Highly Emissive Perylene Diimide-Based Metallacages and Their Host–Guest Chemistry for Information Encryption

Here we report two highly emissive perylene diimide (PDI)-based metallacages and explore their complexation with polycyclic aromatic hydrocarbons, such as pyrene, triphenylene, and perylene. The fluorescence quantum yields of metallacages exceed 90% and their binding constants with perylene can reach as high as 2.41 × 104 M–1 in acetonitrile. These features enable further tuning of the emission of the host–guest complexes to obtain white-light emission based on the complementary orange emission of the metallacages and the blue emission of perylene. Moreover, owing to the huge differences of their quantum yields in solution and in the solid state, the host–guest complexes are successfully employed for information encryption. This study offers a general approach for the construction of emissive metallacages and explores their application for information encryption

Weather Radar Echo Super-Resolution Reconstruction Based on Nonlocal Self-Similarity Sparse Representation

Weather radar echo plays an important role in early warning and timely forecasting of severe weather. However, the radar echo may not be accurate enough to predict or analyze small-scale weather phenomenon due to the degradation of the observed radar. In order to solve this problem, some radar echo super-resolution reconstruction algorithms have been proposed, but the algorithm may result in an excessively smooth edge and detail in a local region. To reconstruct radar echo with better edges and finer details, a novel nonlocal self-similarity sparse representation (NSSR) model is proposed. The NSSR model is based on the sparse representation of weather radar echoes to better reconstruct the echo edge and detail information. We exploit the radar echo nonlocal self-similarity to recover more realistic details based on the NSSR model. Experiment results demonstrate that the proposed NSSR outperforms current general-purpose radar echo super-resolution approaches on both visual effects and objective radar echo quality

Versatile Construction of Single-Tailed Giant Surfactants with Hydrophobic Poly(ε-caprolactone) Tail and Hydrophilic POSS Head

Giant surfactants refer to a new kind of amphiphile by incorporating functional molecular nanoparticles with polymer tails. As a size-amplified counterpart of small-molecule surfactants, they serve to bridge the gap between small-molecule surfactants and amphiphilic block copolymers. This work reports the design and synthesis of single-tailed giant surfactants carrying a hydrophobic poly(ε-caprolactone) (PCL) as the tail and a hydrophilic cage-like polyhedral oligomeric silsesquioxane (POSS) nanoparticle as the head. The modular synthetic strategy features an efficient “growing-from„ and “click-modification„ approach. Starting from a monohydroxyl and heptavinyl substituted POSS (VPOSS-OH), a PCL chain with controlled molecular weight and narrow polydispersity was first grown by the ring-opening polymerization (ROP) of ε-CL under the catalysis of stannous octoate, leading to a PCL chain end-capped with heptavinyl substituted POSS (VPOSS-PCL). To endow the POSS head with adjustable polarity and functionality, three kinds of hydrophilic groups, including hydroxyl groups, carboxylic acids, and amine groups, were installed to the periphery of POSS molecule by a high-efficiency thiol-ene “click„ reaction. The compounds were fully characterized by NMR, gel permeation chromatography (GPC), MALDI-TOF mass spectrometry, and TGA analysis. In addition, the preliminary self-assembly study of these giant surfactants was also investigated by TEM and dynamic laser light scattering (DLS), which indicated that they can form spherical nanoparticles with different diameters in aqueous solution. This work affords a straightforward and versatile way for synthesizing single-tailed giant surfactants with diverse head surface functionalities

A Meta-Analysis of Risk Factors for Post-Traumatic Stress Disorder (PTSD) in Adults and Children after Earthquakes.

PTSD is considered the most common negative psychological reactions among survivors following an earthquake. The present study sought to find out the determinants of PTSD in earthquake survivors using a systematic meta-analysis. Four electronic databases (PubMed, Embase, Web of Science, and PsycInfo) were used to search for observational studies about PTSD following earthquakes. The literature search, study selection, and data extraction were conducted independently by two authors. 52 articles were included in the study. Summary estimates, subgroup analysis, and publication bias tests were performed on the data. The prevalence of PTSD after earthquakes ranged from 4.10% to 67.07% in adults and from 2.50% to 60.00% in children. For adults, the significant predictors were being female, low education level or socio-economic status, prior trauma; being trapped, experiencing fear, injury, or bereavement during the disaster. For children, the significant predictors were being older age, high education level; being trapped, experiencing fear, injury, or bereavement, witnessing injury/death during the earthquakes. Our study provides implications for the understanding of risk factors for PTSD among earthquake survivors. Post-disaster mental health recovery programs that include early identification, on-going monitoring, and sustained psychosocial support are needed for earthquake survivors

Weather Radar Data Compression Based on Spatial and Temporal Prediction

The transmission and storage of weather radar products will be an important problem for future weather radar applications. The aim of this work is to provide a solution for real-time transmission of weather radar data and efficient data storage. By upgrading the capabilities of radar, the amount of data that can be processed continues to increase. Weather radar compression is necessary to reduce the amount of data for transmission and archiving. The characteristics of weather radar data are not considered in general-purpose compression programs. The sparsity and data redundancy of weather radar data are analyzed. A lossless compression of weather radar data based on prediction coding is presented, which is called spatial and temporal prediction compression (STPC). The spatial and temporal correlations in weather radar data are utilized to improve the compression ratio. A specific prediction scheme for weather radar data is given, while the residual data and motion vectors are used to replace the original values for entropy coding. After this, the Level-II product from CINRAD SA is used to evaluate STPC. Experimental results show that the STPC achieves a better performance than the general-purpose compression programs, with the STPC yield being approximately 26% better than the next best approach

Optimal energy storage configuration for joint energy‐regulation market participating renewable energy plants with excess revenue recovery mechanism

Abstract The fluctuation and stochastic characteristics of renewable energy resources challenge the secure system operation and also impose significant financial risks for the market participating renewable energy plants (REPs). Energy storage systems (ESSs) can serve as effective tools in enhancing the operating flexibility of REPs, thus improving their profitability while making them grid‐friendly. However, current studies focussing on the energy market participation of ESS‐equipped REPs neglect ESSs' frequency regulation performance. Moreover, power output deviation and power curtailment of REPs bring difficulties to the integration of renewable energy. To address these challenges, an optimal ESS configuration method for REPs participating in the joint energy‐regulation market is proposed first. A method considering constraints on frequency regulation performance is applied. Then, to reduce power output deviation and power curtailment of REPs, an incentive mechanism based on the excess revenue recovery is implemented to induce the grid‐friendly power outputs of REPs. Numerical analysis on XJTU‐ROTS2017 systems demonstrates that the optimal ESS configuration results obtained from the proposed method could promote the profitability of ESS‐equipped REPs. The results also verify the effectiveness of the excess revenue recovery mechanism in facilitating the grid‐friendly operation behaviours of the ESS‐equipped REPs

A Meta-Analysis of Risk Factors for Post-Traumatic Stress Disorder (PTSD) in Adults and Children after Earthquakes

PTSD is considered the most common negative psychological reactions among survivors following an earthquake. The present study sought to find out the determinants of PTSD in earthquake survivors using a systematic meta-analysis. Four electronic databases (PubMed, Embase, Web of Science, and PsycInfo) were used to search for observational studies about PTSD following earthquakes. The literature search, study selection, and data extraction were conducted independently by two authors. 52 articles were included in the study. Summary estimates, subgroup analysis, and publication bias tests were performed on the data. The prevalence of PTSD after earthquakes ranged from 4.10% to 67.07% in adults and from 2.50% to 60.00% in children. For adults, the significant predictors were being female, low education level or socio-economic status, prior trauma; being trapped, experiencing fear, injury, or bereavement during the disaster. For children, the significant predictors were being older age, high education level; being trapped, experiencing fear, injury, or bereavement, witnessing injury/death during the earthquakes. Our study provides implications for the understanding of risk factors for PTSD among earthquake survivors. Post-disaster mental health recovery programs that include early identification, on-going monitoring, and sustained psychosocial support are needed for earthquake survivors

X-Band Radar Attenuation Correction Method Based on LightGBM Algorithm

X-band weather radar can provide high spatial and temporal resolution data, which is essential to precipitation observation and prediction of mesoscale and microscale weather. However, X-band weather radar is susceptible to precipitation attenuation. This paper presents an X-band attenuation correction method based on the light gradient machine (LightGBM) algorithm (the XACL method), then compares it with the ZH correction method and the ZH-KDP comprehensive correction method. The XACL method was validated using observations from two radars in July 2021, the X-band dual-polarization weather radar at the Shouxian National Climatology Observatory of China (SNCOC), and the S-band dual-polarization weather radar at Hefei. During the rainfall cases on July 2021, the results of the attenuation correction were used for precipitation estimation and verified with the rainfall data from 1204 automatic ground-based meteorological network stations in Anhui Province, China. We found that the XACL method produced a significant correction effect and reduced the anomalous correction phenomenon of the comparison methods. The results show that the average error in precipitation estimation by the XACL method was reduced by 39.88% over 1204 meteorological stations, which is better than the effect of the other two correction methods. Thus, the XACL method proved good local adaptability and provided a new X-band attenuation correction scheme

ADASYN-LOF Algorithm for Imbalanced Tornado Samples

Early warning and forecasting of tornadoes began to combine artificial intelligence (AI) and machine learning (ML) algorithms to improve identification efficiency in the past few years. Applying machine learning algorithms to detect tornadoes usually encounters class imbalance problems because tornadoes are rare events in weather processes. The ADASYN-LOF algorithm (ALA) was proposed to solve the imbalance problem of tornado sample sets based on radar data. The adaptive synthetic (ADASYN) sampling algorithm is used to solve the imbalance problem by increasing the number of minority class samples, combined with the local outlier factor (LOF) algorithm to denoise the synthetic samples. The performance of the ALA algorithm is tested by using the supporting vector machine (SVM), artificial neural network (ANN), and random forest (RF) models. The results show that the ALA algorithm can improve the performance and noise immunity of the models, significantly increase the tornado recognition rate, and have the potential to increase the early tornado warning time. ALA is more effective in preprocessing imbalanced data of SVM and ANN, compared with ADASYN, Synthetic Minority Oversampling Technique (SMOTE), SMOTE-LOF algorithms