Search results for: data analyses

Authors: Sajjad Esmaeili Aghdam

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Economics typically conceptualizes individual behavior as the consequence of external states, for example, budgets and prices (or respective beliefs) and choices. As the main goal, we focus on the influence of a range of Behavioral Economics factors on Strategies of Digital Marketing, evaluation of strategies and deformation of it into highly prospective marketing strategies. The different forms of behavioral prospects all lead to the succeeding two main results. First, the steadiness of the economic dynamics in a currency union be contingent fatefully on the level of economic incorporation. More economic incorporation leads to more steady economic dynamics. Electronic word-of-mouth (eWOM) is “all casual communications focused at consumers through Internet-based technology connected to the usage or characteristics of specific properties and services or their venders.” eWOM can take many methods, the most significant one being online analyses. Writing this paper, 72 articles have been gathered, focusing on the title and the aim of the article from research search engines like Google Scholar, Web of Science, and PubMed. Recent research in strategic management and marketing proposes that markets should not be viewed as a given and deterministic setting, exogenous to the firm. Instead, firms are progressively abstracted as dynamic inventors of market prospects. The use of new technologies touches all spheres of the modern lifestyle. Social and economic life becomes unbearable without fast, applicable, first-class and fitting material. Psychology and economics (together known as behavioral economics) are two protruding disciplines underlying many theories in marketing. The wide marketing works papers consumers’ none balanced behavior even though behavioral biases might not continuously be steadily called or officially labeled.

Keywords: behavioral economics, digital marketing, marketing strategy, high impact strategies

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Authors: Marc Sader, Michiel Stock, Bernard De Baets

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Considering a large amount of adsorption data of adsorbate gases on adsorbent materials in literature, it is interesting to predict such adsorption data without experimentation. A quantitative structure-activity relationship (QSAR) is developed to correlate molecular characteristics of gases and existing knowledge of materials with their respective adsorption properties. The application of Random Forest, a machine learning method, on a set of adsorption isotherms at a wide range of partial pressures and concentrations is studied. The predicted adsorption isotherms are fitted to several adsorption equations to estimate the adsorption properties. To impute the adsorption properties of desired gases on desired materials, leave-one-out cross-validation is employed. Extensive experimental results for a range of settings are reported.

Keywords: adsorption, predictive modeling, QSAR, random forest

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Authors: L. Siva Rama Krishna, Sriram Venkatesh, M. Sastish Kumar, M. Uma Maheswara Chary

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Rapid Prototyping (RP) is a technology that produces models and prototype parts from 3D CAD model data, CT/MRI scan data, and model data created from 3D object digitizing systems. There are several RP process like Stereolithography (SLA), Solid Ground Curing (SGC), Selective Laser Sintering (SLS), Fused Deposition Modelling (FDM), 3D Printing (3DP) among them SLS and FDM RP processes are used to fabricate pattern of custom cranial implant. RP technology is useful in engineering and biomedical application. This is helpful in engineering for product design, tooling and manufacture etc. RP biomedical applications are design and development of medical devices, instruments, prosthetics and implantation; it is also helpful in planning complex surgical operation. The traditional approach limits the full appreciation of various bony structure movements and therefore the custom implants produced are difficult to measure the anatomy of parts and analyse the changes in facial appearances accurately. Cranioplasty surgery is a surgical correction of a defect in cranial bone by implanting a metal or plastic replacement to restore the missing part. This paper aims to do a comparative study on the dimensional error of CAD and SLS RP Models for reconstruction of cranial defect by comparing the virtual CAD with the physical RP model of a cranial defect.

Keywords: rapid prototyping, selective laser sintering, cranial defect, dimensional error

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Authors: Dalia Rimawi, Walid Salameh, Amal Al-Omari, Hadeel AbdelKhaleq

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Predication of Survival time of patients with cancer, is a core factor that influences oncologist decisions in different aspects; such as offered treatment plans, patients’ quality of life and medications development. For a long time proportional hazards Cox regression (ph. Cox) was and still the most well-known statistical method to predict survival outcome. But due to the revolution of data sciences; new predication models were employed and proved to be more flexible and provided higher accuracy in that type of studies. Artificial neural network is one of those models that is suitable to handle time to event predication. In this study we aim to compare ph Cox regression with artificial neural network method according to data handling and Accuracy of each model.

Keywords: Cox regression, neural networks, survival, cancer.

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Authors: Al Omari Mohammed Ahmed

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This paper focuses on Maximum Likelihood Estimator with Covariate. Covariates are incorporated into the Weibull model. Under this regression model with regards to maximum likelihood estimator, the parameters of the covariate, shape parameter, survival function and hazard rate of the Weibull regression distribution with right censored data are estimated. The mean square error (MSE) and absolute bias are used to compare the performance of Weibull regression distribution. For the simulation comparison, the study used various sample sizes and several specific values of the Weibull shape parameter.

Keywords: weibull regression distribution, maximum likelihood estimator, survival function, hazard rate, right censoring

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Authors: Forrest Kaatz, Adhemar Bultheel

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Au-Cu and Pt-M (M = Fe, Co, and Ni) nanocluster alloys are currently being investigated worldwide by many researchers for their interesting catalytic and nanophase properties. The low-temperature behavior of the phase diagrams is not well understood for alloys with nanometer sizes and shapes. These systems have similar bulk phase diagrams with the L12 (Au3Cu, Pt3M, AuCu3, and PtM3) structurally ordered intermetallics and the L10 structure for the AuCu and PtM intermetallics. We consider three models for low temperature ordering in the phase diagrams of Au–Cu and Pt–M nanocluster alloys. These models are valid for sizes ~ 5 nm and approach bulk values for sizes ~ 20 nm. We study the phase transition in nanoclusters with cubic, octahedral, and cuboctahedral shapes, covering the compositions of interest. These models are based on studying the melting temperatures in nanoclusters using the regular solution, mixing model for alloys. Experimentally, it is extremely challenging to determine thermodynamic data on nano–sized alloys. Reasonable agreement is found between these models and recent experimental data on nanometer clusters in the Au–Cu and Pt–M nanophase systems. From our data, experiments on nanocubes about 5 nm in size, of stoichiometric AuCu and PtM composition, could help differentiate between the models. Some available evidence indicates that ordered intermetallic nanoclusters have better catalytic properties than disordered ones. We conclude with a discussion of physical mechanisms whereby ordering could improve the catalytic properties of nanocluster alloys.

Keywords: catalytic reactions, gold nanoalloys, phase transitions, platinum nanoalloys

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Authors: Priyanka Kumari

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Menstrual hygiene is an important aspect in the life of young girls. Menstrual Hygiene Management (MHM) is defined as ‘Women and adolescent girls using a clean material to absorb or collect menstrual blood that can be changed in privacy as often as necessary for the duration of the menstruation period, using soap and water for washing the body as required and having access to facilities to dispose of used menstrual management materials. This paper aims to investigate the prevalence of hygienic menstrual practices and socio-demographic correlates of hygienic menstrual practices among women aged 15-24 in India. Data from the 2015–2016 National Family Health Survey–4 for 244,500 menstruating women aged 15–24 were used. The methods have been categorized into two, women who use sanitary napkins, locally prepared napkins and tampons considered as a hygienic method and those who use cloth, any other method and nothing used at all during menstruation considered as an unhygienic method. Women’s age, year of schooling, religion, place of residence, caste/tribe, marital status, wealth index, type of toilet facility used, region, the structure of the house and exposure to mass media are taken as an independent variables. Bivariate analysis was carried out with selected background characteristics to analyze the socio-economic and demographic factors associated with the use of hygienic methods during menstruation. The odds for the use of the hygienic method were computed by employing binary logistic regression. Almost 60% of the women use cloth as an absorbent during menstruation to prevent blood stains from becoming evident. The hygienic method, which includes the use of locally prepared napkins, sanitary napkins and tampons, is 16.27%, 41.8% and 2.4%. The proportion of women who used hygienic methods to prevent blood stains from becoming evident was 57.58%. Multivariate analyses reveal that education of women, wealth and marital status are found to be the most important positive factors of hygienic menstrual practices. The structure of the house and exposure to mass media also have a positive impact on the use of menstrual hygiene practices. In contrast, women residing in rural areas belonging to scheduled tribes are less likely to use hygienic methods during their menstruation. Geographical regions are also statistically significant with the use of hygienic methods during menstruation. This study reveals that menstrual hygiene is not satisfactory among a large proportion of adolescent girls. They need more education about menstrual hygiene. A variety of factors affect menstrual behaviors; amongst these, the most influential is economic status, educational status and residential status, whether urban or rural. It is essential to design a mechanism to address and access healthy menstrual knowledge. It is important to encourage policies and quality standards that promote safe and affordable options and dynamic markets for menstrual products. Materials that are culturally acceptable, contextually available and affordable. Promotion of sustainable, environmentally friendly menstrual products and their disposal as it is a very important aspect of sustainable development goals. We also need to educate the girls about the services which are provided by the government, like a free supply of sanitary napkins to overcome reproductive tract infections. Awareness regarding the need for information on healthy menstrual practices is very important. It is essential to design a mechanism to address and access healthy menstrual practices. Emphasis should be given to the education of young girls about the importance of maintaining hygiene during menstruation to prevent the risk of reproductive tract infections.

Keywords: adolescent, menstruation, menstrual hygiene management, menstrual hygiene

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Authors: Nadiah Mohamad, Wan Samiati Andriana Wan Mohd Daud, M. Suhaimi Tohid, Mohd Fazli Othman, Mohamad Rizal Salleh

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This is a collaborative project of the mural art between The Department of Fine Art from Universiti Teknologi MARA (UiTM) and Petronas Dagangan Berhad (PDB), the most leading retailer and marketer of downstream oil and gas products in Malaysia. Five different states in the Peninsular of Malaysia that has been identified in showcasing the National Identity of Malaysia at each Petronas gas station, this also includes the Air Keroh in Melaka, Pasir Pekan in Kelantan, Pontian in Johor, Simpang Pulai in Perak, and also Wakaf Bharu in Terengganu. This project is to analyze the element of national identity that has been demonstrated at the Petronas's Mural. The ultimate aim of the mural is to let the community and local people to be aware about what Malaysians are consists and proud of and how everyone is able to connect with the idea through visual art. The method that is being explained in this research is by using visual data through research and also self-experience in collecting the visual data in identifying what images is considered as the national identity and idea development and visual analysis is being transferred based upon the visual data collection. In this stage, elements and principles of design will be the key in highlighting what is necessary for a work of art. In conclusion, visual image of the National Identity of Malaysia is able to connect to the audience from local and also to the people from outside the country to learn and understand the beauty and diversity of Malaysia as a unique country with art through the wall of five Petronas gas station.

Keywords: community, fine art, mural art, national identity

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Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

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Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD

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Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

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Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

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Authors: Mehdi Moghimi

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Doctrines associated with situational prevention considers 'place of committing crime' as one of the fundamental elements of a crime. Meanwhile, with regard to causing or having effect on a crime situation, 'place' can be one of the pivotal indices in situational prevention analyses. This study aims at examining the role of place in construction of a crime situation and explaining the relationship between 'place' and situational preventive measures and procedures. Also, how to identify high-crime places, types of high-crime places and the factors influencing their creation are among the most important secondary objectives of this article. Concerning the purpose, it is a practical study whose material has been written through a documentary method using original sources (English), books and written and translated articles etc. This article is written in two main parts. In the first section, cognitive-conceptual issues about 'place' as one of the main causes of crime situation, and its effective interaction with situational preventive measures will be reviewed. The second part of this paper will focus on criminological examination of places and critical locations of crime and provide situational preventive measures to deal with the situation. 'Crime displacement' and 'geographical distribution of benefits'are also considered as the possible consequences of implementing preventive strategies. The results of the study suggest that the inventory of offenses is distributed according to the spatial characteristics. Moreover, according to the criminological characteristics governing region or location, offenders choose the place of crime based on a logical calculation. Therefore, some locations, regions or neighborhoods are permanent places of occurring lots of crimes. As a result, considering "place", preventive measures and procedures can be systematically directed, and using the most effective ways, limited preventive resources are utilized in the most critical places. Finally, some suggestions for further research and application are provided in line with more favorable promotion of situational preventive measures.

Keywords: crime prevention, place, police crime, situational crime prevention

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Authors: Hans Schumann, Colin Domnauer, Louis Rosenberg

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In this study, machine learning techniques were deployed on real-time human swarm data to forecast the likelihood of outcomes for English Premier League matches in the 2020/21 season. These techniques included ensemble models in combination with neural networks and were tested against an industry standard of Vegas Oddsmakers. Predictions made from the collective intelligence of human swarm participants managed to achieve a positive return on investment over a full season on matches, empirically proving the usefulness of a new artificial intelligence valuing human instinct and intelligence.

Keywords: artificial intelligence, data science, English Premier League, human swarming, machine learning, sports betting, swarm intelligence

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Authors: Lavanya Dharmendran, Shenuka Singh, Sona Baburathanam

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The aim of the study is to understand the level of awareness in a community of a specific region of Kuwait regarding oral cancer and its screening methods so as to enhance the uptake of oral cancer screening methods. This is a cross-sectional study comprising 100 adult participants residing in the governate of Farwaniya, Kuwait. Participants of above 18 years of both genders will be selected using convenience sampling. Data collection includes the administration of a self-administered questionnaire. The questionnaire comprises three sections, each section assessing the knowledge, attitudes and practices of the participants’ opinions about oral cancer and screening methods. Data will be analyzed using Humphris Oral Cancer Knowledge Scale. Inferential statistics will be done using Chi-Square or Fisher’s exact test for categorical data. A level of p<.05 will be established as being significant. All ethical considerations, such as respect for personal confidentiality and informed consent, will be applied in this study. This study revealed that although respondents were aware of the term oral cancer, more than half of the study participants were unaware of the symptoms associated with this condition. Smoking and alcohol were identified as risk factors for oral cancer, but the majority of participants did not identify the Human Papilloma Virus (HPV) as an added risk factor. This suggests a greater need for dental practitioners to include educational strategies in routine dental visits to ensure greater awareness of oral cancer.

Keywords: oral cancer, oral screening, oral public health, oral health

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Authors: Nattabhorn Leamcharaskul

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This study aims to examine determining factors of the dynamics of poverty in Thailand by using panel data of 3,567 households in 2007-2017. Four techniques of estimation are employed to analyze the situation of poverty across households and time periods: the multinomial logit model, the sequential logit model, the quantile regression model, and the difference in difference model. Households are categorized based on their experiences into 5 groups, namely chronically poor, falling into poverty, re-entering into poverty, exiting from poverty and never poor households. Estimation results emphasize the effects of demographic and socioeconomic factors as well as unexpected events on the economic status of a household. It is found that remittances have positive impact on household’s economic status in that they are likely to lower the probability of falling into poverty or trapping in poverty while they tend to increase the probability of exiting from poverty. In addition, not only receiving a secondary source of household income can raise the probability of being a never poor household, but it also significantly increases household income per capita of the chronically poor and falling into poverty households. Public work programs are recommended as an important tool to relieve household financial burden and uncertainty and thus consequently increase a chance for households to escape from poverty.

Keywords: difference in difference, dynamic, multinomial logit model, panel data, poverty, quantile regression, remittance, sequential logit model, Thailand, transfer

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Authors: Sajid Aman

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Jobs in the banking sector in Pakistan are perceived as very tough, due to which employee turnover is very high. However, the managerial role is very important in influencing employees’ attitudes toward their turnout. This paper explores the manager’s role in influencing employees’ effectiveness on the job. The paper adopted a pragmatic approach by combining both qualitative and quantitative data. The study employed an exploratory sequential strategy under a mixed-method research design. Qualitative data was analyzed using thematic analysis. Five major themes, such as the manager’s attitude towards employees, his leadership style, listening to employee’s personal problems, provision of personal loans without interest and future career prospects, emerged as key factors increasing employee’s effectiveness in the banking sector. The quantitative data revealed that a manager’s attitude, leadership style, availability to listen to employees’ personal problems, and future career prospects and listening to employee’s personal problems are strongly associated with employees’ effectiveness at the job. However, personal loan without interest was noted as having no significant association with employee’s effectiveness at the job. The study concludes manager’s role is more important in the effectiveness of the employees at their job in the banking sector. It is suggested that managers should have a positive attitude towards employees and give time to listening to employee’s problems, even personal ones.

Keywords: banking sector, employee’s effectiveness, manager’s role, leadership style

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Authors: A. Benatiallah, D. Benatiallah, F. Abaidi, B. Nasri, A. Harrouz, S. Mansouri

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The region of Adrar is located in the south-western Algeria and covers a total area of 443.782 km², occupied by a population of 432,193 inhabitants. The main activity of population is agriculture, mainly based on the date palm cultivation occupies a total area of 23,532 ha. Adrar region climate is a continental desert characterized by a high variation in temperature between months (July, August) it exceeds 48°C and coldest months (December, January) with 16°C. Rainfall is very limited in frequency and volume with an aridity index of 4.6 to 5 which corresponds to a type of arid climate. Geologically Adrar region is located on the edge North West and is characterized by a Precambrian basement cover stolen sedimentary deposit of Phanerozoic age transgressive. The depression is filled by Touat site Paleozoic deposits (Cambrian to Namurian) of a vast sedimentary basin extending secondary age of the Saharan Atlas to the north hamada Tinhirt Tademaït and the plateau of south and Touat Gourara west to Gulf of Gabes in the Northeast. In this work we have study geothermal potential of Adrar region from the borehole data eatable in various sites across the area of 400,000 square kilometres; from these data we developed a GIS (Adrar_GIS) that plots data on the various points and boreholes in the region specifying information on available geothermal potential has variable depths.

Keywords: sig, geothermal, potenteil, temperature

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Authors: Liliana Rusu

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The goal of the proposed work is to provide a more comprehensive picture of the wind and wave climate in the Romanian nearshore, using the results provided by numerical models. The Romanian coastal environment is located in the western side of the Black Sea, the more energetic part of the sea, an area with heavy maritime traffic and various offshore operations. Information about the wind and wave climate in the Romanian waters is mainly based on observations at Gloria drilling platform (70 km from the coast). As regards the waves, the measurements of the wave characteristics are not so accurate due to the method used, being also available for a limited period. For this reason, the wave simulations that cover large temporal and spatial scales represent an option to describe better the wave climate. To assess the wind climate in the target area spanning 1992–2016, data provided by the NCEP-CFSR (U.S. National Centers for Environmental Prediction - Climate Forecast System Reanalysis) and consisting in wind fields at 10m above the sea level are used. The high spatial and temporal resolution of the wind fields is good enough to represent the wind variability over the area. For the same 25-year period, as considered for the wind climate, this study characterizes the wave climate from a wave hindcast data set that uses NCEP-CFSR winds as input for a model system SWAN (Simulating WAves Nearshore) based. The wave simulation results with a two-level modelling scale have been validated against both in situ measurements and remotely sensed data. The second level of the system, with a higher resolution in the geographical space (0.02°×0.02°), is focused on the Romanian coastal environment. The main wave parameters simulated at this level are used to analyse the wave climate. The spatial distributions of the wind speed, wind direction and the mean significant wave height have been computed as the average of the total data. As resulted from the amount of data, the target area presents a generally moderate wave climate that is affected by the storm events developed in the Black Sea basin. Both wind and wave climate presents high seasonal variability. All the results are computed as maps that help to find the more dangerous areas. A local analysis has been also employed in some key locations corresponding to highly sensitive areas, as for example the main Romanian harbors.

Keywords: numerical simulations, Romanian nearshore, waves, wind

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Authors: Nadia Yaghoobi, Seyed Majid Esmaeilzadeh

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Due to advancements in wireless communication, new sensor capabilities have been created. In addition to the automation industry, the Internet of Things (IoT) has been used in environmental issues and has provided the possibility of communication between different devices for data collection and exchange. Water quality depends on many factors which are essential for maintaining the minimum sustainability of water. Regarding the great dependence of fishes on the quality of the aquatic environment, water quality can directly affect their activity. Therefore, monitoring water quality is an important issue to consider, especially in the fish farming industry. The conventional method of water quality testing is to collect water samples manually and send them to a laboratory for testing and analysis. This time-consuming method is a waste of manpower and is not cost-effective. The water quality measurement system implemented in this project monitors water quality in real-time through various sensors (parameters: water temperature, water level, dissolved oxygen, humidity and ambient temperature, water turbidity, PH). The Wi-Fi module, ESP8266, transmits data collected by sensors wirelessly to ThingSpeak and the smartphone app. Also, with the help of these instantaneous data, water temperature and water level can be controlled by using a heater and a water pump, respectively. This system can have a detailed study of the pollution and condition of water resources and can provide an environment for safe fish farming.

Keywords: dissolved oxygen, IoT, monitoring, ThingSpeak, water level, water quality, WiFi module

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Authors: Cher Rong Matthew Kong, Edmund Tian, Seng Poon Ong, Chee Sian Gan

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Gamma-aminobutyric acid (GABA) is a non-protein amino acid that is a functional constituent of certain rice varieties. When consumed, it decreases blood pressure and reduces the risk of hypertension-related diseases. This has led to more research dedicated towards the development of functional food products (e.g. germinated brown rice) with enhanced GABA content, and the development of these functional food products has led to increased demand for instrument-based methods that can efficiently and effectively determine GABA content. Current analytical methods require analyte derivatisation, and have significant disadvantages such as being labour intensive and time-consuming, and being subject to analyte loss due to the increased complexity of the sample preparation process. To address this, an LC-MS-MS method for the determination of GABA in rice products has been developed and validated. This developed method involves a relatively simple sample preparation process before analysis using HILIC LC-MS-MS. This method eliminates the need for derivatisation, thereby significantly reducing the labour and time associated with such an analysis. Using LC-MS-MS also allows for better differentiation of GABA from any potential co-eluting compounds in the sample matrix. Results obtained from the developed method demonstrated high linearity, accuracy, and precision for the determination of GABA (1ng/L to 8ng/L) in a variety of brown rice products. The method can significantly simplify sample preparation steps, improve the accuracy of quantitation, and increase the throughput of analyses, thereby providing a quick but effective alternative to established instrumental analysis methods for GABA in rice.

Keywords: functional food, gamma-aminobutyric acid, germinated brown rice, method development

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Authors: Guofan Shao, Lina Tang, Hao Zhang

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The values of image classification accuracy are affected by class size distributions and classification schemes, making it difficult to compare the performance of classification algorithms across different remote sensing data sources and classification systems. Based on the term efficacy from medicine and pharmacology, we have developed the metrics of image classification efficacy at the map and class levels. The novelty of this approach is that a baseline classification is involved in computing image classification efficacies so that the effects of class statistics are reduced. Furthermore, the image classification efficacies are interpretable and comparable, and thus, strengthen the assessment of image data classification methods. We use real-world and hypothetical examples to explain the use of image classification efficacies. The metrics of image classification efficacy meet the critical need to rectify the strategy for the assessment of image classification performance as image classification methods are becoming more diversified.

Keywords: accuracy assessment, efficacy, image classification, machine learning, uncertainty

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Authors: Siti Nurhidayah Naqiah Abdull Rani, Herlina Abdul Rahim, Rashidah Ghazali, Noramli Abdul Razak

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Near infrared (NIR) spectroscopy has always been of great interest in the food and agriculture industries. The development of prediction models has facilitated the estimation process in recent years. In this study, 110 crude palm oil (CPO) samples were used to build a free fatty acid (FFA) prediction model. 60% of the collected data were used for training purposes and the remaining 40% used for testing. The visible peaks on the NIR spectrum were at 1725 nm and 1760 nm, indicating the existence of the first overtone of C-H bands. Principal component regression (PCR) was applied to the data in order to build this mathematical prediction model. The optimal number of principal components was 10. The results showed R2=0.7147 for the training set and R2=0.6404 for the testing set.

Keywords: palm oil, fatty acid, NIRS, regression

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Authors: Haifeng Wang, Haili Zhang

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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

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Authors: Lok Raj Joshi, Naveen Prakash Shah, Sharad Kumar Sharma, I. Ratna Bhattarai, Rajendra Basnet, Deepak Dahal, Bahagwan Maharjan, Seraphine Kaminsa

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Context: Tuberculosis (TB) is a significant health issue in Nepal, a country with a high burden of the disease. Despite efforts to control TB, there is still a gap in the notification of TB cases, which hinders effective control and treatment. This paper aims to address this notification gap and proposes strategies to improve TB control in Nepal. Research Aim: The aim of this research is to understand and address the tuberculosis notification gap in Nepal. The focus is on enhancing the healthcare system, involving the private sector and communities, raising awareness, and addressing social determinants to achieve sustainable TB control. Methodology: The research methodology involved a review of existing epidemiological data and research studies related to TB in Nepal. Additionally, consultation with an expert group from the TB control program in Nepal provided insights into the current state of TB control and challenges in addressing the notification gap. Findings: The findings reveal that only 55% of TB cases were reported in 2022, indicating a significant notification gap. Of the reported cases, only 32% and 19% were referred by the private sector and community, respectively. Furthermore, 20% of diagnosed cases were not treated in the initial phase. The estimated number of cases of multidrug-resistant TB (MDR TB) was 2,800, suggesting a low diagnosis rate. Among the diagnosed MDR TB cases, only 60% were receiving treatment. Additionally, it was observed that 20% of diagnosed MDR TB cases were from India and not enrolling in TB treatment in Nepal, indicating a high rate of defaulters. Theoretical Importance: The study highlights the importance of adopting a holistic strategy to address the notification gap in TB cases in Nepal. It emphasizes the need to enhance healthcare infrastructure, raise awareness, involve the private sector and local communities, establish effective methods to trace initial defaulters, implement TB interventions in border regions, and mitigate the social stigma associated with the disease. Data Collection and Analysis Procedures: Data for this study was collected through a review of existing epidemiological data and research studies. The data were then analyzed to identify patterns, trends, and gaps in TB case notification in Nepal.

Keywords: TB, tuberculosis, private sector, community, migrants, nepal

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Authors: Olha Mostova, Maciej Stolarski, Gerald Matthews

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The present paper explores and tests Gary Chapman’s claims that (1) people vary in the ways they prefer to receive and express affection and (2) romantic partners, who communicate their feelings correspondingly to their partner’s preferences, experience greater relationship quality. The author proposes five distinct preferences for and tendencies to express love, including acts of service, physical touch, words of affirmation, quality time, and gifts. In the present study, partners (N = 100 romantic, heterosexual couples) completed measures assessing their preferences and behavioral tendencies reflecting 1) how they a) tend to express and b) prefer to receive signs of affection in correspondence to the five proposed categories; 2) relationship satisfaction; 3) sexual satisfaction and 4) empathy, which was expected to be the factor that leads to a better understanding of and responding to the partner’s needs. The degree of the within-couple match was calculated separately for each individual based on the discrepancies between one’s felt (preferred) and their partner’s expressed love language. The joint discrepancy indicator was a sum of such discrepancies across the five love languages. Conducted analyses provided evidence for significant associations between matching on love languages and both relationship and sexual satisfaction. In particular, people who expressed their affection in the way their partners preferred to receive it experienced greater satisfaction with their relationships and were more sexually satisfied compared to those who met their partner’s needs to a lesser extent. Other results provide some support for mediating effects of certain domains of empathy in the said associations among male but not female participants.

Keywords: affection, empathy, love languages, relationship satisfaction, romantic couples, sexual satisfaction

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Authors: Hamid Reza Behnood, Esmaeel Ayati, Tom Brijs, Mohammadali Pirayesh Neghab

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Investment projects in road safety planning can benefit from an effectiveness evaluation regarding their expected safety outcomes. The objective of this study is to develop a decision support system (DSS) to support policymakers in taking the right choice in road safety planning based on the efficiency of previously implemented safety measures in a set of regions in Iran. The measures considered for each region in the study include performance indicators about (1) police operations, (2) treated black spots, (3) freeway and highway facility supplies, (4) speed control cameras, (5) emergency medical services, and (6) road lighting projects. To this end, inefficiency measure is calculated, defined by the proportion of fatality rates in relation to the combined measure of road safety performance indicators (i.e., road safety measures) which should be minimized. The relative inefficiency for each region is modeled by the Data Envelopment Analysis (DEA) technique. In a next step, a fuzzy decision-making system is constructed to convert the information obtained from the DEA analysis into a rule-based system that can be used by policy makers to evaluate the expected outcomes of certain alternative investment strategies in road safety.

Keywords: performance indicators, road safety, decision support system, data envelopment analysis, fuzzy reasoning

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Authors: Amin Khamoosh, Hamed Faramarzifar

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In the capital of Iran, Tehran, numerous buildings constructed when extreme climates were not prevalent now face the need for rehabilitation, typically within their first decade. Our data delves into the performance metrics and economic advantages of sustainable rehabilitation practices compared to traditional methods. With a focus on the scarcity of water resources, we specifically scrutinize water-efficient techniques throughout construction, rehabilitation, and usage. Examining design elements that optimize natural light while efficiently managing heat transmission is crucial, given the reliance on water for cooling devices in this region. The data aims to present a comprehensive strategy, addressing immediate structural concerns while harmonizing with Iran's unique environmental conditions.

Keywords: sustainable rehabilitation, concrete buildings, iran, solar energy, water-efficient techniques

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Authors: Baba Mbaye

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In collaborative filtering recommendation systems, a user receives suggested items based on the opinions and evaluations of a community of users. This type of recommendation system uses only the information (notes in numerical values) contained in a usage matrix as input data. This matrix can be constructed based on users' behaviors or by offering users to declare their opinions on the items they know. The cold start problem leads to very poor performance for new users. It is a phenomenon that occurs at the beginning of use, in the situation where the system lacks data to make recommendations. There are three types of cold start problems: cold start for a new item, a new system, and a new user. We are interested in this article at the cold start for a new user. When the system welcomes a new user, the profile exists but does not have enough data, and its communities with other users profiles are still unknown. This leads to recommendations not adapted to the profile of the new user. In this paper, we propose an approach that improves cold start by using the notions of similarity and semantic proximity between users profiles during cold start. We will use the cold-metadata available (metadata extracted from the new user's data) useful in positioning the new user within a community. The aim is to look for similarities and semantic proximities with the old and current user profiles of the system. Proximity is represented by close concepts considered to belong to the same group, while similarity groups together elements that appear similar. Similarity and proximity are two close but not similar concepts. This similarity leads us to the construction of similarity which is based on: a) the concepts (properties, terms, instances) independent of ontology structure and, b) the simultaneous representation of the two concepts (relations, presence of terms in a document, simultaneous presence of the authorities). We propose an ontology, OIVCSRS (Ontology of Improvement Visitor Cold Start in Recommender Systems), in order to structure the terms and concepts representing the meaning of an information field, whether by the metadata of a namespace, or the elements of a knowledge domain. This approach allows us to automatically attach the new user to a user community, partially compensate for the data that was not initially provided and ultimately to associate a better first profile with the cold start. Thus, the aim of this paper is to propose an approach to improving cold start using semantic technologies.

Keywords: visitor cold start, recommender systems, collaborative filtering, semantic filtering

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Authors: L. Freeborn

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This paper discusses the application of complex dynamic system theory (DST) to the study of individual differences in second language development. This transdisciplinary framework allows researchers to view the trajectory of language development as a dynamic, non-linear process. A DST approach views language as multi-componential, consisting of multiple complex systems and nested layers. These multiple components and systems continuously interact and influence each other at both the macro- and micro-level. Dynamic systems theory aims to explain and describe the development of the language system, rather than make predictions about its trajectory. Such a holistic and ecological approach to second language development allows researchers to include various research methods from neurological, cognitive, and social perspectives. A DST perspective would involve in-depth analyses as well as mixed methods research. To illustrate, a neurobiological approach to second language development could include non-invasive neuroimaging techniques such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to investigate areas of brain activation during language-related tasks. A cognitive framework would further include behavioural research methods to assess the influence of intelligence and personality traits, as well as individual differences in foreign language aptitude, such as phonetic coding ability and working memory capacity. Exploring second language development from a DST approach would also benefit from including perspectives from the field of applied linguistics, regarding the teaching context, second language input, and the role of affective factors such as motivation. In this way, applying mixed research methods from neurobiological, cognitive, and social approaches would enable researchers to have a more holistic view of the dynamic and complex processes of second language development.

Keywords: dynamic systems theory, mixed methods, research design, second language development

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Authors: Gaya Tridinanti

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Enhanching English vocabulary in early childhood is the main problem often faced by teachers. Thus, the purpose of this study was to determine the enhancement of children’s English vocabulary acquisition by using digital storytelling. This type of research was an action research. It consisted of a series of four activities done in repeated cycles: planning, implementation, observation, and reflection. The subject of the study consisted of 30 students of B group (5-6 years old) attending Happy Kids Kindergarten Palembang, Indonesia. This research was conducted in three cycles. The methods used for data collection were observation and documentation. Descriptive qualitative and quantitative methods were also used to analyse the data. The research showed that the digital storytelling learning activities could enhance the children’s English vocabulary acquisition. It is based on the data in which the enhancement in pre-cycle was 37% and 51% in Cycle I. In Cycle II it was 71% and in Cycle III it was 89.3%. The results showed an enhancement of about 14% from the pre-cycle to Cycle I, 20% from Cycle I to Cycle II, and enhancement of about 18.3% from Cycle II to Cycle III. The conclusion of this study suggests that digital storytelling learning method could enhance the English vocabulary acquisition of B group children at the Happy Kids Kindergarten Palembang. Therefore, digital storytelling can be considered as an alternative to improve English language learning in the classroom.

Keywords: acquisition, enhancing, digital storytelling, English vocabulary

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Authors: Ismail Seçer

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The purpose of this study is to analyze the predictive effect of anxiety sensitivity on obsessive compulsive symptoms. The sample of the study consists of 542 students selected with appropriate sampling method from the secondary and high schools in Erzurum city center. Obsessive Compulsive Inventory and Anxiety Sensitivity Index were used in the study to collect data. The data obtained through the study was analyzed with structural equation modeling. As a result of the study, it was determined that there is a significant relationship between obsessive Compulsive Disorder (OCD) and anxiety sensitivity. Anxiety sensitivity has direct and indirect meaningful effects on the latent variable of OCD in the sub-dimensions of doubting-checking, obsessing, hoarding, washing, ordering, and mental neutralizing, and also anxiety sensitivity is a significant predictor of obsessive compulsive symptoms.

Keywords: obsession, compulsion, structural equation, anxiety sensitivity

Procedia PDF Downloads 532