Search results for: multiple distribution supply chain network

Authors: Thrivikraman Aswathi, S. Advaith

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As there can be cases where the use of real data is somehow limited, such as when it is hard to get access to a large volume of real data, we need to go for synthetic data generation. This produces high-quality synthetic data while maintaining the statistical properties of a specific dataset. In the present work, a generative adversarial network (GAN) is trained to produce multivariate time series (MTS) data since the MTS is now being gathered more often in various real-world systems. Furthermore, the GAN-generated MTS data is fed into a transformer-based deep learning architecture that carries out the data categorization into predefined classes. Further, the model is evaluated across various distinct domains by generating corresponding MTS data.

Keywords: GAN, transformer, classification, multivariate time series

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Authors: Essang Anwana Onuntuei, Chinyere Blessing Azunwoke

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The study focused on determining the impact of multimedia systems and computer graphic design on global and local scale economies. Firstly, the study identified the major players and the world’s top five digital media distribution in the digital media market. It then examined if the Digital Media Vendor and Market Shares differ or remain the same. Also, the research studied whether the global and local desktop, mobile, and tablet markets differ. Again, the study assessed the link between the top five Digital Media and Global Market Shares. Finally, the paper observed the extent of growth rate, economic gains, major setbacks, and opportunities within the digital media industry amidst global and local scale economic flux. A multiple regression analysis method was used to analyse the significant influence of the top five Global Digital Media on the total market share. Furthermore, to analyse the global Digital Media Vendor Market share data, and so on, the Analysis of Variance (ANOVA) was employed. The findings were intriguing and significant.

Keywords: multimedia, systems design, computer graphics, digital media market, media vendor, global market share, user interface, social media, market size

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Authors: Sakshi Dhumale, Madhushree C., Amba Shetty

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The effect of station density on the geostatistical prediction of groundwater levels is of critical importance to ensure accurate and reliable predictions. Monitoring station density directly impacts the accuracy and reliability of geostatistical predictions by influencing the model's ability to capture localized variations and small-scale features in groundwater levels. This is particularly crucial in regions with complex hydrogeological conditions and significant spatial heterogeneity. Insufficient station density can result in larger prediction uncertainties, as the model may struggle to adequately represent the spatial variability and correlation patterns of the data. On the other hand, an optimal distribution of monitoring stations enables effective coverage of the study area and captures the spatial variability of groundwater levels more comprehensively. In this study, we investigate the effect of station density on the predictive performance of groundwater levels using the geostatistical technique of Ordinary Kriging. The research utilizes groundwater level data collected from 121 observation wells within the semi-arid Berambadi watershed, gathered over a six-year period (2010-2015) from the Indian Institute of Science (IISc), Bengaluru. The dataset is partitioned into seven subsets representing varying sampling densities, ranging from 15% (12 wells) to 100% (121 wells) of the total well network. The results obtained from different monitoring networks are compared against the existing groundwater monitoring network established by the Central Ground Water Board (CGWB). The findings of this study demonstrate that higher station densities significantly enhance the accuracy of geostatistical predictions for groundwater levels. The increased number of monitoring stations enables improved interpolation accuracy and captures finer-scale variations in groundwater levels. These results shed light on the relationship between station density and the geostatistical prediction of groundwater levels, emphasizing the importance of appropriate station densities to ensure accurate and reliable predictions. The insights gained from this study have practical implications for designing and optimizing monitoring networks, facilitating effective groundwater level assessments, and enabling sustainable management of groundwater resources.

Keywords: station density, geostatistical prediction, groundwater levels, monitoring networks, interpolation accuracy, spatial variability

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Authors: Sanat K. Dash, Rama S. Verma, Sarit K. Das

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A logarithmic microfluidic shear device was designed and fabricated for cellular mechanotransduction studies. The device contains four cell culture chambers in which flow was modulated to achieve a logarithmic increment. Resistance values were optimized to make the device compact. The network of resistances was developed according to a unique combination of series and parallel resistances as found via optimization. Simulation results done in Ansys 16.1 matched the analytical calculations and showed the shear stress distribution at different inlet flow rates. Fabrication of the device was carried out using conventional photolithography and PDMS soft lithography. Flow profile was validated taking DI water as working fluid and measuring the volume collected at all four outlets. Volumes collected at the outlets were in accordance with the simulation results at inlet flow rates ranging from 1 ml/min to 0.1 ml/min. The device can exert fluid shear stresses ranging four orders of magnitude on the culture chamber walls which will cover shear stress values from interstitial flow to blood flow. This will allow studying cell behavior in the long physiological range of shear stress in a single run reducing number of experiments.

Keywords: microfluidics, mechanotransduction, fluid shear stress, physiological shear

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Authors: Mohd Jaish Siddiqui

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Laryngeal TB is extremely rare in the pediatric population, accounting for 1% of all cases. Here, we present a case of laryngeal TB with miliary tuberculosis and tuberculous encephalitis, presented with sore throat, hoarseness, severe cough and, acute obstruction the larynx, sputum for AFB was negative, T-SPOT was positive and X-pert was positive, bronchoscopy revealed multiple nodules and edema around the larynx, epiglottis, bilateral arytenopharyngeal folds and vocal cord. Enhanced MRI revealed multiple small nodules in bilateral cerebral hemispheres and right thalamus, however CSF was negative. We reviewed the LTB cases that were published up to 2021. A total of twenty fine cases were identified in English literature. The most common manifestation was hoarseness of voice with 80% followed by stridor 40% of cases. Pulmonary involvement was found in 36% of cases, whereas, 45% of cases had no underlying TB. We did not find any case who developed tuberculous encephalitis in the literature.

Keywords: laryngeal tb, treatment, tuberculous encephalitis, children

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Authors: Ya-Hui Lee, Ching-Yi Lu, Hui-Chuan Wei

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The purpose of this research was to explore the relation between proactive coping and well-being of middle-aged adults. We conducted survey research that with t-test, one way ANOVA, Pearson correlation and stepwise multiple regression to analyze. This research drew on a sample of 395 participants from the senior learning centers of Taiwan. The results provided the following findings: 1.The participants from different residence areas associated significant difference with proactive coping, but not with well-being. 2. The participants’ perceived of financial level associated significant difference with both proactive coping and well-being. 3. There was significant difference between participants’ income and well-being. 4. The proactive coping was positively correlated with well-being. 5. From stepwise multiple regression analysis showed that two dimensions of proactive coping had positive predictability. Finally, these results of this study can be provided as references for designing older adult educational programs in Taiwan.

Keywords: middle-age and older adults, learners, proactive coping, well-being

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Authors: Maria M. Giannakou, Athanasios K. Ziliaskopoulos

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Transmission pipelines carrying natural gas are often routed through populated cities, industrial and environmentally sensitive areas. While the need for these networks is unquestionable, there are serious concerns about the risk these lifeline networks pose to the people, to their habitat and to the critical infrastructures, especially in view of natural disasters such as earthquakes. This work presents an Integrated Pipeline Risk Management methodology (IPRM) for assessing the hazard associated with a natural gas pipeline failure due to natural or manmade disasters. IPRM aims to optimize the allocation of the available resources to countermeasures in order to minimize the impacts of pipeline failure to humans, the environment, the infrastructure and the economic activity. A proposed knapsack mathematical programming formulation is introduced that optimally selects the proper mitigation policies based on the estimated cost – benefit ratios. The proposed model is demonstrated with a small numerical example. The vulnerability analysis of these pipelines and the quantification of consequences from such failures can be useful for natural gas industries on deciding which mitigation measures to implement on the existing pipeline networks with the minimum cost in an acceptable level of hazard.

Keywords: cost benefit analysis, knapsack problem, natural gas distribution network, risk management, risk mitigation

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Authors: Xupin Zhang, Maria Caterina Bramati, Enrest Fokoue

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Understanding the career placement of graduates from the university is crucial for both the qualities of education and ultimate satisfaction of students. In this research, we adapt the capabilities of dynamic Bayesian networks to characterize and predict students’ job placement using data from various universities. We also provide elements of the estimation of the indicator (score) of the strength of the network. The research focuses on overall findings as well as specific student groups including international and STEM students and their insight on the career path and what changes need to be made. The derived Bayesian network has the potential to be used as a tool for simulating the career path for students and ultimately helps universities in both academic advising and career counseling.

Keywords: dynamic bayesian networks, indicator estimation, job placement, social networks

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Authors: Erdinc Ozturk, Gizem Akcan

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The aim of this study was to determine some psychosocial variables that predict childhood trauma and dissociation in university students. These psychosocial variables were perceived social support, relationship status, gender and life satisfaction. 250 (125 males, 125 females) university students (bachelor, master and postgraduate degree) were enrolled in this study. They were chosen from universities in Istanbul at the education year of 2016-2017. Dissociative Experiences Scale (DES), Childhood Trauma Questionnaire (CTQ), Multidimensional Perceived Social Support Scale, Life Satisfaction Scale and Relationship Scales Questionnaire were used to assess related variables. Demographic information form was given to students in order to have their demographic information. Frequency distribution, multiple linear regression, and t-test analysis were used for statistical analysis. As together, perceived social support, relationship status and life satisfaction were found to have predictive value on trauma among university students. However, as together, these psychosocial variables did not have predictive value on dissociation. Only, trauma and relationship status had significant predictive value on dissociation. Moreover, there was significant difference between males and females in terms of trauma; however, dissociation scores of participants were not significantly different in terms of gender.

Keywords: childhood trauma, dissociation, perceived social support, relationship status, life satisfaction

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Authors: Preetham Shankapal, Jill King, Kori Murray, Corby Martin, Paula Giselman, Jason Hicks, Owen Carmicheal

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Activation of reward and satiety networks in the brain while processing palatable food cues, as well as functional connectivity during rest has been studied using functional Magnetic Resonance Imaging of the brain in various obesity phenotypes. However, functional connectivity within the reward and satiety network during food cue processing is understudied. 14 obese individuals underwent two fMRI scans during viewing of Macronutrient Picture System images. Each scan included two blocks of images of High Sugar/High Fat (HSHF), High Carbohydrate/High Fat (HCHF), Low Sugar/Low Fat (LSLF) and also non-food images. Seed voxels within seven food reward relevant ROIs: Insula, putamen and cingulate, precentral, parahippocampal, medial frontal and superior temporal gyri were isolated based on a prior meta-analysis. Beta series correlation for task-related functional connectivity between these seed voxels and the rest of the brain was computed. Voxel-level differences in functional connectivity were calculated between: first and the second scan; individuals who saw novel (N=7) vs. Repeated (N=7) images in the second scan; and between the HC/HF, HSHF blocks vs LSLF and non-food blocks. Computations and analysis showed that during food image viewing, reward network ROIs showed significant functional connectivity with each other and with other regions responsible for attentional and motor control, including inferior parietal lobe and precentral gyrus. These functional connectivity values were heightened among individuals who viewed novel HS/HF images in the second scan. In the second scan session, functional connectivity was reduced within the reward network but increased within attention, memory and recognition regions, suggesting habituation to reward properties and increased recollection of previously viewed images. In conclusion it can be inferred that Functional Connectivity within reward network and between reward and other brain regions, varies by important experimental conditions during food photography viewing, including habituation to shown foods.

Keywords: fMRI, functional connectivity, task-based, beta series correlation

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Authors: Gabriele Borg, Alexei De Bono, Charlie Abela

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There on a constant rise in the availability of high volumes of data gathered from multiple sources, resulting in an abundance of unprocessed information that can be used to monitor patterns and trends in user behaviour. Similarly, year after year, Malta is also constantly experiencing ongoing population growth and an increase in mobilization demand. This research takes advantage of data which is continuously being sourced and converting it into useful information related to the traffic problem on the Maltese roads. The scope of this paper is to provide a methodology to create a custom dataset (MalTra - Malta Traffic) compiled from multiple participants from various locations across the island to identify the most common routes taken to expose the main areas of activity. This use of big data is seen being used in various technologies and is referred to as ITSs (Intelligent Transportation Systems), which has been concluded that there is significant potential in utilising such sources of data on a nationwide scale.

Keywords: Big Data, vehicular traffic, traffic management, mobile data patterns

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Authors: Janis E. Campbell

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Most of what is known about complementary, alternative or traditional medicine (CATM) in the United States today is known from either the National Health Interview Survey a cross-sectional survey with a few questions or from small cross-sectional or cohort studies with specific populations. The broad geographical distribution in CATM use or providers is not known. For this project, we used geospatial cluster analysis to determine if there were clusters of CATM provider by county in the US. In this analysis, we used the National Provider Index to determine the geographic distribution of providers in the US. Of the 215,769 CAMT providers 211,603 resided in the contiguous US: Acupuncturist (26,563); Art, Poetry, Music and Dance Therapist (2,752); Chiropractor (89,514); Doula/Midwife (3,535); Exercise (507); Homeopath (380); Massage Therapist (36,540); Mechanotherapist (1,888); Naprapath (146); Naturopath (4,782); Nutrition (42,036); Reflexologist (522); Religious (2,438). ESRI® spatial autocorrelation was used to determine if the geographic location of CATM providers were random or clustered. For global analysis, we used Getis-Ord General G and for Local Indicators of Spatial Associations with an Optimized Hot Spot Analysis using an alpha of 0.05. Overall, CATM providers were clustered with both low and high. With Chiropractors, focusing in the Midwest, religious providers having very small clusters in the central US, and other types of CAMT focused in the northwest and west coast, Colorado and New Mexico, the great lakes areas and Florida. We will discuss some of the implications of this study, including associations with health, economic, social, and political systems.

Keywords: complementary medicine, alternative medicine, geospatial, United States

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Authors: Chonlada Choovanichchanon

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This research paper was aimed to investigate a relationship between the customer service marketing mix and the level of customers’ satisfaction from purchasing goods and service from small business around campus, Suan Sunandha Rajabhat University, Bangkok, Thailand. Based on the survey of 200 customers who frequently purchased goods and service around campus, the level of satisfaction for each factor of marketing mix was reached. An accidental random sampling was applied by using questionnaire in collecting the data. The findings revealed that the means values can help to rank these variables from high to low mean as follows: 1) forms and system of service, 2) physical environment of service center, 3) service from staff and employee, 4) product quality and service, 5) market channel and distribution, 6) market price, and 7) market promotion and distribution.

Keywords: service marketing mix, satisfaction, small business, survey

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Authors: Mohammad Hossein Harirchian, Siavash Babaie, Nika keshtkaran, Sama Bitarafan

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Background: Fatigue is a common complaint of multiple sclerosis (MS) patients, adversely affecting their quality of life. There is a lot of evidence showing that Carnitine deficiency is linked to fatigue development and severity in some conditions. This study aimed to compare the levels of Free L-Carnitine (FLC) between MS patients and healthy people and evaluate its association with the severity of fatigue. Methods: This case-control study included 30 patients with relapsing-remitting MS (RRMS) in 2 sex-matched equal-number groups according to the presence or absence of fatigue and 30 sex-matched healthy people in the control group. In addition, between two patient groups, we compared Serum level of FLC between the patient and healthy group. Fatigue was scored using two valid questionnaires of fatigue Severity Scale (FSS) and Modified Fatigue Impact Scale (MFIS). In addition, association between Serum level of FLC and fatigue severity was evaluated in MS patients. Results: There was no significant difference in serum levels of FLC between MS patients and healthy people. The patients with fatigue had a significantly lower FLC (mg/dl) value than patients without fatigue (22.53 ± 15.84 vs. 75.36 ± 51.98, P < 0.001). The mean value of FSS and MFIS in patients with fatigue were 48.80±8.55 and 62.87 ± 13.63, respectively, which was nearly two-fold higher than group without fatigue (P < 0.001). There was a negative correlation between the serum level of FLC and fatigue severity scales (Spearman rank correlation= 0.76, P < 0.001). Conclusion: We showed healthy people and MS patients were not different in levels of FLC. In addition, patients with lower serum levels of FLC might experience more severe fatigue. Therefore, this could clarify that supplementation with L-Carnitine might be considered as a complementary treatment for MS-related fatigue.

Keywords: fatigue, multiple sclerosis, L-carnitine, modified fatigue impact scale

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Authors: Jaiden Xuan Schraut, Leon Liu, Yiqiao Yin

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Computer vision in medical diagnosis has achieved a high level of success in diagnosing diseases with high accuracy. However, conventional classifiers that produce an image to-label result provides insufficient information for medical professionals to judge and raise concerns over the trust and reliability of a model with results that cannot be explained. In order to gain local insight into cancerous regions, separate tasks such as imaging segmentation need to be implemented to aid the doctors in treating patients, which doubles the training time and costs which renders the diagnosis system inefficient and difficult to be accepted by the public. To tackle this issue and drive AI-first medical solutions further, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional convolutional neural networks (CNN) module for auxiliary classification output. Class activation maps are a method of providing insight into a convolutional neural network’s feature maps that leads to its classification but in the case of lung diseases, the region of interest is enhanced by U-net-assisted Class Activation Map (CAM) visualization. Therefore, our proposed model combines image segmentation models and classifiers to crop out only the lung region of a chest X-ray’s class activation map to provide a visualization that improves the explainability and is able to generate classification results simultaneously which builds trust for AI-led diagnosis systems. The proposed U-Net model achieves 97.61% accuracy and a dice coefficient of 0.97 on testing data from the COVID-QU-Ex Dataset which includes both diseased and healthy lungs.

Keywords: multi-output network model, U-net, class activation map, image classification, medical imaging analysis

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Authors: Mahmoud Fouad, Mervat Badr, Marwa Ibrahim

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Micro-grid system is presently considered a reliable solution for the expected deficiency in the power required from future power systems. Renewable power sources such as wind, solar and hydro offer high potential of benign power for future micro-grid systems. Micro-Grid (MG) is basically a low voltage (LV) or medium voltage (MV) distribution network which consists of a number of called distributed generators (DG’s); micro-sources such as photovoltaic array, fuel cell, wind turbine etc. energy storage systems and loads; operating as a single controllable system, that could be operated in both grid-connected and islanded mode. The capacity of the DG’s is sufficient to support all; or most, of the load connected to the micro-grid. This paper presents a micro-grid system based on wind and solar power sources and addresses issues related to operation, control, and stability of the system. Using Matlab/Simulink, the system is modeled and simulated to identify the relevant technical issues involved in the operation of a micro-grid system based on renewable power generation units.

Keywords: micro-grid system, photovoltaic, wind turbine, energy storage, distributed generation, modeling

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Authors: Yuzhi Cai

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This talk presents a Bayesian approach to quantile autoregressive (QAR) time series model estimation and forecasting. We establish that the joint posterior distribution of the model parameters and future values is well defined. The associated MCMC algorithm for parameter estimation and forecasting converges to the posterior distribution quickly. We also present a combining forecasts technique to produce more accurate out-of-sample forecasts by using a weighted sequence of fitted QAR models. A moving window method to check the quality of the estimated conditional quantiles is developed. We verify our methodology using simulation studies and then apply it to currency exchange rate data. An application of the method to the USD to GBP daily currency exchange rates will also be discussed. The results obtained show that an unequally weighted combining method performs better than other forecasting methodology.

Keywords: combining forecasts, MCMC, quantile modelling, quantile forecasting, predictive density functions

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Authors: Yooseo Pang, Jongwoong Choi, Yong Cho, Yongchae Jeong

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Climate crisis, such as environmental problems related to energy supply, is getting emerged issues, so the use of renewable energy is essentially required to solve these problems, which are mainly managed by the Paris Agreement, the international treaty on climate change. The government of the Republic of Korea announced that the key long-term goal for a low-carbon strategy is “Carbon neutrality by 2050”. It is focused on the role of the internet data centers (IDC) in which large amounts of data, such as artificial intelligence (AI) and big data as an impact of the 4th industrial revolution, are managed. The demand for the cooling system market for IDC was about 9 billion US dollars in 2020, and 15.6% growth a year is expected in Korea. It is important to control the temperature in IDC with an efficient air conditioning system, so hydrothermal energy is one of the best options for saving energy in the cooling system. In order to save energy and optimize the operating conditions, it has been considered to apply ‘the dam deep water air conditioning system. Deep water at a specific level from the dam can supply constant water temperature year-round. It will be tested & analyzed the amount of energy saving with a pilot plant that has 100RT cooling capacity. Also, a target of this project is 1.2 PUE (Power Usage Effectiveness) which is the key parameter to check the efficiency of the cooling system.

Keywords: hydrothermal energy, HVAC, internet data center, free-cooling

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Authors: Nur Fareza Mustapha

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Solutions to policy problems need to be curated to the local context, taking into account the trajectory of the local development path to ensure its efficacy. For Kuala Lumpur, rapid urbanization and migration into the city for the past few decades have increased the demand for housing to accommodate a growing urban population. As a critical factor affecting housing affordability, land supply constraints have been attributed to intensifying market pressures, which grew in tandem with the demands of urban development, along with existing institutional constraints in the governance of land. While demand-side pressures are inevitable given the fixed supply of land, supply-side constraints in regulations distort markets and if addressed inappropriately, may lead to mistargeted policy interventions. Given Malaysia’s historical development, regulatory barriers for land may originate from the British colonial period, when many aspects of the current laws governing tenure were introduced and formalized, and henceforth, became engrained in the system. This research undertakes a postcolonial institutional analysis approach to uncover the causal mechanism driving the evolution of land tenure systems in post-colonial Kuala Lumpur. It seeks to determine the sources of these shifts, focusing on the incentives and bargaining positions of actors during periods of institutional flux/change. It aims to construct a conceptual framework to further this understanding and to elucidate how this historical trajectory affects current access to urban land markets for housing. Archival analysis is used to outline and analyse the evolution of land tenure systems in Kuala Lumpur while stakeholder interviews are used to analyse its impact on the current urban land market, with a particular focus on the provision of and access to affordable housing in the city. Preliminary findings indicate that many aspects of the laws governing tenure that were introduced and formalized during the British colonial period have endured until the present day. Customary rules of tenure were displaced by rules following a European tradition, which found legitimacy through a misguided interpretation of local laws regarding the ownership of land. Colonial notions of race and its binary view of native vs. non-natives have also persisted in the construction and implementation of current legislation regarding land tenure. More concrete findings from this study will generate a more nuanced understanding of the regulatory land supply constraints in Kuala Lumpur, taking into account both the long and short term spatial and temporal processes that affect how these rules are created, implemented and enforced.

Keywords: colonial discourse, historical institutionalism, housing, land policy, post-colonial city

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Authors: Natalia Efremova, Sergey Tarasenko

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We propose a novel neural network architecture for visual saliency detections, which utilizes neuro physiologically plausible mechanisms for extraction of salient regions. The model has been significantly inspired by recent findings from neuro physiology and aimed to simulate the bottom-up processes of human selective attention. Two types of features were analyzed: color and direction of maximum variance. The mechanism we employ for processing those features is PCA, implemented by means of normalized Hebbian learning and the waves of spikes. To evaluate performance of our model we have conducted psychological experiment. Comparison of simulation results with those of experiment indicates good performance of our model.

Keywords: neural network models, visual saliency detection, normalized Hebbian learning, Oja's rule, psychological experiment

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Authors: Akinyemi Tomiwa

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The microstructure of rainfall is important for predicting and modeling various environmental processes, such as rainfall interception by vegetation, soil erosion, and radar signals in rainfall. This rain microstructure was studied with a vertically pointing Micro Rain Radar (MRR) located at a tropical location in Akure South West Nigeria (7o 15’ N, 5o 15’ E). This research utilizes two years of data (2018 and 2019), and the data obtained comprises rainfall parameters such as Rain rates, radar reflectivity, liquid water content, fall velocity and Drop Size Distribution (DSD) based on vertical profiles. The measurement and variations of rain microstructure of these parameters with heights for different rain types were presented from ground level up to the height of 4800 m at 160 m range gates. It has been found that the convective, stratiform and mixed, which are the three major rain types, have different rain microstructures at different heights and were evaluated in this research. The correlation coefficient and the regression line equation were computed for each rain event. The highest rain rate and liquid water content were observed within the height range of 160-4800. It was found that a good correlation exists between the measured parameters. Hence it shows that specific liquid water content increases with increasing rain rate for both stratiform and convective rain types in this part of the world. The results can be very useful for a better understanding of rain structure over tropical regions.

Keywords: rain microstructure, drop size distribution, rain rates, stratiform, convective.

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Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Ali Shareef, Aliha Shareef, Yifeng Zhu

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Energy minimization is of great importance in wireless sensor networks in extending the battery lifetime. One of the key activities of nodes in a WSN is communication and the routing of their data to a centralized base-station or sink. Routing using the shortest path to the sink is not the best solution since it will cause nodes along this path to fail prematurely. We propose a cross-layer energy efficient routing protocol Optrix that utilizes a convex formulation to maximize the lifetime of the network as a whole. We further propose, Optrix-BW, a novel convex formulation with bandwidth constraint that allows the channel conditions to be accounted for in routing. By considering this key channel parameter we demonstrate that Optrix-BW is capable of congestion control. Optrix is implemented in TinyOS, and we demonstrate that a relatively large topology of 40 nodes can converge to within 91% of the optimal routing solution. We describe the pitfalls and issues related with utilizing a continuous form technique such as convex optimization with discrete packet based communication systems as found in WSNs. We propose a routing controller mechanism that allows for this transformation. We compare Optrix against the Collection Tree Protocol (CTP) and we found that Optrix performs better in terms of convergence to an optimal routing solution, for load balancing and network lifetime maximization than CTP.

Keywords: wireless sensor network, Energy Efficient Routing

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Authors: Arūnas Gudinavičius, Andrius Šuminas

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The analysis of scholarly journals formats is fragmented and needs to be studied from a point of view of scientific communication. While PDF is to the author’s best knowledge probably the most popular digital format of XXI century, but there are more formats available: HTML, EPUB, etc. Our aim is to analyze how these formats important to the readers and what is their contribution to scientific communication. We want to investigate how printed journals are still popular between scholars and does different formats are preferred between fields of science . In most cases, publishing of scientific journals are examined from a narrow perspective of a particular university science affair administrators or research funding institution. We believe that more data o n formats used in scholarly periodicals currently published in Lithuania as well as in Eastern Europe are needed. Science communication is often analyzed as a directed chain of information in the author-publisher-reader cycle. The paper is focusing on the publishing part of this chain. A distinction is made between formal and informal forms of scientific communication, which is relevant in today's context, when both forms of communication intertwine and complement each other. In our research, we will analyze formal documentary (formats of publication of scientific articles) communication - scientific information recorded in a certain medium and formatted in certain format (printed, PDF, HTML, EPUB, etc.). In our research, we will analyze the stage of publication of research results in scientific journals and their dissemination through specific publication formats. The paper is to systematize and analyze the various types of formats of scientific journal published in XXI century in Lithuania (small publishing market). The research analyses the case of small European country and presents publishing formats characteristics of the publication of scientific periodicals.

Keywords: scientific communication, scientific journals, publishing formats, reading

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Authors: Wabinyai Fidel Raja, Gideon Lubisa

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Air pollution is becoming a growing concern in Africa due to rapid industrialization and urbanization, leading to implications for public health and the environment. Establishing a comprehensive air quality monitoring network is crucial to combat this issue. However, conventional methods of monitoring are insufficient in African cities due to the high cost of setup and maintenance. To address this, low-cost sensors (LCS) can be deployed in various urban areas through the use of participatory air quality network siting (PAQNS). PAQNS involves stakeholders from the community, local government, and private sector working together to determine the most appropriate locations for air quality monitoring stations. This approach improves the accuracy and representativeness of air quality monitoring data, engages and empowers community members, and reflects the actual exposure of the population. Implementing PAQNS in African cities can build trust, promote accountability, and increase transparency in the air quality management process. However, challenges to implementing this approach must be addressed. Nonetheless, improving air quality is essential for protecting public health and promoting a sustainable environment. Implementing participatory and data-informed air quality monitoring can take a significant step toward achieving these important goals in African cities and beyond.

Keywords: low-cost sensors, participatory air quality network siting, air pollution, air quality management

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Authors: Abootaleb Shirvani, Svetlozar Rachev

Abstract:

ESG stands for Environmental, Social, and Governance and is a non-financial factor that investors use to specify material risks and growth opportunities in their analysis process. ESG ratings provide a quantitative measure of socially responsible investment, and it is essential to incorporate ESG ratings when modeling the dynamics of asset returns. In this article, we propose a triple subordinated Lévy process for incorporating numeric ESG ratings into dynamic asset pricing theory to model the time series properties of the stock returns. The motivation for introducing three layers of subordinator is twofold. The first two layers of subordinator capture the skew and fat-tailed properties of the stock return distribution that cannot be explained well by the existing Lévy subordinated model. The third layer of the subordinator introduces ESG valuation and incorporates numeric ESG ratings into dynamic asset pricing theory and option pricing. We employ the triple subordinator Lévy model for developing the ESG-valued stock return model, derive the implied ESG score surfaces for Microsoft, Apple, and Amazon stock returns, and compare the shape of the ESG implied surface scores for these stocks.

Keywords: ESG scores, dynamic asset pricing theory, multiple subordinated modeling, Lévy processes, option pricing

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Authors: Zhaocheng Shang

Abstract:

In order to study the topic of cities crossing rivers, a Four-Dimensional Analysis Method consisting of timeline, X-axis, Y-axis, and Z-axis is proposed. Policies, plans, and their implications are summarized and researched along with the timeline. The X-axis is the direction which is parallel to the river. The research area was chosen because of its important connection function. It is proposed that more surface water network should be built because of the ecological orientation of the research area. And the analysis of groundwater makes it for sure that the proposal is feasible. After the blue water network is settled, the green landscape network which is surrounded by it could be planned. The direction which is transversal to the river (Y-axis) should run through the transportation axis so that the urban texture could stretch in an ecological way. Therefore, it is suggested that the work of the planning bureau and river bureau should be coordinated. The Z-axis research is on the section view of the river, especially on the Yellow River’s special feature of being a perched river. Based on water control safety demands, river parks could be constructed on the embankment buffer zone, whereas many kinds of ornamental trees could be used to build the buffer zone. City Crossing River is a typical case where we make use of landscaping to build a symbiotic relationship between the urban landscape architecture and the environment. The local environment should be respected in the process of city expansion. The planning order of "Benefit- Flood Control Safety" should be replaced by "Flood Control Safety - Landscape Architecture- People - Benefit".

Keywords: blue-green landscape network, city crossing river, four-dimensional analysis method, planning order

Procedia PDF Downloads 145

Authors: Reza Sirjani, Nobosse Tafem Bolan

Abstract:

Many optimization techniques available in the literature have been developed in order to solve the problem of voltage stability enhancement in power systems. However, there are a number of drawbacks in the use of previous techniques aimed at determining the optimal location and size of reactive compensators in a network. In this paper, an Improved Cuckoo Search algorithm is applied as an appropriate optimization algorithm to determine the optimum location and size of a Static Var Compensator (SVC) in a transmission network. The main objectives are voltage stability improvement and total cost minimization. The results of the presented technique are then compared with other available optimization techniques.

Keywords: cuckoo search algorithm, optimization, power system, var compensators, voltage stability

Procedia PDF Downloads 533

Authors: Eiman Kattan

Abstract:

This study aims to evaluate the impact of classifying labelled remote sensing images conventional neural network (CNN) architecture, i.e., AlexNet on different land cover scenarios based on two remotely sensed datasets from different point of views such as the computational time and performance. Thus, a set of experiments were conducted to specify the effectiveness of the selected convolutional neural network using two implementing approaches, named fully trained and fine-tuned. For validation purposes, two remote sensing datasets, AID, and RSSCN7 which are publicly available and have different land covers features were used in the experiments. These datasets have a wide diversity of input data, number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in training, validation, and testing. As a result, the fully trained approach has achieved a trivial result for both of the two data sets, AID and RSSCN7 by 73.346% and 71.857% within 24 min, 1 sec and 8 min, 3 sec respectively. However, dramatic improvement of the classification performance using the fine-tuning approach has been recorded by 92.5% and 91% respectively within 24min, 44 secs and 8 min 41 sec respectively. The represented conclusion opens the opportunities for a better classification performance in various applications such as agriculture and crops remote sensing.

Keywords: conventional neural network, remote sensing, land cover, land use

Procedia PDF Downloads 351

Authors: Chengcao Sun, Shujun Li, Cuili Yang, Yongyong Xi, Liang Wang, Feng Zhang, Dejia Li

Abstract:

Recently, the long non-coding RNA (lncRNA) NEAT1 has been identified as an oncogenic gene in multiple cancer types and elevated expression of NEAT1 was tightly linked to tumorigenesis and cancer progression. However, the molecular basis for this observation has not been characterized in progression of non-small cell lung cancer (NSCLC). In our studies, we identified NEAT1 was highly expressed in NSCLC patients and was a novel regulator of NSCLC progression. Patients whose tumors had high NEAT1 expression had a shorter overall survival than patients whose tumors had low NEAT1 expression. Further, NEAT1 significantly accelerates NSCLC cell growth and metastasis in vitro and tumor growth in vivo. Additionally, by using bioinformatics study and RNA pull down combined with luciferase reporter assays, we demonstrated that NEAT1 functioned as a competing endogenous RNA (ceRNA) for has-miR-377-3p, antagonized its functions and led to the de-repression of its endogenous targets E2F3, which was a core oncogene in promoting NSCLC progression. Taken together, these observations imply that the NEAT1 modulated the expression of E2F3 gene by acting as a competing endogenous RNA, which may build up the missing link between the regulatory miRNA network and NSCLC progression.

Keywords: long non-coding RNA NEAT1, hsa-miRNA-377-3p, E2F3, non-small cell lung cancer, tumorigenesis

Procedia PDF Downloads 361