Search results for: generalized autoregressive score model

Authors: Mst. Tuhin Akter, Sharun Akter Khushbu, S. M. Shaqib

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A real-time system architecture is highly effective for monitoring and detecting various damaged products or fruits that may deteriorate over time or become infected with diseases. Deep learning models have proven to be effective in building such architectures. However, building a deep learning model from scratch is a time-consuming and costly process. A more efficient solution is to utilize deep neural network (DNN) based transfer learning models in the real-time monitoring architecture. This study focuses on using a novel strawberry dataset to develop effective transfer learning models for the proposed real-time monitoring system architecture, specifically for evaluating and detecting strawberry freshness. Several state-of-the-art transfer learning models were employed, and the best performing model was found to be Xception, demonstrating higher performance across evaluation metrics such as accuracy, recall, precision, and F1-score.

Keywords: strawberry freshness evaluation, deep neural network, transfer learning, image augmentation

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Authors: Nassima Noufail, Sara Bouhali

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In this work, we develop a semi-supervised solution for the purpose of action detection in videos and propose an efficient algorithm for video segmentation. The approach is divided into video segmentation, feature extraction, and classification. In the first part, a video is segmented into clips, and we used the K-means algorithm for this segmentation; our goal is to find groups based on similarity in the video. The application of k-means clustering into all the frames is time-consuming; therefore, we started by the identification of transition frames where the scene in the video changes significantly, and then we applied K-means clustering into these transition frames. We used two image filters, the gaussian filter and the Laplacian of Gaussian. Each filter extracts a set of features from the frames. The Gaussian filter blurs the image and omits the higher frequencies, and the Laplacian of gaussian detects regions of rapid intensity changes; we then used this vector of filter responses as an input to our k-means algorithm. The output is a set of cluster centers. Each video frame pixel is then mapped to the nearest cluster center and painted with a corresponding color to form a visual map. The resulting visual map had similar pixels grouped. We then computed a cluster score indicating how clusters are near each other and plotted a signal representing frame number vs. clustering score. Our hypothesis was that the evolution of the signal would not change if semantically related events were happening in the scene. We marked the breakpoints at which the root mean square level of the signal changes significantly, and each breakpoint is an indication of the beginning of a new video segment. In the second part, for each segment from part 1, we randomly selected a 16-frame clip, then we extracted spatiotemporal features using convolutional 3D network C3D for every 16 frames using a pre-trained model. The C3D final output is a 512-feature vector dimension; hence we used principal component analysis (PCA) for dimensionality reduction. The final part is the classification. The C3D feature vectors are used as input to a multi-class linear support vector machine (SVM) for the training model, and we used a multi-classifier to detect the action. We evaluated our experiment on the UCF101 dataset, which consists of 101 human action categories, and we achieved an accuracy that outperforms the state of art by 1.2%.

Keywords: video segmentation, action detection, classification, Kmeans, C3D

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Authors: Trisha Malhotra

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Sentiment analysis is a recent field of study that computationally assesses the emotional nature of a body of text. To assess its test-validity, sentiment analysis was carried out on the emotional corpus of text from a personal 15-day mood diary. Self-reported mood scores varied more or less accurately with daily mood evaluation score given by the software. On further assessment, it was found that while sentiment analysis was good at assessing ‘global’ mood, it was not able to ‘locally’ identify and differentially score synonyms of various emotional words. It is further critiqued for treating the intensity of an emotion as universal across cultures. Finally, the software is shown not to account for emotional complexity in sentences by treating emotions as strictly positive or negative. Hence, it is posited that a better output could be two (positive and negative) affect scores for the same body of text.

Keywords: analysis, data, diary, emotions, mood, sentiment

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Authors: Asma Ammar

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Being different in theory and practice from their conventional counterparts, this research aims to assess the performance of Islamic banks beyond the financial performance by emphasizing their ethical and social identity based on the higher purposes of Islamic law, namely Maqasid al-Shariah. Using Imam al-Ghazali’s theory of Maqasid al-Shariah and Sekaran’s (2000) method, we develop a Maqasid-based index including the five objectives of Shariah (preservation of life, religion, intellect, posterity, and wealth). Our sample covers 9 Islamic banks considered among the largest Islamic banks in the world. For the five years of study (2017-2021), our results reveal that the highest score is performed by Bank Muamalat while the least score is given to Dubai Islamic Bank. The overall Maqasid performance of the sample is unimpressive, indicating that there is a lack of achievement in Maqasid al-Shariah performance of Islamic banks. Consequently, serious measures should be taken by Islamic banks to improve their Maqasid performance and thus contribute effectively to the socio-economic development of the countries in which they operate.

Keywords: Maqasid al-Shariah, Maqasid al-Shariah index, Islamic banks, performance assessment

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Authors: Bernur Acikgoz

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The purpose of this paper is to investigate the effects of public spending on economic growth and examine the sources of economic growth in developed countries since the 1990s. This paper analyses whether public spending effect on economic growth based on Cobb-Douglas Production Function with the two econometric models with Autoregressive Distributed Lag (ARDL) and Dynamic Fixed Effect (DFE) for 21 developed countries (high-income OECD countries), over the period 1990-2013. Our models results are parallel to each other and the models support that public spending has an important role for economic growth. This result is accurate with theories and previous empirical studies.

Keywords: public spending, economic growth, panel data, ARDL models

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Authors: Roza Dzierzak, Waldemar Wojcik, Piotr Kacejko

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

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

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Authors: Samad Lotfollahzadeh

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Escherichia coli (E.coli) is the most isolated bacteria from blood circulation of septicemic calves. Given the prevalence of septicemia in animals and its economic importance in veterinary practice, better understanding of changes in clinical signs following disease, may contribute to early detection of the disorder. The present study has been carried out to detect changes of clinical signs in induced sepsis in calves with E.coli. Colisepticemia has been induced in 10 twenty-day old healthy Holstein- Frisian calves with intravenous injection of 1.5 X 109 colony forming units (cfu) of O111: H8 strain of E.coli. Clinical signs including rectal temperature, heart rate, respiratory rate, shock, appetite, sucking reflex, feces consistency, general behavior, dehydration and standing ability were recorded in experimental calves during 24 hours after induction of colisepticemia. Blood culture was also carried out from calves four times during the experiment. ANOVA with repeated measure is used to see changes of calves’ clinical signs to experimental colisepticemia, and values of P≤ 0.05 was considered statistically significant. Mean values of rectal temperature and heart rate as well as median values of respiratory rate, appetite, suckling reflex, standing ability and feces consistency of experimental calves increased significantly during the study (P<0.05). In the present study, median value of shock score was not significantly increased in experimental calves (P> 0.05). The results of present study showed that total score of clinical signs in calves with experimental colisepticemia increased significantly, although the score of some clinical signs such as shock did not change significantly.

Keywords: calves, clinical signs scoring, E. coli O111:H8, experimental colisepticemia

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Authors: John Lorenzo Bautista, Yoon-Joong Kim

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In this paper, a set of Filipino Phonetically Balanced Word list consisting of 250 words (PBW250) were constructed for a phoneme-level ASR system for the Filipino language. The Entropy Maximization is used to obtain phonological balance in the list. Entropy of phonemes in a word is maximized, providing an optimal balance in each word’s phonological distribution using the Add-Delete Method (PBW algorithm) and is compared to the modified PBW algorithm implemented in a dynamic algorithm approach to obtain optimization. The gained entropy score of 4.2791 and 4.2902 for the PBW and modified algorithm respectively. The PBW250 was recorded by 40 respondents, each with 2 sets data. Recordings from 30 respondents were trained to produce an acoustic model that were tested using recordings from 10 respondents using the HMM Toolkit (HTK). The results of test gave the maximum accuracy rate of 97.77% for a speaker dependent test and 89.36% for a speaker independent test.

Keywords: entropy maximization, Filipino language, Hidden Markov Model, phonetically balanced words, speech recognition

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Authors: Alexandre Gurchumelia, Luca Sorriso-Valvo, David Burgess, Khatuna Elbakidze, Oleg Kharshiladze, Diana Kvaratskhelia

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The terrestrial magnetosheath is a highly turbulent medium, with a high level of magnetic1field fluctuations throughout a broad range of scales. These often include an inertial range where a2magnetohydrodynamic turbulent cascade is observed. The multifractal properties of the turbulent3cascade, strictly related to intermittency, are observed here during the transition from quasi-parallel to4quasi-perpendicular magnetic field with respect to the bow-shock normal. The different multifractal5behavior in the two regions is analyzed. A standard coarse-graining technique has been used6to evaluate the generalized dimensions and the corresponding multifractal spectrumf(α). A7p-model fit provided a quantitative measure of multifractality and intermittency, to be compared with8standard indicators: the width of the multifractal spectrum, the peak of the kurtosis, and its scaling9exponent. Results show a clear transition and sharp differences in the intermittency properties for the two regions.

Keywords: magnetos heath, turbulence, multifractal, instabilities

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Authors: Tsedeke Lambore Gemecho, Ayele Taye Goshu

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The main objective of this study is to identify the determinants of the number of international migrants in a household and to compare regression models for count response. This study is done by collecting data from total of 2288 household heads of 16 randomly sampled districts in Hadiya and Kembata-Tembaro zones of Southern Ethiopia. The Poisson mixed models, as special cases of the generalized linear mixed model, is explored to determine effects of the predictors: age of household head, farm land size, and household size. Two ethnicities Hadiya and Kembata are included in the final model as dummy variables. Stepwise variable selection has indentified four predictors: age of head, farm land size, family size and dummy variable ethnic2 (0=other, 1=Kembata). These predictors are significant at 5% significance level with count response number of migrant. The Poisson mixed model consisting of the four predictors with random effects districts. Area specific random effects are significant with the variance of about 0.5105 and standard deviation of 0.7145. The results show that the number of migrant increases with heads age, family size, and farm land size. In conclusion, there is a significantly high number of international migration per household in the area. Age of household head, family size, and farm land size are determinants that increase the number of international migrant in households. Community-based intervention is needed so as to monitor and regulate the international migration for the benefits of the society.

Keywords: Poisson regression, GLM, number of migrant, Hadiya and Kembata Tembaro zones

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Authors: Helmi Baazaoui, Mounir Zrigui

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This study addresses the challenge of hate speech detection in Tunisian Arabic text, a critical issue for online safety and moderation. Leveraging the strengths of the AraBERT model, we fine-tuned and evaluated its performance against the Bi-LSTM model across four distinct datasets: T-HSAB, TNHS, TUNIZI-Dataset, and a newly compiled dataset with diverse labels such as Offensive Language, Racism, and Religious Intolerance. Our experimental results demonstrate that AraBERT significantly outperforms Bi-LSTM in terms of Recall, Precision, F1-Score, and Accuracy across all datasets. The findings underline the robustness of AraBERT in capturing the nuanced features of Tunisian Arabic and its superior capability in classification tasks. This research not only advances the technology for hate speech detection but also provides practical implications for social media moderation and policy-making in Tunisia. Future work will focus on expanding the datasets and exploring more sophisticated architectures to further enhance detection accuracy, thus promoting safer online interactions.

Keywords: hate speech detection, Tunisian Arabic, AraBERT, Bi-LSTM, Gemini annotation tool, social media moderation

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Authors: Fiza Azam, Sahar Zia, Fatima Nazir Ali, Aysha Hanif

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Health is a fundamental human right, and a healthy population is essential for the prosperity and sustainable development of any country. This research is aligned with United Nations' Goal 3: Good Health and Well-being. It aims to assess and rank key health indicators across selected South Asian countries. The study focuses on urban areas in these nations, drawing on data from the World Bank’s primary collection of relevant indicators and specific health determinants outlined by the World Health Organization (WHO). These determinants include the physical environment, income and social status, education, social support networks, and personal behavior. To evaluate disparities in urban health across the region, the Urban Health Index (UHI) developed by Georgia State University, USA, is employed, followed by a mapping technique including visualization through a choropleth map to identify the pattern of spatial variations in our key variables, such as socioeconomic indicators across the region. This index serves as a comparative tool to rank health outcomes, where higher UHI values indicate better health conditions. The findings reveal notable disparities across South Asia. Afghanistan, with the lowest UHI score of 0.0423, ranks first, indicating the least favorable urban health conditions. Pakistan follows with a UHI score of 0.1190. Bangladesh and India rank third and fourth with UHI scores of 0.3099 and 0.3250, respectively. The Maldives and Sri Lanka rank fifth and sixth, with UHI scores of 0.3432 and 0.3495. Bhutan is ranked seventh with a score of 0.4750. Nepal, with a UHI score of 0.5012, ranks eighth, indicating the best urban health conditions among the countries studied. The findings of this research are crucial for addressing health disparities, improving living conditions, and enhancing social well-being in the region. These insights can inform policy measures aimed at reducing inequalities and promoting sustainable urban health in South Asia.

Keywords: urban health index, health disparities, sustainable development, South Asia, World Health Organization, United Nations, living conditions, public health

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Authors: Chau-Kuang Chen, Juanita Buford, Colette Davis, Raisha Allen, John Hughes, James Tyus, Dexter Samuels

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During the post-Civil War era, the city of Nashville, Tennessee, had the highest mortality rate in the country. The elevated death and disease among ex-slaves were attributable to the unavailability of healthcare. To address the paucity of healthcare services, the College, an institution with the mission of educating minority professionals and serving the under served population, was established in 1876. This study was designed to assess if the College has accomplished its mission of serving under served communities and contributed to the elimination of health disparities in the United States. The study objective was to quantify the impact of socioeconomic status and adverse health outcomes on primary care professionals serving disadvantaged communities, which, in turn, was significantly associated with a health professional shortage score partly designated by the U.S. Department of Health and Human Services. Various statistical methods were used to analyze the alumni data in years 1975 – 2013. K-means cluster analysis was utilized to identify individual medical and dental graduates into the cluster groups of the practice communities (Disadvantaged or Non-disadvantaged Communities). Discriminant analysis was implemented to verify the classification accuracy of cluster analysis. The independent t test was performed to detect the significant mean differences for clustering and criterion variables between Disadvantaged and Non-disadvantaged Communities, which confirms the “content” validity of cluster analysis model. Chi-square test was used to assess if the proportion of cluster groups (Disadvantaged vs Non-disadvantaged Communities) were consistent with that of practicing specialties (primary care vs. non-primary care). Finally, the partial least squares (PLS) path model was constructed to explore the “construct” validity of analytics model by providing the magnitude effects of socioeconomic status and adverse health outcome on primary care professionals serving disadvantaged community. The social ecological theory along with statistical models mentioned was used to establish the relationship between medical and dental graduates (primary care professionals serving disadvantaged communities) and their social environments (socioeconomic status, adverse health outcome, health professional shortage score). Based on social ecological framework, it was hypothesized that the impact of socioeconomic status and adverse health outcomes on primary care professionals serving disadvantaged communities could be quantified. Also, primary care professionals serving disadvantaged communities related to a health professional shortage score can be measured. Adverse health outcome (adult obesity rate, age-adjusted premature mortality rate, and percent of people diagnosed with diabetes) could be affected by the latent variable, namely socioeconomic status (unemployment rate, poverty rate, percent of children who were in free lunch programs, and percent of uninsured adults). The study results indicated that approximately 83% (3,192/3,864) of the College’s medical and dental graduates from 1975 to 2013 were practicing in disadvantaged communities. In addition, the PLS path modeling demonstrated that primary care professionals serving disadvantaged community was significantly associated with socioeconomic status and adverse health outcome (p < .001). In summary, the majority of medical and dental graduates from the College provide primary care services to disadvantaged communities with low socioeconomic status and high adverse health outcomes, which demonstrate that the College has fulfilled its mission.

Keywords: disadvantaged community, K-means cluster analysis, PLS path modeling, primary care

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Authors: Ainura Tursunalieva, Irene Hudson

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Continuous improvement of both the quality and safety of health care is an important goal in Australia and internationally. The intensive care unit (ICU) receives patients with a wide variety of and severity of illnesses. Accurately identifying patients at risk of developing complications or dying is crucial to increasing healthcare efficiency. Thus, it is essential for clinicians and researchers to have a robust framework capable of evaluating the risk profile of a patient. ICU scoring systems provide such a framework. The Acute Physiology and Chronic Health Evaluation III and the Simplified Acute Physiology Score II are ICU scoring systems frequently used for assessing the severity of acute illness. These scoring systems collect multiple risk factors for each patient including physiological measurements then render the assessment outcomes of individual risk factors into a single numerical value. A higher score is related to a more severe patient condition. Furthermore, the Mortality Probability Model II uses logistic regression based on independent risk factors to predict a patient’s probability of mortality. An important overlooked limitation of SAPS II and MPM II is that they do not, to date, include interaction terms between a patient’s vital signs. This is a prominent oversight as it is likely there is an interplay among vital signs. The co-existence of certain conditions may pose a greater health risk than when these conditions exist independently. One barrier to including such interaction terms in predictive models is the dimensionality issue as it becomes difficult to use variable selection. We propose an innovative scoring system which takes into account a dependence structure among patient’s vital signs, such as systolic and diastolic blood pressures, heart rate, pulse interval, and peripheral oxygen saturation. Copulas will capture the dependence among normally distributed and skewed variables as some of the vital sign distributions are skewed. The estimated dependence parameter will then be incorporated into the traditional scoring systems to adjust the points allocated for the individual vital sign measurements. The same dependence parameter will also be used to create an alternative copula-based model for predicting a patient’s probability of mortality. The new copula-based approach will accommodate not only a patient’s trajectories of vital signs but also the joint dependence probabilities among the vital signs. We hypothesise that this approach will produce more stable assessments and lead to more time efficient and accurate predictions. We will use two data sets: (1) 250 ICU patients admitted once to the Chui Regional Hospital (Kyrgyzstan) and (2) 37 ICU patients’ agitation-sedation profiles collected by the Hunter Medical Research Institute (Australia). Both the traditional scoring approach and our copula-based approach will be evaluated using the Brier score to indicate overall model performance, the concordance (or c) statistic to indicate the discriminative ability (or area under the receiver operating characteristic (ROC) curve), and goodness-of-fit statistics for calibration. We will also report discrimination and calibration values and establish visualization of the copulas and high dimensional regions of risk interrelating two or three vital signs in so-called higher dimensional ROCs.

Keywords: copula, intensive unit scoring system, ROC curves, vital sign dependence

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Authors: Lukas Reznak, Maria Reznakova

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Recession of an economy has a profound negative effect on all involved stakeholders. It follows that timely prediction of recessions has been of utmost interest both in the theoretical research and in practical macroeconomic modelling. Current mainstream of recession prediction is based on standard OLS models of continuous GDP using macroeconomic data. This approach is not suitable for two reasons: the standard continuous models are proving to be obsolete and the macroeconomic data are unreliable, often revised many years retroactively. The aim of the paper is to explore a different branch of recession forecasting research theory and verify the findings on real data of the Czech Republic and Germany. In the paper, the authors present a family of discrete choice probit models with parameters estimated by the method of maximum likelihood. In the basic form, the probits model a univariate series of recessions and expansions in the economic cycle for a given country. The majority of the paper deals with more complex model structures, namely dynamic and bivariate extensions. The dynamic structure models the autoregressive nature of recessions, taking into consideration previous economic activity to predict the development in subsequent periods. Bivariate extensions utilize information from a foreign economy by incorporating correlation of error terms and thus modelling the dependencies of the two countries. Bivariate models predict a bivariate time series of economic states in both economies and thus enhance the predictive performance. A vital enabler of timely and successful recession forecasting are reliable and readily available data. Leading indicators, namely the yield curve and the stock market indices, represent an ideal data base, as the pieces of information is available in advance and do not undergo any retroactive revisions. As importantly, the combination of yield curve and stock market indices reflect a range of macroeconomic and financial market investors’ trends which influence the economic cycle. These theoretical approaches are applied on real data of Czech Republic and Germany. Two models for each country were identified – each for in-sample and out-of-sample predictive purposes. All four followed a bivariate structure, while three contained a dynamic component.

Keywords: bivariate probit, leading indicators, recession forecasting, Czech Republic, Germany

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Authors: Mohammadreza Khanmohammadi, Noureddin Nakhostin Ansari, Soofia Naghdi

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Purpose: Our study aimed to validate single item presenteeism question (SIPQ) into the Persian language for patients with low back pain. Background information: low back pain is a common health problem, and it is one of the most prevalent disorder in working people. There are the different subjective way to evaluate the effect of back pain on work productivity that one of them is by implementing single item presenteeism question. This question has not been validated into the Persian language. Method: Patients were asked to answer SIPQ and pain from 0 to 10 according to numerical rating scale (NRS). The functional rating index was administrated to evaluate construct validity. For test-retest reliability, almost 50 patients re-completed the Persian SIPQ. The construct validity of SIPQ was assessed by analyzing Spearman rank correlation between this question and the Persian version of Functional rating index questionnaire. To analyze test-retest reliability, we assessed intraclass correlation coefficient (agreement) (ICC agreement) (two-way random effects model, single measure). Results: The SIPQ score of two groups of patients (84 males, 16 females, mean age ±SD: 33.85±11.16 years, range: 19-67 years) and healthy subjects (48 male, 2 female ones, mean age ±SD: 24.24 ±8.07 years) was statistically significant. (Mann-Whitney U =198.00, P<.001). The Spearman correlation of data showed that there is a significant correlation between Persian SIPQ score and Persian FRI band (r= .559, P<.001). The ICC was .62. So, the analysis indicated good, test-retest reliability. Conclusion: This study showed that Persian version of SIPQ is reliable and valid when applied to back pain patients.

Keywords: cross cultural adaptation, economic burden, low back pain, Persian language, translation

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Authors: S. Levitsky, R. Bergman

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Fluid rheology may have essential impact on sound propagation in a liquid-filled pipe, especially, in a low frequency range. Rheological parameters of liquid are temperature-sensitive, which ultimately results in a temperature dependence of the wave speed and attenuation in the waveguide. The study is devoted to modeling of this effect at sound propagation in an elastic pipe with polymeric liquid, described by generalized Maxwell model with non-zero high-frequency viscosity. It is assumed that relaxation spectrum is distributed according to the Spriggs law; temperature impact on the liquid rheology is described on the basis of the temperature-superposition principle and activation theory. The dispersion equation for the waveguide, considered as a thin-walled tube with polymeric solution, is obtained within a quasi-one-dimensional formulation. Results of the study illustrate the influence of temperature on sound propagation in the system.

Keywords: elastic tube, sound propagation, temperature effect, viscoelastic liquid

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Authors: Renzo Munoz-Najar, Cristina Wong, Daniel De La Torre Ugarte

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The literature on price transmission usually deals with asymmetries related to different commodities and/or the short and long term. The role of domestic regional differences and the relationship with asymmetries within a country are usually left out. This paper looks at the asymmetry in the transmission of rice prices from the international price to the farm gate prices in four northern regions of Peru for the last period 2001-2016. These regions are San Martín, Piura, Lambayeque and La Libertad. The relevance of the study lies in its ability to assess the need for policies aimed at improving the competitiveness of the market and ensuring the benefit of producers. There are differences in planting and harvesting dates, as well as in geographic location that justify the hypothesis of the existence of differences in the price transition asymmetries between these regions. Those differences are due to at least three factors geography, infrastructure development, and distribution systems. For this, the Threshold Vector Error Correction Model and the Autoregressive Vector Model with Threshold are used. Both models, collect asymmetric effects in the price adjustments. In this way, it is sought to verify that farm prices react more to falls than increases in international prices due to the high bargaining power of intermediaries. The results of the investigation suggest that the transmission of prices is significant only for Lambayeque and La Libertad. Likewise, the asymmetry in the transmission of prices for these regions is checked. However, these results are not met for San Martin and Piura, the main rice producers nationwide. A significant price transmission is verified only in the Lambayeque and La Libertad regions. San Martin and Piura, in spite of being the main rice producing regions of Peru, do not present a significant transmission of international prices; a high degree of self-sufficient supply might be at the center of the logic for this result. An additional finding is the short-term adjustment with respect to international prices, it is higher in La Libertad compared to Lambayeque, which could be explained by the greater bargaining power of intermediaries in the last-mentioned region due to the greater technological development in the mills.

Keywords: asymmetric price transmission, rice prices, price transmission, regional economics

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Authors: Dimiter M. Dimitrov, Abdullah Sadaawi

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The National Assessment for Schools (NAFS) in Saudi Arabia consists of standardized tests in Mathematics, Reading, and Science for school grade levels 3, 6, and 9. One main goal is to classify students into four categories of NAFS performance (minimal, basic, proficient, and advanced) by schools and the entire national sample. The NAFS scoring and equating is performed on a bounded scale (D-scale: ranging from 0 to 1) in the framework of the recently developed “D-scoring method of measurement.” The specificity of the NAFS measurement framework and data complexity presented both challenges and opportunities to (a) the estimation of score reliability for schools, (b) setting cut-scores for the classification of students into categories of performance, and (c) generating plausible values for distributions of student performance on the D-scale. The estimation of score reliability at the school level was performed in the framework of generalizability theory (GT), with students “nested” within schools and test items “nested” within test forms. The GT design was executed via a multilevel modeling syntax code in R. Cut-scores (on the D-scale) for the classification of students into performance categories was derived via a recently developed method of standard setting, referred to as “Response Vector for Mastery” (RVM) method. For each school, the classification of students into categories of NAFS performance was based on distributions of plausible values for the students’ scores on NAFS tests by grade level (3, 6, and 9) and subject (Mathematics, Reading, and Science). Plausible values (on the D-scale) for each individual student were generated via random selection from a statistical logit-normal distribution with parameters derived from the student’s D-score and its conditional standard error, SE(D). All procedures related to D-scoring, equating, generating plausible values, and classification of students into performance levels were executed via a computer program in R developed for the purpose of NAFS data analysis.

Keywords: large-scale assessment, reliability, generalizability theory, plausible values

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Authors: Othmane Touri, Sanaa El Filali, El Habib Benlahmar

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Natural Language Processing (NLP) has gained significant attention lately. It has proved its ability to analyze and extract insights from unstructured text data in various languages. It is found that one of the most popular NLP applications is sentiment analysis which aims to identify the sentiment expressed in a piece of text, such as positive, negative, or neutral, in multiple languages. While there are several multilingual NLP models available for sentiment analysis, there is a need to investigate their effectiveness in different contexts and applications. In this study, we aim to investigate the effectiveness of different multilingual NLP models for sentiment analysis on a dataset of online product reviews in multiple languages. The performance of several NLP models, including Google Cloud Natural Language API, Microsoft Azure Cognitive Services, Amazon Comprehend, Stanford CoreNLP, spaCy, and Hugging Face Transformers are being compared. The models based on several metrics, including accuracy, precision, recall, and F1 score, are being evaluated and compared to their performance across different categories of product reviews. In order to run the study, preprocessing of the dataset has been performed by cleaning and tokenizing the text data in multiple languages. Then training and testing each model has been applied using a cross-validation approach where randomly dividing the dataset into training and testing sets and repeating the process multiple times has been used. A grid search approach to optimize the hyperparameters of each model and select the best-performing model for each category of product reviews and language has been applied. The findings of this study provide insights into the effectiveness of different multilingual NLP models for Multilingual Sentiment Analysis and their suitability for different languages and applications. The strengths and limitations of each model were identified, and recommendations for selecting the most performant model based on the specific requirements of a project were provided. This study contributes to the advancement of research methods in multilingual NLP and provides a practical guide for researchers and practitioners in the field.

Keywords: NLP, multilingual, sentiment analysis, texts

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Authors: Ayse Inel Manav, Saliha Bozdogan Yesilot, Pinar Yesil Demirci, Gursel Oztunc

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Introduction: This study was conducted with a view to evaluating cognitive function level, depression, and quality of life of elderly people living in a nursing home. Methods: This study, which is cross-sectional and descriptive in nature, was conducted in the Nursing and Rehabilitation Center for the Elderly in Adana/Turkey between 1st of May and 1st of August, 2016. The participants included 118 elderly people who were chosen using simple random sampling method. The data were collected using the Personal Information Form, the Standardized Mini Mental State Exam (SMMSE), the Geriatric Depression Scale (GDS), and the World Health Organization Quality of Life-OLD (WHOQOL-OLD) module. The data were analyzed using IBM SPSS Statistics 22 (IBM, SPSS, Turkey) program. Results: Of all the participants, 36,4% (n=43) were female, 63,6% (n=75) were male, and average age was 74,08 ± 8,23 years. The participants’ SMMSE mean score was found 20,37 ± 7,08, GDS mean score was 14,92 ± 4,29, and WHOQOL-OLD module mean score was 69,76 ± 11,54. There was a negative, significant relationship between SMMSE and GDS scores, a positive relationship between WHOQOL-OLD module total scores and a negative, significant relationship between GDS scores and WHOQOL-OLD module total scores. Discussıon and Conclusion: Results showed that more than half of the elderly people living in the nursing home experienced cognitive deterioration and depression; and cognitive state, depression, and quality of life were found to be significantly related to each other.

Keywords: depression, cognitive function level, quality of life

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Authors: Siti Sabariah Buhari, Ruzita Abdul Talib, Poh Bee Koon

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This study aims to develop and evaluate an intervention to improve eating habits, active lifestyle and weight status of overweight and obese children in Negeri Sembilan. The H.E.B.A.T! Program involved children, parents, and school and focused on behaviour and environment modification to achieve its goal. The intervention consists of H.E.B.A.T! Camp, parent’s workshop and school-based activities. A total of 21 children from intervention school and 22 children from control school who had BMI for age Z-score ≥ +1SD participated in the study. Mean age of subjects was 10.8 ± 0.3 years old. Four phases were included in the development of the intervention. Evaluation of intervention was conducted through process, impact and outcome evaluation. Process evaluation found that intervention program was implemented successfully with minimal modification and without having any technical problems. Impact and outcome evaluation was assessed based on dietary intake, average step counts, BMI for age z-score, body fat percentage and waist circumference at pre-intervention (T0), post-intervention 1 (T1) and post-intervention 2 (T2). There was significant reduction in energy (14.8%) and fat (21.9%) intakes (at p < 0.05) at post-intervention 1 (T1) in intervention group. By controlling for sex as covariate, there was significant intervention effect for average step counts, BMI for age z-score and waist circumference (p < 0.05). In conclusion, the intervention made an impact on positive behavioural intentions and improves weight status of the children. It is expected that the HEBAT! Program could be adopted and implemented by the government and private sector as well as policy-makers in formulating childhood obesity intervention.

Keywords: childhood obesity, diet, obesity intervention, physical activity

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Authors: Usman Bawa

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Macro-invertebrates have been used to monitor organic pollution in rivers and streams. Several biotic indices based on macro-invertebrates have been developed over the years including the Biological Monitoring Working Party (BMWP). A new biotic index, the Gammarus:Asellus ratio has been recently proposed as an index of organic pollution. This study tested the validity of the Gammarus:Asellus ratio as an index of organic pollution, by examining the relationship between the Gammarus:Asellus ratio and physical-chemical parameters, and other biotic indices such as BMWP and, Average Score Per Taxon (ASPT) from lakes and streams at Markeaton Park, Allestree Park, and Kedleston Hall, Derbyshire. Macro invertebrates were sampled using the standard five-minute kick sampling techniques physical and chemical environmental variables were obtained based on standard sampling techniques. Eighteen sites were sampled, six sites from Markeaton Park (three sites across the stream and three sites across the lake). Six sites each were also sampled from Allestree Park and Kedleston Hall lakes. The Gammarus:Asellus ratio showed an opposite significant positive correlations with parameters indicative of organic pollution such as the level of nitrates, phosphates, and calcium and also revealed a negatively significant correlations with other biotic indices (BMWP/ASPT). The BMWP score correlated positively significantly with some water quality parameters such as dissolved oxygen and flow rate, but revealed no correlations with other chemical environmental variables. The BMWP score was significantly higher in the stream than the lake in Markeaton Park, also The ASPT scores appear to be significantly higher in the upper Lakes than the middle and lower lakes. This study has further strengthened the use of BMWP/ASPT score as an index of organic pollution. But, additional application is required to validate the use of Gammarus:Asellus as a rapid bio monitoring tool.

Keywords: Asellus, biotic index, Gammarus, macro invertebrates, organic pollution

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Authors: Ari Prasetyo, Taufik Faturohman

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The COVID-19 pandemic in Indonesia is part of the ongoing worldwide pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was confirmed to have spread to Indonesia on 2 March 2020. Moreover, the government of Indonesia has been conducting a local lockdown to limit people's movement from one city to another city. Therefore, this situation has impact on business operation, especially on highway companies listed on the Indonesia stock exchange. This study evaluates and measures three companies’ financial performance and health conditions before and during the COVID-19 pandemic from 2016 – 2020. The measurement is conducted by using financial ratio analysis and the Altman Z-score method. The ratio used to measure the financial ratio analysis is taken from the decree of the Ministry of SOE’s KEP-100/MBU/2002 about the company’s health level condition. From the decree, there are eight financial ratios used such as return on equity (ROE), return on investment (ROI), current ratio, cash ratio, collection period, inventory turnover, total asset turnover, and total equity to total asset. Altman Z-score is used to calculate the financial distress condition. The result shows that the highway companies for the period 2016 – 2020 are as follows: PT Jasa Marga (Persero) Tbk (AA, BB, BB, BB, C), PT Citra Marga Nusaphala Persada Tbk (BB, AA, BB, BBB, C), and PT Nusantara Infrastructure Tbk (BB, BB, AA, BBB, C). In addition, the Altman Z-score assessment performed in 2016-2020 shows that PT Jasa Marga (Persero) Tbk was in the grey zone area for 2015-2018 and in the distress zone for 2019-2020. PT Citra Marga Nusaphala Persada Tbk was in the grey zone area for 2015-2019 and in the distress zone for 2020. PT Nusantara Infrastructure Tbk was in the grey zone area for 2015-2018 and in the distress zone for 2019-2020.

Keywords: financial performance, financial ratio, Altman Z-score, financial distress, highway company

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Authors: Paul Bigala, Samuel Oladipo, Steven Adebowale

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This study examines factors influencing knowledge of HIV/AIDS Prevention Practices and Transmission (KHAPPT) among young undergraduate students (15-24 years). Knowledge composite index was computed for 820 randomly selected students. Chi-square, ANOVA, and multinomial logistic regression were used for the analyses (α=.05). The overall mean knowledge score was 16.5±3.4 out of a possible score of 28. About 83% of the students have undergone HIV test, 21.0% have high KHAPPT, 18% said there is cure for the disease, 23% believed that asking for condom is embarrassing and 11.7% said it is safe to share unsterilized sharp objects with friends or family members. The likelihood of high KHAPPT was higher among students who have had HIV test (OR=3.314; C.I=1.787-6.145, p<0.001) even when other variables were used as control. The identified predictors of high KHAPPT were; ever had HIV test, faculty, and ever used any HIV/AIDS prevention services. North-West University Mafikeng should intensify efforts on the HIV/AIDS awareness program on the campus.

Keywords: HIV/AIDS knowledge, undergraduate students, HIV testing, Mafikeng

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Authors: José Davi Oliveira De Moura, Chou Sin Chan

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In this study, the effect of convective momentum transport (CMT) on the life of cyclone systems and their organization is analyzed. A case of strong precipitation, in the southeast of Brazil, was simulated using Eta model with two kinds of convective parameterization: Kain-Fritsch without CMT and Kain-fritsch with CMT. Reanalysis data from CFSR were used to compare Eta model simulations. The Wind, mean sea level pressure, rain and temperature are included in analysis. The rain was evaluated by Equitable Threat Score (ETS) and Bias Index; the simulations were compared among themselves to detect the influence of CMT displacement on the systems. The result shows that CMT process decreases the intensity of meso cyclones (higher pressure values on nuclei) and change the positions and production of rain. The decrease of intensity in meso cyclones should be caused by the dissolution of momentum from lower levels from up levels. The rain production and rain distribution were altered because the displacement of the larger systems scales was changed. In addition, the inclusion of CMT process is very important to improve the simulation of life time of meteorological systems.

Keywords: convection, Kain-Fritsch, momentum, parameterization

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Authors: Ebenezer Amoquandoh, Daniel Bruce Sarpong, Godfred K. Ofosu-Budu, Andreas Fliessbach

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Soil quality is at stake globally, but under tropical conditions, the loss of soil fertility may be existential. The current rates of soil nutrient depletion, erosion and environmental degradation in most of Africa’s farmland urgently require methods for soil fertility restoration through affordable agricultural management techniques. The study assessed the effects of locally available organic resources to improve soil fertility, crop yield and profitability compared to business as usual on farms in the Eastern and Greater Accra regions of Ghana. Apart from this, we analyzed the change of farmers’ perceptions and knowledge upon the experience with the new techniques; the effect of using locally available organic resource on farmers’ yield and determined the factors influencing the profitability of farming. Using the Difference in Mean Score and Proportion to estimate the extent to which farmers’ perceptions, knowledge and practices have changed, the study showed that farmers’ perception, knowledge and practice on the use of locally available organic resources have changed significantly. This paves way for the sustainable use of locally available organic resource for soil fertility improvement. The Propensity Score Matching technique and Endogenous Switching Regression model used showed that using locally available organic resources have the potential to increase crop yield. It was also observed that using the Profit Margin, Net Farm Income and Return on Investment analysis, it is more profitable to use locally available organic resources than other soil fertility amendments techniques studied. The results further showed that socioeconomic, farm characteristics and institutional factors are significant in influencing farmers’ decision to use locally available organic resources and profitability.

Keywords: soil fertility, locally available organic resources, perception, profitability, sustainability

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Authors: Byungchae Kim, Jiwoo Nam

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Aging of weapon systems can cause the failure and degeneration of components which results in increase of operating and support costs. However, whether this aging effect is significantly strong and it influences a lot on national defense spending due to the rapid increase in operating and support (O&S) costs is questionable. To figure out this, we conduct a literature review analyzing the aging effect of US weapon systems. We also conduct an empirical research using a maintenance database of Korean weapon systems, Defense Logistics Integrated Information System (DAIIS). We run regression of various types of O&S cost on weapon system age to investigate the statistical significance of aging effect and use generalized linear model to find relations between the failure of different priced components and the age. Our major finding is although aging effect exists, its impacts on weapon system cost seem to be not too large considering several characteristics of O&S cost elements not relying on the age.

Keywords: O&S cost, aging effect, weapon system, GLM

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Authors: Roselyn Chipojola, Shu-yu Kuo

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Background: Breastfeeding self-efficacy in both mothers and fathers play a crucial role in improving exclusive breastfeeding rates. However, less is known on the relationship and predictors of paternal and maternal breastfeeding self-efficacy. This study aimed to examine the relationship and associated factors of breastfeeding self-efficacy (BSE) among mothers and fathers in Malawi. Methods: A cross-sectional study was conducted on 180 pairs of postpartum mothers and fathers at a tertiary maternity facility in central Malawi. BSE was measured using the Breastfeeding Self-Efficacy Scale Short-Form. Depressive symptoms were assessed by the Edinburgh Postnatal Depression Scale. A structured questionnaire was used to collect demographic and health variables. Data were analyzed using multivariable logistic regression and multinomial logistic regression. Results: A higher score of self-efficacy was found in mothers (mean=55.7, Standard Deviation (SD) =6.5) compared to fathers (mean=50.2, SD=11.9). A significant association between paternal and maternal breastfeeding self-efficacy was found (r= 0. 32). Age, employment status, mode of birth was significantly related to maternal and paternal BSE, respectively. Older age and caesarean section delivery were significant factors of combined BSE scores in couples. A higher BSE score in either the mother or her partner predicted higher exclusive breastfeeding rates. BSE scores were lower when couples’ depressive symptoms were high. Conclusion: BSE are highly correlated between Malawian mothers and fathers, with a relatively higher score in maternal BSE. Importantly, a high BSE in couples predicted higher odds of exclusive breastfeeding, which highlights the need to include both mothers and fathers in future breastfeeding promotion strategies.

Keywords: paternal, maternal, exclusive breastfeeding, breastfeeding self‑efficacy, malawi

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Authors: Eisa Al-Balhan

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This study explores the effects of parents’ personality and family variables on aggressive behavior in children from the State of Kuwait. The sample of 117children aged between 6 and 10 years (M=7.79 years, SD =1.4 years),117 fathers, and 117mothers from Kuwait. The following tools were used: a) the Aggressive Behavior Scale for Children (ABSC), b) the Personality Scales Inventory (PSI), and c) the Family Climate Scale (FCS). The results show that there were significant differences between children with highly aggressive behavior and those with low aggressive behavior for most of the personality traits of the father and mother, as well as most of the family climate and its different dimensions according to the father’s knowledge and the mother’s knowledge. Furthermore, there was a significant difference between males and females in the total score of aggressive behavior, verbal aggression, physical aggression, self-aggression, and aggression toward others, with higher scores occurring among males. Most of the correlations of the children’s aggressive behavior were with the personality traits of the father. The personality traits of the mother, family climate, and most of its different dimensions according to the father's and mother's knowledge had significant negative correlations with the child's aggression. There was no effect of the mother's and father's education levels on their child’s aggressive behavior. There was a significant difference between normal families and separated families in the total score of aggressive behavior, verbal aggression, and self-aggression, with a higher score occurring among separated families, and there was no significant difference between the two groups in physical aggression and aggression towards others.

Keywords: aggressive behavior, personality traits of parents, family variables, parents

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