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Objective: Green computing meets the needs of a low-carbon society and it is an important aspect of promoting social sustainable development and technological progress. In the investigation, green computing for resource management and allocation issues is only discussed. Therefore, in the context of the 5G communication network, the investigation of the data classification and resource optimization of the Internet of Things are conducted. Method: The virtualization architecture of the heterogeneous wireless network resource based on 5G technology is designed. The related investigation is conducted based on 5G network and Internet of Things technology. Under the traditional method, the transfer learning is introduced to improve the AdaBoost (Adaptive Boosting) algorithm to classify the data. The investigated complete resource reuse method is used to optimize resources. A method that a sub-channel can be reused by a cellular link and any number of D2D links at the same time is proposed to conduct resource optimization investigation. Results: The investigation indicates that the classification accuracy of the algorithm is excellent for the data classification of the Internet of Things and has different advantages in various aspects compared with other algorithms. The designed algorithm can find a larger set of resource reuse and have a significant increase in spectrum utilization efficiency. Conclusion: The investigation can contribute to the boom in the Internet of Things in terms of data classification and resource optimization based on 5G.

The new physics of magic-angle twisted bilayer graphene (TBG) motivated extensive studies of flat bands hosted by moiré superlattices in van der Waals structures, inspiring the investigations into their photonic counterparts with potential applications including Bose-Einstein condensation. However, correlation between photonic flat bands and bilayer photonic moiré systems remains unexplored, impeding further development of moiré photonics. In this work, we formulate a coupled-mode theory for low-angle twisted bilayer honeycomb photonic crystals as a close analogy of TBG, discovering magic-angle photonic flat bands with a non-Anderson-type localization. Moreover, the interlayer separation constitutes a convenient degree of freedom in tuning photonic moiré bands without high pressure. A phase diagram is constructed to correlate the twist angle and separation dependencies to the photonic magic angles. Our findings reveal a salient correspondence between fermionic and bosonic moiré systems and pave the avenue toward novel applications through advanced photonic band or state engineering.

The recent discovery of ferromagnetism in two-dimensional van der Waals crystals has provoked a surge of interest in the exploration of fundamental spin interaction in reduced dimensions. However, existing material candidates have several limitations, notably lacking intrinsic room-temperature ferromagnetic order and air stability. Here, motivated by the anomalously high Curie temperature observed in bulk diluted magnetic oxides, we demonstrate room-temperature ferromagnetism in Co-doped graphene-like Zinc Oxide, a chemically stable layered material in air, down to single atom thickness. Through the magneto-optic Kerr effect, superconducting quantum interference device and X-ray magnetic circular dichroism measurements, we observe clear evidences of spontaneous magnetization in such exotic material systems at room temperature and above. Transmission electron microscopy and atomic force microscopy results explicitly exclude the existence of metallic Co or cobalt oxides clusters. X-ray characterizations reveal that the substitutional Co atoms form Co2+ states in the graphitic lattice of ZnO. By varying the Co doping level, we observe transitions between paramagnetic, ferromagnetic and less ordered phases due to the interplay between impurity-band-exchange and super-exchange interactions. Our discovery opens another path to 2D ferromagnetism at room temperature with the advantage of exceptional tunability and robustness.

The new physics of magic-angle twisted bilayer graphene (TBG) motivated extensive studies of flat bands hosted by moiré superlattices in van der Waals structures, inspiring the investigations into their photonic counterparts with potential applications including Bose-Einstein condensation. However, correlation between photonic flat bands and bilayer photonic moiré systems remains unexplored, impeding further development of moiré photonics. In this work, we formulate a coupled-mode theory for low-angle twisted bilayer honeycomb photonic crystals as a close analogy of TBG, discovering magic-angle photonic flat bands with a non-Anderson-type localization. Moreover, the interlayer separation constitutes a convenient degree of freedom in tuning photonic moiré bands without high pressure. A phase diagram is constructed to correlate the twist angle and separation dependencies to the photonic magic angles. Our findings reveal a salient correspondence between fermionic and bosonic moiré systems and pave the avenue toward novel applications through advanced photonic band or state engineering.

The sky is a natural heat sink that has been extensively used for passive radiative cooling of households. A lot of focus has been on maximizing the radiative cooling power of roof coating in the hot daytime using static, cooling-optimized material properties. However, the resultant overcooling in cold night or winter times exacerbates the heating cost, especially in climates where heating dominates energy consumption. We approached thermal regulation from an all-season perspective by developing a mechanically flexible coating that adapts its thermal emittance to different ambient temperatures. The fabricated temperature-adaptive radiative coating (TARC) optimally absorbs the solar energy and automatically switches thermal emittance from 0.20 for ambient temperatures lower than 15°C to 0.90 for temperatures above 30°C, driven by a photonically amplified metal-insulator transition. Simulations show that this system outperforms existing roof coatings for energy saving in most climates, especially those with substantial seasonal variations.

The recent discovery of ferromagnetism in two-dimensional van der Waals crystals has provoked a surge of interest in the exploration of fundamental spin interaction in reduced dimensions. However, existing material candidates have several limitations, notably lacking intrinsic room-temperature ferromagnetic order and air stability. Here, motivated by the anomalously high Curie temperature observed in bulk diluted magnetic oxides, we demonstrate room-temperature ferromagnetism in Co-doped graphene-like Zinc Oxide, a chemically stable layered material in air, down to single atom thickness. Through the magneto-optic Kerr effect, superconducting quantum interference device and X-ray magnetic circular dichroism measurements, we observe clear evidences of spontaneous magnetization in such exotic material systems at room temperature and above. Transmission electron microscopy and atomic force microscopy results explicitly exclude the existence of metallic Co or cobalt oxides clusters. X-ray characterizations reveal that the substitutional Co atoms form Co2+ states in the graphitic lattice of ZnO. By varying the Co doping level, we observe transitions between paramagnetic, ferromagnetic and less ordered phases due to the interplay between impurity-band-exchange and super-exchange interactions. Our discovery opens another path to 2D ferromagnetism at room temperature with the advantage of exceptional tunability and robustness.

The new physics of magic-angle twisted bilayer graphene (TBG) motivated extensive studies of flat bands hosted by moiré superlattices in van der Waals structures, inspiring the investigations into their photonic counterparts with potential applications including Bose-Einstein condensation. However, correlation between photonic flat bands and bilayer photonic moiré systems remains unexplored, impeding further development of moiré photonics. In this work, we formulate a coupled-mode theory for low-angle twisted bilayer honeycomb photonic crystals as a close analogy of TBG, discovering magic-angle photonic flat bands with a non-Anderson-type localization. Moreover, the interlayer separation constitutes a convenient degree of freedom in tuning photonic moiré bands without high pressure. A phase diagram is constructed to correlate the twist angle and separation dependencies to the photonic magic angles. Our findings reveal a salient correspondence between fermionic and bosonic moiré systems and pave the avenue toward novel applications through advanced photonic band or state engineering.

The sky is a natural heat sink that has been extensively used for passive radiative cooling of households. A lot of focus has been on maximizing the radiative cooling power of roof coating in the hot daytime using static, cooling-optimized material properties. However, the resultant overcooling in cold night or winter times exacerbates the heating cost, especially in climates where heating dominates energy consumption. We approached thermal regulation from an all-season perspective by developing a mechanically flexible coating that adapts its thermal emittance to different ambient temperatures. The fabricated temperature-adaptive radiative coating (TARC) optimally absorbs the solar energy and automatically switches thermal emittance from 0.20 for ambient temperatures lower than 15°C to 0.90 for temperatures above 30°C, driven by a photonically amplified metal-insulator transition. Simulations show that this system outperforms existing roof coatings for energy saving in most climates, especially those with substantial seasonal variations.

Multiview learning problem refers to the problem of learning a classifier from multiple view data. In this data set, each data points is presented by multiple different views. In this paper, we propose a novel method for this problem. This method is based on two assumptions. The first assumption is that each data point has an intact feature vector, and each view is obtained by a linear transformation from the intact vector. The second assumption is that the intact vectors are discriminative, and in the intact space, we have a linear classifier to separate the positive class from the negative class. We define an intact vector for each data point, and a view-conditional transformation matrix for each view, and propose to reconstruct the multiple view feature vectors by the product of the corresponding intact vectors and transformation matrices. Moreover, we also propose a linear classifier in the intact space, and learn it jointly with the intact vectors. The learning problem is modeled by a minimization problem, and the objective function is composed of a Cauchy error estimator-based view-conditional reconstruction term over all data points and views, and a classification error term measured by hinge loss over all the intact vectors of all the data points. Some regularization terms are also imposed to different variables in the objective function. The minimization problem is solve by an iterative algorithm using alternate optimization strategy and gradient descent algorithm. The proposed algorithm shows it advantage in the compression to other multiview learning algorithms on benchmark data sets.

Qingjun Wang,1,2,* Tao Sun,3,* Yunfeng Cao,1,2,4,5 Peng Gao,2,4,6 Jun Dong,2,4 Yanhua Fang,2 Zhongze Fang,2 Xiaoyu Sun,2 Zhitu Zhu1,2 1Oncology Department 2, The First Affiliated Hospital of Liaoning Medical University, 2Personalized Treatment and Diagnosis Research Center, The First Affiliated Hospital of Liaoning Medical University and Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Jinzhou, 3Department of Internal Medicine 1, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Insititute, Shenyang, 4CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 5Key Laboratory of Contraceptives and Devices Research (NPFPC), Shanghai Engineer and Technology Research Center of Reproductive Health Drug and Devices, Shanghai Institute of Planned Parenthood Research, Shanghai, 6Clinical Laboratory, Dalian Sixth People’s Hospital, Dalian, People’s Republic of China *These authors contributed equally to this work Objective: Breast cancer (BC) is still a lethal threat to women worldwide. An accurate screening and diagnosis strategy performed in an easy-to-operate manner is highly warranted in clinical perspective. Besides the routinely focused protein markers, blood is full of small molecular metabolites with diverse structures and properties. This study aimed to screen metabolite markers with BC diagnosis potentials.Methods: A dried blood spot-based direct infusion mass spectrometry (MS) metabolomic analysis was conducted for BC and non-BC differentiation. The targeted analytes included 23 amino acids and 26 acylcarnitines.Results: Multivariate analysis screened out 21 BC-related metabolites in the blood. Regression analysis generated a diagnosis model consisting of parameters Pip, Asn, Pro, C14:1/C16, Phe/Tyr, and Gly/Ala. Tested with another set of BC and non-BC samples, this model showed a sensitivity of 9

Xiaoyu Wang,1 Jiaqi Wang,2 Zhenghua Wang,1 Qingjun Wang,1 Hua Li1 1Second Ward of Oncology Department, 2Traditional Chinese Medicine Department, The First Affiliated Hospital of Liaoning Medical University, Jinzhou, People’s Republic of ChinaObjective: The aim of this study was to observe the plasma amino acid and carnitine characteristics in patients with metastatic gastrointestinal malignancies during chemotherapy and to identify markers for the early diagnosis and evaluation of adverse reactions and prognosis of the digestive tract malignant tumor patients.Methods: Blood samples of 30 patients with metastatic gastrointestinal malignancies were collected at four time points: before chemotherapy, the first day after chemotherapy (+1 day), bone marrow depression period (+14 days), and hematopoietic recovery period (+21 days). The plasma amino acids and carnitine from those 30 patients were determined by high-performance liquid chromatography–tandem mass spectrometry method. Simultaneously, the levels of 21 amino acids were detected in 30 healthy individuals, who were considered as control. Biochemical indexes were also detected at four time points, adverse reactions were recorded during the chemotherapy process, and patients were followed up for 1 year to observe time to progression (TTP) and progression-free survival (PFS).Results: Compared to healthy people in the control group, patients with malignancies showed significantly increased levels of plasma amino acids such as Arg, Asp, Cit, Gly, Orn, Tyr, Val, and carnitine (such as C2). The levels of compounds such as C3, Asn, Leu, Lys, Pip, Pro, C0, C5:1 decreased significantly before chemotherapy. The levels of Cit, Cys, Lys, Pro, Tyr, Val, C0, and C2 decreased significantly on the second day of chemotherapy (+1 day), whereas the level of C3 increased significantly. During myelosuppression (+14 days), the levels of Asp, Cit, Met, and Orn were observed to still decrease significantly, whereas the

Gold mines such as Zhuanshanzi, which extract steeply inclined, extremely thin orebodies and backfill with waste stone have been studied. A formula has been derived for the pressure distribution in the concrete bottom post after filling., Gold mines such as Zhuanshanzi, which extract steeply inclined, extremely thin orebodies and backfill with waste stone have been studied. A formula has been derived for the pressure distribution in the concrete bottom post after filling.