Search results for: mobile network

Authors: S. Neelima, P. S. Subramanyam

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A distribution system is an interface between the bulk power system and the consumers. Among these systems, radial distributions system is popular because of low cost and simple design. In distribution systems, the voltages at buses reduces when moved away from the substation, also the losses are high. The reason for a decrease in voltage and high losses is the insufficient amount of reactive power, which can be provided by the shunt capacitors. But the placement of the capacitor with an appropriate size is always a challenge. Thus, the optimal capacitor placement problem is to determine the location and size of capacitors to be placed in distribution networks in an efficient way to reduce the power losses and improve the voltage profile of the system. For this purpose, in this paper, two stage methodologies are used. In the first stage, the load flow of pre-compensated distribution system is carried out using ‘dimension reducing distribution load flow algorithm (DRDLFA)’. On the basis of this load flow the potential locations of compensation are computed. In the second stage, Genetic Algorithm (GA) technique is used to determine the optimal location and size of the capacitors such that the cost of the energy loss and capacitor cost to be a minimum. The above method is tested on IEEE 9 and 34 bus system and compared with other methods in the literature.

Keywords: dimension reducing distribution load flow algorithm, DRDLFA, genetic algorithm, electrical distribution network, optimal capacitors placement, voltage profile improvement, loss reduction

Procedia PDF Downloads 373

Authors: Bi Zhao

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Neural network technology has greatly improved the output of machine translation in terms of both fluency and accuracy, which greatly increases its appeal for young users. The present exploratory study aims to find out how the Chinese undergraduates perceive and use machine translation in their daily life. A survey is conducted to collect data from 100 undergraduate students from multiple Chinese universities and with varied academic backgrounds, including arts, business, science, engineering, and medicine. The survey questions inquire about their use (including frequency, scenarios, purposes, and preferences) of and attitudes (including trust, quality assessment, justifications, and ethics) toward machine translation. Interviews and tasks of evaluating machine translation output are also employed in combination with the survey on a sample of selected respondents. The results indicate that Chinese undergraduate students use machine translation on a daily basis for a wide range of purposes in academic, communicative, and entertainment scenarios. Most of them have preferred machine translation tools, but the availability of machine translation tools within a certain scenario, such as the embedded machine translation tool on the webpage, is also the determining factor in their choice. The results also reveal that despite the reportedly limited trust in the accuracy of machine translation output, most students lack the ability to critically analyze and evaluate such output. Furthermore, the evidence is revealed of the inadequate awareness of ethical responsibility as machine translation users among Chinese undergraduate students.

Keywords: Chinese undergraduates, machine translation, trust, usage

Procedia PDF Downloads 116

Authors: M. Kosacka, I. Kudelska

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Vehicle is one of the most influential and complex product worldwide, which affects people’s life, state of the environment and condition of the economy (all aspects of sustainable development concept) during each stage of lifecycle. With the increase of vehicles’ number, there is growing potential for management of End of Life Vehicle (ELV), which is hazardous waste. From one point of view, the ELV should be managed to ensure risk elimination, but from another point, it should be treated as a source of valuable materials and spare parts. In order to obtain materials and spare parts, there are established recycling networks, which are an example of sustainable policy realization at the national level. The basic object in the polish recycling network is dismantling facility. The output material streams in dismantling stations include waste, which very often generate costs and spare parts, that have the biggest potential for revenues creation. Both outputs are stored into warehouses, according to the law. In accordance to the revenue creation and sustainability potential, it has been placed a strong emphasis on storage process. We present the concept of storage method, which takes into account the specific of the dismantling facility in order to support decision-making process with regard to the principles of sustainable development. The method was developed on the basis of case study of one of the greatest dismantling facility in Poland.

Keywords: dismantling, end of life vehicles, sustainability, storage

Procedia PDF Downloads 255

Authors: Bircan Demiral

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Cognitive radio (CR) is the promising technology that addresses the spectrum scarcity problem for future wireless communications. Orthogonal Frequency Division Multiplexing (OFDM) technology provides more power band ratios for cognitive radio networks (CRNs). While CR is a solution to the spectrum scarcity, it also brings up the capacity problem. In this paper, a novel power allocation algorithm that aims at maximizing the sum capacity in the OFDM based cognitive radio networks is proposed. Proposed allocation algorithm is based on the previously developed water-filling algorithm. To reduce the computational complexity calculating in water filling algorithm, proposed algorithm allocates the total power according to each subcarrier. The power allocated to the subcarriers increases sum capacity. To see this increase, Matlab program was used, and the proposed power allocation was compared with average power allocation, water filling and general power allocation algorithms. The water filling algorithm performed worse than the proposed algorithm while it performed better than the other two algorithms. The proposed algorithm is better than other algorithms in terms of capacity increase. In addition the effect of the change in the number of subcarriers on capacity was discussed. Simulation results show that the increase in the number of subcarrier increases the capacity.

Keywords: cognitive radio network, OFDM, power allocation, water filling

Procedia PDF Downloads 122

Authors: Wen Li, Zhengyu Bai, Qi Zhang

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The demand for smart scheduling platform in agriculture, particularly in the scheduling process of machinery equipment, is high. With the continuous development of agricultural machinery equipment technology, a large number of agricultural machinery equipment and agricultural machinery cooperative service organizations continue to appear in China. The large area of cultivated land and a large number of agricultural activities in the central and western regions of China have made the demand for smart and efficient agricultural machinery equipment scheduling platforms more intense. In this study, we design and implement a platform for agricultural machinery equipment scheduling to allocate agricultural machinery equipment resources reasonably. With agricultural machinery equipment scheduling platform taken as the research object, we discuss its research significance and value, use the service blueprint technology to analyze and characterize the agricultural machinery equipment schedule workflow, the network analytic method to obtain the demand platform function requirements, and divide the platform functions through the platform function division diagram. Simultaneously, based on the case-based reasoning (CBR) algorithm, the equipment scheduling module of the agricultural machinery equipment scheduling platform is realized; finally, a design scheme of the agricultural machinery equipment scheduling platform architecture is provided, and the visualization interface of the platform is established via VB programming language. It provides design ideas and theoretical support for the construction of a modern agricultural equipment information scheduling platform.

Keywords: case-based reasoning, service blueprint, system design, ANP, VB programming language

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Authors: Chen Yang, Jun Hu, Ping Li, Lili Xue

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Most companies pay careful attention to consumer feedback collection, so it is popular to find the ‘feedback’ button of all kinds of mobile apps. Yet it is much more changeling to analyze these feedback texts and to catch the true feelings of a consumer regarding either a problem or a complimentary of consumers who hands out the feedback. Especially to the Chinese content, it is possible that; in one context the Chinese feedback expresses positive feedback, but in the other context, the same Chinese feedback may be a negative one. For example, in Chinese, the feedback 'operating with loudness' works well with both refrigerator and stereo system. Apparently, this feedback towards a refrigerator shows negative feedback; however, the same feedback is positive towards a stereo system. By introducing Bradley, M. and Lang, P.'s Affective Norms for English Text (ANET) theory and Bucci W.’s Referential Activity (RA) theory, we, usability researchers at Pingan, are able to decipher the feedback and to find the hidden feelings behind the content. We subtract 2 disciplines ‘valence’ and ‘dominance’ out of 3 of ANET and 2 disciplines ‘concreteness’ and ‘specificity’ out of 4 of RA to organize our own rating system with a scale of 1 to 5 points. This rating system enables us to judge the feelings/emotion behind each feedback, and it works well with both single word/phrase and a whole paragraph. The result of the rating reflects the strength of the feeling/emotion of the consumer when he/she is typing the feedback. In our daily work, we first require a consumer to answer the net promoter score (NPS) before writing the feedback, so we can determine the feedback is positive or negative. Secondly, we code the feedback content according to company problematic list, which contains 200 problematic items. In this way, we are able to collect the data that how many feedbacks left by the consumer belong to one typical problem. Thirdly, we rate each feedback based on the rating system mentioned above to illustrate the strength of the feeling/emotion when our consumer writes the feedback. In this way, we actually obtain two kinds of data 1) the portion, which means how many feedbacks are ascribed into one problematic item and 2) the severity, how strong the negative feeling/emotion is when the consumer is writing this feedback. By crossing these two, and introducing the portion into X-axis and severity into Y-axis, we are able to find which typical problem gets the high score in both portion and severity. The higher the score of a problem has, the more urgent a problem is supposed to be solved as it means more people write stronger negative feelings in feedbacks regarding this problem. Moreover, by introducing hidden Markov model to program our rating system, we are able to computerize the scoring system and are able to process thousands of feedback in a short period of time, which is efficient and accurate enough for the industrial purpose.

Keywords: computerized scoring system, feeling/emotion of consumer feedback, referential activity, text mining

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Authors: Arth Bohra, Marwa Mahmoud

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In this paper, we utilize computer vision to develop a tool for youth coaches to formulate set-piece tactics for their players. We used the Soccernet database to extract the ResNet features and camera calibration data for over 3000 corner kick across 500 professional matches in the top 6 European leagues (English Premier League, UEFA Champions League, Ligue 1, La Liga, Serie A, Bundesliga). Leveraging the provided homography matrix, we construct a feature vector representing the formation of players on these corner kicks. Additionally, labeling the videos manually, we obtained the pass-trajectory of each of the 3000+ corner kicks by segmenting the field into four zones. Next, after determining the localization of the players and ball, we used event data to give the corner kicks a rating on a 1-4 scale. By employing a Convolutional Neural Network, our model managed to predict the success of a corner kick given the formations of players. This suggests that with the right formations, teams can optimize the way they approach corner kicks. By understanding this, we can help coaches formulate set-piece tactics for their own teams in order to maximize the success of their play. The proposed model can be easily extended; our method could be applied to even more game situations, from free kicks to counterattacks. This research project also gives insight into the myriad of possibilities that artificial intelligence possesses in transforming the domain of sports.

Keywords: soccer, corner kicks, AI, computer vision

Procedia PDF Downloads 157

Authors: Ping-Hung Chen, Han-Liang Lin

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Taiwan has formed diverse settlement patterns in different time and space backgrounds. Various socio-network links are created between individuals, families, communities, and societies, and different living cultures are also derived. But rapid urbanization and social structural change have caused the creation of densely-packed assembly housing complexes and made neighborhood community upward developed. This, among others, seemed to have affected neighborhood relationship and also created social problems. To understand the complex relations and socio-spatial structure of the community, it is important to use mixed methods. This research employs the theory of space syntax to analyze the layout and structural indicators of the selected communities in Tainan city. On the other hand, this research does the survey about residents' interactions and the sense of community by questionnaire of the selected communities. Then the mean values of the syntax measures from each community were correlated with the results of the questionnaire using a Pearson correlation to examine how elements in physical design affect the sense of community and neighborhood relationship. In Taiwan, most urban morphology research methods are qualitative study. This paper tries to use space syntax to find out the correlation between the community layout and the neighborhood relationship. The result of this study could be used in future studies or improve the quality of residential communities in Taiwan.

Keywords: community layout, neighborhood relationship, space syntax, mixed-method

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Authors: Parisa Mansour

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The coronavirus disease 2019 (COVID-19) was discovered and rapidly spread to various countries around the world since the end of 2019. Computed tomography (CT) images have been used as an important alternative to the time-consuming RT. PCR test. However, manual segmentation of CT images alone is a major challenge as the number of suspected cases increases. Thus, accurate and automatic segmentation of COVID-19 infections is urgently needed. Because the imaging features of the COVID-19 infection are different and similar to the background, existing medical image segmentation methods cannot achieve satisfactory performance. In this work, we try to build a deep convolutional neural network adapted for the segmentation of chest CT images with COVID-19 infections. First, we maintain a large and novel chest CT image database containing 165,667 annotated chest CT images from 861 patients with confirmed COVID-19. Inspired by the observation that the boundary of an infected lung can be improved by global intensity adjustment, we introduce a feature variable block into the proposed deep CNN, which adjusts the global features of features to segment the COVID-19 infection. The proposed PV array can effectively and adaptively improve the performance of functions in different cases. We combine features of different scales by proposing a progressive atrocious space pyramid fusion scheme to deal with advanced infection regions with various aspects and shapes. We conducted experiments on data collected in China and Germany and showed that the proposed deep CNN can effectively produce impressive performance.

Keywords: chest, COVID-19, chest Image, coronavirus, CT image, chest CT

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Authors: Noel Busch-Armendariz, Caitlin Sulley

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Introduction: This study investigated the development of college experiences, COVID-19 pandemic experiences, alcohol use, and sexual violence. The longitudinal study includes 656 college students living in the same dormitory. Students' alcohol use and social network structure were investigated to better understand the relationship with sexual violence risk. Basic Methodologies: Over two years, students repeated five web-based surveys, including a pre-college survey and surveys during four consecutive semesters. Questions were added in the fourth wave to assess students’ experiences of the COVID-19 pandemic, administered from November-January 2021, including mental and behavioral health. Analyses include the impact of COVID on living arrangements, drinking behaviors, and daily life; experiences of COVID symptoms, testing, and diagnosis, responses to COVID such as social distancing, quarantining, not working, increased health care needs; experience of fear, worry, stigma, emotional well-being, loneliness, and mental health; experiences of financial loss, lack of basic supplies, receiving emotional and financial support, and comparison with academic disengagement. Concluding Statement: Findings and discussion will include strategies to strengthen mental and behavioral health programs and policies.

Keywords: COVID, mental health, substance abuse, college students, sexual misconducts

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Authors: Abdul Mannan Akhtar

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In this paper a novel concept of a self learning smart personalized computer assistant (ACPCA) is established which is a context aware system. Based on user habits, moods, and other routines/situational reactions the system will manage various services and suggestions at appropriate times including what schedule to follow, what to watch, what software to be used, what should be deleted etc. This system will utilize a hybrid fuzzyNeural model to predict what the user will do next and support his actions. This will be done by establishing fuzzy sets of user activities, choices, preferences etc. and utilizing their combinations to predict his moods and immediate preferences. Various application of context aware systems exist separately e.g. on certain websites for music or multimedia suggestions but a personalized autonomous system that could adapt to user’s personality does not exist at present. Due to the novelty and massiveness of this concept, this paper will primarily focus on the problem establishment, product features and its functionality; however a small mini case is also implemented on MATLAB to demonstrate some of the aspects of ACPCA. The mini case involves prediction of user moods, activity, routine and food preference using a hybrid fuzzy-Neural soft computing technique.

Keywords: context aware systems, APCPCA, soft computing techniques, artificial intelligence, fuzzy logic, neural network, mood detection, face detection, activity detection

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Authors: Irina A. Gudkova, Yousra Demigha

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Scientific research is moving more and more towards the study of complex systems in several areas of economics, biology physics, and computer science. In this paper, we will work on complex systems in communication networks, Wireless Sensor Networks (WSN) that are considered as stochastic systems composed of interacting entities. The current advancements of the sensing in computing and communication systems is an investment ground for research in several tracks. A detailed presentation was made for the WSN, their use, modeling, different problems that can occur in their application and some solutions. The main goal of this work reintroduces the idea of mean field method since it is a powerful technique to solve this type of models especially systems that evolve according to a Continuous Time Markov Chain (CTMC). Modeling of a CTMC has been focused; we obtained a large system of interacting Continuous Time Markov Chain with population entities. The main idea was to work on one entity and replace the others with an average or effective interaction. In this context to make the solution easier, we consider a wireless sensor network as a multi-body problem and we reduce it to one body problem. The method was applied to a system of WSN modeled as a Markovian queue showing the results of the used technique.

Keywords: Continuous-Time Markov Chain, Hidden Markov Chain, mean field method, Wireless sensor networks

Procedia PDF Downloads 144

Authors: Gabriela V. Angeles Perez, Jose Castillejos Lopez, Araceli L. Reyes Cabello, Emilio Bravo Grajales, Adriana Perez Espinosa, Jose L. Quiroz Fabian

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Road traffic accidents are among the principal causes of traffic congestion, causing human losses, damages to health and the environment, economic losses and material damages. Studies about traditional road traffic accidents in urban zones represents very high inversion of time and money, additionally, the result are not current. However, nowadays in many countries, the crowdsourced GPS based traffic and navigation apps have emerged as an important source of information to low cost to studies of road traffic accidents and urban congestion caused by them. In this article we identified the zones, roads and specific time in the CDMX in which the largest number of road traffic accidents are concentrated during 2016. We built a database compiling information obtained from the social network known as Waze. The methodology employed was Discovery of knowledge in the database (KDD) for the discovery of patterns in the accidents reports. Furthermore, using data mining techniques with the help of Weka. The selected algorithms was the Maximization of Expectations (EM) to obtain the number ideal of clusters for the data and k-means as a grouping method. Finally, the results were visualized with the Geographic Information System QGIS.

Keywords: data mining, k-means, road traffic accidents, Waze, Weka

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Authors: Massimiliano Condotta, Giovanni Borga, Chiara Scanagatta

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Urban systems are places where the various actors involved interact and enter in conflict, in particular with reference to topics such as traffic congestion and security. But topics of discussion, and often clash because of their strong complexity, are air and noise pollution. For air pollution, the complexity stems from the fact that atmospheric pollution is due to many factors, but above all, the observation and measurement of the amount of pollution of a transparent, mobile and ethereal element like air is very difficult. Often the perceived condition of the inhabitants does not coincide with the real conditions, because it is conditioned - sometimes in positive ways other in negative ways - from many other factors such as the presence, or absence, of natural elements such as trees or rivers. These problems are seen with noise pollution as well, which is also less considered as an issue even if it’s problematic just as much as air quality. Starting from these opposite positions, it is difficult to identify and implement valid, and at the same time shared, mitigation solutions for the problem of urban pollution (air and noise pollution). The LOOPER (Learning Loops in the Public Realm) project –described in this paper – wants to build and test a methodology and a platform for participatory co-design, planning, and construction process inside a learning loop process. Novelties in this approach are various; the most relevant are three. The first is that citizens participation starts since from the research of problems and air quality analysis through a participatory data collection, and that continues in all process steps (design and construction). The second is that the methodology is characterized by a learning loop process. It means that after the first cycle of (1) problems identification, (2) planning and definition of design solution and (3) construction and implementation of mitigation measures, the effectiveness of implemented solutions is measured and verified through a new participatory data collection campaign. In this way, it is possible to understand if the policies and design solution had a positive impact on the territory. As a result of the learning process produced by the first loop, it will be possible to improve the design of the mitigation measures and start the second loop with new and more effective measures. The third relevant aspect is that the citizens' participation is carried out via Urban Living Labs that involve all stakeholder of the city (citizens, public administrators, associations of all urban stakeholders,…) and that the Urban Living Labs last for all the cycling of the design, planning and construction process. The paper will describe in detail the LOOPER methodology and the technical solution adopted for the participatory data collection and design and construction phases.

Keywords: air quality, co-design, learning loops, noise pollution, urban living labs

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Authors: Oseni Taiwo Amoo, Bloodless Dzwairo

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In this paper, we extend the versatility and usefulness of GIS as a methodology for any river basin hydrologic characteristics analysis (HCA). The Gurara River basin located in North-Central Nigeria is presented in this study. It is an on-going research using spatial Digital Elevation Model (DEM) and Arc-Hydro tools to take inventory of the basin characteristics in order to predict water abstraction quantification on streamflow regime. One of the main concerns of hydrological modelling is the quantification of runoff from rainstorm events. In practice, the soil conservation service curve (SCS) method and the Conventional procedure called rational technique are still generally used these traditional hydrological lumped models convert statistical properties of rainfall in river basin to observed runoff and hydrograph. However, the models give little or no information about spatially dispersed information on rainfall and basin physical characteristics. Therefore, this paper synthesizes morphometric parameters in generating runoff. The expected results of the basin characteristics such as size, area, shape, slope of the watershed and stream distribution network analysis could be useful in estimating streamflow discharge. Water resources managers and irrigation farmers could utilize the tool for determining net return from available scarce water resources, where past data records are sparse for the aspect of land and climate.

Keywords: hydrological characteristic, stream flow, runoff discharge, land and climate

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Authors: Nazanin Naderi, Milton Smith

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Since worldwide demand for renewable energy is increasing rapidly because of the climate problem and the limitation of fossil fuels, technologies of alternative energy sources have been developed and the electric power network now includes renewable energy resources such as wind energy. Because of the huge advantages that wind energy has, like reduction in natural gas use, price pressure, emissions of greenhouse gases and other atmospheric pollutants, electric sector water consumption and many other contributions to the nation’s economy like job creation it has got too much attention these days from different parts of the world especially in the United States which is trying to provide 20% of the nation’s energy from wind by 2030. This study is trying to evaluate the effect of splitting wind farms on power output. We are trying to find if we can get more output by installing wind turbines in different sites rather than installing all wind turbines in one site. Five potential sites in Texas have been selected as a case study and two years wind data has been gathered for these sites. Wind data are analyzed and effect of correlation between sites on power output has been evaluated. Standard deviation and autocorrelation effect has also been considered for this study. The paper has been organized as follows: After the introduction the second section gives a brief overview of wind analysis. The third section addresses the case study and evaluates correlation between sites, auto correlation of sites and standard deviation of power output. In section four we describe the results.

Keywords: auto correlation, correlation between sites, splitting wind farms, power output, standard deviation

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Authors: Sekkal Nawel

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The Web of Things (WoT), integration of smart technologies from the Internet or network to Web architecture or application, is becoming more complex, larger, and dynamic. The WoT is associated with various elements such as sensors, devices, networks, protocols, data, functionalities, and architectures to perform services for stakeholders. These services operate in the context of the interaction of stakeholders and the WoT elements. Such context is becoming a key information source from which data are of various nature and uncertain, thus leading to complex situations. In this paper, we take interest in the development of intelligent Web services. The key ingredients of this “intelligent” notion are the context diversity, the necessity of a semantic representation to manage complex situations and the capacity to reason with uncertain data. In this perspective, we introduce a multi-layered architecture based on a generic intelligent Web service model dealing with various contexts, which proactively predict future situations and reactively respond to real-time situations in order to support decision-making. For semantic context data representation, we use PR-OWL, which is a probabilistic ontology based on Multi-Entity Bayesian Networks (MEBN). PR-OWL is flexible enough to represent complex, dynamic, and uncertain contexts, the key requirements of the development for the intelligent Web services. A case study was carried out using the proposed architecture for intelligent plant watering to show the role of proactive and reactive contextual reasoning in terms of WoT.

Keywords: smart web service, the web of things, context reasoning, proactive, reactive, multi-entity bayesian networks, PR-OWL

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Authors: Veepaul Kaur Mann Balwinder Singh

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The COVID-19 had an intense impact on several countries across the world. National governments have imposed widespread restrictions to prevent the growth of this pandemic. The new health competitive scenario induced by the COVID-19 crisis raised many issues on how business activities should be reorganized due to the difficulties of physical interactions with distributors, suppliers and customers. The pandemic has particularly affected the whole selling process because of the relevant issues that emerged in managing physical sale channels and interactions with one another, both in the Business-to-Consumer and in the Business-to-Business markets. Recent research about the appropriate actions and strategies that could help firms overcome the crisis has highlighted the key role of digital expertise that may ensure connections and, thus, help business activities run smoothly. This could be true, especially with the occurrence of strong limitations on physical interactions during the COVID-19 pandemic. The catastrophe changes life publically and economically. People are living alone for following the social distancing norms. In that set-up, Digital Marketing is playing an important role in civilization. Anyone can buy any item, pay bills, transfer money and compare items through Digital Marketing without physical interactions. After COVID-19, people will be more aware of health safety and trust. So, through Digital Marketing, organizations can approach customers and provide good service environments. In such a situation, the online network becomes the most important encouraging for online customers to get in contact with the firm and carry out online selling and purchasing activities around the world.

Keywords: COVID-19, business, digital marketing, online customer

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Authors: Suteera Pradubwong, Darawan Augsornwan, Pornpen Pathumwiwathana, Benjamas Prathanee, Bowornsilp Chowchuen

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Background: Cleft lip and palate (CLP) congenital anomalies have a high prevalence in the Northeast of Thailand. A care team’s understand of treatment plan would help to guide the family of patients with CLP to achieve the treatment. Objectives: To examine the impact of the empowering volunteer project, established in the northeast Thailand. Materials and Methods: The Empowering Volunteer project was conducted in 2008 under the Tawanchai Royal Granted project. The patients and family’s general information, treatment, the group brainstorming, and satisfaction with the project were analyzed. Results: Participants were 12 children with CLP, their families and five volunteers with CLP; the participating patients were predominantly females and the mean, age was 12.2 years. The treatment comprised of speech training, dental hygiene care, bone graft and orthodontic treatment. Four issues were addressed including: problems in taking care of breast feeding; instructions’ needs for care at birth; difficulty in access information and society impact; and needs in having a network of volunteers. Conclusions: Empowering volunteer is important for holistic care of patients with CLP which provides easy access and multiple channels for patients and their families. It should be developed as part of the self-help and family support group, the development of community based team and comprehensive CLP care program.

Keywords: self-help and family support group, community based model, volunteer, cleft lip-cleft palate

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Authors: Nahla H. Alaswadko, Rayya A. Hassan, Bayar N. Mohammed

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Empirical deterministic models have been developed to predict roughness progression of heavy duty spray sealed pavements for a dataset representing rural arterial roads. The dataset provides a good representation of the relevant network and covers a wide range of operating and environmental conditions. A sample with a large size of historical time series data for many pavement sections has been collected and prepared for use in multilevel regression analysis. The modelling parameters include road roughness as performance parameter and traffic loading, time, initial pavement strength, reactivity level of subgrade soil, climate condition, and condition of drainage system as predictor parameters. The purpose of this paper is to report the approaches adopted for models development and validation. The study presents multilevel models that can account for the correlation among time series data of the same section and to capture the effect of unobserved variables. Study results show that the models fit the data very well. The contribution and significance of relevant influencing factors in predicting roughness progression are presented and explained. The paper concludes that the analysis approach used for developing the models confirmed their accuracy and reliability by well-fitting to the validation data.

Keywords: roughness progression, empirical model, pavement performance, heavy duty pavement

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Authors: Raymond Xu, Cindy Jingru Wang

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Social media has become a globally connected and influencing platform. Social media data, such as tweets, can help predict the spread of pandemics and provide individuals and healthcare providers early warnings. Public psychological reactions and opinions can be efficiently monitored by AI models on the progression of dominant topics on Twitter. However, statistics show that as the coronavirus spreads, so does an infodemic of misinformation due to pandemic-related factors such as unemployment and lockdowns. Social media algorithms are often biased toward outrage by promoting content that people have an emotional reaction to and are likely to engage with. This can influence users’ attitudes and cause confusion. Therefore, social media is a double-edged sword. Combating fake news and biased content has become one of the essential tasks. This research analyzes the variety of methods used for fake news detection covering random forest, logistic regression, support vector machines, decision tree, naive Bayes, BoW, TF-IDF, LDA, CNN, RNN, LSTM, DeepFake, and hierarchical attention network. The performance of each method is analyzed. Based on these models’ achievements and limitations, a multi-dimensional AI framework is proposed to achieve higher accuracy in infodemic detection, especially pandemic-related news. The model is trained on contextual content, images, and news metadata.

Keywords: artificial intelligence, fake news detection, infodemic detection, image recognition, sentiment analysis

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Authors: Natália Bruno Chaves, Richardes Caúla, Roosevelt do Vale, Daniela Toneti, Rafaela Carvalho, Renata Silva Lopes, Antônio Carlos Júnior, Adner Nobre, Viviane Santiago, Yara Alana Caldato, Estefania Rodriguez Urrego, André Buarque Lemos, Catarina Nucci Stetner, Marcos Mauro Barreto, Stefany Moreira Lima, Mara Cavalcante, Ticiane Ribeiro

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The Well Born (Nascer Bem – in Portuguese) Program was created in the Hapvida health network with the aim of improving access to safe and quality prenatal care for users. In addition to offering a line of prenatal care, the inclusion of obstetric nursing and the decentralization of childbirth, bring security that professionals did not indicate the route of delivery for professional convenience. The introduction of the nursing consultation came to reinforce the care to our users, strengthening their bond and reception. In 2021, the program maintained an average of 40% of normal births in the north, northeast and central-west regions of Brazil, an average above that observed in the rest of the country's private health systems, around 20%. In addition, the neonatal hospitalization rate of this population remained around 5.1%, a figure below the national average. With these data, the “Nascer Bem” program is affirmed as a safe and effective strategy for the promotion of safe normal birth.

Keywords: quality, safe, prenatal, obstetric nursing

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Authors: Juliza Hidayati, Sukardi, Ani Suryani, Sugiharto, Anas M. Fauzi

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The idea of making North Sumatera as a barometer of national oil palm industry requires efforts commodities and agro-industry development of oil palm. One effort that can be done is by successful execution plantation revitalization. The plantation Revitalization is an effort to accelerate the development of smallholder plantations, through expansion and replanting by help of palm Estate Company as business partner and bank financed plantation revitalization fund. Business partner agreement obliged and bound to make at least the same smallholder plantation productivity with business partners, so that the refund rate to banks become larger and prosperous people as a plantation owner. Generally low productivity of smallholder plantations under normal potential caused a lot of old and damaged plants with plant material at random. The purpose of revitalizing oil palm plantations is which are to increase their competitiveness through increased farm productivity. The research aims to identify potential criteria in influencing plantation productivity improvement priorities to be observed and followed up in order to improve the competitiveness of destinations and make North Sumatera barometer of national palm oil can be achieved. Research conducted with Analytical Network Process (ANP), to find the effect of dependency relationships between factors or criteria with the knowledge of the experts in order to produce an objective opinion and relevant depict the actual situation.

Keywords: palm barometer, acceleration of plantation development, productivity, revitalization

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Authors: Hicham Gourgue, Ahmed Aharoune, Ahmed Ihlal

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We present in this work our model of road traffic emissions (line sources) and dispersion of these emissions, named DISPOLSPEM (Dispersion of Poly Sources and Pollutants Emission Model). In its emission part, this model was designed to keep the consistent bottom-up and top-down approaches. It also allows to generate emission inventories from reduced input parameters being adapted to existing conditions in Morocco and in the other developing countries. While several simplifications are made, all the performance of the model results are kept. A further important advantage of the model is that it allows the uncertainty calculation and emission rate uncertainty according to each of the input parameters. In the dispersion part of the model, an improved line source model has been developed, implemented and tested against a reference solution. It provides improvement in accuracy over previous formulas of line source Gaussian plume model, without being too demanding in terms of computational resources. In the case study presented here, the biggest errors were associated with the ends of line source sections; these errors will be canceled by adjacent sections of line sources during the simulation of a road network. In cases where the wind is parallel to the source line, the use of the combination discretized source and analytical line source formulas minimizes remarkably the error. Because this combination is applied only for a small number of wind directions, it should not excessively increase the calculation time.

Keywords: air pollution, dispersion, emissions, line sources, road traffic, urban transport

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Authors: Shogo Nagano, Yusuke Tanigaki, Hirokazu Fukuda

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Vegetables harvested early in the morning or late in the afternoon are valued in plant production, and so the time of harvest is important. The biological functions known as circadian clocks have a significant effect on this harvest timing. The purpose of this study was to non-destructively estimate the circadian clock and so construct a method for determining a suitable harvest time. We took eight samples of green busil (Perilla frutescens var. crispa) every 4 hours, six times for 1 day and analyzed all samples at the same time. A hyperspectral camera was used to collect spectrum intensities at 141 different wavelengths (350–1050 nm). Calculation of correlations between spectrum intensity of each wavelength and harvest time suggested the suitability of the hyperspectral camera for non-destructive estimation. However, even the highest correlated wavelength had a weak correlation, so we used machine learning to raise the accuracy of estimation and constructed a machine learning model to estimate the internal time of the circadian clock. Artificial neural networks (ANN) were used for machine learning because this is an effective analysis method for large amounts of data. Using the estimation model resulted in an error between estimated and real times of 3 min. The estimations were made in less than 2 hours. Thus, we successfully demonstrated this method of non-destructively estimating internal time.

Keywords: artificial neural network (ANN), circadian clock, green busil, hyperspectral camera, non-destructive evaluation

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: Anouschka Swart

Abstract:

In 2023, sport faces a plethora of challenges including but not limited to match-fixing, corruption and doping to its integrity that, threatens both the commercial and public appeal. The continuous changes and commercialisation that has occurred within sport have led to a variety of consequences resulting in the need for ethics to be revived, as it used to be in the past to ensure sport is not in danger. In order to understand governance better, the Institute of Directors in Southern Africa, a global network of professional firms providing Audit, Tax and Advisory services, outlined a process explaining all elements with regards to corporate governance. This process illustrates a governing body’s responsibilities as strategy, policy, oversight and accountability. These responsibilities are further elucidated to 16 governing principles which are highlighted as essential for all organisations in order to achieve and deliver on effective governance outcomes. These outcomes are good ethical culture, good performance, effective control and legitimacy therefore, the aim of the study was to investigate the general state of governance within the clubs affiliated with a rugby club in South Africa by utilizing the King IV Code as the framework. The results indicated that the King Code IV principles are implemented by these rugby clubs to ensure they demonstrate commitment to corporate governance to both internal and external stakeholders. It is however evident that a similar report focused solely on sport is a necessity in the industry as this will provide more clarity on sport specific problems.

Keywords: South Africa, sport, King IV, responsibilities

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Authors: Mohamed Al-Badrashiny, Abdelghani Bellaachia

Abstract:

This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the features in SEER database into four groups. After that it tries to find the most important features among the four groups that maximizes the weighted average F-score of a certain classification algorithm. The ensemble component uses three different classifiers, each of which models different set of features from SEER through the features selection module. On top of them, another classifier is used to give the final decision based on the output decisions and confidence scores from each of the underlying classifiers. Different classification algorithms have been examined; the best setup found is by using the decision tree, Bayesian network, and Na¨ıve Bayes algorithms for the underlying classifiers and Na¨ıve Bayes for the classifier ensemble step. The system outperforms all published systems to date when evaluated against the exact same data of SEER (period of 1973-2002). It gives 87.39% weighted average F-score compared to 85.82% and 81.34% of the other published systems. By increasing the data size to cover the whole database (period of 1973-2014), the overall weighted average F-score jumps to 92.4% on the held out unseen test set.

Keywords: classifier ensemble, breast cancer survivability, data mining, SEER

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Authors: Tohid Rahimi, Yahya Naderi, Babak Yousefi, Seyed Hossein Hoseini

Abstract:

Today’s modern interconnected power system is highly complex in nature. In this, one of the most important requirements during the operation of the electric power system is the reliability and security. Power and frequency oscillation damping mechanism improve the reliability. Because of power system stabilizer (PSS) low speed response against of major fault such as three phase short circuit, FACTs devise that can control the network condition in very fast time, are becoming popular. However, FACTs capability can be seen in a major fault present when nonlinear models of FACTs devise and power system equipment are applied. To realize this aim, the model of multi-machine power system with FACTs controller is developed in MATLAB/SIMULINK using Sim Power System (SPS) blockiest. Among the FACTs device, Static synchronous series compensator (SSSC) due to high speed changes its reactance characteristic inductive to capacitive, is effective power flow controller. Tuning process of controller parameter can be performed using different method. However, Genetic Algorithm (GA) ability tends to use it in controller parameter tuning process. In this paper, firstly POD controller is used to power oscillation damping. But in this station, frequency oscillation dos not has proper damping situation. Therefore, FOD controller that is tuned using GA is using that cause to damp out frequency oscillation properly and power oscillation damping has suitable situation.

Keywords: power oscillation damping (POD), frequency oscillation damping (FOD), Static synchronous series compensator (SSSC), Genetic Algorithm (GA)

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Authors: Andrew Anis Fakhrey Mosaad

Abstract:

Objectives: Physiotherapy in the intensive care unit (ICU) improves patient outcomes. We aimed to determine the characteristics of physiotherapy practice and critical barriers to applying physiotherapy in ICUs. Materials and Methods: A 54-item survey for determining the characteristics physiotherapists and physiotherapy applications in the ICU was developed. The survey was electronically sent to potential participants through the Turkish Physiotherapy Association network. Sixty-five physiotherapists (47F and 18M; 23–52 years; ICU experience: 6.0±6.2 years) completed the survey. The data were analyzed using quantitative and qualitative methods. Results: The duration of ICU practice was 3.51±2.10 h/day. Positioning (90.8%), active exercises (90.8%), breathing exercises (89.2%), passive exercises (87.7%), and percussion (87.7%) were the most commonly used applications. The barriers were related to physiotherapists (low level of employment and practice, lack of shift); patients (unwillingness, instability, participation restriction); teamwork (lack of awareness and communication); equipment (inadequacy, non-priority to purchase); and legal (reimbursement, lack of direct physiotherapy access, non-recognition of autonomy) procedures. Conclusion: The most common interventions were positioning, active, passive, breathing exercises, and percussion. Critical barriers toward physiotherapy are multifactorial and related to physiotherapists, patients, teams, equipment, and legal procedures. Physiotherapist employment, service maintenance, and multidisciplinary teamwork should be considered for physiotherapy effectiveness in ICUs.

Keywords: intensive care units, physical therapy, physiotherapy, exercises

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