Miracle-dz/UniFAR-FTUs huggingface dataset:
TRACE: Tennessee Research and Creative TRACE: Tennessee Research and Creative Exchange Exchange

Retinoids are recognized as important regulators of vertebrate development, cell differentiation, and tissue function. Previous studies, performed both in vivo and in vitro, indicate that retinoids influence several reproductive events, including follicular development, oocyte maturation and early embryonic development. The present study evaluated in vitro effects of retinol addition to media containing maturing bovine oocytes and developing embryos in both a low oxygen atmosphere (7%) and under atmospheric oxygen conditions (20%). In the first experiment, abbatoir collected bovine oocytes were matured in the presence or absence of varying concentrations of retinol. After a 22–24 hour maturation period the oocytes were fertilized, denuded 18 hours later and cultured in a modified synthetic oviductal fluid (mSOF) in a humidified atmosphere at 38.5 degrees C, 5% CO2, 7% O2 and 88% N2. Cleavage rates did not differ among control and retinol-treated oocytes in all three experiments. Addition of 5 micromolar retinol to the maturation medium (IVM) tended (p < 0.07) to increase blastocyst formation (blastocyst/putative zygote; 26.1% +/- 2.2%) compared to the controls (21.9% +/- 1.9%). Further analysis revealed when blastocyst development rates fell below 20% in the control groups, 5 micromolar retinol treatment dramatically improved embryonic development, measured by blastocyst/putative zygote rate (14.4 +/- 2.1 vs 23.7 +/- 2.5; p < 0.02). The 5 micomolar retinol treatment also enhanced the blastocyst/ cleaved rate by nearly 10% (23.7% vs 34.6%; p < 0.02). In the second and third experiments addition of 5 micromolar retinol to the embryo culture medium (IVC) under low oxygen conditions did not significantly improve cleavage or blastocyst rates, but 5 micromolar retinol significantly increased blastocyst development under 20% O2 conditions (p < 0.001). These studies demonstrate that supplementation of 5 micromolar retinol to the maturation medium may improve embryonic development of bovine oocytes indicated by their increased blastocyst rate. A significant improvement in the blastocyst development with the 5 micromolar retinol treatment under atmospheric conditions suggests a beneficial antioxidant effect during embryo culture.
Numerical Investigation of the Effects of Diffusion Time on the Mechanisms of Transition from a Turbulent Jet Flame to Detonation in a H2-Air Mixture

The current study primarily aimed to simulate detonation initiation via turbulent jet flame acceleration in partial-premixed H2-air mixtures. Different vertical concentration gradients were generated by varying the duration of hydrogen injection (diffusion time) within an enclosed channel filled with air. H2-air mixtures with average hydrogen concentrations of 22.5% (lean mixture) and 30% (near stoichiometric mixture) were investigated at diffusion times of 3, 5, and 60 s. Numerical results show that the vertical concentration gradient significantly influences the early stage of flame acceleration (FA). In the stratified lean mixture, detonation began at all the diffusion times, and comparing the flame-speed graphs showed that a decrease in the diffusion time and an increase in the mixture inhomogeneity speeded up the flame propagation and the jet flame-to-detonation transition occurrence in the channel. In the stratified H2-air mixture with an average hydrogen concentration of 30%, the transition from a turbulent jet flame to detonation occurred in all the cases, and the mixture inhomogeneity weakened the FA and delayed the detonation initiation.
Comparative Analysis of Machine Learning Models for Lung Cancer Prediction

Because of the COVID-19 pandemic in recent years, conventional lung diseases have been gradually neglected, especially lung cancer, which used to have a high mortality rate. This paper focuses on the prediction of lung cancer to help provide some suggestions for lung cancer prevention. In this paper, we will compare different types of machine learning prediction models and algorithms to select a class of algorithmic models that are most suitable for lung cancer prediction to achieve the purpose of the study. After the experimental comparison, after combining the results of the secondary and tertiary indicators and ROC curves generated by the confusion matrix, it is concluded that the ensemble learning algorithm model represented by the random forest algorithm model is the optimal class of prediction algorithm models for lung cancer prediction.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Nov 24, 2022 Revised Jan 11, 2023 Accepted Mar 10, 2023 The use of mobile applications extends to the telecommunication sector, mainly due to COVID-19. Failure to provide it can cause dissatisfaction and result in the removal of the mobile application. Moreover, this leads to lost service opportunities, so paying attention to the mobile application's quality is essential. There has yet to be a study on measuring the service quality of a self-service mobile application in the telecommunication sector using online customer reviews. This study uses sentiment analysis and topic modeling to determine the service quality of a self-service mobile application in the telecommunication sector from reviews on Google Play Store and Apple App Store. This study uses myIndiHome as a case study. The total data obtained from both platforms are 20,452 reviews. Sentiment analysis was performed using Naïve Bayes, support vector machine, and logistic regression, while topic modeling was performed using latent dirichlet allocation. The results show that logistic regression performs better than support vector machine and Naïve Bayes. Meanwhile, topic modeling shows that the positive review data has three topics, including application features, products/services, and application interfaces. Moreover, the negative review data has five topics, including application availability, application feature reliability, application processing speed, bugs, and application reliability.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Dec 8, 2022 Revised Feb 1, 2023 Accepted Mar 10, 2023 Bantuan Langsung Tunai Dana Desa (BLT-DD), or known as Village Fund Direct Cash Assistance is assistance from the Indonesian government which causes problems and conflicts in the community when the assistance is not on target. The classification algorithm is proven to use in determining BLT-DD recipients. In this study, the radial basis function (RBF) and elman recurrent neural network (ERNN) models compare to classify the eligibility of BLTDD recipients. In the experiment, the optimal performance of the RBF and ERNN compare in determining the eligibility of BLT-DD recipients. Also, it’s compared with the classification algorithm that implements the same data, namely BLT-DD data for Kubu Raya District. The experimental results show the effectiveness of the RBF model in recognizing test data, while the ERNN model is effective in identifying test data. The RBF and ERNN models can achieve the same total accuracy of 98.10%.
Glove based wearable devices for sign language-GloSign

Loss of the capability to talk or hear has psychological and social effects onthe affected individuals due to the absence of appropriate interaction. SignLanguage is used by such individuals to assist them in communicating witheach other. This paper proposes a glove called GloSign that can convertAmerican sign language to characters. This glove consists of flex and inertialmeasurement unit (IMU) sensors to identify gestures. The data from glove isuploaded on IoT platform, which makes the glove portable and wireless. Thedata from gloves is passed through a k-nearest neighbors (KNN) Algorithmmachine learning algorithm to improve the accuracy of the system. Thesystem was able to achieve an accuracy of 96.8%. The glove can also be usedto form sentences. The output is displayed on the screen or is converted tospeech. This glove can be used in communicating with people who don’t knowsign language.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Jan 12, 2023 Revised Mar 19, 2023 Accepted Mar 27, 2023 Person re-identification (Person Re-ID) is a research direction on tracking and identifying people in surveillance camera systems with non-overlapping camera perspectives. Despite much research on this topic, there are still some practical problems that Person Re-ID has not yet solved, in reality, human objects can easily be obscured by obstructions such as other people, trees, luggage, umbrellas, signs, cars, motorbikes. In this paper, we propose a multibranch deep learning network architecture. In which one branch is for the representation of global features and two branches are for the representation of local features. Dividing the input image into small parts and changing the number of parts between the two branches helps the model to represent the features better. In addition, we add an attention module to the ResNet50 backbone that enhances important human characteristics and eliminates irrelevant information. To improve robustness, the model is trained by combining triplet loss and label smoothing cross-entropy loss (LSCE). Experiments are carried out on datasets Market1501, and duke multi-target multi-camera (DukeMTMC) datasets, our method achieved 96.04% rank-1, 88,11% mean average precision (mAP) on the Market1501 dataset, and 88.78% rank-1, 78,6% mAP on the DukeMTMC dataset. This method achieves performance better than some state-of-the-art methods.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Aug 11, 2021 Revised Jan 27, 2022 Accepted Feb 12, 2022 Language identification (LI) in textual documents is the process of automatically detecting the language contained in a document based on its content. The present language identification techniques presume that a document contains text in one of the fixed set of languages. However, this presumption is incorrect when dealing with multilingual document which includes content in more than one possible language. Due to the unavailability of standard corpora for Hindi-English mixed lingual language processing tasks, we propose the language lexicons, a novel kind of lexical database that augments several bilingual language processing tasks. These lexicons are built by learning classifiers over English and transliterated Hindi vocabulary. The designed lexicons possess condensed quantitative characteristics which reflect their linguistic strength in respect of Hindi and English language. On evaluating the lexicons, it is observed that words of the same language tend to cluster together and are separable over language classes. On comparing the classifier performance with existing works, the proposed lexicon models exhibit the better performance.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Sep 30, 2022 Revised Feb 16, 2023 Accepted Mar 10, 2023 Every animal meat has different color and texture, for example, beef has a dark red color with a chewy texture, while pork has a pale red color and smooth fiber. A previous study has classified types of meat using gray level co-ocurrence matrix (GLCM), hue saturation value (HSV), and color intensity. In this research, we created meat classification between beef, pork, and horse meat using a convolutional neural network (CNN) develop in jupyter notebook, using the MobileNetV2 model, and 315 meat images as a dataset divided into 3 groups, 70% image for the training dataset, 20% image for the testing dataset, and 10% image for validation dataset. Before dividing the image into 3 groups, the image is resized to 224×224, and convert the color to grayscale. The model is trained with a training dataset, the epoch of 50, and Adam optimizer, the results show an accuracy of 93.15%.
Fire and Rescue Services Reconﬁguration for Better Dealing with Post-Flashover Building Fires

: The reconﬁguration of ﬁre and rescue services is the focus of this research. The main purpose is to propose an alternative organizational model for better dealing with post-ﬂashover building ﬁres. The primary source of empirical information is a survey involving 267 managers from the Bulgarian ﬁre and rescue services to gather insights. The research methodology meticulously encompasses the identiﬁcation of changes concerning the efﬁciency and effectiveness of the ﬁre and rescue services, the discernment of inherent challenges, the deﬁnition of speciﬁc factors and criteria relevant to their activities, and the determination of strategic priorities. The goal is to suggest a new, more efﬁcient way to organize these services in Bulgaria, whose approach can be applicable to other contexts in similar conditions as well. The proposed model suggests that the Bulgarian ﬁre and rescue services should work independently from the Ministry of Interior as the State Agency under the Council of Ministers. In addition, a comparison between this newly proposed model and the current one is conducted to highlight its potential advantages.
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Sep 9, 2022 Revised Dec 29, 2022 Accepted Jan 10, 2023 Traditional word embedding models have been used in the feature extraction process of deep learning models for sentiment analysis. However, these models ignore the sentiment properties of words while maintaining the contextual relationships and have inadequate representation for domainspecific words. This paper proposes a method to develop a meta embedding model by exploiting domain sentiment polarity and adverse drug reaction (ADR) features to render word embedding models more suitable for medical sentiment analysis. The proposed lexicon is developed from the medical blogs corpus. The polarity scores of the existing lexicons are adjusted to assign new polarity score to each word. The neural network model utilizes sentiment lexicons and ADR in learning refined word embedding. The refined embedding obtained from the proposed approach is concatenated with original word vectors, lexicon vectors, and ADR feature to form a meta-embedding model which maintains both contextual and sentimental properties. The final meta-embedding acts as a feature extractor to assess the effectiveness of the model in drug reviews sentiment analysis. The experiments are conducted on global vectors (GloVE) and skip-gram word2vector (Word2Vec) models. The empirical results demonstrate the proposed meta-embedding model outperforms traditional word embedding in different performance measures.
Forest health monitoring: national status, trends, and analysis 2013

The annual national report of the Forest Health Monitoring (FHM) Program of the Forest Service, U.S. Department of Agriculture, presents forest health status and trends from a national or multi-State regional perspective using a variety of sources, introduces new techniques for analyzing forest health data, and summarizes results of recently completed Evaluation Monitoring projects funded through the FHM national program. In this 13th edition in a series of annual reports, survey data are used to identify geographic patterns of insect and disease activity. Satellite data are employed to detect geographic patterns of forest fire occurrence. Recent drought conditions are compared across the conterminous United States. Data collected by the Forest Inventory and Analysis (FIA) Program are employed to detect regional differences in tree mortality. A satellite-derived change detection system operating across the conterminous United States is described. A conceptual organization of existing and future technologies to support and improve forest health monitoring is presented. FIA data are used to produce a national map of invasive plant species infestation and to evaluate changes in crown conditions during the last decade. Five recently completed Evaluation Monitoring projects are summarized, addressing forest health concerns at smaller scales.
Distributed Model Predictive Control for Multiple Trains with Coupled Constraints

This paper presents a distributed model predictive control of multiple trains, aiming at overcoming coupled constraints and achieving cooperative operation while reducing the computational burden. According to the coupled constraints associated with maintaining safety distance between adjacent trains, trains are divided into sub-systems and distributed optimal control problem is then formulated. To solve the problem, an alternating direction method of multipliers (ADMM) is employed with a fully distributed scheme, decomposing the original optimal control problem into smaller sub-problems that can be computed in parallel and meet the real-time control requirements. The proposed method effectively guarantees the cooperative operation of trains under local and coupled safety constraints. Numerical simulations are provided to demonstrate the feasibility and effectiveness of the proposed train control model and methods.
Development and validation of a machine learning model for differential diagnosis of malignant pleural effusion using routine laboratory data

Background: The differential diagnosis of malignant pleural effusion (MPE) and benign pleural effusion (BPE) presents a clinical challenge. In recent years, the use of artificial intelligence (AI) machine learning models for disease diagnosis has increased. Objective: This study aimed to develop and validate a diagnostic model for early differentiation between MPE and BPE based on routine laboratory data. Design: This was a retrospective observational cohort study. Methods: A total of 2352 newly diagnosed patients with pleural effusion (PE), between January 2008 and March 2021, were eventually enrolled. Among them, 1435, 466, and 451 participants were randomly assigned to the training, validation, and testing cohorts in a ratio of 3:1:1. Clinical parameters, including age, sex, and laboratory parameters of PE patients, were abstracted for analysis. Based on 81 candidate laboratory variables, five machine learning models, namely extreme gradient boosting (XGBoost) model, logistic regression (LR) model, random forest (RF) model, support vector machine (SVM) model, and multilayer perceptron (MLP) model were developed. Their respective diagnostic performances for MPE were evaluated by receiver operating characteristic (ROC) curves. Results: Among the five models, the XGBoost model exhibited the best diagnostic performance for MPE (area under the curve (AUC): 0.903, 0.918, and 0.886 in the training, validation, and testing cohorts, respectively). Additionally, the XGBoost model outperformed carcinoembryonic antigen (CEA) levels in pleural fluid (PF), serum, and the PF/serum ratio (AUC: 0.726, 0.699, and 0.692 in the training cohort; 0.763, 0.695, and 0.731 in the validation cohort; and 0.722, 0.729, and 0.693 in the testing cohort, respectively). Furthermore, compared with CEA, the XGBoost model demonstrated greater diagnostic power and sensitivity in diagnosing lung cancer-induced MPE. Conclusion: The development of a machine learning model utilizing routine laboratory biomarkers significantly enhances the diagnostic capability for distinguishing between MPE and BPE. The XGBoost model emerges as a valuable tool for the diagnosis of MPE.
UR Scholarship Repository UR Scholarship Repository

The current methods of simulating the Cosmic Microwave Background (CMB) involve either simulating the entire sky using spherical transforms or simulating a ﬂat patch with fast Fourier transforms (FFTs). For patches that are too large to be considered ﬂat but much less than the full sky, the former method is ineﬃcient and the latter is inaccurate. One alternative method of CMB simulation is to simulate the random processes behind the CMB in a 3-dimensional box that contains the part of the sphere that we want to measure. Then, we can select the points we want from the box. This method should be more eﬃcient than previous methods because it performs simulations over a box instead of a sphere, allowing for the use of FFTs in place of much slower spherical harmonic transforms. For this method to work, there must be a 3-dimensional power spectrum deﬁned on the box that has the same correlation function as the angular power spectrum. Since the angular power spectrum is known, this becomes a linear programming problem, where the constraints for the 3-D power spectrum are that it matches the angular power spectrum over the observed region and that it be non-negative. If a power spectrum satisfying these constraints exists, we can use it to create maps of the CMB with the same statistical properties as the observed CMB. These maps can then be used to test theories about the early universe. We have solved this linear programming problem for a variety of realistic scenarios. We have performed extensive statistical tests comparing the statistics of maps produced via FFT simulation with maps produced via standard spherical harmonic methods, and found the two methods to be statistically indistinguishable.
The herpetofauna of a small and unprotected patch of tropical rainforest in Morningside, Sri Lanka

Morningside is an exceptional area in Sri Lanka with highly endemic herpetofauna. How- ever, this relictual forest area lies inside a tea plantation and is mostly lacking conservation protec- tion. Species inventories of remaining rainforest patches are currently incomplete, and information about the behavior and ecology of the herpetofauna of Morningside is poorly known. In our survey, we identified 13 amphibian species and recorded an additional two species that could not be identi - fied with existing keys. We determined 11 reptile species from this patch of forest, and another un- identified Cnemaspis gecko was recorded. We did not assess the herpetofauna outside of this forest patch. Some species are described for the first time in Morningside, suggesting a wider distribution in Sri Lanka. We also document a call from a male Pseudophilautus cavirostris for the first time. Perspectives for future surveys are given.
The study of Izeh and Dezful Shear Zones from North to Dezful Embayment on East and South West of Zagros

Background: The term shear zone is generally used for areas with a strain higher than the surrounding rock. These areas are found in either Planer or Non-planer which formed both conjugated and single shear zones. Objective: The research aims were to determine in the exact boundaries of the Izeh and Dezful shear zones, Geometry of them, angle (2) between these two shear zones, widths zones shearing, degree of overlap between them and type of shearing in addition to its stresses cause deformation phases. Results: Tectonic and Seismotectonic studies of the province faults show that the transformed region is under a pressure with north–north east trend. This trend is along strike slip faults and is almost parallel. The convergence towards north reveal right-lateral strike-slip movements occurred throughout Izeh shear zone (with N-S trend) while left-lateral strike-slip movements occurred all over the Dezful shear zone (with W-E trend). Conclusion: Izeh and Dezful shear zones manifest a brittle behavior based on their location in folded Zagros. Izeh shear zone is a right lateral one and its west and east borders are 28 to 30 kilometers wide. The new shear zone introduced here as east – west zone is located in north of Dezful embayment. It is a left lateral shear zone which is 180 to 190 kilometers long and almost 45 kilometers wide. Izeh and Dezful shear zones have collided in a very small region. Kamestan anticline is the collision spot. The consequences of the collision which is actually a mingle of north – south elements belonging to Izeh shear zone and east – west elements of Dezful shear zone can be seen in the spot.