Search results for: data quality filtering

Authors: Diego De Castro Rodrigues, Marcelo Lisboa Rocha, Daniela M. De Q. Trevisan, Marcos Dias Da Conceicao, Gabriel Rosa, Rommel M. Barbosa

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The use of association rules algorithms within data mining is recognized as being of great value in the knowledge discovery in databases. Very often, the number of rules generated is high, sometimes even in databases with small volume, so the success in the analysis of results can be hampered by this quantity. The purpose of this research is to present a method for reducing the quantity of rules generated with association algorithms. Therefore, a computational algorithm was developed with the use of a Weka Application Programming Interface, which allows the execution of the method on different types of databases. After the development, tests were carried out on three types of databases: synthetic, model, and real. Efficient results were obtained in reducing the number of rules, where the worst case presented a gain of more than 50%, considering the concepts of support, confidence, and lift as measures. This study concluded that the proposed model is feasible and quite interesting, contributing to the analysis of the results of association rules generated from the use of algorithms.

Keywords: data mining, association rules, rules reduction, artificial intelligence

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Authors: Chia-Pei Chen, Yu-Ju Chen, Yu-Juei Hsu

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The prevalence and incidence of end-stage renal disease in Taiwan ranks the highest in the world. According to the statistical survey of the Ministry of Health and Welfare in 2019, kidney disease is the ninth leading cause of death in Taiwan. It leads to autonomic dysfunction, inflammatory response and symptom distress, and further increases the damage to the structure and function of the kidneys, leading to increased demand for renal replacement therapy and risks of cardiovascular disease, which also has medical costs for the society. If we can intervene in a feasible manual to effectively regulate the autonomic nerve function of CKD patients, reduce the inflammatory response and symptom distress. To prolong the progression of the disease, it will be the main goal of caring for CKD patients. This study aims to test the effect of heart rate variability biofeedback (HRVBF) on improving autonomic nerve function (Heart Rate Variability, HRV), inflammatory response (Interleukin-6 [IL-6], C reaction protein [CRP] ), symptom distress (Piper fatigue scale, Pittsburgh Sleep Quality Index [PSQI], and Beck Depression Inventory-II [BDI-II] ) in patients with chronic kidney disease. This study was experimental research, with a convenience sampling. Participants were recruited from the nephrology clinic at a medical center in northern Taiwan. With signed informed consent, participants were randomly assigned to the HRVBF or control group by using the Excel BINOMDIST function. The HRVBF group received four weekly hospital-based HRVBF training, and 8 weeks of home-based self-practice was done with StressEraser. The control group received usual care. We followed all participants for 3 months, in which we repeatedly measured their autonomic nerve function (HRV), inflammatory response (IL-6, CRP), and symptom distress (Piper fatigue scale, PSQI, and BDI-II) on their first day of study participation (baselines), 1 month, and 3 months after the intervention to test the effects of HRVBF. The results were analyzed by SPSS version 23.0 statistical software. The data of demographics, HRV, IL-6, CRP, Piper fatigue scale, PSQI, and BDI-II were analyzed by descriptive statistics. To test for differences between and within groups in all outcome variables, it was used by paired sample t-test, independent sample t-test, Wilcoxon Signed-Rank test and Mann-Whitney U test. Results: Thirty-four patients with chronic kidney disease were enrolled, but three of them were lost to follow-up. The remaining 31 patients completed the study, including 15 in the HRVBF group and 16 in the control group. The characteristics of the two groups were not significantly different. The four-week hospital-based HRVBF training combined with eight-week home-based self-practice can effectively enhance the parasympathetic nerve performance for patients with chronic kidney disease, which may against the disease-related parasympathetic nerve inhibition. In the inflammatory response, IL-6 and CRP in the HRVBF group could not achieve significant improvement when compared with the control group. Self-reported fatigue and depression significantly decreased in the HRVBF group, but they still failed to achieve a significant difference between the two groups. HRVBF has no significant effect on improving the sleep quality for CKD patients.

Keywords: heart rate variability biofeedback, autonomic nerve function, inflammatory response, symptom distress, chronic kidney disease

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Authors: Elin Mordal, Meseret Tsegaye, Hirut Gemeda, Ingeborg Ulvund

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Background: Even the rural-urban gap in the provision of skilled care during childbirth has narrowed, developing countries have the highest percentage of maternal deaths. More important than uncovering deficiencies during pregnancy, is preventing situations of risk during childbirth. The aim of this study was to identify factors women in the rural area consider before they decide where to give birth. Methods: This study utilizes a qualitative descriptive design based on individual interviews with 25 women of childbearing age who has given birth at least once, where women who delivered both at home and a health centre were included. Data collection took place in rural areas around Hawassa Zuriya Worda in Ethiopia February 2015. To identify conditions associated to where women prefer to give birth a thematic analysis was carried out. Result: Experienced risks regarding child birth were the most common reason for women and their families to seek help from skilled birth attendants. Decision-making and planning were identified as a major factor contributing to where women give birth. The women’s position and responsibilities pointed to the fact that women's role is mainly to take care of children and manage the household, while husbands, mother in laws and the elderly are the family members who take most of the decisions. This includes decision about where women give birth. The infrastructure also influences where women choose to give birth. Conclusion: To further improve childbirth care in Hawassa Zuriya Worda it’s important that women get positive experiences, and are met in a safe and supportive way at Health Centers. Challenges appear to women’s autonomy, quality aspects, and infrastructure.

Keywords: childbirth, women, health care utilization, Hawassa Zuriya Worda, Ethiopia, rural area

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Authors: Xiaoyong He

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The manipulation of THz waves is still a challenging task due to lack of natural materials interacted with it strongly. Designed by tailoring the characters of unit cells (meta-molecules), the advance of metamaterials (MMs) may solve this problem. However, because of Ohmic and radiation losses, the performance of MMs devices is subjected to the dissipation and low quality factor (Q-factor). This dilemma may be circumvented by Fano resonance, which arises from the destructive interference between a bright continuum mode and dark discrete mode (or a narrow resonance). Different from symmetric Lorentz spectral curve, Fano resonance indicates a distinct asymmetric line-shape, ultrahigh quality factor, steep variations in spectrum curves. Fano resonance is usually realized through symmetry breaking. However, if concentric double rings (DR) are placed closely to each other, the near-field coupling between them gives rise to two hybridized modes (bright and narrowband dark modes) because of the local asymmetry, resulting into the characteristic Fano line shape. Furthermore, from the practical viewpoint, it is highly desirable requirement that to achieve the modulation of Fano spectral curves conveniently, which is an important and interesting research topics. For current Fano systems, the tunable spectral curves can be realized by adjusting the geometrical structural parameters or magnetic fields biased the ferrite-based structure. But due to limited dispersion properties of active materials, it is still a tough work to tailor Fano resonance conveniently with the fixed structural parameters. With the favorable properties of extreme confinement and high tunability, graphene is a strong candidate to achieve this goal. The DR-structure possesses the excitation of so-called “trapped modes,” with the merits of simple structure and high quality of resonances in thin structures. By depositing graphene circular DR on the SiO2/Si/ polymer substrate, the tunable Fano resonance has been theoretically investigated in the terahertz regime, including the effects of graphene Fermi level, structural parameters and operation frequency. The results manifest that the obvious Fano peak can be efficiently modulated because of the strong coupling between incident waves and graphene ribbons. As Fermi level increases, the peak amplitude of Fano curve increases, and the resonant peak position shifts to high frequency. The amplitude modulation depth of Fano curves is about 30% if Fermi level changes in the scope of 0.1-1.0 eV. The optimum gap distance between DR is about 8-12 μm, where the value of figure of merit shows a peak. As the graphene ribbon width increases, the Fano spectral curves become broad, and the resonant peak denotes blue shift. The results are very helpful to develop novel graphene plasmonic devices, e.g. sensors and modulators.

Keywords: graphene, metamaterials, terahertz, tunable

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Authors: Innocensia John, Henrik Egelyng, Razack Lokina

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As the world`s population is constantly growing, food security has become a thorny trending issue. The impact has particularly been felt more in Africa as most of the people depend on food Agriculture products. Geographical Indications can aid in transforming the Tanzania agriculture-dependent economy through tapping the unique attributes of their quality products like soil, taste color etc. Consumers worldwide demand more uniquer products featuring a ´connect´ with the land use systems producing particular qualities. Tanzania has demonstrated the capacity to tap into the organic world market and has untapped potential for harvesting market value from geographical indications. This paper presents preliminary results from VALOR — a research project investigating conditions under which Tanzanian origin food producers can add value by incorporating territory specific cultural, environmental and social qualities into marketing, production and processing of unique local, niche and specialty products. Cases are investigated of the prospects for Tanzania to leapfrog perhaps into exports of geographical indications products, and certainly into allowing smallholders to create employment and build monetary value, while stewarding local food cultures and natural environments and resources, and increasing the diversity of supply of natural and unique quality products and so contribute to enhanced food security. Rice from Kyela, coffee and Sugar from Kilimanjaro, are some of the product cases investigated and provides for the in-depth case study, as ´landscape´ products incorporating ´taste of place´. Framework conditions for producers creating or capturing market value as stewards of cultural and landscape values and environments and institutional requirements for such creation or capturing to happen, including presence of export opportunities, are discussed.

Keywords: food origins, food security, protected geographical indications, case study analysis

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Authors: Shirin Alabdulqader

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This research aims to investigate the concept of code-switching (CS) between English, Arabic, and the CS practices of Saudi online users via a Translanguaging (TL) lens for more inclusive view towards the nature of the data from the study. It employs Digitally Mediated Communication (DMC), specifically the WhatsApp and Twitter platforms, in order to understand how the users employ online resources to communicate with others on a daily basis. This project looks beyond language and considers the multimodal affordances (visual and audio means) that interlocutors utilise in their online communicative practices to shape their online social existence. This exploratory study is based on a data-driven interpretivist epistemology as it aims to understand how meaning (reality) is created by individuals within different contexts. This project used a mixed-method approach, combining a qualitative and a quantitative approach. In the former, data were collected from online chats and interview responses, while in the latter a questionnaire was employed to understand the frequency and relations between the participants’ linguistic and non-linguistic practices and their social behaviours. The participants were eight bilingual Saudi nationals (both men and women, aged between 20 and 50 years old) who interacted with others online. These participants provided their online interactions, participated in an interview and responded to a questionnaire. The study data were gathered from 194 WhatsApp chats and 122 Tweets. These data were analysed and interpreted according to three levels: conversational turn taking and CS; the linguistic description of the data; and CS and persona. This project contributes to the emerging field of analysing online Arabic data systematically, and the field of multimodality and bilingual sociolinguistics. The findings are reported for each of the three levels. For conversational turn taking, the CS analysis revealed that it was used to accomplish negotiation and develop meaning in the conversation. With regard to the linguistic practices of the CS data, the majority of the code-switched words were content morphemes. The third level of data interpretation is CS and its relationship with identity; two types of identity were indexed; absolute identity and contextual identity. This study contributes to the DMC literature and bridges some of the existing gaps. The findings of this study are that CS by its nature, and most of the findings, if not all, support the notion of TL that multiliteracy is one’s ability to decode multimodal communication, and that this multimodality contributes to the meaning. Either this is applicable to the online affordances used by monolinguals or multilinguals and perceived not only by specific generations but also by any online multiliterates, the study provides the linguistic features of CS utilised by Saudi bilinguals and it determines the relationship between these features and the contexts in which they appear.

Keywords: social media, code-switching, translanguaging, online interaction, saudi bilinguals

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Authors: Ghadeer Mohammad Said El-Sheikh, Samer Mohamad Shalhoob

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Medical equipment operation state is a valid reflection of health care organizations' performance, where such equipment highly contributes to the quality of healthcare services on several levels in which quality improvement has become an intrinsic part of the discourse and activities of health care services. In healthcare organizations, clinical and biomedical engineering departments play an essential role in maintaining the safety and efficiency of such equipment. One of the most challenging topics when it comes to such sophisticated equipment is the lifespan of medical equipment, where many factors will impact such characteristics of medical equipment through its life cycle. So far, many attempts have been made in order to address this issue where most of the approaches are kind of arbitrary approaches and one of the criticisms of existing approaches trying to estimate and understand the lifetime of a medical equipment lies under the inquiry of what are the environmental factors that can play into such a critical characteristic of a medical equipment. In an attempt to address this shortcoming, the purpose of our study rises where in addition to the standard technical factors taken into consideration through the decision-making process by a clinical engineer in case of medical equipment failure, the dimension of environmental factors shall be added. The investigations, researches and studies applied for the purpose of supporting the decision making process by a clinical engineers and assessing the lifespan of healthcare equipment’s in the Lebanese society was highly dependent on the identification of technical criteria’s that impacts the lifespan of a medical equipment where the affecting environmental factors didn’t receive the proper attention. The objective of our study is based on the need for introducing a new well-designed plan for evaluating medical equipment depending on two dimensions. According to this approach, the equipment that should be replaced or repaired will be classified based on a systematic method taking into account two essential criteria; the standard identified technical criteria and the added environmental criteria.

Keywords: technical, environmental, healthcare, characteristic of medical equipment

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Authors: Hanan Begali, Shahi Dost, Annett Ziegler, Markus Cornberg, Maria-Esther Vidal, Anke R. M. Kraft

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Chronic hepatitis B virus (HBV) infection can be treated with nucleot(s)ide analog (NA), for example, which inhibits HBV replication. However, they have hardly any influence on the functional cure of HBV, which is defined by hepatitis B surface antigen (HBsAg) loss. NA needs to be taken life-long, which is not available for all patients worldwide. Additionally, NA-treated patients are still at risk of developing cirrhosis, liver failure, or hepatocellular carcinoma (HCC). Although each patient has the same components of the immune system, immune responses vary between patients. Therefore, a deeper understanding of the immune response against HBV in different patients is necessary to understand the parameters leading to HBV cure and to use this knowledge to optimize HBV therapies. This requires seamless integration of an enormous amount of diverse and fine-grained data from viral markers, e.g., hepatitis B core-related antigen (HBcrAg) and hepatitis B surface antigen (HBsAg). The data integration system relies on the assumption that profiling human immune systems requires the analysis of various variables (e.g., demographic data, treatments, pre-existing conditions, immune cell response, or HLA-typing) rather than only one. However, the values of these variables are collected independently. They are presented in a myriad of formats, e.g., excel files, textual descriptions, lab book notes, and images of flow cytometry dot plots. Additionally, patients can be identified differently in these analyses. This heterogeneity complicates the integration of variables, as data management techniques are needed to create a unified view in which individual formats and identifiers are transparent when profiling the human immune systems. The proposed study (HBsRE) aims at integrating heterogeneous data sets of 87 chronically HBV-infected patients, e.g., clinical data, immune cell response, and HLA-typing, with knowledge encoded in biomedical ontologies and open-source databases into a knowledge-driven framework. This new technique enables us to harmonize and standardize heterogeneous datasets in the defined modeling of the data integration system, which will be evaluated in the knowledge graph (KG). KGs are data structures that represent the knowledge and data as factual statements using a graph data model. Finally, the analytic data model will be applied on top of KG in order to develop a deeper understanding of the immune profiles among various patients and to evaluate factors playing a role in a holistic profile of patients with HBsAg level loss. Additionally, our objective is to utilize this unified approach to stratify patients for new effective treatments. This study is developed in the context of the project “Transforming big data into knowledge: for deep immune profiling in vaccination, infectious diseases, and transplantation (ImProVIT)”, which is a multidisciplinary team composed of computer scientists, infection biologists, and immunologists.

Keywords: chronic hepatitis B infection, immune response, knowledge graphs, ontology

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Authors: Natalia Limones, Javier Marzo, Marcus Wijnen, Aleix Serrat-Capdevila

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In this research we developed a structured approach for the detection of areas under the highest levels of drought risk that is suitable for data-scarce environments. The methodology is based on recent scientific outcomes and methods and can be easily adapted to different contexts in successive exercises. The research reviews the history of drought in the south of Angola and characterizes the experienced hazard in the episode from 2012, focusing on the meteorological and the hydrological drought types. Only global open data information coming from modeling or remote sensing was used for the description of the hydroclimatological variables since there is almost no ground data in this part of the country. Also, the study intends to portray the socioeconomic vulnerabilities and the exposure to the phenomenon in the region to fully understand the risk. As a result, a map of the areas under the highest risk in the south of the country is produced, which is one of the main outputs of this work. It was also possible to confirm that the set of indicators used revealed different drought vulnerability profiles in the South of Angola and, as a result, several varieties of priority areas prone to distinctive impacts were recognized. The results demonstrated that most of the region experienced a severe multi-year meteorological drought that triggered an unprecedent exhaustion of the surface water resources, and that the majority of their socioeconomic impacts started soon after the identified onset of these processes.

Keywords: drought risk, exposure, hazard, vulnerability

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Authors: Jummalla Aashrith, N. L. Shiva Sai, K. Bhavya Sri

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The swift progress of web technology has not only amassed a vast reservoir of internet data but also triggered a substantial surge in data generation. The internet has metamorphosed into one of the dynamic hubs for online education, idea dissemination, as well as opinion-sharing. Notably, the widely utilized social networking platform Twitter is experiencing considerable expansion, providing users with the ability to share viewpoints, participate in discussions spanning diverse communities, and broadcast messages on a global scale. The upswing in online engagement has sparked a significant curiosity in subjective analysis, particularly when it comes to Twitter data. This research is committed to delving into sentiment analysis, focusing specifically on the realm of Twitter. It aims to offer valuable insights into deciphering information within tweets, where opinions manifest in a highly unstructured and diverse manner, spanning a spectrum from positivity to negativity, occasionally punctuated by neutrality expressions. Within this document, we offer a comprehensive exploration and comparative assessment of modern approaches to opinion mining. Employing a range of machine learning algorithms such as Naive Bayes and Logistic Regression, our investigation plunges into the domain of Twitter data streams. We delve into overarching challenges and applications inherent in the realm of subjectivity analysis over Twitter.

Keywords: machine learning, sentiment analysis, visualisation, python

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Authors: Majid Alizadeh, Sina Nematizadeh, Hassan Esmailpour

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The mutual effects of hospitality and the environment are undeniable, so that the tourism industry has major harmful effects on the environment. Hotels, as one of the most important pillars of the hospitality industry, have significant effects on the environment. Green marketing is a promising strategy in response to the growing concerns about the environment. A green hotel marketing model was proposed using a grounded theory approach in the hotel industry. The study was carried out as a mixed method study. Data gathering in the qualitative phase was done through literature review and In-depth, semi-structured interviews with 10 experts in green marketing using snowball technique. Following primary analysis, open, axial, and selective coding was done on the data, which yielded 69 concepts, 18 categories and six dimensions. Green hotel (green product) was adopted as the core phenomenon. In the quantitative phase, data were gleaned using 384 questionnaires filled-out by hotel guests and descriptive statistics and Structural equation modeling (SEM) were used for data analysis. The results indicated that the mediating role of behavioral response between the ecological literacy, trust, marketing mix and performance was significant. The green marketing mix, as a strategy, had a significant and positive effect on guests’ behavioral response, corporate green image, and financial and environmental performance of hotels.

Keywords: green marketing, sustainable development, hospitality, grounded theory, structural equations model

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Authors: Mahsa Zargaran

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Introduction: Bronchiectasis, a chronic respiratory ailment, has grown progressively widespread globally. Common Variable Immunodeficiency (CVID) has been recognized as a notable contributing factor for bronchiectasis. In order to effectively manage this condition, a thorough and focused strategy is necessary. Material and Methods: A systematic literature search was conducted in Web of Science, PubMed, and EMBASE from January 2000 to December 2023 using established keywords. In addition, we discovered randomized controlled trials (RCTs) by searching the Cochrane Airways Group Register of trials and online trials registries. Two reviewers autonomously retrieved and recorded data from the papers that were included, and evaluated the potential for bias in each study. Results: The majority of research have shown that the prevalence of bronchiectasis in individuals with CVID is 24.9%. Furthermore, bronchiectasis is the most commonly observed radiological abnormality in these patients. Also, there is a significant occurrence of bronchiectasis in the Granulomatous Lymphocytic Interstitial Lung Disease (GL-ILD) group, with a prevalence rate of 31.3%. Research indicates that individuals diagnosed with CVID who also have bronchiectasis have insufficient forced expiratory volume in one second (FEV1). Furthermore, patients with bronchiectasis experience a higher frequency of respiratory tract infections and a diminished quality of life. Conclusion: Bronchiectasis is the predominant radiological observation in individuals with CVID, resulting in a reduction in FEV1, as well as recurrent infections in the lower respiratory tract. Additionally, individuals diagnosed with bronchiectasis exhibited reduced levels of serum immunoglobulin A (IgA) and immunoglobulin M (IgM). This study offers a fresh outlook and emphasizes the significance of early diagnosis and the need for enhancements in treatment approaches.

Keywords: common variable immunodeficiency -, bronchiectasis, forced expiratory volume in one second (FEV1), respiratory tract infections

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Authors: Wenyu Guo, Malachy O’Rourke, Mark Bowkett, Michael Gilchrist

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Many devices for real-time monitoring of surface water have been developed in the past few years to provide early warning of pollutions and so to decrease the risk of environmental pollution efficiently. One of the most common methodologies used in the detection system is a colorimetric process, in which a container with fixed volume is filled with target ions and reagents to combine a colorimetric dye. The colorimetric ions can sensitively absorb a specific-wavelength radiation beam, and its absorbance rate is proportional to the concentration of the fully developed product, indicating the concentration of target nutrients in the pre-mixed water samples. In order to achieve precise and rapid detection effect, channels with dimensions in the order of micrometers, i.e., microfluidic systems have been developed and introduced into these diagnostics studies. Microfluidics technology largely reduces the surface to volume ratios and decrease the samples/reagents consumption significantly. However, species transport in such miniaturized channels is limited by the low Reynolds numbers in the regimes. Thus, the flow is extremely laminar state, and diffusion is the dominant mass transport process all over the regimes of the microfluidic channels. The objective of this present work has been to analyse the mixing effect and chemistry kinetics in a stop-flow microfluidic device measuring Nitride concentrations in fresh water samples. In order to improve the temporal resolution of the Nitride microfluidic sensor, we have used computational fluid dynamics to investigate the influence that the effectiveness of the mixing process between the sample and reagent within a microfluidic device exerts on the time to completion of the resulting chemical reaction. This computational approach has been complemented by physical experiments. The kinetics of the Griess reaction involving the conversion of sulphanilic acid to a diazonium salt by reaction with nitrite in acidic solution is set in the Laminar Finite-rate chemical reaction in the model. Initially, a methodology was developed to assess the degree of mixing of the sample and reagent within the device. This enabled different designs of the mixing channel to be compared, such as straight, square wave and serpentine geometries. Thereafter, the time to completion of the Griess reaction within a straight mixing channel device was modeled and the reaction time validated with experimental data. Further simulations have been done to compare the reaction time to effective mixing within straight, square wave and serpentine geometries. Results show that square wave channels can significantly improve the mixing effect and provides a low standard deviations of the concentrations of nitride and reagent, while for straight channel microfluidic patterns the corresponding values are 2-3 orders of magnitude greater, and consequently are less efficiently mixed. This has allowed us to design novel channel patterns of micro-mixers with more effective mixing that can be used to detect and monitor levels of nutrients present in water samples, in particular, Nitride. Future generations of water quality monitoring and diagnostic devices will easily exploit this technology.

Keywords: nitride detection, computational fluid dynamics, chemical kinetics, mixing effect

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Authors: R. R. Dewi Rahmawaty Aktyani Putri, Earmporn Thongkrajai, Dedy Purwito

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There were many instruments have been developed to assess social support which has the different dimension in breast cancer patients. The Issue of measurement is a challenge to determining the component of dimensional concept, defining the unit of measurement, and establishing the validity and reliability of the measurement. However, the instruments where need to know how much support which obtained and perceived among women with breast cancer who undergone chemotherapy which it can help nurses to prevent of non-adherence in chemotherapy. This study aimed to measure the reliability of BC-SSAS instrument among 30 Indonesian women with breast cancer aged 18 years and above who undergone chemotherapy for six cycles in the oncological unit of Outpatient Department (OPD), Margono Soekardjo Hospital, Central Java, Indonesia. Data were collected during October to December 2015 by using modified the Breast Cancer Social Support Assessment (BC-SSAS). The Cronbach’s alpha analysis was carried out to measure internal consistency for reliability test of BC-SSAS instrument. This study used five experts for content validity index. The results showed that for content validity, I-CVI was 0.98 and S-CVI was 0.98; Cronbach’s alpha value was 0.971 and the Cronbach’s alpha coefficients for the subscales were high, with 0.903 for emotional support, 0.865 for informational support, 0.901 for tangible support, 0.897 for appraisal support and 0.884 for positive interaction support. The results confirmed that the BC-SSAS instrument has high reliability. BC-SSAS instruments were reliable and can be used in health care services to measure the social support received and perceived among women with breast cancer who undergone chemotherapy so that preventive interventions can be developed and the quality of health services can be improved.

Keywords: BC-SSAS, women with breast cancer, chemotherapy, Indonesia

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Authors: Asawari Alok Pawaskar

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This study was aimed at investigating quality assurance (QA) done with IBA matrix, the discrepan­cies observed for helical tomotherapy plans. A selection of tomotherapy plans that initially failed the with Matrix process was chosen for this investigation. These plans failed the fluence analysis as assessed using gamma criteria (3%, 3 mm). Each of these plans was modified (keeping the planning constraints the same), beamlets rebatched and reoptimized. By increasing and decreasing the modula­tion factor, the fluence in a circumferential plane as measured with a diode array was assessed. A subset of these plans was investigated using varied pitch values. Factors for each plan that were examined were point doses, fluences, leaf opening times, planned leaf sinograms, and uniformity indices. In order to ensure that the treatment constraints remained the same, the dose-volume histograms (DVHs) of all the modulated plans were compared to the original plan. It was observed that a large increase in the modulation factor did not significantly improve DVH unifor­mity, but reduced the gamma analysis pass rate. This also increased the treatment delivery time by slowing down the gantry rotation speed which then increases the maximum to mean non-zero leaf open time ratio. Increasing and decreasing the pitch value did not substantially change treatment time, but the delivery accuracy was adversely affected. This may be due to many other factors, such as the complexity of the treatment plan and site. Patient sites included in this study were head and neck, breast, abdomen. The impact of leaf tim­ing inaccuracies on plans was greater with higher modulation factors. Point-dose measurements were seen to be less susceptible to changes in pitch and modulation factors. The initial modulation factor used by the optimizer, such that the TPS generated ‘actual’ modulation factor within the range of 1.4 to 2.5, resulted in an improved deliverable plan.

Keywords: dose volume histogram, modulation factor, IBA matrix, tomotherapy

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Authors: Maryam Ziaee, Himanshu Shee, Amrik Sohal

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Big Data Analytics (BDA) in supply chain management has recently drawn the attention of academics and practitioners. Big data refers to a massive amount of data from different sources, in different formats, generated at high speed through transactions in business environments and supply chain networks. Traditional statistical tools and techniques find it difficult to analyse this massive data. BDA can assist organisations to capture, store, and analyse data specifically in the field of supply chain. Currently, there is a paucity of research on BDA in the pharmaceutical supply chain context. In this research, the Australian pharmaceutical supply chain was selected as the case study. This industry is highly significant since the right medicine must reach the right patients, at the right time, in right quantity, in good condition, and at the right price to save lives. However, drug shortages remain a substantial problem for hospitals across Australia with implications on patient care, staff resourcing, and expenditure. Furthermore, a massive volume and variety of data is generated at fast speed from multiple sources in pharmaceutical supply chain, which needs to be captured and analysed to benefit operational decisions at every stage of supply chain processes. As the pharmaceutical industry lags behind other industries in using BDA, it raises the question of whether the use of BDA can improve transparency among pharmaceutical supply chain by enabling the partners to make informed-decisions across their operational activities. This presentation explores the impacts of BDA on supply chain management. An exploratory qualitative approach was adopted to analyse data collected through interviews. This study also explores the BDA potential in the whole pharmaceutical supply chain rather than focusing on a single entity. Twenty semi-structured interviews were undertaken with top managers in fifteen organisations (five pharmaceutical manufacturers, five wholesalers/distributors, and five public hospital pharmacies) to investigate their views on the use of BDA. The findings revealed that BDA can enable pharmaceutical entities to have improved visibility over the whole supply chain and also the market; it enables entities, especially manufacturers, to monitor consumption and the demand rate in real-time and make accurate demand forecasts which reduce drug shortages. Timely and precise decision-making can allow the entities to source and manage their stocks more effectively. This can likely address the drug demand at hospitals and respond to unanticipated issues such as drug shortages. Earlier studies explore BDA in the context of clinical healthcare; however, this presentation investigates the benefits of BDA in the Australian pharmaceutical supply chain. Furthermore, this research enhances managers’ insight into the potentials of BDA at every stage of supply chain processes and helps to improve decision-making in their supply chain operations. The findings will turn the rhetoric of data-driven decision into a reality where the managers may opt for analytics for improved decision-making in the supply chain processes.

Keywords: big data analytics, data-driven decision, pharmaceutical industry, supply chain management

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Authors: Joshua Harrell, Gideon Osei Bonsu, Susan Garza, Clarence Conner, Da’Neisha Harris, Emma Bukoswki, Zohreh Safari

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The Social Vulnerability Index (SVI) stands as a pivotal tool for gauging community resilience amidst diverse stressors, including pandemics like COVID-19. This paper synthesizes recent research and underscores the significance of SVI in elucidating the differential impacts of crises on communities. Drawing on studies by Fox et al. (2023) and Mah et al. (2023), we delve into the application of SVI alongside emerging data sources to uncover nuanced insights into community vulnerability. Specifically, we explore the utilization of SVI in conjunction with mobility data from platforms like SafeGraph to probe the intricate relationship between social vulnerability and mobility dynamics during the COVID-19 pandemic. By leveraging 16 community variables derived from the American Community Survey, including socioeconomic status and demographic characteristics, SVI offers actionable intelligence for guiding targeted interventions and resource allocation. Building upon recent advancements, this paper contributes to the discourse on harnessing AI techniques to mitigate health disparities and fortify public health resilience in the face of pandemics and other crises.

Keywords: social vulnerability index, mobility dynamics, data analytics, health equity, pandemic preparedness, targeted interventions, data integration

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Authors: Ruomeng Xiao, Zhulin Zong, Longfa Yang

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Aiming at the problem that the target signal is difficult to detect under the strong ground clutter environment, this paper proposes a clutter suppression algorithm based on the combination of singular value decomposition and the Mallat fast wavelet algorithm. The method first carries out singular value decomposition on the radar echo data matrix, realizes the initial separation of target and clutter through the threshold processing of singular value, and then carries out wavelet decomposition on the echo data to find out the target location, and adopts the discard method to select the appropriate decomposition layer to reconstruct the target signal, which ensures the minimum loss of target information while suppressing the clutter. After the verification of the measured data, the method has a significant effect on the target extraction under low SCR, and the target reconstruction can be realized without the prior position information of the target and the method also has a certain enhancement on the output SCR compared with the traditional single wavelet processing method.

Keywords: clutter suppression, singular value decomposition, wavelet transform, Mallat algorithm, low SCR

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Authors: Amel Osman, Stewart McKenna

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Aim: Fever may present as the sole sign of a serious underlying infection in young infants. Febrile Infants aged less than 90 days are at an elevated susceptibility to invasive bacterial infections, thus presenting a challenge in ensuring the appropriate management of these cases. This study aims to ensure strict adherence to NICE guidelines for the management of fever in infants between 0 and 90 days presenting to Tallaght Hospital ED. A comprehensive audit, followed by a re-audit, was conducted to enhance the quality of care delivered to these patients. In accordance with NICE guidelines, all febrile infants should undergo blood tests. Additionally, LP should be performed in all neonates under 28 days, infants displaying signs of illness, and those with WCC below 5 or above 15. Method: A retrospective case review was performed, encompassing all patients aged between 0 to 90 days who presented with fever at Tallaght ED. Data retrieval was conducted from electronic records on two separate occasions, six months apart. The evaluation encompassed the assessment of body temperature as well as both partial and full septic workups. Results: Over the study period, 150 infants presented to the ED with fever in the initial audit, and 120 in the re-audit. In the first study, 81 patients warranted a full septic workup as per NICE, but only 48 received it. Conversely, 40 patients met criteria for a partial septic workup, with 12 undergoing blood tests. In the second study, 73 patients qualified for a full septic workup, of which 52 were completed. Additionally, 27 patients were indicated for a partial workup, with 20 undergoing blood tests. Conclusion: Managing febrile infants under three months of age presenting to Tallaght ED remains a persistent challenge, underscoring the need for continuous educational initiatives to guarantee that these patients receive the requisite assessments and treatments.

Keywords: infants, fever, septic workup, tallaght

Procedia PDF Downloads 55

Authors: Chunhua Liao, Jinfei Wang, Bo Shan, Yang Song, Yongjun He, Taifeng Dong

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Winter wheat is one of the main crops in Canada. Forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. It was found that the four-band reflectance (blue, green, red, near-infrared) performed better than their vegetation indices (NDVI, EVI, WDRVI and OSAVI) in wheat yield prediction. The optimum phenological stage for wheat yield prediction with highest accuracy was at the growing stages from the end of the flowering to the beginning of the filling stage. The best MLR model was therefore built to predict wheat yield before harvest using Sentinel-2 data acquired at the end of the flowering stage. Further, to improve the ability of the yield prediction at the early stages, three simple unsupervised domain adaptation (DA) methods were adopted to transform the reflectance data at the early stages to the optimum phenological stage. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. The average testing RMSE of the MLR model at the end of the flowering stage was 604.48 kg/ha. Near the booting stage, the average testing RMSE of yield prediction using the best MLR was reduced to 799.18 kg/ha when applying the mean matching domain adaptation approach to transform the data to the target domain (at the end of the flowering) compared to that using the original data based on the models developed at the booting stage directly (“MLR at the early stage”) (RMSE =1140.64 kg/ha). This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that “DA then MLR at the optimum stage” performed better than “MLR directly at the early stages” for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages. This study indicated that the simple domain adaptation methods had a great potential in crop yield prediction at the early stages using remote sensing data.

Keywords: wheat yield prediction, domain adaptation, Sentinel-2, within-field scale

Procedia PDF Downloads 68

Authors: Dan Kanmegne

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Africa will become the most populated continent by the end of the century, with around 4 billion inhabitants. Food security and climate changes will become continental issues since agricultural practices depend on climate but also contribute to global emissions and land degradation. Agroforestry has been identified as a cost-efficient and reliable strategy to address these two issues. It is defined as the integrated management of trees and crops/animals in the same land unit. Agroforestry provides benefits in terms of goods (fruits, medicine, wood, etc.) and services (windbreaks, fertility, etc.), and is acknowledged to have a great potential for carbon sequestration; therefore it can be integrated into reduction mechanisms of carbon emissions. Particularly in sub-Saharan Africa, the constraint stands in the lack of information about both areas under agroforestry and the characterization (composition, structure, and management) of each agroforestry system at the country level. This study describes and quantifies “what is where?”, earliest to the quantification of carbon stock in different systems. Remote sensing (RS) is the most efficient approach to map such a dynamic technology as agroforestry since it gives relatively adequate and consistent information over a large area at nearly no cost. RS data fulfill the good practice guidelines of the Intergovernmental Panel On Climate Change (IPCC) that is to be used in carbon estimation. Satellite data are getting more and more accessible, and the archives are growing exponentially. To retrieve useful information to support decision-making out of this large amount of data, satellite data needs to be organized so to ensure fast processing, quick accessibility, and ease of use. A new solution is a data cube, which can be understood as a multi-dimensional stack (space, time, data type) of spatially aligned pixels and used for efficient access and analysis. A data cube for Burkina Faso has been set up from the cooperation project between the international service provider WASCAL and Germany, which provides an accessible exploitation architecture of multi-temporal satellite data. The aim of this study is to map and characterize agroforestry systems using the Burkina Faso earth observation data cube. The approach in its initial stage is based on an unsupervised image classification of a normalized difference vegetation index (NDVI) time series from 2010 to 2018, to stratify the country based on the vegetation. Fifteen strata were identified, and four samples per location were randomly assigned to define the sampling units. For safety reasons, the northern part will not be part of the fieldwork. A total of 52 locations will be visited by the end of the dry season in February-March 2020. The field campaigns will consist of identifying and describing different agroforestry systems and qualitative interviews. A multi-temporal supervised image classification will be done with a random forest algorithm, and the field data will be used for both training the algorithm and accuracy assessment. The expected outputs are (i) map(s) of agroforestry dynamics, (ii) characteristics of different systems (main species, management, area, etc.); (iii) assessment report of Burkina Faso data cube.

Keywords: agroforestry systems, Burkina Faso, earth observation data cube, multi-temporal image classification

Procedia PDF Downloads 152

Authors: Tak Mau Simon Chan

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While there is growing reflection of the adverse impacts instigated by the flourishing gaming industry on the physical health and job satisfaction of those who work in Macau casinos, there is also a critical void in our understanding of the mental health of croupiers and how casino employment interacts with the family system. From a systemic approach, it would be most effective to examine the ‘dealer issues’ collectively and offer assistance to both the individual dealer and the family system of dealers. Therefore, with the use of a mixed method study design, the levels of anxiety, depression and sleeping quality of a sample of 1124 dealers who are working in Macau casinos have been measured in the present study, and 113 dealers have been interviewed about the impacts of casino employment on their family life. This study presents some very important findings. First, the quantitative study indicates that gender is a significant predictor of depression and anxiety levels, whilst lower income means less quality sleep. The Pearson’s correlation coefficients show that as the Zung Self-rating Anxiety Scale (ZSAS) scores increase, the Zung Self-rating Depression Scale (ZSDS) and Pittsburgh Sleep Quality Index (PSQI) scores will also simultaneously increase. Higher income, therefore, might partly explain for the reason why mothers choose to work in the gaming industry even with shift work involved and a stressful work environment. Second, the findings from the qualitative study show that aside from the positive impacts on family finances, the shift work and job stress to some degree negatively affect family responsibilities and relationships. There are resultant family issues, including missed family activities, and reduced parental care and guidance, marital intimacy, and communication with family members. Despite the mixed views on the gender role differences, the respondents generally agree that female dealers have more family and child-minding responsibilities at home, and thus it is more difficult for them to balance work and family. Consequently, they may be more vulnerable to stress at work. Thirdly, there are interrelationships between work and family, which are based on a systemic inquiry that incorporates work- individual- family. Poor physical and psychological health due to shift work or a harmful work environment could affect not just work performance, but also life at home. Therefore, a few practice points about 1) work-family conflicts in Macau; 2) families-in- transition in Macau; and 3) gender and class sensitivity in Macau; are provided for social workers and family practitioners who will greatly benefit these families, especially whose family members are working in the gaming industry in Macau. It is concluded that in addressing the cultural phenomenon of “dealer’s complex” in Macau, a systemic approach is recommended that addresses both personal psychological needs and family issue of dealers.

Keywords: family, work stress, mental health, Macau, dealers, gaming industry

Procedia PDF Downloads 308

Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

Procedia PDF Downloads 176

Authors: Kalpana Sharma, Jyoti Mann

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The study was designed towards institutional analysis for a clear understanding of the process involved in functioning and layout of determinants influencing physical education teacher’s education program in India. This further can be recommended for selection of parameters for creating sector benchmarking for physical education teachers training institutions across India. 165 stakeholders involving students, teachers, parents, administrators were surveyed from the identified seven institutions and universities from different states of India. They were surveyed on the basis of seven broad parameters which were associated with the post graduate physical education program in India. A physical education program assessment tool of 52 items was designed to administer it among the stakeholders selected for the survey. An item analysis of the contents was concluded through the review process from selected experts working in higher education with experience in teacher training program in physical education. The data was collected from the stakeholders of the selected institutions through Physical Education Program Assessment Tool (PEPAT). The hypothesis that PE teacher education program is independent of physical education institutions was significant. The study directed a need towards robust admission process emphasizing on identification, selection of potential candidates and quality control of intake with the scientific process developed according to the Indian education policies and academic structure. The results revealed that the universities do not have similar functional and delivery process related to the physical education teacher training program. The study reflects towards the need for physical education universities and institutions to identify the best practices to be followed regarding the functioning of delivery of physical education programs at various institutions through strategic management studies on the identified parameters before establishing strict standards and norms for achieving excellence in physical education in India.

Keywords: assessment, benchmarking, curriculum, physical education, teacher education

Procedia PDF Downloads 566

Authors: Shuofen Hsu, Ya-Yi Chao, Chao-Wei Li

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This paper investigates the relationship between voluntary audit (hereafter, VA) of semi-annual consolidated financial statements decision and accounting conservatism. In general, there are four kinds of auditors' assurance services, which include audit, review, agreed-upon procedure and compliance engagements base on degree of assurance. The VA work by auditors may not only have the higher audit quality but an important signal of more reliable information than the review work. In Taiwan, The listed companies must prepare the semi-annual consolidated financial statements and with auditors' review before 2012, but some of the listed companies choose the assurance work from review to audit voluntarily. Due to the adoption of International Financial Reporting Standards, the listed companies were required to prepare the second quarterly consolidated financial statements which should be reviewed by auditors since 2013. This rule will change some of the assurance work from audit to review by auditors, and the information asymmetry maybe increased. To control the selection bias, we use two-stage model to test the relationship between VA decision and accounting conservatism. Our empirical results indicate that the VA decision and accounting conservatism have a significant positive relationship in firms with family-controlled. That is, firms with family-controlled are more likely to do VA and to prepare more conservative consolidated financial statements to reduce the information asymmetry, meaning that there is a complementary effect between VA and accounting conservatism for firms with more information asymmetry. But on the contrary, we find that the VA decision and accounting conservatism have a significant negative relationship in firms with professional managers-controlled, meaning that there is a substitution effect between VA and accounting conservatism for firms with less information asymmetry. Finally, the accounting conservatism of consolidated financial statements decrease after the adoption of IFRSs (International Financial Reporting Standards) in Taiwan. It means that the disclosure and transparency of consolidated financial statements had be improved.

Keywords: voluntary audit, accounting conservatism, audit quality, information asymmetry

Procedia PDF Downloads 230

Authors: Ricardo Paiva, Rui Soares, Felix Harnau, Bruno Fragoso

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Nickel-based alloys are materials well suited for service in extreme environments subjected to pressure and heat. Some industrial applications for Nickel-based alloys are aerospace and jet engines, oil and gas extraction, pollution control and waste processing, automotive and marine industry. It is generally recognized that grain refinement is an effective methodology to improve the quality of casted parts. Conventional grain refinement techniques involve the addition of inoculation substances, the control of solidification conditions, or thermomechanical treatment with recrystallization. However, such methods often lead to non-uniform grain size distribution and the formation of hard phases, which are detrimental to both wear performance and biocompatibility. Stirring of the melt by electromagnetic fields has been widely used in continuous castings with success for grain refinement, solute redistribution, and surface quality improvement. Despite the advantages, much attention has not been paid yet to the use of this approach on functional castings such as investment casting. Furthermore, the effect of electromagnetic stirring (EMS) fields on Nickel-based alloys is not known. In line with the gaps/needs of the state-of-art, the present research work targets to promote new advances in controlling grain size and morphology of investment cast Nickel based alloys. For such a purpose, a set of experimental tests was conducted. A high-frequency induction furnace with vacuum and controlled atmosphere was used to cast the Inconel 718 alloy in ceramic shells. A coil surrounded the casting chamber in order to induce electromagnetic stirring during solidification. Aiming to assess the effect of the electromagnetic stirring on Ni alloys, the samples were subjected to microstructural analysis and mechanical tests. The results show that electromagnetic stirring can be an effective methodology to modify the grain size and mechanical properties of investment-cast parts.

Keywords: investment casting, grain refinement, electromagnetic stirring, nickel alloys

Procedia PDF Downloads 136

Authors: D. Sarkar, M. Pal, A. K. Sarkar

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Asphaltic concrete for pavement construction in India are produced by using crushed stone, gravels etc. as aggregate. In north-Eastern region of India, there is a scarcity o f stone aggregate. Therefore the road engineers are always in search of an optional material as aggregate which can replace the regularly used material. The purpose of this work was to evaluate the utilization of substandard or marginal aggregates in flexible pavement construction. The investigation was undertaken to evaluate the effects of using lower quality aggregates such as over burnt brick aggregate on the preparation of asphalt concrete for flexible pavements. The scope of this work included a review of available literature and existing data, a laboratory evaluation organized to determine the effects of marginal aggregates and potential techniques to upgrade these substandard materials, and a laboratory evaluation of these upgraded marginal aggregate asphalt mixtures. Over burnt brick aggregates are water susceptible and can leads to moisture damage. Moisture damage is the progressive loss of functionality of the material owing to loss of the adhesion bond between the asphalt binder and the aggregate surface. Hence, zycosoil as an anti striping additive were evaluated in this study. This study summarizes the results of the laboratory evaluation carried out to investigate the properties of asphalt concrete prepared with zycosoil modified over burnt brick aggregate. Marshall specimen were prepared with stone aggregate, zycosoil modified stone aggregate, over burnt brick aggregate and zycosoil modified over burnt brick aggregate. Results show that addition of zycosoil with stone aggregate increased stability by 6% and addition of zycosoil with over burnt brick aggregate increased stability by 30%.

Keywords: asphalt concrete, over burnt brick aggregate, marshall stability, zycosoil

Procedia PDF Downloads 363

Authors: Juliana A. Knocikova

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Methods of nonlinear signal analysis are based on finding that random behavior can arise in deterministic nonlinear systems with a few degrees of freedom. Considering the dynamical systems, entropy is usually understood as a rate of information production. Changes in temporal dynamics of physiological data are indicating evolving of system in time, thus a level of new signal pattern generation. During last decades, many algorithms were introduced to assess some patterns of physiological responses to external stimulus. However, the reflex responses are usually characterized by short periods of time. This characteristic represents a great limitation for usual methods of nonlinear analysis. To solve the problems of short recordings, parameter of approximate entropy has been introduced as a measure of system complexity. Low value of this parameter is reflecting regularity and predictability in analyzed time series. On the other side, increasing of this parameter means unpredictability and a random behavior, hence a higher system complexity. Reduced neurophysiological data complexity has been observed repeatedly when analyzing electroneurogram and electromyogram activities during defence reflex responses. Quantitative phrenic neurogram changes are also obvious during severe hypoxia, as well as during airway reflex episodes. Concluding, the approximate entropy parameter serves as a convenient tool for analysis of reflex behavior characterized by short lasting time series.

Keywords: approximate entropy, neurophysiological data, nonlinear dynamics, reflex

Procedia PDF Downloads 305

Authors: Pan Long, Bi Dongjie, Li Xifeng, Xie Yongle

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The near-field synthetic aperture radar (SAR) imaging is an advanced nondestructive testing and evaluation (NDT&E) technique. This paper investigates the complex-valued signal processing related to the near-field SAR imaging system, where the measurement data turns out to be noncircular and improper, meaning that the complex-valued data is correlated to its complex conjugate. Furthermore, we discover that the degree of impropriety of the measurement data and that of the target image can be highly correlated in near-field SAR imaging. Based on these observations, A modified generalized sparse Bayesian learning algorithm is proposed, taking impropriety and noncircularity into account. Numerical results show that the proposed algorithm provides performance gain, with the help of noncircular assumption on the signals.

Keywords: complex-valued signal processing, synthetic aperture radar, 2-D radar imaging, compressive sensing, sparse Bayesian learning

Procedia PDF Downloads 138

Authors: Kung-Jen Tu, Danny Vernatha

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To assist individual departments within universities in their energy management tasks, this study explores the application of Building Information Modeling in establishing the ‘BIM based Energy Management Support System’ (BIM-EMSS). The BIM-EMSS consists of six components: (1) sensors installed for each occupant and each equipment, (2) electricity sub-meters (constantly logging lighting, HVAC, and socket electricity consumptions of each room), (3) BIM models of all rooms within individual departments’ facilities, (4) data warehouse (for storing occupancy status and logged electricity consumption data), (5) building energy management system that provides energy managers with various energy management functions, and (6) energy simulation tool (such as eQuest) that generates real time 'standard energy consumptions' data against which 'actual energy consumptions' data are compared and energy efficiency evaluated. Through the building energy management system, the energy manager is able to (a) have 3D visualization (BIM model) of each room, in which the occupancy and equipment status detected by the sensors and the electricity consumptions data logged are displayed constantly; (b) perform real time energy consumption analysis to compare the actual and standard energy consumption profiles of a space; (c) obtain energy consumption anomaly detection warnings on certain rooms so that energy management corrective actions can be further taken (data mining technique is employed to analyze the relation between space occupancy pattern with current space equipment setting to indicate an anomaly, such as when appliances turn on without occupancy); and (d) perform historical energy consumption analysis to review monthly and annually energy consumption profiles and compare them against historical energy profiles. The BIM-EMSS was further implemented in a research lab in the Department of Architecture of NTUST in Taiwan and implementation results presented to illustrate how it can be used to assist individual departments within universities in their energy management tasks.

Keywords: database, electricity sub-meters, energy anomaly detection, sensor

Procedia PDF Downloads 310