Search

Ferroelectric photovoltaic cells have attracted particular interest owing to their potential applications in the green energy field. But there are two drawbacks: weak polarization and wide bandgap, which make them suffer from the limitation of energy conversion efficiency. In this study, the key issue is solved in Ag2O nanoparticle-dispersed Bi5Ti3FeO15 composites. In order to clarify the mechanism, performances of the bandgap, polarization-dependent J–V curves, dielectric response, and switchable photocurrents were investigated. The Maxwell–Wagner polarization effect is confirmed by permittivity Cole–Cole plots with two or more semicircles overlapping. The spatial polarization gradient matrices can reduce the effective mass of the electron–hole pairs and further promote their separation via the Maxwell–Wagner polarization effect. The synchronous mobility of the separated carriers is enhanced. An improved ferroelectric photovoltaics is achieved in Bi5Ti3FeO15⋅3%Ag2O composites, and the key parameters are as follows: VOC ∼ −3.1 V, energy converse efficiency 9.2 × 10−4%. Furthermore, this work shows the first step toward polarization gradient composites for application in ferroelectric photovoltaic cells. [ABSTRACT FROM AUTHOR]

Tumor metastasis remains a leading factor in the failure of cancer treatments and patient mortality. To address this, a silver‐induced absorption red‐shifted core‐shell nano‐particle is developed, and surface‐modified with triphenylphosphonium bromide (TPP) and hyaluronic acid (HA) to obtain a novel nanodiagnosis‐treatment agent (Ag@CuS‐TPP@HA). This diagnosis‐treatment agent can dual‐targets cancer cells and mitochondria, and exhibits maximal light absorption at 1064 nm, thereby enhancing nesr‐infrared II (NIR‐II) photoacoustic (PA) signal and photothermal effects under 1064 nm laser irradiation. Additionally, the silver in Ag@CuS‐TPP@HA can catalyze the Fenton‐like reactions with H2O2 in the tumor tissue, yielding reactive oxygen species (ROS). The ROS production, coupled with enhanced photothermal effects, instigates immunogenic cell death (ICD), leading to a substantial release of tumor‐associated antigens (TAAs) and damage‐associated molecular patterns, which have improved the tumor immune suppression microenvironment and boosting immune checkpoint blockade therapy, thus stimulating a systemic antitumor immune response. Hence, Ag@CuS‐TPP@HA, as a cancer diagnostic‐treatment agent, not only accomplishes targeted the NIR‐II PA imaging of tumor tissue and addresses the challenge of accurate diagnosis of deep cancer tissue in vivo, but it also leverages ROS/photothermal therapy to enhance immune checkpoint blockade, thereby eliminating primary tumors and effectively inhibiting distant tumor growth. [ABSTRACT FROM AUTHOR]

Protein misfolding and aggregation, a key contributor to the progression of numerous neurodegenerative diseases, results in functional deficiencies and the creation of harmful intermediates. Detailed visualization of this misfolding process is of paramount importance for improving our understanding of disease mechanisms and for the development of potential therapeutic strategies. While in vitro studies using purified proteins have been instrumental in delivering significant insights into protein misfolding, the behavior of these proteins in the complex milieu of living cells often diverges significantly from such simplified environments. Biomedical imaging performed in cell provides cellular-level information with high physiological and pathological relevance, often surpassing the depth of information attainable through in vitro methods. This review highlights a variety of methodologies used to scrutinize protein misfolding within biological systems. This includes optical-based methods, strategies leaning on mass spectrometry, in-cell nuclear magnetic resonance, and cryo-electron microscopy. Recent advancements in these techniques have notably deepened our understanding of protein misfolding processes and the features of the resulting misfolded species within living cells. The progression in these fields promises to catalyze further breakthroughs in our comprehension of neurodegenerative disease mechanisms and potential therapeutic interventions. [ABSTRACT FROM AUTHOR]

Recently, the high efficient and environment–friendly electrocaloric cooling technology has become a hot topic. Unfortunately, the single process and extreme conditions have greatly limited its application. Due to the similar perovskite structure, Bi 5 Ti 3 FeO 15 and BiFeO 3 were selected to fabricate mesoscopic composites in this work to realize the double consecutive cooling effect. We propose that the non–collinear interfacial polarization is the origin of near room temperature electrocaloric cycling refrigeration. The electrons are trapped at the Bi 5 Ti 3 FeO 15 /BiFeO 3 interfacial potential well, where the electrons possess discontinuous energy and jump by thermal activity. The structure, dielectric capacitance and ferroelectric polarization of the composite films were further analyzed. Finally, the performance of the mesoscale cooling device is simulated, which shows a promising avenue to high efficient double cycling refrigeration. More importantly, the results are helpful to understand the cycling principle of electrocaloric cooling. [ABSTRACT FROM AUTHOR]

[Display omitted] • The clusters-ferroelectrics surface-wrapped structure is high efficient transport layer of carriers. • The detectors possess ultra-high detectivity at the wavelength range 320 nm – 400 nm. • The energy-level alignments at the interface between non-polar ZnO clusters and Bi 4.5 Na 0.5 Ti 4 O 15 ferroelectrics are favorable for the separation of photogenerated carriers. High-sensitivity detectors for near-ultraviolet rays are of great importance in civil and military fields. Recently, great efforts have been made to enhance the sensitivity, but mainly at the cost of increasing energy consumption or bulk volume. High-performance near-ultraviolet ray detector was fabricated using ZnO clusters compounded in ferroelectric Bi 4.5 Na 0.5 Ti 4 O 15 composites, which exhibit ultrahigh sensitivity detectivity (D *), linear dynamic response (LDR) and photoconductance σ(λ) of up to 3.9 × 1011 Jones, 52.04 dB, and 3.9 × 10-10 S/m, respectively. The three-parameter criterions are over a wide wavelength range larger than that of a semiconductor. High external quantum efficiency 4.6 % is to ensure the self-powered device. Such a brilliant virtue is attributed to the artificial transport layer by the superior ZnO cluster surface-wrapped structure, which effectively transports the electrons or holes induced by photons. These pioneering findings indicate that the ZnO cluster surface-wrapped structure may be useful for the assembly of ferroelectric high-performance UV photoelectronic devices in the near future. [ABSTRACT FROM AUTHOR]

Due to the nonlinearity, fluctuation, and intermittency of wind speed, its accurate prediction is essential for improving efficiency in wind power operation systems. In this regard, a hybrid model that combines the rime optimization algorithm (RIME), variational mode decomposition (VMD), multi-headed self-attention (MSA) mechanism and long short-term memory (LSTM) is proposed for wind speed prediction. First, the number of modes and VMD penalty parameter are optimized with RIME, the optimized parameters are brought into the VMD to decompose the raw wind speeds, and a Lagrange multiplier and quadratic penalty function are introduced to obtain the input series. Then, a LSTM short-term wind speed prediction model is constructed based on the MSA mechanism and solved for the hidden states and weights of each layer of attention in the model. Finally, a ReLU activation function is used to activate the hidden states of the LSTM model, and a weighted sum vector is used as the final sequence representation, which is inputted to the output layer for specific prediction to obtain the short-term wind speed prediction results. To verify the effectiveness of the proposed model, wind speed data from four wind farms in Ningxia, China, and two sets of wind speed data from an M2 tower in the USA are selected, and 19 models are built to compare the performance of the proposed model. The results show that the proposed model outperforms other models on all datasets in terms of all five performance metrics, with smaller errors and higher prediction accuracy. • Tuning the parameters of the VMD by the RIME optimization algorithm. • Proposing a new MSA-LSTM module to prevent long-range dependencies. • An MSA is introduced to the LSTM input and to enhance sequence correlation. • The stability and robustness of the method are verified in different experiments. • The model was validated using six sets of data with different characteristic wind speeds. [ABSTRACT FROM AUTHOR]

By absorbing and transferring the photons (nearby bandgap) to photoelectrons, non–compensated (Ce4+ dopants) doping Bi 5 Ti 3 FeO 15 ferroelectric films present switchable and stable photovoltaics. The defect dipole attraction in non–compensated doping films not only narrows the bandgap effectively by the creation of tunable intermediate sub–band (gap state), but enhances separation of electron–hole pairs in the visible–light region. The concept is proved by dramatic redshift of optical absorbance and intense photovoltaics. Subsequently, contrast investigations of equivalent–compensated doping films offer solid experimental evidences. Furthermore, the intensity of Coulomb attraction between defect dipoles can be destroyed by thermal perturbation, which is empirically supported by the abrupt drop of temperature dependent photovoltage at 340 K. These results demonstrate that non–compensated doping can be a promising route to construct more efficient energy transfer devices. [Display omitted] [ABSTRACT FROM AUTHOR]

Background Alternative polyadenylation (APA) causes shortening or lengthening of the 3ʹ-untranslated region (3ʹ-UTR) of genes (APA genes) in diverse cellular processes such as cell proliferation and differentiation. To identify cell-type–specific APA genes in scRNA-Seq data, current bioinformatic methods have several limitations. First, they assume certain read coverage shapes in the scRNA-Seq data, which can be violated in multiple APA genes. Second, their identification is limited between 2 cell types and not directly applicable to the data of multiple cell types. Third, they do not control undesired source of variance, which potentially introduces noise to the cell-type–specific identification of APA genes. Findings We developed a combination of a computational change-point algorithm and a statistical model, single-cell Multi-group identification of APA (scMAPA). To avoid the assumptions on the read coverage shape, scMAPA formulates a change-point problem after transforming the 3ʹ biased scRNA-Seq data to represent the full-length 3ʹ-UTR signal. To identify cell-type–specific APA genes while adjusting for undesired source of variation, scMAPA models APA isoforms in consideration of the cell types and the undesired source. In our novel simulation data and data from human peripheral blood mononuclear cells, scMAPA outperforms existing methods in sensitivity, robustness, and stability. In mouse brain data consisting of multiple cell types sampled from multiple regions, scMAPA identifies cell-type–specific APA genes, elucidating novel roles of APA for dividing immune cells and differentiated neuron cells and in multiple brain disorders. Conclusions scMAPA elucidates the cell-type–specific function of APA events and sheds novel insights into the functional roles of APA events in complex tissues. [ABSTRACT FROM AUTHOR]

Protein misfolding and aggregation processes involve local polarity and viscosity fluctuation. Herein we modulated the polarity and viscosity sensitivities of merocyanine dyes to detect protein aggregation. We demonstrated how structural modulation balanced these two fluorescence sensitivities and affected the detection of misfolded and aggregated proteins. [ABSTRACT FROM AUTHOR]

A size effect of magnetic FeCo clusters in Na0.5Bi0.5TiO3/FeCo composites is presented, which is embodied by a strong size‐dependent magnetostrictive effect and magnetoelectric (ME) coupling effect. The size evolution of magnetic clusters induces the change of elastic energy in the magnetic phase, which is the key reason for the tunable magnetostrictive and mangetoelectric effects. An interface stress transferring mode is used to describe the coupling effect of the composites. This work explores a route to modulate ME materials by amorphous alloy clusters, which is of great significance to the development of magnetoelectronics. [ABSTRACT FROM AUTHOR]

The methods of searching for optimized parameters have substantial effects on the forecast accuracy of ensemble data assimilation systems. The selection of these factors is usually performed using trial-and-error methods, and poor parameterizations may lead to filter divergence. Combined with the local ensemble transform Kalman filtering method (LETKF), a technique for an automated search of the best configuration (parameters) of a data assimilation system is proposed. To obtain better assimilation, a differential evolution (DE) algorithm-based multiple-factor parameterization method results in the corresponding circumstances. By combining with fast-searching DE algorithms, we may retrieve the most ideal parameter combinations. Several numerical experiments performed with the Lorenz-96 model show that new methods performed better than the original one-parameter optimization methods. As the basis of DE methods, the best combinations of the local radius and the covariance inflation parameter, which can guarantee the best DA performances in the corresponding circumstances, are retrieved. It is found that the new method is capable of outperforming previous search algorithms under both perfect and imperfect model scenarios, and the calculation cost in Lorenz-96 model is lower. However, how to apply the new proposed method to more complex atmospheric or land surface models requires further verification. [ABSTRACT FROM AUTHOR]

Protein phase separation process involving protein unfolding, misfolding, condensation and aggregation etc. has been associated with numerous human degenerative diseases. The complexity in protein conformational transitions results in multi‐step and multi‐species biochemical pathways upon protein phase separation. Recent progresses in designing novel fluorescent probes have unraveled the enriched details of phase separated proteins and provided mechanistic insights towards disease pathology. In this review, we summarized the design and characterizations of fluorescent probes that selectively illuminated proteins at different phase separated states with a focus on aggregation‐induced emission probes, fluorescent molecular rotors, and solvatochromic fluorophores. Inspired by these pioneering works, a design blueprint was proposed to further develop fluorescent probes that can potentially shed light on the unresolved protein phase separated states in the future. [ABSTRACT FROM AUTHOR]

Alternative polyadenylation (APA) in breast tumor samples results in the removal/addition of cis -regulatory elements such as microRNA (miRNA) target sites in the 3′-untranslated region (3′-UTRs) of genes. Although previous computational APA studies focused on a subset of genes strongly affected by APA (APA genes), we identify miRNAs of which widespread APA events collectively increase or decrease the number of target sites [probabilistic inference of microRNA target site modification through APA (PRIMATA-APA)]. Using PRIMATA-APA on the cancer genome atlas (TCGA) breast cancer data, we found that the global APA events change the number of the target sites of particular microRNAs [target sites modified miRNA (tamoMiRNA)] enriched for cancer development and treatments. We also found that when knockdown (KD) of NUDT21 in HeLa cells induces a different set of widespread 3′-UTR shortening than TCGA breast cancer data, it changes the target sites of the common tamoMiRNAs. Since the NUDT21 KD experiment previously demonstrated the tumorigenic role of APA events in a miRNA dependent fashion, this result suggests that the APA-initiated tumorigenesis is attributable to the miRNA target site changes, not the APA events themselves. Further, we found that the miRNA target site changes identify tumor cell proliferation and immune cell infiltration to the tumor microenvironment better than the miRNA expression levels or the APA events themselves. Altogether, our computational analyses provide a proof-of-concept demonstration that the miRNA target site information indicates the effect of global APA events with a potential as predictive biomarker. [ABSTRACT FROM AUTHOR]

The oxygen vacancies (V O) modulating inverse and conventional magnetocaloric effects in double perovskite Bi 2 NiMnO 6-δ polycrystalline films are claimed. The vacuum annealing processor showed large regulated artificial V O concentration from 5.1% to 17.6%, which has been verified by X-photoelectron spectrum results. The role of V O concentration in magnetocaloric effect is clarified by the charge transfer mechanism. The band length and angle (O-Ni-O, O-Mn-O) are sensitive to the valence confusion of elements (Mn and Ni), which is disturbed by V O concentration. This scenario contributes to the spin orientation and antiferromagnetic/ferromagnetic (AFM/FM) competition near the phase transition point by coulomb exchange interaction. The temperature dependent transport behavior, meanwhile, presents metallic-FM state in Bi 2 NiMnO 6-δ (high V O concentration). While stoichiometric Bi 2 NiMnO 6 (low V O concentration) films show dielectric traits. The structure, magnetization and optical absorption properties were characterized systematically. The present work explores a route to design magnetocaloric materials via controllable artificial V O defects. [ABSTRACT FROM AUTHOR]

TbFe2/Bi5Ti3FeO15 heterostructural films were prepared by inserting cluster-assembled TbFe2 microdiscs into a Bi5Ti3FeO15 matrix using low energy cluster beam deposition combined with sol-gel methods. The phase structure, ferroelectric properties, bandgap, photovoltaic spectral response, and performances of the ferroelectric photovoltaic effect were modulated by the in situ stress driven by magnetostriction of TbFe2 clusters under external magnetic fields. The short-circuit current, open-circuit voltage, and power conversation efficient increase with the in situ stress, reaching 0.026 mA/cm2, 9.5 V, and 5.88 × 10−2%, respectively, under a maximum in-stress of 0.075 GPa. So the high open-circuit voltage above bandgap is attributed to the distinct bandgap shifting and the effective separation of photogenerated electron-hole pairs derived from the in situ stress induced large built-in field. The in situ stress dominated symmetry breaking contributes to the improvement of the power conversation coefficient. The in situ dynamic internal stress provides a high efficient approach to modulate and improve ferroelectric photovoltaic effects. [ABSTRACT FROM AUTHOR]

Cluster−assembled Ni−NiO core−shell structures were periodically immersed into multiferroic Bi 5 Ti 3 FeO 15 (BTFO) film matrices to form pseudo 1–3 BTFO/Ni−NiO/BTFO heterostructural films with multiferroic/antiferromagnetic/ferromagnetic (MF/AFM/FM) multi−interfaces. A biased magnetoelectric coupling effect was obtained at room temperature by a magnetic annealing process. The biased direction depends on the orientation of the annealing magnetic field, with magnetoelectric coefficient curves moving to left after positive annealing magnetic field, while moving to right with negative annealing magnetic field. This biased magnetoelectric effect originates from the multi−interface spin exchange coupling. The immersion of cluster−assembled Ni−NiO core−shells in the multiferroic matrices enhance the interfacial coupling effect due to the large surface-to-volume ratio. Thus, the interfacial spin exchange induced biased magnetoelectric effect is enhanced and reversible. The work provides an avenue on the weak magnetic field detection, spintronic devices and multistate switching memories. [ABSTRACT FROM AUTHOR]

La doped Bi 5 Ti 3 FeO 15 (BTFO) multiferroic films with various doping concentration were synthesized by chemical solution deposition method. The structure, ferroelectric, magnetic properties, and magnetoelectric coupling effect were systemically investigated. Room temperature magnetoelectric effect was obtained. And the magnetoelectric coupling and magnetic exchange bias effects are regulated by the doping concentration of La. Such regulation originates from the evolution of magnetic structure derived from doping rare earth La. The coexistence of ferromagnetic (FM) and antiferromagnetic (AFM) orders can be controlled by the doping concentration of La. Thus, the superexchange interaction between FM and AFM orders was enhanced strongly, which contributes to the strong magnetoelectric coupling effect. This work provides an avenue on regulating the magnetic structure and magnetoelectric coupling effects. [ABSTRACT FROM AUTHOR]

Poor mechanical properties and a relatively low stability in physiological environments have significantly limited hydroxyapatite (HA) for many practical implant applications. In this study, we developed a reduced graphene oxide/fluorhydroxyapatite (rGO/FHA) biocomposite with enhanced mechanical properties, dissolution resistance, biocompatibility and antibacterial activity. rGO and fluorine (F) are simultaneously incorporated into HA to form the rGO/FHA composite by an in situ chemical synthesis method and spark plasma sintering technology. The uniform distribution of rGO and the good interfacial bonding help to improve the hardness by 86% and the fracture toughness by 137%. The chemical stability of the rGO/FHA composite is evaluated by determining the Ca 2+ ion release. The result indicates that the substitution of F − ions into the HA structure has a positive effect on the dissolution resistance of HA. The in vitro bioactivity assessment shows that the osteoblast adhesion and proliferation ability on the rGO/FHA composite are improved. In addition, a higher alkaline phosphate activity is also detected in cells cultured on the rGO/FHA composite. More importantly, it is observed that the rGO/FHA composite can effectively inhibit the adhesion and proliferation of Streptococcus mutans . These findings suggest that the rGO/FHA biocomposite would be highly useful as a dental implant material. [ABSTRACT FROM AUTHOR]

An error covariance matrix plays an important role in maintaining the statistical property of the ensemble in an ensemble Kalman filter method. However, data assimilation filter divergence may occur from an inaccurate estimate of the covariance matrix. In this study, based on an ensemble time-local H-infinity filter, which inflates the eigenvalues of the analysis error covariance matrix, a new robust ensemble data assimilation method is proposed, referred to as an inflation transform matrix eigenvalues. By design, new filters may be preferred over other traditional ensemble filters, when model performances are not well known, or change unpredictably. The primary aim is to improve the performance of the assimilation system in the framework of the ensemble filtering, according to the minimum/maximum rule of robust filtering. The proposed estimation method is tested using the well-known Lorenz-96 model, in order to investigate how the ensemble time-local H-infinity filter method of the inflation transform matrix impacts the robustness of the assimilation system under selected special conditions, such as the assimilation steps, force parameters, ensemble sizes, and observation information. The experiments show that the proposed inflation transform matrix method displays good robustness to the changes in the system's parameters. Also, when compared with the traditional filtering methods, this robust filtering method is found to improve the assimilation performance. [ABSTRACT FROM AUTHOR]

BiFeO 3 /Bi 5 Ti 3 FeO 15 (BFO/BTFO) bilayer multiferroic composite films were deposited on Pt (100)/Ti/SiO 2 /Si substrates using chemical solution deposition. Well-defined microstructure, improved multiferroic properties, and strong magnetoelectric coupling effect were obtained at room temperature. Such strong magnetoelectric effect originates from not only spin–orbit coupling in spin-driven ferroelectric systems for respective single-phase multiferroic layer, but the interface coupling between BiFeO 3 and Bi 5 Ti 3 FeO 15 film layers. The present work provides a simple way to fabricate lead-free multiferroic films with strong magnetoelectric effect. [ABSTRACT FROM AUTHOR]

Bi 4 Ti 3 O 12 /Bi 5 Ti 3 FeO 15 composite films were prepared by chemistry solution deposition. The microstructure, ferroelectric properties, piezoelectric properties, magnetic properties as well as magnetoelectric effect were investigated for the composite films. It is shown that the composite films display well-defined microstructure with the clear interface. Compared with the pure Bi 4 Ti 3 O 12 or Bi 5 Ti 3 FeO 15 films, the composite films not only exhibit enhanced ferromagnetic and ferroelectric properties, but also possess strong magnetoelectric effect. Thus enhanced multiferroic and magnetoelectric properties are attributed to the similar bismuth-layered perovskite structure between Bi 5 Ti 3 FeO 15 and Bi 4 Ti 3 O 12 films, which leads to a strong coupling interaction between ferromagnetic and ferroelectric phases. The present work provides a feasible way of preparing magnetoelectric composite films and facilitating their applications on micro-electro-mechanical system and information storage devices. [ABSTRACT FROM AUTHOR]

Ho, Mn co-doped Bi 5 Ti 3 FeO 15 films were prepared and the microstructures, ferroelectric properties, piezoelectric properties, magnetic properties as well as magnetoelectric effect were investigated. It shows that the ferroelectric, piezoelectric, ferromagnetic, magnetoelectric properties are enhanced obviously after doping with Ho and Mn. The origin of thus enhanced multiferroic and magnetoelectric properties was discussed detailedly. The present results suggest a candidate for magnetoelectric materials. [ABSTRACT FROM AUTHOR]

Effects of the solution pH value and reaction time on the precursor morphology and photoluminescence properties are investigated for hydrothermally prepared monoclinic Y 2 WO 6 phosphors. In the near-neutral environment, sodium dodecyl benzene sulfonate (SDBS) surfactant forms small microspheres micelles as template to synthesize microspherical precursor. H + ions concentration affects the arrangement of negative ionic surfactant SDBS. As a result, jujube-liked and popcorn-like loose microspheres formed at low pH value. When the pH value is 5.2 and the hydrothermal reaction time reaches 24 h, respectively, the strongest luminescent intensity can be obtained. Under this condition, the precursor presented regular microsphere with diameter of 4.0 μm. After high-temperature heat treatment, the obtained phosphor particles still exhibit microsphere-like shape. Therefore, we provide an effective method to tune the morphology of Y 2 WO 6 phosphors and study the relationship between morphology and luminescent performance. [ABSTRACT FROM AUTHOR]

The effective utilization of wind energy is a positive development trend in renewable energy that provides an effective impetus to alleviate global warming. However, the complex volatility and extreme suddenness of wind speeds make it difficult to accurately predict wind speeds. To overcome this challenge, a novel short-term wind speed prediction model based on double decomposition, piecewise error correction, Elman neural network and the autoregressive integrated moving average model is proposed. In this model, the fuzzy entropy is applied to judge the complexity of each subseries generated by variational mode decomposition. To address error correction, a piecewise error correction method that needs to extract only a part of the error sequence as a training set is proposed. Three experiments utilizing 6 Datasets and 14 compared models are conducted to verify the effectiveness of the proposed model. The results show that (i) the hybrid strategy based on double decomposition and piecewise error correction can improve the final prediction accuracy of different single models; (ii) the proposed hybrid model can effectively handle both linear and nonlinear problems; (iii) the root mean squared error of the proposed model for Dataset1-6 in seven-step forecasting are 0.1243, 0.1444, 0.0233, 0.1015, 0.1846 and 0.2513, respectively. • The double decomposition strategy is applied to wind speed prediction. • A novel piecewise error correction method has been developed. • Fuzzy entropy is used to judge the complexity of subsequences. • The proposed model is verified using 6 wind speed datasets in multi-step forecasting. [ABSTRACT FROM AUTHOR]

Abstract: Single-phase multiferroic BiFeO3 and Holmium (Ho) doped BiFeO3 thin films have been prepared via solution–gelation technique. It shows that ferroelectric properties are enhanced and leakage current is reduced obviously after doping Ho. Such improved ferroelectric and leakage properties are attributed to the structural transforms from rhombohedral to the coexistence of tetragonal and orthorhombic symmetry structure after doping Ho. At the mean time, the fatigue behaviors of the present films were improved obviously after doped Ho. The present work provides an available way on enhancing multiferroic and fatigue properties of BiFeO3 based thin films. [Copyright &y& Elsevier]

An evolutionary strategy-based error parameterization method that searches for the most ideal error adjustment factors was developed to obtain better assimilation results. Numerical experiments were designed using some classical nonlinear models (i.e., the Lorenz-63 model and the Lorenz-96 model). Crossover and mutation error adjustment factors of evolutionary strategy were investigated in four aspects: the initial conditions of the Lorenz model, ensemble sizes, observation covariance, and the observation intervals. The search for error adjustment factors is usually performed using trial-and-error methods. To solve this difficult problem, a new data assimilation system coupled with genetic algorithms was developed. The method was tested in some simplified model frameworks, and the results are encouraging. The evolutionary strategy-based error handling methods performed robustly under both perfect and imperfect model scenarios in the Lorenz-96 model. However, the application of the methodology to more complex atmospheric or land surface models remains to be tested. [ABSTRACT FROM AUTHOR]

The methods of parameterizing model errors have a substantial effect on the accuracy of ensemble data assimilation. After a review of the current error-handling methods, a new blending error parameterization method was designed to combine the advantages of multiplicative inflation and additive inflation. Motivated by evolutionary algorithm concepts that have been developed in the control engineering field for years, the authors propose a new data assimilation method coupled with crossover principles of genetic algorithms based on ensemble transform Kalman filters (ETKFs). The numerical experiments were developed based on the classic nonlinear model (i.e., the Lorenz model). Convex crossover, affine crossover, direction-based crossover, and blending crossover data assimilation systems were consequently designed. When focusing on convex crossover and affine crossover data assimilation problems, the error adjustment factors were investigated with respect to four aspects, which were the initial conditions of the Lorenz model, the number of ensembles, observation covariance, and the observation interval. A new data assimilation system, coupled with genetic algorithms, is proposed to solve the difficult problem of the error adjustment factor search, which is usually performed using trial-and-error methods. The results show that all of the methods can adaptively obtain the best error factors within the constraints of the fitness function. [ABSTRACT FROM AUTHOR]

• The VMD decomposition algorithm is applied to wind speed prediction. • A novel double-layer echo state network (D-ESN) has been developed and applied in wind speed prediction. • A genetic algorithm is used to optimize the hyperparameters of the D-ESN model. • The proposed model is verified using 6 wind speed datasets and 10 comparison models. Due to the strong randomness of wind speed, wind power generation is difficult to integrate into the grid. It is very important to predict wind speed reliably and accurately so that wind energy can be utilized effectively. In this study, to obtain accurate wind speed prediction results, a combined VMD-D-ESN model based on variational mode decomposition (VMD), a double-layer staged training echo state network (D-ESN) and genetic algorithm (GA) optimization is proposed. First, the VMD-D-ESN model preprocesses the original wind speed data with VMD and then uses the D-ESN model to predict each decomposed subsequence. Lastly, the final prediction value is obtained by combining all of the predicted subsequences. In the D-ESN model's double-layer structure, the first layer selects the length of the training set, and the second layer has the ability to correct the prediction error in the first layer. In a practical application of case prediction using six different data collection sites, ten models are established to compare the performance of the proposed model. Compared with other traditional models, the results show that the model that combines the VMD decomposition algorithm with the D-ESN structure achieves high prediction accuracy and strong stability on all available datasets. Additionally, the model also shows that the use of the VMD decomposition algorithm strongly improves the prediction ability of the model. [ABSTRACT FROM AUTHOR]

Keywords: aggregation; aggregation-induced emission; fluorescent molecular rotor; protein phase separation; solvatochromic fluorophore EN aggregation aggregation-induced emission fluorescent molecular rotor protein phase separation solvatochromic fluorophore 1 1 1 10/27/21 20211021 NES 211021 We have reviewed reported fluorescent sensors that detect protein phase separation process, including aggregation-induced emission, fluorescent molecular rotor and solvatochromic fluorophore probes. Frontispiece: Illuminating Protein Phase Separation: Reviewing Aggregation-Induced Emission, Fluorescent Molecular Rotor and Solvatochromic Fluorophore Based Probes Aggregation, aggregation-induced emission, fluorescent molecular rotor, protein phase separation, solvatochromic fluorophore. [Extracted from the article]

A layered perovskite structure K 0.5 Na 0.5 NbO 3 Bi 4−x Pr x Ti 3 O 12 lead-free relaxation ferroelectric capacitor was fabricated by inserting piezoelectric K 0.5 Na 0.5 NbO 3 unit into Bi 4 Ti 3 O 12 matrix. Pr element doping induced lattice distortion assists the matching between inserting unit and matrix. K 0.5 Na 0.5 NbO 3 Bi 2.6 Pr 1.4 TiO 12 capacitor has aroused the large energy storage density (U re) 69.7 J/cm3 and high efficiency (η) 78.13% at room temperature. The well performances can be captured by the randomly dispersed polar nano-regions. The inserted piezoelectric unit hosts regulate the long-range ferroelectric order, which leads to the disturbed ferroelectric order by Pr3+ doping and further forms the polar nano-regions. Raman spectra evolution is well documented to the polar nano-regions. What's more, the energy capacitors exhibit excellent thermal stability in a temperature range of −20 ℃–140 ℃ and fatigue stability with charge/discharge cycling reaching 1 × 108. Inserting piezoelectric unit provides a strategy to design dielectric energy capacitors. • A piezoelectric unit is inserted into layered perovskite matrix. • Pr element doping induced lattice distortion assists the matching between inserting unit and matrix. • Piezoelectric unit hosts regulate the long-range ferroelectric orders. • The capacitors exhibit excellent thermal and durable stability. [ABSTRACT FROM AUTHOR]

Magnetic field is used to explore the mechanism of ferroelectric-dynamic coupling in the photovoltaic effect, where the spin–orbit coupling gives rise to and the expansion of relaxation times for electron–hole pair recombination. Thus, phonons with spin-1 and spin-1/2 electrons can be regulated by the magnetic field, which contributes to ferroelectric photovoltaics. The ballistic photocurrents originate from the asymmetric scattering of spin carriers by multiferroics, where significant photocurrent and magneto-spectrum responses are observed in Bi 6- x Pr x Ti 3 Fe 2 O 18 (x = 0.0, 0.3, 0.6, 0.9, 1.2) multiferroics. The magnetic field-dependent photoelectric spectra and J - V curves under illumination have validated the prolonged relaxation lifetime and improvement of density of electron–hole pairs. The evolution of multiferroic orders and magnetic field-dependent photovoltaic response spectra correlate to the ratio of phase B 2 cb / F 2 mm. These results reveal the distinct advantages of multiferroics, which are expected to be an ideal platform to improve efficiency and further clarify the ferroelectric photovoltaic mechanism. • The observation of ballistic photocurrents in multiferroics. • The expansion of relaxation lifetime and generation of electron–hole pairs. • The perspective of ferroelectric photovoltaic mechanism from spatial-temporal symmetry broken. [ABSTRACT FROM AUTHOR]

Schizophrenia is a serious mental disease that is regulated by multiple genes and influenced by multiple factors. Due to the complexity of its etiology, the pathogenesis is still unclear. MicroRNAs belong to a class of small non-coding RNAs that are highly conserved in endogenous evolution and play critical roles in multiple biological pathways. In recent years, aberrant miRNA expression has been implicated in schizophrenia, with certain miRNAs emerging as potential diagnostic and prognostic biomarkers for this disorder. In this review, our objective is to investigate the differential expression of miRNAs in schizophrenia, elucidate their potential mechanisms of action, and assess their feasibility as biomarkers. The PubMed electronic database and Google Scholar were searched for the years 2003 to 2024. The study focused on schizophrenia and miRNA as the research topic, encompassing articles related to biomarkers, etiology, action mechanisms, and differentially expressed genes associated with schizophrenia and miRNA. A total of 1488 articles were retrieved, out of which 49 were included in this scope review. This study reviewed 49 articles and identified abnormal expression of miRNA in different tissues of both schizophrenia patients and healthy controls, suggesting its potential role in the pathogenesis and progression of schizophrenia. Notably, several specific miRNAs, including miR-34a, miR-130b, miR-193-3p, miR-675-3p, miR-1262, and miR-218-5p, may serve as promising biological markers for diagnosing schizophrenia. Furthermore, this study summarized potential mechanisms through which miRNAs may contribute to the development of schizophrenia. The studies within the field of miRNA's role in schizophrenia encompass a broad spectrum of focus. Several selected studies have identified dysregulated miRNAs associated with schizophrenia across various tissues, thereby highlighting the potential utility of specific miRNAs as diagnostic biomarkers for this disorder. Various mechanisms underlying dysregulated miRNAs in schizophrenia have been explored; however, further investigations are needed to determine the exact mechanisms by which these dysregulated miRNAs contribute to the pathogenesis of this condition. The exploration of miRNA's involvement in the etiology and identification of biomarkers for schizophrenia holds significant promise in informing future clinical trials and advancing our understanding in this area. [ABSTRACT FROM AUTHOR]

In ensemble data assimilation systems, the impracticalities of full sampling and systematic error often lead to spurious correlations between two variables with low actual correlations. To solve these problems, researchers have previously proposed a covariance localization (CL) method, which mainly involves the Schur product between a state error covariance matrix and a distance-based correlation matrix. Although this CL method can reduce spurious correlations to a certain extent, observational data remain difficult to be used effectively, which results in unreasonable assimilation. In this study, we develop a new CL method coupled with a fuzzy logic control algorithm, which we call the covariance fuzzy (CF) method. The proposed CF method is a distance-based localization method with "fuzzy" vanishing correlations in data assimilation (DA) systems. To verify the effectiveness of the new algorithm, we conducted a set of experiments using an ensemble Kalman filter (EnKF) that combines the nonlinear Lorenz-96 model or the quasi-geostrophic (QG) models. First, the performances of the CL and CF methods are discussed with respect to different strength forcings, ensemble sizes, and covariance inflation factors. The experimental results show that the proposed CF method can obtain a more effective observation weight than the CL method and can reduce the errors caused by spurious correlations. Additionally, using power spectral density (PSD) as a performance evaluation index, the robustness of the proposed fuzzy logic localization method is demonstrated. However, the application of the fuzzy logic-based localization methodology to a real atmospheric model remains to be tested. [ABSTRACT FROM AUTHOR]

Due to the ever-increasing environmental pollution becoming progressively more serious, wind power has been widely used around the world in recent years. However, because of their randomness and intermittence, the accurate prediction of wind speeds is difficult. To address this problem, this article proposes a hybrid system for short-wind-speed prediction. The system combines the autoregressive differential moving average (ARIMA) model with a three-layer feedforward neural network. An ARIMA model was employed to predict linear patterns in series, while a feedforward neural network was used to predict the nonlinear patterns in series. To improve accuracy of the predictions, the neural network models were trained by using two methods: first-order transition rules and fuzzy first-order transition rules. The Levenberg–Marquardt (LM) algorithm was applied to update the weight and deviation of each layer of neural network. The dominance matrix method was employed to calculate the weight of the hybrid system, which was used to establish the linear hybrid system. To evaluate the performance, three statistical indices were used: the mean square error (MSE), the root mean square error (RMSE) and the mean absolute percentage error (MAPE). A set of Lorenz-63 simulated values and two datasets collected from different wind fields in Qilian County, Qinghai Province, China, were utilized as to perform a comparative study. The results show the following: (a) compared with the neural network trained by first-order transition rules, the prediction accuracy of the neural network trained by the fuzzy first-order transition rules was higher; (b) the proposed hybrid system attains superior performance compared with a single model; and (c) the proposed hybrid system balances the forecast accuracy and convergence speed simultaneously during forecasting. Therefore, it was feasible to apply the hybrid model to the prediction of real time-series. [ABSTRACT FROM AUTHOR]

In this study, we investigated the effects of mirror therapy (MT) combined with contralaterally controlled functional electrical stimulation (CCFES) on upper limb motor function, activities of daily life, and corticospinal excitability in post-stroke patients. Sixty post-stroke patients were randomly divided into four groups: CCFES, MT, MT combined with CCFES, and control. All the patients underwent routine rehabilitation. Those in the MT, CCFES, MT combined with CCFES, and control groups received MT, CCFES, MT combined with CCFES, and routine rehabilitation alone, respectively. Upper limb motor function, activities of daily living, and corticospinal excitability were evaluated before and after a 3-week intervention period. MT combined with CCFES demonstrated a significantly greater therapeutic effect on motor function of the paretic wrist than CCFES, MT, or routine rehabilitation alone. However, there was no significant difference in the overall motor function of the affected upper limb, activities of daily life, or corticospinal excitability between the MT combined with CCFES group and the other three groups. MT combined with CCFES may be a potential adjuvant therapy to promote motor function in paretic wrist after stroke. The combined therapy of mirror therapy (MT) and contralaterally controlled functional electrical stimulation (CCFES) may be a potential adjuvant therapy to promote motor function in paretic wrists after stroke. MT combined with CCFES could be incorporated into rehabilitation programs for post-stroke patients with impaired upper limb motor function. Exploring the potential benefits of combining different rehabilitation therapies could inform the development of more effective interventions. [ABSTRACT FROM AUTHOR]

Spatiotemporal symmetry breaking induced the coexistence of ferroelectric and magnetic order phases. To realize multiferroic orders at room temperature in BiFeO 3 ceramics, structure symmetry modulation was introduced by doping Tb, where the R 3 c - P bnm phase transition and unlocking ferromagnetism by destroying the cycloidal spin structure were observed. The phase structure, its fraction, and properties were investigated, including ferroelectric polarization, magnetic orders, dielectric trait, and optical absorption. The electronic structure in doped ceramics can support the parasitic ferromagnetic phase and robust ferroelectric order. The energy band is composed of the hybridization of the O-2 p state and Fe-3 d states, where charge transfer occurs from the O-2 p valence band to the Fe-3 d conduction band, driven by the localized Coulomb repulsion between d - f electrons. The present work provides insight into the regulation of the multiferroic traits of BiFeO 3 from the electronic structure. [ABSTRACT FROM AUTHOR]

Unlike amyloid aggregates, amorphous protein aggregates with no defined structures have been challenging to target and detect in a complex cellular milieu. In this study, we rationally designed sensors of amorphous protein aggregation from aggregation‐induced‐emission probes (AIEgens). Utilizing dicyanoisophorone as a model AIEgen scaffold, we first sensitized the fluorescence of AIEgens to a nonpolar and viscous environment mimicking the interior of amorphous aggregated proteins. We identified a generally applicable moiety (dimethylaminophenylene) for selective binding and fluorescence enhancement. Regulation of the electron‐withdrawing groups tuned the emission wavelength while retaining selective detection. Finally, we utilized the optimized probe to systematically image aggregated proteome upon proteostasis network regulation. Overall, we present a rational approach to develop amorphous protein aggregation sensors from AIEgens with controllable sensitivity, spectral coverage, and cellular performance. [ABSTRACT FROM AUTHOR]

Unlike amyloid aggregates, amorphous protein aggregates with no defined structures have been challenging to target and detect in a complex cellular milieu. In this study, we rationally designed sensors of amorphous protein aggregation from aggregation‐induced‐emission probes (AIEgens). Utilizing dicyanoisophorone as a model AIEgen scaffold, we first sensitized the fluorescence of AIEgens to a nonpolar and viscous environment mimicking the interior of amorphous aggregated proteins. We identified a generally applicable moiety (dimethylaminophenylene) for selective binding and fluorescence enhancement. Regulation of the electron‐withdrawing groups tuned the emission wavelength while retaining selective detection. Finally, we utilized the optimized probe to systematically image aggregated proteome upon proteostasis network regulation. Overall, we present a rational approach to develop amorphous protein aggregation sensors from AIEgens with controllable sensitivity, spectral coverage, and cellular performance. [ABSTRACT FROM AUTHOR]

Epstein-Barr virus (EBV) is a ubiquitous γ-herpesvirus with latent and lytic cycles. EBV replicates in the stratified epithelium but the nasopharynx is also composed of pseudostratified epithelium with distinct cell types. Latent infection is associated with nasopharyngeal carcinoma (NPC). Here, we show with nasopharyngeal conditionally reprogrammed cells cultured at the air-liquid interface that pseudostratified epithelial cells are susceptible to EBV infection. Donors varied in susceptibility to de novo EBV infection, but susceptible cultures also displayed differences with respect to pathogenesis. The cultures from one donor yielded lytic infection but cells from two other donors were positive for EBV-encoded EBERs and negative for other lytic infection markers. All cultures stained positive for the pseudostratified markers CK7, MUC5AC, α-tubulin in cilia, and the EBV epithelial cell receptor Ephrin receptor A2. To define EBV transcriptional programs by cell type and to elucidate latent/lytic infection-differential changes, we performed single cell RNA-sequencing on one EBV-infected culture that resulted in alignment with many EBV transcripts. EBV transcripts represented a small portion of the total transcriptome (~0.17%). All cell types in the pseudostratified epithelium had detectable EBV transcripts with suprabasal cells showing the highest number of reads aligning to many EBV genes. Several restriction factors (IRF1, MX1, STAT1, C18orf25) known to limit lytic infection were expressed at lower levels in the lytic subcluster. A third of the differentially-expressed genes in NPC tumors compared to an uninfected pseudostratified ALI culture overlapped with the differentially-expressed genes in the latent subcluster. A third of these commonly perturbed genes were specific to EBV infection and changed in the same direction. Collectively, these findings suggest that the pseudostratified epithelium could harbor EBV infection and that the pseudostratified infection model mirrors many of the transcriptional changes imposed by EBV infection in NPC. Author summary: It has been known for over 50 years that EBV latent infection is associated with NPC. Despite many advances from studies in 2-dimensional cell culture, many aspects of EBV molecular pathogenesis in the nasopharynx remain undefined because the cell types and the biology of the nasopharyngeal epithelium can only be faithfully captured in 3-dimensional cell culture. In the stratified epithelium, cellular differentiation triggers lytic infection but it is not clear to what degree the pseudostratified epithelium is involved. The pseudostratified epithelium is abundant in the lateral wall where the lymphoid-rich fossa of Rosenmüller is located and is a site where NPC tumors most often arises. While the oral epithelium is a site of EBV replication, whether the nasopharyngeal epithelium is a major source of EBV shedding in the nasopharynx is not well defined. Here, we present a 3-dimensional organoid model of the nasopharyngeal pseudostratified epithelium showing that such cells can be infected with EBV in some donor cultures, with examples of both latent and lytic infection. We propose that the cell types of the pseudostratified epithelium should be considered a component of EBV pathogenesis in the nasopharynx and that the difference in donor susceptibility and latent/lytic infection could influence EBV's fitness in the nasopharynx. [ABSTRACT FROM AUTHOR]

[Display omitted] • Polysiloxane has a flexible main chain and methyl group with small steric hindrance, which may make its backbone have a good affinity for clay with siloxane tetrahedral structure, cooperating with the quaternary ammonium group to bring the clay interlayer space closer and inhibit hydration. This shows the application potential of modified polysiloxane in the field of drilling fluid additives. • The anti-high temperature thought used to introduce the anti-high temperature group and monomer into the carbon main chain polymer. In this study, the anti-high temperature thought was to use the Si-O-Si chain of the polysiloxane itself, which has impressive rigidity, to maintain the stability of the structure at high temperatures, and to have a certain temperature resistance. • The zwitterionic group was introduced based on the main chain's high-temperature resistance, facilitating the strong inhibition properties of SC-POTD and maintaining compatibility with the drilling fluid system. In oil and gas well engineering, shale wellbore stability poses significant challenges. Developing shale inhibitors with high inhibition and resistance to elevated temperatures is crucial to address wellbore stability in deep wells. In this study, an amphoteric polysiloxane known as SC-POTD was synthesized as a heat-resistant shale inhibitor for water-based drilling fluid. The inhibition properties were evaluated using linear swelling, rolling recovery, and rheological tests, and the results were compared with the properties of potassium chloride and commercial polyamines. Various analysis methods were conducted to understand the interaction mechanism between the clay particles and polymer molecules, including X-ray diffraction test, Zeta potential determination, particle size analysis, contact angle measurement, and scanning electron microscope observation. SC-POTD with an amphoteric structure can effectively adsorb on the clay surface through electrostatic force, compressing the electric double layer of clay and forming a hydrophobic layer. Furthermore, SC-POTD can embed in the crystal interlayer, exchange cations and discharge interlayer water, effectively inhibiting hydration. With the flexibility of the Si-O backbone and the small steric hindrance of methyl side groups, SC-POTD may have a better affinity with bentonite containing siloxane tetrahedrons, and cooperate with the quaternary ammonium group to enhance clay interlayer interaction. In addition, the temperature resistance of SC-POTD can be improved by the siloxane backbone with larger bond energy. The results indicated that modified polysiloxane may find a promising application as heat-resistant drilling fluid additives. [ABSTRACT FROM AUTHOR]

Data assimilation plays a dual role in advancing the "scientific" understanding and serving as an "engineering tool" for the Earth system sciences. Land data assimilation (LDA) has evolved into a distinct discipline within geophysics, facilitating the harmonization of theory and data and allowing land models and observations to complement and constrain each other. Over recent decades, substantial progress has been made in the theory, methodology, and application of LDA, necessitating a holistic and in‐depth exploration of its full spectrum. Here, we present a thorough review elucidating the theoretical and methodological developments in LDA and its distinctive features. This encompasses breakthroughs in addressing strong nonlinearities in land surface processes, exploring the potential of machine learning approaches in data assimilation, quantifying uncertainties arising from multiscale spatial correlation, and simultaneously estimating model states and parameters. LDA has proven successful in enhancing the understanding and prediction of various land surface processes (including soil moisture, snow, evapotranspiration, streamflow, groundwater, irrigation and land surface temperature), particularly within the realms of water and energy cycles. This review outlines the development of global, regional, and catchment‐scale LDA systems and software platforms, proposing grand challenges of generating land reanalysis and advancing coupled land‒atmosphere DA. We lastly highlight the opportunities to expand the applications of LDA from pure geophysical systems to coupled natural and human systems by ingesting a deluge of Earth observation and social sensing data. The paper synthesizes current LDA knowledge and provides a steppingstone for its future development, particularly in promoting dual driven theory‐data land processes studies. Plain Language Summary: Land Data Assimilation (LDA) integrates numerical models with observation data to enhance predictions of key variables related to land surface processes, including soil moisture, snow, evapotranspiration, and groundwater. Consequently, LDA effectively tackles two of the fundamental scientific challenges in the Earth system: how to enhance the usability of exponentially increasing land observations and how to improve the predictive capability and accuracy of land models. Significant advancements have been made in the theory, methods, and applications of LDA since the 21st century. Breakthroughs have been attained in characterizing uncertainties, estimating multiple variables and parameters simultaneously, and incorporating big data analytics. LDA systems at global, regional, and catchment‐scales, along with widely used software platforms, have been developed. However, future challenges facing LDA comprise improving meteorological forcing data, creating long‐term land reanalysis, devising operational applications, and broadening its range to cover the critical zone. In the era of big data, LDA will evolve further by assimilating big Earth data and incorporating machine learning to develop digital twins from pure geophysical systems to coupled natural and human systems. Key Points: Land data assimilation (LDA) advances scientific understanding and serves as an engineering tool for Earth system sciencesLDA reflects the trend of harmonizing theory and data in the era of big data and artificial intelligenceFuture LDA research should expand the applications from pure geophysical systems to coupled natural and human systems [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is an efficient population-based search algorithm for solving numerical optimization problems. However, the performance of DE is very sensitive to the choice of mutation strategies and their associated control parameters. In this paper, we propose a self-adaptive multi-population differential evolution algorithm, called SAMDE. The population is randomly divided into three equally sized sub-populations, each with different mutation strategies. At the end of each generation, all sub-populations are updated independently and recombined. Each sub-population uses an adaptive mechanism for selecting how current generation control parameters are generated. An improved mutation strategy, "rand assemble/1", is proposed, its base vector is composed proportionally of three randomly selected individuals. The performance of SAMDE is evaluated on the suite of CEC 2005 benchmark functions. A comparative study is carried out with other state-of-the-art optimization techniques. The results show that SAMDE has a competitive performance compared to several other efficient DE variants. [ABSTRACT FROM AUTHOR]

Wind speed forecasting is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speeds accurately is difficult. Aims at this challenge, a new hybrid model is proposed for short-term wind speed forecasting, where the short-term forecasting period is ten minutes. The model combines extreme learning machine with improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and autoregressive integrated moving average (ARIMA). The extreme learning machine model is employed to obtain short-term wind speed predictions, while the autoregressive model is used to determine the best input variables. An ensemble method is used to improve the robustness of the extreme learning machine. To improve the prediction accuracy, the ICEEMDAN-ARIMA method is developed to postprocess the errors; this method can also be used to preprocess original wind speed. Additionally, this paper reports the results of a comparative study on preprocessing and postprocessing time series data. Three experimental results show that: (1) the error correction is effective in decreasing the prediction error, and the proposed models with error correction are suitable for short-term wind speed forecasting; (2) the ICEEMDAN method is more powerful than other variants of empirical mode decomposition in performing non-stationary decomposition, and the ICEEMDAN-ARIMA method achieves satisfactory performance both for preprocessing and postprocessing; and (3) for prediction, the preprocessing of time series is more effective than its postprocessing. [ABSTRACT FROM AUTHOR]

Solar energy is one of the most promising new energy sources, and making full use of it is the main way to reduce carbon emissions. The prediction of short-term solar radiation is of great significance to the stable operation of grid-connected photovoltaic power stations and the efficient conversion of solar energy. In this paper, a multistep short-term solar radiation prediction method based on the WRF-Solar model, deep fully convolution networks and a chaotic aquila optimization algorithm is proposed. First, the WRF-Solar model is used to predict solar radiation, and the results are spliced with historical satellite observations. Then, the spliced data are fed into five fully convolution networks for separate prediction, and each network has multilayer convolution networks to extract spatial features of different scales. Finally, the final solar radiation prediction is obtained using a chaotic aquila optimization algorithm and combining the results of the five networks. Experiments in Northwest China show that although the prediction performance varies from month to month, on the whole, the proposed method is better than other models, making it easier for the optimizer to jump out of the local optimal solution. The accuracy and robustness of the proposed model can better guide power grid dispatching. • Hybrid NWP and fully convolutional network for predicting solar radiation. • High frequency multistep solar radiation prediction for the region. • Multiple fully convolutional network combinations for radiation prediction. • Model evaluations were conducted over multiple time periods. [ABSTRACT FROM AUTHOR]

Stroke is a common cerebrovascular disease with high morbidity, mortality, and disability worldwide. Post-stroke dysfunction is related to the death of neurons and impairment of synaptic structure, which results from cerebral ischemic damage. Currently, transcranial magnetic stimulation (TMS) techniques are available to provide clinically effective interventions and quantitative diagnostic and prognostic biomarkers. The development of TMS has been 40 years and a range of repetitive TMS (rTMS) protocols are now available to regulate neuronal plasticity in many neurological disorders, such as stroke, Parkinson disease, psychiatric disorders, Alzheimer disease, and so on. Basic studies in an animal model with ischemic stroke are significant for demonstrating potential mechanisms of neural restoration induced by rTMS. In this review, the mechanisms were summarized, involving synaptic plasticity, neural cell death, neurogenesis, immune response, and blood–brain barrier (BBB) disruption in vitro and vivo experiments with ischemic stroke models. Those findings can contribute to the understanding of how rTMS modulated function recovery and the exploration of novel therapeutic targets. The mechanisms of rTMS in treating ischemic stroke from animal models. rTMS can prompt synaptic plasticity by increasing NMDAR, AMPAR and BDNF expression; rTMS can inhibit pro-inflammatory cytokines TNF and facilitate the expression of anti-inflammatory cytokines IL-10 by shifting astrocytic phenotypes from A1 to A2, and shifting microglial phenotypes from M1 to M2; rTMS facilitated the release of angiogenesis-related factors TGFβ and VEGF in A2 astrocytes, which can contribute to vasculogenesis and angiogenesis; rTMS can suppress apoptosis by increasing Bcl-2 expression and inhibiting Bax, caspase-3 expression; rTMS can also suppress pyroptosis by decreasing caspase-1, IL-1β, ASC, GSDMD and NLRP1 expression. rTMS, repetitive transcranial magnetic stimulation; NMDAR, N-methyl-D-aspartic acid receptors; AMPAR: α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors; BDNF, brain-derived neurotrophic factor; VEGF, vascular endothelial growth factor; GSDMD: cleaved Caspase-1 cleaves Gasdermin D; CBF: cerebral blood flow. [ABSTRACT FROM AUTHOR]

A series of Bi1-xSrxMn2-yTiyO5 ceramics were synthesized via a conventional solid-state reaction method, and their crystal structures and magnetic properties were investigated. The strong correlation between the magnetic behavior and mixed–valence states of the Mn ions was characterized by a vibrating sample magnetometer and XPS measurements. Here, we disclose the equation of band angle, length, and magnetic ground state, which is ascribed to the doping element and concentration. Magnetic characterization reveals that Sr- doped (x = 0.3) ceramics exhibit oxygen vacancy-induced ferromagnetic behaviors, which is understood by the bound magnetic polarons mechanism. To clarify the origin of ferromagnetic behavior in non–magnetic doped ceramics, contrast experiments of temperature-dependent magnetic traits of Ti-doped ceramics were analyzed. Furthermore, the tunable Néel temperature is ascribed to the large effective exchange coupling constant. The significance of this work is that we demonstrate an effective tool for manipulating the antiferromagnetic state and offer practical doping principles. [ABSTRACT FROM AUTHOR]

Approximately two-thirds of stroke survivors experience chronic upper-limb paresis; however, treatment options are limited. Repetitive transcranial magnetic stimulation (rTMS) can enhance motor function recovery in stroke survivors, but its efficacy is controversial. We compared the efficacy of stimulating different targets in 10 chronic stroke patients with severe upper-limb motor impairment. Motor imagery-based brain–computer interface training augmented with virtual reality was used to induce neural activity in the brain region during an imagery task. Participants were then randomly assigned to two groups: an experimental group (received high-frequency rTMS delivered to the brain region activated earlier) and a comparison group (received low-frequency rTMS delivered to the contralesional primary motor cortex). Behavioural metrics and diffusion tensor imaging were compared pre- and post rTMS. After the intervention, participants in both groups improved somewhat. This preliminary study indicates that in chronic stroke patients with severe upper-limb motor impairment, inducing activation in specific brain regions during motor imagery tasks and selecting these regions as a target is feasible. Further studies are needed to explore the efficacy of this intervention. [ABSTRACT FROM AUTHOR]

Wind speed forecasting is the key to wind power conversion and management in smart grids. In this paper, a new hybrid model is proposed, which is composed of empirical mode decomposition, a convolutional neural network, a recurrent neural network and a linear regression network considering the model error. In this model, empirical mode decomposition is used to decompose the original wind speed series into multiple subseries, five types of neural networks are used as predictors for each subseries, and a linear regression network is used as the second-level predictor to forecast the wind speed and error series of each model. To verify the prediction ability of the model, experiments are performed using the wind speed data of three stations in the actual wind farm for four months. The results show that the proposed hybrid prediction model has better accuracy and stability than any single neural network model and any neural network model with decomposition preprocessing, which also shows that combination forecasting is a robust and reliable wind speed forecasting method. • Decomposition-based hybrid wind speed forecasting model using CNN–RNN. • CNN is used to extract multi-scale features of wind speed. • Linear regression network is used to combine forecasting while considering error. • The accuracy and stability of the model are evaluated on the wind farm datasets. [ABSTRACT FROM AUTHOR]

This work involves an investigation of the structure, ferroelectricity, optical band-gap, photovoltaic spectral response and J - V performance of BiCrO 3 /BiFeO 3 bilayer composite films prepared via the solution-gelation technique. It is shown that the enhanced ferroelectric properties are observed for BiCrO 3 /BiFeO 3 bilayer composite films by interaction resulting from the coupling between BiFeO 3 and BiCrO 3 layers. The photovoltaic spectral responses of the normalized current for BiCrO 3 /BiFeO 3 bilayer composite films presents a noteworthy red-shift towards visible region compared with the pure BiFeO 3 films and the pure BiCrO 3 films. Thus photovoltaic response is attributed to the narrow band-gap of BiCrO 3 /BiFeO 3 bilayer composite films. The short circuit current density and open circuit voltage of the BiCrO 3 /BiFeO 3 bilayer composite films under white-light illumination are much higher than the values of BiFeO 3 and BiCrO 3 films. The present work provides an available way of controlling photovoltaic response of ferroelectric films and accelerating its application in light sensors, light drivers and ferroelectric photovoltaic cells. [ABSTRACT FROM AUTHOR]

Multiferroic BiFeO 3 /Bi 4 Ti 3 O 12 bilayer films were prepared using chemical solution deposition. Well-defined interfaces between BiFeO 3 and Bi 4 Ti 3 O 12 layers and clear domain structures were observed. The ferroelectric, leakage, dielectric, piezoelectric, magnetic as well as magnetoelectric properties were investigated for the composite films. It is shown that the composite films not only exhibit ferromagnetic and ferroelectric properties, but also possess strong magnetoelectric effect at room temperature. Thus outstanding magnetoelectric properties are attributed to the excellent coupling effect between BiFeO 3 and Bi 4 Ti 3 O 12 layers, which leads to a strong coupling interaction between ferromagnetic and ferroelectric orders. The present work provides a feasible way of preparing magnetoelectric composite films and facilitating their applications in micro-electro-mechanical system and information storage devices. [ABSTRACT FROM AUTHOR]