Search

The osteoporotic bone defect caused by excessive activity of osteoclasts has posed a challenge for public healthcare. However, most existing bioinert bone cement fails to effectively regulate the pathological bone microenvironment and reconstruct bone homeostasis in the presence of osteoclast overactivity and osteoblast suppression. Herein, inspired by natural bone tissue, an in-situ modulation system for osteoporotic bone regeneration is developed by fabricating an injectable double-crosslinked PEGylated poly(glycerol sebacate) (PEGS)/calcium phosphate cement (CPC) loaded with sodium alendronate (ALN) (PEGS/CPC@ALN) adhesive bone cement. By incorporating ALN, the organic-inorganic interconnection within PEGS/CPC@ALN results in a 100 % increase in compression modulus and energy dissipation efficiency. Additionally, PEGS/CPC@ALN effectively adheres to the bone by bonding with amine and calcium ions present on the bone surface. Moreover, this in-situ regulation system comprehensively mitigates excessive bone resorption through the buffering effect of CPC to improve the acidic microenvironment of osteoporotic bone and the release of ALN to inhibit hyperactive osteoclasts, and facilitates stem cell proliferation and differentiation into osteoblasts through calcium ion release. Overall, the PEGS/CPC@ALN effectively regulates the pathological microenvironment of osteoporosis while promoting bone regeneration through synergistic effects of drugs and materials, thereby improving bone homeostasis and enabling minimally invasive treatment for osteoporotic defects.

ABSTRACTVaccines utilizing modified messenger RNA (mRNA) technology have shown robust protective efficacy against SARS-CoV-2 in humans. As the virus continues to evolve in both human and non-human hosts, risk remains that the performance of the vaccines can be compromised by new variants with strong immune escape abilities. Here we present preclinical characterizations of a novel bivalent mRNA vaccine RQ3025 for its safety and effectiveness in animal models. The mRNA sequence of the vaccine is designed to incorporate common mutations on the SARS-CoV-2 spike protein that have been discovered along the evolutionary paths of different variants. Broad-spectrum, high-titer neutralizing antibodies against multiple variants were induced in mice (BALB/c and K18-hACE2), hamsters and rats upon injections of RQ3025, demonstrating advantages over the monovalent mRNA vaccines. Effectiveness in protection against several newly emerged variants is also evident in RQ3025-vaccinated rats. Analysis of splenocytes derived cytokines in BALB/c mice suggested that a Th1-biased cellular immune response was induced by RQ3025. Histological analysis of multiple organs in rats following injection of a high dose of RQ3025 showed no evidence of pathological changes. This study proves the safety and effectiveness of RQ3025 as a broad-spectrum vaccine against SARS-CoV-2 variants in animal models and lays the foundation for its potential clinical application in the future.

Driven by China’s carbon peaking and carbon neutrality goals, this study constructs a carbon emission inversion model for 57 county-level in the core city cluster of the Huaihai Economic Zone. It analyzes the spatial-temporal characteristics of carbon emissions in these counties between 2001 and 2021. It also explores the impacts of different socio-economic factors on carbon emissions by adopting geographically and temporally weighted regression methods. The results showed that: (1) From 2001 to 2021, the total carbon emissions of counties increased from 63.42 Mt to 279.89 Mt, and the distribution pattern of the region is “high in the middle and low in the east and west.” (2) Carbon emissions have significant positive spatial correlations, with the global Moran’s I index showing a wave-like downward trend from 0.233 to 0.0954, indicating that the spatial concentration of carbon emissions in different counties is gradually decreasing, and the differences show a trend of narrowing. (3) Significant spatial and temporal heterogeneity is among the influencing factors. It is mainly manifested in population size and industrial structure, while economic scale and technological progress are the dominant factors in carbon emissions. The research results will inform county-level low-carbon development.

The utilization of high-strength and tough petroleum casing steel has become the focus of various research groups worldwide, particularly in the past decade. However, defining its forming behavior presents several challenges, stemming from the complexity of enhancing strength and toughness on the steel's characteristics. To address this issue, this study employed a low-carbon microalloyed steel and introduced V and Nb elements sequentially, resulting in three groups of steels under investigation. Subsequently, mechanical properties and microstructure characteristics were examined. Interestingly, the addition of V and Nb elements resulted in both tensile and yield strengths above 1200 MPa and 1173 MPa, respectively, satisfying the criteria for the V170 oil casing. Furthermore, after adding V, V(C,N) precipitates uniformly in the steel. Still, the Nb element is not completely precipitated as there is a significant amount of Nb in solid solution. The effective grain size of the investigated steel increases from 0.92 μm to 0.98 μm after adding V and Nb elements, accompanied by a reduction in the fraction of HAGB from 47.4 % to 42.4 %. Therefore, V element plays a vital and positive role in improving the mechanical properties of the experimental steel, while Nb element has only a minor effect.

Abstract Purpose This research was conducted to investigate the potential of short-chain fatty acids (SCFAs) in protecting organs from heat stress-induced injuries and gut microbiota modulation. Methods Sprague–Dawley rats were randomly assigned to various groups including a control group, a room temperature training group, a hyperthermia training group, SCFAs pretreatment group, and recipients received feces from the HT group. After strenuous training at high temperatures, the levels of plasma enzymes AST, ALT, BUN, and Cr were evaluated. The changes in gut microbiota and fecal metabolites were detected using 16S rRNA sequencing and GC–MS methods. Pathological examination of colon and liver tissues was conducted, and immunohistochemical techniques were employed to assess intestinal barrier function. Results The findings indicate that SCFAs hold the potential for mitigating liver and colon damage caused by heat stress. With the intervention of SCFAs, there were observable changes in the structure and metabolites of the intestinal microbiota, as well as improvements in intestinal barrier function. Further support for the benefits of SCFAs was found through fecal microbiota transplantation, which demonstrated that modified gut microbiota can effectively reduce organ damage. Conclusions This study provides evidence that SCFAs, as metabolites of the gut microbiota, have a valuable role to play in regulating gut health and mitigating the harmful effects of heat stress.

This study aimed to solve the quantitative problem of the magnetic flux leakage (MFL) internal detection signals of long-distance oil and gas pipelines. Based on the magnetic charge theory, the numerical–analytical model for MFL internal detection of pipeline complex defects was constructed by introducing stress factor in this work. This model was used to investigate the changes of magnetic signals caused by different division sizes of the defective end-face. The distribution pattern of MFL signal under different geometric sizes and stresses of defective end-face was analyzed, and its influence on the characteristics of magnetic signals was assessed. The effectiveness of the model was verified through experiments. The results indicated that the overall accuracy of magnetic signal analysis by determining the optimal division size on the end face defects was improved by 35% compared with the conventional magnetic charge model, in which the calculation accuracy for defects with depth less than 25% of wall thickness was 41%. The axial signal component had extreme values. The radial component showed sinusoidal fluctuations, accompanied by peaks and troughs. The magnetic signal component increased non-linearly with the increase of the depth and width of the defect, and the incremental change increased first and then decreased. The magnetic signal components exponentially decreased with the increase of defect stress under magnetic saturation, and the decay rate gradually decreased with the increase of stress.

Addressing the issue of inadequate convergence and suboptimal accuracy in classical data-driven algorithms for coherent polarization–direction-of-arrival (DOA) estimation, a novel high-precision two-dimensional coherent polarization–DOA estimation method utilizing a sequence-embedding fusion (SEF) transformer is proposed for the first time. Drawing inspiration from natural language processing (NLP), this approach employs transformer-based multitasking text inference to facilitate joint estimation of polarization and DOA. This method leverages the multi-head self-attention mechanism of the transformer to effectively capture the multi-dimensional features within the spatial-polarization domain of the covariance matrix data. Additionally, an SEF module was proposed to fuse the spatial-polarization domain features from different dimensions. The module is a combination of a convolutional neural network (CNN) with local information extraction capabilities and a feature dimension transformation function, serving to improve the model’s ability to fuse information about features in the spatial-polarization domain. Moreover, to enhance the model’s expressive capacity, we designed a multi-task parallel output mode and a multi-task weighted loss function. Simulation results demonstrate that our method outperforms classical data-driven approaches in both accuracy and generalization, and the estimation accuracy of our method is improved relative to the traditional model-driven algorithm.

The image integrity of real-time monitoring is crucial for monitoring crop growth, helping farmers and researchers improve production efficiency and crop yields. Unfortunately, existing schemes just focus on ground equipment and drone imaging, neglecting satellite networks in remote or extreme environments. Given that satellite internet features wide area coverage, we propose SEAIS, a secure and efficient agricultural image storage scheme combining blockchain and satellite networks. SEAIS presents the mathematical model of image processing and transmission based on satellite networks. Moreover, to ensure the integrity and authenticity of image data during pre-processing such as denoising and enhancement, SEAIS includes a secure agricultural image storage and verification method based on blockchain, homomorphic encryption, and zero-knowledge proof. Specifically, images are stored via IPFS, with hash values and metadata recorded on the blockchain, ensuring immutability and transparency. The simulation results show that SEAIS exhibits more stable and efficient processing times in extreme environments. Also, it maintains low on-chain storage overhead, enhancing scalability.

Isorhamnetin (ISO) is a phenolic compound belonging to flavonoid family, showcasing important in vitro pharmacological activities such as antitumor, anti-inflammation, and organ protection. ISO is predominantly extracted from Hippophae rhamnoides L. This plant is well-known in China and abroad because of its “medicinal and food homologous” characteristics. As a noteworthy natural drug candidate, ISO has received considerable attention in recent years owing to its low cost, wide availability, high efficacy, low toxicity, and minimal side effects. To comprehensively elucidate the multiple biological functions of ISO, particularly its antitumor activities and other pharmacological potentials, a literature search was conducted using electronic databases including Web of Science, PubMed, Google Scholar, and Scopus. This review primarily focuses on ISO’s ethnopharmacology. By synthesizing the advancements made in existing research, it is found that the general effects of ISO involve a series of in vitro potentials, such as antitumor, protection of cardiovascular and cerebrovascular, anti-inflammation, antioxidant, and more. This review illustrates ISO’s antitumor and other pharmacological potentials, providing a theoretical basis for further research and new drug development of ISO.

Inland arid basins face the challenge of ecological deterioration due to insufficient water availability. The irrigation water consumption depletes the water flowing into the downstream tailrace ecological wetland, leading to increasing ecological deterioration. It is urgent to optimize the management of irrigation water resources in the middle reaches and improve the ecological sustainability of the lower reaches. To ensure sustainable development, improving water use efficiency and preserving the health of basin ecosystems should be simultaneously considered in the agricultural water management of these regions. Therefore, a 0–1 integer multi-objective programming approach was proposed to optimize midstream crop planting. This method has advantages in (1) effectively balancing ecological sustainability, agricultural production, and water-saving goals; (2) linking irrigation district management with grid geographic information to develop land use strategies; and (3) obtaining optimal solutions for multi-objective synergies. The proposed approach is applied to a typical inland river basin in China, the Heihe River Basin in Gansu Province. Results indicate that the optimization schemes can increase agricultural benefits, crop suitability, water use efficiency, and ecological quality by 12.37%, 6.82%, 13.00%, and 8.04% (compared to 2022), respectively, while irrigation water can be saved about 7.53%. The optimization results and proposed approach can help decision-makers manage water resources in the Heihe River Basin and similar regions.

Blue-emission (∼480 ​nm) CsPbBr3 nanoparticles with ultra-small size (∼2.1 ​nm) are synthesized using the liquid nitrogen freezing with the ligand of dodecylbenzene sulfonic acid (DBSA). Asymmetric narrow emissions at the low energy side, with the full width at half-maximum of ∼20 ​nm, are observed in solution and film at room temperature. The spectral asymmetry is mainly ascribed to phonon vibronic replica with averaged phonon energy of ∼40 ​meV. Moreover, exciting this CsPbBr3 nanoparticles solution using linearly polarized 6 ns pulsed laser at 355 ​nm, we observe polarized emission with polarization degree (PPL) of ∼7%, and PPL decreases more than 20% in the vibronic progression. However, the PPL goes to zero in frozen solutions as well as in films. Thus we speculate the polarized emission is due to the photoinduced re-alignment of nanoparticles, and the diminished PPL at the phonon side band may be due to the non-adiabatic electronic-to-vibronic transitions. The novel phenomena from the ultra-small CsPbBr3 nanoparticle demonstrated in this work may provide fundamental insights into its photophysics with direct implications for optoelectronics.

With the explosive growth of edge computing, huge amounts of data are being generated in billions of edge devices. It is really difficult to balance detection efficiency and detection accuracy at the same time for object detection on multiple edge devices. However, there are few studies to investigate and improve the collaboration between cloud computing and edge computing considering realistic challenges, such as limited computation capacities, network congestion and long latency. To tackle these challenges, we propose a new multi-model license plate detection hybrid methodology with the tradeoff between efficiency and accuracy to process the tasks of license plate detection at the edge nodes and the cloud server. We also design a new probability-based offloading initialization algorithm that not only obtains reasonable initial solutions but also facilitates the accuracy of license plate detection. In addition, we introduce an adaptive offloading framework by gravitational genetic searching algorithm (GGSA), which can comprehensively consider influential factors such as license plate detection time, queuing time, energy consumption, image quality, and accuracy. GGSA is helpful for Quality-of-Service (QoS) enhancement. Extensive experiments show that our proposed GGSA offloading framework exhibits good performance in collaborative edge and cloud computing of license plate detection compared with other methods. It demonstrate that when compared with traditional all tasks are executed on the cloud server (AC), the offloading effect of GGSA can be improved by 50.31%. Besides, the offloading framework has strong portability when making real-time offloading decisions.

The refractory diabetic wound has remained a worldwide challenge as one of the major health problems. The impaired angiogenesis phase during diabetic wound healing partly contributes to the pathological process. MicroRNA (miRNA) is an essential regulator of gene expression in crucial biological processes and is a promising nucleic acid drug in therapeutic fields of the diabetic wound. However, miRNA therapies have limitations due to lacking an effective delivery system. In the present study, we found a significant reduction of miR-31-5p expression in the full-thickness wounds of diabetic mice compared to normal mice. Further, miR-31-5p has been proven to promote the proliferation, migration, and angiogenesis of endothelial cells. Thus, we conceived the idea of exogenously supplementing miR-31-5p mimics to treat the diabetic wound. We used milk-derived exosomes as a novel system for miR-31-5p delivery and successfully encapsulated miR-31-5p mimics into milk exosomes through electroporation. Then, we proved that the miR-31-5p loaded in exosomes achieved higher cell uptake and was able to resist degradation. Moreover, our miRNA-exosomal formulation demonstrated dramatically improved endothelial cell functions in vitro, together with the promotion of angiogenesis and enhanced diabetic wound healing in vivo. Collectively, our data showed the feasibility of milk exosomes as a scalable, biocompatible, and cost-effective delivery system to enhance the bioavailability and efficacy of miRNAs.

Comprehensive annotating of cell types in spatially resolved transcriptomics to understand biological processes at the single cell level remains challenging. Here the authors introduce Spatial-ID, a supervision-based cell typing method, that combines the existing knowledge of reference single-cell RNA-seq data and the spatial information of spatially resolved transcriptomics data.

The key to realizing a high-performance frequency-modulated continuous wave (FMCW) laser frequency-sweeping light source is how to extend the frequency-swept bandwidth and eliminate the effect of nonlinearity. To solve these issues, this paper designs a broadband high-linear FMCW frequency-sweeping light source system based on the combination of fixed temperature control and digital optoelectronic phase-locked loop (PLL), which controls the temperatures of the two lasers separately and attempts to achieve the coarse spectral stitching based on a time-division multiplexing scheme. Furthermore, we uses the PLL to correct the frequency error more specifically after the coarse stitching, which achieves the spectrum fine stitching and, meanwhile, realizes the nonlinearity correction. The experimental results show that our scheme can successfully achieve bandwidth expansion and nonlinearity correction, and the sweeping bandwidth is twice as much as that of the original single laser. The full-width half-maximum (FWHM) of the FMCW output is reduced from 150 kHz to 6.1 kHz, which exhibits excellent nonlinear correction performance. The relative error of the FMCW ranging system based on this frequency-swept light source is also reduced from 1.628% to 0.673%. Therefore, our frequency-swept light source with excellent performance has a promising application in the FMCW laser ranging system.

Photodynamic therapy (PDT) has garnered significant attention in the fields of cancer treatment and drug-resistant bacteria eradication due to its non-invasive nature and spatiotemporal controllability. Iridium complexes have captivated researchers owing to their tunable structure, exceptional optical properties, and substantial Stokes displacement. However, most of these complexes suffer from aggregation-induced quenching, leading to diminished luminous efficiency. In contrast to conventional photosensitizers, photosensitizers exhibiting aggregation-induced luminescence (AIE) properties retain the ability to generate a large number of reactive oxygen species when aggregated. To overcome these limitations, we designed and synthesized a novel iridium complex named Ir-TPA in this study. It incorporates quinoline triphenylamine cyclomethylated ligands that confer AIE characteristics for Ir-TPA. We systematically investigated the photophysical properties, AIE behavior, spectral features, and reactive oxygen generation capacity of Ir-TPA. The results demonstrate that Ir-TPA exhibits excellent optical properties with pronounced AIE phenomenon and robust capability for producing singlet oxygen species. This work not only introduces a new class of metal iridium complex photosensitizer with AIE attributes but also holds promise for achieving remarkable photodynamic therapeutic effects in future cellular experiments and biological studies.

Abstract Clinical work and research on diabetic wound repair remain challenging globally. Although various conventional wound dressings have been continuously developed, the efficacy is unsatisfactory. The effect of drug delivery is limited by the depth of penetration. The sustained release of biomolecules from biological wound dressings is a promising treatment approach to wound healing. An assortment of cell-derived exosomes (exos) have been proved to be instrumental in tissue regeneration, and researchers are dedicated to developing biomolecules carriers with unique properties. Herein, we reported a methacrylate gelatin (GelMA) microneedles (MNs) patch to achieve transdermal and controlled release of exos and tazarotene. Our MNs patch comprising GelMA/PEGDA hydrogel has distinctive biological features that maintain the biological activity of exos and drugs in vitro. Additionally, its unique physical structure prevents it from being tightly attached to the skin of the wound, it promotes cell migration, angiogenesis by slowly releasing exos and tazarotene in the deep layer of the skin. The full-thickness cutaneous wound on a diabetic mouse model was carried out to demonstrate the therapeutic effects of GelMA/PEGDA@T + exos MNs patch. As a result, our GelMA/PEGDA@T + exos MNs patch presents a potentially valuable method for repairing diabetic wound in clinical applications. Graphic Abstract

Due to the rapid growth in network traffic and increasing security threats, Intrusion Detection Systems (IDS) have become increasingly critical in the field of cyber security for providing secure communications against cyber adversaries. However, there exist many challenges for designing a robust, efficient and accurate IDS, especially when dealing with high-dimensional anomaly data with unforeseen and unpredictable attacks. In this paper, we propose a Robust Transformer-based Intrusion Detection System (RTIDS) reconstructing feature representations to make a trade-off between dimensionality reduction and feature retention in imbalanced datasets. The proposed method utilizes positional embedding technique to associate sequential information between features, then a variant stacked encoder-decoder neural network is used to learn low-dimensional feature representations from high-dimensional raw data. Furthermore, we apply self-attention mechanism to facilitate network traffic type classifications. Extensive experiments reveal the effectiveness of the proposed RTIDS on two publicly available real traffic intrusion detection datasets named CICIDS2017 and CIC-DDoS2019 with F1-Score of 99.17% and 98.48% respectively. A comparative study with classical machine learning algorithm support vector machine (SVM) and deep learning algorithms that include recurrent neural network (RNN), fuzzy neural network (FNN), and long short-term memory network (LSTM) is conducted to demonstrate the validity of the proposed method.

Diabetic wound complications are financially costly and difficult to heal in worldwide. Whereas the therapies of diabetic wound, such as wound dressing, endocrine therapy or flap-transplantations, were not satisfied. Based on our previous study of exosome secreted by adipose-derived stem cell (ADSC-exo), we loaded ADSC-exo into the matrix metalloproteinase degradable polyethylene glycol (MMP-PEG) smart hydrogel. Physical and chemical properties of ADSC-exo@MMP-PEG smart hydrogel were tested by scanning electron microscope (SEM), Fourier transform infrared spectroscopy (FTIR), weight loss examination, etc. As the hydrogel degraded in response to MMP, ADSC-exo was released and subsequently enhanced cell function via Akt signaling. Moreover, treatment with ADSC-exo@MMP-PEG smart hydrogel significantly relieved the H2O2-induced oxidative stress, which was widely recognized as a major cause of diabetic wound nonhealing. Similar results were achieved in mice diabetic wound models, in which the ADSC-exo@MMP-PEG treatment group displayed a significantly accelerated wound healing. To summarize, the present smart hydrogel with enzyme-response and exosome-release was proved to be benefit for diabetic wounds healing, which provides a reliable theoretical basis for application of ADSC-exo in treatment of diabetic wounds.

The frequency-swept laser (FSL) is applied widely in various sensing systems in the scientific and industrial fields, especially in the light detection and ranging (Lidar) area. However, the inherent nonlinearity limits its performance in application systems, especially in the broadband frequency-swept condition. In this work, from the perspective of data-driven control, we adopt the reinforcement learning-based broadband frequency-swept linearization method (RL-FSL) to optimize the control policy and generate the modulation signals. The nonlinearity measurement system and the system simulator are established. Since the powerful learning ability of the reinforcement learning algorithm, the linearization policy is optimized off-line and the generated modulation signals reduce the nonlinearity almost 20 times, compared to the case without control. In the long-term operation, the regular updated modulation signals perform better than the traditional iteration results, demonstrating the efficiency of the proposed data-driven control method in application systems. Therefore, the RL-FSL method has the potential to be the candidate of optical system control.

Abstract Eukaryotic RNA polymerase I (Pol I) transcribes ribosomal DNA and generates RNA for ribosome synthesis. Pol I accounts for the majority of cellular transcription activity and dysregulation of Pol I transcription leads to cancers and ribosomopathies. Despite extensive structural studies of yeast Pol I, structure of human Pol I remains unsolved. Here we determined the structures of the human Pol I in the pre-translocation, post-translocation, and backtracked states at near-atomic resolution. The single-subunit peripheral stalk lacks contacts with the DNA-binding clamp and is more flexible than the two-subunit stalk in yeast Pol I. Compared to yeast Pol I, human Pol I possesses a more closed clamp, which makes more contacts with DNA. The Pol I structure in the post-cleavage backtracked state shows that the C-terminal zinc ribbon of RPA12 inserts into an open funnel and facilitates “dinucleotide cleavage” on mismatched DNA–RNA hybrid. Critical disease-associated mutations are mapped on Pol I regions that are involved in catalysis and complex organization. In summary, the structures provide new sights into human Pol I complex organization and efficient proofreading.

Lipid membranes are heterogeneous and dynamically regulated in cells. Here the authors report a Spectrum and Polarisation Optical Tomography (SPOT) method where they use Nile Red dye to resolve membrane morphology, polarity and phase in cells.

In order to solve the problem of the quantification of detection signals in the magnetic flux leakage (MFL) of defective in-service oil and gas pipelines, a non-uniform magnetic charge model was established based on magnetic effects. The distribution patterns of magnetic charges under different stresses were analyzed. The influences of the elastic load and plastic deformation on the characteristic values of MFL signals were quantitatively assessed. The experimental results showed that the magnetic charge density was large at the edges of the defect and small at the center, and approximately decreased linearly with increasing stress. The eigenvalues of the axial and radial components of the MFL signals were compared, and it was found that the eigenvalues of the radial component exhibited a larger decline rate and were more sensitive to stress. With the increase in the plastic deformation, the characteristic values of the MFL signals initially decreased and then increased, and there was an inflection point. The location of the inflection point was associated with the magnetostriction coefficient. Compared with the uniform magnetic charge model, the accuracy of the axial and radial components of the MFL signals in the elastic stage of the improved magnetic charge model rose by 17% and 16%, respectively. The accuracy of the axial and radial components of the MFL signals were elevated by 9.15% and 9%, respectively, in the plastic stage.

Aiming at the existing Direction of Arrival (DOA) methods based on neural network, a large number of samples are required to achieve signal-scene adaptation and accurate angle estimation. In the coherent signal environment, the problems of a larger amount of training sample data are required. In this paper, the DOA of coherent signal is converted into the DOA parameter estimation of the angle interval of incident signal. The accurate estimation of coherent DOA under the condition of small samples based on meta−reinforcement learning (MRL) is realized. The meta−reinforcement learning method in this paper models the process of angle interval estimation of coherent signals as a Markov decision process. In the inner loop layer, the sequence to sequence (S2S) neural network is used to express the angular interval feature sequence of the incident signal DOA. The strategy learning of the existence of angle interval under small samples is realized through making full use of the context relevance of spatial spectral sequence through S2S neural network. Thus, according to the optimal strategy, the output sequence is sequentially determined to give the angle interval of the incident signal. Finally, DOA is obtained through one-dimensional spectral peak search according to the angle interval obtained. The experiment shows that the meta−reinforcement learning algorithm based on S2S neural network can quickly converge to the optimal state by only updating the gradient of S2S neural network parameters with a small sample set when a new signal environment appears.

The transfer space realizes the connectivity of subway intersections. Passengers generally express that they have a poor experience in the use of this space, so improving the environmental suitability of transfer spaces at subway stations is a top priority. Based on a literature review and field research, this study established an environmental suitability evaluation system for transfer spaces and used the fuzzy comprehensive evaluation method to evaluate the environmental suitability of eight samples in Shanghai. The results showed that the evaluation results of the eight samples were ranked as follows: Hanzhong Road Station > People’s Square Station > East Nanjing Road Station > Century Avenue Station > Xujiahui Station > Laoximen Station > Jiangsu Road Station > Shanghai Railway Station. Through the analysis of the relationship between the indicators, it was found that the environmental suitability of a transfer space is greatly affected by safety and convenience, while practicality, comfort, and aesthetics were found to have a weak influence on the suitability of transfer spaces. These evaluation methods and results provide a reference for the improvement of the environmental quality of subway transfer spaces in other cities.

In this study, high-sensitively colorimetric detection of mercury ions was developed according to the surface plasmon resonance of modified gold nanoparticles. While in a citrate buffer solution, the selected modifier Bismuthiol II can cause gold nanoparticles to aggregate. Gold nanoparticles with a spherical shape and average diameter of about 13 nm were prepared. While mercury ions can inhibit gold nanoparticle aggregation, after the addition of mercury ions, the color of the gold nanoparticles solution changes from light violet to red with a hypochromatic shift at the absorption peak. By collecting the UV-Vis spectrum of the solution after the reaction, the characteristic bands are selected to build a partial least squares model. Both the 0.9477 correlation coefficient with a 0.0986 mg/L standard deviation for the calibration set, and 0.9274 correlation coefficient with a 0.1020 mg/L standard deviation for the prediction set were obtained via a partial least squares model.

Summary: The entorhinal-hippocampal circuit can encode features of elapsed time, but nearly all previous research focused on neural encoding of “implicit time.” Recent research has revealed encoding of “explicit time” in the medial entorhinal cortex (MEC) as mice are actively engaged in an interval timing task. However, it is unclear whether the MEC is required for temporal perception and/or learning during such explicit timing tasks. We therefore optogenetically inactivated the MEC as mice learned an interval timing “door stop” task that engaged mice in immobile interval timing behavior and locomotion-dependent navigation behavior. We find that the MEC is critically involved in learning of interval timing but not necessary for estimating temporal duration after learning. Together with our previous research, these results suggest that activity of a subcircuit in the MEC that encodes elapsed time during immobility is necessary for learning interval timing behaviors.

Magnetic MXene composite Fe3O4@Ti3C2 was successfully prepared and employed as 17α-ethinylestradiol (EE2) adsorbent from water solution. The response surface methodology was employed to investigate the interactive effects of adsorption parameters (adsorption time, pH of the solution, initial concentration, and the adsorbent dose) and optimize these parameters for obtaining maximum adsorption efficiency of EE2. The significance of independent variables and their interactions were tested by the analysis of variance (ANOVA) and t-test statistics. Optimization of the process variables for maximum adsorption of EE2 by Fe3O4@Ti3C2 was performed using the quadratic model. The model predicted maximum adsorption of 97.08% under the optimum conditions of the independent variables (adsorption time 6.7 h, pH of the solution 6.4, initial EE2 concentration 0.98 mg L−1, and the adsorbent dose 88.9 mg L−1) was very close to the experimental value (95.34%). pH showed the highest level of significance with the percent contribution (63.86%) as compared to other factors. The interactive influences of pH and initial concentration on EE2 adsorption efficiency were significant (p < 0.05). The goodness of fit of the model was checked by the coefficient of determination (R2) between the experimental and predicted values of the response variable. The response surface methodology successfully reflects the impact of various factors and optimized the process variables for EE2 adsorption. The kinetic adsorption data for EE2 fitted well with a pseudo-second-order model, while the equilibrium data followed Langmuir isotherms. Thermodynamic analysis indicated that the adsorption was a spontaneous and endothermic process. Therefore, Fe3O4@Ti3C2 composite present the outstanding capacity to be employed in the remediation of EE2 contaminated wastewaters.