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arXiv 2024年

作者： Guo, Qiangbing Wu, Yun-Kun Zhang, Di Zhang, Qiuhong Guo, Guang-Can Alù, Andrea Ren, Xi-Feng Qiu, Cheng-Wei Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Centre for Advanced 2D Materials Graphene Research Centre National University of Singapore Singapore Singapore CAS Key Laboratory of Quantum Information CAS Synergetic Innovation Center of Quantum Information & Quantum Physics University of Science and Technology of China Hefei China Hefei National Laboratory University of Science and Technology of China Hefei China Photonics Initiative Advanced Science Research Center City University of New York New York United States Physics Program Graduate Center City University of New York New York United States The Key Laboratory of Weak-Light Nonlinear Photonics Ministry of Education School of Physics TEDA Applied Physics Institute Nankai University Tianjin China School of Chemistry and Materials Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou China

Polarization entanglement holds significant importance for photonic quantum technologies. Recently emerging subwavelength nonlinear quantum light sources, e.g., GaP and LiNbO3 thin films, benefiting from the relaxed phase-matching constraints and volume confinement, has shown intriguing properties, such as high-dimensional hyperentanglement and robust entanglement anti-degradation. Van der Waals (vdW) NbOCl2 crystal, renowned for its superior optical nonlinearities, has emerged as one of ideal candidates for ultrathin quantum light sources [Nature 613, 53 (2023)]. However, polarization-entanglement is inaccessible in NbOCl2 crystal due to its unfavorable nonlinear susceptibility tensor. Here, by leveraging the twist-stacking degree of freedom inherently in vdW systems, we showcase the preparation of tunable polarization entanglement and quantum Bell states. Our work not only provides a new and tunable polarization-entangled vdW photon-pair source, but also introduces a new knob in engineering the entanglement state of quantum light at the nanoscale. © 2024, CC BY-NC-ND.

关键词： Van der Waals forces

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An intelligent applied Fuzzy Logic to prediction the Parts per Million (PPM) as hydroponic nutrition on the based Internet of Things (IoT)

An intelligent applied Fuzzy Logic to prediction the Parts p...

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International Conference on Informatics and Computing (ICIC)

作者： Purnawarman Musa Herik Sugeru Haya Fadhila Mufza Faculty of Computer Science and Information Technology Universitas Gunadarma Indonesia Faculty of Industrial Technology Study Program of Agrotechnology Universitas Gunadarma Indonesia

Hydroponic plants require regular monitoring of plants that grow well and can even cause plants to die. Because too late in providing nutrition or getting too little or too little food or routine monitoring in the hydroponic system. The innovation proposed in the agricultural sector that we suggest is the process of monitoring hydroponic plants in real-time. This study aims to predict the excess or lack of nutrient content based on the results of routine monitoring and has the analytical ability to determine the prediction of the value of Parts per Million (PPM) to find the nutritional value of plant media. Measurement of nutritional value using Electrical Conductivity (EC) and the potential of Hydrogen (pH) in hydroponic plant media. In this research, using the NFT hydroponic system as the application of fuzzy logic with the Mamdani method to determine the predictions of PPM values in providing nutrients at plants to adjusting the nutritional at each plant (e.g., for water spinach plants are need 1050 until 1400 PPM). The implemented is method Internet of Things (IoT) to remote sensing on measure using a smartphone as the representation of the measurement results on the hydroponic system.

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Influence of blended learning on learning result of algorithm and programming 3

Influence of blended learning on learning result of algorith...

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3rd International Conference on Informatics and Computing, ICIC 2018

作者： Anggrawan, Anthony Ibrahim, Nurdin Suyitno, M. Satria, Christofer Informatics Engineering Study Program of College of Informatics Management and Computer Science Bumigora West Nusa Tenggara Indonesia Education Technology Study Program of Jakarta State University Jakarta Indonesia Education Technology Study Program of Jakarta State University Jakarta Indonesia Visual Communication Design Study Program of College of Informatics Management and Computer Science Bumigora West Nusa Tenggara Indonesia

ISBN: (纸本)9781538669204

The advancement of today's learning technology makes the learning process no longer fully bases on face to face learning, but can achieve by online learning. Online learning is not only useful in overcoming deficiencies in conventional classroom learning, but also a means to meet the learning styles of students who are less fond of and/or dislike face-to-face learning. On the other hand, the main disadvantage of online learning is the lack of face-to-face interaction, while the strength of face-to-face learning is the intensity of student interaction with teachers, the ease of cooperative learning and the clarity of learning materials. In turn, the superiorities on face-to-face learning and online self-learning can be acquired by blended learning. However, blended learning is a learning solution by taking the best advantage of face-to-face classroom learning and online learning. Blended learning has been a learning solution of today's learning. In this study, two blended learning that has different learning content portions between the portion of face-to-face learning materials and online self-learning was conducted to find out which blended learning results produce better pedagogy between two blended learning, and how well the learning outcomes of blended learning are, especially for Algorithm and programming subject. The research results showed the blended learning with 60% face-to-face learning content and 40% online self-learning content delivers learning cognitive competency outcomes worse than 40% face-to-face learning content and 60% online self-learning content. The learning outcome of both blended learning is good, that is greater than 60% from the ideal number. © 2018 IEEE.

关键词： Students

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Evaluation QOS and energy consumption for DSDV and DSR in MANET (Mobile ad hoc network) 1

Evaluation QOS and energy consumption for DSDV and DSR in MA...

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2017 1st International Conference on Engineering and Applied technology, ICEAT 2017

作者： Sidharta, H.A. Sidharta Huda, C. Computer Science Study Program Bina Nusantara Institute of Creative Technology Malang East-Java Indonesia

A mobile ad hoc network (MANET) is an infrastructure less, autonomous, and standalone network. Routing protocol mechanism on MANET can be classified proactive, reactive and hybrid. On this paper researcher comparing QOS with reactive and proactive, reactive represented by DSR and proactive by DSDV. Performance of these routing protocols is evaluated with performance metrics such as Dropped Packets, Packet Delivery Ratio, Throughput, End to End Delay, Jitter, and Residual energy. Based on experiment shows that DSDV is better than DSR, performance DSDV more stable than DSR, DSR on little node better than DSDV, but in line with increment number of node performance is degrade. © Published under licence by IOP Publishing Ltd.

关键词： Mobile ad hoc networks

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Scientific discovery in the age of artificial intelligence

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NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

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Load balancing factor using greedy algorithm in the routing protocol for improving internet access 2

Load balancing factor using greedy algorithm in the routing ...

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2nd Nommensen International Conference on technology and Engineering, NICTE 2018

作者： Satria, Ady Sihombing, Poltak Sawaluddin Informatics Engineering Study Program Magister Faculty of Computer Science and Information Technology University of Sumatera Utara Indonesia

Load Balancing is very well applied to the distribution of internet access at any point of the Wi-Fi area so that the use of limited devices can be optimal and on target. The proper use of the Greedy Algorithms when applied to an access point device is very capable in resolving excessive loading in a single resource to take the best choice at every stage in an optimum process. Access point device is also a success factor in running the optimization of the distribution of Wi-Fi access on the Access Point is strongly influenced by the parameters set. In this research, user access factor in one very high Wi-Fi area also influences the possibility of incoming access failure. For the application of the results of this study can be utilized on all access point devices that have limits, but for speed internet access remains at the capacity provided bandwidth. © Published under licence by IOP Publishing Ltd.

关键词： Wireless local area networks (WLAN)

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Nonreciprocal transport in a bilayer of MnBi2Te4 and Pt

arXiv

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arXiv 2022年

作者： Ye, Chen Xie, Xiangnan Lv, Wenxing Huang, Ke Yang, Allen Jian Jiang, Sicong Liu, Xue Zhu, Dapeng Qiu, Xuepeng Tong, Mingyu Zhou, Tong Hsu, Chuang-Han Chang, Guoqing Lin, Hsin Li, Peisen Yang, Kesong Wang, Zhenyu Jiang, Tian Wang, Xiao Renshaw Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 21 Nanyang Link 637371 Singapore State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Physics Laboratory Industrial Training Center Shenzhen Polytechnic Guangdong Shenzhen518055 China Department of NanoEngineering Program of Chemical Engineering University of California La Jolla San DiegoCA92093-0448 United States Program of Materials Science and Engineering University of California La Jolla San DiegoCA92093-0418 United States Institutes of Physical Science and Information Technology Anhui University Hefei230601 China Fert Beijing Institute MIIT Key Laboratory of Spintronics School of Integrated Circuit Science and Engineering Beihang University Beijing100191 China Beihang-Goertek Joint Microelectronics Institute Qingdao Research Institute Beihang University Qingdao266000 China Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology School of Physics Science and Engineering Tongji University Shanghai200092 China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha410073 China Insitute of Physics Academia Sinica Taipei115229 Taiwan College of Intelligence Science and Technology National University of Defense Technology Changsha410073 China National Innovation Institute of Defense Technology Academy of Military Sciences PLA China Beijing100010 China Beijing Academy of Quantum Information Sciences Beijing100084 China Beijing Institute for Advanced Study National University of Defense Technology Changsha410073 China School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Ave 639798 Singapore

MnBi2Te4 (MBT) is the first intrinsic magnetic topological insulator with the interaction of spin-momentum locked surface electrons and intrinsic magnetism, and it exhibits novel magnetic and topological phenomena. Recent studies suggested that the interaction of electrons and magnetism can be affected by the Mn-doped Bi2Te3 phase at the surface due to inevitable structural defects. Here we report an observation of nonreciprocal transport, i.e. current-direction-dependent resistance, in a bilayer composed of antiferromagnetic MBT and nonmagnetic Pt. The emergence of the nonreciprocal response below the Néel temperature confirms a correlation between nonreciprocity and intrinsic magnetism in the surface state of MBT. The angular dependence of the nonreciprocal transport indicates that nonreciprocal response originates from the asymmetry scattering of electrons at the surface of MBT mediated by magnon. Our work provides an insight into nonreciprocity arising from the correlation between magnetism and Dirac surface electrons in intrinsic magnetic topological insulators. © 2022, CC BY.

关键词： Magnetism

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Multi-parameter prediction of oil palm fruit quality through near infrared spectroscopy combined with chemometric analysis

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Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy 2025年 343卷 126505页

作者： Muhammad Achirul Nanda Kharistya Amaru S Rosalinda Inna Novianty Tusan Park Department of Agricultural and Biosystem Engineering Faculty of Agro-Industrial Technology Universitas Padjadjaran Sumedang 45363 Indonesia. Electronic address: m.achirul@unpad.ac.id. Department of Agricultural and Biosystem Engineering Faculty of Agro-Industrial Technology Universitas Padjadjaran Sumedang 45363 Indonesia. Computer Engineering Technology Study Program College of Vocational Studies IPB University Bogor 16680 Indonesia. Department of Smart Bio-Industrial Mechanical Engineering Kyungpook National University Daegu 41566 Republic of Korea Department of Applied Biosciences Kyungpook National University Daegu 41566 Republic of Korea Smart Agriculture Innovation Center Kyungpook National University Daegu 41566 Republic of Korea.

Early information concerning water, oil, and free fatty acid (FFA) content in palm fruit on-site is crucial in determining the commercial value of fresh fruit bunches (FFB) and maintaining oil palm quality. Conventional methods are destructive, labor-intensive, single-target, costly, and time-consuming. Therefore, this study aimed to develop a method for the multi-parameter prediction of oil palm fruit quality using near-infrared (NIR) spectroscopy. Empirical Wavelet Transform (EWT) and Gaussian Process Regression (GPR) were implemented as a novel chemometric method. A total of 750 fruit samples of the Tenera variety (Elaeis guineensis Jacq. var. tenera) with various maturity levels were collected from the Cikabayan Oil Palm Plantation. Each sample was scanned using an NIR instrument at 1000-1500 nm wavelengths to obtain absorbance data. The EWT was applied to decompose the NIR spectra into empirical modes, and GPR was used to build a regression model for fitting. Based on numerical analysis, the combination of EWT and GPR produced the root mean square error (RMSE) values of 2.877 ± 0.900 % (R = 0.955 ± 0.018), 1.256 ± 0.543 % (R = 0.942 ± 0.030), and 0.065 ± 0.04 % (R = 0.964 ± 0.044) for water, oil, and FFA content, respectively. The results showed that the model could accurately predict the internal quality of oil palm fruit without the need for solvents or reagents, supporting environmental sustainability. These were expected to enhance oil palm production management by improving quality control, optimizing harvest timing, and promoting sustainability across the value chain.

关键词： Chemometric Near-infrared spectroscopy Nondestructive evaluation Oil palm fruit Quality

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Automatic Food Leftover Estimation in Tray Box Using Image Segmentation

Automatic Food Leftover Estimation in Tray Box Using Image S...

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International Conference on Sustainable Information Engineering and technology (SIET)

作者： Yuita Arum Sari Ratih Kartika Dewi Jaya Mahar Maligan Anindya Sasri Ananta Sigit Adinugroho Computer Vision Research Group Faculty of Computer Science Malang Indonesia Mobile Game and Multimedia Research Group Faculty of Computer Science Malang Indonesia Food Nutrition Study Program Faculty of Agricultural Technology Malang Indonesia

Nutritional food is the most important aspect for people to fulfil their daily needs, but nowadays, the change of lifestyle can affect the style of food consumption, for instance leaving food when they take a meal. Besides, food leftover causes loss of consumed nutrients. We took an observation in a canteen in Faculty of Agricultural technology in University of Brawijaya produces lost of food about 1,142.5 gram per day. To prevent the amount of food waste, this paper proposes an early stage framework to create an automatic leftover estimation method using image segmentation based on color channel. So that, we can optimize of the amount of food served and avoid loss food nutritions hereinafter. We experimented in two types of background of tray box: black and gray, then an automatic cropping process based on each sub area of tray box is implemented. Each part of them contains an item of food, so we detect the area using color channel segmentation and implements B component in LAB color space. After getting the main area of food, then two areas between segmented food image are compared before and after being consumed. The calculation of the predicted leftover portion is calculated based on original weight of food item. The result shows that tray box with black background with constant (cd) is the best to project leftover, in which reaches 2.37 of Root Mean Square Error (RMSE). It proves that the propose method is sufficient to handle the leftover prediction of food.

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A pretrained transformer model for decoding individual glucose dynamics from continuous glucose monitoring data

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National Science Review 2025年第5期 362-381页

作者： Yurun Lu Dan Liu Zhongming Liang Rui Liu Pei Chen Yitong Liu Jiachen Li Zhanying Feng Lei M.Li Bin Sheng Weiping Jia Luonan Chen Huating Li Yong Wang Center for Excellence in Mathematical Sciences National Center for Mathematics and Interdisciplinary Sciences Hua Loo-Keng Center for Mathematical Sciences Key Laboratory of Management Decision and Information System Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematics University of Chinese Academy of SciencesChinese Academy of Sciences Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai Diabetes InstituteShanghai Clinical Center for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences BGI-Research School of Mathematics South China University of Technology Department of Statistics Department of Biomedical Data Science Bio-X ProgramStanford University Department of Computer Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Cell Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Guangdong Institute of Intelligence Science and Technology Pazhou Laboratory (Huangpu)

Continuous glucose monitoring(CGM) technology has grown rapidly to track real-time blood glucose levels and trends with improved sensor accuracy. The ease of use and wide availability of CGM will facilitate safe and effective decision making for diabetes management. Here, we developed an attention-based deep learning model, CGMformer, pretrained on a well-controlled and diverse corpus of CGM data to represent individual's intrinsic metabolic state and enable clinical applications. During pretraining, CGMformer encodes glucose dynamics including glucose level, fluctuation, hyperglycemia, and hypoglycemia into latent space with self-supervised learning. It shows generalizability in imputing glucose value across five external datasets with different populations and metabolic states(MAE = 3.7 mg/d L). We then fine-tuned CGMformer towards a diverse panel of downstream tasks in the screening of diabetes and its complications using task-specific data, which demonstrated a consistently boosted predictive accuracy over direct fine-tuning on a single task(AUROC = 0.914 for type 2 diabetes(T2D) screening and 0.741 for complication screening). By learning an intrinsic representation of an individual's glucose dynamics,CGMformer classifies non-diabetic individuals into six clusters with elevated T2D risks, and identifies a specific cluster with lean body-shape but high risk of glucose metabolism disorders, which is overlooked by traditional glucose measurements. Furthermore, CGMformer achieves high accuracy in predicting an individual's postprandial glucose response with dietary modelling(Pearson correlation coefficient = 0.763)and helps personalized dietary recommendations. Overall, CGMformer pretrains a transformer neural network architecture to learn an intrinsic representation by borrowing information from a large amount of daily glucose profiles, and demonstrates predictive capabilities fine-tuned towards a broad range of downstream applications, holding promise for the ear

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