Search results for: multiple regression model

Authors: Gyeongbeom Yi

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The purpose of this study is to introduce the analytic solution for determining the optimal capacity (lot-size) of a multiproduct, multistage production and inventory system to meet the finished product demand. Reasonable decision-making about the capacity of processes and storage units is an important subject for industry. The industrial solution for this subject is to use the classical economic lot sizing method, EOQ/EPQ (Economic Order Quantity/Economic Production Quantity) model, incorporated with practical experience. However, the unrealistic material flow assumption of the EOQ/EPQ model is not suitable for chemical plant design with highly interlinked processes and storage units. This study overcomes the limitation of the classical lot sizing method developed on the basis of the single product and single stage assumption. The superstructure of the plant considered consists of a network of serially and/or parallelly interlinked processes and storage units. The processes involve chemical reactions with multiple feedstock materials and multiple products as well as mixing, splitting or transportation of materials. The objective function for optimization is minimizing the total cost composed of setup and inventory holding costs as well as the capital costs of constructing processes and storage units. A novel production and inventory analysis method, PSW (Periodic Square Wave) model, is applied. The advantage of the PSW model comes from the fact that the model provides a set of simple analytic solutions in spite of a realistic description of the material flow between processes and storage units. The resulting simple analytic solution can greatly enhance the proper and quick investment decision for plant design and operation problem confronted in diverse economic situations.

Keywords: analytic solution, optimal design, process-storage network

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Authors: Amit Das

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Background and significance of the study: Generally investors and market forecasters use financial statement for investigation while it awakens contribute to investing. The main vicinity of financial accounting and reporting practices recommends a few basic financial performance indicators, namely, return on capital employed, return on assets and earnings per share, which is associated considerably with share prices. It is principally true in case of Indian pharmaceutical companies also. Share investing is intriguing a financial risk in addition to investors look for those financial evaluations which have noteworthy shock on share price. A crucial intention of financial statement analysis and reporting is to offer information which is helpful predominantly to exterior clients in creating credit as well as investment choices. Sound financial performance attracts the investors automatically and it will increase the share price of the respective companies. Keeping in view of this, this research work investigates the impact of financial performance indicators on share price of pharmaceutical companies in India which is listed in the Bombay Stock Exchange. Methodology: This research work is based on secondary data collected from moneycontrol database on September 28, 2015 of top 101 pharmaceutical companies in India. Since this study selects four financial performance indicators purposively and availability in the database, that is, earnings per share, return on capital employed, return on assets and net profits as independent variables and one dependent variable, share price of 101 pharmaceutical companies. While analysing the data, correlation statistics, multiple regression technique and appropriate test of significance have been used. Major findings: Correlation statistics show that four financial performance indicators of 101 pharmaceutical companies are associated positively and negatively with its share price and it is very much significant that more than 80 companies’ financial performances are related positively. Multiple correlation test results indicate that financial performance indicators are highly related with share prices of the selected pharmaceutical companies. Furthermore, multiple regression test results illustrate that when financial performances are good, share prices have been increased steadily in the Bombay stock exchange and all results are statistically significant. It is more important to note that sensitivity indices were changed slightly through financial performance indicators of selected pharmaceutical companies in India. Concluding statements: The share prices of pharmaceutical companies depend on the sound financial performances. It is very clear that share prices are changed with the movement of two important financial performance indicators, that is, earnings per share and return on assets. Since 101 pharmaceutical companies are listed in the Bombay stock exchange and Sensex are changed with this, it is obvious that Government of India has to take important decisions regarding production and exports of pharmaceutical products so that financial performance of all the pharmaceutical companies are improved and its share price are increased positively.

Keywords: financial performance indicators, share prices, pharmaceutical companies, India

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Authors: Achut Manandhar, Kenneth D. Morton, Peter A. Torrione, Leslie M. Collins

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The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing.

Keywords: dimensional affect prediction, output-associative RVM, multivariate regression, fast testing

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Authors: Mohamed Sharof Mostafa, Atefeh Ahmadi

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Most of traditional single modality psychotherapy and counselling approaches to clients’ problems are based on the application of one therapy in all sessions. Modern developments in these sciences focus on eclectic and integrative interventions to consider all dimensions of an issue and all characteristics of the clients. This paper presents and overview eclectic therapy and its pros and cons. In addition, multiple intelligence theory and its application in eclectic therapy approaches are mentioned.

Keywords: eclectic therapy, client, multiple intelligence theory, dimensions

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Authors: Susana Marques

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Research on how store environment cues influence consumers’ store choice decision criteria, such as store operations, product quality, monetary price, store image and sales promotion, is sparse. Especially absent research on the simultaneous impact of multiple store environment cues. The authors propose a comprehensive store choice model that includes: three types of store environment cues as exogenous constructs; various store choice criteria as possible mediating constructs, and store patronage intentions as an endogenous construct. On the basis of testing with a sample of 561 customers of hypermarkets, the model is partially supported. This study used structural equation modelling to test the proposed model.

Keywords: store choice, store patronage, structural equation modelling, retailing

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Authors: Mehmet Alpaslan Köroğlu, Musa Hakan Arslan, Muslu Kazım Körez

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Basic objective of this study is to create a regression analysis method that can estimate the length of a plastic hinge which is an important design parameter, by making use of the outcomes of (lateral load-lateral displacement hysteretic curves) the experimental studies conducted for the reinforced square concrete columns. For this aim, 170 different square reinforced concrete column tests results have been collected from the existing literature. The parameters which are thought affecting the plastic hinge length such as cross-section properties, features of material used, axial loading level, confinement of the column, longitudinal reinforcement bars in the columns etc. have been obtained from these 170 different square reinforced concrete column tests. In the study, when determining the length of plastic hinge, using the experimental test results, a regression analysis have been separately tested and compared with each other. In addition, the outcome of mentioned methods on determination of plastic hinge length of the reinforced concrete columns has been compared to other methods available in the literature.

Keywords: columns, plastic hinge length, regression analysis, reinforced concrete

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Authors: Junjie Peng, Jinbao Chen, Shuai Kong, Danxu Liu

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On the basis of traditional resource allocation strategies, the usage of resources on physical servers in cloud data center is great uncertain. It will cause waste of resources if the assignment of tasks is not enough. On the contrary, it will cause overload if the assignment of tasks is too much. This is especially obvious when the applications are the same type because of its resource preferences. Considering CPU intensive application is one of the most common types of application in the cloud, we studied the optimization strategy for CPU intensive applications on the same server. We used resource preferences to analyze the case that multiple CPU intensive applications run simultaneously, and put forward a model which can predict the execution time for CPU intensive applications which run simultaneously. Based on the prediction model, we proposed the method to select the appropriate number of applications for a machine. Experiments show that the model can predict the execution time accurately for CPU intensive applications. To improve the execution efficiency of applications, we propose a scheduling model based on priority for CPU intensive applications. Extensive experiments verify the validity of the scheduling model.

Keywords: cloud computing, CPU intensive applications, resource optimization, strategy

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Authors: Faiz N. K. Yusufi, Aquil Ahmed, Jamal Ahmad

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Predicting the risk of diabetic retinopathy (DR) in Indian type 2 diabetes patients is immensely necessary. India, being the second largest country after China in terms of a number of diabetic patients, to the best of our knowledge not a single risk score for complications has ever been investigated. Diabetic retinopathy is a serious complication and is the topmost reason for visual impairment across countries. Any type or form of DR has been taken as the event of interest, be it mild, back, grade I, II, III, and IV DR. A sample was determined and randomly collected from the Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India. Collected variables include patients data such as sex, age, height, weight, body mass index (BMI), blood sugar fasting (BSF), post prandial sugar (PP), glycosylated haemoglobin (HbA1c), diastolic blood pressure (DBP), systolic blood pressure (SBP), smoking, alcohol habits, total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), physical activity, duration of diabetes, diet control, history of antihypertensive drug treatment, family history of diabetes, waist circumference, hip circumference, medications, central obesity and history of DR. Cox proportional hazard regression is used to design risk scores for the prediction of retinopathy. Model calibration and discrimination are assessed from Hosmer Lemeshow and area under receiver operating characteristic curve (ROC). Overfitting and underfitting of the model are checked by applying regularization techniques and best method is selected between ridge, lasso and elastic net regression. Optimal cut off point is chosen by Youden’s index. Five-year probability of DR is predicted by both survival function, and Markov chain two state model and the better technique is concluded. The risk scores developed can be applied by doctors and patients themselves for self evaluation. Furthermore, the five-year probabilities can be applied as well to forecast and maintain the condition of patients. This provides immense benefit in real application of DR prediction in T2DM.

Keywords: Cox proportional hazard regression, diabetic retinopathy, ROC curve, type 2 diabetes mellitus

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Authors: Shahadut Hossain

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Measurement errors in continuous covariates and/or misclassifications in categorical covariates are common in epidemiological studies. Regression analysis ignoring such mismeasurements seriously biases the estimated main and interaction effects of covariates on the outcome of interest. Thus, adjustments for such mismeasurements are necessary. In this research, we propose a Bayesian parametric framework for eliminating deleterious impacts of covariate mismeasurements in logistic regression. The proposed adjustment method is unified and thus can be applied to any generalized linear and non-linear regression models. Furthermore, adjustment for covariate mismeasurements requires validation data usually in the form of either gold standard measurements or replicates of the mismeasured covariates on a subset of the study population. Initial investigation shows that adequacy of such adjustment depends on the sizes of main and validation samples, especially when prevalences of the categorical covariates are low. Thus, we investigate the impact of main and validation sample sizes on the adjusted estimates, and provide a general guideline about these sample sizes based on simulation studies.

Keywords: measurement errors, misclassification, mismeasurement, validation sample, Bayesian adjustment

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Authors: Abbas Vahedian, Rijun Shrestha, Keith Crews

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In recent years, fibre reinforced polymers as applications of strengthening materials have received significant attention by civil engineers and environmentalists because of their excellent characteristics. Currently, these composites have become a mainstream technology for strengthening of infrastructures such as steel, concrete and more recently, timber and masonry structures. However, debonding is identified as the main problem which limit the full utilisation of the FRP material. In this paper, a preliminary analysis of factors affecting bond strength of FRP-to-concrete and timber bonded interface has been conducted. A novel theoretical method through regression analysis has been established to evaluate these factors. Results of proposed model are then assessed with results of pull-out tests and satisfactory comparisons are achieved between measured failure loads (R² = 0.83, P < 0.0001) and the predicted loads (R² = 0.78, P < 0.0001).

Keywords: debonding, fibre reinforced polymers (FRP), pull-out test, stepwise regression analysis

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Authors: Jin-Ming Wu, Te-Wei Ho, Yu-Wen Tien

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Background: The aim of this population-based study was to determine the occurrence of diabetes and exocrine pancreatic insufficiencies (EPI) on non-diabetes subjects receiving distal pancreatectomy (DP). Method: A nationwide cohort study between 2000 and 2010 was collected from the Taiwan National Health Insurance Research Database. Among 3264 DP patients, we identified 1410 non-diabetes and 966 non-diabetes non-EPI. Results. Of 1410 non-diabetes DP subjects, 312 patients (22.1%) developed newly-diagnosed diabetes after PD. On a multiple logistic regression model, co-morbid hyperlipidemia (odds ratio, 1.640; 95% CI, 1.362–2.763; P < 0.001) and pancreatitis (odds ratio, 2.428; 95% CI, 1.889–3.121; P < 0.001) significantly contributed to higher incidences of diabetes after DP. Moreover, 380 subjects (39.3%) developed EPI, and pancreatic cancer is the statistically significant risk factor (odds ratio, 4.663; 95% CI, 2.108–6.085; P < 0.001). Conclusion: The patients with co-morbid hyperlipidemia and chronic pancreatitis had higher rates of newly-diagnosed diabetes after DP, moreover, pancreatic cancer subjects had higher rates of pancreatic exocrine insufficiency after DP. The clinicians should be alert to follow up glucose metabolism and clinical symptoms of fat intolerance for DP patients.

Keywords: distal pancreatectomy, National database, diabetes, exocrine insufficiency

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Authors: Duncan Fraser

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A 3-Dimensional (3D) conceptual site model was developed using the Leapfrog Works® platform utilising a comprehensive historical dataset for a large former Gasworks site in Fitzroy, Melbourne. The gasworks had been constructed across two fractured geological units with varying hydraulic conductivities. A Newer Volcanic (basaltic) outcrop covered approximately half of the site and was overlying a fractured Melbourne formation (Siltstone) bedrock outcropping over the remaining portion. During the investigative phase of works, a dense non-aqueous phase liquid (DNAPL) plume (coal tar) was identified within both geological units in the subsurface originating from multiple sources, including gasholders, tar wells, condensers, and leaking pipework. The first stage of model development was undertaken to determine the horizontal and vertical extents of the coal tar in the subsurface and assess the potential causality between potential sources, plume location, and site geology. Concentrations of key contaminants of interest (COIs) were also interpolated within Leapfrog to refine the distribution of contaminated soils. The model was subsequently used to develop a robust soil remediation strategy and achieve endorsement from an Environmental Auditor. A change in project scope, following the removal and validation of the three former gasholders, necessitated the additional excavation of a significant volume of residual contaminated rock to allow for the future construction of two-story underground basements. To assess financial liabilities associated with the offsite disposal or thermal treatment of material, the 3D model was updated with three years of additional analytical data from the active remediation phase of works. Chemical concentrations and the residual tar plume within the rock fractures were modelled to pre-classify the in-situ material and enhance separation strategies to prevent the unnecessary treatment of material and reduce costs.

Keywords: 3D model, contaminated land, Leapfrog, remediation

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Authors: S. A. Khan, B. Mishra, O. Sherchan

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Background and Aims: Thyroid disorder is the most common endocrine disorder after diabetes mellitus. Females are more vulnerable to this disease, and old age is an important risk factor. This study was undertaken to investigate the burden of thyroid disorder in Nepalese postmenopausal women. Methods: In the present cross-sectional study, we included 271 post-menopausal women. Three ml of blood was collected following standard protocol after taking the written consent. Serum was separated and analyzed for free T3, free T4, and Thyroid Stimulating Hormone (TSH) by Chemiluminescence Immunoassay (CLIA) method in Snibe Maglumi 1000 analyzer. Data obtained was analyzed in SPSS Version 21. P < 0.05 was set for statistical significant at 95% Confidence Interval (CI). Results: Majority of the participants belong to Janjati (46.5%) ethnicity, followed by Brahmin/Chhetri (41.7%), residing either in urban or suburban locality. Most of them were non-vegetarian, non-smoker, and non-alcoholic. Subjects were divided into hyperthyroid (TSH < 0.3 uIU/ml), hypothyroid (TSH > 4.5 uIU/ml), and euthyroid (TSH=0.3-4.5 uIU/ml) based on TSH value. We reported 10.3% hyperthyroid and 29.2% hypothyroid cases. TSH was significantly correlated with T3 (r=-0.244; p < 0.001) T4 (r=-0.398; p < 0.001); age (r=-0.138; p=0.023) and BMI (r=0.123; p=0.043). Multiple linear regression model for TSH reveals only T3 and T4 were significantly associated with TSH (p < 0.001; p=0.001). Conclusion: To conclude, nearly 39.5% of the postmenopausal women had thyroid disorder. Postmenopausal women are vulnerable to thyroid disorder; therefore, requires regular thyroid monitoring.

Keywords: thyroid stimulating hormone, TSH, T3, T4, thyroid disorder

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Authors: Shi Yu, Rong Liu, Jingyun Lv

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Compression textiles with ergonomic-fit and controllable pressure performance have demonstrated positive effect on prevention and treatment of chronic venous insufficiency (CVI). Well-designed compression textile products contribute to improving user compliance in their daily application. This study explored the effects of multiple knitting variables (yarn-machinery settings) on the physical-mechanical properties and the produced pressure magnitudes of tubular compression fabrics (TCFs) through experimental testing and multiple regression modeling. The results indicated that fabric physical (stitch densities and circumference) and mechanical (tensile) properties were affected by the linear density (yarn diameters) of inlay yarns, which, to some extent, influenced pressure magnitudes of the TCFs. Knitting variables (e.g., feeding velocity of inlay yarns and loop size settings) can alter circumferences and tensile properties of tubular fabrics, respectively, and significantly varied pressure values of the TCFs. This study enhanced the understanding of the effects of knitting factors on pressure controlling of TCFs, thus facilitating dimension and pressure design of compression textiles in future development.

Keywords: laid-in knitted fabric, yarn-machinery settings, pressure magnitudes, quantitative analysis, compression textiles

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Authors: Abderrahmane Soufi

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The building sector is the largest consumer of energy in France, accounting for 44% of French consumption. To reduce energy consumption and improve energy efficiency, France implemented an energy transition law targeting 40% energy savings by 2030 in the tertiary building sector. Building simulation tools are used to predict the energy performance of buildings but the reliability of these tools is hampered by discrepancies between the real and simulated energy performance of a building. This performance gap lies in the simplified assumptions of certain factors, such as the behavior of occupants on air conditioning and heating, which is considered deterministic when setting a fixed operating schedule and a fixed interior comfort temperature. However, the behavior of occupants on air conditioning and heating is stochastic, diverse, and complex because it can be affected by many factors. Probabilistic models are an alternative to deterministic models. These models are usually derived from statistical data and express occupant behavior by assuming a probabilistic relationship to one or more variables. In the literature, logistic regression has been used to model the behavior of occupants with regard to heating and air conditioning systems by considering univariate logistic models in residential buildings; however, few studies have developed multivariate models for higher education and vocational training buildings in a Mediterranean climate. Therefore, in this study, occupant behavior on heating and air conditioning systems was modeled using logistic regression. Occupant behavior related to the turn-on heating and air conditioning systems was studied through experimental measurements collected over a period of one year (June 2023–June 2024) in three classrooms occupied by several groups of students in engineering schools and professional training. Instrumentation was provided to collect indoor temperature and indoor relative humidity in 10-min intervals. Furthermore, the state of the heating/air conditioning system (off or on) and the set point were determined. The outdoor air temperature, relative humidity, and wind speed were collected as weather data. The number of occupants, age, and sex were also considered. Logistic regression was used for modeling an occupant turning on the heating and air conditioning systems. The results yielded a proposed model that can be used in building simulation tools to predict the energy performance of teaching buildings. Based on the first months (summer and early autumn) of the investigations, the results illustrate that the occupant behavior of the air conditioning systems is affected by the indoor relative humidity and temperature in June, July, and August and by the indoor relative humidity, temperature, and number of occupants in September and October. Occupant behavior was analyzed monthly, and univariate and multivariate models were developed.

Keywords: occupant behavior, logistic regression, behavior model, mediterranean climate, air conditioning, heating

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Authors: Yuan Huang, Xiangping Chen, Xiaonan Luo

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Current software are continuously updating. The change between two versions usually involves multiple program entities (e.g., packages, classes, methods, attributes) with multiple purposes (e.g., changed requirements, bug fixing). It is hard for developers to understand which changes are made for the same purpose. Whether two changes are related is not decided by the relationship between this two entities in the program. In this paper, we summarized 4 coupling rules(16 instances) and 4 state-combination types at the class, method and attribute levels for software change. Related Change Vector (RCV) are defined based on coupling rules and state-combination types, and applied to classify related software changes by using Probabilistic Neural Network during a software updating.

Keywords: PNN, related change, state-combination, logical coupling, software entity

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Authors: D. S. Nagesh, G. L. Datta

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In the field of welding, various studies had been made by some of the previous investigators to predict as well as optimize weld bead geometric descriptors. Modeling of weld bead shape is important for predicting the quality of welds. In most of the cases, design of experiments technique to postulate multiple linear regression equations have been used. Nowadays, Genetic Algorithm (GA) an intelligent information treatment system with the characteristics of treating complex relationships as seen in welding processes used as a tool for inverse mapping/optimization of the process is attempted.

Keywords: smaw, genetic algorithm, bead geometry, optimization/inverse mapping

Procedia PDF Downloads 445

Authors: D. S. Nagesh, G. L. Datta

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In the field of welding, various studies had been made by some of the previous investigators to predict as well as optimize weld bead geometric descriptors. Modeling of weld bead shape is important for predicting the quality of welds. In most of the cases design of experiments technique to postulate multiple linear regression equations have been used. Nowadays Genetic Algorithm (GA) an intelligent information treatment system with the characteristics of treating complex relationships as seen in welding processes used as a tool for inverse mapping/optimization of the process is attempted.

Keywords: SMAW, genetic algorithm, bead geometry, optimization/inverse mapping

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Authors: Ehsan Motamedian

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Finding all optimal flux distributions of a metabolic model is an important challenge in systems biology. In this paper, a new algorithm is introduced to identify all alternate optimal solutions of a large scale metabolic network. The algorithm reduces the model to decrease computations for finding optimal solutions. The algorithm was implemented on the Escherichia coli metabolic model to find all optimal solutions for lactate and acetate production. There were more optimal flux distributions when acetate production was optimized. The model was reduced from 1076 to 80 variable fluxes for lactate while it was reduced to 91 variable fluxes for acetate. These 11 more variable fluxes resulted in about three times more optimal flux distributions. Variable fluxes were from 12 various metabolic pathways and most of them belonged to nucleotide salvage and extra cellular transport pathways.

Keywords: flux variability, metabolic network, mixed-integer linear programming, multiple optimal solutions

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Authors: Xia Huo

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The composition and metabolites of the gut microbiota can be altered by environmental pollutants. However, the effect of co-exposure to multiple pollutants on the human gut microbiota has not been sufficiently studied. In this study, gut microorganisms and their metabolites were compared between 33 children from Guiyu and 34 children from Haojiang. The exposure level was assessed by estimating the daily intake (EDI) of polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs), 6PPD-quinone (6PPDQ), and metal(loid)s in dust. Significant correlations were found between the EDIs of 6PPDQ, BDE28, PCB52, Ni, Cu, and both the alpha diversity index and specific metabolites in single-element models. The study found that the Bayesian kernel machine regression (BKMR) model showed a negative correlation between the EDIs of five pollutants (6PPDQ, BDE28, PCB52, Ni, and Cu) and the Chao 1 index, particularly beyond the 55th percentile. Furthermore, the EDIs of these five pollutants were positively correlated with the levels of the metabolite 2,4-diaminobutyric acid while negatively correlated with the levels of d-erythro-sphingosine and d-threitol. Our research suggests that exposure to 6PPDQ, BDE28, PCB52, Ni, and Cu in kindergarten dust is associated with alterations in the gut microbiota and its metabolites. These alterations may be associated with neurodevelopmental abnormalities in children.

Keywords: gut microbiota, 6PPDQ, PBDEs, PCBs, metal(loid)s, BKMR

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Authors: I. Gokasar, G. Bakioglu

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With the unprecedented rates of technological improvements, people start to solve their problems with the help of technological tools. According to application stores and websites in which people evaluate and comment on the traffic apps, there are more than 100 traffic applications which have different features with respect to their purpose of usage ranging from the features of traffic apps for public transit modes to the features of traffic apps for private cars. This study focuses on the top 30 traffic applications which were chosen with respect to their download counts. All data about the traffic applications were obtained from related websites. The purpose of this study is to analyze traffic applications in terms of their categorical attributes with the help of developing a regression model. The analysis results suggest that negative interpretations (e.g., being deficient) does not lead to lower star ratings of the applications. However, those negative interpretations result in a smaller increase in star rate. In addition, women use higher star rates than men for the evaluation of traffic applications.

Keywords: traffic app, real–time information, traffic congestion, regression analysis, dummy variables

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Authors: Tatsuya Yamazaki, Kazuya Miyakawa, Tomohiko Sugiyama, Toshitaka Iwatani

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The introduction of sensor technologies into agriculture is a necessary step to realize Precision Agriculture. Although sensing methodologies themselves have been prevailing owing to miniaturization and reduction in costs of sensors, there are some difficulties to analyze and understand the sensing data. Targeting at pears ’Le Lectier’, which is particular to Niigata in Japan, cultivation environmental data have been collected at pear fields by eight sorts of sensors: field temperature, field humidity, rain gauge, soil water potential, soil temperature, soil moisture, inner-bag temperature, and inner-bag humidity sensors. With regard to the inner-bag temperature and humidity sensors, they are used to measure the environment inside the fruit bag used for pre-harvest bagging of pears. In this experiment, three kinds of fruit bags were used for the pre-harvest bagging. After over 100 days continuous measurement, volumes of sensing data have been collected. Firstly, correlation analysis among sensing data measured by respective sensors reveals that one sensor can replace another sensor so that more efficient and cost-saving sensing systems can be proposed to pear farmers. Secondly, differences in characteristic and performance of the three kinds of fruit bags are clarified by the measurement results by the inner-bag environmental sensing. It is found that characteristic and performance of the inner-bags significantly differ from each other by statistical analysis. Lastly, a relational model between the sensing data and the pear outlook quality is established by use of Structural Equation Model (SEM). Here, the pear outlook quality is related with existence of stain, blob, scratch, and so on caused by physiological impair or diseases. Conceptually SEM is a combination of exploratory factor analysis and multiple regression. By using SEM, a model is constructed to connect independent and dependent variables. The proposed SEM model relates the measured sensing data and the pear outlook quality determined on the basis of farmer judgement. In particularly, it is found that the inner-bag humidity variable relatively affects the pear outlook quality. Therefore, inner-bag humidity sensing might help the farmers to control the pear outlook quality. These results are supported by a large quantity of inner-bag humidity data measured over the years 2014, 2015, and 2016. The experimental and analytical results in this research contribute to spreading Precision Agriculture technologies among the farmers growing ’Le Lectier’.

Keywords: precision agriculture, pre-harvest bagging, sensor fusion, structural equation model

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Authors: Chandra Prayaga, Aaron Wade, Lakshmi Prayaga, Gopi Shankar Mallu

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This paper presents the use of machine learning algorithms to predict the success of students in an introductory physics course. Data having 140 rows pertaining to the performance of two batches of students was used. The lack of sufficient data to train robust machine learning models was compensated for by generating synthetic data similar to the real data. CTGAN and CTGAN with Gaussian Copula (Gaussian) were used to generate synthetic data, with the real data as input. To check the similarity between the real data and each synthetic dataset, pair plots were made. The synthetic data was used to train machine learning models using the PyCaret package. For the CTGAN data, the Ada Boost Classifier (ADA) was found to be the ML model with the best fit, whereas the CTGAN with Gaussian Copula yielded Logistic Regression (LR) as the best model. Both models were then tested for accuracy with the real data. ROC-AUC analysis was performed for all the ten classes of the target variable (Grades A, A-, B+, B, B-, C+, C, C-, D, F). The ADA model with CTGAN data showed a mean AUC score of 0.4377, but the LR model with the Gaussian data showed a mean AUC score of 0.6149. ROC-AUC plots were obtained for each Grade value separately. The LR model with Gaussian data showed consistently better AUC scores compared to the ADA model with CTGAN data, except in two cases of the Grade value, C- and A-.

Keywords: machine learning, student success, physics course, grades, synthetic data, CTGAN, gaussian copula CTGAN

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Authors: Pedro F. D. Oliveira, Rangel S. Maia, Aline S. Paula

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As the electric energy consumption grows, the necessity of energy transmission lines increases. One of the problems caused by an oscillatory response to dynamical loads (such as wind effects) in transmission lines is the cable fatigue. Thus, the dynamical behavior of transmission cables understanding and its control is extremely important. The socioeconomic damage caused by a failure in these cables can be quite significant, from large economic losses to energy supply interruption in large regions. Dynamic Vibration Absorbers (DVA) are oscillatory elements used to mitigate the vibration of a primary system subjected to harmonic excitation. The positioning of Stockbridge (DVA for overhead transmission lines) plays an important role in mitigating oscillations of transmission lines caused by airflows. Nowadays, the positioning is defined by technical standards or commercial software. The aim of this paper is to conduct an analysis of multiple DVAs performances in cable conductors of overhead transmission lines. The cable is analyzed by a finite element method and the model is calibrated by experimental results. DVAs performance is analyzed by evaluating total cable energy, and a study of multiple DVAs positioning is conducted. The results are compared to the existing regulations showing situations where proper positioning, different from the standard, can lead to better performance of the DVA. Results also show situations where the use of multiple DVAs is appropriate.

Keywords: dynamical vibration absorber, finite element method, overhead transmission lines, structural dynamics

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Authors: Javad Izadi, Sahar Majioud

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This study presents an approach of assessing the choice of performance measures of companies in the United Kingdom after the application of IFRS in 2005. The aim of this study is to investigate the effects of IFRS on the choice of performance evaluation methods for UK companies. We analyse through an econometric model the relationship of the dependent variable, the firm’s performance, which is a nominal variable with the independent ones. Independent variables are split into two main groups: the first one is the group of accounting-based measures: Earning per share, return on assets and return on equities. The second one is the group of market-based measures: market value of property plant and equipment, research and development, sales growth, market to book value, leverage, segment and size of companies. Concerning the regression used, it is a multinomial logistic regression performed on a sample of 130 UK listed companies. Our finding shows after IFRS adoption, and companies give more importance to some variables such as return on equities and sales growth to assess their performance, whereas the return on assets and market to book value ratio does not have as much importance as before IFRS in evaluating the performance of companies. Also, there are some variables that have no impact on the performance measures anymore, such as earning per share. This article finding is empirically important for business in subjects related to IFRS and companies’ performance measurement.

Keywords: performance’s Measure, nominal variable, econometric model, evaluation methods

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Authors: Aminu Hamza

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This study examines the factors that play vital role in the adoption of mobile payment system among consumers in Nigeria. Technology Acceptance Model (TAM) was used with two additional variables to form the conceptual model. The study was conducted in three Universities in Kano state, Nigeria. Convenience sampling method was used with a total valid 202 respondents which involved the students of Bayero University Kano (BUK), Northwest University, and Kano University of Science and Technology (KUST) Wudil, Kano, Nigeria. Results of the regression analysis revealed that Perceived ease of use (PEOU) and Perceived usefulness (PU) have significant and positive correlation with the behavioral intention to adopt mobile payment system. The findings of this study would be useful to the policy makers Central Bank of Nigeria (CBN), mobile network operators and providers of the services.

Keywords: mobile payment system, Nigeria, technology adoption, technology acceptance model

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Authors: Man-Yu Wong, Siyu Zhou, Zhiqiang Cao

Abstract:

In quality of life, health service utilization is an important determinant of medical resource expenditures on Colorectal cancer (CRC) care, a better understanding of the increased utilization of health services is essential for optimizing the allocation of healthcare resources to services and thus for enhancing the service quality, especially for high expenditure on CRC care like Hong Kong region. In assessing the association between the health-related quality of life (HRQOL) and health service utilization in patients with colorectal neoplasm, count data models can be used, which account for over dispersion or extra zero counts. In our data, the HRQOL evaluation is a self-reported measure obtained from a questionnaire completed by the patients, misreports and variations in the data are inevitable. Besides, there are more zero counts from the observed number of clinical consultations (observed frequency of zero counts = 206) than those from a Poisson distribution with mean equal to 1.33 (expected frequency of zero counts = 156). This suggests that excess of zero counts may exist. Therefore, we study tests for detecting zero-inflation in models with measurement error in covariates. Method: Under classical measurement error model, the approximate likelihood function for zero-inflation Poisson regression model can be obtained, then Approximate Maximum Likelihood Estimation(AMLE) can be derived accordingly, which is consistent and asymptotically normally distributed. By calculating score function and Fisher information based on AMLE, a score test is proposed to detect zero-inflation effect in ZIP model with measurement error. The proposed test follows asymptotically standard normal distribution under H0, and it is consistent with the test proposed for zero-inflation effect when there is no measurement error. Results: Simulation results show that empirical power of our proposed test is the highest among existing tests for zero-inflation in ZIP model with measurement error. In real data analysis, with or without considering measurement error in covariates, existing tests, and our proposed test all imply H0 should be rejected with P-value less than 0.001, i.e., zero-inflation effect is very significant, ZIP model is superior to Poisson model for analyzing this data. However, if measurement error in covariates is not considered, only one covariate is significant; if measurement error in covariates is considered, only another covariate is significant. Moreover, the direction of coefficient estimations for these two covariates is different in ZIP regression model with or without considering measurement error. Conclusion: In our study, compared to Poisson model, ZIP model should be chosen when assessing the association between condition-specific HRQOL and health service utilization in patients with colorectal neoplasm. and models taking measurement error into account will result in statistically more reliable and precise information.

Keywords: count data, measurement error, score test, zero inflation

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Authors: James Kinsella

Abstract:

There is currently very little understanding of how the interaction between in- vestors, consumers, the firms (agents) affect a) the transmission of information, and b) the creation and transfer of value and wealth between these two groups. Employing scholarly ideas from multiple research areas (behavioural finance, emotional finance, econo-biology, and game theory) we develop a conceptual the- oretic model (the ‘bow-tie’ model) as a framework for considering this interaction. Our bow-tie model views information transfer, value and wealth creation, and transfer through the lens of “investor-consumer connection facilitated through the communicative medium of the ‘firm’ (agents)”. We confront our bow-tie model with theoretical and practical examples. Next, we utilise consumer and business confidence data alongside index data, to conduct quantitative analy- sis, to support our bow-tie concept, and to introduce the concept of “investor- consumer connection”. We highlight the importance of information persuasiveness, knowledge, and emotional categorization of characteristics in facilitating a communicative relationship between investors, consumers, and the firm (agents), forming academic and practical applications of the conceptual bow-tie model, alongside applications to wider instances, such as those seen within the Covid-19 pandemic.

Keywords: behavioral finance, emotional finance, economy-biology, social mood

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Authors: Sara El Mansouria Beghdadi

Abstract:

Quantitative structure–activity relationship (QSAR) modelling is one of the main computer tools used in medicinal chemistry. Over the past two decades, the incidence of fungal infections has increased due to the development of resistance. In this study, the QSAR was performed on a series of esters of 2-carboxamido-3-(1H-imidazole-1-yl) propanoic acid derivatives. These compounds have showed moderate and very good antifungal activity. The multiple linear regression (MLR) was used to generate the linear 2d-QSAR models. The dataset consists of 115 compounds with their antifungal activity (log MIC) against «Candida albicans» (ATCC SC5314). Descriptors were calculated, and different models were generated using Chemoffice, Avogadro, GaussView software. The selected model was validated. The study suggests that the increase in lipophilicity and the reduction in the electronic character of the substituent in R1, as well as the reduction in the steric hindrance of the substituent in R2 and its aromatic character, supporting the potentiation of the antifungal effect. The results of QSAR could help scientists to propose new compounds with higher antifungal activities intended for immunocompromised patients susceptible to multi-resistant nosocomial infections.

Keywords: quantitative structure–activity relationship, imidazole, antifungal, candida albicans (ATCC SC5314)

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Authors: Naomi W. Maina

Abstract:

Fertility preference is a subject of great significance in developing countries. Studies reveal that the preferences of fertility are actually significant in determining the society’s fertility levels because the fertility behavior of the future has a high likelihood of falling under the effect of currently observed fertility inclinations. The objective of this study was to establish the factors associated with fertility preference amongst couples in Kenya by fitting a multinomial logistic regression model against 5,265 couple data obtained from Kenya demographic health survey 2014. Results revealed that the type of place of residence, the region of residence, age and spousal age gap significantly influence desire for additional children among couples in Kenya. There was the notable high likelihood of couples living in rural settlements having similar fertility preference compared to those living in urban settlements. Moreover, geographical disparities such as in northern Kenya revealed significant differences in a couples desire to have additional children compared to Nairobi. The odds of a couple’s desire for additional children were further observed to vary dependent on either the wife or husbands age and to a large extent the spousal age gap. Evidenced from the study, was the fact that as spousal age gap increases, the desire for more children amongst couples decreases. Insights derived from this study would be attractive to demographers, health practitioners, policymakers, and non-governmental organizations implementing fertility related interventions in Kenya among other stakeholders. Moreover, with the adoption of devolution, there is a clear need for adoption of population policies that are County specific as opposed to a national population policy as is the current practice in Kenya. Additionally, researchers or students who have little understanding in the application of multinomial logistic regression, both theoretical understanding and practical analysis in SPSS as well as application on real datasets, will find this article useful.

Keywords: couples' desire, fertility, fertility preference, multinomial regression analysis

Procedia PDF Downloads 170