Miracle-dz/UniFAR-FTUs huggingface dataset:
Decision Tree Analysis on Management of Deep Carious Lesion

An ongoing active carious lesion is the main cause of dental pain, this is often associated with presence of dental cavities, presence of adherent plaque-based biofilm, lack of aesthetics and associated structural and functional problems. To further prevent the spread of the active carious lesion restorative intervention is of great significance. The tooth structure preservation is important to have a positive response when pulpal sensibility tests are performed, in addition to this carious tissue removal should be adapted accordingly to cause minimal damage to the remaining dentin structure, this can be done by employing a minimally invasive approach. The removal of the carious tissue depends on the proximity of the lesion to the pulp, pulpal sensibility responses, the extent of the remaining supragingival tooth structure and the operator control-based factors like moisture control and access. The removal of the soft dentine is recommended in deep carious lesion, avoiding the pulpal exposure followed by sealing of the dentinal tubules beneath the adhesive restoration. The modern technologies of air abrasion, the use of chemo mechanical agents, and rotary plastic burs are the recent advancement in soft tissue caries removal. Avoiding the pulpal exposure must be the key factor in managing such lesions, with healthy enamel/dentine margins at the cavity periphery and by using adhesive restorative biomaterials is very critical to manage such lesions.
Spectral Clustering with Sampling — A Graph Signal Processing Perspective

Graph signal processing is an extension of classical signal processing to signals that are defined over the vertex set of graphs. This type of signal processing is necessary in applications where the data is best represented by a graph (like in social networks) and when the amount of data is big. Usually a graph is generated that represents the similarity of different data points and then the typically sparse weight matrix can be used for efficient analysis. One application of graph signal processing is clustering, where the aim is to identify groups of similar data items. This is used for data analysis in several fields ranging from biology over image processing to machine learning. In this work we consider various clustering algorithms. We focus on signed spectral clustering with sampling, which is an algorithm that modifies the weight matrix of a given graph based on some sampled cluster labels and then performs signed spectral clustering. In machine learning nomenclature methods that are supported by sampled cluster labels are referred to as a semi-supervised clustering. At first, algorithms for graph learning are presented. They are used as a pre-processing stage for graph-based clustering. Those algorithms are the -neighborhood graph and the k-nearest neighborhood graph. Next, k-means and spectral clustering, which are well known examples of unsupervised clustering algorithms, are discussed and then we introduce an original method that combines signed spectral clustering with sampling. In the last part signed spectral clustering with sampling is compared to clustering by harmonic functions and singular value projection in Monte-Carlo simulations. Our numerical results show that signed spectral clustering with sampling performs superior compared to existing algorithms.
Research on the International Roughness Index Threshold of Road Rehabilitation in Metropolitan Areas: A Case Study in Taipei City

The International Roughness Index (IRI) is the standard scale for evaluating road roughness in many countries in the world. The Taipei City government actively promotes a Road Smoothing Project and plans to complete the rehabilitation of the main and minor roads within its jurisdiction. This study aims to detect the road surface roughness in Taipei City and recommend appropriate IRI thresholds for road rehabilitation. A total of 171 asphalt concrete pavement sections in Taipei City with a total length of 803.49 km were analyzed and compared by IRI. The longitudinal profile of the detected road sections was measured using an inertial profiler. The statistical analysis showed that the IRI value prior to road leveling was mainly distributed between 5 and 8 m/km, while the IRI value after road leveling was mainly distributed between 3 and 4.5 m/km. This confirms that the implementation of the Road Smoothing Project has a significant effect on improving road smoothness. Moreover, based on the analysis results, it is recommended that the IRI threshold value for road rehabilitation in Taipei City be set at 4.50 m/km.
Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty

The integration of renewable generation adds complexity to the operation of the power system due to its unpredictable characteristics. Therefore, the development of methods to accurately model the uncertainty is necessary. In this paper, the spatio-temporal kriging and analog approaches are used to forecast wind power generation and used as the input to solve an economic dispatch problem, considering the uncertainties of wind generation. Spatio-temporal kriging captures the spatial and temporal information available in the database to improve wind forecasts. We evaluate the performance of using the spatio-temporal kriging, and comparisons are carried out versus other approaches in the framework of the economic power dispatch problem, for which simulations are developed on the modified IEEE 3-bus and IEEE 24-bus test systems. The results demonstrate that the use of kriging based spatio-temporal models in the context of economic power dispatch can provide an opportunity for lower operating costs in the presence of uncertainty when compared to other approaches.
Automatic Enrollment System for Student Dormitory

The Directorate of Dormitories at the University of Kirkuk offers full accommodated rooms to the male and female students. However, the directorate registers and assigns the accommodations manually. Therefore, a visiting has been made to the directorate to identify the problems of this system. It is found that the manual system suffers from several issues such as wasting time, losing data and extra charges in documenting students' information. Additionally, delays in replying to students’ enquiries, and ignoring their discipline in distributing them among the accommodations. This paper proposes using an e-governance system to tackle the identified problems. The proposed system permits the student to register online and provides him/her with an id card. The id card consists of a serial number, ISBN and student's room information in the accommodation. To design the system, a number of programming languages is used: (PHP, HTML, JAVA SCRIPT and CSS). Furthermore, SPSS software has implemented to evaluate the system. The evaluation has shown significant merits in terms of the reliability and speeding up of assigning rooms to the students among the accommodations. The distribution of the students is according to students discipline. Furthermore, the system determines the capacity of each accommodation precisely. Moreover, decreasing in time wasting and tiredness that may students face in the traditional manual system. Additionally, reducing the physical contact between the students and the employees. Consequently, the proposed system gathers students' information in an electronic archiving which may lead to easiness in importing their information in the future.
Colon Versus Rectal Cancers among Surgical Approach: An Observational Study

1. Abstract 1.1. Background: Colorectal cancer is the most common tumor of the gastrointestinal tract, and it is the third leading cause of mortality throughout the world. 1.2. Objectives: An observational study of colorectal cancers. 1.3. Methods: The research was carried out in Abu Ghraib General hospital in the period from January 2019 to May 2019. The primary data reported included colorectal cancer, and patients study characterizes. These including gender, age, family history, smoking, comorbidity, IBD, tumor sites, histopathology, stages, grading, the distance for anal verge, local recurrence, distant metastasis, type of surgery, and chemoradiation. 1.4. Results: The gender was composed of male 40%, and female 60%, with mean age, was 44.89±21.6 years for males and 47.55±20.07 years for females. There was only 4% had a positive family history. Smoking patients in this study were 48%, yet the non-smoker patients were 52%. Rectosigmoid cancer represented the most common site of cancer figured in 40% of patients. The results showed that adenocarcinoma was the most common histopathology in 72% of patients. The localized tumors were recorded in 44%, whereas metastasis was found in 34% of patients. Regarding grading, the moderate differentiation was the dominant grade in 60%. The tumor distant from anal verge results exhibited as <5 cm in 7(14%), 5-10 cm in 30(60%), and >10 cm in 13(26%). The majority of patients in our study have no metastatic disease 66%. Indeed, the hepatic was the most frequent site of distant metastasis. All patients underwent different surgical procedures. 1.5. Conclusions: Middle age group was mostly effect and more in the female gender. Many factors are shown to increase the risk of developing colorectal cancer including increasing age; consumption of processed meat, and smoking. Rectosigmoid cancer represented the most current sites. The localized diseases were common. Moderate differentiation was the dominant grade. The tumor location and the distal tumor margin are essential factors upon which the surgical plan for patients with rectal cancer is based. Accurate measurement of the distal tumor margin is necessary for planning the surgical procedure. The liver was the most frequent site of distant metastasis that figured. 2. Introduction The highest colon cancer incidence rates are found in parts of Europe (eg, in Hungary, Slovenia, Slovakia, the Netherlands, and Norway), Australia/New Zealand, Northern America, and Eastern Asia (Japan and the Republic of Korea, Singapore [in females]), with Hungary and Norway ranking first among males and females, respectively [1]. The incidences are elevated in Uruguay among both men and women. Rectal cancer incidence rates have a similar regional distribution, although the highest rates are seen in the Republic of Korea among males and Macedonia among females [1]. Rates of both colon and rectal cancer incidence tend to be low in most regions
Identifikasi Konten Kasar pada Tweet Bahasa Indonesia

This study aims to identify tweets containing abusive or offensive content. To do this, we performed five steps, such as, data collection, preprocessing, feature extraction, classification, and evaluation. We employed Multinomial Naïve Bayes and Support Vector Machine with linear kernel as our classification algorithm. Based on the experiment, it is known that the performance of the Support Vector Machine algorithm with linear kernel is superior overall compared to the Multinomial Naïve Bayes algorithm. It can be seen from the result of the values of accuracy, precision, recall, and F1-score for the SVM algorithm, respectively 0.9928; 0.9914; 0.9946; and 0.9930. Whereas the value of accuracy, precision, recall, and F1-score of the Multinomial Naïve Bayes algorithm are 0.9834; 0.9912; 0.9762; and 0.9836. However, it can be concluded that the Support Vector Machine and Multinomial Naïve Bayes algorithm have almost the same performance. This is evidenced by the difference in performance achievements that are not too striking from both algorithm. Keywords— Twitter, classificaion, Multinomial Naïve Bayes, Support Vector Machine Abstrak—Penelitian ini bertujuan untuk melakukan identifikasi tweet yang mengandung konten kasar atau ofensif. Untuk melakukan hal tersebut, ada lima tahap yang dilalui yaitu pengumpulan data, preprocessing, ekstraksi fitur, klasifikasi, dan evaluasi. Adapun algoritma klasifikasi yang digunakan adalah Multinomial Naïve Bayes dan Support Vector Machine dengan linear kernel. Berdasarkan eksperimen, diketahui bahwa performa algoritma Support Vector Machine dengan linear kernel lebih unggul secara keseluruhan dibandingkan dengan algoritma Multinomial Naïve Bayes. Hal tersebut dilihat dari perolehan nilai accuracy, precision, recall, dan F1-score untuk algoritma SVM berturut-turut adalah 0.9928; 0.9914; 0.9946; dan 0.9930. Sedangkan perolehan accuracy, precision, recall, dan F1-score algoritma Multinomial Naïve Bayes berturut-turut adalah 0.9834; 0.9912; 0.9762; dan 0.9836. Namun demikian, dapat disimpulkan bahwa algoritma Support Vector Machine dan Multinomial Naïve Bayes memiliki performa yang hampir sama baiknya. Hal tersebut dibuktikan dengan selisih capaian performa yang tidak terlalu mencolok dari keduanya. Kata kunci— Twitter, klasifikasi, Multinomial Naïve Bayes, Support Vector Machine
Modelling fish trade liberalisation

Recent research has on the basis of general equilibrium models warned that trade liberalisation in non-optimally managed renewable resources might cause over-exploitation and reduced steady-state welfare. Welfare effects of trade liberalisation in open access and optimal management are found case specific and dependent on factors such as the country’s status as importer or exporter, the state of the fish stocks and the size of the countries on the world market. The present paper develops an alternative partial equilibrium framework capable of identifying welfare effects of fish trade liberalisation also in the presence of complex but realistic management schemes, such as regulated open access and regulated restricted access. The case dependency known from the general equilibrium analysis is confirmed, but the introduction of realistic fisheries management schemes in the partial equilibrium model extended this case dependency. The welfare effect of trade liberalisation in an exporter country is negative under open access, but was found positive under regulated restricted access in the present paper.
Cooperative learning methods in mathematics education – 1.5 year experience from teachers’ perspective

Inclusive education and inclusive learning environment have become a major issue in Hungarian schools in Slovakia in the last decade. The number of underprivileged students with marginalised social background has risen tremendously. The primary questions of education including the provision of a proper learning environment in heterogeneous classes, the tackling of status problems among students have become inevitable. The introduction of the Complex Instruction Methodology (CIP) into the education process created an opportunity for teachers to work with heterogeneous groups in classes. Mathematics by definition creates heterogeneous groups in schools as due to large knowledge gaps among students the differentiation of work and the cooperation of students is extremely difficult. The CIP method is a special group work designed for heterogeneous groups which could provide enormous help for the work of the teachers. In the current work we would like to present the adaptation process of the CIP method in two Hungarian schools in Slovakia. By conducting a series of interviews with teachers we are going to analyse the efficiency of the CIP method in cooperative mathematics
An Efficient Domain-Adaptation Method using GAN for Fraud Detection

In this paper, an efficient domain-adaptation method is proposed for fraud detection. The proposed method employs the discriminative characteristics used in feature maps and generative adversarial networks (GANs), to minimize the deviation that occurs when a common feature is shifted between two domains. To solve class imbalance problem and increase the model’s detection accuracy, new data samples are generated by applying a minority class data augmentation method, which uses a GAN. We evaluate the classification performance of the proposed domain-adaption model by comparing it against support vector machine (SVM) and convolutional neural network (CNN) models, using classification performance evaluation indicators. The experimental results indicated that the proposed model is applicable to both test datasets; furthermore, it requires less time for learning. Although the SVM offers a better detection performance than the CNN and proposed domain-adaptation model, its learning time exceeds those of the other two models when dataset increases. Also, although the detection performance of the CNN-based model is similar to that of the proposed domain-adaptation model, its learning process is longer. In addition, although the GAN used to solve the class imbalance problem of the two datasets requires slightly more time than SMOTE (synthetic minority oversampling technique), it shows a better classification performance and is effective for datasets featuring class imbalances.
Title Swelling-activated and arachidonic acid-induced currents are TREK-1 in rat bladder smooth muscle cells

Using the perforated patch voltage clamp, we investigated swelling-activated ionic channels (SACs) in rat urinary bladder smooth muscle cells. Hypo-osmotic (60%) bath solution increased a membrane current which was inhibited by the SAC inhibitor, gadolinium. The reversal potential of the hypotonicity-induced current shifted in the positive direction by increasing external K concentration. The hypotonicity-induced current was inhibited by extracellular acidic pH, phorbol ester and forskolin. These pharmacological properties are identical to those of arachidonic acid-induced current present in these cells, suggesting the presence of TREK-1, a four-transmembrane two pore domain K channel. Using RT-PCR we screened rat bladder smooth muscles and cerebellum for expression of TREK-1, TREK-2 and TRAAK mRNAs. Only TREK-1 mRNA was expressed in the bladder, while all three were expressed in the cerebellum. We conclude that a mechanosensitive K channel is present in rat bladder myocytes, which is activated by arachidonic acid and most likely is TREK-1. This K channel may have an important role in the regulation of bladder smooth muscle tone during urine storage.
Variability in gaseous elemental mercury at Villum Research Station, Station Nord, in North Greenland from 1999 to 2017

Abstract. Mercury is ubiquitous in the atmosphere, and atmospheric transport is an important source for this element in the Arctic. Measurements of gaseous elemental mercury (GEM) have been carried out at Villum Research Station (Villum) at Station Nord, situated in northern Greenland. The measurements cover the period 1999–2017, with a gap in the data for the period 2003–2008 (for a total of 11 years). The measurements were compared with model results from the Danish Eulerian Hemispheric Model (DEHM) that describes the contribution from direct anthropogenic transport, marine emissions and general background concentration. The percentage of time spent over different surfaces was calculated by back-trajectory analysis, and the reaction kinetics were determined by a comparison with ozone. The GEM measurements were analysed for trends, both seasonal and annual. The only significant trends found were negative ones for the winter and autumn months. Comparison of the measurements to simulations using the Danish Eulerian Hemispheric Model (DEHM) indicated that direct transport of anthropogenic emissions of mercury accounts for between 14 % and 17 % of the measured mercury. Analysis of the kinetics of the observed atmospheric mercury depletion events (AMDEs) confirms the results of a previous study at Villum of the competing reactions of GEM and ozone with Br, which suggests that the lifetime of GEM is about a month. However, a GEM lifetime of 12 months gave the best agreement between the model and measurements. The chemical lifetime is shorter, and thus, the apparent lifetime appears to be the result of deposition followed by reduction and re-emission; for this reason, the term “relaxation time” is preferred to “lifetime” for GEM. The relaxation time for GEM causes a delay between emission reductions and the effect on actual concentrations. No significant annual trend was found for the measured concentrations of GEM over the measurement period, despite emission reductions. This is interesting, and together with low direct transport of GEM to Villum as found by the DEHM model, it shows that the dynamics of GEM are very complex. Therefore, in the coming years, intensive measurement networks are needed to describe the global distribution of mercury in the environment as the use of models to predict future levels will still be highly uncertain. The situation is increasingly complex due to global changes that most likely will change the transport patterns of mercury, not only in the atmosphere but also between matrixes.
Issues in Information Systems

This study investigates the impact of various demographic factors such as age, race, gender, and education levels on student acceptance of new learning management systems. The investigators first did a through review of the literature to find substantial increases in online learning throughout the country, University selection criteria for new Learning Management Systems (LMS), reasons for University LMS system changes, and research on UTAUT and TAM theories of technology acceptance. Then, a survey was distributed to students which measured several technology acceptance factors, utilizing the technology acceptance model (TAM). Regression analysis was performed. The results supported the following relationships between the variables: Perceived Ease of Use (PE) has a significant positive influence on perceived usefulness (PU); Perceived usefulness has a significant positive influence on attitude toward using (AT); Perceived Ease of Use (PE) has a significant positive influence on attitude toward using (AT); Perceived Usefulness (PU) has a significant positive influence on Behavioral Intentions to use (BI); Attitude toward using has a significant positive influence on behavioral intentions to use; The higher the educational level, the more likely new LMS systems will be accepted; The higher a person’s age, the more likely he/she is to perceive a new LMS more negatively.
ampah merupakan salah satu masalah yang selalu muncul di Negara Indonesia bahkan didunia. Semakin lama semakin meningkat

Garbage is one of the problems that always arise in Indonesia and even in the world. Increasingly, the production of waste is increased along with the increase in population and consumption. Therefore, need a prevention to stop wasting or producing garbage through recycle. This research do garbage recycle classification of cardboard, glass, metal, paper and plastic by using Local Binary Pattern (LBP) texture feature extraction methode and Support Vector Machine (SVM) as classification methode. For examination technic and dataset distribution is using K-Fold Cross Validation methode type Leave One Out (LOO). From examination result had been done were using fold 5 until fold 10. Polynomial kernel get highest accuracy result from every fold used with mean point 87.82%. Based on SVM classification examination result whether linear kernel, polynomial nor gaussian by using fold 5 until fold 10. The best accuracy point for cardboard garbage is 96.01%. For glass garbage, the best accuracy point is 90.62%. Then, metal garbage get the best accuracy point 89.72%. While paper garbage with highest accuracy point 96.01%. And plastic garbage with highest accuracy point 87.64%.