Search results for: conflicting claim on credit of discovery of ridge regression

Authors: Katarzyna Wyka, Dana Sylvan, JoAnn Difede

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Background and significance: The following disaster, population-based screening programs are routinely established to assess physical and psychological consequences of exposure. These data sets are highly skewed as only a small percentage of trauma-exposed individuals develop health issues. Commonly used statistical methodology in post-disaster mental health generally involves population-averaged models. Such models aim to capture the overall response to the disaster and its aftermath; however, they may not be sensitive enough to accommodate population heterogeneity in symptomatology, such as post-traumatic stress or depressive symptoms. Methods: We use an archival longitudinal data set from Weill-Cornell 9/11 Mental Health Screening Program established following the World Trade Center (WTC) terrorist attacks in New York in 2001. Participants are rescue and recovery workers who participated in the site cleanup and restoration (n=2960). The main outcome is the post-traumatic stress symptoms (PTSD) severity score assessed via clinician interviews (CAPS). For a detailed understanding of response to the disaster and its aftermath, we are adapting quantile regression methodology with particular focus on predictors of extreme distress and resilience to trauma. Results: The response variable was defined as the quantile of the CAPS score for each individual under two different scenarios specifying the unconditional quantiles based on: 1) clinically meaningful CAPS cutoff values and 2) CAPS distribution in the population. We present graphical summaries of the differential effects. For instance, we found that the effect of the WTC exposures, namely seeing bodies and feeling that life was in danger during rescue/recovery work was associated with very high PTSD symptoms. A similar effect was apparent in individuals with prior psychiatric history. Differential effects were also present for age and education level of the individuals. Conclusion: We evaluate the utility of quantile regression in disaster research in contrast to the commonly used population-averaged models. We focused on assessing the distribution of risk factors for post-traumatic stress symptoms across quantiles. This innovative approach provides a comprehensive understanding of the relationship between dependent and independent variables and could be used for developing tailored training programs and response plans for different vulnerability groups.

Keywords: disaster workers, post traumatic stress, PTSD, quantile regression

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Authors: Meltem Gurunlu

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One of the central problems in corporate finance is the allocation of funds. This usually takes two forms: allocation of funds across firms in an economy or allocation of funds across projects or business units within a firm. The first one is typically related to the external markets (the bond market, the stock market, banks and finance companies) whereas the second form of the capital allocation is related to the internal capital markets in which corporate headquarters allocate capital to their business units. (within-group transfers, within-group credit markets, and within-group equity market). The main aim of this study is to investigate the nature of capital allocation dynamics by comparing the relevant studies carried out on external and internal capital markets with paying special significance to the business groups.

Keywords: internal capital markets, external capital markets, capital structure, capital allocation, business groups, corporate finance

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Authors: Hadi Rouhi Belvirdi, Davoud Beheshtizadeh

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The increasing demand for sustainable construction materials has led to a growing interest in high-strength recycled concrete (HSRC). Utilizing recycled materials not only reduces waste but also minimizes the depletion of natural resources. This study explores the application of artificial intelligence (AI) techniques to model and predict the properties of HSRC. In the past two decades, the production levels in various industries and, consequently, the amount of waste have increased significantly. Continuing this trend will undoubtedly cause irreparable damage to the environment. For this reason, engineers have been constantly seeking practical solutions for recycling industrial waste in recent years. This research utilized the results of the compressive strength of 90-day high-strength recycled concrete. The method for creating recycled concrete involved replacing sand with crushed glass and using glass powder instead of cement. Subsequently, a feedforward artificial neural network was employed to model the compressive strength results for 90 days. The regression and error values obtained indicate that this network is suitable for modeling the compressive strength data.

Keywords: high-strength recycled concrete, feedforward artificial neural network, regression, construction materials

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Authors: Jungeun Cho

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Social finance has received increasing attention as a means to promote the growth of regional economies. Despite the plenty of research discussed their critical role and functions in regional economic development such as the financing and promotion of co-operatives or social enterprises and the offering credit to the financially excluded in the region, however, rarely are efforts made to measure the contribution of social finance in the regional economy. It is essential to establish an evaluation model in order to encourage social finance institutions to perform their supposed role and functions on regional economic development. The objective of this paper is to formulate an evaluation model of the contribution of social finance to the regional economy through an analytic hierarchy process (AHP) approach. This study is expected to provide useful guidelines for social finance institutions’ strategies and the policies of local or central government regarding social finance.

Keywords: social finance, regional economy, social economy, policies of local or central government

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Authors: Mensah-Akoto Julius, Kenichi Matsui

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This paper examines the impact of COVID-19 on waste management workers in Ghana. A questionnaire survey was conducted among 60 waste management workers in Accra metropolis, the capital region of Ghana, to understand the impact of the COVID-19 pandemic on waste generation, workers’ safety in collecting solid waste, and service delivery. To find out correlations between the pandemic and safety of waste management workers, a regression analysis was used. Regarding waste generation, the results show the pandemic led to the highest annual per capita solid waste generation, or 3,390 tons, in 2020. Regarding the safety of workers, the regression analysis shows a significant and inverse association between COVID-19 and waste management services. This means that contaminated wastes may infect field workers with COVID-19 due to their direct exposure. A rise in new infection cases would have a negative impact on the safety and service delivery of the workers. The result also shows that an increase in economic activities negatively impacts waste management workers. The analysis, however, finds no statistical relationship between workers’ service deliveries and employees’ salaries. The study then discusses how municipal waste management authorities can ensure safe and effective waste collection during the pandemic.

Keywords: Covid-19, waste management worker, waste collection, Ghana

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Authors: Alexander Gretchenko, Liudmila Goncharenko, Sergey Sybachin

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This article summarizes the history of the discovery of N.D. Kondratiev of large cycles of economic conditions, as well as the creation and justification of the theory of innovation-cyclical economic development of Kondratiev-Schumpeter. An analysis of it in modern conditions is providing. The main conclusion in this article is that in general terms today it can be argued that the Kondratiev-Schumpeter theory is sufficiently substantiated. Further, the possibility of making a forecast of the development of the economic situation in the direction of applying this theory in practice, which demonstrate its effectiveness, is considered.

Keywords: Kondratiev's big cycles of economic conjuncture, Schumpeter's theory of innovative economic development, long-term cyclical forecasting, dating of Kondratiev cycles

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Authors: Xuan Hua Huang, Shu Fen Wu, Yi Ting Huang, Pi Yueh Lee

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Background: In Taiwan, the Taiwan healthcare care Indicator Series regards the rate of hospital readmission as an important indicator of healthcare quality. Unplanned readmission not only effects patient’s condition but also increase healthcare utilization rate and healthcare costs. Purpose: The purpose of this study was explored the effects of adult unplanned readmission within 14 days of discharge at a regional teaching hospital in South Taiwan. Methods: The retrospectively review design was used. A total 495 participants of unplanned readmissions and 878 of non-readmissions within 14 days recruited from a regional teaching hospital in Southern Taiwan. The instruments used included the Charlson Comorbidity Index, and demographic characteristics, and disease-related variables. Statistical analyses were performed with SPSS version 22.0. The descriptive statistics were used (means, standard deviations, and percentage) and the inferential statistics were used T-test, Chi-square test and Logistic regression. Results: The unplanned readmissions within 14 days rate was 36%. The majorities were 268 males (54.1%), aged >65 were 318 (64.2%), and mean age was 68.8±14.65 years (23-98years). The mean score for the comorbidities was 3.77±2.73. The top three diagnosed of the readmission were digestive diseases (32.7%), respiratory diseases (15.2%), and genitourinary diseases (10.5%). There were significant relationships among the gender, age, marriage, comorbidity status, and discharge planning services (χ2: 3.816-16.474, p: 0.051~0.000). Logistic regression analysis showed that old age (OR = 1.012, 95% CI: 1.003, 1.021), had the multi-morbidity (OR = 0.712~4.040, 95% CI: 0.559~8.522), had been consult with discharge planning services (OR = 1.696, 95% CI: 1.105, 2.061) have a higher risk of readmission. Conclusions: This study finds that multi-morbidity was independent risk factor for unplanned readmissions at 14 days, recommended that the interventional treatment of the medical team be provided to provide integrated care for multi-morbidity to improve the patient's self-care ability and reduce the 14-day unplanned readmission rate.

Keywords: unplanned readmission, comorbidities, Charlson comorbidity index, logistic regression

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Authors: Hui-Yen Lee, Hsiu-Yun Wei, Guey-Jen Lin, Pi-Yueh Lee Lee

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Background: Unplanned hospital readmissions increase healthcare costs and have been considered a marker of poor healthcare performance. The elderly face a higher risk of unplanned readmission due to elderly-specific characteristics such as deteriorating body functions and the relatively high incidence of complications after treatment of acute diseases. Purpose: The aim of this study was exploring the factors that relate to the unplanned readmission of elderly within 14 days of discharge at our hospital in southern Taiwan. Methods: We retrospectively reviewed the medical records of patients aged ≥65 years who had been re-admitted between January 2018 and December 2018.The Charlson Comorbidity score was calculated using previous used method. Related factors that affected the rate of unplanned readmission within 14 days of discharge were screened and analyzed using the chi-squared test and logistic regression analysis. Results: This study enrolled 829 subjects aged more than 65 years. The numbers of unplanned readmission patients within 14 days were 318 cases, while those did not belong to the unplanned readmission were 511 cases. In 2018, the rate of elderly patients in unplanned 14 days readmissions was 38.4%. The majority patients were females (166 cases, 52.2%), with an average age of 77.6 ± 7.90 years (65-98). The average value of Charlson Comorbidity score was 4.42±2.76. Using logistic regression analysis, we found that the gastric or peptic ulcer (OR=1.917 , P< 0.002), diabetes (OR= 0.722, P< 0.043), hemiplegia (OR= 2.292, P< 0.015), metastatic solid tumor (OR= 2.204, P< 0.025), hypertension (OR= 0.696, P< 0.044), and skin ulcer/cellulitis (OR= 2.747, P< 0.022) have significantly higher risk of 14-day readmissions. Conclusion: The results of the present study may assist the healthcare teams to understand the factors that may affect unplanned readmission in the elderly. We recommend that these teams give efficient approach in their medical practice, provide timely health education for elderly, and integrative healthcare for chronic diseases in order to reduce unplanned readmissions.

Keywords: unplanning readmission, elderly, Charlson comorbidity score, logistic regression analysis

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Authors: Hafez Abdo, Rob Ackrill

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Renewable energy (RE) promotion in the UK relies on multiple policy instruments, which are required to overcome the path dependency pressures favouring fossil fuels. These instruments include targeted funding schemes and economy-wide instruments embedded in the tax code. The resulting complexity of incentives raises important questions around the coherence and effectiveness of these instruments for RE generation. This complexity is exacerbated by UK RE policy being nested within EU policy in a multi-level governance (MLG) setting. To gain analytical traction on such complexity, this study will analyse policies promoting the on-farm generation of energy for heat and power, from farm and food waste, via anaerobic digestion. Utilising both primary and secondary data, it seeks to address a particular lacuna in the academic literature. Via a localised, in-depth investigation into the complexity of policy instruments promoting RE, this study will help our theoretical understanding of the challenges that MLG and path dependency pressures present to policymakers of multi-dimensional policies.

Keywords: anaerobic digestion, energy, green, policy, renewable, tax, UK

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Authors: Ahsan Ali Khan

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During past few years, researchers are emphasizing more on the need of safety in construction industry. This need of safety is an important issue in developing countries. As due to development they are facing huge construction growth. This research is done to evaluate labor safety condition in construction industry of Pakistan. The research carried out through questionnaire survey at different construction sites. Useful data are gathered from these sites which then factor analyzed resulting in five factors. These factors reflect that most of the workers are aware of the safety need, but they divert this responsibility towards management and claim that the work is more essential for management instead of safety. Moreover, those work force which is unaware of safety state that there is lack of any training and guidance from upper management which lead to many unfavorable events on construction sites. There is need of implementation safety activities by management like training, formulation of rules and policies. This research will be helpful to divert management attention towards safety need so they will make efforts for safety of their manpower—the workers.

Keywords: labor's safety, management role, Pakistan, safety factors

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Authors: Khadija Rafi, Neha Jamil, Laiba Khalid, Meerub Nawaz, Mahwish Farooq

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Speech articulation is a complex process to produce intelligible sounds with the help of precise movements of various structures within the vocal tract. All these structures in the vocal tract are named as articulators, which comprise lips, teeth, tongue, and palate. These articulators work together to produce a range of distinct phonemes, which happen to be the basis of language. It starts with the airstream from the lungs passing through the trachea and into oral and nasal cavities. When the air passes through the mouth, the tongue and the muscles around it form such coordination it creates certain sounds. It can be seen when the tongue is placed in different positions- sometimes near the alveolar ridge, soft palate, roof of the mouth or the back of the teeth which end up creating unique qualities of each phoneme. We can articulate vowels with open vocal tracts, but the height and position of the tongue is different every time depending upon each vowel, while consonants can be pronounced when we create obstructions in the airflow. For instance, the alphabet ‘b’ is a plosive and can be produced only by briefly closing the lips. Articulation disorders can not only affect communication but can also be a hurdle in speech production. To improve articulation skills for such individuals, doctors often recommend speech therapy, which involves various kinds of exercises like jaw exercises and tongue twisters. However, this disorder is more common in children who are going through developmental articulation issues right after birth, but in adults, it can be caused by injury, neurological conditions, or other speech-related disorders. In short, speech articulation is an essential aspect of productive communication, which also includes coordination of the specific articulators to produce different intelligible sounds, which are a vital part of spoken language.

Keywords: linguistics, speech articulation, speech therapy, language learning

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Authors: Jung-Hun Cho, Tae-Heon Moon, Jin-Hak Lee

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Land price contains the comprehensive characteristics of urban space, representing the social and economic features of the city. Accordingly, land price can be utilized as an indicator, which can identify the changes of spatial structure and socioeconomic variations caused by urban development. This study attempted to explore the changes in land price by a new road construction. Methodologically, it adopted Space Syntax, which can interpret urban spatial structure comprehensively, to identify the relationship between the forms of road networks and land price. The result of the regression analysis showed the ‘integration index’ of Space Syntax is statistically significant and has a strong correlation with land price. If the integration value is high, land price increases proportionally. Subsequently, using regression equation, it tried to predict the land price changes of each of the lots surrounding the roads that are newly opened. The research methods or study results have the advantage of predicting the changes in land price in an easy way. In addition, it will contribute to planners and project managers to establish relevant polices and smoothing urban regeneration projects through enhancing residents’ understanding by providing possible results and advantages in their land price before the execution of urban regeneration and development projects.

Keywords: space syntax, urban regeneration, spatial structure, official land price

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Authors: Eric Mwenda, Shadrack Ngene

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Human-wildlife conflicts (HWCs) are the main threat to conservation in Africa. This is because wildlife needs overlap with those of humans. In Kenya, about 70% of wildlife occurs outside protected areas. As a result, wildlife and human range overlap, causing HWCs. The HWCs in Kenya occur in the drylands adjacent to protected areas. The top five counties with the highest incidences of HWC are Taita Taveta, Narok, Lamu, Kajiado, and Laikipia. The common wildlife species responsible for HWCs are elephants, buffaloes, hyenas, hippos, leopards, baboons, monkeys, snakes, and crocodiles. To ensure individuals affected by the conflicts are compensated, Kenya has developed a model of HWC compensation claims data collection and payment. We collected data on HWC from all eight Kenya Wildlife Service (KWS) Conservation Areas from 2009 to 2019. Additional data was collected from stakeholders' consultative workshops held in the Conservation Areas and a literature review regarding payment of injuries and ongoing insurance schemes being practiced in areas. This was followed by the description of the claims administration process and calculation of the pricing of the compensation claims. We further developed a digital platform for data capture and processing of all reported conflict cases and payments. Our product recognized four categories of HWC (i.e., human death and injury, property damage, crop destruction, and livestock predation). Personal bodily injury and human death were provided based on the Continental Scale of Benefits. We proposed a maximum of Kenya Shillings (KES) 3,000,000 for death. Medical, pharmaceutical, and hospital expenses were capped at a maximum of KES 150,000, as well as funeral costs at KES 50,000. Pain and suffering were proposed to be paid for 12 months at the rate of KES 13,500 per month. Crop damage was to be based on farm input costs at a maximum of KES 150,000 per claim. Livestock predation leading to death was based on Tropical Livestock Unit (TLU), which is equivalent to KES 30,000, whick includes Cattle (1 TLU = KES 30,000), Camel (1.4 TLU = KES 42,000), Goat (0.15 TLU = 4,500), Sheep (0.15 TLU = 4,500), and Donkey (0.5 TLU = KES 15,000). Property destruction (buildings, outside structures and harvested crops) was capped at KES 150,000 per any one claim. We conclude that it is possible to use an administrator to collect data on HWC compensation claims and make payments using technology. The success of the new approach will depend on a piloting program. We recommended that a pilot scheme be initiated for eight months in Taita Taveta, Kajiado, Baringo, Laikipia, Narok, and Meru Counties. This will test the claims administration process as well as harmonize data collection methods. The results of this pilot will be crucial in adjusting the scheme before country-wide roll out.

Keywords: human-wildlife conflicts, compensation, human death and injury, crop destruction, predation, property destruction

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Authors: Mansir Abubakar, Hazlina Hamdan, Norwati Mustapha, Teh Noranis Mohd Aris

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In order to build a matching pattern for each class correspondences of ontology, it is required to specify a set of attribute correspondences across two corresponding classes by clustering. Clustering reduces the size of potential attribute correspondences considered in the matching activity, which will significantly reduce the computation workload; otherwise, all attributes of a class should be compared with all attributes of the corresponding class. Most existing ontology matching approaches lack scalable attributes discovery methods, such as cluster-based attribute searching. This problem makes ontology matching activity computationally expensive. It is therefore vital in ontology matching to design a scalable element or attribute correspondence discovery method that would reduce the size of potential elements correspondences during mapping thereby reduce the computational workload in a matching process as a whole. The objective of this work is 1) to design a clustering method for discovering similar attributes correspondences and relationships between ontologies, 2) to discover element correspondences by classifying elements of each class based on element’s value features using K-medoids clustering technique. Discovering attribute correspondence is highly required for comparing instances when matching two ontologies. During the matching process, any two instances across two different data sets should be compared to their attribute values, so that they can be regarded to be the same or not. Intuitively, any two instances that come from classes across which there is a class correspondence are likely to be identical to each other. Besides, any two instances that hold more similar attribute values are more likely to be matched than the ones with less similar attribute values. Most of the time, similar attribute values exist in the two instances across which there is an attribute correspondence. This work will present how to classify attributes of each class with K-medoids clustering, then, clustered groups to be mapped by their statistical value features. We will also show how to map attributes of a clustered group to attributes of the mapped clustered group, generating a set of potential attribute correspondences that would be applied to generate a matching pattern. The K-medoids clustering phase would largely reduce the number of attribute pairs that are not corresponding for comparing instances as only the coverage probability of attributes pairs that reaches 100% and attributes above the specified threshold can be considered as potential attributes for a matching. Using clustering will reduce the size of potential elements correspondences to be considered during mapping activity, which will in turn reduce the computational workload significantly. Otherwise, all element of the class in source ontology have to be compared with all elements of the corresponding classes in target ontology. K-medoids can ably cluster attributes of each class, so that a proportion of attribute pairs that are not corresponding would not be considered when constructing the matching pattern.

Keywords: attribute correspondence, clustering, computational workload, k-medoids clustering, ontology matching

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Authors: Fatemeh Roostaee, Zahra Nikmanesh

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This study was done to investigate the role of religious coping components on occupational stress and the quality of working life of midwives. The method of study was descriptive-correlation. The sample was comprised of all midwives in maternity hospitals in Zahedan during 1393. Participants were selected through applying census method. The instruments of data collection were three questionnaires: the quality of working life, occupational stress, and religious opposition. For statistical analysis, Pearson correlation and step by step regression analysis methods were used. The results showed that there is a significant negative relationship between the component of religious activities (r=-0/454) and occupational stress, and regression analysis was also shown that the variable of religious activities has been explained 45% of occupational stress variable changes. The Pearson correlation test showed that there isn't any significant relationship between religious opposition components and the quality of life. Therefore, it is necessary to present essential trainings on (the field of) strengthening compatibility strategies and religious activities to reduce occupational stress.

Keywords: the quality of working life, occupational stress, religious, midwife

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Authors: Pratyush Agnihotri

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This paper presents a comprehensive overview of the conceptual design process for a fixed-wing vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV). Fixed-wing VTOL UAVs combine the advantages of rotary-wing aircraft, such as vertical take-off and landing capabilities, with the efficiency and speed of fixed-wing flight. The primary objective of this study is to explore the aerodynamic design principles that optimize performance parameters, including range, endurance, and stability while maintaining the VTOL capability. The design process involves selecting appropriate airfoils, optimizing wing configurations, and integrating propulsion systems suitable for both hovering and forward flight. Analytical methods are employed to evaluate aerodynamic performance, with a focus on lift-to-drag ratio, power requirements, and control strategies. The results highlight the challenges and trade-offs inherent in designing such hybrid aircraft, particularly in balancing the conflicting requirements of VTOL and fixed-wing flight. This study contributes to the development of efficient, versatile UAVs capable of operating in diverse environments.

Keywords: fixed wing, hybrid, VTOL, UAV

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Authors: Vinko Vidučić, Ljiljana Vidučić, Damir Boras

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The goals of the research include the determination of the characteristics of SMEs finance in Croatia, as well as the determination of indirect growth rates of the information model of the entrepreneurs` perception of business environment. The research results show that cost of finance and access to finance are most important constraining factor in setting up and running the business of small entrepreneurs in Croatia. Furthermore, small entrepreneurs in Croatia are significantly dissatisfied with the administrative barriers although relatively to a lesser extent than was the case in the pre-crisis time. High collateral requirement represents the main characteristic of bank lending concerning SMEs followed by long credit elaboration process. Formulated information model has defined the individual impact of indirect growth rates of the remaining variables on the model’s specific variable.

Keywords: business environment, information model, indirect growth rates, SME finance

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Authors: Muhamad Hafiz Ismail, Ahmad H., Nelfianty M. R.

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The longitudinal study is conducted to identify predictive factors of soccer talent among male players aged 14 to 17 years. Convenience sampling involving elite respondents (n=20) and sub-elite respondents (n=20) male soccer players. Descriptive statistics were reported as frequencies and percentages. The inferential statistical analysis is used to report the status of reliability, independent samples t-test, paired samples t-test, and multiple regression analysis. Generally, there are differences in mean of height, muscular strength, muscular endurance, cardiovascular endurance, task orientation, cognitive anxiety, self-confidence, juggling skills, short pass skills, long pass skills, dribbling skills, and shooting skills for 20 elite players and sub-elite players. Accordingly, there was a significant difference between pre and post-test for thirteen variables of height, weight, fat percentage, muscle strength, muscle endurance, cardiovascular endurance, flexibility, BMI, task orientation, juggling skills, short pass skills, a long pass skills, and dribbling skills. Based on the first predictive factors (physical), second predictive factors (fitness), third predictive factors (psychological), and fourth predictive factors (skills in playing football) pledged to the soccer talent; four multiple regression models were produced. The first predictive factor (physical) contributed 53.5 percent, supported by height and percentage of fat in soccer talents. The second predictive factor (fitness) contributed 63.2 percent and the third predictive factors (psychology) contributed 66.4 percent of soccer talent. The fourth predictive factors (skills) contributed 59.0 percent of soccer talent. The four multiple regression models could be used as a guide for talent scouting for soccer players of the future.

Keywords: soccer talent identification, fitness and physical test, soccer skills test, psychological test

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: Jerah Mystica B. Novenario, Flor A. Ceballo-Alcantara

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Sweetpotato is grown in tropical countries for its edible tubers, which became an important source of food. It is usually propagated through vine cutting which may be obtained from harvested plants or from nurseries intended for cutting production only. The recurrent use of vines may cause increased weevil infestation. The crop is known to be infested with insect pests, more importantly, the sweetpotato weevil, Cylasformicarius, which targets the tubers and thus cause economic losses. Sweetpotato farmers in Tarlac claim that only one sweetpotato cultivar is being attacked by C. formicarius. However, in was found in this experiment that feeding and feeding behavior of the weevil were not affected by the cultivar provided; such that no significant differences were observed on the average amount of tuber consumed by both male (F=0.86; df=2; P=0.45) and female (F=2.71; df=2; P=0.11) and feeding time (F=0.9; df=2; P=0.43). Conversely, in terms of damage assessment, significantly different (F=1.64; df=2; P=0.23) results were noted.

Keywords: cylas formicarius, feeding behavior, insect pest, sweetpotato

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Authors: Prateep Wajeetongratana

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The purpose of this research was to study the influence of accountants’ potential performance on their working process, a case study of Government Savings Banks in the northeast of Thailand. The independent variables included accounting knowledge, accounting skill, accounting value, accounting ethics, and accounting attitude, while the dependent variable included the success of the working process. A total of 155 accountants working for Government Savings Banks were selected by random sampling. A questionnaire was used as a tool for collecting data. Descriptive statistics in this research included percentage, mean, and multiple regression analyses. The findings revealed that the majority of accountants were female with an age between 35-40 years old. Most of the respondents had an undergraduate degree with ten years of experience. Moreover, the factors of accounting knowledge, accounting skill, accounting a value and accounting ethics and accounting attitude were rated at a high level. The findings from regression analysis of observation data revealed a causal relationship in that the observation data could explain at least 51 percent of the success in the accountants’ working process.

Keywords: influence, potential performance, success, working process

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Authors: Triveni Gogoi, Rima Chatterjee

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Shear wave velocity (Vs) estimation is an important approach in the seismic exploration and characterization of a hydrocarbon reservoir. There are varying methods for prediction of S-wave velocity, if recorded S-wave log is not available. But all the available methods for Vs prediction are empirical mathematical models. Shear wave velocity can be estimated using P-wave velocity by applying Castagna’s equation, which is the most common approach. The constants used in Castagna’s equation vary for different lithologies and geological set-ups. In this study, multiple regression analysis has been used for estimation of S-wave velocity. The EMERGE module from Hampson-Russel software has been used here for generation of S-wave log. Both single attribute and multi attributes analysis have been carried out for generation of synthetic S-wave log in Upper Assam basin. Upper Assam basin situated in North Eastern India is one of the most important petroleum provinces of India. The present study was carried out using four wells of the study area. Out of these wells, S-wave velocity was available for three wells. The main objective of the present study is a prediction of shear wave velocities for wells where S-wave velocity information is not available. The three wells having S-wave velocity were first used to test the reliability of the method and the generated S-wave log was compared with actual S-wave log. Single attribute analysis has been carried out for these three wells within the depth range 1700-2100m, which corresponds to Barail group of Oligocene age. The Barail Group is the main target zone in this study, which is the primary producing reservoir of the basin. A system generated list of attributes with varying degrees of correlation appeared and the attribute with the highest correlation was concerned for the single attribute analysis. Crossplot between the attributes shows the variation of points from line of best fit. The final result of the analysis was compared with the available S-wave log, which shows a good visual fit with a correlation of 72%. Next multi-attribute analysis has been carried out for the same data using all the wells within the same analysis window. A high correlation of 85% has been observed between the output log from the analysis and the recorded S-wave. The almost perfect fit between the synthetic S-wave and the recorded S-wave log validates the reliability of the method. For further authentication, the generated S-wave data from the wells have been tied to the seismic and correlated them. Synthetic share wave log has been generated for the well M2 where S-wave is not available and it shows a good correlation with the seismic. Neutron porosity, density, AI and P-wave velocity are proved to be the most significant variables in this statistical method for S-wave generation. Multilinear regression method thus can be considered as a reliable technique for generation of shear wave velocity log in this study.

Keywords: Castagna's equation, multi linear regression, multi attribute analysis, shear wave logs

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Authors: Gorkem Algan, Ilkay Ulusoy, Saban Gonul, Banu Turgut, Berker Bakbak

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Computer vision systems recently made a big leap thanks to deep neural networks. However, these systems require correctly labeled large datasets in order to be trained properly, which is very difficult to obtain for medical applications. Two main reasons for label noise in medical applications are the high complexity of the data and conflicting opinions of experts. Moreover, medical imaging datasets are commonly tiny, which makes each data very important in learning. As a result, if not handled properly, label noise significantly degrades the performance. Therefore, a label-noise-robust learning algorithm that makes use of the meta-learning paradigm is proposed in this article. The proposed solution is tested on retinopathy of prematurity (ROP) dataset with a very high label noise of 68%. Results show that the proposed algorithm significantly improves the classification algorithm's performance in the presence of noisy labels.

Keywords: deep learning, label noise, robust learning, meta-learning, retinopathy of prematurity

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Authors: Jing Zhang, Daniel Nikovski

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We propose an approximation algorithm called LINKUMP to compute the Pan Matrix Profile (PMP) under the unnormalized l∞ distance (useful for value-based similarity search) using double-ended queue and linear interpolation. The algorithm has comparable time/space complexities as the state-of-the-art algorithm for typical PMP computation under the normalized l₂ distance (useful for shape-based similarity search). We validate its efficiency and effectiveness through extensive numerical experiments and a real-world anomaly detection application.

Keywords: pan matrix profile, unnormalized euclidean distance, double-ended queue, discord discovery, anomaly detection

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Authors: Paul Han, Jun-Geol Baek

Abstract:

In the flat panel display industry, the scheduler and dispatching system to meet production target quantities and the deadline of production are the major production management system which controls each facility production order and distribution of WIP (Work in Process). In dispatching system, delivery time is a key factor for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors and a forecasting model of delivery time. Of survival analysis techniques to select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the Accelerated Failure Time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the Mean Square Error (MSE) criteria, the AFT model decreased by 33.8% compared to the existing prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing a delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

Keywords: delivery time, survival analysis, Cox PH model, accelerated failure time model

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Authors: Brando Vagenende, Marie-Anne Guerry

Abstract:

For stochastic matrices of any order, the geometric description of the convex set of eigenvalues is completely known. The purpose of this study is to investigate the subset of the monotone matrices. This type of matrix appears in contexts such as intergenerational occupational mobility, equal-input modeling, and credit ratings-based systems. Monotone matrices are stochastic matrices in which each row stochastically dominates the previous row. The monotonicity property of a stochastic matrix can be expressed by a nonnegative lower-order matrix with the same eigenvalues as the original monotone matrix (except for the eigenvalue 1). Specifically, the aim of this research is to focus on the properties of eigenvalues of monotone matrices. For those matrices up to order 3, there already exists a complete description of the convex set of eigenvalues. For monotone matrices of order at least 4, this study gives, through simulations, more insight into the geometric description of their eigenvalues. Furthermore, this research treats in a geometric and algebraic way the properties of eigenvalues of monotone matrices of order at least 4.

Keywords: eigenvalues of matrices, finite Markov chains, monotone matrices, nonnegative matrices, stochastic matrices

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Authors: Haoming Ma, Guo Yu, Peiru Zhou

Abstract:

Background: Previous studies show that dysglycemia, mostly hyperglycemia, hypoglycemia and glycemic variability(GV), are associated with excess mortality in critically ill patients, especially those without diabetes. Glycemic variability is an increasingly important measure of glucose control in the intensive care unit (ICU) due to this association. However, there is limited data pertaining to the relationship between different clinical factors and glycemic variability and clinical outcomes categorized by their DM status. This retrospective study of 958 intensive care unit(ICU) patients was conducted to investigate the relationship between GV and outcome in critically ill patients and further to determine the significant factors that contribute to the glycemic variability. Aim: We hypothesize that the factors contributing to mortality and the glycemic variability are different from critically ill patients with or without diabetes. And the primary aim of this study was to determine which dysglycemia (hyperglycemia\hypoglycemia\glycemic variability) is independently associated with an increase in mortality among critically ill patients in different groups (DM/Non-DM). Secondary objectives were to further investigate any factors affecting the glycemic variability in two groups. Method: A total of 958 diabetic and non-diabetic patients with severe diseases in the ICU were selected for this retrospective analysis. The glycemic variability was defined as the coefficient of variation (CV) of blood glucose. The main outcome was death during hospitalization. The secondary outcome was GV. The logistic regression model was used to identify factors associated with mortality. The relationships between GV and other variables were investigated using linear regression analysis. Results: Information on age, APACHE II score, GV, gender, in-ICU treatment and nutrition was available for 958 subjects. Predictors remaining in the final logistic regression model for mortality were significantly different in DM/Non-DM groups. Glycemic variability was associated with an increase in mortality in both DM(odds ratio 1.05; 95%CI:1.03-1.08,p＜0.001) or Non-DM group(odds ratio 1.07; 95%CI:1.03-1.11,p=0.002). For critically ill patients without diabetes, factors associated with glycemic variability included APACHE II score(regression coefficient, 95%CI:0.29,0.22-0.36,p＜0.001), Mean BG(0.73,0.46-1.01,p＜0.001), total parenteral nutrition(2.87,1.57-4.17,p＜0.001), serum albumin(-0.18,-0.271 to -0.082,p＜0.001), insulin treatment(2.18,0.81-3.55,p=0.002) and duration of ventilation(0.006,0.002-1.010,p=0.003).However, for diabetes patients, APACHE II score(0.203,0.096-0.310,p＜0.001), mean BG(0.503,0.138-0.869,p=0.007) and duration of diabetes(0.167,0.033-0.301,p=0.015) remained as independent risk factors of GV. Conclusion: We found that the relation between dysglycemia and mortality is different in the diabetes and non-diabetes groups. And we confirm that GV was associated with excess mortality in DM or Non-DM patients. Furthermore, APACHE II score, Mean BG, total parenteral nutrition, serum albumin, insulin treatment and duration of ventilation were significantly associated with an increase in GV in Non-DM patients. While APACHE II score, mean BG and duration of diabetes (years) remained as independent risk factors of increased GV in DM patients. These findings provide important context for further prospective trials investigating the effect of different clinical factors in critically ill patients with or without diabetes.

Keywords: diabetes, glycemic variability, predictors, severe disease

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Authors: Kanyarat Bussaban, Kunyanuth Kularbphettong

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This research aims to create a model for analysis of student behavior using Library resources based on data mining technique in case of Suan Sunandha Rajabhat University. The model was created under association rules, apriori algorithm. The results were found 14 rules and the rules were tested with testing data set and it showed that the ability of classify data was 79.24 percent and the MSE was 22.91. The results showed that the user’s behavior model by using association rule technique can use to manage the library resources.

Keywords: behavior, data mining technique, a priori algorithm, knowledge discovery

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Authors: Auliya Adzillatin Uzhma, M. Adrian Rizky, Puri Diah Santyarini

Abstract:

Bekasi Industrial Area in Kab. Bekasi and SEZ (Special Economic Zone) Sei Mangkei in Kab. Simalungun are two areas whose have the same main economic activity that are manufacturing industrial. Manufacturing industry in Bekasi Industrial Area contributes more than 70% of Kab. Bekasi’s GDP, while manufacturing industry in SEZ Sei Mangkei contributes less than 20% of Kab. Simalungun’s GDP. The dependent variable in the research is labor productivity, while the independent variable is the amount of labor, the level of labor education, the length of work and salary. This research used linear regression method to find the model for represent actual condition of productivity in two industrial area, then the equalization using dummy variable on labor education level variable. The initial hypothesis (Ho) in this research is that labor productivity in Bekasi Industrial Area will be higher than the productivity of labor in SEZ Sei Mangkei. The variable that supporting the accepted hypothesis are more labor, higher education, longer work and higher salary in Bekasi Industrial Area.

Keywords: labor, industrial city, linear regression, productivity

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Authors: Tomas Berhanu Bekele

Abstract:

Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea of avoiding traffic instabilities and homogenizing traffic flow in such a way that the risk of accidents is minimized and traffic flow is maximized. Lately, Intelligent Transport Systems (ITS) has become an important area of research to solve such road traffic-related issues for making smart decisions. It links people, roads and vehicles together using communication technologies to increase safety and mobility. Moreover, accurate prediction of road traffic is important to manage traffic congestion. The aim of this study is to develop an ANN model for the prediction of traffic flow and to compare the ANN model with the linear regression model of traffic flow predictions. Data extraction was carried out in intervals of 15 minutes from the video player. Video of mixed traffic flow was taken and then counted during office work in order to determine the traffic volume. Vehicles were classified into six categories, namely Car, Motorcycle, Minibus, mid-bus, Bus, and Truck vehicles. The average time taken by each vehicle type to travel the trap length was measured by time displayed on a video screen.

Keywords: intelligent transport system (ITS), traffic flow prediction, artificial neural network (ANN), linear regression

Procedia PDF Downloads 64