then confront these results to the 62 answers from local residents to a questionnaire collected on paper. The analysis of our data, along with observation and other forms of information -such as maps analysis or online applications (Dianping)- demonstrate major improvement in economic sustainability but also significant losses in environmental sustainability, especially in terms of active transportation. As for the social viewpoint, local residents' opinions tend to be rather positive, especially regarding traffic safety and access to consumption, despite the lack of connectivity and radical changes induced by Wujiaochang massive transformations. In general, our investigation exposes the overall positive outcomes of Wujiaochang Plaza reconstruction but also unveils major drawbacks, especially in terms of soft mobility and traffic fluidity. We gather that our approach could be of tremendous help for future major urban interventions, as such approaches in municipal regeneration are widely implemented in Chinese cities and yet still need to be thoroughly assessed in terms of sustainability.

Keywords: China's reform and opening-up, economical revitalization, neighborhood identity, sustainability assessment, urban renewal

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Authors: L. Edirisinghe, P. Mukherjee, H. Edirisinghe

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Virtual Container Yard (VCY) is a modern concept that helps to reduce the empty container repositioning cost of carriers. The concept of VCY is based on container interchange between shipping lines. Although this mechanism has been theoretically accepted by the shipping community as a feasible solution, it has not yet achieved the necessary momentum among container shipping lines (CSL). This paper investigates whether there is any legal influence on this industry myopia about the VCY. It is believed that this is the first publication that focuses on the legal aspects of container exchange between carriers. Not much literature on this subject is available. This study establishes with statistical evidence that there is a phobia prevailing in the shipping industry that exchanging containers with other carriers may lead to various legal implications. The complexity of exchange is two faceted. CSLs assume that offering a container to another carrier (obviously, a competitor in terms of commercial context) or using a container offered by another carrier may lead to undue legal implications. This research reveals that this fear is reflected through four types of perceived components, namely: shipping associate; warehouse associate; network associate; and trading associate. These components carry eighteen subcomponents that comprehensively cover the entire process of a container shipment. The statistical explanation has been supported through regression analysis; INCO terms were used to illustrate the shipping process.

Keywords: virtual container yard, legal, maritime law, inventory

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Authors: Prashansa Singh, Manjot Kaur, Rohit Bajaj

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The proliferation of wireless sensor networks and Internet of Things (IoT) devices in the quickly changing digital landscape has highlighted the urgent need for strong security solutions that can handle these systems’ limited resources. A key solution to this problem is the emergence of ultralightweight security protocols, which provide strong security features while respecting the strict computational, energy, and memory constraints imposed on these kinds of devices. This in-depth analysis explores the field of ultralightweight security protocols, offering a thorough examination of their evolution, salient features, and the particular security issues they resolve. We carefully examine and contrast different protocols, pointing out their advantages and disadvantages as well as the compromises between resource limitations and security resilience. We also study these protocols’ application domains, including the Internet of Things, RFID systems, and wireless sensor networks, to name a few. In addition, the review highlights recent developments and advancements in the field, pointing out new trends and possible avenues for future research. This paper aims to be a useful resource for researchers, practitioners, and developers, guiding the design and implementation of safe, effective, and scalable systems in the Internet of Things era by providing a comprehensive overview of ultralightweight security protocols.

Keywords: wireless sensor network, machine-to-machine, MQTT broker, server, ultralightweight, TCP/IP

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Authors: Dermot O'Brien, Vasileios Christaras, Georgios Fontaras, Igor Nai Fovino, Ioannis Kounelis

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With the rise of the Internet of Things (IoT), 5G, and blockchain (BC) technologies, vehicles are becoming ever increasingly connected and are already transmitting substantial amounts of data to the original equipment manufacturers (OEMs) servers. This data could be used to help detect mileage fraud and enable more accurate vehicle emissions monitoring. This would not only help regulators but could enable applications such as permitting efficient drivers to pay less tax, geofencing for air quality improvement, as well as pollution tolling and trading platforms for transport-related businesses and EU citizens. Other applications could include traffic management and shared mobility systems. BC enables the transmission of data with additional security and removes single points of failure while maintaining data provenance, identity ownership, and the possibility to retain varying levels of privacy depending on the requirements of the applied use case. This research performs simulations of vehicles interacting with European member state authorities and European Commission BC nodes that are running hyperleger fabric and explores whether the technology is currently feasible for transport applications such as the emission monitoring use-case.

Keywords: future transportation systems, technological innovations, policy approaches for transportation future, economic and regulatory trends, blockchain

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Sireen Irsheid

Abstract:

Despite concerted efforts to improve education attainment with progress in recent years, student achievement and attainment remain among the most significant challenges for school districts across the United States. Many scholars have argued that students who do not complete high school do not drop out of school voluntarily but are ‘pushed out’ of schools through multiple mechanisms related to structural and socioeconomic barriers, behavioral health challenges, pedagogical practices, and administrative procedures. Extant literature has shown that living in historically disadvantaged neighborhoods or attending under-resourced schools exacerbates student-level risk factors for grade retention and school pushout. Most efforts to respond to the school pushout phenomenon have focused on individual characteristics of students, with relatively little attention to addressing these multiple system-level characteristics related to perpetuating inequities. This study is built on a growing body of social justice-oriented research concerned with the systemic influences that shape the experiences and mental health challenges of young people. Specifically, this study examined how young people who have been experiencing education inequities make meaning and navigate the structural factors related to neighborhood and school disinvestment and access to resources and supports, and their risk for school pushout. Furthermore, schools as political, cultural, and ideologically reproductive spaces often serve as sites of resistance and can support students who are impacted by educational inequity. Study findings provide education, neighborhood, school psychology, social work practice, and policy considerations.

Keywords: education policy, mental health, school prison nexus, school pushout, structural trauma

Procedia PDF Downloads 48

Authors: Nantakrit Yodpijit, Teppakorn Sittiwanchai

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The number of older adults is rising rapidly. The world’s population becomes aging. Falls is one of common and major health problems in the elderly. Falls may lead to acute and chronic injuries and deaths. The fall-prone individuals are at greater risk for decreased quality of life, lowered productivity and poverty, social problems, and additional health problems. A number of studies on falls prevention using fall detection system have been conducted. Many available technologies for fall detection system are laboratory-based and can incur substantial costs for falls prevention. The utilization of alternative technologies can potentially reduce costs. This paper presents the new design and development of a wearable-based fall detection system using an Accelerometer and Gyroscope as motion sensors for the detection of body orientation and movement. Algorithms are developed to differentiate between Activities of Daily Living (ADL) and falls by comparing Threshold-based values with Artificial Neural Networks (ANN). Results indicate the possibility of using the new threshold-based method with neural network algorithm to reduce the number of false positive (false alarm) and improve the accuracy of fall detection system.

Keywords: aging, algorithm, artificial neural networks (ANN), fall detection system, motion sensorsthreshold

Procedia PDF Downloads 479