Miracle-dz/UniFAR-FTUs huggingface dataset:
Using QSAR model for studying heterocycles activity

Different heterocyclic saccharin derivatives that have been previously prepared were tested here for their pharmacokinetic properties with Marvinsketch program and PreADMET website. The quantitative structure–activity relationship (QSAR) calculations included isoelectronic point (pI) as Protonation factor, logP as a partitioning factor that calculated by Consensus and Chemaxon methods, hydrophilic-lipophilic balance (HLB) as a partitioning factor that calculated by Chemaxon, Davies, and Griffins methods. polar surface area (PSA) beside adsorption, distribution, metabolism, excretion, and toxicity (ADMET) to detect the properties. These calculated QSAR characters confirmed that saccharin derivatives were more hydrophilic than saccharin molecule which manged membrane permeability, mutagenicity, and carcinogenicity descriptions. As a conclusion, the tested properties showed a good chance of some of these prepared saccharin derivatives to be good drugs.
Research and Application of Business Ability Evaluation Based On DBSCAN Algorithm and Entropy Method

Based on the data of 95598 work sheets and quality inspection, this paper constructs customer commissioners business capability evaluation models of different business types. Firstly, through DBSCAN algorithm we mine the potential category characteristics of customer commissioners. Then, entropy method is used to score the customer commissioners under the target category comprehensively. Finally combining the clustering results and entropy score, the customer commissioners with relatively strong and weak business capabilities are identified accurately. By marking the business capacity labels for the corresponding customer commissioners, it has guiding significance for the post adjustment and business training, which can improve the quality of power supply service and customer satisfaction for customer service center.
CAD model update on as-built geometries with morphing technique: ITER winding pack

CAD and CAE model update on manufactured shapes is a key process in the industry 4.0, where design, manufacturing and maintenance are integrated elements. Digital Twin models, together with Reverse Engineering (RE) techniques, allow tracking components performances during production and verifying the compliance with design requirements during the component lifetime. The need to have RE processes with a small impact on the production is driving the development of these models towards numerical techniques with fast computing performances. In this context, morphing techniques, largely deployed in the field of CAE models update, represent a valuable alternative to conventional RE techniques, because of their reduced computational time and capability to preserve the initial topology of the model. In the work presented in this paper, Radial Basis Function morphing technique has been used for the update of ITER Winding Pack CAD model on scan data acquired from the manufactured component.
Build a strong agribusiness institution through collective action

Agribusiness developments are facing challenges in managing limited natural resources. This requires coordination, synchronization, and cooperation through partnerships or collective action. There have been many partnership efforts in a few agribusiness sectors, however, there is no sign of sustainable partnership. Partnerships often fail due to internal factors from both of the partners. On the other hand, there is also a successful partnership, which is called a cooperative. In several countries, cooperatives have been implemented with great success, which also has contributed to a high Gross National Product value for the country. In Indonesia, the success of cooperatives in the agricultural sector was demonstrated by the success of cooperatives in bringing Indonesia to achieve self-sufficiency in rice in 1984. The relationship between farmers and cooperatives at that time was very strong, but the results of previous research indicated that changes in regulations after the reform period weakened the role of cooperatives in maintaining food stability. The question is “what causes it? ” This study tries to conduct a literature review of cooperatives that have succeeded and developed well by using the Systematic Literature Review [SLR] methods. The results show that since the first movement in Indonesia cooperatives is always under government intervention. The lesson to be learned from the success of several countries in building cooperatives is the importance of movements that come from below with collective individuals awareness. Therefore, to build a strong agribusiness institution, it is necessary to re-develop the cooperative management into an integrated agribusiness cooperative based on collective action.
The development of lesson plan of the water pressure booster pump STEM education

The paper will clarify STEM education learning activity of designing the simple the water pressure booster pump. The STEM education learning activity will be developed based on Sutaphan and Yuenyong [12] the context-based STEM education learning approach. The activity will start from identification of social issue of the drought often occurs during the dry season in various regions in Indonesia. The possible solution as the designing simple water pump will be introduced to the students to enhance them to develop the prototypes. Then, the lesson plan will provide activities regarding on the 7 stages of Sutaphan and Yuenyong [12] the context-based STEM education in order to scaffold students to practice knowledge for designing some technology prototypes or products through engineer design process. For making the simple water pressure booster pump, the lesson plan will provide students chance to practice integration of knowledge. That knowledge includes physics (e.g. basic measurement skills, object motion, dynamic fluid, gravitational force, energy, etc.), math (e.g. measurement, unit conversion), art (e.g. an impressive form of water pressure booster pump), and economy (e.g. save money and be able to use natural resources). This paper may have implications for designing STEM education learning activities.
On the application of neural network technologies for control problems in cognitive transportation systems

In the work, the functions of cognition are brought in line with the aspects of the functioning of cognitive transport systems and technologies. We consider the mathematical apparatus of cognitive technologies – neural network control systems, based on trained multi-layer neural networks of direct action in the class of adaptive control systems for dynamic objects. The possibility and variants of their inclusion in the control system of the transport system are clarified. On the basis of the considered approach, a country road recognition system based on a convolutional neural network has been developed, which will automatically segment rural roads using satellite images and build a road network diagram to analyze the spatial development of transport networks and to control unmanned vehicles. The development of a neural network based on U-net was carried out in Python 3x. The training set consists of 880 images prepared by hand mark up. The accuracy of the developed model when testing on prepared samples was 64%. According to the results of the study, conclusions were drawn and prospects for further functional development of the developed tools were determined.
Aerial Image Based Calamity Monitoring Using Deep Learning For Emergency Responsive Applications

Unmanned Aerial Vehicles (UAVs), fitted with camera sensors, can support situational mindfulness for money crisis reaction and catastrophe the board applications as they’re prepared to work remotely and hard to get to regions. In Furthermore, by utilizing an inserted stage and deep learning UAVs, a disaster influenced zone are frequently checked independently, the picture are regularly broke down progressively and cautioned inside the nearness of differed cataclysms like crumbled structures, flood, or fire for quicker moderate their consequences for the earth and on human populace. Through this investigation a light-weight convolution neural system (CNN) engineering is created, fit for running productively on an implanted stage better contrasted with existing models with insignificant memory prerequisites. These fundamental outcomes give strong premise to inquire about on the recognition continuously of a flying picture with UAVs for crisis reaction applications. Observing of cataclysms is essential to limit their consequences for the earth and individuals and can be improved by utilizing unmanned flying vehicles (UAVs) fitted with camera sensors. A contemporary method for acknowledgment of occasion’s bolstered elevated photographs is deep learning. This exploration presents the fine art related with the utilization of deep learning strategies for disaster distinguishing proof. We represent the intensity of this framework in deeply precise catastrophe discovery, utilizing a moderately straightforward deep learning model. With an informational index of 544 pictures (counting cataclysm pictures like fire, tremors, crumbled structures, torrent and floods), results show a precision of 91 percent, demonstrating that deep learning, joined with UAV camera sensors, has a similar exactness, the ability to anticipate high exactness disasters.
Characterization of magnetic Czochralski silicon devices with aluminium oxide field insulator: effect of oxygen precursor on electrical properties and radiation hardness

Aluminium oxide (Al2O3) has been proposed as an alternative to thermal silicon dioxide (SiO2) as field insulator and surface passivation for silicon detectors, where it could substitute p-stop/p-spray insulation implants between pixels due to its negative oxide charge, and enable capacitive coupling of segments by means of its higher dielectric constant. Al2O3 is commonly grown by atomic layer deposition (ALD), which allows the deposition of thin layers with excellent precision. In this work, we report the electrical characterization of single pad detectors (diodes) and MOS capacitors fabricated on magnetic Czochralski silicon substrates and using Al2O3 as field insulator. Devices are studied by capacitance-voltage, current-voltage, and transient current technique measurements. We evaluate the influence of the oxygen precursors in the ALD process, as well as the effect of gamma irradiation, on the properties of these devices. We observe that leakage currents in diodes before the onset of breakdown are low for all studied ALD processes. Charge collection as measured by transient current technique (TCT) is also independent of the choice of oxygen precursor. The Al2O3 films deposited with O3 possess a higher negative oxide charge than films deposited by H2O, However, in diodes a higher oxide charge is linked to earlier breakdown, as has been predicted by simulation studies. A combination of H2and O3 precursors results in a good compromise between the beneficial properties provided by the respective individual precursors.
Improved SIFT algorithm based on image filtering

In this paper, SIFT feature extraction algorithm was optimized through image filtering, so as to highlight the role of stable edge corner and improve the efficiency of stable edge corner collection. Afterwards, the experimental results were compared and verified by the FLANN feature matching method. Experimental results show that the improved SIFT feature extraction algorithm using image filtering can improve the extraction effect of feature points with stable edge response, while suppressing the extraction of feature points with unstable edge response, thus improving the accuracy of matching.
Examination of Lane Changing Duration Time on Expressway

Lane changing (LC) manoeuvre has been regarded as an essential characteristic of driving behaviour, which mostly influences traffic efficiency and safety. Thus, a wrongful performance of LC could lead to accident occurrence in the form of rear-end, sideswipe, or angled crash. Consequently, to develop countermeasures to minimize events associated with LC, an understanding of drivers’ behaviours during LC manoeuvres and its duration are essential. This study explores into characteristics of drivers’ LC duration time along Kuala Lumpur – Seremban expressway. Data were collected along a selected segment using an instrumented vehicle. A passenger car was instrumented with a Video Velocity Box (VBox), which used video recording of field traffic event Hence, VBox attached to the test vehicle was used for recording LC manoeuvres in the field after which the video records were played back in a computer to estimate the LC duration times. A total of 174 LC manoeuvre incidences were observed in this study, the LC duration times of the observed motorists’ LC events ranged from 0.90 to 10.52 seconds with mean and standard deviation values of 3.02 and 1.32 seconds, respectively. Out of 174 sampled drivers, an approximate of 162 representing 93% performed LC manoeuvre in less than 5.0 seconds with most of them executed in less than 3.0 seconds. This implies that more than 90% of the drivers observed are characterized by an aggressive LC driving behaviour. It was also found that the LC duration times data obtained in this study fits a Lognormal distribution reasonably.
A Brief Overview of Deep Learning and Memristor

At present, the deep learning has become a main topic in academic and engineering domain. It has been considered to be the general solutions to solve various of classification, clustering, regression and generate tasks in all fields, but it needs higher computing power at the same time. In other words, it needs the new equipment possessing lower power consumption and higher efficiency, which further triggers the development of new hardware and computing technology. A kind of promising solution is an emerging equipment known as memristor, which can be naturally mixed into a feasible computing device to realize neural computing to extend computing power of existing hardware. In this paper, more convenience and practically of using memristor to realize deep neural networks are obtained by summarizing mechanism of deep learning and structure of memristor.
Real time Localization Method Research with Monoslam and BIM Model

Outdoor navigation technologies based on GPS and Beidou has been very mature. However, due to the weakness of satellite signal and refraction, they can’t satisfy the needs of indoor localization and parking area navigation in large commercial buildings. Most indoor localization technologies, such as Bluetooth and Wi-Fi, are based on active equipment. Due to the implementation cost, they can’t be widely deployed. In recent years, SLAM has been greatly developed and applied in the field of robotics and autonomous driving. For technologies such as stereo SLAM and Lidar SLAM require additional equipment, and meanwhile large scale computing will happen during point cloud matching, they are not suitable for civilian mobile usage. Considering the BIM technology, which can provide accurate 3D data of buildings, are widely used gradually, a real-time localization method combining monocular SLAM and BIM model is proposed to complete vision-based initial localization. First, we create the BIM model with the design drawings, extract the visible edge lines of model, and establish a 3D line feature database; then, intercept the key frames of the mobile camera, and extract the edge line and line feature descriptors from image, match the line features between key frames, triangulate the matched line segments, get the 3D line coordinate in first frame, mistake filter will be rolled out between multiple frames; finally, match the reconstruction result with line feature database form BIM model, register the initial position. This method can quickly and effectively perform the registration of monocular images and BIM models, get the initial position in complex spaces, and support the application scenarios such as indoor navigation, equipment maintenance, facility management, and construction monitoring.
Simulated Analysis of Using Artificial Neural Network to Identify Fake Images Based on Metadata

Many phony pictures are spreading through advanced media these days. Identification of such phony pictures is unavoidable for the revealing of the picture based cybercrimes. Fashioning pictures what’s more, recognizing such pictures are promising examination territories in this advanced time. The altered pictures are an identified utilizing neural organize which additionally perceives the locales of the picture that have been controlled and uncovers the sections of the first picture. It tends to be actualized on Android stage and subsequently made accessible to normal clients. The pressure proportion of the remote content in a phony picture is not the same as that of the first picture and is recognized utilizing Error Level Analysis. Another include utilized alongside pressure proportion is picture metadata. Despite the fact that it is conceivable to change metadata content creation it inconsistent all alone, here it is utilized as a supporting parameter for mistake level examination choice.
Iterative quantum phase estimation on an IBM quantum processor

An elementary algorithm of quantum phase estimation based on the modified Kitaev algorithm is implemented on two qubits of an IBM quantum processor. This algorithm includes adiabatic preparation of the initial state, controlled phase shift with allowance for the results of previous measurements of qubit states, and single measurement of the qubit quantum state in order to obtain each significant bit of the measured phase. Classical error correction is applied to determine the correct sequence of bits, which makes it possible to eliminate the influence of limited accuracy of two-qubit gates.
Comparison of the BM25 and rabinkarp algorithm for plagiarism detection

Plagiarism occurs because of the easy distribution of data. Plagiarism detection of documents such as student assignments and final projects requires a long process, often overlooked. However, to avoid plagiarism, a document must be checked for the level of plagiarism. Plagiarism detection can be done online / offline with the plagiarism checker. However, checking documents with plagiarism checkers such as Turnitin, Dupli Checker, Copyleaks, PaperRater, Grammarly and others requires additional fees. Several studies have been conducted to detect plagiarism. BM25 and Rabin Karp are examples of the Plagiarism Checker method. BM25 is tfidf based, while Rabin Karp is Hashing based. Each method needs to know its performance to detect plagiarism. Based on these problems, a study on the comparison of plagiarism detection with the BM25 algorithm with Rabin-Karp will be conducted. The case study is to use the article in Indonesian. The application of the BM25 and Rabin Karp algorithms goes through the Pre-Processing stage which consists of case folding, cleaning, tokenizing, filtering, and stemming. In this study, Sastrawi stemmer was used in this study. The test was conducted on twenty articles in Indonesian. The test results that are seen are the performance in the form of execution time.
Discussion on The Data Extraction Strategy Of FAQ System Based On Chat Records

Q&A data based on chat records have one characteristic: customers ask, service answers. There is a large amount of knowledge between the questions and answers of customer service. By optimizing the Q&A extraction algorithm to extract knowledge, a very excellent Q&A library can be constructed, thus the accuracy of FAQ system is greatly improved. By analyzing the existing data, this paper cnosiders the extraction strategy of question answering from machine learning and non-machine learning respectively. Then We compares their performance from three aspects of precision, recall and Fl-score according to their different characteristics. In order to ensure the best classification performance, grid search and K-fold crossover are also used to test the optimized classifier performance. After selecting the optimal data extraction strategy, We developed a FAQ system use this strategy, the system results show that the performance is reliable.
CpG islands identification in DNA sequences using modified P-spectrum based algorithm

The identification of CpG Islands play a major role in the analysis of DNA sequences because of association of CpG Islands with many epigenetic events. Some of these events are promoter activity and consequently gene prediction, chromosome inactivation, and for early detection of cancer etc. And hence the exact identification of CpG Islands in the stretch of DNA sequences has always remained a challenging task. Numerous computational algorithms have been developed for the identification of CpG Islands in DNA sequences. Recently various transform based methods have been reported for the CpG Islands detection in literature. In these transform based methods, there is a requirement of transforming the signal from time domain to frequency domain and correspondingly there is probability of transform biasing. Hence to overcome this issue, a modified P-spectrum based algorithm has been proposed in this paper. Also the performance of the proposed method has been compared with recently reported methods of CpG Islands detection using standard evaluation metrics. The performance of proposed method has been proved to be much better than the other methods and hence the proposed approach is an efficient method in detecting CpG Islands.
Combined system for indeterminate non-affine plant with control delay on the set of functioning states

In the present paper authors consider the synthesis of combined nonlinear control law for non-affine control plant with delay in the input variable with step-by-step changing of its dynamics. The control plant operates in conditions of a priori parametric and structural uncertainties in the presence of external noise and measurement of the controlled variable only. The structure of the control system includes the predictor-compensator and the filter-corrector.
Evaluation Metrics for Personalized Recommendation Systems

This paper summarizes and reviews the latest research progress on the evaluation metrics of the recommendation system, expounds from multiple perspectives such as accuracy, diversity, novelty, and conducts an in-depth analysis of their respective advantages and disadvantages and applicable environments, focuses on the three levels of recommended diversity and evaluation criteria, and makes predictions on the development direction of the evaluation metrics of the recommendation system.
Biomaterials as therapeutic agents for treatment of cancer: A review

Biomaterials can be stated as an amalgamation of science in research and development where the field of cellular biology, molecular biology, chemical sciences, materials sciences and engineering plays important role. These are substances which have been modified to interact with biological systems for therapeutic purposes and even diagnosis. They induce a synergistic effect in the environment where implanted. Although as a science, it is years old but at present it is growing as a modern-day research platform that finds its application in many fields of medical science and cancer being the most extensively researched field in the same. Such studies undertaken in the field of biomaterials has been reviewed in the paper that includes the use of biomaterials as therapeutic agents including their use as vaccines and surface modulators to enhance the antigen specific T-cell activity in the immunotherapy for cancers. The application has been found reliable in case of recurrent cancers, poorly immunogenic tumours as well as immunologically resistant tumours. Triple negative breast cancer and skin cancers have been successfully treated with the help of biomaterials. The review highlights the importance and scope of study towards field of biomaterials in cancer treatment.