Perancangan Buku Pop-Up Permainan Tradisional Anak sebagai Media Pelestarian Keaifan Lokal Lombok NTB

This writing aims to (1) describe children's games as ideas or inspiration for the creation of a work of Pop Up Books (2) describe the depiction of the form and process of making Visual Communication Design in the form of a three-dimensional Pop-up Book that elevates Lombok NTB's Traditional Children's Games in order to remain maintain and preserve the local wisdom of the archipelago through children's games. The methods used in the creation of this artwork include exploration and experimentation. In this case, the exploration was carried out by searching for libraries, pictures and all information related to the theme that I took, namely traditional games for children from Lombok, NTB. Then in terms of this exploration, do a sketch activity, which is then visualized in several uses of several bitmap-based applications such as Ibis applications on mobile phones and Coreldraw applications using laptops. The work is done by starting from making a sketch, then designing, printing, then cutting and pasting until finally it becomes a Pop-up book. The results of the discussion and creation are as follows: 1. The theme of the work presented in: Final Project is Traditional Children's Games in Lombok NTB, with the title "Designing Pop-up Books for Traditional Children's Games as Preservation of Local Wisdom in Lombok NTB". 2. These Visual Communication Designs visualize a collaborative design of Ibis and Coreldraw applications and then the results are printed using an albartos paper machine and create a work called Pop-up Book. 3. The number of book pages created is 11 pages consisting of 1 opening page, 9 Pop-up game pages, and 1 closing page along with front and back covers, with a manufacturing period of 4 months in 2021 starting from July-October. Keywords: Children's traditional games, Pop-up Books, Local wisdom.

Ultrasonic Intelligent Diagnosis of Papillary Thyroid Carcinoma Based on Machine Learning

Thyroid diseases are divided into papillary carcinoma and nodular diseases, which are very harmful to the human body. Ultrasound is a common diagnostic method for thyroid diseases. In the process of diagnosis, doctors need to observe the characteristics of ultrasound images, combined with professional knowledge and clinical experience, to give the disease situation of patients. However, different doctors have different clinical experience and professional backgrounds, and the diagnosis results lack objectivity and consistency, so an intelligent diagnosis technology for thyroid diseases based on the ultrasound image is needed in clinic, which can give objective and reliable diagnosis opinions on thyroid diseases by extracting the texture, shape, and other information of the image and assist doctors in clinical diagnosis. This paper mainly studies the intelligent ultrasonic diagnosis of papillary thyroid cancer based on machine learning, compares the ultrasonic characteristics of PTMC diagnosed by using the new ultrasound technology (CEUS and UE), and summarizes the differential diagnosis effect and clinical application value of the two technology methods for PTMC. In this paper, machine learning, diffuse thyroid image features, and RBM learning methods are used to study the ultrasonic intelligent diagnosis of papillary thyroid cancer based on machine learning. At the same time, the new contrast-enhanced ultrasound (CEUS) technology and ultrasound elastography (UE) technology are used to obtain the experimental phenomena in the experiment of ultrasonic intelligent diagnosis of papillary thyroid cancer. The results showed that 90% of the cases were diagnosed by contrast-enhanced ultrasound and confirmed by postoperative pathology. CEUS and UE have reliable practical value in the diagnosis of PTMC, and the combined application of CEUS and UE can improve the sensitivity and accuracy of PTMC diagnosis.

Traffic Flow Prediction for Smart Traffic Lights Using Machine Learning Algorithms

Nowadays, many cities have problems with traffic congestion at certain peak hours, which produces more pollution, noise and stress for citizens. Neural networks (NN) and machine-learning (ML) approaches are increasingly used to solve real-world problems, overcoming analytical and statistical methods, due to their ability to deal with dynamic behavior over time and with a large number of parameters in massive data. In this paper, machine-learning (ML) and deep-learning (DL) algorithms are proposed for predicting traffic flow at an intersection, thus laying the groundwork for adaptive traffic control, either by remote control of traffic lights or by applying an algorithm that adjusts the timing according to the predicted flow. Therefore, this work only focuses on traffic flow prediction. Two public datasets are used to train, validate and test the proposed ML and DL models. The first one contains the number of vehicles sampled every five minutes at six intersections for 56 days using different sensors. For this research, four of the six intersections are used to train the ML and DL models. The Multilayer Perceptron Neural Network (MLP-NN) obtained better results (R-Squared and EV score of 0.93) and took less training time, followed closely by Gradient Boosting then Recurrent Neural Networks (RNNs), with good metrics results but the longer training time, and finally Random Forest, Linear Regression and Stochastic Gradient. All ML and DL algorithms scored good performance metrics, indicating that they are feasible for implementation on smart traffic light controllers.

Production of an energy-storage electrode paper using a pilot-scale paper machine

The global electrification of our society requires an enormous capacity of electrical energy storage. This drives the demand for low-cost and sustainable solutions, where the electrode materials are key components. In the present work, all-organic supercapacitor electrodes have successfully been demonstrated to be produced on a pilot-scale paper machine, thereby showing the feasibility of large-scale production of “paper-based energy storage”. The material concept was based on activated charcoal from pyrolyzed coconut and cationized cellulose pulp, the latter having small amounts of electrostatically adsorbed PEDOT:PSS in order to create a conducting, percolating network. In a pre-trial lab experiment, it was evident that even small addition of 1 wt% PEDOT:PSS gave a large increase in capacitance compared to samples with only activated charcoal. In the pilot trials, the addition of carboxymethylated nanocellulose and/or carbon black was further investigated. The different additions significantly affected several paper properties such as tensile strength and conductivity, but the specific capacitance of the activated charcoal was not affected and was found to be around 65-70 F/g. As more than half of the electrodes mass consisted of pulp fibers, the specific capacitance of the paper electrodes was about 25-30 F/g, which is in the same order of commercial supercapacitor electrodes. The successful production of several 10-meter-long rolls of supercapacitor electrode paper shows the feasibility of producing energy storage devices with papermaking methods, and the work as a whole provides valuable insights on how to further advance bio-based energy storage solutions.

Short-Term Prediction of Traffic State for a Rural Road Applying Ensemble Learning Process

Short-term prediction of traffic variables aims at providing information for travelers before commencing their trips. In this paper, machine learning methods consisting of long short-term memory (LSTM), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) are employed to predict traffic state, categorized into A to C for segments of a rural road network. Since the temporal variation of rural road traffic is irregular, the performance of applied algorithms varies among different time intervals. To find the most precise prediction for each time interval for segments, several ensemble methods, including voting methods and ordinal logit (OL) model, are utilized to ensemble predictions of four machine learning algorithms. The Karaj-Chalus rural road traffic data was used as a case study to show how to implement it. As there are many influential features on traffic state, the genetic algorithm (GA) has been used to identify 25 of 32 features, which are the most influential on models’ fitness. Results show that the OL model as an ensemble learning model outperforms machine learning models, and its accuracy is equal to 80.03 percent. The highest balanced accuracy achieved by OL for predicting traffic states A, B, and C is 89, 73.4, and 58.5 percent, respectively.

Art Paper for Large Wood Relief Block Printing – A Paper Development Study

Wood relief block printing was developed in China in the seventh century and is used today for many art printing applications. The presented research project describes the development of an art paper product applicable for large wood relief block printing from laboratory scale to large semi commercial production of art paper for printing image sizes of up to 44-inch (1118 mm) by 96-inch (2400 mm) at outdoor steam roller printing events or smaller indoor printing press applications. The improvement of the paper properties from laboratory development, small laboratory paper machine and semi commercial paper machine run for the production of the final art paper showed an improvement throughout the process development for the optical and mechanical paper properties and exceeded the set values set by the artist using the art paper. The produced art paper with a basis weight of 260 g/m² and a thickness of 171 µm is produced from a mixture of 70% northern bleached hardwood Kraft pulp and 30% northern bleached softwood Kraft pulp. The ISO brightness of the art paper off-white (egg-shell) colour was at 63.2% and the ISO color value for L, a, b. at 90.8, 1.1, and 12.6 respectively. The art papers surface roughness and porosity as a parameter for ink attachment and penetration is for the top side 2179 ml/min and for the bottom side (wire side) 2326 ml/min, whereas porosity was measured at 1668 ml/min. Bending stiffness in machine direction and cross machine direction was measured at 157mN and 70 mN respectively. Burst strength was measured at 2.24 kPA·m²/g.

Hybrid approach with Deep Auto-Encoder and optimized LSTM based Deep Learning approach to detect anomaly in cloud logs

Anomaly-based detection is coupled with recognizing the uncommon, to catch the unusual activity, and to find the strange action behind that activity. Anomaly-based detection has a wide scope of critical applications, from bank application security to regular sciences to medical systems to marketing apps. Anomaly-based detection adopted by various Machine Learning techniques is really a type of system that consists of artificial intelligence. With the ever-expanding volume and new sorts of information, for example, sensor information from an incontestably enormous amount of IoT devices and from network flow data from cloud computing, it is implicitly understood without surprise that there is a developing enthusiasm for having the option to deal with more conclusions automatically by means of AI and ML applications. But with respect to anomaly detection, many applications of the scheme are simply the passion for detection. In this paper, Machine Learning (ML) techniques, namely the SVM, Isolation forest classifiers experimented and with reference to Deep Learning (DL) techniques, the proposed DA-LSTM (Deep Auto-Encoder LSTM) model are adopted for preprocessing of log data and anomaly-based detection to get better performance measures of detection. An enhanced LSTM (long-short-term memory) model, optimizing for the suitable parameter using a genetic algorithm (GA), is utilized to recognize better the anomaly from the log data that is filtered, adopting a Deep Auto-Encoder (DA). The Deep Neural network models are utilized to change over unstructured log information to training ready features, which are reasonable for log classification in detecting anomalies. These models are assessed, utilizing two benchmark datasets, the Openstack logs, and CIDDS-001 intrusion detection OpenStack server dataset. The outcomes acquired show that the DA-LSTM model performs better than other notable ML techniques. We further investigated the performance metrics of the ML and DL models through the well-known indicator measurements, specifically, the F-measure, Accuracy, Recall, and Precision. The exploratory conclusion shows that the Isolation Forest, and Support vector machine classifiers perform roughly 81%and 79%accuracy with respect to the performance metrics measurement on the CIDDS-001 OpenStack server dataset while the proposed DA-LSTM classifier performs around 99.1%of improved accuracy than the familiar ML algorithms. Further, the DA-LSTM outcomes on the OpenStack log data-sets show better anomaly detection compared with other notable machine learning models.

Opening closed doors: using machine learning to explore factors associated with marital sexual violence in a cross-sectional study from India

ObjectivesSexual violence against women is pervasive in India. Most of this violence is experienced in the context of marriage, and rates of marital sexual violence (MSV) have been relatively stagnant over the past decade. This paper machine learning algorithms paired with qualitative thematic analysis to identify new and potentially modifiable factors influencing MSV in India.Design, setting and participantsThis cross-sectional analysis of secondary data used data from in-person interviews with ever-married women aged 15–49 who responded to gender-based violence questions in the nationally representative 2015–2016 National Family Health Survey (N=66 013), collected between 20 January 2015 and 4 December 2016. Analyses included iterative thematic analysis (L-1 regularised regression followed by iterative qualitative thematic coding of L-2 regularised regression results) and neural network modelling.Outcome measureParticipants reported their experiences of sexual violence perpetrated by their current (or most recent) husband in the previous 12 months. These responses were aggregated into any vs no recent MSV.ResultsNearly 7% of women experienced MSV in the past 12 months. Major themes associated with MSV through iterative thematic analysis included experiences of/exposure to violence, sexual behaviour, decision making and freedom of movement, sociodemographics, access to media, health knowledge, health system interaction, partner control, economic agency, reproductive and maternal history, and health status. A neural network model identified variables that largely corresponded to these themes.ConclusionsThis analysis identified several themes that may be promising avenues to identify and support women experiencing MSV, and to mitigate these traumatic experiences. In particular, amplifying screening activities at health encounters, especially among women who appear to have compromised health or restricted agency, may enable a greater number of women access to essential physical and emotional support services, and merits further consideration.

Determining operating variables that impact internal fiber bonding using Wedge statistical analysis methods

In this study, Wedge statistical analysis tools were used to collect, collate, clean up, plot, and analyze several years of operational data from a commercial paper machine. The z-direction tensile (ZDT) and Scott Bond tests were chosen as representative of fiber bond strength. After analyzing thousands of operational parameters, the ones with the most significant impact upon ZDT involved starch application method, starch penetration, and the amount of starch applied. Scott bond was found to be significantly impacted by formation and refining. Final calendering of the paper web has also shown an impact on internal fiber bonding.

Genomic Variation Prediction: A Summary From Different Views

Structural variations in the genome are closely related to human health and the occurrence and development of various diseases. To understand the mechanisms of diseases, find pathogenic targets, and carry out personalized precision medicine, it is critical to detect such variations. The rapid development of high-throughput sequencing technologies has accelerated the accumulation of large amounts of genomic mutation data, including synonymous mutations. Identifying pathogenic synonymous mutations that play important roles in the occurrence and development of diseases from all the available mutation data is of great importance. In this paper, machine learning theories and methods are reviewed, efficient and accurate pathogenic synonymous mutation prediction methods are developed, and a standardized three-level variant analysis framework is constructed. In addition, multiple variation tolerance prediction models are studied and integrated, and new ideas for structural variation detection based on deep information mining are explored.