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The coordinated developmental nature between regions is explored from two perspectives: the degree of coupled and coordinated development between digital finance and regional green innovation and the spatial characteristics. Firstly, the coupling coordination model is used to calculate the degree of development level of two-way promotion between the two systems, furthermore, the Moran index is used to analyze the spatial correlation of the coupling coordination degree values, and finally, the influence of each digital finance and green innovation influencing factor of neighboring cities on the coupling agglomeration effect is studied through the spatial error model. Through the empirical analysis, it is found that the coordination degree of digital finance development and green innovation degree in the region is good, and the two can show the trend of coordinated development on the whole, and the coordination degree of digital finance development and green innovation degree in the Yangtze River Economic Zone has the characteristics of more obvious spatial non-equilibrium in the studied interval, and improving the coordination value of the provinces on the verge of disorder is the key to improve the coordinated development of digital finance and green innovation in the Yangtze River Economic Zone. The key to improve the coordinated development of digital finance and green innovation in the Yangtze River Economic Zone is to improve the coordination value of the endangered provinces. From the perspective of global autocorrelation, it can be learned that the coupling coordination degree presents significant characteristics in spatial agglomeration, and the local Moran index indicates that the local agglomeration effect in the Yangtze River Economic Belt mainly presents "high-high", "low-low", and "high-low" types. "Finally, the regression results obtained from the spatial error model show that the five indicators of digital financial usage depth, economic growth rate, green total factor productivity, foreign direct investment, and digital financial digitization all play a positive role in promoting the improvement of the regional coupling agglomeration effect.

An audit committee (AC) report is the primary channel through which investors learn about the responsibilities and activities of an AC. AC members may use personal language (“we”) or impersonal language (“the audit committee”) in an AC report. Psychology research suggests that personal (vs. impersonal) language signals that the language user has greater warmth and sense of communion (i.e., being part of a larger group). Applying this theory, we predict that an AC's use of personal (vs. impersonal) language leads investors to perceive a warmer and more communal AC, and that an AC's perceived warmth/communion (cued by personal language) positively impacts investor judgments. We further posit that the positive effect of personal language is stronger when AC compensation is largely short term than long term. This is because investors need more assurance of AC oversight effectiveness when AC compensation is short term, which makes investors rely more on heuristic cues such as AC language to make judgments. Consistent with this prediction, we find that when AC compensation is largely short term, nonprofessional investors (proxied by Master of Business Administration students) react more positively to an AC's use of personal language than impersonal language. The effect of AC language is insignificant when AC compensation is largely long term, as the long-term compensation structure already provides assurance about the AC's oversight effectiveness, and thus, investors rely less on heuristic cues. Furthermore, we find that the perceived warmth of AC members explains the effect of AC language. Finally, our interviews with nonprofessional investors corroborate some of our main findings and validate their practice implications. Ministry of Education (MOE) Published version The authors also thank the Singapore Ministry of Education Academic Research Fund Tier 2 MOE2019-T2-2-137 and the UOB Endowed Chair Fund for financial support.

Most of the existing traditional multi-view clustering algorithms succeed based on the assumption that the multi-view dataset is complete. However, in practical applications, complete data samples rarely exist and incomplete data are more common. To be able to deal with the incomplete information problem, this paper proposes an incomplete multi-view clustering algorithm based on graph regularized low-rank representations. Specifically, we first combine distance regularization terms and non-negative constraints based on low-rank representations to learn graphs with global and local data structures. Then, spectral clustering is used to obtain the corresponding low-dimensional representation of the graph. Finally, a new weighted fusion mechanism is introduced into the model to learn a consistent representation of all views, and the K-means algorithm is used to obtain the final clustering result. Experimental results on six incomplete multi-view datasets demonstrate that our proposed method achieves the best performance compared with the existing state-of-the-art methods.

Aqueous ammonium-ion energy storage systems have recently gained continuous attention owing to the advantages of sustainability and environmental-friendliness in the grid-scale application. However, ammonium-ion supercapacitors are still in their infancy, and it is of great challenge in developing suitable materials for application in wearable energy storage devices. Herein, we develop a vanadium oxide hydration (V

This paper investigates how dominant retailers should choose appropriate markup pricing strategies (a fixed-dollar markup, or a percentage markup), and how their supplier responds by choosing wholesale pricing strategies (a retailer-specific wholesale pricing, or a uniform wholesale pricing). Our results show that, all other things being equal, the uniform wholesale pricing strategy weakens the position of the retailers in the supply chain and thus is more favorable for the supplier. Specifically, when both retailers choose the same markup scheme, it is better for the supplier to choose the uniform wholesale pricing strategy since it can lead a higher profit for himself. The uniform wholesale pricing strategy also benefits the whole channel and the end-consumers, but hurts the dominant retailers. When the two retailers choose different markup schemes, the best choice for the supplier will still be the uniform wholesale pricing scheme if the level of downstream competition is sufficiently high; otherwise, the supplier would prefer the retailer-specific wholesale pricing scheme. The preference of the retailer using fixed-dollar markup pricing is just the opposite as compared to that of the supplier, while the retailer using percentage markup pricing always prefers retailer-specific wholesale pricing. Besides, the uniform wholesale pricing is beneficial to the whole channel. After anticipating the supplier’s reactions, both retailers are better off choosing percentage markup pricing no matter which wholesale pricing strategy the supplier chooses and what the level of competition is. Therefore, the final equilibrium would be the case where the dominant retailers choose the percentage-markup variant and the supplier always responds by adopting the uniform pricing scheme no matter what the level of bcompetition is. This equilibrium enhances the supply chain efficiency and benefits the end-consumers since it leads to the highest channel profits and the lowest retailing prices. Moreover, under this equilibrium, the supplier gets better while the retailers get worse as the level of competition increases.

PurposeIn the context of the digital economy, information technology (IT) investment has become a necessary way for enterprises to transform digitally. However, why and how IT investment can enhance digital transformation is lacking in the literature. Based on the resource-based view (RBV), this study explored the impact mechanism of IT infrastructure on the digital transformation of enterprises from the perspective of the digital transformation strategy. Further, this study examined the moderating role of top management on the relationships between IT infrastructure and digital transformation strategy and between digital transformation strategy and enterprise's digital transformation.Design/methodology/approachThrough a questionnaire survey of Chinese enterprises, 180 sample data were collected, and the partial least squares-structural equation modeling (PLS-SEM) method was used to test the hypothesis.FindingsDigital transformation strategy fully mediates the relationship between IT infrastructure and enterprise digital transformation. Furthermore, top management has a significant positive moderating effect on the relationship between IT infrastructure and digital transformation strategy, as well as the relationship between digital transformation strategy and digital transformation.Originality/valueThis study explores the moderating role of top management in the relationship between IT and enterprise performance, as well as the mediating role of digital transformation strategy in the relationship between IT infrastructure investment and digital transformation performance. As a result, the study adds significantly to the body of knowledge on IT business value, digital transformation and strategic management. The authors' findings can help update managers' perceptions of IT value and provide theoretical guidance on deriving digital transformation performance from IT infrastructure investments.

Acetylene (C

Utilizing a bifunctional organic linker, the first crystalline organic framework based on dative B←N bonds with permanent porosity was synthesized in one step. It sets a benchmark for C2H2 uptake and shows effective C2H2/CO2 separation.

A new microporous Zn(ii)-based MOF 1 has been successfully synthesized by utilizing both amino- and nitro-functionalized ligands. Gas sorption explorations reveal that the activated sample exhibits significant C2H2 uptakes and selective capture from CO2.

HighlightsAn indirect Kalman filter algorithm is proposed to fuse GNSS/INS positioning information.Detailed kinematics and dynamics model of track vehicles was established.An MPC-based double-layer closed-loop controller combined with tracked vehicle model is designed.Tracked transport vehicle performs well in path tracking on soft soil road.Abstract. Orchards in hills and mountainous regions are more occluded and single satellite navigation is unstable. Therefore, the indirect Kalman filter information fusion algorithm was proposed to achieve high-precision positioning by establishing a state error equation based on GNSS/INS. A complete kinematics and dynamics model of tracked chassis was established. A double-layer closed-loop controller based on model predictive control (MPC) was designed. An MPC controller based on the kinematics model in the outer loop was designed to output the expected control value of the tracked transporter. The inner loop design was based on the extended state observer of the dynamic model to estimate and compensate for the internal and external disturbances of the system. The performance test was based on a tracked chassis platform. The test results presented that when driving at a speed of 0.50 m/s under soft soil road conditions, the maximum lateral deviation was 0.15 m, and the average absolute deviation was 0.05 m. This high level of control accuracy means that this control design enables the transfer vehicle to follow the navigation path precisely and complete its task. Keywords: Hills and mountainous regions, Integrated navigation, Model predictive control, Vehicle dynamics model.

This review mainly summarizes the recent progress of MOFs composed of N-heterocyclic carboxylate ligands in gas sorption/separation. This work may help to understand the relationship between the structures of MOFs and gas sorption/separation.

A new C3H6-selective MOF based on infinite helix SBUs was constructed, realizing the effective separation of C2H4–C3H6 to not only yield ≥99.9% purity of C2H4 by a one-step method but also greatly increase the purity of C3H6 in the adsorbed mixtures.

This work focuses on the design of multifunctional lanthanide MOFs platform for smart sensing of methylmalonic acid (MMA) and anti-counterfeiting applications. The platform enables intuitive detection of MMA in urine to further infer vitamin B12 levels.

Abstract: Brain diseases, including Alzheimer's disease (AD), brain tumors and Parkinson's disease (PD), pose heavy pressure on the public healthcare system. The main obstacle to vanquish brain diseases is the blood-brain barrier (BBB), which is a selective barrier mainly formed by brain endothelial cells. BBB prevents almost all drugs from reaching the brain, thereby hindering drug delivery. Over the past few decades, considerable signs of progress have been made in crossing the BBB and treating brain diseases. Gold nanoparticles (AuNPs) demonstrate the characteristics of adjustable size, unique optical properties, flexible surface modification, and good biocompatibility, which all contribute AuNPs as a promising candidate in biomedical fields. This article reviews the structure and properties of BBB, and discusses main transport routes through the BBB. Besides, nanoparticles, specially AuNPs applied in brain diseases as main drug delivery platforms, are systematically summarized, emphasizing several methods to modify AuNPs, including tuning particle size and surface modification, which are aimed at promoting BBB penetration or prolonging circulation time of AuNPs. In addition, AuNPs utilized in brain diseases are introduced in detail from the aspects of brain imaging, AD, brain tumors, and PD. Prospects and challenges that need to be considered in further investigations and clinical transformation of AuNPs used in brain diseases are also included, hoping to bring new insights into the applications of AuNPs in brain diseases.

Intrahepatic cholangiocarcinoma (IHCC) is the second most common malignant neoplasm of the liver. In spite of the increasing incidence worldwide, it is relatively rare in Western countries. IHCC is relatively common in Eastern and Southeastern Asia. Patients with IHCC are usually diagnosed at an advanced stage, therefore, the clinical outcome is dismal. Dysregulation of urea cycle metabolic enzyme expression is found in different types of cancers. Nevertheless, a comprehensive evaluation of genes related to the urea cycle (i.e., GO:0000050) has not been conducted in IHCC. By performing a comparative analysis of gene expression profiles, we specifically examined genes associated with the urea cycle (GO:0000050) in a publicly accessible transcriptomic dataset (GSE26566). Interestingly, CPS1 was identified as the second most prominently down-regulated gene in this context. Tumor tissues of 182 IHCC patients who underwent curative-intent hepatectomy were enrolled. The expression level of CPS1 protein in our IHCC cohort was assessed by immunohistochemical study. Subsequent to that, statistical analyses were carried out to examine the expression of CPS1 in relation to various clinicopathological factors, as well as to assess its impact on survival outcomes. We noticed that lower immunoreactivity of CPS1 in IHCC was associated with tumor progression (pT status) with statistical significance (p = 0.003). CPS1 underexpression was not only negatively correlated to overall survival (OS), disease-free survival (DFS), local recurrence-free survival (LRFS) and metastasis-free survival (MeFS) in univariate analysis but also an independent prognosticator to forecast poorer clinical outcome for all prognostic indices (OS, DFS, LRFS and MeFs) in patients with IHCC (all p ≤ 0.001). These results support that CPS1 may play a crucial role in IHCC oncogenesis and tumor progression and serve as a novel prognostic factor and a potential diagnostic and theranostic biomarker.

Malnutrition is a common problem in patients with metastatic colorectal cancer (mCRC) receiving targeted therapy plus chemotherapy, resulting in severe toxicity and decreased survival rates. This retrospective study employing propensity score matching (PSM) examined the efficacy and safety of a supplemental home parenteral nutrition (HPN) program for patients with RAS wild-type mCRC receiving cetuximab plus chemotherapy. This retrospective nationwide registry study included data from 14 medical centers/hospitals across Taiwan, and the data period ranged from November 2016 to December 2020. Patients with RAS wild-type mCRC receiving cetuximab plus chemotherapy as their first-line therapy were included and divided into HPN and non-HPN program groups. HPN was initiated based on patient-specific factors, such as baseline nutritional status, treatment-related toxicities, and comorbidities. Clinical outcomes were evaluated using response to therapy, duration of response (DoR), progression-free survival (PFS), and overall survival (OS). This study recruited 758 patients, of whom 110 and 648 were included in the HPN and non-HPN program groups, respectively. After 1:3 PSM, the data of 109 and 327 patients from the HPN and non-HPN program groups were analyzed, respectively. The HPN program group had a higher metastasectomy rate (33.9% vs. 20.2%, p = 0.005), and longer duration of treatment and DoR than the non-HPN program group (13.6 vs. 10.3 and 13.6 vs. 9.9 months, p = 0.001 and < 0.001, respectively). The HPN program group tended to have a longer median PFS (18.2 vs. 13.9 months, p = 0.102). Moreover, we noted a significant improvement in the median OS in the same group (53.4 vs. 34.6 months, p = 0.002). Supplemental HPN programs may be recommended for select patients with mCRC receiving targeted therapy plus chemotherapy to improve oncological outcomes.

During the harvesting of cut-stem vegetables, the structural parameters of cutters have an important influence on the harvesting effect. Structural parameters of stalks directly affect the cutting effect of the cutter. The thickness of the stalk xylem has a strong influence on the cutting stability and cutting surface effect of the cutter. In this paper, the effect of the broccoli stalk xylem on conventional toothless cutters was investigated using broccoli stalks as the cutting object. It was found that a thicker xylem leads to shear force fluctuations, which in turn affects the smooth operation of the cutting device. Taking locust mouthparts as the research object, a bionic multi-tooth cutting method is proposed in this paper to obtain the contour curve based on the locust upper jaw cutting tooth lobe. By establishing the regression equation, the contour curve of the cutting teeth is fitted accurately. The cutter edge is designed with the locust’s maxillary incisive lobe as the bionic object. ANSYS software was used to simulate the cutting of a double disc cutter and broccoli stalk. The effect of each factor was analyzed by response-surface regression to determine the optimal cutter speed, machine forward speed, cutting inclination, blade overlap, and optimal cutting position. The cutting test is verified via broccoli stalks and a cutting test bench to further determine the cutting device operating parameters. The optimal operating parameters of the cutting device were 0.239 m/s forward speed, 30.974-degree cutting edge angle, 10.066 mm blade overlap, and 467.511 rpm.

Long-acting antipsychotic injections require that psychiatric nurses choose the proper injection site and technique to avoid harming patients. This research conducted a mixed-method study to examine the long-acting injectable antipsychotic (LAI) knowledge, practice, and administrative barriers in a sample of 269 psychiatric nurses from 3 public psychiatric hospitals in Taiwan. Self-report questionnaires showed female nurses exhibiting higher scores and older nurses demonstrating more knowledge. The dorsogluteal (DG) site was the most widely used for injections, with 57.6% of nurses using the Z-track method. Qualitative data analysis was performed on 20 psychiatric nurses who chose the DG site as their preferred injection site. There were two key themes. The first was a gap between the nurses’ knowledge of LAI administration and their actual practice. The second needed more confidence and training in using the ventrogluteal injection site. These results highlight the need for continued education and training to improve LAI practice among psychiatric nurses.

Supermassive black holes (SMBHs) are ubiquitously found at the centers of most massive galaxies. Measuring SMBH mass is important for understanding the origin and evolution of SMBHs. However, traditional methods require spectroscopic data which is expensive to gather. We present an algorithm that weighs SMBHs using quasar light time series, circumventing the need for expensive spectra. We train, validate, and test neural networks that directly learn from the Sloan Digital Sky Survey (SDSS) Stripe 82 light curves for a sample of $38,939$ spectroscopically confirmed quasars to map out the nonlinear encoding between SMBH mass and multi-color optical light curves. We find a 1$\sigma$ scatter of 0.37 dex between the predicted SMBH mass and the fiducial virial mass estimate based on SDSS single-epoch spectra, which is comparable to the systematic uncertainty in the virial mass estimate. Our results have direct implications for more efficient applications with future observations from the Vera C. Rubin Observatory. Our code, \textsf{AGNet}, is publicly available at \url{https://github.com/snehjp2/AGNet}., Comment: 8 pages, 7 figures, 1 table, Accepted by MNRAS

In this paper, we design a potential game to investigate the interplay between sensors and attackers in wireless remote estimation systems (RESs), where we consider both closed-loop and open-loop scenarios that depend on whether the sensor's actions are known to the attacker or not. As a typical application of Cyber-Physical Systems (CPSs), wireless RESs have been showing vulnerable to channel jamming attacks. As a countermeasure, the wireless sensor in the estimation system can switch to another independent wireless channel to transmit sensor data. However, a smart attacker can also switch the target channel to launch a jamming attack, resulting in a game between the sensor and attacker. Moreover, the reinforcement learning based methods are proposed to obtain solutions of the designed game models in two scenarios. We also provide extensive simulation results to validate the effectiveness of the proposed methods.

Many factors were reported to be associated with diabetic retinopathy (DR); however, their contributions remained unclear. We aimed to evaluate the prognostic and diagnostic accuracy of logistic regression and three machine learning models based on various medical records.This was a cross-sectional study. We investigated the prevalence and associations of DR among 757 participants aged 40 years or older in the 2005-2006 National Health and Nutrition Examination Survey (NHANES). We trained the models to predict if the participants had DR with 15 predictor variables. Area under the receiver operating characteristic (AUROC) and mean squared error (MSE) of each algorithm were compared in the external validation dataset using a replicate cohort from NHANES 2007-2008.Among the 757 participants, 53 (7.00%) subjects had DR, the mean (standard deviation, SD) age was 57.7 (13.04), and 78.0% were male (n = 42). Logistic regression revealed that female gender (OR = 4.130, 95% CI: 1.820-9.380; P 0.05), HbA1c (OR = 1.665, 95% CI: 1.197-2.317; P 0.05), serum creatine level (OR = 2.952, 95% CI: 1.274-6.851; P 0.05), and eGFR level (OR = 1.009, 95% CI: 1.000-1.014, P 0.05) increased the risk of DR. The average performance obtained from internal validation was similar in all models (AUROC ≥ 0.945), and k-nearest neighbors (KNN) had the highest value with an AUROC of 0.984. In external validation, they remained robust or with modest reductions in discrimination with AUROC still ≥ 0.902, and KNN also performed the best with an AUROC of 0.982. Both logistic regression and machine learning models had good performance in the clinical diagnosis of DR.This study highlights the utility of comparing traditional logistic regression to machine learning models. We found that logistic regression performed as well as optimized machine learning methods when classifying DR patients.

Spatiotemporal manipulation of protein distributions, abundances, and functions based on molecular level remains a significant challenge in studying biological systems and developing therapeutics. Particularly, such a nanotherapeutic platform though both specific and internal way is extremely lacking. Herein, we put forward a click chemistry-driven protein sorting (PROCLISORT) strategy, which acted in a cell space station (CSS) to achieve the sequential regulation of specific protein along the entire PD-1 immune checkpoint axis. From the spatial dimension, CSS could achieve comprehensive recognition, anchoring and blocking PD-L1/PD-L2 as well as transport PD-L1 among organelles at the subcellular level. From the time dimension, through the booting control via click reaction, the occurrence of these biological regulatory events became controllable and sequential, thus resulting in rapid and durable down-regulation of PD-L1. Through these smart tasks, this CSS stimulated a satisfactory tumor-immune-therapy effect both in vitro and in vivo. With a rational design, this multistage booting nanoplatform holds promise for molecular manipulation along the disease-related pathway in various living systems.

The toxicity, corrosiveness, and volatility of elemental bromine presents challenges for its safe storage and transportation. Purification from other halogens is also difficult. Here, we report an easy-to-prepare calix[4]pyrrole-based azo-bridged porous organic polymer (

This article investigates the adaptive event-triggered finite-time dissipative filtering problems for the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy Markov jump systems (MJSs) with asynchronous modes. By designing a generalized performance index, the H∞, L₂-L∞, and dissipative fuzzy filtering problems with network transmission delay are addressed. The adaptive event-triggered scheme (ETS) is proposed to guarantee that the IT2 T-S fuzzy MJSs are finite-time boundedness (FTB) and, thus, lower the energy consumption of communication while ensuring the performance of the system with extended dissipativity. Different from the conventional triggering mechanism, in this article, the parameters of the triggering function are based on an adaptive law, which is obtained online rather than as a predefined constant. Besides, the asynchronous phenomenon between the plant and the filter is considered, which is described by a hidden Markov model (HMM). Finally, two examples are presented to show the availability of the proposed algorithms.

Double auctions play a pivotal role in stimulating active participation of a large number of users comprising both task requesters and workers in mobile crowdsourcing. However, most existing studies have concentrated on designing offline two-sided auction mechanisms and supporting single-type tasks and fixed auction service models. Such works ignore the need of dynamic services and are unsuitable for large-scale crowdsourcing markets with extremely diverse demands (i.e., types and urgency degrees of tasks required by different requesters) and supplies (i.e., task skills and online durations of different workers). In this paper, we consider a practical crowdsourcing application with an on-demand service strategy. Especially, we innovatively design three online service models, namely online single-bid single-task (OSS), online single-bid multiple-task (OSM) and online multiple-bid multiple-task (OMM) models to accommodate diversified tasks and bidding demands for different users. Furthermore, to effectively allocate tasks and facilitate bidding, we propose a truthful online double auction mechanism for each service model based on the McAfee double auction. By doing so, each user can flexibly select auction service models and corresponding auction mechanisms according to their current interested tasks and online duration. To illustrate this, we present a three-demand example to explain the effectiveness of our on-demand service strategy in realistic crowdsourcing applications. Moreover, we theoretically prove that our mechanisms satisfy truthfulness, individual rationality, budget balance and consumer sovereignty. Through extensive simulations, we show that our mechanisms can accommodate the various demands of different users and improve social utility including platform utility and average user utility. IEEE