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The primary objective of this study is to identify the criteria under which a unique μ-pseudo almost automorphic solution can exist for a stochastic functional differential equation with Stepanov forcing terms. To achieve this, spectral decomposition, Ito’s isometry property and semigroup theory are utilized. The Lipschitz condition and inequality techniques are then employed to establish the uniqueness of the μ-pseudo almost automorphic solution. The methodology is demonstrated through a representative example of a stochastic process.

This paper empirically examines the extent to which and how local economic growth and asset location affect firm growth based on a sample of US equity real estate investment trusts (REITs) from 2001 to 2016. Using the GDP growth rate by MSA and individual property data of REITs, we construct an aggregated measure of local economic growth for each REIT based on its asset locations in different metropolitan areas. We find that REIT firm growth (measured using both book value and market value of assets) is positively correlated with the lagged firm-level economic growth measure, indicating that REITs allocating assets in areas with higher economic growth tend to experience higher firm growth. Moreover, local economic growth enhances REIT growth mainly through the growth of equity (not through the growth of debt), as REITs with more assets in higher economic growth areas provide higher stock returns to shareholders. These findings suggest that local economic conditions have a significant impact on REIT firm growth and a REIT’s asset allocation strategy can play an important role in its long-term prospects.

Identification of intrinsic disorder in proteins relies in large part on computational predictors, which demands that their accuracy should be high. Since intrinsic disorder carries out a broad range of cellular functions, it is desirable to couple the disorder and disorder function predictions. We report a computational tool, flDPnn, that provides accurate, fast and comprehensive disorder and disorder function predictions from protein sequences. The recent Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment and results on other test datasets demonstrate that flDPnn offers accurate predictions of disorder, fully disordered proteins and four common disorder functions. These predictions are substantially better than the results of the existing disorder predictors and methods that predict functions of disorder. Ablation tests reveal that the high predictive performance stems from innovative ways used in flDPnn to derive sequence profiles and encode inputs. flDPnn’s webserver is available at http://biomine.cs.vcu.edu/servers/flDPnn/ The authors present flDPnn, a computational tool for disorder and disorder function predictions from protein sequences. flDPnn was assessed with the data from the “Critical Assessment of Protein Intrinsic Disorder Prediction” experiment and on an independent and low-similarity test dataset, which show that flDPnn offers accurate predictions of disorder, fully disordered proteins and four common disorder functions.

Efficient electroreduction of carbon dioxide to multicarbon products in aqueous solution is of great importance and challenging. Unfortunately, the low efficiency of the production of C 2 products limits implementation at scale. Here, we report reduction of carbon dioxide to C 2 products (acetic acid and ethanol) over a 3D dendritic copper-cuprous oxide composite fabricated by in situ reduction of an electrodeposited copper complex. In potassium chloride aqueous electrolyte, the applied potential was as low as −0.4 V vs reversible hydrogen electrode, the overpotential is only 0.53 V (for acetic acid) and 0.48 V (for ethanol) with high C 2 Faradaic efficiency of 80% and a current density of 11.5 mA cm −2 . The outstanding performance of the electrode for producing the C 2 products results mainly from near zero contacting resistance between the electrocatalysts and copper substrate, abundant exposed active sites in the 3D dendritic structure and suitable copper(I)/copper(0) ratio of the electrocatalysts. Electrocatalytic reduction of carbon dioxide is attractive for obtaining multicarbon products, but conversion efficiency is low. Here the authors use copper complex materials for electrochemical reduction of carbon dioxide to ethanol and acetic acid with high efficiencies and activities.

A practical fixed‐time composite learning control scheme, by combining dynamic regressor extension and mixing (DREM) parameter identification algorithm and adaptive dynamic surface control (DSC) technique, is proposed for a class of strict‐feedback non‐linear systems subjected to linear‐in‐parameters uncertainties and full‐state constraint. To address the problem of state constraint, a non‐linear transformation function is introduced to convert the originally constrained non‐linear system into an unconstrained one. Meanwhile, the hyperbolic tangent function is employed to avoid singularity issues that often appeared in the traditional fixed‐time (FXT) control designs. In order to relax the requirement of persistency of excitation condition, a modified FXT‐DREM parameter identification approach with an interval excitation condition is constructed by introducing a three‐layer transformation technique derived from the classical DREM algorithm. Then, the modified FXT‐DREM parameter identification algorithm is seamlessly integrated into the adaptive DSC framework, resulting in a composite‐learning control scheme. By employing Lyapunov stability analysis, the fixed‐time convergence of both the parameter estimation error and the trajectory tracking error is proved. Finally, the effectiveness of the proposed design is demonstrated through simulation test. 1. This is the first time to extend the dynamic regressor extension and mixing (DREM) parameter identification algorithm to the fixed‐time adaptive control scheme. 2. This is also the first attempt to integrate the DREM algorithm with the composite‐learning‐control method.

Event Causality Identification (ECI) aims to predict causal relations between events in a text. Existing research primarily focuses on leveraging external knowledge such as knowledge graphs and dependency trees to construct explicit structured features to enrich event representations. However, this approach underestimates the semantic features of the original input sentences and performs poorly in capturing implicit causal relations. Therefore, this paper proposes a new framework based on Hierarchical Feature Extraction and Prompt-aware Attention (HFEPA) to address the issues above. On the one hand, we introduce a Hierarchical Feature Extraction (HFE) module to extract two kinds of features based on the input sentences: event mention level and segment level, enriching the semantic information of events through the interaction between event pairs and different segments. On the other hand, we design a Prompt-aware Attention (PAA) module that utilizes implicit causal knowledge in pre-trained language models to capture potential relationship information between events. This information is then combined with the contextual information of the text sequence to enhance the model’s ability to identify implicit causal relations between events. Additionally, this task faces challenges in the Chinese domain due to the limited scale of annotated datasets, leading to relatively slow research progress. To address this issue, we propose a new Chinese ECI dataset (Chinese News Causality), aiming to solve the current data scarcity problem in the Chinese domain. This dataset contains 25,629 event mentions and 5,569 causal event pairs, making it, to our knowledge, the largest Chinese dataset to date. We evaluate the effectiveness of HFEPA on both the EventStoryLine and Chinese News Causality datasets, and experimental results show that HFEPA significantly outperforms previous methods. The CNC dataset is available at https://github.com/twinkle121/CNC .

Amorphous nano-metal catalysts often exhibit appealing catalytic properties, because the intrinsic linear scaling relationship can be broken. However, accurate control synthesis of amorphous nano-metal catalysts with desired size and morphology is a challenge. In this work, we discover that Cu(0) could be oxidized to amorphous Cu x O species by supercritical CO 2 . The formation process of the amorphous Cu x O is elucidated with the aid of machine learning. Based on this finding, a method to prepare Cu nanoparticles with an amorphous shell is proposed by supercritical CO 2 treatment followed by electroreduction. The unique feature of this method is that the size of the particles with amorphous shell can be easily controlled because their size depends on that of the original crystal Cu nanoparticles. Moreover, the thickness of the amorphous shell can be easily controlled by CO 2 pressure and/or treatment time. The obtained amorphous Cu shell exhibits high selectivity for C2+ products with the Faradaic efficiency of 84% and current density of 320 mA cm −2 . Especially, the FE of C2+ oxygenates can reach up to 65.3 %, which is different obviously from the crystalline Cu catalysts. Amorphous metal catalysts often exhibit appealing catalytic properties, however, accurate control synthesis of amorphous nano-metal catalysts is a challenge. Here, the authors report a feasible strategy to prepare the amorphous Cu catalysts by supercritical CO 2 treatment followed by electroreduction. The resulted catalyst shows high selectivity towards multi-carbon products for CO 2 electroreduction.

Background Immune checkpoint inhibitors (ICIs) have been increasingly applied in the treatment of several kinds of malignancies. Some clinical demographic characteristics were reported to be associated with the ICIs efficacy. The purpose of our current meta-analysis was to clearly evaluated the relationship between BMI and ICIs efficacy for cancer patients receiving immunotherapy. Methods A systematic search of Pubmed, EMBASE and conference proceedings was performed to investigate the influence of BMI on ICIs efficacy. Pooled analysis for overall survival (OS), progression-free survival (PFS) and immune-related adverse effects (IRAEs) were analyzed in current study. Results A total of 13 eligible studies comprising 5279 cancer patients treated with ICIs were included in the analysis. The pooled analysis showed there is positive association between high BMI and improved OS and PFS among patients with ICIs treatment (OS: HR = 0.62, 95% CI 0.55–0.71, P < 0.0001; I 2  = 26.3%, P = 0.202); PFS: HR = 0.71, 95% CI 0.61–0.83, P < 0.0001; I 2  = 0%, P = 0.591). There is no significant difference between the incidence of all grade IRAEs between obese, overweight patients and normal patients (Overweight vs Normal: pooled RR = 1.28, 95% CI 0.76– 2.18, P = 0.356; Obese vs Normal: pooled RR = 1.36, 95% CI 0.85– 2.17, P = 0.207). Conclusion An improved OS and PFS were observed in patients with high BMI after receiving ICIs treatment compared with patients of low BMI. No significant association between BMI and incidence of IRAEs was found in cancer patients after ICIs treatment.

Quantum spin liquid (QSL) is a novel state of matter which refuses the conventional spin freezing even at 0 K. Experimentally searching for the structurally perfect candidates is a big challenge in condensed matter physics. Here we report the successful synthesis of a new spin-1/2 triangular antiferromagnet YbMgGaO 4 with sy m metry. The compound with an ideal two-dimensional and spatial isotropic magnetic triangular-lattice has no site-mixing magnetic defects and no antisymmetric Dzyaloshinsky-Moriya (DM) interactions. No spin freezing down to 60 mK (despite θ w  ~ −4 K), the power-law temperature dependence of heat capacity and nonzero susceptibility at low temperatures suggest that YbMgGaO 4 is a promising gapless (≤|θ w |/100) QSL candidate. The residual spin entropy, which is accurately determined with a non-magnetic reference LuMgGaO 4 , approaches zero (<0.6%). This indicates that the possible QSL ground state (GS) of the frustrated spin system has been experimentally achieved at the lowest measurement temperatures.

Psoriasis is a common chronic inflammatory skin disease. Its treatment is challenged by the limited amount of drug reaching the inflamed skin. The overexpressed CD44 protein in inflamed psoriatic skin can serve as a potential target of novel active-targeting nanocarriers to increase drug accumulation in the skin. Hyaluronic acid (HA) was linked to propylene glycol-based ethosomes by covalent binding to develop a novel topical drug delivery carrier (HA-ES) for curcumin. An imiquimod-induced psoriasis mouse model was established, and curcumin delivery and anti-psoriatic efficacy using HA-ES were compared with those using plain ethosomes (ES). The HA gel network formed on the surface of HA-ES reduced the leakage and release of poorly water-soluble curcumin. Compared with ES, transdermal curcumin delivery was significantly enhanced by using HA-ES as vehicles; the cumulative transdermal amount and the amount retained in the skin after 8 h were, respectively, 1.6 and 1.4 times those observed with ES, as well as 3.1 and 3.3 times those observed with a curcumin propylene glycol solution (PGS), respectively. The psoriatic skin retention of curcumin with HA-ES was 2.3 and 4.0 times that of ES and PGS, respectively. CD44 expression in imiquimod-induced psoriasis-like inflamed skin was 2.7 times that in normal skin. Immunostaining revealed similar results, suggesting that the specific adhesion of HA-ES to CD44 increased drug accumulation in the skin. After topical administration to mice, the HA-ES group showed an alleviation of inflammation symptoms; lower TNF-α, IL-17A, IL-17F, IL-22, and IL-1β mRNA levels; and lower CCR6 protein expression compared to the ES and PGS groups. We demonstrated increased topical drug delivery of curcumin to inflamed tissues using HA-ES targeting the highly expressed CD44 protein. This innovative strategy could be applied for the development of topical drug delivery systems targeting inflamed skin.