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In this study, we developed a novel confinement-synthesis approach to layered double hydroxide nanodots (LDH-NDs) anchored on carbon nanoparticles, which formed a three-dimensional (3D) interconnected network within a porous carbon support derived from pyrolysis of metal-organic frameworks (C-MOF). The resultant LDH-NDs@C-MOF nonprecious metal catalysts were demonstrated to exhibit super-high catalytic performance for oxygen evolution reaction (OER) with excellent operation stability and low overpotential (∼230 mV) at an exchange current density of 10 mAcm−2. The observed overpotential for the LDH-NDs@C-MOF is much lower than that of large-sized LDH nanosheets (321 mV), pure carbonized MOF (411 mV), and even commercial RuO2 (281 mV). X-ray absorption measurements and density functional theory (DFT) calculations revealed partial charge transfer from Fe3+ through an O bridge to Ni2+ at the edge of LDH-NDs supported by C-MOF to produce the optimal binding energies for OER intermediates. This, coupled with a large number of exposed active sides and efficient charge and electrolyte/reactant/product transports associated with the porous 3D C-MOF support, significantly boosted the OER performance of the LDH-ND catalyst with respect to its nanosheet counterpart. Apart from the fact that this is the first active side identification for LDH-ND OER catalysts, this work provides a general strategy to enhance activities of nanosheet catalysts by converting them into edge-rich nanodots to be supported by 3D porous carbon architectures.

Disperse metal atoms can boost the catalytic activities of carbon-based electrocatalysts for various important chemical reactions at the heart of clean energy conversion technologies, such as oxyge...

The development of catalysts with high activity and selectivity for electrochemical reduction of CO2 could provide renewable energy sources and improved environmental remediation. However, most current electrocatalysts have large energy barriers that lower their activity and selectivity. Here, we designed a class of electrocatalysts with 3d transition bimetallic active sites embedded into covalent organic frameworks (COFs) and discovered that they were more efficient than noble metals and single-atom electrocatalysts in CO2 reduction reaction to CO. To facilitate rational design and quick screening of desired catalysts, an intrinsic descriptor was identified for the catalysts, which correlates the catalytic activity with the topological, bonding, and electronic structures. Among the catalysts, Fe/Cu-, Ni/Zn-COFs could be excellent electrocatalysts, with the low overpotential (~0.25 V) while suppressing side reactions. The excellent activity and selectivity of the catalysts stems from their unique bimetallic sites that break the constraint of scaling relations, leading to much more adsorption modes and facilitating intermediates to achieve the ideal states.

Financial models are based on the standard assumptions of frictionless markets, complete information, no transaction costs and no taxes and borrowing and short selling without restrictions. Short-selling bans around the world after the global financial crisis and in several exchanges during the COVID 19 period, become more and more important. This paper bridges the gap by providing for the first time in the literature a model that accounting explicitly and simultaneously for inflation, information costs and short sales in the portfolio performance with regime switching. Our model can be used by portfolio managers to assess the impact of these market imperfections on portfolio decisions.

In this paper, we investigate two families of fully coupled linear Forward-Backward Stochastic Differential Equations (FBSDE). Within these families, one could get the same well-posedness of FBSDEs with totally different structures. The first family of FBSDEs are proved to be equivalent with respect to the Unified Approach. Thus one could get the well-posedness of the whole family if one member exists a unique solution. Another equivalent family of FBSDEs are investigated by introducing a linear transformation method. By reason of the fully coupling structure between the forward and backward equations, it leads to a highly interdependence in solutions. We are able to lower the coupling of FBSDEs, by virtue of the idea of transformation, without losing the well-posedness. Moreover, owing to the non-degeneracy of the transformation matrix, the solution to original FBSDE is totally determined by solutions of FBSDE after transformation. In addition, an example of optimal Linear Quadratic (LQ) problem is presented to illustrate., 18 pages

Longevity was influenced by many complex diseases and traits. However, the relationships between human longevity and genetic risks of complex diseases were not broadly studied. Here, we constructed polygenic risk scores (PRSs) for 225 complex diseases/traits and evaluated their relationships with human longevity in a cohort with 2178 centenarians and 2299 middle-aged individuals. Lower genetic risks of stroke and hypotension were observed in centenarians, while higher genetic risks of schizophrenia (SCZ) and type 2 diabetes (T2D) were detected in long-lived individuals. We further stratified PRSs into cell-type groups and significance-level groups. The results showed that the immune component of SCZ genetic risk was positively linked to longevity, and the renal component of T2D genetic risk was the most deleterious. Additionally, SNPs with very small p-values (p ≤ 1x10

Atrial fibrillation (AF) is a common arrhythmia, which can lead to thrombosis and increase the risk of a stroke or even death. In order to meet the need for a low false-negative rate (FNR) of the screening test in clinical application, a convolutional neural network with a low false-negative rate (LFNR-CNN) was proposed. Regularization coefficients were added to the cross-entropy loss function which could make the cost of positive and negative samples different, and the penalty for false negatives could be increased during network training. The inter-patient clinical database of 21 077 patients (CD-21077) collected from the large general hospital was used to verify the effectiveness of the proposed method. For the convolutional neural network (CNN) with the same structure, the improved loss function could reduce the FNR from 2.22% to 0.97% compared with the traditional cross-entropy loss function. The selected regularization coefficient could increase the sensitivity (SE) from 97.78% to 98.35%, and the accuracy (ACC) was 96.62%, which was an increase from 96.49%. The proposed algorithm can reduce the FNR without losing ACC, and reduce the possibility of missed diagnosis to avoid missing the best treatment period. Meanwhile, it provides a universal loss function for the clinical auxiliary diagnosis of other diseases.心房颤动（房颤）是一种常见的心律失常，可导致血栓形成并增加脑卒中甚至死亡的风险。针对临床应用中疾病筛检低假阴性率的需求，本文提出一种改进的低假阴性率卷积神经网络。通过在交叉熵损失函数中引入正则化系数，差别对待阳性和阴性样本的代价成本，使得网络训练时可加大对假阴性的惩罚。采用三甲医院采集的包含 21 077 位受试者的患者间临床数据集进行验证，相对于传统交叉熵损失函数，使用改进的损失函数可将假阴性率由 2.22% 降低至 0.97%，所选正则化系数可将灵敏度由 97.78% 提升至 98.35%，准确率 96.62% 亦较原来的 96.49% 有所提升。所提算法可在不牺牲准确率的前提下降低假阴性率，降低漏诊可能性以免错过最佳治疗时期，可为其他疾病的临床辅助诊断提供一种可变参数的损失函数。.

Biological age (BA) has been proposed to evaluate the aging status instead of chronological age (CA). Our study shows evidence that there might be multiple "clocks" within the whole-body system: systemic aging drivers/clocks overlaid with organ/tissue-specific counterparts. We utilize multi-omics data, including clinical tests, immune repertoire, targeted metabolomic molecules, gut microbiomes, physical fitness examinations, and facial skin examinations, to estimate the BA of different organs (e.g., liver, kidney) and systems (immune and metabolic system). The aging rates of organs/systems are diverse. People's aging patterns are different. We also demonstrate several applications of organs/systems BA in two independent datasets. Mortality predictions are compared among organs' BA in the dataset of the United States National Health and Nutrition Examination Survey. Polygenic risk score of BAs constructed in the Chinese Longitudinal Healthy Longevity Survey cohort can predict the possibility of becoming centenarian.

Proton exchange fuel cells (PEFCs) are one of the most popular and promising energy conversion devices because of their highly stable and efficient membranes in acidic media, but there is a lack of durable non-noble metal electrocatalysts suitable for acidic environments. Herein, we designed a new type of electrocatalysts consisting of transition metal halide molecules covered by graphene sheets, which is supported by experiments. To rapidly screen the best catalysts from numerous candidate materials, the electronic structures, reaction free energies and overpotentials of those graphene-covered halide catalysts were studied by the first-principles calculations to predict the catalytic activities for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). An intrinsic descriptor, the electrostatic force induced by the metallic ions, was found to well describe the catalytic activities and provide a better understanding of the local electrical field effects on catalytic activities. The spin-down d-band center was also introduced to describe catalytic activities of the catalysts. The results demonstrate that the graphene-covered CrBr2 shows the best bifunctional catalytic activities for fuel cells while graphene-covered CoF2 could well facilitate H2O2 production. These catalysts are better than the best commercial noble metal catalysts (e.g., Pt and RuO2) in terms of overpotentials and activities. This work provides a theoretical base for rationally designing durable electrocatalysts with excellent catalytic activities.

Using a two-country dynamic optimization model, we investigate the impact of exchange risk, incomplete information and short sales constraints on international portfolio decisions around market closure. Using optimal control theory, we provide solutions and simulation results. Our model can be applied to solve several problems in financial economics in the presence of market closure, information asymmetry and short sales constraints.

Postharvest anthracnose of banana caused by Colletotrichum musae is one of the major diseases resulting in huge economic losses worldwide. To control this disease using biocontrol agents, two antagonistic strains SD7 and NB20 with significant inhibitory effects on mycelial growth and conidial germination of C. musae were identified and evaluated in this study. The inhibitory effects of cell-free culture filtrates of SD7 and NB20 on conidial germination of C. musae were both 100%, and those on mycelial growth of C. musae were 97.7 ± 0.9% and 95.0 ± 0.6%, respectively. The antifungal activities of cell-free culture filtrates of both strains were still stable after they were stored at 4°C for 6 months. The control efficacies of cell-free culture filtrates of SD7 and NB20 on postharvest anthracnose of banana were 55.9 ± 4.1% and 33.2 ± 3.9%, respectively. The disease severity (mean scale value) in banana fruit fingers was significantly lower after the treatment with a cultural suspension of the bacterial strain SD7 (1.4 ± 0.49) or actinomycete strain NB20 (2.0 ± 0.63), compared to that in the control (4.8 ± 0.40). After subculturing for 10 generations, the antifungal efficiency of NB20 remained stable, whereas that of strain SD7 declined obviously. Lastly, based on the morphological, physio-biochemical and molecular characteristics, the bacterial strain SD7 was identified as Burkholderia cepacia, while the actinomycete strain NB20 was identified as Streptomyces katrae. The results from this study will provide the basis for developing an effective and novel biofungicide to control banana anthracnose disease.

The theory of corporate international investment is extended to account for the effects of information costs, short sales and taxes on the management decision process. Our results explain how real exchange risk, competition and diversification gains affect corporate international investment decisions in this setting. The model includes clear forward looking explanations of some anomalies in the literature such as the ‘home bias puzzle’ in international finance.

In 3+ co-doped YVO 4 :5%Eu 3+ red phosphors were synthesized via a facile hydrothermal method. The impacts of In 3+ co-doping concentration onto the crystal structure, morphology, and photoluminescence properties were elucidated by using X-ray diffraction (XRD), scanning electron microscopy (SEM), photoluminescence spectra, and the luminescence intensity change. The results indicated that with increasing the In 3+ co-doping concentration, the intensity of excitation and emission spectra was continuously increased, reached maximum at In 3+ concentration of 5 mol%, and then was diminished for higher In 3+ content which indicated concentration quenching. The distinct enhancement of the photoluminescence of YVO 4 :Eu 3+ , In 3+ phosphors at lower In 3+ co-doping concentration can be attributed to the incorporation of In 3+ improving the host lattice absorption of YVO 4 . Thereafter, with the increase of In 3+ concentration exceeding 5 mol%, the emission intensity was decreased which is mainly due to the fact that the electronegativity of indium is greater than yttrium, and it is not beneficial to the charge transfer process of O 2− → V 5+ inside the VO 4 3− ions, thus further weakening the energy transfer from VO 4 3− groups to Eu 3+ ions.

Oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) both have sluggish kinetics, which normally requires the use of noble metal-based catalysts (e.g. RuO2, IrOx and Pt). Unfortunately, the high cost of these noble metals has severely restricted their large-scale applications. Herein, we report the fabrication of a cost-effective OER/ORR bifunctional catalyst by embedding atomic Fe–Ni dual metal pairs into nitrogen-doped carbon hollow spheres (Fe–NiNC-50). The resultant catalyst shows exceptional catalytic performance towards both OER and ORR, which is even comparable to the noble metal-based benchmarks. By virtue of its bi-functionality, a rechargeable zinc-air battery is constructed and presents high power density (~220 mW cm−2), stable discharge voltage and large specific energy density (~932.66 Wh kgZn−1). Moreover, the rechargeable Zn-air battery exhibits long-term durability in a charge-discharge cycling test (100 h) with negligible performance degradation. The outstanding bifunctional catalytic performance can be ascribed to the formation of Fe–Ni atomic pairs, which imposes mutual effects for tuning electronic structures of both Fe and Ni sites. Experiments and theoretical calculations further unravel that the electronically modified Ni and Fe atoms are the active sites for OER and ORR, respectively, facilitating both OER and ORR by tuning the binding energy of the reaction intermediates.

Carbon nanomaterials are promising metal-free catalysts for energy conversion and storage, but the catalysts are usually developed via traditional trial-and-error methods. To rationally design and accelerate the search for the highly efficient catalysts, it is necessary to establish design principles for the carbon-based catalysts. Here, theoretical analysis and material design of metal-free carbon nanomaterials as efficient photo-/electrocatalysts to facilitate the critical chemical reactions in clean and sustainable energy technologies are reviewed. These reactions include the oxygen reduction reaction in fuel cells, the oxygen evolution reaction in metal-air batteries, the iodine reduction reaction in dye-sensitized solar cells, the hydrogen evolution reaction in water splitting, and the carbon dioxide reduction in artificial photosynthesis. Basic catalytic principles, computationally guided design approaches and intrinsic descriptors, catalytic material design strategies, and future directions are discussed for the rational design and synthesis of highly efficient carbon-based catalysts for clean energy technologies.

Covalent organic frameworks (COFs) are promising for catalysis, sensing, gas storage, adsorption, optoelectricity, etc. owning to the unprecedented combination of large surface area, high crystallinity, tunable pore size, and unique molecular architecture. Although COFs are in their initial research stage, progress has been made in the design and synthesis of COF-based electrocatalysis for the oxygen reduction reaction, oxygen evolution reaction, hydrogen evolution reaction, and CO2 reduction in energy conversion and fuel generation. Design principles are also established for some of the COF materials toward rational design and rapid screening of the best electrocatalysts for a specific application. Herein, the recent advances in the design and synthesis of COF-based catalysts for clean energy conversion and storage are presented. Future research directions and perspectives are also being discussed for the development of efficient COF-based electrocatalysts.

Solar fuels produced from sunlight via photocatalytic water splitting and artificial photosynthesis represent one of the most promising clean energy sources. Various semiconductors, including graphitic carbon nitride (g-C3N4) and transition metal oxides, have been considered as photocatalysts for the fuel production, but there lacks of the design principles for rapid screening of the best photocatalysts from numerous candidate materials. Here, we demonstrate, for the first time, a universal guiding principle that governs the photocatalytic activities of p-orbital element-doped C3N4-based photocatalysts for photocatalytic water splitting, which is also applicable to other doped semiconductors including ZnO and TiO2 for water splitting and carbon dioxide reduction. An activity indictor is introduced to determine the efficiency of non-metal-doped semiconductor photocatalysts for the solar fuel production. These predictions are supported by experimental results. This generalized principle could guide a broad photochemical production of solar fuels including hydrogen, hydrocarbon, and other green chemicals.

We present the model of corporate optimal investment with consideration of the influence of inflation and the difference between the market opening and market closure. In our model, the investor has three market activities of his or her choice: investment in project A, investment in project B, and consumption. The optimal strategy for the investor is obtained using the Hamilton-Jacobi-Bellman equation which is derived using the dynamic programming principle. Further along, a specific case, the Hyperbolic Absolute Risk Aversion case, is discussed in detail, where the explicit optimal strategy can be obtained using a very simple and direct method. At the very end, we present some simulation results along with a brief analysis of the relationship between the optimal strategy and other factors.

In this paper, we study the well-posedness of the Forward-Backward Stochastic Differential Equations (FBSDE) in a general non-Markovian framework. The main purpose is to find a unified scheme which combines all existing methodology in the literature, and to address some fundamental longstanding problems for non-Markovian FBSDEs. An important device is a decoupling random field that is regular (uniformly Lipschitz in its spatial variable). We show that the regulariy of such decoupling field is closely related to the bounded solution to an associated characteristic BSDE, a backward stochastic Riccati-type equation with superlinear growth in both components $Y$ and $Z$. We establish various sufficient conditions for the well-posedness of an ODE that dominates the characteristic BSDE, which leads to the existence of the desired regular decoupling random field, whence the solvability of the original FBSDE. A synthetic analysis of the solvability is given, as a "User's Guide," for a large class of FBSDEs that are not covered by the existing methods. Some of them have important implications in applications., Published at http://dx.doi.org/10.1214/14-AAP1046 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)