Search results for: survival data

Authors: Haojie Wang, Xun Li, Rui Yin

Abstract:

In order to solve the memorization overfitting in the meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels, so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to exponential growth of computation, this paper also proposes a key data extraction method, that only extracts part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: data augmentation, mutex task generation, meta-learning, text classification.

Procedia PDF Downloads 99

Authors: Sifat Rahman Ahona, Rifat Tasnim, Naima Hassan

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Data aggregation is a helpful technique for reducing the data communication overhead in wireless sensor network. One of the important tasks of data aggregation is positioning of the aggregator points. There are a lot of works done on data aggregation. But, efficient positioning of the aggregators points is not focused so much. In this paper, authors are focusing on the positioning or the placement of the aggregation points in wireless sensor network. Authors proposed an algorithm to select the aggregators positions for a scenario where aggregator nodes are more powerful than sensor nodes.

Keywords: aggregation point, data communication, data aggregation, wireless sensor network

Procedia PDF Downloads 165

Authors: Innocent Atuhe, Alfred Jatho, Yusuf Mulumba, Babra Nalwadda, Jackson Orem

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Statement of the Problem: Malnutrition and cachexia are common in cancer and often contribute to poor health outcomes. Advanced cervical cancer patients are at an increased risk of malnutrition, chemo-toxicity, poor quality of life (QoL), and higher mortality. It is recommended to assess all cancer patients for malnutrition, risk of malnutrition, and QoL in order to provide targeted care, especially for advanced cancer patients. Objective: The aim of this study was to assess the prevalence of malnutrition or risk of malnutrition and health-related quality of life in advanced cervical cancer patients with cachexia. Methodology: This was a longitudinal study of 212 advanced cervical cancer patients with a confirmed diagnosis of cachexia receiving chemotherapy at the Uganda Cancer Institute between January 2022 and September 2023. We confirmed the diagnosis of cachexia using the international consensus definition. We evaluated each patient for malnutrition using the Mini Nutritional Assessment (MNA) tool and assessed their QoL using the Feeling of well-being (FWB) on the Edmonton Symptom Assessment System (ESAS) questionnaire and the Distress Thermometer (DT) questionnaire. Data was analyzed using Excel and presented using frequencies and percentages. Results: Of the 212 advanced cervical cancer patients with cachexia, 65 (31.7%) of the respondents were malnourished, and 118 (57.6%) were at risk of malnutrition; the average ESAS score was 3, indicating less symptom burden while 115 (54.2%) were extremely distressed with pain as major distress. Conclusion and Significance: The majority of the patients with advanced cervical cancer cachexia were at risk of malnutrition. There is an urgent need to assess all patients with advanced cancer for malnutrition and risk of malnutrition and immediate referral of those with malnutrition to the nutritionist for interventions to improve QoL and overall survival.

Keywords: malnutrition, QoL, cachexia, cancer

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Authors: Paula Simões, Isabel Natário

Abstract:

This paper reviews a number of theoretical aspects for implementing an explicit spatial perspective in econometrics for modelling non-continuous data, in general, and count data, in particular. It provides an overview of the several spatial econometric approaches that are available to model data that are collected with reference to location in space, from the classical spatial econometrics approaches to the recent developments on spatial econometrics to model count data, in a Bayesian hierarchical setting. Considerable attention is paid to the inferential framework, necessary for structural consistent spatial econometric count models, incorporating spatial lag autocorrelation, to the corresponding estimation and testing procedures for different assumptions, to the constrains and implications embedded in the various specifications in the literature. This review combines insights from the classical spatial econometrics literature as well as from hierarchical modeling and analysis of spatial data, in order to look for new possible directions on the processing of count data, in a spatial hierarchical Bayesian econometric context.

Keywords: spatial data analysis, spatial econometrics, Bayesian hierarchical models, count data

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Authors: A. Abyar, R. Khavarzadeh

Abstract:

The most basic and important decision-making tool for industrial and service managers is understanding the market and customer behavior. In this regard, the Apriori algorithm, as one of the well-known machine learning methods, is used to identify customer preferences. On the other hand, with the increasing diversity of goods and services and the speed of changing customer behavior, we are faced with big data. Also, due to the large number of competitors and changing customer behavior, there is an urgent need for continuous analysis of this big data. While the speed of the Apriori algorithm decreases with increasing data volume. In this paper, the big data PCA method is used to reduce the dimension of the data in order to increase the speed of Apriori algorithm. Then, in the simulation section, the results are examined by generating data with different volumes and different diversity. The results show that when using this method, the speed of the a priori algorithm increases significantly.

Keywords: association rules, Apriori algorithm, big data, big data PCA, market basket analysis

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Authors: Gueidi Afef, Gharsellaoui Hamza, Ben Ahmed Samir

Abstract:

This paper deals with the secret of the continual progression data that new data management solutions have been emerged: The NoSQL databases. They crossed several areas like personalization, profile management, big data in real-time, content management, catalog, view of customers, mobile applications, internet of things, digital communication and fraud detection. Nowadays, these database management systems are increasing. These systems store data very well and with the trend of big data, a new challenge’s store demands new structures and methods for managing enterprise data. The new intelligent machine in the e-learning sector, thrives on more data, so smart machines can learn more and faster. The robotics are our use case to focus on our test. The implementation of NoSQL for Robotics wrestle all the data they acquire into usable form because with the ordinary type of robotics; we are facing very big limits to manage and find the exact information in real-time. Our original proposed approach was demonstrated by experimental studies and running example used as a use case.

Keywords: NoSQL databases, database management systems, robotics, big data

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Authors: C. B. Le, V. N. Pham

Abstract:

In modern data analysis, multi-source data appears more and more in real applications. Multi-source data clustering has emerged as a important issue in the data mining and machine learning community. Different data sources provide information about different data. Therefore, multi-source data linking is essential to improve clustering performance. However, in practice multi-source data is often heterogeneous, uncertain, and large. This issue is considered a major challenge from multi-source data. Ensemble is a versatile machine learning model in which learning techniques can work in parallel, with big data. Clustering ensemble has been shown to outperform any standard clustering algorithm in terms of accuracy and robustness. However, most of the traditional clustering ensemble approaches are based on single-objective function and single-source data. This paper proposes a new clustering ensemble method for multi-source data analysis. The fuzzy optimized multi-objective clustering ensemble method is called FOMOCE. Firstly, a clustering ensemble mathematical model based on the structure of multi-objective clustering function, multi-source data, and dark knowledge is introduced. Then, rules for extracting dark knowledge from the input data, clustering algorithms, and base clusterings are designed and applied. Finally, a clustering ensemble algorithm is proposed for multi-source data analysis. The experiments were performed on the standard sample data set. The experimental results demonstrate the superior performance of the FOMOCE method compared to the existing clustering ensemble methods and multi-source clustering methods.

Keywords: clustering ensemble, multi-source, multi-objective, fuzzy clustering

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Authors: Eui-Jin Kim, Hasik Lee, Su-Jin Park, Dong-Kyu Kim

Abstract:

Smart-card data are expected to provide information on activity pattern as an alternative to conventional person trip surveys. The focus of this study is to propose a method for training the person trip surveys to supplement the smart-card data that does not contain the purpose of each trip. We selected only available features from smart card data such as spatiotemporal information on the trip and geographic information system (GIS) data near the stations to train the survey data. XGboost, which is state-of-the-art tree-based ensemble classifier, was used to train data from multiple sources. This classifier uses a more regularized model formalization to control the over-fitting and show very fast execution time with well-performance. The validation results showed that proposed method efficiently estimated the trip purpose. GIS data of station and duration of stay at the destination were significant features in modeling trip purpose.

Keywords: activity pattern, data fusion, smart-card, XGboost

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Authors: Claire Harrison, Raajit K. Rampal, Vikas Gupta, Srdan Verstovsek, Moshe Talpaz, Jean-Jacques Kiladjian, Ruben Mesa, Andrew Kuykendall, Alessandro Vannucchi, Francesca Palandri, Sebastian Grosicki, Timothy Devos, Eric Jourdan, Marielle J. Wondergem, Haifa Kathrin Al-Ali, Veronika Buxhofer-Ausch, Alberto Alvarez-Larrán, Sanjay Akhani, Rafael Muñoz-Carerras, Yury Sheykin, Gozde Colak, Morgan Harris, John Mascarenhas

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Myelofibrosis (MF) is characterized by bone marrow fibrosis, anemia, splenomegaly and constitutional symptoms. Progressive bone marrow fibrosis results from aberrant megakaryopoeisis and expression of proinflammatory cytokines, both of which are heavily influenced by bromodomain and extraterminal domain (BET)-mediated gene regulation and lead to myeloproliferation and cytopenias. Pelabresib (CPI-0610) is an oral small-molecule investigational inhibitor of BET protein bromodomains currently being developed for the treatment of patients with MF. It is designed to downregulate BET target genes and modify nuclear factor kappa B (NF-κB) signaling. MANIFEST-2 was initiated based on data from Arm 3 of the ongoing Phase 2 MANIFEST study (NCT02158858), which is evaluating the combination of pelabresib and ruxolitinib in Janus kinase inhibitor (JAKi) treatment-naïve patients with MF. Primary endpoint analyses showed splenic and symptom responses in 68% and 56% of 84 enrolled patients, respectively. MANIFEST-2 (NCT04603495) is a global, Phase 3, randomized, double-blind, active-control study of pelabresib and ruxolitinib versus placebo and ruxolitinib in JAKi treatment-naïve patients with primary MF, post-polycythemia vera MF or post-essential thrombocythemia MF. The aim of this study is to evaluate the efficacy and safety of pelabresib in combination with ruxolitinib. Here we report updates from a recent protocol amendment. The MANIFEST-2 study schema is shown in Figure 1. Key eligibility criteria include a Dynamic International Prognostic Scoring System (DIPSS) score of Intermediate-1 or higher, platelet count ≥100 × 10^9/L, spleen volume ≥450 cc by computerized tomography or magnetic resonance imaging, ≥2 symptoms with an average score ≥3 or a Total Symptom Score (TSS) of ≥10 using the Myelofibrosis Symptom Assessment Form v4.0, peripheral blast count <5% and Eastern Cooperative Oncology Group performance status ≤2. Patient randomization will be stratified by DIPSS risk category (Intermediate-1 vs Intermediate-2 vs High), platelet count (>200 × 10^9/L vs 100–200 × 10^9/L) and spleen volume (≥1800 cm^3 vs <1800 cm^3). Double-blind treatment (pelabresib or matching placebo) will be administered once daily for 14 consecutive days, followed by a 7 day break, which is considered one cycle of treatment. Ruxolitinib will be administered twice daily for all 21 days of the cycle. The primary endpoint is SVR35 response (≥35% reduction in spleen volume from baseline) at Week 24, and the key secondary endpoint is TSS50 response (≥50% reduction in TSS from baseline) at Week 24. Other secondary endpoints include safety, pharmacokinetics, changes in bone marrow fibrosis, duration of SVR35 response, duration of TSS50 response, progression-free survival, overall survival, conversion from transfusion dependence to independence and rate of red blood cell transfusion for the first 24 weeks. Study recruitment is ongoing; 400 patients (200 per arm) from North America, Europe, Asia and Australia will be enrolled. The study opened for enrollment in November 2020. MANIFEST-2 was initiated based on data from the ongoing Phase 2 MANIFEST study with the aim of assessing the efficacy and safety of pelabresib and ruxolitinib in JAKi treatment-naïve patients with MF. MANIFEST-2 is currently open for enrollment.

Keywords: CPI-0610, JAKi treatment-naïve, MANIFEST-2, myelofibrosis, pelabresib

Procedia PDF Downloads 209

Authors: Haojie Wang, Xun Li, Rui Yin

Abstract:

In order to solve the memorization overfitting in the model-agnostic meta-learning MAML algorithm, a method of generating mutually exclusive tasks based on data augmentation is proposed. This method generates a mutex task by corresponding one feature of the data to multiple labels so that the generated mutex task is inconsistent with the data distribution in the initial dataset. Because generating mutex tasks for all data will produce a large number of invalid data and, in the worst case, lead to an exponential growth of computation, this paper also proposes a key data extraction method that only extract part of the data to generate the mutex task. The experiments show that the method of generating mutually exclusive tasks can effectively solve the memorization overfitting in the meta-learning MAML algorithm.

Keywords: mutex task generation, data augmentation, meta-learning, text classification.

Procedia PDF Downloads 150

Authors: Milind Chaudhari, Suhail Balasinor

Abstract:

Due to massive deforestation and an ever-increasing population, the organic content of the soil is depleting at a much faster rate. Due to this, there is a big chance that the entire food production in the world will drop by 40% in the next two decades. Vertical farming can help in aiding food production by leveraging big data and cloud computing to ensure plants are grown naturally by providing the optimum nutrients sunlight by analyzing millions of data points. This paper outlines the most important parameters in vertical farming and how a combination of big data and AI helps in calculating and analyzing these millions of data points. Finally, the paper outlines how different organizations are controlling the indoor environment by leveraging big data in enhancing food quantity and quality.

Keywords: big data, IoT, vertical farming, indoor farming

Procedia PDF Downloads 179

Authors: Gideon Abagna Azunre

Abstract:

Informal urbanism is perhaps the most ubiquitous urban phenomenon in sub-Saharan Africa (SSA) and Ghana specifically. Estimates suggest that about two-fifths of urban dwellers (37.9%) in Ghana live in informal settlements, while two-thirds of the working labour force are within the informal economy. This makes Ghana invariably an ‘informal country.’ Informal urbanism involves economic and housing activities that are – in law or in practice – not covered (or insufficiently covered) by formal regulations. Many urban folks rely on informal urbanism as a survival strategy due to limited formal waged employment opportunities or rising home prices in the open market. In an era of globalizing neoliberalism, this struggle to survive in cities resonates with several people globally. For years now, there have been intense debates on the utility of informal urbanism – both its economic and housing dimensions – in developing sustainable cities. While some scholars believe that informal urbanism is beneficial to the sustainable city development agenda, others argue that it generates unbearable negative consequences and it symbolizes lawlessness and squalor. Consequently, the main aim of this research was to dig below the surface of the narratives to locate the role of informal urbanism in the quest for sustainable cities. The research geographically focused on Ghana and its burgeoning informal sector. Also, both primary and secondary data were utilized for the analysis; Secondary data entailed a synthesis of the fragmented literature on informal urbanism in Ghana, while primary data entailed interviews with informal stakeholders (such as informal settlement dwellers), city authorities, and planners. These two data sets were weaved together to discover the nexus between informal urbanism and the tripartite dimensions of sustainable cities – economic, social, and environmental. The results from the research showed a two-pronged relationship between informal urbanism and the three dimensions of sustainable city development. In other words, informal urbanism was identified to both positively and negatively affect the drive for sustainable cities. On the one hand, it provides employment (particularly to women), supplies households’ basic needs (shelter, health, water, and waste management), and enhances civic engagement. However, on the other hand, it perpetuates social and gender inequalities, insecurity, congestion, and pollution. The research revealed that a ‘black and white’ interpretation and policy approach is incapable of capturing the complexities of informal urbanism. Therefore, trying to eradicate or remove it from the urbanscape because it exhibits some negative consequences means cities will lose their positive contributions. The inverse also holds true. A careful balancing act is necessary to maximize the benefits and minimize the costs. Overall, the research presented a de-colonial theorization of informal urbanism and thus followed post-colonial scholars’ clarion call to African cities to embrace the paradox of informality and find ways to integrate it into the city-building process.

Keywords: informal urbanism, sustainable city development, economic sustainability, social sustainability, environmental sustainability, Ghana

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Authors: A. Anis, W. Bekheet, A. El Hakim

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Implementing a Road Safety Management System (SMS) in a crowded developing country such as Egypt is a necessity. Beginning a sustainable SMS requires a comprehensive reliable data system for all information pertinent to road crashes. In this paper, a survey for the available data in Egypt and validating it for using in an SMS in Egypt. The research provides some missing data, and refer to the unavailable data in Egypt, looking forward to the contribution of the scientific society, the authorities, and the public in solving the problem of missing or unreliable crash data. The required data for implementing an SMS in Egypt are divided into three categories; the first is available data such as fatality and injury rates and it is proven in this research that it may be inconsistent and unreliable, the second category of data is not available, but it may be estimated, an example of estimating vehicle cost is available in this research, the third is not available and can be measured case by case such as the functional and geometric properties of a facility. Some inquiries are provided in this research for the scientific society, such as how to improve the links among stakeholders of road safety in order to obtain a consistent, non-biased, and reliable data system.

Keywords: road safety management system, road crash, road fatality, road injury

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Authors: Angelis P. Barlampas

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The purpose of this study is to inform the reader, about the various applications of artificial intelligence (AI), in cardiac imaging. AI grows fast and its role is crucial in medical specialties, which use large amounts of digital data, that are very difficult or even impossible to be managed by human beings and especially doctors.Artificial intelligence (AI) refers to the ability of computers to mimic human cognitive function, performing tasks such as learning, problem-solving, and autonomous decision making based on digital data. Whereas AI describes the concept of using computers to mimic human cognitive tasks, machine learning (ML) describes the category of algorithms that enable most current applications described as AI. Some of the current applications of AI in cardiac imaging are the follows: Ultrasound: Automated segmentation of cardiac chambers across five common views and consequently quantify chamber volumes/mass, ascertain ejection fraction and determine longitudinal strain through speckle tracking. Determine the severity of mitral regurgitation (accuracy > 99% for every degree of severity). Identify myocardial infarction. Distinguish between Athlete’s heart and hypertrophic cardiomyopathy, as well as restrictive cardiomyopathy and constrictive pericarditis. Predict all-cause mortality. CT Reduce radiation doses. Calculate the calcium score. Diagnose coronary artery disease (CAD). Predict all-cause 5-year mortality. Predict major cardiovascular events in patients with suspected CAD. MRI Segment of cardiac structures and infarct tissue. Calculate cardiac mass and function parameters. Distinguish between patients with myocardial infarction and control subjects. It could potentially reduce costs since it would preclude the need for gadolinium-enhanced CMR. Predict 4-year survival in patients with pulmonary hypertension. Nuclear Imaging Classify normal and abnormal myocardium in CAD. Detect locations with abnormal myocardium. Predict cardiac death. ML was comparable to or better than two experienced readers in predicting the need for revascularization. AI emerge as a helpful tool in cardiac imaging and for the doctors who can not manage the overall increasing demand, in examinations such as ultrasound, computed tomography, MRI, or nuclear imaging studies.

Keywords: artificial intelligence, cardiac imaging, ultrasound, MRI, CT, nuclear medicine

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Authors: Anand K. Vinu, Vaishnav Vimal, Sasi Gopalan

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All spacecraft components have a range of allowable temperatures that must be maintained to meet survival and operational requirements during all mission phases. Due to heat absorption, transfer, and emission on one side, the satellite surface presents an asymmetric temperature distribution and causes a change in momentum, which can manifest in spinning and non-spinning satellites in different manners. This problem can cause orbital decays in satellites which, if not corrected, will interfere with its primary objective. The thermal analysis of any satellite requires data from the power budget for each of the components used. This is because each of the components has different power requirements, and they are used at specific times in an orbit. There are three different cases that are run, one is the worst operational hot case, the other one is the worst non-operational cold case, and finally, the operational cold case. Sunlight is a major source of heating that takes place on the satellite. The way in which it affects the spacecraft depends on the distance from the Sun. Any part of a spacecraft or satellite facing the Sun will absorb heat (a net gain), and any facing away will radiate heat (a net loss). We can use the state-of-the-art foldable hybrid insulator/radiator panel. When the panels are opened, that particular side acts as a radiator for dissipating the heat. Here the insulator, in our case, the aerogel, is sandwiched with solar cells and radiator fins (solar cells outside and radiator fins inside). Each insulated side panel can be opened and closed using actuators depending on the telemetry data of the CubeSat. The opening and closing of the panels are dependent on the special code designed for this particular application, where the computer calculates where the Sun is relative to the satellites. According to the data obtained from the sensors, the computer decides which panel to open and by how many degrees. For example, if the panels open 180 degrees, the solar panels will directly face the Sun, in turn increasing the current generator of that particular panel. One example is when one of the corners of the CubeSat is facing or if more than one side is having a considerable amount of sun rays incident on it. Then the code will analyze the optimum opening angle for each panel and adjust accordingly. Another means of cooling is the passive way of cooling. It is the most suitable system for a CubeSat because of its limited power budget constraints, low mass requirements, and less complex design. Other than this fact, it also has other advantages in terms of reliability and cost. One of the passive means is to make the whole chase act as a heat sink. For this, we can make the entire chase out of heat pipes and connect the heat source to this chase with a thermal strap that transfers the heat to the chassis.

Keywords: passive cooling, CubeSat, efficiency, satellite, stationary satellite

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Authors: Oualid Walid Ben Ali

Abstract:

Big Data has become one of the buzzwords today. The recent explosion of digital data has led the organization, either private or public, to a new era towards a more efficient decision making. At some point, business decided to use that concept in order to learn what make their clients tick with phrases like ‘sales funnel’ analysis, ‘actionable insights’, and ‘positive business impact’. So, it stands to reason that Big Data was viewed through green (read: money) colored lenses. Somewhere along the line, however someone realized that collecting and processing data doesn’t have to be for business purpose only, but also could be used for other purposes to assist law enforcement or to improve policing or in road safety. This paper presents briefly, how Big Data have been used in the fields of policing order to improve the decision making process in the daily operation of the police. As example, we present a big-data driven system which is sued to accurately dispatch the patrol cars in a geographic environment. The system is also used to allocate, in real-time, the nearest patrol car to the location of an incident. This system has been implemented and applied in the Emirate of Abu Dhabi in the UAE.

Keywords: big data, big data analytics, patrol car allocation, dispatching, GIS, intelligent, Abu Dhabi, police, UAE

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Authors: Thomas Gerstner, Bastian von Harrach, Daniel Roth

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The Monte Carlo pathwise sensitivities approach is well established for smooth payoff functions. In this work, we present a new Monte Carlo algorithm that is able to calculate the pathwise sensitivities for discontinuous payoff functions. Our main tool is the one-step survival idea of Glasserman and Staum. Although this technique yields to new terms per observation, while differentiating, the algorithm is still efficient. As an application, we use the results for a two-dimensional calibration of a Coco-Bond, which we model with different types of discretely monitored barrier options.

Keywords: Monte Carlo, discretely monitored barrier options, pathwise sensitivities, Coco-Bond

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Authors: Eakarat Boonreang, Anothai Harasarn

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The objectives of this study are to investigate the flood disaster management capacity of communities in Ubon Ratchathani province, Thailand, and to recommend the sustainable flood management approaches of communities in Ubon Ratchathani province, Thailand. The selected population consisted of the community leaders and committees, the executives of local administrative organizations, and the head of Ubon Ratchathani provincial office of disaster prevention and mitigation. The data was collected by in-depth interview, focus group, and observation. The data was analyzed and classified in order to determine the communities’ capacity in flood disaster management. The results revealed that communities’ capacity were as follows, before flood disaster, the community leaders held a meeting with the community committees in order to plan disaster response and determined evacuation routes, and the villagers moved their belongings to higher places and prepared vehicles for evacuation. During flood disaster, the communities arranged motorboats for transportation and villagers evacuated to a temporary evacuation center. Moreover, the communities asked for survival bags, motorboats, emergency toilets, and drinking water from the local administrative organizations and the 22nd Military Circle. After flood disaster, the villagers cleaned and fixed their houses and also collaborated in cleaning the temple, school, and other places in the community. The recommendation approaches for sustainable flood disaster management consisted of structural measures, such as the establishment of reservoirs and building higher houses, and non-structural measures such as raising awareness and fostering self-reliance, establishing disaster management plans, rehearsal of disaster response procedures every year, and transferring disaster knowledge among younger generations. Moreover, local administrative organizations should formulate strategic plans that focus on disaster management capacity building at the community level, particularly regarding non-structural measures. Ubon Ratchathani provincial offices of disaster prevention and mitigation should continually monitor and evaluate the outcomes of community based disaster risk management program, including allocating more flood disaster management-related resources among local administrative organizations and communities.

Keywords: capacity building, community based disaster risk management, flood disaster management, Thailand

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Authors: Andri Setiyoso, Agus Purwadi, Nanda Avianto Wicaksono

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This paper present about induction generator design for 100kW power output capacity. Induction machine had been chosen because of the capability for energy conversion from electric energy to mechanical energy and vise-versa with operation on variable speed condition. Stator Controlled Induction Generator (SCIG) was applied as wind power plant in Desa Taman Jaya, Sukabumi, Indonesia. Generator was designed to generate power 100 kW with wind speed at 12 m/s and survival condition at speed 21 m/s.

Keywords: wind energy, induction generator, Stator Controlled Induction Generator (SCIG), variable speed generator

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Authors: B. F. Espiritu

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This paper examines the resistance of the Lumad people, the indigenous peoples in Mindanao, Southern Philippines, and of environmental and human rights activists to the Philippine government's neoliberal policies and their call for justice and a new economic paradigm that will uphold peoples' rights and environmental protection in two alternative media online sites. The study contributes to the body of knowledge on indigenous resistance to neoliberal globalization and the quest for a new economic paradigm that upholds social justice for the marginalized in society, empathy and compassion for those who depend on the land for their survival, and environmental sustainability. The study analyzes the discourses in selected news articles from Davao Today and Kalikasan (translated to English as 'Nature') People's Network for the Environment’s statements and advocacy articles for the Lumad and the environment from 2018 to February 2020. The study reveals that the alternative media news articles and the advocacy articles contain statements that expose the oppression and violation of human rights of the Lumad people, farmers, government environmental workers, and environmental activists as shown in their killings, illegal arrest and detention, displacement of the indigenous peoples, destruction of their schools by the military and paramilitary groups, and environmental plunder and destruction with the government's permit for the entry and operation of extractive and agribusiness industries in the Lumad ancestral lands. Anchored on Christian Fuch's theory of alternative media as critical media and Bert Cammaerts' theorization of alternative media as counterhegemonic media that are part of civil society and form a third voice between state media and commercial media, the study reveals the counterhegemonic discourses of the news and advocacy articles that oppose the dominant economic system of neoliberalism which oppresses the people who depend on the land for their survival. Furthermore, the news and advocacy articles seek to advance social struggles that transform society towards the realization of cooperative potentials or a new economic paradigm that upholds economic democracy, where the local people, including the indigenous people, are economically empowered their environment and protected towards the realization of self-sustaining communities. The study highlights the call for justice, empathy, and compassion for both the people and the environment and the need for a new economic paradigm wherein indigenous peoples and local communities are empowered towards becoming self-sustaining communities in a sustainable environment.

Keywords: alternative media, environmental sustainability, human rights, indigenous resistance

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Authors: Xu Du, Aparna S. Varde

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In this research, we propose to conduct diagnostic and predictive analysis about the key factors and consequences of urban population relocation. To achieve this goal, urban simulation models extract the urban development trends as land use change patterns from a variety of data sources. The results are treated as part of urban big data with other information such as population change and economic conditions. Multiple data mining methods are deployed on this data to analyze nonlinear relationships between parameters. The result determines the driving force of population relocation with respect to urban sprawl and urban sustainability and their related parameters. Experiments so far reveal that data mining methods discover useful knowledge from the multicity urban data. This work sets the stage for developing a comprehensive urban simulation model for catering to specific questions by targeted users. It contributes towards achieving sustainability as a whole.

Keywords: data mining, environmental modeling, sustainability, urban planning

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Authors: C. Nathanailides, S. Anastasiou, A. Dimitroglou, P. Logothetis, G. Kanlis

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In the present work we reviewed historical data of the Greek Marine aquaculture industry including adoption of new methods and technological innovation. The results indicate that the industry exhibited a rapid rise in production efficiency, employment and adoption of new technologies which reduced outbreaks of diseases, reduced production risk and the price of the farmed fish. The improvements of total quality practices and technological input on the Greek Aquaculture industry include improved survival, growth and body shape of farmed fish, which resulted from development of new aquaculture feeds and the genetic selection of the bloodstock. Also improvements in the quality of the final product were achieved via technological input in the methods and technology applied during harvesting, packaging, and transportation-preservation of farmed fish ensuring high quality of the product from the fish farm to the plate of the consumers. These parameters (health management, nutrition, genetics, harvesting and post-harvesting methods and technology) changed significantly over the last twenty years and the results of these improvements are reflected in the production efficiency of the Aquaculture industry and the quality of the final product. It is concluded that the Greek aquaculture industry exhibited a rapid growth, adoption of technologies and supply was stabilized after the global financial crisis, nevertheless, the development of the Greek aquaculture industry is currently limited by international trade sanctions, credit crunch, and increased taxation and not by limited technology or resources.

Keywords: innovation, aquaculture, total quality, management

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Authors: Aref Ghafouri, Mohammad javad Mollakazemi, Farhad Asadi

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In this paper, model order reduction method is used for approximation in linear and nonlinearity aspects in some experimental data. This method can be used for obtaining offline reduced model for approximation of experimental data and can produce and follow the data and order of system and also it can match to experimental data in some frequency ratios. In this study, the method is compared in different experimental data and influence of choosing of order of the model reduction for obtaining the best and sufficient matching condition for following the data is investigated in format of imaginary and reality part of the frequency response curve and finally the effect and important parameter of number of order reduction in nonlinear experimental data is explained further.

Keywords: frequency response, order of model reduction, frequency matching condition, nonlinear experimental data

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Authors: Diogo Santos, Nelson Zagalo, Carla Morais

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No Man’s Sky (NMS) is a sci-fi video game about survival and exploration where players fly spaceships, search for elements, and use them to survive. NMS isn’t a serious game, and not all the science in the game is presented with scientific evidence. To find how players felt about the scientific content in the game and how they perceive the chemistry in it, a survey was sent to NMS’s players, from which were collected answers from 124 respondents from 23 countries. Chemophobia is still a phenomenon when chemistry or chemicals are a subject of discussion, but 68,9% of our respondents showed a positive attitude towards the presence of chemistry in NMS, with 57% stating that playing the video game motivated them to know more about science. 8% of the players stated that NMS often prompted conversations about the science in the video game between them and teachers, parents, or friends. These results give us ideas on how an entertainment game can potentially help scientists, educators, and science communicators reach a growing, evolving, vibrant, diverse, and demanding audience.

Keywords: digital games, science communication, chemistry, informal learning, No Man’s Sky

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Authors: Thuan Nguyen

Abstract:

In the present time, data to a data-driven knowledge-based economy is the same as oil to the industrial age hundreds of years ago. Data is everywhere in vast volumes! Big data analytics is expected to help firms not only efficiently improve performance but also completely transform how they should run their business. However, employing the emergent technology successfully is not easy, and assessing the roles of big data in improving firm performance is even much harder. There was a lack of studies that have examined the impacts of big data analytics on organizational performance. This study aimed to fill the gap. The present study suggested using firms’ intellectual capital as a proxy for big data in evaluating its impact on organizational performance. The present study employed the Value Added Intellectual Coefficient method to measure firm intellectual capital, via its three main components: human capital efficiency, structural capital efficiency, and capital employed efficiency, and then used the structural equation modeling technique to model the data and test the models. The financial fundamental and market data of 100 randomly selected publicly listed firms were collected. The results of the tests showed that only human capital efficiency had a significant positive impact on firm profitability, which highlighted the prominent human role in the impact of big data technology.

Keywords: big data, big data analytics, intellectual capital, organizational performance, value added intellectual coefficient

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Authors: Hitesh Tahbildar

Abstract:

The cost of test data generation for a program is computationally very high. In general case, no algorithm to generate test data for all types of algorithms has been found. The cost of generating test data for different types of algorithm is different. Till date, people are emphasizing the need to generate test data for different types of programming constructs rather than different types of algorithms. The test data generation methods have been implemented to find heuristics for different types of algorithms. Some algorithms that includes divide and conquer, backtracking, greedy approach, dynamic programming to find the minimum cost of test data generation have been tested. Our experimental results say that some of these types of algorithm can be used as a necessary condition for selecting heuristics and programming constructs are sufficient condition for selecting our heuristics. Finally we recommend the different heuristics for test data generation to be selected for different types of algorithms.

Keywords: ongest path, saturation point, lmax, kL, kS

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Authors: Hatice Kübra Koç

Abstract:

For academia, collecting reliable and valid data is one of the most significant issues for researchers. However, it is not the same procedure for all different target groups; meanwhile, during data collection from teenagers, young adults, or adults, researchers can use common data collection tools such as questionnaires, interviews, and semi-structured interviews; yet, for young learners and very young ones, these reliable and valid data collection tools cannot be easily designed or applied by the researchers. In this study, firstly, common data collection tools are examined for ‘very young’ and ‘young learners’ participant groups since it is thought that the quality and efficiency of an academic study is mainly based on its valid and correct data collection and data analysis procedure. Secondly, two different data collection instruments for very young and young learners are stated as discussing the efficacy of them. Finally, a suggested data collection tool – a performance-based questionnaire- which is specifically developed for ‘very young’ and ‘young learners’ participant groups in the field of teaching English to young learners as a foreign language is presented in this current study. The designing procedure and suggested items/factors for the suggested data collection tool are accordingly revealed at the end of the study to help researchers have studied with young and very learners.

Keywords: data collection instruments, performance-based questionnaire, young learners, very young learners

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Authors: Chang Liang, Weizhi Gong, Yan Zhang

Abstract:

Purpose: Hepatocellular carcinoma (HCC) is a malignant tumor with a high mortality rate and poor prognosis worldwide. Murine double minute 2 (MDM2) regulates the tumor suppressor p53, increasing cancer risk and accelerating tumor progression. Speckle-type POX virus and zinc finger protein (SPOP), a key of subunit of Cullin-Ring E3 ligase, inhibits tumor genesis and progression by the ubiquitination of its downstream substrates. This study aimed to clarify whether SPOP and MDM2 are mutually regulated in HCC and the correlation between SPOP and MDM2 and the prognosis of HCC patients. Methods: First, the expression of SPOP and MDM2 in HCC tissues were detected by TCGA database. Then, 53 paired samples of HCC tumor and adjacent tissues were collected to evaluate the expression of SPOP and MDM2 using immunohistochemistry. Chi-square test or Fisher’s exact test were used to analyze the relationship between clinicopathological features and the expression levels of SPOP and MDM2. In addition, Kaplan‒Meier curve analysis and log-rank test were used to investigate the effects of SPOP and MDM2 on the survival of HCC patients. Last, the Multivariate Cox proportional risk regression model analyzed whether the different expression levels of SPOP and MDM2 were independent risk factors for the prognosis of HCC patients. Results: Bioinformatics analysis revealed the low expression of SPOP and high expression of MDM2 were related to worse prognosis of HCC patients. The relationship between the expression of SPOP and MDM2 and tumor stem-like features showed an opposite trend. The immunohistochemistry showed the expression of SPOP protein was significantly downregulated while MDM2 protein significantly upregulated in HCC tissue compared to that in para-cancerous tissue. Tumors with low SPOP expression were related to worse T stage and Barcelona Clinic Liver Cancer (BCLC) stage, but tumors with high MDM2 expression were related to worse T stage, M stage, and BCLC stage. Kaplan–Meier curves showed HCC patients with high SPOP expression and low MDM2 expression had better survival than those with low SPOP expression and high MDM2 expression (P < 0.05). A multivariate Cox proportional risk regression model confirmed that a high MDM2 expression level was an independent risk factor for poor prognosis in HCC patients (P <0.05). Conclusion: The expression of SPOP protein was significantly downregulated, while the expression of MDM2 significantly upregulated in HCC. The low expression of SPOP and high expression. of MDM2 were associated with malignant progression and poor prognosis of HCC patients, indicating a potential therapeutic target for HCC patients.

Keywords: hepatocellular carcinoma, murine double minute 2, speckle-type POX virus and zinc finger protein, ubiquitination

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Authors: Yaxin Bi

Abstract:

Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.

Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors

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Authors: Hyun-Woo Cho

Abstract:

For ensuring the safety of a manufacturing process one should quickly identify an assignable cause of a fault in an on-line basis. To this end, many statistical techniques including linear and nonlinear methods have been frequently utilized. However, such methods possessed a major problem of small sample size, which is mostly attributed to the characteristics of empirical models used for reference models. This work presents a new method to overcome the insufficiency of measurement data in the monitoring and diagnosis tasks. Some quasi-measurement data are generated from existing data based on the two indices of similarity and importance. The performance of the method is demonstrated using a real data set. The results turn out that the presented methods are able to handle the insufficiency problem successfully. In addition, it is shown to be quite efficient in terms of computational speed and memory usage, and thus on-line implementation of the method is straightforward for monitoring and diagnosis purposes.

Keywords: data analysis, diagnosis, monitoring, process data, quality control

Procedia PDF Downloads 485