Search results for: positive and negative models matching

Authors: Vera Diyanty, Akhmad Syahroza

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This research aims to describe an empirical evidence of the influence of family control on the company’s financing policy. Additionally, this research also shows the effect of leadership from family member and the effectiveness of the board of commissioners on companies’ financing policy. The result of this study found that family control through direct and indirect ownership mechanism have a positive impact on the choice of bank loan compare to public debt. Nevertheless, this research also shows that companies’ founders who become CEO and the effectiveness of board of commissioners do not prove to increase the alignment effect nor decrease the negative impact of entrenchment effect on the bank loan preference.

Keywords: family controlling, family CEO, board effectiveness, financing policy

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Authors: Jing Jin

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The incorporation of artificial intelligence (AI) and machine learning (ML) in aircraft manufacturing and aerospace supply chains leads to the generation of a substantial amount of data among various tiers of suppliers and OEMs. Identifying the high-quality information challenges decision-makers. The application of AI/ML models necessitates access to 'high-quality' information to yield desired outputs. However, the process of information sharing introduces complexities, including distortion through various communication channels and biases introduced by both human and AI entities. This phenomenon significantly influences the quality of information, impacting decision-makers engaged in configuring supply chain systems. Traditionally, distorted information is categorized as 'low-quality'; however, this study challenges this perception, positing that distorted information, contributing to stakeholder goals, can be deemed high-quality within supply chains. The main aim of this study is to identify and evaluate the dimensions of information quality crucial to the UK aerospace supply chain. Guided by a central research question, "What information quality dimensions are considered when defining information quality in the UK aerospace supply chain?" the study delves into the intricate dynamics of information quality in the aerospace industry. Additionally, the research explores the nuanced impact of information distortion on stakeholders' decision-making processes, addressing the question, "How does the information distortion phenomenon influence stakeholders’ decisions regarding information quality in the UK aerospace supply chain system?" This study employs deductive methodologies rooted in positivism, utilizing a cross-sectional approach and a mono-quantitative method -a questionnaire survey. Data is systematically collected from diverse tiers of supply chain stakeholders, encompassing end-customers, OEMs, Tier 0.5, Tier 1, and Tier 2 suppliers. Employing robust statistical data analysis methods, including mean values, mode values, standard deviation, one-way analysis of variance (ANOVA), and Pearson’s correlation analysis, the study interprets and extracts meaningful insights from the gathered data. Initial analyses challenge conventional notions, revealing that information distortion positively influences the definition of information quality, disrupting the established perception of distorted information as inherently low-quality. Further exploration through correlation analysis unveils the varied perspectives of different stakeholder tiers on the impact of information distortion on specific information quality dimensions. For instance, Tier 2 suppliers demonstrate strong positive correlations between information distortion and dimensions like access security, accuracy, interpretability, and timeliness. Conversely, Tier 1 suppliers emphasise strong negative influences on the security of accessing information and negligible impact on information timeliness. Tier 0.5 suppliers showcase very strong positive correlations with dimensions like conciseness and completeness, while OEMs exhibit limited interest in considering information distortion within the supply chain. Introducing social network analysis (SNA) provides a structural understanding of the relationships between information distortion and quality dimensions. The moderately high density of ‘information distortion-by-information quality’ underscores the interconnected nature of these factors. In conclusion, this study offers a nuanced exploration of information quality dimensions in the UK aerospace supply chain, highlighting the significance of individual perspectives across different tiers. The positive influence of information distortion challenges prevailing assumptions, fostering a more nuanced understanding of information's role in the Industry 4.0 landscape.

Keywords: information distortion, information quality, supply chain configuration, UK aerospace industry

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Authors: Awol Seid Ebrie

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HIV results as an incurable disease, AIDS. After a person is infected with virus, the virus gradually destroys all the infection fighting cells called CD4 cells and makes the individual susceptible to opportunistic infections which cause severe or fatal health problems. Several studies show that the CD4 cells count is the most determinant indicator of the effectiveness of the treatment or progression of the disease. The objective of this paper is to investigate the progression of the disease over time among patient under HAART treatment. Two main approaches of the generalized multilevel ordinal models; namely the proportional odds model and the nonproportional odds model have been applied to the HAART data. Also, the multilevel part of both models includes random intercepts and random coefficients. In general, four models are explored in the analysis and then the models are compared using the deviance information criteria. Of these models, the random coefficients nonproportional odds model is selected as the best model for the HAART data used as it has the smallest DIC value. The selected model shows that the progression of the disease increases as the time under the treatment increases. In addition, it reveals that gender, baseline clinical stage and functional status of the patient have a significant association with the progression of the disease.

Keywords: nonproportional odds model, proportional odds model, random coefficients model, random intercepts model

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Authors: Oyuntsetseg Sandag, Lkhagvadorj Khosbayar, Naidansuren Tsendeekhuu, Densenbal Dansran, Bandi Solongo

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Introduction: Obstructive sleep apnea (OSA) is a common disorder affecting at least 2% to 4% of the adult population. It is estimated that nearly 80% of men and 93% of women with moderate to severe sleep apnea are undiagnosed. A number of screening questionnaires and clinical screening models have been developed to help identify patients with OSA, also it’s indeed to clinical practice. Purpose of study: Determine dependence of obstructive sleep apnea between for severe risk and risk factor. Material and Methods: A cross-sectional study included 114 patients presenting from theCentral state 3th hospital and Central state 1th hospital. Patients who had obstructive sleep apnea (OSA)selected in this study. Standard StopBang questionnaire was obtained from all patients.According to the patients’ response to the StopBang questionnaire was divided into low risk, intermediate risk, and high risk.Descriptive statistics were presented mean ± standard deviation (SD). Each questionnaire was compared on the likelihood ratio for a positive result, the likelihood ratio for a negative test result of regression. Statistical analyses were performed utilizing SPSS 16. Results: 114 patients were obtained (mean age 48 ± 16, male 57)that divided to low risk 54 (47.4%), intermediate risk 33 (28.9%), high risk 27 (23.7%). Result of risk factor showed significantly increasing that mean age (38 ± 13vs. 54 ± 14 vs. 59 ± 10, p<0.05), blood pressure (115 ± 18vs. 133 ± 19vs. 142 ± 21, p<0.05), BMI(24 IQR 22; 26 vs. 24 IQR 22; 29 vs. 28 IQR 25; 34, p<0.001), neck circumference (35 ± 3.4 vs. 38 ± 4.7 vs. 41 ± 4.4, p<0.05)were increased. Results from multiple logistic regressions showed that age is significantly independently factor for OSA (odds ratio 1.07, 95% CI 1.02-1.23, p<0.01). Predictive value of age was significantly higher factor for OSA (AUC=0.833, 95% CI 0.758-0.909, p<0.001). Our study showing that risk of OSA is beginning 47 years old (sensitivity 78.3%, specifity74.1%). Conclusions: According to most of all patients’ response had intermediate risk and high risk. Also, age, blood pressure, neck circumference and BMI were increased such as risk factor was increased for OSA. Especially age is independently factor and highest significance for OSA. Patients’ age one year is increased likelihood risk factor 1.1 times is increased.

Keywords: obstructive sleep apnea, Stop-Bang, BMI (Body Mass Index), blood pressure

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Authors: Rithi Baruah

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The aviation industry is one such sectors whose primary aim is to work for the safety and comfort of their clients and customers. The crew members in the aviation industry include pilots, flight attendants, air traffic controllers, baggage personnel and maintenance personnel. This study will concentrate on the frontline employees of the aviation industry, the flight attendants. Flight attendants belong to the niche group of population who are paid to smile. Although the profession seems to be very glamorous, it is physically and psychologically very taxing. Energy at workplace is a fairly new concept and is an organizational resource which helps employee attain their goals. Therefore, the researcher will aim to establish the relationship between relational energy and the major issue of emotional labor and cognitive flexibility among flight attendants. The researcher will hypothesize that there will be a negative relationship between relational energy and emotional labour, and a positive relationship between relational energy and cognitive flexibility. Also, a positive relationship will be expected between cognitive flexibility and emotional labour of cabin crew. A quantitative research design will be used to study the relationship among 50 flight attendants in India. The findings of the research will not only help the aviation sector but will be a major contribution to the existing literature of aviation psychology in India which is scanty. The relationships can also provide scope to develop a model using the same. From crew resource management and aviation psychology perspectives, relationships among the study variables will not only provide scope for helping the aviation employees in particular but also develop the performance and safety of aviation sector at large.

Keywords: cabin crew, cognitive flexibility, emotional labour, relational energy

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Authors: Abhishek Saha, Serene Tay, Gerard Pijcke

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The availability of high-resolution topography and rainfall information in urban areas has made it necessary to revise modeling approaches used for simulating flood risk assessments. Lidar derived elevation models that have 1m or lower resolutions are becoming widely accessible. The classical approaches of 1D-2D flow models where channel flow is simulated and coupled with a coarse resolution 2D overland flow models may not fully utilize the information provided by high-resolution data. In this context, a study was undertaken to compare three different modeling approaches to simulate flooding in an urban area. The first model used is the base model used is Sobek, which uses 1D model formulation together with hydrologic boundary conditions and couples with an overland flow model in 2D. The second model uses a full 2D model for the entire area with shallow water equations at the resolution of the digital elevation model (DEM). These models are compared against another shallow water equation solver in 2D, which uses a subgrid method for grid refinement. These models are simulated for different horizontal resolutions of DEM varying between 1m to 5m. The results show a significant difference in inundation extents and water levels for different DEMs. They are also sensitive to the different numerical models with the same physical parameters, such as friction. The study shows the importance of having reliable field observations of inundation extents and levels before a choice of model and data can be made for spatial flood risk assessments.

Keywords: flooding, DEM, shallow water equations, subgrid

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Authors: Yuchien Lin

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This study attempted to investigate the changes in electroencephalography (EEG) and emotion regulation following eight-week inward-attention meditation training program. The subjects were 24 adults without meditation experiences divided into meditation and control groups. The quantitatively analyzed changes in psychophysiological parameters during inward-attention meditation, and evaluated the emotion scores assessed by the State-Trait Anxiety Inventory (STAI), the Positive and Negative Affect Schedule (PANAS), and the Emotion Regulation Scale (ERS). The results were found: (1) During meditation, significant EEG increased for theta-band activity in the frontal and the bilateral temporal areas, for alpha-band activity in the left and central frontal areas, and for gamma-band activity in the left frontal and the left temporal areas. (2) The meditation group had significantly higher positive affect in posttest than in pretest. (3) There was no significant difference in the changes of EEG spectral characteristics and emotion scores in posttest and pretest for the control group. In the present study, a unique meditative concentration task with a constant level of moderate mental effort focusing on the center of brain was used, so as to enhance frontal midline theta, alpha, and gamma-band activity. These results suggest that this mental training allows individual reach a specific mental state of relaxed but focused awareness. The gamma-band activity, in particular, enhanced over left frontoparietal area may suggest that inward-attention meditation training involves temporal integrative mechanisms and may induce short-term and long-term emotion regulation abilities.

Keywords: meditation, EEG, emotion regulation, gamma activity

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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

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

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Authors: Sahar Jami, Iman Valizadeh

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The purpose of the study: The aim of this study was to evaluate the influence of culture on the choice of capital structure management companies are listed in the Tehran Stock Exchange. Methods: This study was a cross-document using data after the event (Retrospective) in 1394 was performed. To select a sample of elimination sampling (screening) is used to determine the sample size was 123 companies. Results: The results showed that the variables of culture, return on equity, a significant positive impact on the capital structure (ROA, QTobins) and financial leverage and firm size variables and a significant negative impact on the capital structure (ROA, QTobins).

Keywords: culture management, capital structure, ROA, QTobins, variables of culture

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Authors: Sandeep Kaur, Jenny Vuong, Marcel Julliard, Sean O'Donoghue

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Time-series data are useful for modelling as they can enable model-evaluation. However, when reconstructing models from phosphoproteomic data, often non-exact methods are utilised, as the knowledge regarding the network structure, such as, which kinases and phosphatases lead to the observed phosphorylation state, is incomplete. Thus, such reactions are often hypothesised, which gives rise to uncertainty. Here, we propose a framework, implemented via a web-based tool (as an extension to Minardo), which given time-series phosphoproteomic datasets, can generate κ models. The incompleteness and uncertainty in the generated model and reactions are clearly presented to the user via the visual method. Furthermore, we demonstrate, via a toy EGF signalling model, the use of algorithmic verification to verify κ models. Manually formulated requirements were evaluated with regards to the model, leading to the highlighting of the nodes causing unsatisfiability (i.e. error causing nodes). We aim to integrate such methods into our web-based tool and demonstrate how the identified erroneous nodes can be presented to the user via the visual method. Thus, in this research we present a framework, to enable a user to explore phosphorylation proteomic time-series data in the context of models. The observer can visualise which reactions in the model are highly uncertain, and which nodes cause incorrect simulation outputs. A tool such as this enables an end-user to determine the empirical analysis to perform, to reduce uncertainty in the presented model - thus enabling a better understanding of the underlying system.

Keywords: κ-models, model verification, time-series phosphoproteomic datasets, uncertainty and error visualisation

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Authors: Muhammad Umar Kibiya

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The study aims to examine whether regulatory changes have an impact on the financial reporting quality of Microfinance Banks in Nigeria. The research employed a panel data analysis technique, using data from 2018 to 2022. The sample includes 72 microfinance banks, using regression analyses to examine the relationship between variables. The findings indicate that Board Chairmanship has a positive and significant effect on financial reporting quality. It also reveals that share ownership has a negative and significant impact on financial reporting quality. The results suggest that regulatory changes have a positive and significant influence on financial reporting quality. Thus, findings have important implications for microfinance banks in Nigeria. It suggests that having a strong and competent board chairperson can enhance financial reporting quality, leading to more transparent and reliable information for stakeholders. Furthermore, the study highlights the importance of regulatory changes in improving financial reporting practices in the microfinance banking sector. The study contributes to the extant literature by providing empirical evidence on the relationship between board chairmanship, share ownership, financial reporting quality, and regulatory changes in microfinance banks. It further supports the concept that governance mechanisms and regulatory reforms play a crucial role in ensuring transparency and accountability within the microfinance banking sector. It recommends that microfinance banks should appoint experienced and qualified individuals as board chairpersons to enhance financial reporting quality. Furthermore, policymakers and regulatory authorities should continue to implement and enforce regulations that promote transparent financial reporting practices in microfinance banks.

Keywords: board chairman, share ownership, financial reporting quality, microfinance, regulatory changes

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Authors: Abd Elrahman Elgamala, N. Darwish, I. Bondouk, Sh. Hamada

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Available experimental angular distributions for ⁶Li elastically scattered from ¹⁶O nucleus in the energy range 13.0–50.0 MeV are investigated and reanalyzed using optical model of the conventional phenomenological potential and also using double folding optical model of different interaction models: DDM3Y1, CDM3Y1, CDM3Y2, and CDM3Y3. All the involved models of interaction are of M3Y Paris except DDM3Y1 which is of M3Y Reid and the main difference between them lies in the different values for the parameters of the incorporated density distribution function F(ρ). We have extracted the renormalization factor N_R for ⁶Li+¹⁶O nuclear system in the energy range 13.0–50.0 MeV using the aforementioned interaction models.

Keywords: elastic scattering, optical model, folding potential, density distribution

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Authors: D.Mendes, J. Henriques, P. Carvalho, T. Rocha, S. Paredes, R. Cabiddu, R. Trimer, R. Mendes, A. Borghi-Silva, L. Kaminsky, E. Ashley, R. Arena, J. Myers

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The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).

Keywords: cardiorespiratory fitness, data-driven models, knowledge extraction, machine learning

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Authors: Amedeo D'Angiulli, Kylie Schibli

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Traditionally, children’s brain development research has emphasized the limitative aspects of disability and impairment, electing as an explanatory model the classical clinical notions of brain lesion or functional deficit. In contrast, Positive Educational Neuroscience (PEN) is a new approach that emphasizes strengths and human flourishing related to the brain by exploring how learning practices have the potential to enhance neurocognitive flexibility through neuroplastic overcompensation. This mini-review provides an overview of PEN and shows how it links to the concept of neurocognitive flexibility. We provide examples of how the present concept of neurocognitive flexibility can be applied to special education by exploring examples of neuroplasticity in the learning domain, including: (1) learning to draw in congenitally totally blind children, and (2) music training in children from disadvantaged neighborhoods. PEN encourages educators to focus on children’s strengths by recognizing the brain’s capacity for positive change and to incorporate activities that support children’s individual development.

Keywords: neurocognitive development, positive educational neuroscience, sociocultural approach, special education

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Authors: Roza Dzierzak, Waldemar Wojcik, Piotr Kacejko

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Analysis of the texture of medical images consists of determining the parameters and characteristics of the examined tissue. The main goal is to assign the analyzed area to one of two basic groups: as a healthy tissue or a tissue with pathological changes. The CT images of the thoracic lumbar spine from 15 healthy patients and 15 with confirmed osteoporosis were used for the analysis. As a result, 120 samples with dimensions of 50x50 pixels were obtained. The set of features has been obtained based on the histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and Haar wavelet. As a result of the image analysis, 290 descriptors of textural features were obtained. The dimension of the space of features was reduced by the use of three selection methods: Fisher coefficient (FC), mutual information (MI), minimization of the classification error probability and average correlation coefficients between the chosen features minimization of classification error probability (POE) and average correlation coefficients (ACC). Each of them returned ten features occupying the initial place in the ranking devised according to its own coefficient. As a result of the Fisher coefficient and mutual information selections, the same features arranged in a different order were obtained. In both rankings, the 50% percentile (Perc.50%) was found in the first place. The next selected features come from the co-occurrence matrix. The sets of features selected in the selection process were evaluated using six classification tree methods. These were: decision stump (DS), Hoeffding tree (HT), logistic model trees (LMT), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). In order to assess the accuracy of classifiers, the following parameters were used: overall classification accuracy (ACC), true positive rate (TPR, classification sensitivity), true negative rate (TNR, classification specificity), positive predictive value (PPV) and negative predictive value (NPV). Taking into account the classification results, it should be stated that the best results were obtained for the Hoeffding tree and logistic model trees classifiers, using the set of features selected by the POE + ACC method. In the case of the Hoeffding tree classifier, the highest values of three parameters were obtained: ACC = 90%, TPR = 93.3% and PPV = 93.3%. Additionally, the values of the other two parameters, i.e., TNR = 86.7% and NPV = 86.6% were close to the maximum values obtained for the LMT classifier. In the case of logistic model trees classifier, the same ACC value was obtained ACC=90% and the highest values for TNR=88.3% and NPV= 88.3%. The values of the other two parameters remained at a level close to the highest TPR = 91.7% and PPV = 91.6%. The results obtained in the experiment show that the use of classification trees is an effective method of classification of texture features. This allows identifying the conditions of the spongy tissue for healthy cases and those with the porosis.

Keywords: classification, feature selection, texture analysis, tree algorithms

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Authors: Nadia Ayub

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The purpose of the study was to explore the impact of personal identity on self-esteem among adolescents. Two hypotheses were tested in the study, i.e., personal identity effects on self-esteem; and gender difference in the variables of personal identity and self-esteem. The total of 300 (150 female; 150 male) adolescents participated in the study. Personal identity scale (Ayub, N., In Press), and self-esteem scale (Rosenberg, 1985) were administered. The findings of the study suggest that positive personal identity impact on self-esteem and gender difference was found on the variables of personal identity and self-esteem. In conclusion, the results of the study are beneficial for researchers, policymakers, psychologists. The strong positive personal identity and self-esteem help in healthy mental development not only in adolescence but throughout the life of individuals.

Keywords: personal identity, self-esteem, adolescents, positive psychology

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Authors: Rebecca Cervantes, Elishah Maro Pangilinan

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The current research used Uses and gratifications theory to further understand the motivations and satisfaction students get from Facebook and Twitter. The researchers relate the objectives in developing uses and gratifications theory 1) to explain how individuals use mass communication to gratify their needs, “what do people do with the media” many of these young adults use social media networks to communicate with family, friends, and even strangers. Social media sites have created new and non-personal ways for people to interact with others and young adults have taken advantage of this technological trend; 2) to discover underlying motives for individuals’ media use 3) to identify the positive and the negative consequences of individual media use. The researchers use survey questionnaires to gather information that is used in this study. A descriptive analysis was used to measure the answers to a 24-item questionnaire.

Keywords: adolescent, communication, social media, #Hashtag

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Authors: Eyitayo Francis Adanlawo

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Television is one of humanity’s most important means of communication, a channel through which societal norms and values can be transferred to youths. The imagination created by foreign television programmes ultimately leads to strong emotional responses. Though some foreign films and programmes are educational in nature, the view that the majority of them are inimical to the youths’ positive-believe-system is rife. This has been occasioned by the adoption of repugnant alien cultures, imitation of vulgar slangs, weird hairdo and most visibly an adjustment in values. This study theoretically approaches two research questions: do youths act out the life style of characters seeing in foreign films? Is moral decadence, indiscipline, and vulgar habits being the results of the contents of foreign programmes and films? To establish the basis for relating foreign films watched to social vices as violence, sexual pervasiveness, cultural and traditional moral pollution on youths; Observational learning Theory and Reinnforcement Theory were utilized to answer the research questions and established the effect of foreign films content on youths. We conclude that constant showcasing of violent themes was highly responsible for the upsurge in social vices prevalent among the youths and can destroy the basis of the societal, cultural orientation. Recommendations made range from the need for government to halt the importation of foreign films not censored; the need for local films to portray more positive messages and the need for concrete steps to be taken to eradicate or minimise the use of programme capable of exerting negative influence.

Keywords: media (television), moral decadence, youths, values, observation learning theory, reinforcement theory

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Authors: Bruna Goncalves, Jennifer Henck, Romulo Uliana, Eliana Kamimura, Carlos Oliveira, Carlos Corassin

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Studies have shown evidence of human exposure to aflatoxin M1 due to the consumption of contaminated milk and dairy products (mainly cheeses). This poses a great risk to public health, since milk and milk products are frequently consumed by a portion of the population considered immunosuppressed, children and the elderly. Knowledge of the negative impacts of aflatoxins on health and economics has led to investigations of strategies to prevent their formation in food, as well as to eliminate, inactivate or reduce the bioavailability of these toxins in contaminated products This study evaluated the effect of microbiological methods using lactic acid bacteria on aflatoxin M1 (AFM1) reduction in Minas Frescal cheese (typical Brazilian product, being among the most consumed cheeses in Brazil) spiked with 1 µg/L AFM1. Inactivated lactic acid bacteria (0,5%, v/v de L. rhamnosus e L. lactis) were added during the cheese production process. Nine cheeses were produced, divided into three treatments: negative controls (without AFM1 or lactic acid bacteria), positive controls (AFM1 only), and lactic acid bacteria + AFM1. Samples of cheese were collected on days 2, 10, 20 and 30 after the date of production and submitted to composition analyses and determination of AFM1 by high-performance liquid chromatography. The reductions of AFM1 in cheese by lactic acid bacteria at the end of the trial indicate a potential application of inactivated lactic acid bacteria in reducing the bioavailability of AFM1 in Minas frescal cheese without physical-chemical and microbiological modifications during the 30-day experimental period. The authors would like to thank São Paulo Research Foundation – FAPESP (grants #2017/20081-6 and #2017/19683-1).

Keywords: aflatoxin, milk, minas frescal cheese, decontamination

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Authors: Reihaneh Rafiemanzelat, Maryam Baradaran

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Creating healthy communities that are sustainable and livable is a desire of policy makers in European countries. Indicators have used at the level of international, national, state to evaluate the level of health in cities and regions. Therefore, there are many challenges in the assumption of health and planning indicators. This research provides an overview of health indicators used to date in Europe according to World Health Organization (WHO) strategy. It then discusses on how indicators have been successful to the creation of healthy, livable and sustainable cities in Europe. This research is based on qualitative research to review the documentary researches on health issue and urban planning. The result will show the positive and negative effects of in process indicators on European cities.

Keywords: healthy community, livability, sustainability, WHO strategy

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Authors: Sang-Ju Yu

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This paper examines the changing essences and effects of ‘affective atmosphere’ during the COVID-19 pandemic crisis, which have been facilitated and shaped the ‘affective governance’ in Taiwan. Due to long-term uncertainty and unpredictability, the COVID-19 pandemic not only caused unprecedented global crisis but triggered the public’s negative emotional responses. This paper unravels how the shortage of Personal Protective Equipment and the proliferating fake news heightened people’s fear and anxiety and how specific affective atmospheres can be provoked and manipulated to harness emotional appeals of citizens strategically in Taiwan. Through the in-depth interviews with diverse stakeholders involved, it unfolds the dynamics and strategies of affective governance, wherein public emotions and concerns are now given significant consideration in both policy measures and the affective expression of leadership, spatial arrangement, service delivery, and the interaction with citizens. Addressing psychosocial and emotional needs has become the core of crisis response mechanisms suited to dynamic affective atmospheres and pandemic situation. This paper also demonstrates that epidemic prevention and control is not merely the production of neutral or rational policy-making processes, as it is dominated by multiple emotions resulted from unexpected and salient events at different moments. It provides explicit insight into how different prevention scenarios operated effectively through political and affective mobilisation to strengthen emotional bonding and collective identity which energises collective action. Basically, successful affective governance calls for both negative and positive emotions, for both scientific and political decision-making, for both community and bureaucracy, and both quality and efficiency of private–public collaboration.

Keywords: affective atmospheres, affective governance, COVID-19 pandemic, private-public collaboration

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Authors: Elias K. Maragos, Petros E. Maravelakis

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In Dynamic Data Envelopment Analysis (DDEA), which is a subfield of Data Envelopment Analysis (DEA), the productivity of Decision Making Units (DMUs) is considered in relation to time. In this case, as it is accepted by the most of the researchers, there are outputs, which are produced by a DMU to be used as inputs in a future time. Those outputs are known as intermediates. The common models, in DDEA, do not take into account the shape of the distribution of those inputs, outputs or intermediates data, assuming that the distribution of the virtual value of them does not deviate from linearity. This weakness causes the limitation of the accuracy of the analytical power of the traditional DDEA models. In this paper, the authors, using the concept of piecewise linear inputs and outputs, propose an extended DDEA model. The proposed model increases the flexibility of the traditional DDEA models and improves the measurement of the dynamic performance of DMUs.

Keywords: Dynamic Data Envelopment Analysis, DDEA, piecewise linear inputs, piecewise linear outputs

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Authors: A. G. Matveev

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The copyrights system is a combination of different elements. The number, content and the correlation of these elements are different for different legal orders. The models of copyrights systems display this system in terms of the interaction of economic and author's moral rights. Monistic and dualistic models are the most popular ones. The article deals with different points of view on the monism and dualism in copyright system. A specific model of the copyright in Switzerland in the XXth century is analyzed. The evolution of a French dualistic model of copyright is shown. The author believes that one should talk not about one, but rather about a number of dualism forms of copyright system.

Keywords: copyright, exclusive copyright, economic rights, author's moral rights, rights of personality, monistic model, dualistic model

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Authors: Herlandson C. Moura, Jorge H. Bidinotto, Eduardo M. Belo

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The present work proposes the development of an adaptive control system which enables the suppression of Pilot Induced Oscillations (PIO) in Digital Fly-By-Wire (DFBW) aircrafts. The proposed system consists of a Modified Model Reference Adaptive Control (M-MRAC) integrated with the Gain Scheduling technique. The PIO oscillations are detected using a Real Time Oscillation Verifier (ROVER) algorithm, which then enables the system to switch between two reference models; one in PIO condition, with low proneness to the phenomenon and another one in normal condition, with high (or medium) proneness. The reference models are defined in a closed loop condition using the Linear Quadratic Regulator (LQR) control methodology for Multiple-Input-Multiple-Output (MIMO) systems. The implemented algorithms are simulated in software implementations with state space models and commercial flight simulators as the controlled elements and with pilot dynamics models. A sequence of pitch angles is considered as the reference signal, named as Synthetic Task (Syntask), which must be tracked by the pilot models. The initial outcomes show that the proposed system can detect and suppress (or mitigate) the PIO oscillations in real time before it reaches high amplitudes.

Keywords: adaptive control, digital Fly-By-Wire, oscillations suppression, PIO

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Authors: Riona Dighe

Abstract:

This research attempts to identify an alternative approach to the measurement of Gross National Happiness (GNH). It uses artificial intelligence (AI), incorporating natural language processing (NLP) and sentiment analysis to measure GNH. We use ‘off the shelf’ NLP models responsible for the sentiment analysis of a sentence as a building block for this research. We constructed an algorithm using NLP models to derive a sentiment analysis score against sentences. This was then tested against a sample of 20 respondents to derive a sentiment analysis score. The scores generated resembled human responses. By utilising the MLP classifier, decision tree, linear model, and K-nearest neighbors, we were able to obtain a test accuracy of 89.97%, 54.63%, 52.13%, and 47.9%, respectively. This gave us the confidence to use the NLP models against sentences in websites to measure the GNH of a country.

Keywords: artificial intelligence, NLP, sentiment analysis, gross national happiness

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Authors: S. R. Nikhashemi, Laily Haj Paim, Ali Khatibi

Abstract:

Structural Equation Modeling (SEM) was used to test a hypothesized model explaining Malaysian hypermarket customers’ perceptions of brand trust (BT), customer perceived value (CPV) and perceived service quality (PSQ) on building their brand loyalty (CBL) and generating positive word-of-mouth communication (WOM). Self-administered questionnaires were used to collect data from 374 Malaysian hypermarket customers from Mydin, Tesco, Aeon Big and Giant in Kuala Lumpur, a metropolitan city of Malaysia. The data strongly supported the model exhibiting that BT, CPV and PSQ are prerequisite factors in building customer brand loyalty, while PSQ has the strongest effect on prediction of customer brand loyalty compared to other factors. Besides, the present study suggests the effect of the aforementioned factors via customer brand loyalty strongly contributes to generate positive word of mouth communication.

Keywords: brand trust, perceived value, Perceived Service Quality, Brand loyalty, positive word of mouth communication

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Authors: Fazıl Gökgöz, Fahrettin Filiz

Abstract:

Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

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Authors: Nitish Sharma, G. G. Karady

Abstract:

This paper presents a scheme for the protection of loop system from all type of faults using the direction of fault current. The presence of distributed generation in today’s system increases the complexity of fault detection as the power flow is bidirectional. Hence, protection scheme specific to this purpose needs to be developed. This paper shows a fast protection scheme using communication which can be fiber optic or wireless. In this paper, the possibility of wireless communication for protection is studied to exchange the information between the relays. The negative sequence and positive sequence directional elements are used to determine the direction of fault current. A PSCAD simulation is presented and validated using commercial SEL relays.

Keywords: smart grid protection, pilot protection, power system simulation, wireless communication

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Authors: Adel Bougamouza, Boualam Remini, Abd El Hadi Ammari, Feteh Sakhraoui

Abstract:

The assessment of sediments being carried by a river is importance for planning and designing of various water resources projects. In this study, Artificial Neural Network Techniques are used to estimate the daily suspended sediment concentration for the corresponding daily discharge flow in the upstream of Foum El Gherza dam, Biskra, Algeria. The FFNN, GRNN, and RBNN models are established for estimating current suspended sediment values. Some statistics involving RMSE and R2 were used to evaluate the performance of applied models. The comparison of three AI models showed that the RBNN model performed better than the FFNN and GRNN models with R2 = 0.967 and RMSE= 5.313 mg/l. Therefore, the ANN model had capability to improve nonlinear relationships between discharge flow and suspended sediment with reasonable precision.

Keywords: artificial neural network, Oued Abiod watershed, feedforward network, generalized regression network, radial basis network, sediment concentration

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Authors: Joel S. Akintayo, Ramat T. Salman

Abstract:

The stakeholders’ pressure on corporate managers to maintain firm’s profitability has created economic incentives for management to engage in earnings management practices. Therefore, this study examines the effects of audit quality and corporate governance on earnings management of quoted deposit money banks (DMBs) in Nigeria. This study specifically investigates the influence of audit tenure, audit fee, board independence, and board size on earnings management of DMBs. Explanatory research design was employed in carrying out the study while secondary data were sourced from the annual reports and accounts of all the 15 quoted DMBs in Nigerian Stock Exchange as at December 31, 2015 for a period of 10 years covering from 2006 to 2015. The data obtained for the study were analyzed using panel regression analysis approach. The findings reveal that board independence has a negative significant effect on earnings management at a 5% level of significance (p=0.002), while audit fee has a positive significant effect on earnings management at a 5% level of significance (p=0.013) and audit tenure has a negative significant effect on earnings management of DMBs at a 5% level of significance (p=0.003). Surprisingly, board size was statistically not significant at a 5% level of significance (p=0.086). The study concludes that high audit quality and sound corporate governance could improve the earnings quality of DMBs. Hence, the study recommends that the authorities saddled with the responsibility of banking supervision in Nigeria such the Securities and Exchange Commission (SEC) and CBN to advise the National Assembly in Nigeria to pass into law the three years professional requirement for audit tenure.

Keywords: audit quality, audit tenure, audit fee, board independence, corporate governance, earnings management

Procedia PDF Downloads 198