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The next cryptocurrency price movement prediction application uses patterned datasets

AIP Conference Proceedings 2024年第1期3116卷

作者： Rizky Parlika Mustafid Basuki Rahmat Diponegoro University Information Systems Doctoral Study Program Graduate School 50241 Jl. Imam Bardjo SH Pleburan Semarang Indonesia University of Pembangunan Nasional “Veteran” Jawa Timur Department of Informaton Technology Faculty of Computer Science 60294 Rungkut Madya Gunung Anyar Surabaya Indonesia

Researchers and experts have developed various techniques, models, and methods to predict the price movements of cryptocurrencies, especially Bitcoin. However, among the many techniques studied in the literature, there is still a lack of focus on mining, creating, and developing datasets with specific patterns for predicting the next cryptocurrency price movement. This is an exciting reason to conduct further research. A web-based Patterned Dataset Application and a Telegram bot were constructed to address this issue. These tools read the price position of each cryptocurrency and predict the next price direction based on the last position indicated by the Patterned Dataset Application. The experiment’s results show that when the Patterned Dataset Application shows a diamond crash position, it is time to make a purchase; conversely, when it shows a diamond moon position, it is time to make a sale. It is hoped that by utilizing the Patterned Dataset Application, potential losses can be minimized, and there is more potential for profit in cryptocurrency trading. Even though the initial data source comes from Indonesia’s most prominent digital cryptocurrency trading market, according to coinmarketcap, namely Indodax, the results of this patterned dataset application can often describe the same cryptocurrency conditions globally. The novelty of this research is to produce a new way of predicting the next cryptocurrency price movement using patterned datasets. At the end of this paper, it will be proven that hypothesis 1 and hypothesis 2 on the results of the patterned dataset are true.

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Recent developments of quantum dot based micro-LED based on non-radiative energy transfer mechanism

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Opto-Electronic Advances 2021年第4期4卷 1-15页

作者： Xiaotong Fan Tingzhu Wu Bin Liu Rong Zhang Hao-Chung Kuo Zhong Chen School of Electronic Science and Engineering Fujian Engineering Research Center for Solid-State LightingXiamen UniversityXiamen 361005China Fujian Science&Technology Innovation Laboratory for Energy Materials of China Xiamen 361005China Jiangsu Provincial Key Laboratory of Advanced Photonic and Electronic Materials Nanjing National Laboratory of MicrostructuresSchool of Electronic Science and EngineeringNanjing UniversityNanjing 210093China Department of Photonics and Graduate Institute of Electro-Optical Engineering College of Electrical and Computer EngineeringChiao Tung UniversityHsinchu 30010China

With regard to micro-light-emitting diodes(micro-LEDs),their excellent brightness,low energy consumption,and ultrahigh resolution are significant ***,the large size of traditional inorganic phosphors and the number of side defects have restricted the practical applications of small sized ***,quantum dot(QD)and nonradiative energy transfer(NRET)have been proposed to solve existing *** possess nanoscale dimensions and high luminous efficiency,and they are suitable for NRET because they are able to nearly contact the micro-LED *** NRET between QDs and micro-LED chip further improves the color conversion efficiency(CCE)and effective quantum yield(EQY)of full-color micro-LED *** this review,we discuss the NRET mechanism for QD micro-LED devices,and then nano-pillar LED,nano-hole LED,and nano-ring LED are introduced in *** structures are beneficial to the NRET between QD and micro-LED,especially nano-ring ***,the challenges and future envisions have also been described.

关键词： quantum dot based micro-LED non-radiative energy transfer atomic layer deposition sidewall defects

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Secondary spin current driven efficient THz spintronic emitters

arXiv

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arXiv 2022年

作者： Agarwal, Piyush Yang, Yingshu Medwal, Rohit Asada, Hironori Fukuma, Yasuhiro Battiato, Marco Singh, Ranjan Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link Singapore637371 Singapore Center for Disruptive Photonic Technologies The Photonics Institute Nanyang Technological University Singapore639798 Singapore Department of Physics Indian Institute of Technology Uttar Pradesh Kanpur208016 India Department of Electronic Devices and Engineering Graduate School of Science and Engineering Yamaguchi University Ube755-8611 Japan Department of Physics and Information Technology Faculty of Computer Science and System Engineering Kyushu Institute of Technology Iizuka820-8502 Japan

Femtosecond laser-induced photoexcitation of ferromagnet (FM)/heavy metal (HM) heterostructures have attracted attention by emitting broadband terahertz frequencies. The phenomenon relies on the formation of ultrafast spin current, which is largely attributed to the direct photoexcitation of the FM layer. However, we reveal that during the process, the FM layer also experiences a secondary excitation led by the hot electrons from the HM layer that travel across the FM/HM interface and transfer additional energy in the FM. Thus, the generated secondary spins enhance the total spin current formation and lead to amplified spintronic terahertz emission. The results also emphasize the significance of the secondary spin current, which even exceeds the primary spin currents when FM/HM heterostructures with thicker HM are used. An analytical model is developed to provide deeper insights into the microscopic processes within the individual layers, underlining the generalized ultrafast superdiffusive spin-transport mechanism. Copyright © 2022, The Authors. All rights reserved.

关键词： Hot electrons

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Effects of Span Length and Additional Structure on Flow-Induced Transverse Vibration Characteristic of a Cantilevered Rectangular Prism

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Journal of Flow Control, Measurement & Visualization 2020年第3期8卷 102-120页

作者： La Ode Ahmad Barata Takahiro Kiwata Takaaki Kono Toshiyuki Ueno Graduate School of Natural Science and Technology Kanazawa University Kanazawa Japan School of Mechanical Engineering Kanazawa University Kanazawa Japan School of Electrical and Computer Engineering Kanazawa University Kanazawa Japan

We consider the effects of the aspect ratio L/H (where L is the length of a prism, and H is the height of a prism normal to the flow direction) and the size of additional structures (which are a plate and a fin on the surface of a prism) on a vibration characteristic of a cantilevered rectangular prism. The present research is intended to support the analysis of energy harvesting research on the flow-induced vibration in water flow using a magnetostrictive phenomenon. The prisms are constructed from stainless steel and mounted elastically to a plate spring attached to the ceiling wall of the water tunnel. The prisms with aspect ratios of L/H ≥ 5 have reasonably identical vibration characteristics. However, the difference in the vibration characteristic appears distinctly on a rectangular prism with an aspect ratio of L/H = 2.5. The rectangular prism with an aspect ratio of L/H = 10 and a side ratio of D/H = 0.2 has a stable and large response amplitude and oscillates with a lower velocity. The length of the added plate and the size of the added fin influence the velocity of vibration onset. If the length of the added plate and fin size on the rectangular prism with D/H = 0.2 becomes large, the curve of the response amplitude shifts to that of the rectangular prism with D/H= 0.5. The response amplitude of the rectangular prism with/without plate or fin is found to be related to the second moment of area of the prism.

关键词： Flow-Induced Vibration Cantilevered Rectangular Prism Aspect Ratio Stepped Prism Finned Prism Response Amplitude Resonant Reduced Velocity

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CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data

CompressedMediQ: Hybrid Quantum Machine Learning Pipeline fo...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Kuan-Cheng Chen Yi-Tien Li Tai-Yu Li Chen-Yu Liu Po-Heng Henry Lee Cheng-Yu Chen Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Translational Imaging Research Center Taipei Medical University Hospital Taipei Taiwan Research Center for Neuroscience Taipei Medical University Taipei Taiwan Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei Taiwan National Center for High-Performance Computing NARlabs Hsinchu Taiwan Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Department of Medical Imaging Taipei Medical University Hospital Taipei Taiwan Department of Radiology National Defense Medical Center Taipei Taiwan Department of Radiology School of Medicine College of Medicine Taipei Taiwan

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroimaging datasets, such as large-scale MRI data from the Alzheimer’s Disease Neuroimaging Initiative and Neuroimaging in Frontotemporal Dementia, present significant hurdles due to their vast size and complexity. CompressedMediQ integrates classical HPC nodes for advanced MRI pre-processing and Convolutional Neural Network (CNN)-PCA-based feature extraction and reduction, addressing the limited-qubit availability for quantum data encoding in the NISQ era. This is followed by Quantum Support Vector Machine (QSVM) classification. By utilizing quantum kernel methods, the pipeline optimizes feature mapping and classification, enhancing data separability and outperforming traditional neuroimaging analysis techniques. Experimental results highlight the pipeline’s superior accuracy in dementia staging, validating the practical use of quantum machine learning in clinical diagnostics. Despite the limitations of NISQ devices, this proof-of-concept demonstrates the transformative potential of quantum-enhanced learning, paving the way for scalable and precise diagnostic tools in healthcare and signal processing.

关键词： Neuroimaging Support vector machines Quantum computing Accuracy Magnetic resonance imaging Pipelines Machine learning Medical services Signal processing Standards

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Learner Action Patterns in the Problem-Solving Process Related to program Code Composition Based on Tracking System Activities

Learner Action Patterns in the Problem-Solving Process Relat...

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International Conference on Informatics and Computing (ICIC)

作者： Aulia Akhrian Syahidi Ahmad Afif Supianto Tsukasa Hirashima Yutaka Watanobe Smart City Information System Applied Undergraduate Program Politeknik Negeri Banjarmasin Banjarmasin City Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Bandung City Indonesia Learning Engineering Laboratory Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Higashihiroshima Japan Department of Computer Science and Engineering Division of Information Systems University of Aizu Aizu-Wakamatsu City Fukushima Japan

ISBN: (纸本)9798350345728

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the “if-then” rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process.

关键词： Visualization Codes Media Data models Recording Data mining Problem-solving

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LM22A-4-loaded smart mesoporous balls enhance neuroprotection and functional recovery after ischemic stroke

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Biomedicine and Pharmacotherapy 2025年 183卷 117863-117863页

作者： Lee, Jae Ho Lee, Kyeong Hyeon Ryu, Ji Hyeon Kim, Min Jae Kim, Eunji Lee, Seo-Yeon Han, Sang-Cheol Choi, Byung Tae Shin, Yong-Il Shin, Hwa Kyoung Department of Korean Medical Science School of Korean Medicine Pusan National University Gyeongnam Yangsan 50612 South Korea Graduate Training Program of Korean Medical Therapeutics for Healthy-Aging Pusan National University Gyeongnam Yangsan 50612 South Korea Department of Rehabilitation Medicine School of Medicine Pusan National University Gyeongnam Yangsan 50612 South Korea Research Institute for Convergence of Biomedical Science and Technology Pusan National University Yangsan Hospital Gyeongnam Yangsan 50612 South Korea Department of Pharmacology Wonkwang University School of Medicine Jeonbuk Iksan 54538 South Korea CEN Co. Ltd. Nano-Convergence Center 761 Muan-ro Miryang 50404 South Korea

Stroke is globally recognized as the second leading cause of death, significantly impairing both motor and cognitive functions. Enhancing regeneration after stroke is crucial for restoring these functions and necessitates strategies to promote neuroregeneration to achieve better post-stroke outcomes. Brain-derived neurotrophic factor (BDNF) plays a key role in neuroregeneration by influencing motor ability, learning, memory, and rehabilitation after stroke. However, challenges such as the substantial protein size, short half-life of BDNF, and blood-brain barrier hinder its efficient delivery to the brain. In this study, LM22A-4, a BDNF mimetic, was utilized and delivered through a Smart Mesoporous Ball (SMB-3) system to target the ischemic injured brain and explore its potential therapeutic effects in a mouse ischemic stroke model. Treatment with LM22A-4-loaded SMB-3 (LM22A-4-SMB-3) markedly restored neurological, motor, and cognitive deficits following ischemic stroke compared to LM22A-4 alone. Additionally, administration of LM22A-4-SMB-3 reduced apoptotic cell death and glial activation, as evidenced by the TUNEL assay results, and decreased GFAP and Iba-1 expression levels. Furthermore, the phosphorylation of TrkB and Akt, but not that of Erk, was considerably increased in the LM22A-4-SMB-3-treated group. Treatment also enhanced the number of BrdU+/NeuN+ cells, with a marked reduction in post-stroke brain atrophy. These findings suggest that LM22A-4-SMB-3 can attenuate ischemic damage and recover neurological, motor, and cognitive functions by increasing p-TrkB and p-Akt levels and promoting neurogenesis. Therefore, SMB-3-mediated delivery of LM22A-4 presents a potentially applicable delivery system, and LM22A-4-SMB-3 use could be considered a novel therapeutic strategy to improve post-stroke outcomes. © 2025 The Authors

关键词： BDNF mimetic Drug delivery system Ischemic stroke LM22A-4 Smart mesoporous ball

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Are we ready for a new paradigm shift? A Survey on Visual Deep MLP

arXiv

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arXiv 2021年

作者： Liu, Ruiyang Li, Yinghui Tao, Linmi Liang, Dun Zheng, Hai-Tao Department of Computer Science and Technology BNRist Tsinghua University Key Lab of Pervasive Computing Ministry of Education of China Beijing100084 China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China

Recently, the proposed deep MLP models have stirred up a lot of interest in the vision community. Historically, the availability of larger datasets combined with increased computing capacity leads to paradigm shifts. This review paper provides detailed discussions on whether MLP can be a new paradigm for computer vision. We compare the intrinsic connections and differences between convolution, self-attention mechanism, and Token-mixing MLP in detail. Advantages and limitations of Token-mixing MLP are provided, followed by careful analysis of recent MLP-like variants, from module design to network architecture, and their applications. In the GPU era, the locally and globally weighted summations are the current mainstreams, represented by the convolution and self-attention mechanism, as well as MLP. We suggest the further development of paradigm to be considered alongside the next-generation computing devices. Copyright © 2021, The Authors. All rights reserved.

关键词： computer vision

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Texture and Shape Biased Two-Stream Networks for Clothing Classification and Attribute Recognition

Texture and Shape Biased Two-Stream Networks for Clothing Cl...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yuwei Zhang Peng Zhang Chun Yuan Zhi Wang Department of Computer Science and Technology Tsinghua University Tsinghua Shenzhen International Graduate School Peng Cheng Laboratory

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Clothes category classification and attribute recognition have achieved distinguished success with the development of deep learning. People have found that landmark detection plays a positive role in these tasks. However, little research is committed to analyzing these tasks from the perspective of clothing attributes. In our work, we explore the usefulness of landmarks and find that landmarks can assist in extracting shape features; and using landmarks for joint learning can increase classification and recognition accuracy effectively. We also find that texture features have an impelling effect on these tasks and that the pre-trained ImageNet model has good performance in extracting texture features. To this end, we propose to use two streams to enhance the extraction of shape and texture, respectively. In particular, this paper proposes a simple implementation, Texture and Shape biased Fashion Networks (TS-FashionNet). Comprehensive and rich experiments demonstrate our discoveries and the effectiveness of our model. We improve the top-3 classification accuracy by 0.83% and improve the top-3 attribute recognition recall rate by 1.39% compared to the state-of-the-art models.

关键词： Shape Clothing Feature extraction Streaming media Task analysis Image recognition Heating systems

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Optimization of Multi-Channel Quartz Crystal Microbalance for Gas Sensing Application

Optimization of Multi-Channel Quartz Crystal Microbalance fo...

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Recent Advances and Innovations in Engineering (ICRAIE)

作者： Ahmad Anwar Zainuddin Soumik Bhattacharjee Aliza Aini Md Ralib Nurain Sufi Sabreena Mohd Sukri Saidatul Izyanie Binti Kamarudin Krishnan Subramaniam Dini Handayani M. Reyasudin Basir Khan Nik Nor Muhammad Saifudin Computer Science Kuliyyah of Information Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia Digital Media Masters Universität Bremen Germany Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur Malaysia Kolej Pengajian Pengkomputeran Informatik dan Matematik (KPPIM) Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Mechanical Engineering Mila University Nilai Negeri Sembilan Malaysia Tun Razak Graduate School (TRGS) Universiti Tun Abdul Razak (UNIRAZAK) Kuala Lumpur Malaysia

Mass detection sensor Quartz Crystal Microbalance (QCM) measures mass sensitivity by piezoelectric, high frequency shaving on nanograms. These sensors detect with high precise geometry, low cost and ease of production. Conventional QCM sensors are limited to one electrode structure that minimizes the range of sensing. On a single quartz crystal substrate, three new QCM Multi-Channel sensors are developed and optimized that include 2-channel, 3-channel and 4-channel QCM sensors. The COMSOL Multiphysics software is used to design and simulate the multi-channel sensors to enhance the device structure for applications with less interference frequency. From the results, it shows that the least interferences exhibit frequency values of 9.58MHz as both QCMs for the 5mm centre-to-centre (c2c). However, due to small c2c distances that decrease accuracy and operating disadvantages, this design was not selected. For 3 channel sensors, however, three resonant peaks show a similar value of 9.58MHz when c2c is maintained at 7mm. With the highest c2c distance this design shows less interference. Finally, for 4-channel sensors, a distance of 6 mm c2c also has a minimum interference with a 9.58 MHz resonance frequency. This shows that 4 channel sensors with the highest c2c can provide greater operational convenience and less interference at frequencies and thus maximize measurement accuracy. Multi-sensor arrays are advantageous when detecting a large number of analytes at the same time or when only selective coatings (coatings that respond to more than one analyte at the same time) are available. These sensor arrays, which use only one sensor and combine it with enhanced signal evaluation methods or prior sample separation to achieve the results of multi-sensor arrays, are becoming increasingly popular.

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