Search results for: statistical machine translation

Authors: Michael Stewart

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Mutation testing is a type of software testing proposed in the 1970s where program statements are deliberately changed to introduce simple errors so that test cases can be validated to determine if they can detect the errors. Test cases are executed against the mutant code to determine if one fails, detects the error and ensures the program is correct. One major issue with this type of testing was it became intensive computationally to generate and test all possible mutations for complex programs. This paper used reinforcement learning and parallel processing within the context of mutation testing for the selection of mutation operators and test cases that reduced the computational cost of testing and improved test suite effectiveness. Experiments were conducted using sample programs to determine how well the reinforcement learning-based algorithm performed with one live mutation, multiple live mutations and no live mutations. The experiments, measured by mutation score, were used to update the algorithm and improved accuracy for predictions. The performance was then evaluated on multiple processor computers. With reinforcement learning, the mutation operators utilized were reduced by 50 – 100%.

Keywords: automated-testing, machine learning, mutation testing, parallel processing, reinforcement learning, software engineering, software testing

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Authors: Ibrahim Khan, Waqas Khalid

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The Healthcare sector is known to consume a higher proportion of total energy consumption in the HVAC market owing to an excessive cooling and heating requirement in maintaining human thermal comfort in indoor conditions, catering to patients undergoing treatment in hospital wards, rooms, and intensive care units. The indoor thermal comfort conditions in selected hospitals of Islamabad, Pakistan, were measured on a real-time basis with the collection of first-hand experimental data using calibrated sensors measuring Ambient Temperature, Wet Bulb Globe Temperature, Relative Humidity, Air Velocity, Light Intensity and CO2 levels. The Experimental data recorded was analyzed in conjunction with the Thermal Comfort Questionnaire Surveys, where the participants, including patients, doctors, nurses, and hospital staff, were assessed based on their thermal sensation, acceptability, preference, and comfort responses. The Recorded Dataset, including experimental and survey-based responses, was further analyzed in the development of a correlation between operative temperature, operative relative humidity, and other measured operative parameters with the predicted mean vote and adaptive predicted mean vote, with the adaptive temperature and adaptive relative humidity estimated using the seasonal data set gathered for both summer – hot and dry, and hot and humid as well as winter – cold and dry, and cold and humid climate conditions. The Machine Learning Logistic Regression Algorithm was incorporated to train the operative experimental data parameters and develop a correlation between patient sensations and the thermal environmental parameters for which a new ML-based adaptive thermal comfort model was proposed and developed in our study. Finally, the accuracy of our model was determined using the K-fold cross-validation.

Keywords: predicted mean vote, thermal comfort, energy management, logistic regression, machine learning

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Authors: Simon Mauma Efange

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The main aims of this research were to describe, in co-relational terms, the relationship, if any, between reading comprehension and academic achievement in science studied at the secondary level and, secondly, to find out possible trends in gender differences, such as whether boys would perform better than girls or vice versa. This research employed a quantitative design. Two kinds of instruments were employed: the Oxford Online Placement Test and the Local Assessment System Test. The Oxford Online Placement Test assesses students' English level quickly and easily. The results of these tests were subjected to statistical analysis using a special statistical software called SPSS. Statistical tools such as mean, standard deviation, percentages, frequencies, t-tests, and Pearson’s coefficient of correlation were used for the analysis of the results. Results of the t-test showed that the means are significantly different. Calculating the p-value revealed that the results were extremely statistically significant at p <.05. The value of r (Pearson correlation coefficient) was 0.2868. Although technically there is a positive correlation, the relationship between the variables is only weak (the closer the value is to zero, the weaker the relationship). However, in conclusion, calculations from the t-test using SPSS revealed that the results were statistically significant at p <.05, confirming a relationship between the two variables, and high scores in reading will give rise to slightly high scores in science. The research also revealed that having a high score in reading comprehension doesn’t necessarily mean having a high score in science or vice versa. Female subjects performed much better than male subjects in both tests, which is in line with the literature reviewed for this research.

Keywords: achievement in science, achievement in English, and bilingual students, relationship

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Authors: Bassem Nassar, Wessam Hussein, Medhat Mokhtar

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The crucial concern of satellite operations is to ensure the health and safety of satellites. The worst case in this perspective is probably the loss of a mission but the more common interruption of satellite functionality can result in compromised mission objectives. All the data acquiring from the spacecraft are known as Telemetry (TM), which contains the wealth information related to the health of all its subsystems. Each single item of information is contained in a telemetry parameter, which represents a time-variant property (i.e. a status or a measurement) to be checked. As a consequence, there is a continuous improvement of TM monitoring systems in order to reduce the time required to respond to changes in a satellite's state of health. A fast conception of the current state of the satellite is thus very important in order to respond to occurring failures. Statistical multivariate latent techniques are one of the vital learning tools that are used to tackle the aforementioned problem coherently. Information extraction from such rich data sources using advanced statistical methodologies is a challenging task due to the massive volume of data. To solve this problem, in this paper, we present a proposed unsupervised learning algorithm based on Principle Component Analysis (PCA) technique. The algorithm is particularly applied on an actual remote sensing spacecraft. Data from the Attitude Determination and Control System (ADCS) was acquired under two operation conditions: normal and faulty states. The models were built and tested under these conditions and the results shows that the algorithm could successfully differentiate between these operations conditions. Furthermore, the algorithm provides competent information in prediction as well as adding more insight and physical interpretation to the ADCS operation.

Keywords: space telemetry monitoring, multivariate analysis, PCA algorithm, space operations

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Authors: Till Gramberg

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In the dynamic world of machine and plant engineering, stagnation in the growth of new product sales compels companies to reconsider their business models. The increasing shift toward service orientation, known as "servitization," along with challenges posed by digitalization and sustainability, necessitates an adaptation of product portfolio management (PPM). Against this backdrop, this study investigates the current challenges and requirements of PPM in this industrial context and develops a framework for the application of generative artificial intelligence (AI) to enhance agility and efficiency in PPM processes. The research approach of this study is based on a mixed-method design. Initially, qualitative interviews with industry experts were conducted to gain a deep understanding of the specific challenges and requirements in PPM. These interviews were analyzed using the Gioia method, painting a detailed picture of the existing issues and needs within the sector. This was complemented by a quantitative online survey. The combination of qualitative and quantitative research enabled a comprehensive understanding of the current challenges in the practical application of machine and plant engineering PPM. Based on these insights, a specific framework for the application of generative AI in PPM was developed. This framework aims to assist companies in implementing faster and more agile processes, systematically integrating dynamic requirements from trends such as digitalization and sustainability into their PPM process. Utilizing generative AI technologies, companies can more quickly identify and respond to trends and market changes, allowing for a more efficient and targeted adaptation of the product portfolio. The study emphasizes the importance of an agile and reactive approach to PPM in a rapidly changing environment. It demonstrates how generative AI can serve as a powerful tool to manage the complexity of a diversified and continually evolving product portfolio. The developed framework offers practical guidelines and strategies for companies to improve their PPM processes by leveraging the latest technological advancements while maintaining ecological and social responsibility. This paper significantly contributes to deepening the understanding of the application of generative AI in PPM and provides a framework for companies to manage their product portfolios more effectively and adapt to changing market conditions. The findings underscore the relevance of continuous adaptation and innovation in PPM strategies and demonstrate the potential of generative AI for proactive and future-oriented business management.

Keywords: servitization, product portfolio management, generative AI, disruptive innovation, machine and plant engineering

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Authors: Sara Fioretti

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During the Middle Bronze Age, southern Italy underwent a reorganisation of social structures where local cultures, such as the sub-Apennine and Nuragic, flourished and participated in maritime trade. This paper explores the socio-economic relationships, in both cross-cultural and potentially inter-regional settings, present within the archaeological repertoire of the southern Italian Late Bronze Age (LBA 1600 -1050 BCE). The emergence of economic relations within the connectivity of the regional settlements is explored through ceramic contexts found in the case studies Punta di Zambrone, Broglio di Trebisacce, and Nuraghe Antigori. This paper discusses the findings of a statistical and theoretical approach from an ongoing study in relation to the Mediterranean’s characterisation as a period dominated by Mycenaean influence. This study engages with a theoretical bricolage of Social Networks Entanglement, and Assertive Objects Theory to address the selective and assertive dynamics evident in the cross-cultural trade exchanges as well as consider inter-regional dynamics. Through this intersection of theory and statistical analysis, the case studies establish a small percentage of pottery as imported, whilst assertive productions have a relatively higher quantity. Overall, the majority still adheres to regional Italian traditions. Therefore, we can dissect the rhizomatic relationships cultivated by the Italian coasts and Mycenaeans and their roles within their networks through the intersection of theoretical and statistical analysis. This research offers a new perspective on the complex nature of the Late Bronze Age relational structures.

Keywords: late bronze age, mediterranean archaeology, exchanges and trade, frequency distribution of ceramic assemblages, social network theory, rhizomatic exchanges

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Authors: Kateriina Rannula, Elle Sõrmus, Siret Piirsalu

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Mixed generation classroom is a phenomenon that current higher education establishments are faced with daily trying to meet the needs of modern labor market with its emphasis on lifelong learning and retraining. Representatives of mainly X and Y generations in one classroom acquiring higher education is a challenge to lecturers considering all the characteristics that differ one generation from another. The importance of outlining different strategies and considering the needs of the students lies in the necessity for everyone to acquire the maximum of the provided knowledge as well as to understand each other to study together in one classroom and successfully cooperate in future workplaces. In addition to different generations, there are also learners with different native languages which have an impact on reading and understanding texts in third languages, including possible translation. Current research aims to investigate, describe and compare reading strategies among the representatives of generation X and Y. Hypotheses were formulated - representatives of generation X and Y use different reading strategies which is also different among first and third year students of the before mentioned generations. Current study is an empirical, qualitative study. To achieve the aim of the research, relevant literature was analyzed and a semi-structured questionnaire conducted among the first and third year students of Tallinn Health Care College. Questionnaire consisted of 25 statements on the text reading strategies, 3 multiple choice questions on preferences considering the design and medium of the text, and three open questions on the translation process when working with a text in student’s third language. The results of the questionnaire were categorized, analyzed and compared. Both, generation X and Y described their reading strategies to be 'scanning' and 'surfing'. Compared to generation X, first year generation Y learners valued interactivity and nonlinear texts. Students frequently used strategies of skimming, scanning, translating and highlighting together with relevant-thinking and assistance-seeking. Meanwhile, the third-year generation Y students no longer frequently used translating, resourcing and highlighting while Generation X learners still incorporated these strategies. Knowing about different needs of the generations currently inside the classrooms and on the labor market enables us with tools to provide sustainable education and grants the society a work force that is more flexible and able to move between professions. Future research should be conducted in order to investigate the amount of learning and strategy- adoption between generations. As for reading, main suggestions arising from the research are as follows: make a variety of materials available to students; allow them to select what they want to read and try to make those materials visually attractive, relevant, and appropriately challenging for learners considering the differences of generations.

Keywords: generation X, generation Y, learning strategies, reading strategies

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Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: Weiwei Cui, Dejian Lin, Leigang Zhang, Yao Wang, Zheng Sun, Lianfeng Li

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Built-in test (BIT) is an important technology in testability field, and it is widely used in state monitoring and fault diagnosis. With the improvement of modern equipment performance and complexity, the scope of BIT becomes larger, and it leads to the emergence of false alarm problem. The false alarm makes the health assessment unstable, and it reduces the effectiveness of BIT. The conventional false alarm suppression methods such as repeated test and majority voting cannot meet the requirement for a complicated system, and the intelligence algorithms such as artificial neural networks (ANN) are widely studied and used. However, false alarm has a very low frequency and small sample, yet a method based on ANN requires a large size of training sample. To recognize the false alarm, we propose a method based on multi-classification support vector machine (SVM) in this paper. Firstly, we divide the state of a system into three states: healthy, false-alarm, and faulty. Then we use multi-classification with '1 vs 1' policy to train and recognize the state of a system. Finally, an example of fault injection system is taken to verify the effectiveness of the proposed method by comparing ANN. The result shows that the method is reasonable and effective.

Keywords: false alarm, fault diagnosis, SVM, k-means, BIT

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Authors: Sergio Paez Moncaleano, Alvaro Mauricio Montenegro

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This paper presents a novel statistical methodology for measuring and founding constructs in Latent Regression Analysis. This approach uses the qualities of Factor Analysis in binary data with interpretations on Item Response Theory (IRT). In addition, based on the fundamentals of submodel theory and with a convergence of many ideas of IRT, we propose an algorithm not just to solve the dimensionality problem (nowadays an open discussion) but a new research field that promises more fear and realistic qualifications for examiners and a revolution on IRT and educational research. In the end, the methodology is applied to a set of real data set presenting impressive results for the coherence, speed and precision. Acknowledgments: This research was financed by Colciencias through the project: 'Multidimensional Item Response Theory Models for Practical Application in Large Test Designed to Measure Multiple Constructs' and both authors belong to SICS Research Group from Universidad Nacional de Colombia.

Keywords: item response theory, dimensionality, submodel theory, factorial analysis

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Authors: Harini Chakkera

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Background: Inpatient hyperglycemia is an established independent risk factor among several patient cohorts with hospital readmission. This has not been studied after kidney transplantation. Nearly one-third of patients who have undergone a kidney transplant reportedly experience 30-day readmission. Methods: Data on first-time solitary kidney transplantations were retrieved between September 2015 to December 2018. Information was linked to the electronic health record to determine a diagnosis of diabetes mellitus and extract glucometeric and insulin therapy data. Univariate logistic regression analysis and the XGBoost algorithm were used to predict 30-day readmission. We report the average performance of the models on the testing set on five bootstrapped partitions of the data to ensure statistical significance. Results: The cohort included 1036 patients who received kidney transplantation, and 224 (22%) experienced 30-day readmission. The machine learning algorithm was able to predict 30-day readmission with an average AUC of 77.3% (95% CI 75.30-79.3%). We observed statistically significant differences in the presence of pretransplant diabetes, inpatient-hyperglycemia, inpatient-hypoglycemia, and minimum and maximum glucose values among those with higher 30-day readmission rates. The XGBoost model identified the index admission length of stay, presence of hyper- and hypoglycemia and recipient and donor BMI values as the most predictive risk factors of 30-day readmission. Additionally, significant variations in the therapeutic management of blood glucose by providers were observed. Conclusions: Suboptimal glucose metrics during hospitalization after kidney transplantation is associated with an increased risk for 30-day hospital readmission. Optimizing the hospital blood glucose management, a modifiable factor, after kidney transplantation may reduce the risk of 30-day readmission.

Keywords: kidney, transplant, diabetes, insulin

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Authors: Jincan Li, Mingyu Gao, Zhiwei He, Yuxiang Yang, Zhongfei Yu, Yuanyuan Liu

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Building an appropriate motion model is crucial for trajectory planning of robots and determines the operational quality directly. An adaptive acceleration and deceleration motion planning based on trigonometric functions for the end-effector of 6-DOF robots in Cartesian coordinate system is proposed in this paper. This method not only achieves the smooth translation motion and rotation motion by constructing a continuous jerk model, but also automatically adjusts the parameters of trigonometric functions according to the variable inputs and the kinematic constraints. The results of computer simulation show that this method is correct and effective to achieve the adaptive motion planning for linear trajectories.

Keywords: kinematic constraints, motion planning, trigonometric function, 6-DOF robots

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Authors: R. Ramesh, K. K. Shivaraman

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The main aim of this paper is to handle the energy requirement in an efficient manner by merging the advanced digital communication and control technologies for smart grid applications. In order to reduce user home load during peak load hours, utility applies several incentives such as real-time pricing, time of use, demand response for residential customer through smart meter. However, this method provides inconvenience in the sense that user needs to respond manually to prices that vary in real time. To overcome these inconvenience, this paper proposes a convolutional neural network (CNN) with k-means clustering machine learning model which have ability to forecast energy requirement in short term, i.e., hour of the day or day of the week. By integrating our proposed technique with home energy management based on Bluetooth low energy provides predicted value to user for scheduling appliance in advanced. This paper describes detail about CNN configuration and k-means clustering algorithm for short-term energy forecasting.

Keywords: convolutional neural network, fuzzy logic, k-means clustering approach, smart home energy management

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Authors: Larry Turkish

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Introduction: The NBA is an example of mediocrity and this will be evident in the following paper. The study examines and evaluates the characteristics of the NBA champions. As divisions and playoff teams increase, there is an increase in the probability that the champion originates from the mediocre category. Since it’s inception in 1947, the league has been mediocre and continues to this day. Why does a professional league allow any team with a less than 50% winning percentage into the playoffs? As long as the finances flow into the league, owners will not change the current algorithm. The objective of this paper is to determine if the regular season has meaning in finding an NBA champion. Statistical Analysis: The data originates from the NBA website. The following variables are part of the statistical analysis: Rank, the rank of a team relative to other teams in the league based on the regular season win-loss record; Winning Percentage of a team based on the regular season; Divisions, the number of divisions within the league and Playoff Teams, the number of playoff teams relative to a particular season. The following statistical applications are applied to the data: Pearson Product-Moment Correlation, Analysis of Variance, Factor and Regression analysis. Conclusion: The results indicate that the divisional structure and number of playoff teams results in a negative effect on the winning percentage of playoff teams. It also prevents teams with higher winning percentages from accessing the playoffs. Recommendations: 1. Teams that have a winning percentage greater than 1 standard deviation from the mean from the regular season will have access to playoffs. (Eliminates mediocre teams.) 2. Eliminate Divisions (Eliminates weaker teams from access to playoffs.) 3. Eliminate Conferences (Eliminates weaker teams from access to the playoffs.) 4. Have a balanced regular season schedule, (Reduces the number of regular season games, creates equilibrium, reduces bias) that will reduce the need for load management.

Keywords: alignment, mediocrity, regression, z-score

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Authors: Haikal Achmad Thaha, Pujo Laksono, Dhamma Nibbana Putra

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Customer service is one of the core interest in a service sector of a company, whether as the core business or as service part of the operation. Most of the time, the people and the previous research in service industry is focused on finding the best business model solution for the service sector, usually to decide between total in house customer service, outsourcing, or something in between. Conventionally, to take this decision is some important part of the management job, and this is a process that usually takes some time and staff effort, meanwhile market condition and overall company needs may change and cause loss of income and temporary disturbance in the companies operation . However, in this paper we have offer a new concept model to assist decision making process in service industry. This model will featured information platform as central tool to integrate service industry operation. The result is service information model which would ideally increase response time and effectivity of the decision making. it will also help service industry in switching the service solution system quickly through machine learning when the companies growth and the service solution needed are changing.

Keywords: service industry, customer service, machine learning, decision making, information platform

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Authors: Akshay B. Pawar, Rohit Y. Parasnis

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Heart Rate Variability (HRV) is a widely used indicator of the regulation between the autonomic nervous system (ANS) and the cardiovascular system. Besides being non-invasive, it also has the potential to predict mortality in cases involving critical injuries. The gold standard method for determining HRV is based on the analysis of RR interval time series extracted from ECG signals. However, because it is much more convenient to obtain photoplethysmogramic (PPG) signals as compared to ECG signals (which require the attachment of several electrodes to the body), many researchers have used pulse cycle intervals instead of RR intervals to estimate HRV. They have also compared this method with the gold standard technique. Though most of their observations indicate a strong correlation between the two methods, recent studies show that in healthy subjects, except for a few parameters, the pulse-based method cannot be a surrogate for the standard RR interval- based method. Moreover, the former tends to overestimate short-term variability in heart rate. This calls for improvements in or alternatives to the pulse-cycle interval method. In this study, besides the systolic peak-peak interval method (PP method) that has been studied several times, four recent PPG-based techniques, namely the first derivative peak-peak interval method (P1D method), the second derivative peak-peak interval method (P2D method), the valley-valley interval method (VV method) and the tangent-intersection interval method (TI method) were compared with the gold standard technique. ECG and PPG signals were obtained from 10 young and healthy adults (consisting of both males and females) seated in the armchair position. In order to de-noise these signals and eliminate baseline drift, they were passed through certain digital filters. After filtering, the following HRV parameters were computed from PPG using each of the five methods and also from ECG using the gold standard method: time domain parameters (SDNN, pNN50 and RMSSD), frequency domain parameters (Very low-frequency power (VLF), Low-frequency power (LF), High-frequency power (HF) and Total power or “TP”). Besides, Poincaré plots were also plotted and their SD1/SD2 ratios determined. The resulting sets of parameters were compared with those yielded by the standard method using measures of statistical correlation (correlation coefficient) as well as statistical agreement (Bland-Altman plots). From the viewpoint of correlation, our results show that the best PPG-based methods for the determination of most parameters and Poincaré plots are the P2D method (shows more than 93% correlation with the standard method) and the PP method (mean correlation: 88%) whereas the TI, VV and P1D methods perform poorly (<70% correlation in most cases). However, our evaluation of statistical agreement using Bland-Altman plots shows that none of the five techniques agrees satisfactorily well with the gold standard method as far as time-domain parameters are concerned. In conclusion, excellent statistical correlation implies that certain PPG-based methods provide a good amount of information on the pattern of heart rate variation, whereas poor statistical agreement implies that PPG cannot completely replace ECG in the determination of HRV.

Keywords: photoplethysmography, heart rate variability, correlation coefficient, Bland-Altman plot

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Authors: Ayu Nabila Kusuma Pradana, Aprinaldi Jasa Mantau, Tomohiko Akahoshi

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The incidence of Disseminated intravascular coagulation (DIC) following gastrointestinal surgery has a poor prognosis. Therefore, it is important to determine the factors that can predict the prognosis of DIC. This study will investigate the factors that may influence the outcome of DIC in patients after gastrointestinal surgery. Eighty-one patients were admitted to the intensive care unit after gastrointestinal surgery in Kyushu University Hospital from 2003 to 2021. Acute DIC scores were estimated using the new Japanese Association for Acute Medicine (JAAM) score from before and after surgery from day 1, day 3, and day 7. Acute DIC scores will be compared with The Sequential Organ Failure Assessment (SOFA) score, platelet count, lactate level, and a variety of biochemical parameters. This study applied machine learning algorithms to predict the prognosis of DIC after gastrointestinal surgery. The results of this study are expected to be used as an indicator for evaluating patient prognosis so that it can increase life expectancy and reduce mortality from cases of DIC patients after gastrointestinal surgery.

Keywords: the survival rate, gastrointestinal surgery, JAAM score, neural network, machine learning, disseminated intravascular coagulation (DIC)

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Authors: Farnoush Haghanipour

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Recent research studies relation between coping strategies with stress and mental health situation in flying addicted family of self-introducer and private, Units of Guilan province. For this purpose 251 family (parent, spouse), that referred to private and self-introducer centers to break out of drug are selected in random sampling form. Research method was cross sectional-descriptive and purpose of research was fixing of between kinds of coping strategies with stress and mental health condition with attention to demographic variables. Therefore to collection of information, coping strategies questionnaire (CSQ) and mental health questionnaire (GHQ) was used and finally data analyzed by descriptive statistical methods (average, standard deviation) and inferential statistical correlation coefficient and regression. Study of correlation coefficient between mental healths with problem focused emotional focused and detachment strategies in level more than %99 is confirmed. Also mental health with avoidant focused hasn't correlation in other words relation is between mental health with problem focused strategies (r= 0/34) and emotional focused with mental health (r=0.52) and detachment with mental health (r= 0.18) in meaningful level 0.05. And also relation is between emotional focused strategies and mental health (r= 0.034) that is meaningless in Alpha 0.05. Also relation between problem processed coping strategies and mental health situation with attention to demographic variable is meaningful and relation level verified in confidence level more than 0.99. And result of anticipation equation regression statistical test has most a have in problem focused coping strategy, mental health, but relation of the avoidant emotional, detachment strategy with mental health was meaningless with attention to demographic variables.

Keywords: stress, coping strategy with stress, mental health, self introducer and private

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Authors: Raquel P. F. Guiné, Susana R. Matos, Daniela V. T. A. Costa, Fernando J. Gonçalves

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Because blueberries are worldwide recognized as a good source of beneficial components, their consumption has increased in the past decades, and so have the scientific works about their properties. Hence this work was undertaken to evaluate the effect of some production and conservation factors on the properties of blueberries from cultivar Bluecrop. The physical and chemical analyses were done according to established methodologies and then all data was treated using software SPSS for assessment of the possible differences among the factors investigated and/or the correlations between the variables at study. The results showed that location of production influenced some of the berries properties (caliber, sugars, antioxidant activity, color and texture) and that the age of the bushes was correlated with moisture, sugars and acidity, as well as lightness. On the other hand, altitude of the farm only was correlated to sugar content. With regards to conservation, it influenced only anthocyanins content and DPPH antioxidant activity. Finally, the type of extract and the order of extraction had a pronounced influence on all the phnolic properties evaluated.

Keywords: Antioxidant activity, blueberry, conservation, geographical origin, phenolic compounds, statistical analysis

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Authors: Nida Rizvi, Deeksha Katyal, Varun Joshi

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River Hindon is an important river catering the demand of highly populated rural and industrial cluster of western Uttar Pradesh, India. Water quality of river Hindon is deteriorating at an alarming rate due to various industrial, municipal and agricultural activities. The present study aimed at identifying the pollution sources and quantifying the degree to which these sources are responsible for the deteriorating water quality of the river. Various water quality parameters, like pH, temperature, electrical conductivity, total dissolved solids, total hardness, calcium, chloride, nitrate, sulphate, biological oxygen demand, chemical oxygen demand and total alkalinity were assessed. Water quality data obtained from eight study sites for one year has been subjected to the two multivariate techniques, namely, principal component analysis and cluster analysis. Principal component analysis was applied with the aim to find out spatial variability and to identify the sources responsible for the water quality of the river. Three Varifactors were obtained after varimax rotation of initial principal components using principal component analysis. Cluster analysis was carried out to classify sampling stations of certain similarity, which grouped eight different sites into two clusters. The study reveals that the anthropogenic influence (municipal, industrial, waste water and agricultural runoff) was the major source of river water pollution. Thus, this study illustrates the utility of multivariate statistical techniques for analysis and elucidation of multifaceted data sets, recognition of pollution sources/factors and understanding temporal/spatial variations in water quality for effective river water quality management.

Keywords: cluster analysis, multivariate statistical techniques, river Hindon, water quality

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Authors: Tew Leh Ghee

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The study's goal is to determine how celebrity endorsement affects Malaysian consumers' attitudes and intentions to buy skincare products. Since customers now largely rely on celebrity endorsement to influence purchasing decisions in almost every business, celebrity endorsement is not, in reality, a new phenomenon. Even though the market for skincare products has a vast potential to be exploited, corporations have yet to seize this niche via celebrity endorsement. Basically, there hasn't been much study done to recognize the significance of celebrity endorsement in this industry. This research combined descriptive and quantitative methods with a self-administered survey as the primary data-gathering tool. All of the characteristics under study were measured using a 5-point Likert scale, and the questionnaire was written in English. A convenience sample method was used to choose respondents, and 360 sets of valid questionnaires were gathered for the study's statistical analysis. Preliminary statistical analyses were analyzed using SPSS version 20.0 (Statistical Package for the Social Sciences). The backdrop of the respondents' demographics was examined using descriptive analysis. All concept assessments' validity and reliability were examined using exploratory factor analysis, item-total statistics, and reliability statistics. Pearson correlation and regression analysis were used, respectively, to assess relationships and impacts between the variables under study. The research showed that, apart from competence, celebrity endorsements of skincare products in Malaysia had a favorable impact on attitudes and purchase intentions as evaluated by attractiveness and dependability. The research indicated that the most significant element influencing attitude and buy intention was the credibility of a celebrity endorsement. The study offered implications in order to provide potential improvements of celebrity endorsement in skincare goods in Malaysia. The study's last portion includes its limits and ideas for the future.

Keywords: trustworthiness, influential, phenomenon, celebrity emdorsement

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Authors: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong

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Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.

Keywords: data refinement, machine learning, mutual information, short-term latency prediction

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Authors: Rupali Satsangi

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14 years ago, leaders from every country agreed on a vision for the future – a world with less poverty, hunger and disease, greater survival prospects for mothers and their infants, better educated children, equal opportunities for women, and a healthier environment; a world in which developed and developing countries work in partnership for the betterment of all. This vision took the shape of eight Millennium Development Goals, which provide countries around the world a framework for development and time-bound targets by which progress can be measured. However, India has found 35 of the indicators as relevant to India. India’s MDG-framework has been contextualized through a concordance with the existing official indicators of corresponding dimensions in the national statistical system. The present study based on secondary data analyzed the status of India towards achieving the MDGs after reviewing the data study find out that India can miss the MDGs Bus in women health, sanitation and global partnership. These goals were less addressed by India in his policies and takeoffs.

Keywords: millennium development goals, national statistical system, global partnership, healthier environment

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Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125Hz to 8000Hz. The data contains patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R\textsuperscript{2} values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R\textsuperscript{2} values for the best models for KNN ranges from .89 to .95. The best imputation models received R\textsuperscript{2} between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our best imputation models versus constant imputations by a two percent increase.

Keywords: machine learning, audiograms, data imputations, single imputations

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Authors: Nasrine Olson

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With over 70,000 scholarly publications on the topic of biosensors, an overview of the field has become a challenge. To facilitate, there are currently over 700 expert-reviews of publications on biosensors and related topics. This study focuses on these review papers in order to provide a Meta-Review of the area. This paper provides a statistical analysis and overview of biosensor-related review papers. Comprehensive searches are conducted in the Web of Science, and PubMed databases and the resulting empirical material are analyzed using bibliometric methods and tools. The study finds that the biosensor-related review papers can be categorized in five related subgroups, broadly denoted by (i) properties of materials and particles, (ii) analysis and indicators, (iii) diagnostics, (iv) pollutant and analytical devices, and (v) treatment/ application. For an easy and clear access to the findings visualization of clusters and networks of connections are presented. The study includes a temporal dimension and identifies the trends over the years with an emphasis on the most recent developments. This paper provides useful insights for those who wish to form a better understanding of the research trends in the area of biosensors.

Keywords: bibliometrics, biosensors, meta-review, statistical analysis, trends visualization

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Authors: Swarali Hingse, Shraddha Digole, Uday Annapure

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The present study investigates the biotransformation of ferulic acid to vanillin by Pycnoporus cinnabarinus and its optimization using one-factor-at-a-time method as well as statistical approach. Effect of various physicochemical parameters and medium components was studied using one-factor-at-a-time method. Screening of the significant factors was carried out using L25 Taguchi orthogonal array and then these selected significant factors were further optimized using response surface methodology (RSM). Significant media components obtained using Taguchi L25 orthogonal array were glucose, KH2PO4 and yeast extract. Further, a Box Behnken design was used to investigate the interactive effects of the three most significant media components. The final medium obtained after optimization using RSM containing glucose (34.89 g/L), diammonium tartrate (1 g/L), yeast extract (1.47 g/L), MgSO4•7H2O (0.5 g/L), KH2PO4 (0.15 g/L), and CaCl2•2H2O (20 mg/L) resulted in amplification of vanillin production from 30.88 mg/L to 187.63 mg/L.

Keywords: ferulic acid, pycnoporus cinnabarinus, response surface methodology, vanillin

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Authors: Samar Swaid

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Today, corporates are moving toward the adoption of Design-Thinking techniques to develop products and services, putting their consumer as the heart of the development process. One of the leading companies in Design-Thinking, IDEO (Innovation, Design, Engineering Organization), defines Design-Thinking as an approach to problem-solving that relies on a set of multi-layered skills, processes, and mindsets that help people generate novel solutions to problems. Design thinking may result in new ideas, narratives, objects or systems. It is about redesigning systems, organizations, infrastructures, processes, and solutions in an innovative fashion based on the users' feedback. Tim Brown, president and CEO of IDEO, sees design thinking as a human-centered approach that draws from the designer's toolkit to integrate people's needs, innovative technologies, and business requirements. The application of design thinking has been witnessed to be the road to developing innovative applications, interactive systems, scientific software, healthcare application, and even to utilizing Design-Thinking to re-think business operations, as in the case of Airbnb. Recently, there has been a movement to apply design thinking to machine learning and artificial intelligence to ensure creating the "wow" effect on consumers. The Association of Computing Machinery task force on Data Science program states that" Data scientists should be able to implement and understand algorithms for data collection and analysis. They should understand the time and space considerations of algorithms. They should follow good design principles developing software, understanding the importance of those principles for testability and maintainability" However, this definition hides the user behind the machine who works on data preparation, algorithm selection and model interpretation. Thus, the Data Science program includes design thinking to ensure meeting the user demands, generating more usable machine learning tools, and developing ways of framing computational thinking. Here, describe the fundamentals of Design-Thinking and teaching modules for data science programs.

Keywords: data science, design thinking, AI, currculum, transformation

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Authors: H. Abas Nejad, A. R. Teymoori

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Facial expression recognition is one of the open problems in computer vision. Robust neutral face recognition in real time is a major challenge for various supervised learning based facial expression recognition methods. This is due to the fact that supervised methods cannot accommodate all appearance variability across the faces with respect to race, pose, lighting, facial biases, etc. in the limited amount of training data. Moreover, processing each and every frame to classify emotions is not required, as the user stays neutral for the majority of the time in usual applications like video chat or photo album/web browsing. Detecting neutral state at an early stage, thereby bypassing those frames from emotion classification would save the computational power. In this work, we propose a light-weight neutral vs. emotion classification engine, which acts as a preprocessor to the traditional supervised emotion classification approaches. It dynamically learns neutral appearance at Key Emotion (KE) points using a textural statistical model, constructed by a set of reference neutral frames for each user. The proposed method is made robust to various types of user head motions by accounting for affine distortions based on a textural statistical model. Robustness to dynamic shift of KE points is achieved by evaluating the similarities on a subset of neighborhood patches around each KE point using the prior information regarding the directionality of specific facial action units acting on the respective KE point. The proposed method, as a result, improves ER accuracy and simultaneously reduces the computational complexity of ER system, as validated on multiple databases.

Keywords: neutral vs. emotion classification, Constrained Local Model, procrustes analysis, Local Binary Pattern Histogram, statistical model

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Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

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Authors: Nuo Xu, Kok Hun Goh, Jeyatharan Kumarasamy

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Pile load testing is significant during foundation construction due to its traditional role of design validation and routine quality control of the piling works. In order to verify whether piles can take loadings at specified settlements, piles will have to undergo working load test where the test load should normally up to 150% of the working load of a pile. Selection or sampling of piles for the working load test is done subject to the number specified in Singapore National Annex to Eurocode 7 SS EN 1997-1:2010. This paper presents an innovative way to rationalize the number of pile load test by adopting statistical analysis approach and looking at the coefficient of variance of pile elastic modulus using a case study at Singapore Tuas depot. Results are very promising and have shown that it is possible to reduce the number of working load test without influencing the reliability and confidence on the pile quality. Moving forward, it is suggested that more load test data from other geological formations to be examined to compare with the findings from this paper.

Keywords: elastic modulus of pile under soil interaction, jurong formation, kentledge test, pile load test

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