Miracle-dz/UniFAR-FTUs huggingface dataset:
Antixenosis of soybean promising lines and the level of resistance against Etiella zinckenella, the pod borer

This study aimed to determine the antixenosis of soybean genotypes and the resistance to pod borer. 16 successful lines of soybean were used. Planting dates were arranged in a way to integrate the flowering period of 16 promising lines of soybean. For two days, a pair of four-day adult pod borers were infested in each plant at the R4 stage. The result revealed that the difference in soybean promising lines had a major impact on the population of egg and larva, and on the pod damage severity. The lowest egg (1 egg/plant) was identified in Grb/Myp-16 and GH-Gn-8, while the lowest larval population (1 individual/plant) was in GH-Gn-8. The lowest impaired pod intensity was found on Grb/Myp-16 and Brg/Myp-13. The weight of the seed ranged from 2.4 to 6.5 g/plant. The trichome density ranged from 28.3-72/mm 2 with three different positions (leaning, skewed and upright). In summary, four soybean promising lines were consistently resistant to pod borer, Brg/Myp-13, Grb/Myp-16, GH-Gn-8, and GH/KR-13. The resistance was antixenosis or non-preference as both a place to lay eggs so as a feed. These lines had the possibility as a genetic source to improve resistance to Etiella zinckenella.
Research on Dispatch Optimization of Self-owned Power Plant under High New Energy Penetration

Considering that self-owned power plant (SPP) can accommodate more new energy generation, this paper proposes a joint dispatch optimization method of SPP. First, two market patterns of SPP considering its load characteristics were established. Then, a self-optimization model of SPP was built with the goal of minimizing SPP operating costs, and a joint optimization model of both SPP and the grid was constructed with the goal of minimizing total system operating costs which comprehensively considered the constraints of SPP interests, unit generation capacity, new energy accommodation requirement and grid security in optimizing the output of SPP and grid-connected units. Next, the optimization models were solved by NSGA-II algorithm. Finally, a comparative case-study on the two models was carried out, and the results showed that SPP can help to accommodate more new energy and reduce system operating costs.
Tofu wastewater industry with urea fertilizer as a cultivation medium for the microalga Spirulina plantensis

One of the limiting problem in fish larvae rearing is the availability of live feed such as zooplankton and phytoplankton. Spirulina platensis is a phytoplankton with important uses in fish larvae culture either as a live feed or an ingredient supplementation in the feed. However, its cultivation has high economic costs associated with the growth medium. One of the alternative medium that potentially reduce costs and environmentally friendly is tofu wastewater. The purpose of this research is to evaluate the combination of tofu wastewater with urea for the cultivation of S. platensis. We investigated the growth of S. platensis in tofu wastewater containing various concentrations of urea: 0 g (control), 10 g, 20 g, 30 g, or 40 g urea added to 3 L diluted (2:1) tofu wastewater. The S. platensis productivity parameters were measured are population density, specific growth rate, and doubling time. The population density growth was significantly different, while the specific growth rate and doubling time were not significantly different for the various urea concentrations. The optimum growth was observed with 10 g urea per 3 L medium. This combination of tofu wastewater with urea could be a cost-effective medium for the cultivation of S. platensis.
Research of Incremental Learning Algorithm for SVM Based on Class Center Diameter

In the learning process based on ISVM, how to effectively retain the history information and selectively discard some new training data, so as to maintain the classification accuracy and save the storage space after adding new samples every time, which is the key of the current ISVM classification algorithm. This thesis proposes a new incremental learning algorithm, that is, SVM incremental learning algorithm based on cluster diameter (CD-ISVM). This algorithm firstly calculates the two centers of the positive and negative samples, and then to build the boundary vector set by the coordinates of the two class centers. Moreover, KKT conditions are combined to filter incremental data and boundary vector set, and feedback information is provided to adjust the position of the boundary vector set. Then, the union of SV set and boundary vector set is taken as the increment set. After several increments, a classifier with strong generalization is finally trained.
Face Detection for Color Image Based on MATLAB

Amazing results in many works in the field of deep convolutional neural networks using them in many fields of machine learning such as image classification control of atrial play, and image annotation. Mathematical analysis of deep convolutional neural networks (CNN) has a broad role in many applications including image classification applications. Face recognition technology has occupied a wide field in research that is still continuing in this work. Face recognition technology is being developed depending on the MATLAB program to develop the subjecting of the color image to the neural convolution network and the work of a program to reduce the time the network deals with the image to reduce the number of layers to only two layers and access the algorithm Suggested in this work. Computer vision of a color image in a face detection study. In this work, the researcher created a fast algorithm to reach the research goal based on new standards in the field of face detection. A new face detection program was designed using MATLAB, CascadeObjectDetector and trainCascadeObjectDetector the face detection devices were developed. In this work, a technique for panning and color image using the convolutional neural network for deep learning is proposed, the method used to reach the goal of this work.
Research and implementation of convolution k-means algorithm based on unsupervised learning

This paper proposes a framework combining the advantages of unsupervised learning, convolutional neural network and K-means clustering algorithm. When there are few label data available, convolution k-means algorithm is formed by combining k-means algorithm with convolution neural network. Through the learning of convolutional neural network and clustering analysis, as well as the continuous optimization and adjustment of the network, the convolutional k-means algorithm is used to train the deep convolutional network, and the hierarchical function of unsupervised learning technology is used to reduce the dependence on a large number of labeled data. Through the analysis of the experimental results, compared with the supervised learning convolution k-means algorithm, the unsupervised learning convolution k-means algorithm can better represent the data clustering with the increase of the number of filters, and can improve the accuracy of test classification. The unsupervised learning convolution k-means algorithm is better than other unsupervised learning filter methods.
Literature review of the development of ZnO and TiO2 as photocatalytic material in water splitting processes

Water with hydrogen content is a source of renewable fuels that are available in abundance. However, the main constraint of the process of water splitting or hydrolysis is the amount of energy needed in the process. Photocatalysis material is a material that can function as a catalyst that helps the water-splitting process. The characteristics of photocatalytic material are mainly determined by the energy gap or bandgap that will allow the excitation of electrons and holes. This paper will discuss the literature review on the development of ZnO and TiO2 semiconductors as photocatalysis materials.
A Case Study of Building Information Modelling (BIM) for Subsurface in Malaysia

Unforeseen ground condition often leads to the occurrence of cost overrun and project delay in Malaysia’s architecture, engineering, and construction (AEC) industries. The implementation of Building Information Modelling (BIM) for subsurface was attempted to reduce uncertainties associated with the underground conditions. A pilot case study was performed to understand the 3D subsurface modelling workflow in BIM environment. A construction site in Ara Damansara, Petaling Jaya, Malaysia was selected for the pilot case study. The site investigation data of the study area were interpreted and input into AutoCAD Civil 3D with geotechnical module to perform the 3D subsurface modelling at various detail levels. The workflow of the modelling process in case study was documented for future references. Through this pilot study, it was found that the adoption of BIM for subsurface modelling could produce soil profiles for different engineering purposes such as visualisation of subsurface conditions during project initiation stage, preliminary conceptual design, and detailed analysis and design of geotechnical structures, particularly foundation. The BIM implementation for geotechnical data improves the management of site investigation data, enhances the visualisation of subsurface profile, and improves the efficiency in communicating the subsurface conditions to all stakeholders in a project team.
Investigation of a Proper Sampling Position for Freshness Assessment of Cabbage by Spectrophotometric-Chemo Metric Approach

The objective of this study was to identify the differences of cabbage’s layers based on their responses to the environmental conditions. The differences between layers were observed according to the spectrophotometric evaluation. The three outer layers of a fresh cabbage were separated, sampled and put into extraction tubes. The metabolites inside the each layer were extracted by the mixture of isopropanol and hexane (2:1) and analyzed by a spectrophotometer with a scanning analytical mode from 200 nm to 800 nm. The obtained data were analyzed with a metabolomics statistical software. Based on the statistical results, it can be concluded that each layer of cabbage has a specific profile of metabolites distribution. Each layer has produced different responses to the environment after harvest where the first layer indicated more sensitive to the environmental conditions. Therefore, the selection of the first layer of cabbage is not proper for any evaluation relating to postharvest investigations.
Genetic algorithm for minimization of ESOP representations for multiple-output logic functions

This article concerns ESOP representations for multiple-output logic functions. Exclusive-or sum-of-products expression (ESOP) is a formula representing a logic function as an exclusive-or sum of products of variables and their negations. EXOR-based realization leads to reduction in size of logic circuits for many practical logic functions and improves their testability. Minimization of a logic function is a problem of constructing ESOP representation for the given function having the minimal number of summands. We introduce a correspondence between ESOPs for multiple-output logic functions and single-output logic functions and propose genetic algorithm obtaining close to minimal ESOP representations for multiple-output logic functions.
Image Mosaic in Visual Detection of Wellbore

Based on the research on the visual detection system, this paper analyzes the characteristics of wellbore video (image), proposes the key techniques of image processing, and designs the methods of image mosaic. The experiment results show that when the image correction, the perspective transform, the image registration, and image fusion are implemented, a panoramic image of wellbore can be obtained. This study provides a new perspective for the visual detection and the technology reserves for further quantitative evaluation of visual detection.
Discrete cosine transform and multi class support vector machines for classification cardiac atrial arrhythmia and cardiac normal

The electrocardiogram signal is the most important analysis to detect cardiac arrhythmia. Machine learning classification is used as a first step to detect someone’s arrhythmia or normal heart. This paper discusses one method for detecting arrhythmia by using digital images of cardiac signals and R-R intervals. The process electrocardiogram digital image is divided into two, first the process of calculating the R-R intervals and second the process of extraction feature using Discrete Cosine Transform, followed by calculating the Euclidean Distance or Cityblock Distance with normal electrocardiogram signal reference. Euclidean Distance results or Cityblock Distance and R-R distance of electrocardiogram signals are then classified using Multiclass Support Vector Machine. The results of accuracy the classification four classes that are cardiac normal, atrial premature beat arrhythmia, atrial flutter arrhythmia, and atrial fibrillation arrhythmia, are 81.9%. The originality is used image to detect cardiac normal or cardiac arrhythmia by combined Discrete Cosine Transform, Euclidean distance or City block distance and Multiclass Support Vector Machine.
Detection of Leukemia Using Machine Learning Algorithms

Detection of disorders of Blood usually include visual inspection through a microscope. This may help to classify various conditions related to human blood. The paper details about evolving a system that could help in diagnosis and detection of Leukemia at an early stage. Only then controlling and prevention can be done. The proposed system focuses on White Blood Cells as they are the affected ones in case of Leukemia. The affected cells differ in shape and color features, which are used as classifier inputs.
One Line Soft Keyboard with T9-keys Layout Proposal on Smartwatch Touch Screen Device

A smartwatch is a mini-sized digital computer device that functions apart from being a watch and can also perform intelligent computing tasks. Due to screen size limitations, a smartwatch requires special design techniques on its input and output to allow interaction with its users. Text entry is often required by many applications, but because the smartwatch screen is small, text entry techniques commonly used on personal computers are impractical when applied to smartwatches. In this study, we introduce a design concept proposal for a soft keyboard called One Line Soft Keyboard with T9-Keys Layout. The soft keyboard design concept is expected to reduce the space on the keyboard on the smartwatch screen, along with the use of widely used button layouts that are familiar to users.
MoO3–MoS2 vertical heterostructures synthesized via one-step CVD process for optoelectronics

The 2D transitional metal oxides/transition-metal dichalcogenides vertical heterostructures of MoO3–MoS2 are successfully synthesized on SiO2/Si substrates via one-step chemical vapor deposition process. The vertical MoO3–MoS2 heterostructures exhibit the average size of ∼20 μm and the thickness down to ∼10 nm. Moreover, the phototransistor device based on MoO3–MoS2 heterostructures presents responsivity of 5.41 × 103 A W−1, detectivity of 0.89 × 1010 Jones and external quantum efficiency of 1263.4%, respectively, under a 532 nm wavelength light. This study affords a new path to simplify process of fabricating MoO3–MoS2 vertical heterostructures for electronic and optoelectronic applications.
A multiscale insight into the growth of h-BN: effect of the enclosure

There is a lack of knowledge on the fundamental growth mechanisms governing the characteristics of 2D materials synthesized by the chemical vapor deposition (CVD) technique and their correlation with experimentally controllable parameters, which hindered their wafer-scale synthesis. Here, we pursued an analytical and computational approach to access the system states that are not experimentally viable to address these critical needs. We developed a multiscale computational framework correlating the macroscale heat and mass flow with the mesoscale morphology of the as-grown 2D materials by solving the coupled system of heat/mass transfer and phase-field equations. We used hexagonal boron nitride (h-BN) as our model material and investigated the effect of substrate enclosure on its growth kinetics and final morphology. We revealed a lower concentration with a more uniform distribution on the substrate in an enclosed-growth than open-growth. It leads to a more uniform size distribution of the h-BN islands, consistent with existing experimental investigations.
Refreshable Braille Module Using Cam Actuated Mechanism

Less than 3% of 145 million blind people in developing countries at present are literate [1]. At present, in most of the cases, speech output has been the medium for blind people to access materials. For content which require deep understanding such as technical texts etc., it is not an appropriate modality, moreover blind people are more accustomed to braille transcripts and adaptability issues to all audio resources is difficult. And thus, arises the need for a cheap, easily adaptable, less complex solution to the problem. Braille points are varied arrangements of raised dots representing characters which are identified by touch by visually impaired people. Here we discuss in detail, development of a dynamic/refresh-able Braille display which presents Braille points by the up-and-down movement of pins using a completely new, first of its kind, Cam actuated mechanism with just two actuation points instead of a standard 6 used by every single refresh-able braille board in the world at present. Two nested shafts having 8 arrays of precisely calculated and placed cam, each were responsible for keeping the pins in up and down state. The shafts were actuated using two micro-servo motors placed next to each other. X degree rotation of servo after transmission through gear and subsequently shaft resulted in a particular configuration. Henceforth calibration was done so that all braille alphabet could be produced with specific servo rotations.
A Sampling-based Distributed Source Coding Scheme

In many practical distributed source coding (DSC) schemes that apply channel coding and iterative decoding, correlation estimation provides the likelihood information about the source bits to initialize the decoding algorithms. Therefore, the decoding depends strongly on the accuracy of this correlation estimation information. In this study, a sampling-based scheme for DSC is proposed that takes advantage of the source redundancy without requiring prior knowledge of its statistics. At the sender, the source sequence is sampled to obtain the sampled and unsampled sub-sequences. Then, the un-sampled sub-sequence is compressed by an arithmetic coder. Meanwhile the syndromes of the sampled sub-sequence are also calculated. At the receiver, the correlation between the side information and the un-sampled sub-sequence is used to estimate the conditional marginal distribution of the sampled sub-sequence. Then, the estimated information is used to initialize the likelihood information of the decoding algorithms. Finally, the likelihood information is combined with the received syndromes to perform iterative decoding to recover the original sampled sub-sequence. Experiment results show that the proposed scheme achieves better rate-distortion performance compared with the existing DSC schemes.
Acoustic monitoring using PyzoFlex®: a novel printed sensor for smart consumer products

Acoustic monitoring has always been a niche area in the field of monitoring applications compared to other modalities, such as computer vision. Over the last decades, the number of applications for acoustic monitoring has been growing and ranges from predictive maintenance within the industrial sector to acoustic scene classification and security monitoring in traffic and urban scenarios. With the rise of the internet-of-things (IoT) and artificial intelligence (AI) in recent years, smart consumer products and devices have pushed forward using different sensor technologies to enhance the user experience. To this end, acoustic monitoring is still an underestimated discipline with great potential to serve as a missing link in smart sensing within environments where other modalities face difficulties. In this paper, we present PyzoFlex®, a printable sensor technology which facilitates accurate measurement of pressure and temperature changes in objects and their environment, as a sensor interface for acoustic monitoring applications. In contrast to microphones or acceleration sensors, PyzoFlex may be printed onto any curved or textured surface. To demonstrate the possibilities, we present a case study in which we equip a coffee machine with PyzoFlex to acoustically monitor the machine states in real-time using a machine learning model.
Implementation of cleaner production to improve wastewater treatment performance

Wastewater management in hospitals is needed to process wastewater generated from each hospital’s activities to reduce polluters’ burden in its disembodied. Wastewater management, in general in hospitals, applies the end of the pipe, which certainly does not completely solve existing environmental problems. The implementation of clean production is expected to improve wastewater treatment performance by identifying inefficiency opportunities in wastewater treatment and efforts to reuse processed water. The research was conducted by conducting laboratory analysis of wastewater treatment results using chemical and biological physics parameters and looking at the opportunities that can be done from the inefficient wastewater treatment described using flow diagrams. The analysis results showed that the processed wastewater from the wastewater treatment process still meets the standard requirements based on physical, chemical, and biological parameters so that it can be utilized back into raw water for hospitals. Identification of aspects found in the existence of inefficiencies is humans, methods, materials, and machinery. The application of cleaner production to provide recommendations for other hospitals to improve wastewater treatment performance as well as wastewater treatment utilization. Implementation of cleaner production to improve wastewater treatment performance through identification in each process and investigate the potential application of cleaner production that can be applied to the system.
Cyberculture: Beauty media animation field in the Z generation in the information flow

Today’s young generation prefers searching for information over the Internet. Whatever information is easily obtained quickly on smartphones through the web and or social media such as Instagram or YouTube. Search for beauty information is done because of curiosity or find the beauty products they need. Looking for information on beauty, YouTube is a form of video impression to be an easier choice than to be able to surf the web first. The aim of the study wanted to know what kind of video was used by the young generation in search of information about beauty and how the adolescent’s reaction was in seeing YouTube’s beauty video views. The method of this study is qualitative research. This study conducted a beauty video view analysis on YouTube and in strengthening the data was conducted interviews. The results showed that YouTuber can influence the audience to see its channel. The beauty of animation video has not been able to give more reliable results in watching the impression.