The boundedness and compactness of weighted composition operators on the logarithmic Bloch-Orlicz space Formula: see text are investigated in this paper.
We introduce an SU(2)X dark sector without any fermions and then realize a non-abelian kinetic mixing between the dark SU(2)X gauge fields and the standard model SU(2)L×U(1)Y gauge fields. While one ...of the dark gauge bosons becomes a dark photon, the others can keep stable to form a dark matter particle. The nearly degenerate masses of dark photon and dark matter could be tested if the dark photon and the dark matter are both observed in the future.
This study aims to develop a deep learning based classification framework for remotely sensed time series. The experiment was carried out in Yolo County, California, which has a very diverse ...irrigated agricultural system dominated by economic crops. For the challenging task of classifying summer crops using Landsat Enhanced Vegetation Index (EVI) time series, two types of deep learning models were designed: one is based on Long Short-Term Memory (LSTM), and the other is based on one-dimensional convolutional (Conv1D) layers. Three widely-used classifiers were also tested for comparison, including a gradient boosting machine called XGBoost, Random Forest, and Support Vector Machine. Although LSTM is widely used for sequential data representation, in this study its accuracy (82.41%) and F1 score (0.67) were the lowest among all the classifiers. Among non-deep-learning classifiers, XGBoost achieved the best result with 84.17% accuracy and an F1 score of 0.69. The highest accuracy (85.54%) and F1 score (0.73) were achieved by the Conv1D-based model, which mainly consists of a stack of Conv1D layers and an inception module. The behavior of the Conv1D-based model was inspected by visualizing the activation on different layers. The model employs EVI time series by examining shapes at various scales in a hierarchical manner. Lower Conv1D layers of the optimized model capture small scale temporal variations, while upper layers focus on overall seasonal patterns. Conv1D layers were used as an embedded multi-level feature extractor in the classification model which automatically extracts features from input time series during training. The automated feature extraction reduces the dependency on manual feature engineering and pre-defined equations of crop growing cycles. This study shows that the Conv1D-based deep learning framework provides an effective and efficient method of time series representation in multi-temporal classification tasks.
•Deep neural networks were developed for crop classification.•Deep neural network achieved 85.54% accuracy and an F1 score of 0.73.•The best non-deep-learning classifier achieved 84.17% accuracy and an F1 score of 0.69.•One-dimensional convolutional neural network was used as automated temporal feature extractor.•One-dimensional convolutional neural network identifies complex seasonal dynamics of economic crops.
Improved durability, enhanced interfacial stability, and room temperature applicability are desirable properties for all‐solid‐state lithium metal batteries (ASSLMBs), yet these desired properties ...are rarely achieved simultaneously. Here, in this work, it is noticed that the huge resistance at Li metal/electrolyte interface dominantly impeded the normal cycling of ASSLMBs especially at around room temperature (<30 °C). Accordingly, a supramolecular polymer ion conductor (SPC) with “weak solvation” of Li+ was prepared. Benefiting from the halogen‐bonding interaction between the electron‐deficient iodine atom (on 1,4‐diiodotetrafluorobenzene) and electron‐rich oxygen atoms (on ethylene oxide), the O‐Li+ coordination was significantly weakened. Therefore, the SPC achieves rapid Li+ transport with high Li+ transference number, and importantly, derives a unique Li2O‐rich SEI with low interfacial resistance on lithium metal surface, therefore enabling stable cycling of ASSLMBs even down to 10 °C. This work is a new exploration of halogen‐bonding chemistry in solid polymer electrolyte and highlights the importance of “weak solvation” of Li+ in the solid‐state electrolyte for room temperature ASSLMBs.
PEO‐based electrolytes suffer from huge interfacial resistance, poor Li+ transport, and Li dendrite formation in all‐solid‐state lithium metal batteries (ASSLMBs) operating at around room‐temperature. This work proposes the regulation of the Li+ solvation environment through halogen‐bonding interaction and highlights the importance of “weak solvation” of Li+ in solid electrolytes for room temperature ASSLMBs.
We say φ∈U if a constant Cφ>1 could be found, such that φst≤Cφsφt,s>1,t>0. Under the assumption φ∈U, boundedness and compactness of weighted composition operators on the Zygmund-Orlicz space Zφ are ...investigated in this paper.
Let (
, 𝒜,
) be a σ−finite measure space. A transformation
:
→
is non-singular if
∘
is absolutely continuous with respect with
. For this non-singular transformation, the composition operator
: 𝒟(
...) →
) is defined by
=
∘
,
∈ 𝒟(
).
For a fixed positive integer
≥ 2, basic properties of product
· · ·
in
) are presented in Section 2, including the boundedness and adjoint. Under the assistance of these properties, normality and quasinormality of specific bounded
· · ·
in
) are characterized in Section 3 and 4 respectively, where
,
, · · ·,
are all densely defined.
The electrolytes for lithium metal batteries (LMBs) are plagued by a low Li+ transference number (T+) of conventional lithium salts and inability to form a stable solid electrolyte interphase (SEI). ...Here, we synthesized a self‐folded lithium salt, lithium 2‐2‐(2‐methoxy ethoxy)ethoxyethanesulfonyl(trifluoromethanesulfonyl) imide (LiETFSI), and comparatively studied with its structure analogue, lithium 1,1,1‐trifluoro‐N‐2‐2‐(2‐methoxyethoxy)ethoxy)ethylmethanesulfonamide (LiFEA). The special anion chemistry imparts the following new characteristics: i) In both LiFEA and LiETFSI, the ethylene oxide moiety efficiently captures Li+, resulting in a self‐folded structure and high T+ around 0.8. ii) For LiFEA, a Li−N bond (2.069 Å) is revealed by single crystal X‐ray diffraction, indicating that the FEA anion possesses a high donor number (DN) and thus an intensive interphase “self‐cleaning” function for an ultra‐thin and compact SEI. iii) Starting from LiFEA, an electron‐withdrawing sulfone group is introduced near the N atom. The distance of Li−N is tuned from 2.069 Å in LiFEA to 4.367 Å in LiETFSI. This alteration enhances ionic separation, achieves a more balanced DN, and tunes the self‐cleaning intensity for a reinforced SEI. Consequently, the fast charging/discharging capability of LMBs is progressively improved. This rationally tuned anion chemistry reshapes the interactions among Li+, anions, and solvents, presenting new prospects for advanced LMBs.
By attaching an electron‐withdrawing sulfone group near N atom, the distance of Li−N is tuned from 2.069 Å in LiFEA to 4.367 Å in LiETFSI, which enhances ionic separation degree and achieves a more balanced donor number and tuned self‐cleaning intensity for a reinforced SEI. Consequently, the fast charging/discharging capability of lithium metal batteries with this self‐folded lithium salt is progressively improved.
Although many efforts have been devoted to the adsorptive removal of phosphate from aqueous solutions and eutrophic water, it is still highly desirable to develop novel adsorbents with high ...adsorption capacities. In this study, Fe-based metal-organic frameworks (MOFs), MIL-101 and NH
-MIL-101, are fabricated through a general facile strategy. Their performance as an adsorbent for phosphate removal is investigated. Experiments are performed to study the effects of various factors on the phosphate adsorption, including adsorbent dosage, contact time and co-existing ions. Both MIL-101(Fe) and NH
-MIL-101(Fe) show highly effective removal of phosphates from aqueous solutions, and the concentration of phosphates decrease sharply from the initial 0.60 mg·L
to 0.045 and 0.032 mg·L
, respectively, within just 30 min of exposure. The adsorption kinetics and adsorption isotherms reveal that NH
-MIL-101(Fe) has higher adsorption capacity than MIL-101(Fe) possibly due to the amine group. Furthermore, the Fe-based MOFs also exhibit a high selectivity towards phosphate over other anions such as chloride, bromide, nitrate and sulfate. Particularly, the prepared Fe-based MIL-101 materials are also capable of adsorbing phosphate in an actual eutrophic water sample and display better removal effect.
Magnesium (Mg) is the fourth most abundant element in the human body and is important in terms of specific osteogenesis functions. Here, we provide a comprehensive review of the use of ...magnesium-based biomaterials (MBs) in bone reconstruction. We review the history of MBs and their excellent biocompatibility, biodegradability and osteopromotive properties, highlighting them as candidates for a new generation of biodegradable orthopedic implants. In particular, the results reported in the field-specific literature (280 articles) in recent decades are dissected with respect to the extensive variety of MBs for orthopedic applications, including Mg/Mg alloys, bioglasses, bioceramics, and polymer materials. We also summarize the osteogenic mechanism of MBs, including a detailed section on the physiological process, namely, the enhanced osteogenesis, promotion of osteoblast adhesion and motility, immunomodulation, and enhanced angiogenesis. Moreover, the merits and limitations of current bone grafts and substitutes are compared. The objective of this review is to reveal the strong potential of MBs for their use as agents in bone repair and regeneration and to highlight issues that impede their clinical translation. Finally, the development and challenges of MBs for transplanted orthopedic materials are discussed.
In many real-world applications, different types of misclassification usually suffer from different costs, but the accurate cost is often hard to be determined and usually one can only get an ...interval-estimation like that one type of mistake is about 5 to 10 times more serious than the other type. On the other hand, there are usually abundant unlabeled data available, leading to great research effort about semi-supervised learning. It is noticeable that cost interval and unlabeled data usually appear simultaneously in practice tasks; however, there is rare study tackling them together. In this paper, we propose the cisLDM approach which is able to handle cost interval and exploit unlabeled data in a principled way. Rather than maximizing the minimum margin like traditional large margin classifiers, cisLDM tries to optimize the margin distribution on both labeled and unlabeled data when minimizing the worst-case totalcost and the mean total-cost simultaneously according to the cost interval. Experiments on a broad range of datasets and cost settings exhibit the impressive performance of cisLDM. In particular, cisLDM is able to reduce 47 percent more total-cost than standard SVM and 27 percent more total-cost than cost-sensitive semi-supervised SVM which assumes the true cost value is known in advance.
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