Search

Background and objective: MicroRNAs (miRNAs) are implicated in cancer by exerting roles in tumor growth, metastasis, and even drug resistance. The general trends of miRNA research in diverse cancers are not fully understood. In this work, miRNA-related research in colorectal cancer, prostate cancer, leukemia, and brain tumors was analyzed in search of key research trends with clinical potential. Methods: A bibliometric analysis of articles, spanning from 2014 to 2024, was carried out with the major focus laid on four types of cancers. The Co-citation network analysis, keyword bursts, and the collaborative pattern were done in VOSviewer and CiteSpace, respectively. Results: Colorectal cancer had the highest publication volume, with research primarily focusing on gene expression, extracellular vesicles, and non-coding RNAs. Prostate cancer showed a shift toward clinical applications, while leukemia and brain tumor research, though less extensive, highlighted miRNA's potential in early diagnosis and treatment. Co-citation analysis identified emerging research collaborations and key contributors. Conclusion: miRNA plays a pivotal role in cancer diagnosis, biomarker development, and therapeutic interventions. With advancements in non-invasive diagnostics and personalized medicine, miRNA offers significant potential for clinical applications. Future research should focus on miRNA's role in drug resistance and combination therapies to accelerate its clinical translation. [ABSTRACT FROM AUTHOR]

Urban biodiversity is crucial for ecological security, balance, and important for fostering awareness on human-nature interconnectedness among the public. The diversity of birds, as an urban ecosystem indicator, reflects ecosystem services and is impacted by urban development. To explore the impacts of urbanization on bird diversity, stratified to songbirds, terrestrial birds, climbers, swimming birds, wading birds, and raptors, we specifically investigated the existing and potential distributions of selected bird species, analyzed different contributions of environmental factors, and compared these with urban biodiversity conservation policies. We used bird records from the China Birdwatching Record Center (over 1400 species of birds for querying) and remotely-sensed landcover data, based on the MaxEnt model, to analyze bird spatial distribution characteristics and potential habitat corridors throughout Beijing. The results showed that: (1) Songbirds and terrestrial birds were predominantly concentrated in water areas in urban areas. Wading birds, climbers, swimming birds, and raptors were gathered in forest-covered areas, near wetlands and farmland in suburban areas. Projections indicated that the raptor species Common Kestrel (Falco tinnunculus) showed a notable shift toward urban cores. (2) Among climbers, Gray-headed Pygmy Woodpecker (Dendrocopos canicapillus) occupied the highest proportion of high-quality habitats (10.34%), contrasting with the representative songbird species Blackbird (Turdus merula) at 1.38%, which demonstrated adaptability to urban environments. Critical habitats were concentrated in shrub forests, supporting habitat connectivity. Proximity to water bodies was critical for raptors, wading, swimming, and climbers, whereas terrestrial birds and songbirds were more affected by artificial lighting. (3) The "urban and suburban park rings" policy has effectively enhanced habitat quality and connectivity, promoting urban biodiversity resilience. This study improves our understanding of how different bird communities adapt to urbanization in terms of habitats and movement corridors, and provides useful information for formulating urban bird biodiversity conservation strategies. [ABSTRACT FROM AUTHOR]

Introduction: Chronic kidney disease is a significant public health issue. Dapagliflozin has been shown to improve the quality of life for patients with chronic kidney disease. This review aimed to systematically assess the cost-effectiveness of adding dapagliflozin to standard care compared with standard care alone for treating chronic kidney disease. Methods: The relevant studies were searched in PubMed, Web of Science, Scopus, Embase, and Cochrane from the inception date to June 1, 2024. The titles, abstracts, and full texts were independently evaluated and screened by two authors. Additionally, the economic evaluation studies were assessed independently by two authors using the consolidated health economic evaluation reporting standards checklist. Results: 14 studies were included which were about the economic evaluations of adding dapagliflozin in the treatment of chronic kidney disease. The minimum consolidated health economic evaluation reporting standards score for the studies was 0.77, indicating very good quality. Adding dapagliflozin to the standard of care would be more effective and cost-saving in Mexico, Malaysia, Canada, Thailand, and China. The highest incremental cost-effectiveness ratio of dapagliflozin ($67962.75/QALY) originated from the USA. According to the available studies, adding dapagliflozin to standard of care for the treatment of chronic kidney disease is considered cost-effectiveness from both the healthcare system and the payer's perspective. Conclusion: Adding dapagliflozin to standard care in the treatment of chronic kidney disease is cost-effective from both the healthcare system and the payer's perspective in well-developed countries. [ABSTRACT FROM AUTHOR]

Toxic acetone gas emissions and leakage are a potential threat to the environment and human health. Gas sensors founded on metal oxide semiconductors (MOS) have become an effective strategy for toxic gas detection with their mature process. In the present work, an efficient acetone gas sensor based on Au-modified ZnO porous nanofoam (Au/ZnO) is synthesized by polyvinylpyrrolidone-blowing followed by a calcination method. XRD and XPS spectra were utilized to investigate its structure, while SEM and TEM characterized its morphology. The gas sensitivity of the Au/ZnO sensors was investigated in a static test system. The results reveal that the gas-sensitive performance of porous ZnO toward the acetone can be enhanced by adjusting the loading ratio of noble Au nanoparticles. Specifically, the Au/ZnO sensor prepared by the Au loading ratio of 3.0% (Au/ZnO-3.0%) achieved a 100 ppm acetone gas response of 20.02 at the optimum working temperature of 275 °C. Additionally, a portable electronic device used a STM32 primary control chip to integrate the Au/ZnO-3.0% gas sensor with other modules to achieve the function of detecting and alarming toxic acetone gas. This work is of great significance for efficiently detecting and reducing acetone emissions. [ABSTRACT FROM AUTHOR]

Tax incentives are a powerful driver for the development of infrastructure real estate investment trusts (REITs). Complementary antiavoidance regulations are essential to ensure the sustainable and healthy development of the REIT market. This paper breaks through the traditional policy approach that emphasizes incentives but neglects supervision. Based on factors such as information asymmetry and regulatory capture, a tripartite game model of REIT tax incentives and antiavoidance regulation is established. Utilizing global REIT policy and market data, the paper sets parameter values and conducts numerical simulations to explore the dynamic strategic interaction mechanisms among the government, REIT stakeholders, and financial regulators. The study finds that (1) government's tax incentive policies and regulatory strategies show a phase transition characteristic with the changes of REIT market returns; and (2) based on the project's profit status, financial regulators and investors are prone to develop a collusive avoidance evolutionary strategy. Therefore, antiavoidance regulations and mechanisms to prevent regulatory capture should be implemented in tandem to ensure the efficiency of tax incentives. The conclusion of the study provides a theoretical basis for the formulation of REIT tax incentive policy and the promotion of the sustainable development of the REIT market. [ABSTRACT FROM AUTHOR]

According to the information disclosure quality and investment return efficiency of ESG enterprises, this paper establishes a dynamic incentive mechanism based on return regulation considering the reciprocal preferences of enterprises and investors. The strategy evolution path of ESG enterprises is explored from the perspective of external regulation in the investment market. The evolutionary game analysis of ESG investment returns in five scenarios shows that the increase in reciprocal preferences of investors and ESG enterprises will promote enterprises to make high efforts to improve the quality of information disclosure and credit rating behavior. However, with the increase of project return, the degree of reciprocity of investors should be appropriately adjusted, otherwise it is easy to cause speculation of ESG enterprises. In the case of heterogeneous returns, the effects of positive and negative incentives differ greatly, so the incentive mechanism should be set to match the return state of ESG investments. [ABSTRACT FROM AUTHOR]

This study examined the impact of returnee entrepreneurs' internal migration experience on enterprise innovation and explored possible mechanisms. Using data from the 2018 Enterprise Survey for Innovation and Entrepreneurship in China (ESIEC), which collected a large sample of entrepreneurs from small and medium enterprises, the results of our study showed that entrepreneurs' internal migration experience positively affected enterprise innovation activities and expenditure. Further, the effects were larger for the entrepreneurs who had the first migration at a younger age and had a longer duration of migration exposure. The mechanism analysis suggested that human capital accumulation was a potential channel underlying the innovation consequences of entrepreneurs' internal migration experience. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) plays an important role in agricultural and environmental systems. With the continuous improvement in the availability of remote sensing data, satellite technology has experienced widespread development in the monitoring of large-scale SSM. Synthetic Aperture Radar (SAR) and optical remote sensing data have been extensively utilized due to their complementary advantages in this field. However, the limited information from single-band SARs or single optical remote sensing data has restricted the accuracy of SSM retrieval, posing challenges for precise SSM monitoring. In contrast, multi-source and multi-band remote sensing data contain richer and more comprehensive surface information. Therefore, a method of combining multi-band SAR data and employing machine learning models for SSM inversion was proposed. C-band Sentinel-1 SAR data, X-band TerraSAR data, and Sentinel-2 optical data were used in this study. Six commonly used feature parameters were extracted from these data. Three machine learning methods suitable for small-sample training, including Genetic Algorithms Back Propagation (GA-BP), support vector regression (SVR), and Random Forest (RF), were employed to construct the SSM inversion models. The differences in SSM retrieval accuracy were compared when two different bands of SAR data were combined with optical data separately and when three types of data were used together. The results show that the best inversion performance was achieved when all three types of remote sensing data were used simultaneously. Additionally, compared to the C-band SAR data, the X-band SAR data exhibited superior performance. Ultimately, the RF model achieved the best accuracy, with a determinable coefficient of 0.9186, a root mean square error of 0.0153 cm3/cm3, and a mean absolute error of 0.0122 cm3/cm3. The results indicate that utilizing multi-band remote sensing data for SSM inversion offers significant advantages, providing a new perspective for the precise monitoring of SSM. [ABSTRACT FROM AUTHOR]

Allergic inflammation is the foundation of multiple allergic disorders, such as allergic rhinitis and asthma. Mast cells are effector cells that initiate inflammatory response. 5-hydroxymethylfurfural (5-HMF), a furfural compound, is the heat-processed product of various fruit, foods, drinks, as well as some Chinese herbal medicines. 5-HMF was previously reported to inhibit mast cell activation. Our study aimed to explore the functions of 5-HMF in both phorbol 12-mystate 13-acetate (PMA) plus calcium ionophore (A23187)-induced allergic inflammation in human mast cell line HMC-1 and ovalbumin (OVA)-induced asthma mouse models. HMC-1 cells were pretreated with 5-HMF and then stimulated by PMA+A23187. The cytotoxicity of 5-HMF on HMC-1 cells was evaluated by MTT assay. Histamine content in cell supernatants was measured by the o-phthaldialdehyde spectrofluorometric procedure. Intracellular calcium was determined using the fluorescent dye Fura-2AM. The production and expression of pro-inflammatory cytokines were detected by ELISA and RT-qPCR. Caspase-1 colorimetric assay was employed to examine the enzymatic activity of caspase-1. Asthma mouse models were induced by OVA sensitization. The bronchoalveolar lavage fluid (BALF) and blood samples were collected for the detection of total and differential cell count as well as aspartate aminotransferase (AST), alanine aminotransferase (ALT), OVA-immunoglobulin E (OVA-IgE), OVA-immunoglobulin G1 (OVA-IgG1), and pro-inflammatory cytokine levels. The left lung of mouse was dissected for histopathological examination by hematoxylin and eosin (H&E) staining. The protein expression of pro-caspase-1 and the phosphorylation of NF-κB and MAPK pathway-associated molecules were assessed by Western blotting. Our findings revealed that 5-HMF efficiently suppressed the PMA+A23187-induced enhancement in histamine production and intracellular calcium in HMC-1 cells. Pro-inflammatory cytokine production and expression in HMC-1 cells were elevated after PMA plus A23187 stimulation, which, however, were inhibited by pretreatment of 5-HMF. Additionally, 5-HMF suppressed the activity of caspase-1 and the phosphorylation of NF-κB and MAPK-associated molecules including p65 NF-κB, p38 MAPK, ERK, and JNK in HMC-1 cells. In vivo experiments demonstrated that 5-HMF treatment reduced the lung/body weight index and total and differential (macrophages, neutrophils, lymphocytes, and eosinophils) cell counts in BALF of asthmatic mice, but exerted no influence on serum AST and ALT levels. Besides, 5-HMF reduced serum OVA-IgE and OVA-IgG1 levels, mitigated lung inflammation, and inhibited the NF-κB and MAPK signaling pathways in asthma mouse models. 5-HMF mitigates allergic inflammation in asthma by inactivating caspase-1 and NF-κB and MAPK signaling pathways. [ABSTRACT FROM AUTHOR]

Background: The sharp increase in fungal infections, insufficient diagnostic and treatment capabilities for fungal infections, poor prognosis of patients with fungal infections as well as the increasing drug resistance of fungi are serious clinical problems. It is necessary to explore the implementation and evaluation methods of antifungal stewardship (AFS) to promote the standardized use of antifungal drugs. Methods: The AFS programme was implemented at a tertiary first-class hospital in China using a plan-do-check-act (PDCA) quality management tool. A baseline investigation was carried out to determine the utilization of antifungal drugs in pilot hospitals, analyse the existing problems and causes, and propose corresponding solutions. The AFS programme was proposed and implemented beginning in 2021, and included various aspects, such as team building, establishment of regulations, information construction, prescription review and professional training. The management effectiveness was recorded from multiple perspectives, such as the consumption of antifungal drugs, the microbial inspection rate of clinical specimens, and the proportion of rational prescriptions. The PDCA management concept was used for continuous improvement to achieve closed-loop management. Results: In the first year after the implementation of the AFS programme, the consumption cost, use intensity and utilization rate of antifungal drugs decreased significantly (P < 0.01). The proportion of rational antifungal drug prescriptions markedly increased, with the proportion of prescriptions with indications increasing from 86.4% in 2019 to 97.0% in 2022, and the proportion of prescriptions with appropriate usage and dosage increased from 51.9 to 87.1%. In addition, after the implementation of the AFS programme, physicians' awareness of the need to complete microbial examinations improved, and the number of fungal cultures and serological examinations increased substantially. Statistics from drug susceptibility tests revealed a decrease in the resistance rate of Candida to fluconazole. Conclusion: This study indicated that the combination of AFS and the PDCA cycle could effectively reduce antifungal consumption and promote the rational use of antifungal drugs, providing a reference for other health care systems to reduce the overuse of antifungal drugs and delay the progression of fungal resistance. [ABSTRACT FROM AUTHOR]

Ligand modulation of the electrical properties and surface solvation plays a crucial role in the development of functionalized colloidal quantum dots (CQDs) inks for solution‐processed optoelectronics. While inorganic halide ionic ligands can facilitate the n‐type doping of PbS CQDs and establish an electrical double layer in polar solvents for stable high‐concentration n‐type CQD inks, a plausible solvation strategy is still lacking to stabilize p‐type PbS CQDs, thereby resulting in a tedious multi‐step deposition involving short‐chain dithiol molecular ligands for advanced heterojunction CQD solar cells. Here an effective hydrogen bonding solvation strategy is proposed using 2‐mercaptoethanol (ME) ligands to realize stable p‐type PbS CQD ink. This strategy enables DMSO to form a dense solvation layer surrounding the ME‐modified PbS CQDs. With this approach, the PbS‐ME CQD ink exhibits a photoluminescence quantum yield of 52.03%, which is the highest record for PbS CQD inks. Finally, one‐step deposition of a p‐type PbS‐ME layer for PbS CQD photovoltaics is successfully achieved with an impressive power conversion efficiency (PCE) of 10.91%. This p‐type solar ink facilitates the fabrication of ready‐to‐use devices, enabling extensive applications in large‐scale and flexible optoelectronic devices. [ABSTRACT FROM AUTHOR]

The sugar apple (Annona squamosa) is valued for its taste, nutritional richness, and versatility, making it suitable for fresh consumption and medicinal use with significant commercial potential. Widely found in the tropical Americas and Asia's tropical or subtropical regions, it faces challenges in post-harvest ripeness assessment, predominantly reliant on manual inspection, leading to inefficiency and high labor costs. This paper explores the application of computer vision techniques in detecting ripeness levels of harvested sugar apples and proposes an improved deep learning model (ECD-DeepLabv3+) specifically designed for ripeness detection tasks. Firstly, the proposed model adopts a lightweight backbone (MobileNetV2), reducing complexity while maintaining performance through MobileNetV2′s unique design. Secondly, it incorporates the efficient channel attention (ECA) module to enhance focus on the input image and capture crucial feature information. Additionally, a Dense ASPP module is introduced, which enhances the model's perceptual ability and expands the receptive field by stacking feature maps processed with different dilation rates. Lastly, the proposed model emphasizes the spatial information of sugar apples at different ripeness levels by the coordinate attention (CA) module. Model performance is validated using a self-made dataset of harvested optical images categorized into three ripeness levels. The proposed model (ECD-DeepLabv3+) achieves values of 89.95% for MIoU, 94.58% for MPA, 96.60% for PA, and 94.61% for MF1, respectively. Compared to the original DeepLabv3+, it greatly reduces the number of model parameters (Params) and floating-point operations (Flops) by 89.20% and 69.09%, respectively. Moreover, the proposed method could be directly applied to optical images obtained from the surface of the sugar apple, which provides a potential solution for the detection of post-harvest fruit ripeness. [ABSTRACT FROM AUTHOR]

To detect and analyze risk signals of the drug-related adverse events (AEs) of 4 gadolinium-based contrast agents (GBCAs) (gadopentetate dimeglumine (Gd-DTPA), gadobenate dimeglumine (Gd-BOPTA), gadoteridol (Gd-HP-DO3A), and gadobutrol (Gd-BT-DO3A)) according to the US Food and Drug Administration Adverse Event Reporting System (FAERS) database and ensure the clinical safety. The AEs that are associated with the 4 GBCAs were collected from the FAERS database from 2004Q1 to 2022Q3. The risk signals were mined using reporting odds ratio (ROR) and proportional reporting ratio (PRR). 424 risk signals were excavated, in which 151 risk signals were associated with Gd-DTPA, 93 risk signals were related to Gd-BOPTA, 79 risk signals were relevant to Gd-HP-DO3A, and 101 risk signals were associated with Gd-BT-DO3A. The AE signals involved 20 system organ classes (SOCs). Two of the top four SOCs were identical, namely 'skin and subcutaneous tissue disorders' and 'general disorders and administration site conditions.' The safety signals of 4 GBCAs were detected, and the SOCs associated with the AEs of the 4 GBCAs were different. Besides, some AEs obtained in this study were not mentioned in the package inserts, which need more attention and research to ensure the clinical safety. [ABSTRACT FROM AUTHOR]

The all-trans conformation (β -phase) possesses a significant impact on the piezoelectric polymer polyvinylidene fluoride (PVDF). Inducing more molecular chain [−CH2−CF2−] n to form all-trans conformation is one of the biggest obstacles for manufacturing high-performance piezoelectric sensing devices. Herein, the continuous vacuum technology is used to modulate the polarity of binary solvents by the proportion of the lower solvent. The regulated solvent forms a high dipole moment, an interaction between the dipole of β -phase and the dipole moment makes the phase reversal in PVDF. Fourier transform infrared spectroscopy, piezoelectric constant test and other characterization results show that when the weakly polar acetone and the strongly polar solvent DMF reach a ratio of 4:6, the pure PVDF film possesses high piezoelectricity (d 33 ∼ −44.8 pC N−1) and strong self-polarization. Additionally, the A4D6 device exhibits high sensitivity (S 1 = 0.182 V/N, 0.5 N ∼ 30 N), driven capability (0.49 mW m−2), and reliability during the electrical tests as a pressure device. This work provides an effective and cost-effective route of optimizing the solvent's polarity to improve the piezoelectric characteristics of the polymer. [ABSTRACT FROM AUTHOR]

The prediction of major histocompatibility complex (MHC)-peptide binding affinity is an important branch in immune bioinformatics, especially helpful in accelerating the design of disease vaccines and immunity therapy. Although deep learning-based solutions have yielded promising results on MHC-II molecules in recent years, these methods ignored structure knowledge from each peptide when employing the deep neural network models. Each peptide sequence has its specific combination order, so it is worth considering adding the structural information of the peptide sequence to the deep model training. In this work, we use positional encoding to represent the structural information of peptide sequences and validly combine the positional encoding with existing models by different strategies. Experiments on three datasets show that the introduction of position-coding information can further improve the performance built upon the existing model. The idea of introducing positional encoding to this field can provide important reference significance for the optimization of the deep network structure in the future. [ABSTRACT FROM AUTHOR]

Purpose: Hypoxia is one of the most frequent adverse events under deep sedation in the semiprone position. We hypothesized that supraglottic jet oxygenation and ventilation (SJOV) via Wei nasal jet tube (WNJ) can reduce the incidence of hypoxia in patients under deep sedation during endoscopic retrograde cholangiopancreatography (ERCP). Methods: A total of 171 patients were divided into three groups: N group, supplementary oxygen via a nasopharyngeal airway (4–6 L/min); W group, supplementary oxygen via WNJ (4–6 L/min); WS group, SJOV via WNJ. The primary outcome was the incidence of adverse events, including sedation-related adverse events [SRAEs, hypoxemia (SpO2 = 75–89% lasted less than 60 s); severe hypoxemia (SpO2 < 75% at any time or SpO2 < 90% lasted more than 60 s] and subclinical respiratory depression (SpO2 = 90–95%). Other intraoperative and post-operative adverse events were also recorded as secondary outcomes. Results: Compared with the N group, the incidence of hypoxemia and subclinical respiratory depression in the WS group was significantly lower (21% vs. 4%, P = 0.005; 27% vs. 6%, P = 0.002). Compared with Group W, the incidence of hypoxemia and subclinical respiratory depression in Group WS was also significantly less frequent (20% vs. 4%, P = 0.009; 21% vs. 6%, P = 0.014). No severe hypoxia occurred in the group WS, while four and one instances were observed in the group N and group W respectively. There were no significant differences in other adverse events among the three groups. Conclusion: SJOV can effectively improve oxygenation during ERCP in deeply sedated semiprone patients. [ABSTRACT FROM AUTHOR]

Electrolyte with copper redox shuttles has endowed dye‐sensitized solar cells (DSSCs) with a high open‐circuit voltage (Voc) over 1.0 V and promising efficiencies under the sun and indoor light, but further combining these copper redox shuttles with quasi‐solid‐state gel polymer electrolytes (GPEs) is still a great challenge. The crystallization of polymers inside GPEs is one of the important factors to limit the mass transport of large‐sized copper redox shuttles and dramatically reduce the efficiency of quasi‐solid‐state DSSCs. Herein, the copper‐based GPEs are fabricated via in situ UV‐induced copolymerization of acrylic monomers. This chemical cross‐linking strategy can significantly reduce the crystallinity of polymers inside the GPEs and thus improve the apparent diffusion coefficient two times compared with the generally used poly(ethylene oxide) GPEs. Finally, optimized GPEs, copolymerized by diacrylic and triacrylic monomers, successfully generate the quasi‐solid‐state DSSCs with an efficiency of 10.1% and a high Voc of 1.03 V. These photovoltaic parameters are comparable to those of DSSCs with liquid copper‐based electrolytes, demonstrating that the chemical cross‐linking is an effective strategy to fabricate high‐performance copper‐based GPEs. This strategy will facilitate the future efficiency and stability improvement of DSSCs for the commercial optoelectronic applications. [ABSTRACT FROM AUTHOR]

In this paper, we study the inverse scattering problem and propose a new method to determine the shape of a cavity with Dirichlet boundary conditions from the knowledge of acoustic near-field measurements. The Fourier–Bessel method (FBM) is used to reconstruct the boundary of the cavity from scattered fields which are incited by point sources and measured on a closed curve inside the cavity. We not only analyze the stability but also present the numerical experiments to show the effectiveness of the FBM. [ABSTRACT FROM AUTHOR]

Multi-walled carbon nanotubes (MWCNTs) with one-dimensional nanostructure are an ideal support for oxygen reduction reaction (ORR) catalysts thanks to their intrinsic outstanding electrical conductivity and high specific surface area. Iron and nitrogen doping could alter the local electronic structure and therefore enhance the ORR activity of MWCNTs, but the preparation process always includes complicated growth conditions and post-treatment. Herein, an iron and nitrogen co-modified multi-walled carbon nanotubes (Fe–N-MWCNTs) with hierarchical nanostructure is engineered and synthesized via a simple two-step pyrolysis approach. Large specific surface area, low resistivity, and intensified charge density near the Fermi level synergistically endow the Fe–N-MWCNTs with outstanding ORR activity. The optimal Fe–N-MWCNTs exhibit a higher onset potential value of 0.92 V (versus RHE) and half-wave potential (E 1/2) of 0.85 V (versus RHE) in 0.1 M KOH medium, which exceeds the benchmark Pt/C electrocatalyst (E 1/2 = 0.84 V). This strategy of modifying MWCNTs support by a simple calcination process provides a feasible method to prepare cost-efficient ORR electrocatalysts. [ABSTRACT FROM AUTHOR]

The difficulty in financing has become the primary obstacle in the process of promoting a public–private partnership (PPP). Asset securitization is a financial model that can exchange the predictable cash flow of illiquid PPP project assets with the current cash flow, and credit enhancement is crucial in this process. Different from the traditional external credit enhancement of PPP projects based on government guarantees, this paper introduces an innovative credit risk management tool, i.e., credit default swaps (CDS), into the process of asset securitization in the PPP model. In order to avoid opportunistic behavior caused by information asymmetry in the process of financial product transaction, a CDS trading contract between PPP project investors and risk management institutions based on the principal–agent theory and a flexible CDS pricing model is proposed. Through a demonstration case of the Leshan to Rongjiang Expressway in China, the volatility correlation among CDS contract default probability, pricing, and related factors, such as default limits, operation period, compensation amount, project investment return, investment return volatility, and PPP project investors' expected inputs, is obtained. The study shows that (1) appropriate CDS pricing helps to generate investors' high expected inputs and diligence with controls; (2) there is a positive correlation between the change of default limit and compensation amount, and the set value of default limit should not be too high, otherwise it may induce the increase of default probability; and (3) based on the high return on assets and moderate asset volatility of PPP projects, the probability of default of CDS contracts is low. The innovative financial mechanism developed by the principal–agent theory could enrich the credit enhancement methods in asset securities of PPP projects. [ABSTRACT FROM AUTHOR]

Unsupervised multi-modal hashing has received considerable attention in large-scale multimedia retrieval areas since its low storage and high search speed. Existing unsupervised multi-modal hashing methods usually aim to mine the complementary information and the structural information for different modalities and preserve them in low-dimensional discrete space. The main limitations are two folds: (1) The shared semantic properties and the specific-modality information between multi-modal data are not explored simultaneously, which limits the improvement of retrieval accuracy. (2) Most multi-modal hashing methods with rough fusion manners cause greatly the information loss. In this paper, we present an unsupervised Joint and Individual Feature Fusion Hashing (JIFFH) that jointly performs the unified feature learning and individual feature learning. A two-layer fusion architecture with an adaptive weighting scheme is adopted to fuse effectively the common semantic properties and the specific-modality data information. The experimental results on three public multi-modal datasets show that our proposed method is better than state-of-the-art unsupervised multi-modal hashing methods. In conclusion, the proposed JIFFH method is very effective to learn discriminative hash codes and can boost retrieval performance. [ABSTRACT FROM AUTHOR]

Soil moisture is an important land environment characteristic that connects agriculture, ecology, and hydrology. Surface soil moisture (SSM) prediction can be used to plan irrigation, monitor water quality, manage water resources, and estimate agricultural production. Multi-source remote sensing is a crucial tool for assessing SSM in agricultural areas. The field-measured SSM sample data are required in model building and accuracy assessment of SSM inversion using remote sensing data. When the SSM samples are insufficient, the SSM inversion accuracy is severely affected. An SSM inversion method suitable for a small sample size was proposed. The alpha approximation method was employed to expand the measured SSM samples to offer more training data for SSM inversion models. Then, feature parameters were extracted from Sentinel-1 microwave and Sentinel-2 optical remote sensing data, and optimized using three methods, which were Pearson correlation analysis, random forest (RF), and principal component analysis. Then, three common machine learning models suitable for small sample training, which were RF, support vector regression, and genetic algorithm-back propagation neural network, were built to retrieve SSM. Comparison experiments were carried out between various feature optimization methods and machine learning models. The experimental results showed that after sample augmentation, SSM inversion accuracy was enhanced, and the combination of utilizing RF for feature screening and RF for SSM inversion had a higher accuracy, with a coefficient of determination of 0.7256, a root mean square error of 0.0539 cm3/cm3, and a mean absolute error of 0.0422 cm3/cm3, respectively. The proposed method was finally used to invert the regional SSM of the study area. The inversion results indicated that the proposed method had good performance in regional applications with a small sample size. [ABSTRACT FROM AUTHOR]

Interface engineering, which efficiently optimizes the interfacial carrier collection and recombination, has been proven to be of importance for the emerging colloidal quantum dot solar cells (CQDSCs). Compared with the attractive modification efforts at the interface between quantum dots and the anode/electron transport layer (ETL), the interface between fluorine-doped tin-oxide (FTO) cathodes and the ETL, for which there exists a band alignment mismatch and high trap density in ZnO, has been investigated less. Herein, two kinds of ethylenimine-based polymers, branched by only ethylenimine groups (b-PEI) and by both ethylenimine/ethoxylated groups (e-PEI), respectively, are introduced as bifunctional interfacial layers (BILs) in lead sulfide (PbS) CQDSCs. PEI-based BILs were utilized to modulate the work function of an FTO cathode for optimizing the band alignment at the FTO/ZnO interface and to control the crystallinity of ZnO for reducing its traps. These BILs suppressed the interfacial carrier recombination and achieved a power conversion efficiency (PCE) of 11.28% in CQDSCs, which was much superior to the PCE of the reference device without BIL (10.29%). Also, the branched side chain of PEI-based BILs plays a crucial role in rationally modulating the Schottky barrier to gain different interface-optimization effects. Our work has laid a foundation for the commercial application of CQDSCs due to the advantage of low-temperature solution processability, low-cost, and scalable manufacturing. [ABSTRACT FROM AUTHOR]

Due to the numerous cross-operations and poor information communication, it is easy to cause production safety accidents in traditional assembled steel plants. The transformation and upgrading of smart production in the assembly steel plants is helpful to improve the efficiency of safety management. In order to effectively reduce the safety risks in the production of assembled steel components, this paper integrates policy incentives and safety supervision, constructs an evolutionary game model between the government and assembled steel producers, and analyzes the strategic evolution rules and stability conditions of stakeholders through the replication dynamics equation. Moreover, based on the empirical simulation of the Fuzhou X Steel Structure Plant project, the effectiveness of the evolutionary model incentive strategy setting is verified. The results show that whether an assembled steel plants adopt a smart management strategy or not is influenced by the government's incentive subsidy mechanism, penalty mechanism, the benefits and costs generated by traditional/ smart management, the probability and loss of safety accidents and other factors. The conclusion is important for upgrading the safety management mode, improving the safety production efficiency and constructing the safety supervision system of the assembled steel smart plant. [ABSTRACT FROM AUTHOR]

The surface ligand environment plays a dominant role in determining the physicochemical, optical, and electronic properties of colloidal quantum dots (CQDs). Specifically, the ligand‐related electronic traps are the main reason for the carrier nonradiative recombination and the energetic losses in colloidal quantum dot solar cells (CQDSCs), which are usually solved with numerous advanced ligand exchange reactions. However, the synthesis process, as the essential initial step to control the surface ligand environment of CQDs, has lagged behind these post‐synthesis ligand exchange reactions. The current PbS CQDs synthesis tactic generally uses lead oxide (PbO) as lead precursor, and thus suffers from the water byproducts issue increasing the surface‐hydroxyl ligands and aggravating trap‐induced recombination in the PbS CQDSCs. Herein, an organic‐Pb precursor, lead (II) acetylacetonate (Pb(acac)2), is used instead of a PbO precursor to avoid the adverse impact of water byproducts. Consequently, the Pb(acac)2 precursor successfully optimizes the surface ligands of PbS CQDs by reducing the hydroxyl ligands and increasing the iodine ligands with trap‐passivation ability. Finally, the Pb(acac)2‐based CQDSCs possess remarkably reduced trap states and suppressed nonradiative recombination, generating a certified record Voc of 0.652 V and a champion power conversion efficiency (PCE) of 11.48% with long‐term stability in planar heterojunction‐structure CQDSCs. [ABSTRACT FROM AUTHOR]

The aspect sentiment triplet extraction (ASTE) task aims to extract the target term and the opinion term, and simultaneously identify the sentiment polarity of target-opinion pairs from the given sentences. While syntactic constituency information and commonsense knowledge are both important and valuable for the ASTE task, only a few studies have explored how to integrate them via flexible graph convolutional networks (GCNs) for this task. To address this gap, this paper proposes a novel end-to-end model, namely GCN-EGTS, which is an enhanced Grid Tagging Scheme (GTS) for ASTE leveraging syntactic constituency parsing tree and a commonsense knowledge graph based on GCNs. Specifically, two types of GCNs are developed to model the information involved, namely span GCN for syntactic constituency parsing tree and relational GCN (R-GCN) for commonsense knowledge graph. In addition, a new loss function is designed by incorporating several constraints for GTS to enhance the original tagging scheme. The extensive experiments on several public datasets demonstrate that GCN-EGTS outperforms the state-of-the-art approaches significantly for the ASTE task based on the evaluation metrics. The outcomes of this research indicate that effectively incorporating syntactic constituency parsing information and commonsense knowledge is a promising direction for the ASTE task. [ABSTRACT FROM AUTHOR]

Solution‐processed solar cells are promising for the cost‐effective, high‐throughput production of photovoltaic devices. Colloidal quantum dots (CQDs) are attractive candidate materials for efficient, solution‐processed solar cells, potentially realizing the broad‐spectrum light utilization and multi‐exciton generation effect for the future efficiency breakthrough of solar cells. The emerging quantum junction solar cells (QJSCs), constructed by n‐ and p‐type CQDs only, open novel avenue for all‐quantum‐dot photovoltaics with a simplified device configuration and convenient processing technology. However, the development of high‐efficiency QJSCs still faces the challenge of back carrier diffusion induced by the huge carrier density drop at the interface of CQDs and conductive glass substrate. Herein, an ultra‐thin atomic layer deposited tin oxide (SnOx) layer is employed to buffer this carrier density drop, significantly reducing the interfacial recombination and capacitance caused by the back carrier diffusion. The SnOx‐modified QJSC achieves a record‐high efficiency of 11.55% and a suppressed hysteresis factor of 0.04 in contrast with reference QJSC with an efficiency of 10.4% and hysteresis factor of 0.48. This work clarifies the critical effect of interfacial issues on the carrier recombination and hysteresis of QJSCs, and provides an effective pathway to design high‐performance all‐quantum‐dot devices. [ABSTRACT FROM AUTHOR]

Reasoning on temporal knowledge graphs, which aims to infer new facts from existing knowledge, has attracted extensive attention and in-depth research recently. One of the important tasks of reasoning on temporal knowledge graphs is entity prediction, which focuses on predicting the missing objects in facts at current time step when relevant histories are known. The problem is that, for entity prediction task on temporal knowledge graphs, most previous studies pay attention to aggregating various semantic information from entities but ignore the impact of semantic information from relation types. We believe that relation types is a good supplement for our task and making full use of semantic information of facts can promote the results. Therefore, a framework of Enhanced Relational Graph Convolution Network (ERGCN) is put forward in this paper. Rather than only considering representations of entities, the context semantic information of both relations and entities is considered and merged together in this framework. Experimental results show that the proposed approach outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The natural language model BERT uses a large-scale unsupervised corpus to accumulate rich linguistic knowledge during its pretraining stage, and then, the information is fine-tuned for specific downstream tasks, which greatly improves the understanding capability of various natural language tasks. For some specific tasks, the capability of the model can be enhanced by introducing external knowledge. In fact, these methods, such as ERNIE, have been proposed for integrating knowledge graphs into BERT models, which significantly enhanced its capabilities in related tasks such as entity recognition. However, for two types of tasks, commonsense causal reasoning and predicting the ending of stories, few previous studies have combined model modification and process optimization to integrate external knowledge. Therefore, referring to ERNIE, in this paper, we propose enhanced language representation with event chains (EREC), which focuses on keywords in the text corpus and their implied relations. Event chains are integrated into EREC as external knowledge. Furthermore, various graph networks are used to generate embeddings and to associate keywords in the corpus. Finally, via multi-task training, external knowledge is integrated into the model generated in the pretraining stage so as to enhance the effect of the model in downstream tasks. The experimental process of the EREC model is carried out with a three-stage design, and the experimental results show that EREC has a deeper understanding of the causal relationship and event relationship contained in the text by integrating the event chains, and it achieved significant improvements on two specific tasks. [ABSTRACT FROM AUTHOR]

Advance in wide‐bandgap‐oxide electron transport layers (WBO‐ETLs) is essential for the development of high‐efficiency and stable solution‐processed quantum dot photovoltaics (PVs). Herein, a novel dip‐coated SnO2 ETL to fabricate PbS colloidal quantum dot solar cells (CQDSCs) is employed, replacing the benchmarked ZnO sol–gel ETL, in pursuit of both high efficiency and high operational stability. Taking advantage of the high light‐transmittance and desired electrical characteristics of the dip‐coated SnO2 ETL, the CQDSC with a structure of fluorine‐doped tin oxide (FTO)/SnO2/I‐capped PbS CQDs/EDT‐capped PbS/Au (EDT: 1,2‐ethanedithiol), exhibits a champion power conversion efficiency (PCE) of 10.70%, higher than the 9.52% of the ZnO‐based control device. Particularly, the unencapsulated SnO2‐based CQDSC demonstrated a remarkably improved operational stability in comparison to the ZnO‐based device, maintaining 93.4% of the initial efficiency after a 200 h light‐soaking at the maximum power point tracking under AM 1.5G irradiation, which is attributed to the robust SnO2/PbS CQD heterojunction under operational conditions. The improved efficiency and operational stability suggest the superiority of the dip‐coated SnO2 ETL to its ZnO counterpart, paving a way for the development of high‐efficiency and high‐stability CQDSCs via WBO‐ETL engineering. [ABSTRACT FROM AUTHOR]

Multicriteria group decision-making environments that have a high number of criterion values can be difficult for the experts to handle. This is due to the fact that the experts have to take too much information into account. Thus, they get lost among all the possibilities and have difficulties making the right decision. In order to solve this problem, we present a novel multicriteria group decision-making method that reduces the initial set of criterion values in an organized way. Hierarchical clustering methods are used in order to generate a new reduced criteria set that can be handled by the experts. Fuzzy ontologies are used as an aid system that stores how much each alternative fulfills each criterion. The presented method makes it possible for the experts to carry out the group decision-making process by focusing on ranking the reduced set of criterion values. As a result, a comfortable decision environment is generated, in which the experts can make decisions by managing a fair amount of information. The aid provided by fuzzy ontologies allows the experts to focus on establishing the importance of the criterion values, leaving the rest to the computational system. [ABSTRACT FROM AUTHOR]

Population spatialization reveals the distribution and quantity of the population in geographic space with gridded population maps. Fine-scale population spatialization is essential for urbanization and disaster prevention. Previous approaches have used remotely sensed imagery to disaggregate census data, but this approach has limitations. For example, large-scale population censuses cannot be conducted in underdeveloped countries or regions, and remote sensing data lack semantic information indicating the different human activities occurring in a precise geographic location. Geospatial big data and machine learning provide new fine-scale population distribution mapping methods. In this paper, 30 features are extracted using easily accessible multisource geographic data. Then, a building-scale population estimation model is trained by a random forest (RF) regression algorithm. The results show that 91% of the buildings in Lin'an District have absolute error values of less than six compared with the actual population data. In a comparison with a multiple linear (ML) regression model, the mean absolute errors of the RF and ML models are 2.52 and 3.21, respectively, the root mean squared errors are 8.2 and 9.8, and the R2 values are 0.44 and 0.18. The RF model performs better at building-scale population estimation using easily accessible multisource geographic data. Future work will improve the model accuracy in densely populated areas. [ABSTRACT FROM AUTHOR]

This work developed a method for calculating the Jones-Wilkins-Lee (JWL) equation of state (EOS) for the detonation products of aluminized explosives based on hexanitrohexaazaisowurtzitane (CL-20). On the basis of previous studies, chemical reaction equations, Hess' law and Avogadro's law were all incorporated in this process because of the non-ideal detonations of aluminized explosives. The EOS parameters were determined for explosive formulations comprising pure CL-20 as well as aluminized versions and compared to literature reports. Calculated pressure-specific volume curves were in good agreement with prior studies. A 50 mm cylinder test using a CL-20/Al/wax composition with a 75/20/5 mass ratio was conducted and the calculated EOS parameters were used to simulate the expansion of the cylinder wall. The experimental and simulation plots were found to agree within 5%, which confirms the validity of this method with regard to engineering analyses. [ABSTRACT FROM AUTHOR]

Background: Rice (Oryza sativa L.) is one of the most widely grown food crops, and its yield and quality are particularly important for a warm-saturated diet. Cold stress restricts rice growth, development, and yield; however, the specific mechanism of cold tolerance in rice remains unknown. Results: The analysis of leaf physiological and photosynthetic characteristics showed that the two rice varieties were significantly affected by cold stress, but the cold-tolerant variety KY131 had more stable physiological characteristics, maintaining relatively good photosynthetic capacity. To better explore the transcriptional regulation mechanism and biological basis of rice response to cold stress, a comprehensive analysis of the rice transcriptome and lipidome under low temperature and control temperature conditions was carried out. The transcriptomic analysis revealed that lipid metabolism, including membrane lipid and fatty acid metabolism, may be an important factor in rice cold tolerance, and 397 lipid metabolism related genes have been identified. Lipidomics data confirmed the importance of membrane lipid remodeling and fatty acid unsaturation for rice adaptation to cold stress. This indicates that the changes in the fluidity and integrity of the photosynthetic membrane under cold stress lead to the reduction of photosynthetic capacity, which could be relieved by increased levels of monogalactosyldiacylglycerol that mainly caused by markedly increased expression of levels of 1,2-diacylglycerol 3-beta-galactosyltransferase (MGD). The upregulation of phosphatidate phosphatase (PAP2) inhibited the excessive accumulation of phosphatidate (PA) to produce more phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylglycerol (PG), thereby preventing of membrane phase transition under cold stress. In addition, fatty acid β-oxidation is worth further study in rice cold tolerance. Finally, we constructed a metabolic model for the regulatory mechanism of cold tolerance in rice, in which the advanced lipid metabolism system plays a central role. Conclusions: Lipidome analysis showed that membrane lipid composition and unsaturation were significantly affected, especially phospholipids and galactolipids. Our study provides new information to further understand the response of rice to cold stress. [ABSTRACT FROM AUTHOR]

The estimation of the vascular direction of celiac trunk plays an important role in the resection of gastric cancer as it can help doctors to plan the specific gastrectomy. The traditional manual estimation is the current clinical standard but time-consuming and subjective. Still, it is a challenging task for current computer vision methods to fully use the volume data in computer-aided clinical diagnosis because the segmented contour of object is of low quality and the small target can't be completely segmented in the existing methods of convolutional neural network. In this paper, we proposed a novel contour constraint multi-branches network which used an additional contour segmentation branch as constraint for celiac trunk segmentation. The experiments show that our method outperforms other compared methods both quantitively and qualitatively, which provides effective framework for automated, accurate, and reliable vascular direction of celiac trunk estimation to help doctors in clinical diagnosis. [ABSTRACT FROM AUTHOR]

Long project cycle and uncertainties are important characteristics of public-private partnership (PPP) projects. Since the introduction of PPP projects in China, the timing of capital withdrawal has become important. With the emergence of risk factors during the course of the project, it will face the problem of investment withdrawal by social capital financial investors. Escalation of commitment (EOC) refers to the erroneous behaviour of project decision makers who do not promptly withdraw from a project when they receive negative feedback and continue to invest resources in the project. EOC not only causes more unnecessary losses but also adversely affects decision makers. Therefore, it is crucial to clarify the impact of EOC on the choice of the exit timing of social capital. This article adopts literature survey method and quantitative analysis method: introducing the theory of maximization of income into the real option model, combining the net present value method with the binary tree option pricing model, constructing the decision-making model to analyze the exit timing of PPP social capital in the context of EOC. Then combined numerical simulation and empirical analysis to verify the effectiveness of the decision-making model, discussed the reasons why the social capital party chooses EOC, and proposes measures for controlling EOC. The higher the degree of completion of the project, the easier it is for the person in charge of the project to make inaccurate judgements about the project due to personal psychological factors, and the easier it is for EOC to occur. Therefore, after setting the minimum goal of the project, the decision maker needs to accurately evaluate the existing value of the project to avoid falling into decision-making errors. [ABSTRACT FROM AUTHOR]

Elaborating the sensitization effects of different noble metals on In2O3 has great significance in providing an optimum method to improve ethanol sensing performance. In this study, long-range ordered mesoporous In2O3 has been fabricated through replicating the structure of SBA-15. Different noble metals (Au, Ag, Pt and Pd) with the same doping amount (1 at%) have been introduced by an in situ doping routine. The results of the gas sensing investigation indicate that the gas responses towards ethanol can be obviously increased by doping different noble metals. In particular, the best sensing performance towards ethanol detection can be achieved through Pd doping, and the sensors based on Pd-doped In2O3 not only possess the highest response (39.0–100 ppm ethanol) but also have the shortest response and recovery times at the optimal operating temperature of 250 °C. The sensing mechanism of noble metal doped materials can be attributed to the synergetic effect combining 'catalysis' and 'electronic and chemical sensitization' of noble metals. In particular, the chemical state of the noble metal also has a great influence on the gas sensing mechanism. A detailed explanation of the enhancement of gas sensing performance through noble metal doping is presented in the gas sensing mechanism part of the manuscript. [ABSTRACT FROM AUTHOR]

Large-scale and well-aligned in situ growth SnO2 nanotube (NT) arrays have been synthesized directly on the surface of the Al2O3 ceramic tube by a cost-effective template self-etching method. The morphology of in situ SnO2 NTs can be adjusted by changing the concentration of urea. The structure and morphology characteristics of SnO2 NT were examined via x-ray diffraction, BET, and scanning electron microscopy, respectively. A series of detections were carried out to evaluate the gas sensing performances. The results indicated that in situ growth SnO2 NT arrays sensor exhibited an excellent response (S = 20.3), good linearity under the concentration range of ppm level (5–300 ppm), and outstanding selectivity to 100 ppm of acetone gas. Compared with the sensors fabricated by a slurry-coating method, the controllable in situ assembled SnO2 NT arrays exhibited a more stable structure and easier fabrication process. The high acetone sensing performance might due to the unique hollow structure and favorable orientation growth. The dominant sensing mechanism about the in situ growth SnO2 NT arrays sensor has been discussed in detail. It is expected that in situ growth SnO2 NT arrays sensor with the general working principle and controllable growth strategy will become a promising functional material in monitoring and detecting acetone. [ABSTRACT FROM AUTHOR]

Highly transparent and active Pt–Mo2C counter electrodes were successfully fabricated by the strong metal–support interaction, with high dispersity of Pt nanoclusters on Mo2C support, which endowed bifacial dye-sensitized solar cells with a rear-to-front efficiency ratio as high as 0.75. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet has penetrated into all aspects of people's lives. A large number of online customer reviews have been accumulated in several product forums, which are valuable resources to be analyzed. However, these customer reviews are unstructured textual data, in which a lot of ambiguities exist, so analyzing them is a challenging task. At present, the effective deep semantic or fine-grained analysis of customer reviews is rare in the existing literature, and the analysis quality of most studies is also low. Therefore, in this paper a fine-grained opinion mining method is introduced to extract the detailed semantic information of opinions from multiple perspectives and aspects from Chinese automobile reviews. The conditional random field (CRF) model is used in this method, in which semantic roles are divided into two groups. One group relates to the objects being reviewed, which includes the roles of manufacturer, the brand, the type, and the aspects of cars. The other group of semantic roles is about the opinions of the objects, which includes the sentiment description, the aspect value, the conditions of opinions and the sentiment tendency. The overall framework of the method includes three major steps. The first step distinguishes the relevant sentences with the irrelevant sentences in the reviews. At the second step the relevant sentences are further classified into different aspects. At the third step fine-grained semantic roles are extracted from sentences of each aspect. The data used in the training process is manually annotated in fine granularity of semantic roles. The features used in this CRF model include basic word features, part-of-speech (POS) features, position features and dependency syntactic features. Different combinations of these features are investigated. Experimental results are analyzed and future directions are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we study the initial-boundary value problem of the Rosenau–Burgers equation by the multiple integral finite volume method (MIFVM). The MIFVM can keep the original equation property very well. We propose a two-level implicit nonlinear discrete scheme, which preserves the energy decline property of the original equation. Existence and uniqueness of the numerical solution are derived. The convergence with the order of O(τ2 + h3) and unconditional stability of the numerical scheme are verified. Numerical examples demonstrate that the scheme is reliable and effective. [ABSTRACT FROM AUTHOR]

In this study, long-term ordered mesoporous In2O3 was prepared by replicating the structure of SBA-15. Samples prepared with Au-doped and Au-loaded mesoporous In2O3 were aimed at studying the influence of the modification method (doping and loading) on acetone sensing properties, and the corresponding sensing mechanism was discussed in detail. The structural and chemical characterizations of the samples were carried out by XRD, BET, TEM and XPS. The best sensing performance was achieved in the In2O3 Au-doped (IO-Au- D) sample; the response reached 19.01 to 100 ppm of acetone at 250 °C. The effects of the polarity and bond dissociation energy of acetone benefited the improved selectivity among volatile organic compounds (VOCs). The excellent gas sensing performance not only depended on the ordered mesoporous structure of the In2O3 matrix, but could also be attributed to the "electronic sensitization" and "chemical sensitization" of Au. Furthermore, uniformly dispersed Au doping depressed the lattice growth and enlarged the depletion region, playing a great role in enhancing the sensing performance. However, the limited surface decorated region of the Au-loaded sample suppressed the enhancement of the acetone sensing performance. This work significantly reveals that the sensing performance can be controlled by different methods of introducing additives. [ABSTRACT FROM AUTHOR]

PbS colloidal quantum dot solar cells (CQDSCs) have recently achieved remarkable performance enhancement due to the development of the phase-transfer ligand exchange (PTLE) method. However, the lack of compact packing of the PTLE-passivated CQDs impairs the interdot electronic coupling and thereby severely restricts further improvement in performance. To address this electronic coupling issue, we report a simple yet effective process of external pressure (0–2 MPa). We find that the interdot distance is reduced after the application of the pressure. Both optical and electrical measurements clearly demonstrate that the distance reduction can effectively strengthen the interdot electronic coupling, thus promoting the carrier transport of the CQD layer. However, too much pressure (>2 MPa) could accelerate the detrimental carrier recombination processes of CQDSCs. Accordingly, by optimizing the carrier transport and recombination processes, we achieve the maximum power conversion efficiency of 8.2% with a moderate pressure of 1.5 MPa, which is 25.5% higher than the solar cell without the external pressure. This effective strategy of external pressure could also be applied to other CQD-based optoelectronic devices to realize a better device performance. [ABSTRACT FROM AUTHOR]

In recent years, the number of services, together with their types, is expanding rapidly. Confronted with such a large number of services, people are hard to discover the desired services. Service ranking is regarded as a significant step to solve the problem. But, how to evaluate the importance of services correctly is still a significant issue. Though there are some literatures to rank services, it is not enough. In this paper, we proposed to apply parameters in complex networks to measure the importance of services. First, our approach models services and the relations between every pair of services as a service network. Second, we apply a set of parameters in complex networks to measure the corresponding parameter values as their importance. Finally, services will be ranked according to their importance in a descending order. Empirical experiments are performed on a real-world data set crawled from ProgrammableWeb, and the results show the similarity and difference between the results of different parameters. [ABSTRACT FROM AUTHOR]

The CdS interlayer plays an important role as the seed layer and energy barrier in photovoltaic performance enhancement of CdSe quantum-dot sensitized solar cells (QDSCs). However, the CdS (CdS-CBD) interlayer is generally synthesized by the chemical bath deposition method (CBD) and suffers from excessive Cd2+ due to the poor controllability of interfacial chemical reactions. Here, we employed a sol–gel method to prepare a CdS interlayer (CdS-SG) with a stoichiometric ratio of Cd2+ to S2− on a TiO2 mesoporous photoanode and then fabricated CdSe QDSCs. A power conversion efficiency of 3.37% was obtained in the CdS-SG QDSCs (Cell-SG), which is higher than that of 2.64% in the CdS-CBD QDSCs (Cell-CBD) under AM 1.5G solar illumination of 100 mW cm−2. Charge extraction measurements showed that the Cell-SG contained fewer trap states than the Cell-CBD. A comprehensive study of the electron dynamics based on open-circuit voltage decay (OCVD), electrochemical impedance spectroscopy (EIS), intensity-modulated voltage spectroscopy (IMVS), and intensity-modulated photocurrent spectroscopy (IMPS) measurements revealed that the stoichiometric CdS-SG interlayer significantly reduced the charge recombination and prolonged the lifetime of photogenerated electrons in contrast to the CdS-CBD interlayer in the QDSCs. [ABSTRACT FROM AUTHOR]