In this study, we report the antibacterial mechanisms of action of uniform silver nanoparticles (AgNPs) and decorated activated carbon nanocomposite (CAC-AgNPs) obtained using a green synthesis approach. The nanomaterials were characterized by ultraviolet-visible (UV-vis) absorption spectra and Fourier transform infrared (FTIR) spectra. The antibacterial activity of the as-prepared nanomaterials was evaluated against an array of bacterial strains by microdilution method, whereas their cytotoxicity profile was evaluated on Vero cells (human mammalian cells). The antibacterial mechanistic studies of active nanomaterials were carried out through bacterial growth kinetics, nucleic acid leakage test, and catalase inhibition assay. A silver nanocomposite was successfully fabricated from Croton macrostachyus-based activated carbon. The as-prepared nanomaterials exhibited antibacterial activity against an array of bacterial strains (minimum inhibitory concentration (MIC) range: 62.5 to 500 µg/mL), the most susceptible being Escherichia coli and Staphylococcus aureus. Cytotoxicity studies of the nanomaterials on Vero cells revealed that the nanocomposite (median cytotoxic concentration (CC50): 213.6 µg/mL) was less toxic than the nanoparticles (CC50 value: 164.75 µg/mL) counterpart. Antibacterial mechanistic studies unveiled that the nanomaterials induced (i) bacteriostatic activity vis-à-vis E. coli and S. aureus and (ii) inhibition of catalase in these bacteria. This novel contribution on the antibacterial mechanisms of action of silver nanocomposite from C. macrostachyus-based activated carbon might contribute to the understanding of antibacterial action of these biomaterials. Nevertheless, more chemistry and in vivo experiments, as well as in depth antibacterial mechanistic studies are warranted for the successful utilization of these antibacterial biomaterials.

In the seasonally frozen regions, during the grouting of prestressed bridge ducts in low-temperature environments, incompletely cured grout materials undergo volumetric changes due to the freeze-thaw cycling, resulting in structural cracks along the prestressing ducts of the bridge, thereby diminishing the bridge's operational lifespan. To investigate the frost heaving characteristics of grout materials under freeze-thaw cycles and propose potential enhancements, this study was devised to conduct frost heaving stress tests on grout materials. Through this research, we gained insights into how various additives affect the frost heaving stress characteristics of grout materials under freeze-thaw cycles. Incorporating analyses of free water content, setting time, compressive strength, as well as macroscopic examinations such as XRD and SEM, this study delves into the mechanisms for enhancing frost heaving characteristics of grout materials. Furthermore, it proposes a research approach that utilizes multiple admixtures to improve the frost heaving properties of grout materials. Through frost heaving stress tests and other macroscopic experiments, the study investigates the influence of these combined admixtures on frost heaving stress in grout materials and elucidates the underlying improvement mechanisms. The results indicate that reducing the water-cement ratio, incorporating calcium formate, sulfoaluminate cement, air-entraining agents, and carbamide all have a positive impact on mitigating frost heaving stress in grout materials. However, the improvement mechanisms differ, and employing a single measure alone is insufficient to effectively reduce frost heaving stress while meeting performance criteria such as compressive strength, setting time, and flowability. Free water content emerges as a crucial indicator determining the magnitude of frost heaving stress in grout materials, with 11.5% of free water content representing the critical threshold for frost heaving in grout materials. Utilizing composite admixtures can simultaneously decrease free water content, lower the freezing point of free water, and alleviate frost heaving deformation, resulting in more efficient reduction of frost heaving stress. When the admixture content reaches 9.9%, frost heaving stress is eliminated, and the comprehensive performance parameters including compressive strength, setting time, and flowability meet the specified requirements.

Gentiopicroside (GPS) is a leading component of several plant species from Gentianaceae botanical family. As a compound with plenty biological activities and a component that is often consumed in dietary supplements and herbal drugs, GPS has an important role in the regulation of physiological processes in humans. Recent studies on the type 2 receptors shed new light on GPS applications showing its agonistic properties that could be meaningful in the future therapeutic strategies of Parkinson’s disease, thyroid disorders or cancer states. This review aims to collect previously published reports on the biological properties of GPS as a single compound that were confirmed by in vitro and in vivo studies, and to draw attention at the newly discovered role of this bitter-tasting secoiridoid as a type 2 receptor agonist. Thanks to these properties, the research on this substance could be revisited.

In the FCC conversion of heavy petroleum fractions as atmospheric residues, the main challenge for refiners to achieve the quantity and quality of various commercial products depends essentially on the catalyst used in the process. A deep characterization of the catalyst at different steps of the process (fresh, regenerated, and spent catalyst) was investigated to study the catalyst's behavior including the physicochemical evolution, the deactivation factor, and kinetic-thermodynamic parameters. All samples have been characterized by various spectroscopy methods such as N2 adsorption-desorption, UV-visible spectroscopy, Raman spectroscopy, LECO carbon analysis, Scanning electron microscopy (SEM), X-ray diffraction (XRD), X‐ray fluorescence (XRF), nuclear magnetic resonance spectroscopy (NMR13C) analysis, and Thermogravimetric analysis. The results of N2 adsorption-desorption, UV-vis, Raman, LECO carbon, and SEM images showed that the main causes of catalyst deactivation and coking were the deposition of carbon species that covered the active sites and clogged the pores, and the attrition factor due to thermal conditions and poisonous metals. The XRD and XRF results showed the catalyst’s physicochemical evolution during the process and the different interlinks between catalyst and feedstock (Nickel, Vanadium, Sulfur, and Iron) elements which should be responsible for the coking and catalyst attrition factor. It has been found that, in addition to the temperature, the residence time of the catalyst in the process also has an influence on catalyst structure transformation. NMR13C analysis revealed that polyaromatic hydrocarbon is the main component in the deposited coke of the spent catalyst. Thermogravimetric analysis showed that the order of catalyst mass loss was fresh > regenerated > spent catalyst due to the progressive losses of the hydroxyl bonds (OH) and the structure change along the catalyst thermal treatment. Moreover, the kinetic and thermodynamic parameters showed that all zones are non-spontaneous endothermic reactions.

This study investigates the microstructures and deformation mechanism of hetero-structured pure titanium under different high strain rates (500 s-1，1000 s-1，2000 s-1). The influence of strain rate on the deformation mechanism is examined. As the strain rate increases, the dominance of dislocation slip decreases while deformation twinning becomes more prominent. Notably, at a strain rate of 2000s-1, nanoscale twin lamellae are activated within grain with a size of 500nm, which is a rarely observed phenomenon in pure Ti. Additionally, martensitic phase transformation is also identified. These findings can greatly enhance our understanding of the mechanical responses of Ti and broaden the potential applications of Ti in dynamic deformation scenarios.

Immune checkpoint inhibitors (ICIs), including anti-cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) and anti-programmed death-1 (PD-1) antibodies, have initiated a new era in the treatment of malignant melanoma. ICIs can be used in various settings, including as first-line, adjuvant, and neoadjuvant therapy. In the scope of this review, we examined clinical studies utilizing ICIs in the context of treating oral mucosal melanoma, a rare disease albeit with an extremely poor prognosis, with a specific focus on unraveling the intricate web of resistance mechanisms. The absence of a comprehensive review focusing on ICIs in oral mucosal melanoma is notable. Therefore, this review seeks to address this deficiency by offering a novel and thorough analysis of the current status, potential resistance mechanisms, and future prospects of applying ICIs specifically to oral malignant melanoma. Clarifying and thoroughly understanding these mechanisms will facilitate the advancement of effective therapeutic approaches and enhance the prospects for patients suffering from oral mucosal melanoma.

Underwater image processing faces significant challenges due to the absorption and scattering of light as it travels through water. A self-supervised learning network based on the self-attention mechanism is proposed for underwater visual applications in this paper. The goal is to improve the effectiveness and stability of feature extraction from underwater images. By incorporating self-attention mechanism, the sensitivity of the original network architecture to degraded features of blurred underwater images is enhanced. The network proposed in this paper is trained using transfer learning and evaluated on various underwater image datasets. With distributive, quantitative, and qualitative advantages compared with other methods, experimental results have demonstrated that the algorithm presented shows a slighter decline in feature extraction ability for blurred images as the turbidity of water increases.

The phenomenon of interference is a characteristic of all waves. This paper reviews the interference of classical waves and of matter waves in quantum mechanics. In quantum mechanics, there are two interference mechanisms between identical particles. One is that the de Broglie wavelengths of two interference waves are the same, which is called the equal wavelength interference mechanism. The other is that the wavelengths of interference waves can also be different, in which case there should be an exchange term in additional to the direction term in the wave function. There is a fixed phase difference between the two terms, which causes interference. This is called exchange effect interference mechanism. We propose an experiment, which is simpler to what has been done, to show the exchange effect interference. There is a special case where two interference waves can satisfy both the two mechanisms. We propose an experiment to discriminate that in this case which mechanism plays a role on earth.

Semantic segmentation of remote sensing urban scene imagery is a pixel-wise prediction, which is applied to identify the land-cover or land-use category. However, semantic segmentation demands huge computation cost. In order to reduce the huge computation cost, a common method is to introduce transformer and CNN hybrid method to have a good trade-off between accuracy and computation cost. However, recent CNN-transformer hybrid methods often capture local-global context and cross-window interaction information simultaneously. Then they introduce the local-global context to fuse the local feature, which repeat fuse the local and global feature and result in the additional computation cost. And previous methods often ignore to filter the local and global feature. To fix the problem, we design a lightweight decoder-EDDformer, which is Efficient Global Value Transformer with Dynamic Gatefusion module. Efficient global value transformer only response for extracting global feature. Dynamic Gatefusion module filter the local and global semantic feature and fuse them to capture the local-global context in one time. Extensive experiments reveal that our method not only runs faster but also achieves higher accuracy compared with other state-of-the-art lightweight models.

Electrocatalytic and photocatalytic hydrogen evolution reaction (HER) provides a promising approach to clean energy generation. Bimetallic single-atom catalysts raised and have been explored to be advanced catalysts for HER. It is urgent to review and summarize the recent advances in developing bimetallic single-atom HER catalysts. Firstly, the fundamentals of bimetallic single-atom catalysts are presented, highlighting their unique configuration of two isolated metal atoms on supports and resultant synergistic effects. Secondly, recent advances in bimetallic single-atom catalysts for electrocatalytic HER under acidic/alkaline conditions are then reviewed, including W-Mo, Ru-Bi, Ni-Fe, Co-Ag, and other dual-atom systems on graphene and transition metal dichalcogenides (TMDs) with enhanced HER activity versus monometallic analogs due to geometric and electronic synergies. Then, Photocatalytic bimetallic single-atom catalysts on semiconducting carbon nitrides for solar H2 production are also discussed. Finally, an outlook is provided on opportunities and challenges in precisely controlling bimetallic single-atom catalyst synthesis and gaining in-depth mechanistic insights into bimetallic interactions.

Underwater robotics equipped with visual detection systems have the function of detecting un-derwater artifacts, which is of great significance to deep-sea archaeology. Underwater artifacts are located in complex environments with poor imaging conditions, and the targets have chal-lenging problems such as breakage, stacking, and sediment burial, which causes the marine cul-tural target detection failures. To solve these problems, this paper proposes an underwater cul-tural target detection algorithm based on the deformable deep aggregation network model for underwater robotics exploration. To fully extract the target feature information of underwater targets in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. Besides, the BAM attention module is designed for fea-ture optimization, which enhances the potential feature information of the target while weak-ening the background interference information. Finally, the target prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validat-ed and analyzed on the collected underwater artifact datasets, and the precision, recall, and mAP of the algorithm have reached 93.1%, 91.4%, and 92.8%, respectively. In addition, the present al-gorithm has been practically deployed on the underwater robotics. In the deep-sea tests, the ar-tifact detection frame rate reaches up to 19 fps, which satisfies the real-time target detection。

At present Coronavirus disease-2019 (COVID-19) is one of the leading contributing factor to mortality and the impact of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection on the sinonasal tract has become more prominent, particularly with the rising awareness of olfactory dysfunction (OD). We extracted data from published papers available in electronic databases (Wiley online library, PubMed, and Nature). We used the following search terms alone or with combinations - Olfactory dysfunction, SARS-CoV-2, mechanism and treatments. We found worldwide up to 98% of patients confirmed OD due to COVID-19. Current studies have implied that regardless of the high self-reported recovery rate, 25–40% of patients after 1 or 2 months and approximately 15%–28% of patients at six months struggle to fully restore their sense of smell. Moreover, female sex, younger and older age, active smoking, and chronic lung disease are reported as the associated risk factors of OD. Although the pathophysiological mechanism of action(s) of the OD is yet to be explored in depth, central nervous system (CNS) entrance, olfactory bulb (OB) and sustentacular cell damage, neural routes inflammation, non-neuronal cells damage, decreased OB volume and deregulation of olfactory receptor genes are among the commonly reported mechanisms for the development of OD.

The fatigue crack growth rate (FCGR) of aluminium alloys under the combined influence of temperature and humidity remains a relatively unexplored area, receiving limited attention due to its intricate nature and challenges in predicting the combined impact of these factors. The challenge was to investigate and address the specific mechanisms and interactions between temperature and humidity, as in coastal environment conditions, on the FCGR of aluminium alloy. The present study conducts a comprehensive investigation on the combined influence of temperature and humidity on the FCGR of Al6082 alloy. The fatigue pre-cracked compact tension specimens were corroded for 7 days and then subjected to various temperature and humidity conditions in a thermal chamber for 3 days to simulate coastal environments. The obtained data were analysed to determine the influence of temperature and humidity on the FCGR of Al6082 alloy. An empirical model was also established to predict fatigue life cycle values under these environmental conditions precisely. The correlation between FCGR and fracture toughness models was also examined. The Al6082 alloy exhibits a 34% increase in the Paris constant C, indicating reduced FCGR resistance due to elevated temperature and humidity levels. At the same time, fatigue, corrosion, moisture-assisted crack propagation, and hydrogen embrittlement lead to a 27% decrease in threshold fracture toughness. The developed model exhibited accurate predictions for fatigue life cycles, and the correlation between fracture toughness and FCGR showed less than 10% error, indicating a strong relationship between these parameters.

Background: Myasthenia Gravis (MG) is a rare autoimmune disease presenting with au-to-antibodies that affect the neuromuscular junction. Aside from symptomatic treatment options, novel therapeutics include monoclonal antibodies (mAbs). IMGT®, the international ImMunoGe-neTics information system® (https://www.imgt.org), extends the characterization of therapeutic antibodies with a systematic description of their mechanisms of action (MOA) and makes them available through its database for mAbs and fusion proteins, IMGT/mAb-DB. Methods: Using the available literature data combined with the amino acid sequence analyses from mAbs managed in IMGT/2Dstructure-DB, the IMGT® protein database, biocuration allowed to define in a standardized way descriptions of MOA of mAbs that target molecules towards MG treatment. Results: New therapeutic targets include FcRn and molecules such as CD38, CD40, CD19, MS4A1 and interleukin-6 receptor. A standardized graphical representation of the MOA of selected mAbs was created and integrated within IMGT/mAb-DB. The main mechanisms involved in these mAbs are either blocking or neutralizing. Therapies directed to B cell depletion and plasma cells have a blocking MOA with an immunosuppressant effect along with Fc-effector function (MS4A1, CD38) or FcγRIIb engager effect (CD19). Monoclonal antibodies targeting the complement also have blocking MOA with a complement inhibitor effect and treatments targeting T cells have a blocking MOA with an immunosuppressant effect (CD40) and Fc-effector function (IL6R). On the other hand, FcRn antagonists present a neutralizing MOA with an FcRn inhibitor effect. Conclusion: The MOA of each new mAb needs to be considered in association with the immuno-pathogenesis of each of the subtypes of MG in order to integrate the new mAbs as a viable and safe option in the therapy decision process. In IMGT/mAb-DB, mAbs for MG are characterized by their sequence, domains, chains and their MOA is described.

Crossostephium chinense is a wild species with strong salt tolerance, which has great potential to improve the salt tolerance of cultivated chrysanthemums. Conversely, the unique salt-tolerant molecular mechanisms of Cr. chinense are still unclear. This study performed a comparative physiological and transcriptome analysis of Cr. chinense, Chrysanthemum lavandulifolium and their hybrids. The physiological results showed that Cr. chinense maintained a higher superoxide dismutase (SOD) activity, to alleviate the oxidative damage to membrane. KEGG enrichment analysis showed that the plant hormone signaling transduction and the MAPK signaling pathway were mostly enriched in Cr. chinense and hybrids under salt stress. Further weighted gene co-expression network analysis (WGCNA) of DEGs suggested that the abscisic acid (ABA) signaling transduction may play a significant role in the salt-tolerant mechanisms of Cr. chinense and hybrids. The tissue-specific expression patterns of the candidate genes related to ABA signaling transduction and MAPK signaling pathway indicated that genes related to ABA signaling transduction performed significant expression levels under salt stress. This study provides a theoretical foundation for future research into the molecular mechanism of salt tolerance in chrysanthemums under salt stress.

The ankle is a complex joint with a high injury incidence. Rehabilitation Robotics applied to the ankle is a very active research field. We present cable-driven reconfigurable robot kinematics and statics. We studied how the tendons pull mid-foot bones around the talocrural and subtalar axes. Likewise, we propose a hybrid serial-parallel mechanism analogous to the ankle. Then, using screw theory, we synthesized a cable-driven robot with the human ankle in the closed-loop kinematics. We incorporate a draw-wire sensor to measure the axes’ pose and compute the product of exponentials. We reconfigure the cables to balance the tension and pressure forces using the axis projection on the base and platform planes. Likewise, we also computed the workspace to show that the reconfigurable design fits several sizes. The data used is from anthropometry and statistics. Finally, we validated the robot’s statics with MuJoCo for various cable length groups corresponding to the axes’ range of motion. We suggested a platform adjusting system and an alignment method. The design is lightweight, and the cable-driven robot has advantages over rigid parallel robots, such as Stewart platforms. We will use compliant actuators.

Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and high missed detection rates of offshore ship targets in large-scene SAR images are due to the occlusion of objects or mutual occlusion among targets, especially for small ship targets. To solve this problem, this study proposes a target detection model (TAC_CSAC_Net) that incorporates a multi-attention mechanism for detecting marine vessels in large-scene SAR images. Experiments were conducted on two public datasets, the SAR-Ship-Dataset and high-resolution SAR image dataset (HRSID), with multi-scene and multi-size, and the results showed that the proposed TAC_CSAC_Net model achieves good performance for both small and occluded target detection. Experiments were conducted on a real large-scene dataset, LS-SSDD, to obtain the detection results of subgraphs of the same scene. Quantitative comparisons were made with classical and recently developed deep learning models, and the experiments demonstrated that the proposed model outperformed other models for large-scene SAR image target detection.

In this paper we will provide a new equation that explains why there are three generations or families of leptons and quarks, respectively. We will also explain why all those particles have the known mass ratios amongst their three respective generations and flavors. We will also tackle the problem of Yukawa couplings being arbitrary parameters in the Standard Model Higgs mechanism, which is a long standing problem do to their formulaic dependence on the Higgs Vacuum Expectation Value (VEV). We will attempt to solve this problem and provide a strong argument through an equation for Yukawa couplings of all leptons and quarks via a new methodology that depends on the running of the fine-structure constant on the Q scale, quantum numbers and the Weinberg angle (also on the Q scale). We will also make predictions for all three left-chiral neutrino mass eigenstates and we will provide upper limits for the three right-chiral neutrino mass eigenstates

To address the deficiency of existing relation extraction models in effectively extracting relational triplets pertaining to railway traffic knowledge in Tibet, this paper constructs a Tibet Railway Traffic text dataset and provides an enhanced relation extraction model. The proposed model incorporates subject feature enhancement and relational attention mechanisms. It leverages a pre-trained model as the embedding layer to obtain vector representations of text. Subsequently, the subject is extracted and its semantic information is augmented using an LSTM neural network. Furthermore, during object extraction, the multi-head attention mechanism enables the model to prioritize relations associated with the aforementioned features. Finally, objects are extracted based on the subjects and relations. The proposed method has been comprehensively evaluated on multiple datasets, including the Tibet Railway Traffic text dataset and two public datasets. The results on the Tibet dataset achieves an F1-score of 93.3\%, surpassing the baseline model CasRel by 0.8\%, indicating a superior applicability of the proposed model. On the other hand, the model achieves F1-scores of 91.1\% and 92.6\% on two public datasets, NYT and WebNLG, respectively, outperforming the baseline CasRel by 1.5\% and 0.8\%, which highlights the good generalization ability of the proposed model.

Background: The seasonal variation of peptic ulcers is a rhythmic phenomenon reported worldwide, exhibiting diverse patterns and controversies. Unfortunately, it has remained an unresolved mystery for more than 90 years. Numerous studies have found that this phenomenon was closely associated with multiple environmental factors, but the underlying mechanism has never been elucidated. Objectives: This study aims to elucidate the seasonal variation of peptic ulcers and identify the role of environmental factors in the disease. Methods: Based on a recently identified etiology of peptic ulcers, two inverse operations in calculus, differentiation and integration, are iterated to analyze the existing data. First, the fluctuation curve in the seasonal variation is differentiated twice into the monthly incidences caused by multiple individual environmental factors, and the fluctuation curve due to each individual environmental factor is generated separately. Second, the monthly incidences caused by the individual environmental factors are integrated twice to reproduce the fluctuation curves in the seasonal variation of peptic ulcers. Results: The differentiations of the fluctuation curves in the season variation reveal a parallel relationship between the psychological impacts of each individual environmental factor and the monthly incidences of peptic ulcers. The integrations of the monthly incidences caused by 3 environmental factors reproduce the fluctuation curves in 3 representative seasonal patterns of peptic ulcers but make the parallel relationships invisible. Discussion: The parallel relationships revealed a causal role of environmental factors in peptic ulcers, whereas the reproduction of the fluctuation curves elucidated that multiple environmental factors cause the seasonal variation of peptic ulcers by Superposition Mechanism. The regional differences in environmental factors result in the diverse patterns, as well as the controversy questioning the seasonality of peptic ulcers. Significantly, the data analyses exemplify the application of a new concept, Superposition Mechanism, which might be an indispensable methodological complement to life science and medicine.

Speech Emotion Recognition (SER) is an interesting and difficult problem to handle. In this paper, we deal with it through the implementation of deep learning networks. We have designed and implemented six different deep learning networks, a Deep Belief Network (DBN), a simple deep neural network (SDNN), a LSTM network (LSTM), a LSTM network with the addition of an attention mechanism (LSTM-ATN), a Convolutional neural network (CNN), and a Convolutional neural network with the addition of an attention mechanism (CNN-ATN), having in mind, apart from solving the SER problem, to test the impact of attention mechanism to the results. Dropout and Batch Normalization techniques are also used to improve the generalization ability (prevention of overfitting) of the models as well as to speed up the training process. The Surrey Audio-Visual Expressed Emotion database (SAVEE), and the Ryerson Audio-Visual Database (RAVDESS) database were used for training and evaluation of our models. The results showed that networks with the addition of the attention mechanism did better than the others. Furthermore, they showed that CNN-ATN was the best among tested networks, achieving an accuracy of 74% for the SAVEE and 77% for the RAVDESS dataset, and exceeded existing state-of-the-art systems for the same datasets.

Upcycling recycled carbon fibers recovered from waste carbon composites can reduce the price of carbon fibers while improving disposal-related environmental problems. This study assessed and characterized recycled carbon fibers subjected to sizing treatment using N-(2-aminoethyl)-3-aminopropyltrimethoxysilane (APS) chemically coordinated with polyamide 6 (PA6) and polypropylene (PP) resins. Sizing treatment with 1 wt.% APS for 10 s yielded O=C-O on the surface of the carbon fiber, and the -SiOH in the APS underwent a dehydration-condensation reaction that converted O=C-O (lactone groups) into bonds of C-O (hydroxyl groups) and C=O (carbonyl groups). The effects of C-O and C=O on the interfacial bonding force increased to a maximum, resulting in an oxygen-to-carbon ratio (O/C) of 0.26. The polar/surface energy ratio showed the highest value of 32.29% at 10 s, and the interfacial bonding force showed the maximum value of 32 MPa at 10 s, which is about 15% better than that of commercial carbon fiber (PA6-based condition). In 10-s resizing treatments with 0.5 wt.% 3-methacryloxypropyltrimethoxysilane (MPS), C-O, C=O, and O=C-O underwent a dehydration-condensation reaction with -SiOH, which broke the bonds between carbon and oxygen and introduced a methacrylate group (H2C=C(CH3)CO2H), resulting in a significant increase in C-O and C=O, with an O/C of 0.51. The polar/surface free energy ratio was about 38% at 10 s, with the interfacial bonding force increasing to 27% compared to commercial carbon fiber (PP-based conditions). The MPS exhibited a superior interfacial shear strength improvement, two times higher than that of APS, with excellent coordination with PP resin and commercial carbon fiber, although the interfacial bonding strength of the PP resin was significantly lower.

Background: In 1962, Susser and Stein observed that the mortality rates of peptic ulcers in England and Wales increased markedly in the 1910s, maintained at a high level during the 1910s-1940s, and began to decline steadily since the early 1950s. They termed this epidemic pattern a birth-cohort phenomenon, but its mechanism has never been elucidated. Susser and Stein speculated that the occurrences of extraordinary social environmental factors roughly fit the fluctuations, but the role of environmental factors in peptic ulcers has never been identified. Objectives: This study aims to elucidate the mechanism of the birth-cohort phenomenon of peptic ulcers and identify the role of environmental factors in the disease. Methods: Starting from an etiology identified recently, this study uses the two inverse operations in calculus, differentiation, and integration, to analyze the existing data. First, a fluctuation curve in the birth-cohort phenomenon is differentiated into multiple annual mortality rates caused by individual environmental factors. Second, these annual mortality rates are integrated to reproduce the fluctuation curve in the birth-cohort phenomenon. Results: The differentiation reveals a parallel relationship between the psychological impacts of environmental factors and the mortality rates of peptic ulcers, whereas the integration reproduces a representative fluctuation curve in the birth-cohort phenomenon. The successive occurrences of multiple social environmental factors from the 1910s to 1940s maintained the increase in mortality rates, while the sustained improvements in living environments explained the steady decline of the mortality rates from the early 1950s. Conclusion: The parallel relationship suggests a causal role of environmental factors in peptic ulcers, whereas the reproduction of a representative fluctuation curve indicates that multiple environmental factors caused the birth-cohort phenomenon by Superposition Mechanism. Significantly, this study challenges the current reductionist approach used to study disease in modern medicine by demonstrating the effectiveness of an opposite methodological concept focused on integration.

Consciousness is a special type of interaction between subjects exchanging by lingua quanta (phonemes). A set of lingua quanta composes a thesaurus placed on the edge of chaos. Its library is a memory, modification of which is due to tuning of memristive neural elements scattered in the brain volume. The memristive neural model takes into consideration two types of neurons - excitatory and inhibitory and the current leakage, all at the body temperature, T = 309 K. At such temperatures only heavy ions like hydrogen ions - protons can pass robustly through the water medium of the brain. The robust ion transport is proton water wires supported by the Grotthuss mechanism. Final aims of the ions are the gap junctions (electric synapses) linking nearest neurons. Following these observations, a model of excitable nervous tissue is constructed. Namely, a difference mapping on the basis of sigmoid curves can reproduce chaotic modes of neural activity proved by positive values of the Lyapunov exponent. The edge of chaos is placed near the bifurcation boundary dividing the chaos and the periodic convulsive activity typical at the epileptic discharges. In this region the self-sustained spiral waves occur. An intermittent activity of the competing excitatory and inhibitory neurons is observed on the edge of chaos. The intermittent electrical activity of neural tissues is demonstrated by records both from different literature issues and records made by the author together with DR. A. Dudkin on slices of CA1 field of the hippocampus.

This investigation delves into the profound interplay between the e-commerce environment and the agricultural system, while scrutinizing the intricate human-land coupling dynamics engen-dered by transformations within the agricultural domain. Focusing on the expansion of orchards in Pinghe County, a pivotal epicenter for Chinese sweet pomelo production, this study eluci-dates the reverberations of Rural E-commerce Environment Development on Orchard Expansion through the lens of Tele-coupling. In doing so, it unveils the nuanced tapestry of the human-land coupling associations latent within the evolutionary trajectory of the agricultural system amidst the blossoming information society. The findings underscore a symbiotic relationship between the augmentation of orchard acreage and the burgeoning rural e-commerce landscape, effec-tively propelling the economic prosperity of the agricultural system. Moreover, a tele-coupling phenomenon has emerged, underscoring the intricate web of interconnections binding the mat-uration of the rural e-commerce ecosystem with the proliferation of orchards. The research illu-minates that the transformation of land utilization, imbricated within the agricultural system, manifests as a distinctive form of tele-coupling intricately woven into the fabric of urbanization and information technology advancements. Consequently, adopting a comprehensive theoreti-cal perspective, amalgamating disparate domains across distinct geographic realms, becomes imperative for discerning the intricate nuances of the human-land coupling nexus within these multifaceted, open systems.

The diverse physical appearances and wide density range of polymer bead foams offer immense potential across various applications and future advancements. The multi-scale and multi-level structural features of bead foams involve many fundamental scientific topics. This review presents a comprehensive overview of recent progress in bead foams preparation and molding techniques. Initially, a comparative analysis is conducted among bead foam characteristics of distinct polymers, based on their unique properties. Subsequently, a summary and comparison of molding processes employed for fabricating bead foam parts are provided. Beyond traditional methods like steam-chest molding (SCM) and adhesive-assisted molding (AAM), emerging techniques like in-mold foaming and molding (IMFM) and microwave selective sintering (MSS) are highlighted. Lastly, the bonding mechanisms behind the diverse molding methods are discussed.

Understanding the thermal cracks of rock caused by high temperature is of great help to the development of underground engineering, such as geothermal mining, underground coal gasification. Although there has been many study on thermal cracking, it is not systematic and in-depth enough. In order to deeply study the thermal crack mechanism of rock, this paper conducted thermal cracking experiments on granite at different temperatures and heating/cooling paths, and qualitative and quantitative analysis was conducted on the crack initiation characteristics, propagation paths, and crack network morphology of rock thermal crack under different test conditions. The thermal crack mechanism was also analyzed from the perspectives of mineral petrology, fracture mechanics, thermodynamics, and other aspects. The research results show that there are two obvious mutation points in the study temperature range for samples with fast cooling paths (SF path: slow heating and fast cooling; FF path: fast heating and fast cooling), around 200-300 ℃ and 600 ℃, respectively, while for samples with slow cooling paths (SS path: slow heating and slow cooling; FS path: fast heating and slow cooling), there is only one mutation point around 600 ℃. The initiation positions of thermal cracking under all temperature paths are relatively similar, with intergranular crack located between particles at the edge of the sample or intercrystalline crack in the middle of feldspar or quartz aggregates. The initiation temperature of SF and FF path specimens is relatively low compared to SS and FS path specimens, and the number and size of cracks is small. The crack network structure formed by the SF path is the most complex, with the largest crack ratio and cumulative crack length; The crack network structure formed by the FF path is relatively complex, with a larger main crack size but relatively fewer secondary cracks; The FS and SS paths do not form a complex network with good connectivity. The process of thermal crack development can be divided into three stages: the development of small cracks, the joint development of main cracks and small cracks, and the connection of cracks into a network structure. The mechanism of thermal crack propagation is that the expansion and thermal conductivity between different crystals are different. The thermal stress caused by temperature gradient and the tension or shear stress caused by the inconsistent deformation of crystals form stress concentration in weak areas such as particles boundary, cleavage, and original cracks. Firstly, it causes some crystals with smaller strength or less rounded shape to crack, and when the combined stress is large, the cracks will gradually expand along the existing fine cracks. This also explains that the most main cracks of the SF and FF path specimens mainly surround some large mineral aggregates or between particles.

Addressing global environmental problem, water splitting to produce hydrogen fuel by solar energy is getting so much attention. In the water splitting, the essential problem to solve is the development of efficient　catalysts for oxygen production. In this paper, having the prospect for a practical application of photocatalysts to artificial photosynthesis, molecular mechanisms in the current literature are briefly reviewed. At first, recent progress in the function of Mn cluster at the natural photosystem II is briefly described. The kinds of devices in which oxygen evolution reaction (OER) catalysts are used were designated; water electrolyzers, photoelectrodes, and photocatalysts. Some methods for analyzing molecular mechanism in OER catalysis, emphasized by FTIR method, are shown briefly. After describing common OER mechanisms, the molecular mechanisms are discussed for TiO2 and BiVO4 photoelectrodes with our novel data, followed by presenting of co-catalysts IrO2, RuO2, NiO2, and other metal oxides for OER catalysts. Recent reports describing OER catalysts of perovskites, layered double hydroxides (LDH), metal-organic frameworks (MOF), single atom catalysts, as well as metal complexes are reviewed. Finally, by comparing with natural photosystem, the requiring factors to improve the activity of the catalysts for artificial photosynthesis will be discussed.

Diabetic kidney disease (DKD) is one of the leading causes of end-stage renal disease worldwide and significantly increases the risk of premature death due to cardiovascular diseases. Elevated urinary albumin levels are an important clinical feature of DKD. Effective control of albuminuria not only delays glomerular filtration rate (GFR) decline but also markedly reduces cardiovascular disease risk and all-cause mortality. New drugs for treating DKD proteinuria, including sodium-glucose cotransporter 2 inhibitors (SGLT2i), mineralocorticoid receptor antagonists (MRAs), and endothelin receptor antagonists (REAs), have shown significant efficacy. Auxiliary treatment with proprietary Chinese medicine has also yielded promising results; however, it also faces a broader scope for development. The mechanisms by which these drugs treat albuminuria in patients with DKD should be described more thoroughly. The positive effects of combination therapy with two or more drugs in reducing albuminuria and protecting the kidneys warrant further investigation. Therefore, this review explores the pathophysiological mechanism of albuminuria in patients with DKD, the value of clinical diagnosis and prognosis, new progress and mechanisms of treatment, and multidrug therapy in patients who have type 2 diabetic kidney disease (T2DKD), providing a new perspective on the clinical diagnosis and treatment of DKD.

In order to solve the problems of complicated steering control of unmanned vehicles in the field and difficult steering on complex roads, we designed a wheel-track composite vehicle equipped with a novel power differential steering mechanism with dual driving, which drove the steering of the vehicle through the differential rotation of the rear two wheels. The unmanned vehicle was simple to control, small in size, and able to work under the conditions of complex roads, such as hills, mountains, and muddy land. Firstly, a steering mechanism with both differential speed and force was designed to prevent the vehicle from skidding into muddy land and stopping motionless. Secondly, the kinematics model and dynamics model of the two drive shafts and the two output shafts (wheel shafts) were established. Thirdly, according to the relationship between the rotational speed of the two output shafts and the steering radius of the vehicle, the kinematic model of the rotational speed of the two input shafts and the steering radius of the wheel-track composite vehicle was obtained. Finally, according to the test data, the mathematical model of rotational speeds of the two input shafts and the actual steering radius of the vehicle was obtained by neural network fitting, and the maximum relative error between the model results and the actual steering radius value was 3.53%. The combination of power differential steering mechanism and wheel-track composite unmanned vehicle increased the adhesion with the ground and could better adapt to the complex road environment. In conclusion, the unmanned vehicle had the advantages of continuous radius steering, deceleration and torsion increase, differential lock, etc. It was suitable for all-terrain military and civilian vehicles and various special equipment mobile platforms of the walking device, and the research results could provide theoretical references for the steering control and structural optimization of the wheel-track composite vehicle under the environment of complex road surface.

Fused silica glass is a material with outstanding mechanical, thermal and optical properties. Being a brittle material, it is challenging to shape. In the last decade, the manufacturing of monolithic flexible mechanisms in fused silica has evolved with the femtosecond laser-assisted etching process. However, instrumenting those structures is demanding. To address this obstacle, this article proposes to inscribe a Bragg Grating sensor inside a flexure and interface it with an optical fibre to record the strain using a spectrum analyzer. The strain sensitivity of this Bragg Grating sensor is characterized at 1.2 pm/μϵ (1 μϵ = 1 microstrain). Among other applications, deformation sensing can be used to record a force. Its use as a micro-force sensor is estimated. The sensor resolution is limited by our recording equipment to 30 μN over a measurement range above 10 mN. This technology can offer opportunities for surgery applications or others where precision and stability in harsh environments are required.

Polyhedral Oligomeric Silsesquioxanes (POSS) are one of the smallest silica nano particles. The presence of ordered ladder-like or cage conformation in their structure leads to remarkable thermal stability far greater than that for a linear polysiloxane. Reinforcement with nano particles is one of the recently favored strategy used to enhance the performance of polymers materials. The cyclic silica structure present in POSS ensures excellent thermal and mechanical stability. As a reinforcing filler, POSS can enhance the resistance of polymer matrix against pyrolysis and corrosion. In this paper, amine-terminated POSS with ladder like structure was synthesized by condensation of hydrolyzed aminosilane coupling agent. The ladder-like structure of POSS severely limits the motion of the chain backbone. The absence of phase transition for the synthesized POSS was confirmed by differential scanning calorimetry (DSC). Thermogravimetric analysis (TGA) was utilized to evaluate the thermal stability of POSS. TGA data show that the ladder-like POSS reported here has high char yield of about 75 % in nitrogen atmosphere and 54% in air atmosphere at 800℃. The TGA derivative curves show that the degradation of ladder-like POSS involves multiple stages and that the degradation rate is directly proportional to the heating rate. With the aid of Fourier Transform Infrared Spectroscopy (FT-IR), the cyclic structure of Si-O-Si bonding was determined to be the predominant structure. By analyzing the FTIR spectra of the cured POSS residue obtained at the specified temperatures, the detailed degradation mechanism of POSS was inferred. From the FTIR spectra of the low temperature cured samples, it was shown that the terminal silanol group was eliminated below 400℃. Some intermediate polymeric siloxane structures were observed by means of FT-IR and they eventually transitioned into silica which is the final product of pyrolysis of POSS. The presence of the FTIR transmission peaks at 1000 and 1100 cm-1, due to asymmetrical vertical and horizontal stretching of Si-O-Si, were the key evidence for the inferred ladder-like structure of POSS.

Real-time tracking is one of the most challenging problems in computer vision. Most Transformer-based trackers usually require expensive computational and storage power, which leads to the inability of these robust trackers to achieve real-time performance in resource-constrained devices. In this work, we propose a lightweight tracker, AnteaTrack. To localize the target more precisely, we develop a scaling-invariant max-filtering operator employing a sliding window combined with local max-pooling, which filters out the suspected target from the feature and performs an augmented representation while suppressing the background. For a more tight bounding box, we employ Pixel-Shuffle to increase the resolution of the feature map and get a more fine-grained representation of the target. In addition, AnteaTrack can run in real-time at 47 frames per second(FPS) on the CPU. We tested AnteaTrack on 5 datasets, and a large number of experiments have shown that AnteaTrack provides the most efficient solution compared to the same type of CPU real-time trackers. The code will be available at https://github.com/cnchange/AnteaTrack.

MoS2 has long been considered as a promising catalyst for hydrogen production. At present, there are many strategies to further improve its catalytic performance, such as edge engineering, defect engineering, phase engineering and so on. However, at present, there is still a great deal of controversy about the mechanism of MoS2 catalytic hydrogen production. For example, it is generally believed that the base plane of MoS2 is inert, but it has been reported that the inert base plane can undergo a transient phase transition in the catalytic process to play the catalytic role, which is contrary to the common understanding that the catalytic activity is only at the edge. Therefore, it is necessary to further understand the mechanism of MoS2 catalytic hydrogen production. In this article, we summarized the latest research progress on the catalytic hydrogen production of MoS2, which is of great significance for revisited the mechanism of MoS2 catalytic hydrogen production.

Individual investors often struggle to predict stock prices due to the limitations imposed by the computational capacities of personal laptop Graphics Processing Units (GPUs) when running intensive deep learning models. This study proposes solving these GPU constraints by integrating deep learning models with technical analysis methods. This integration significantly reduces analysis time and equips individual investors with the ability to identify stocks that may yield potential gains or losses in an efficient manner. Thus, a comprehensive buy and sell algorithm, compatible with average laptop GPU performance, is introduced in the study. This algorithm offers a lightweight analysis method that emphasizes factors identified by technical analysis methods, thereby providing a more accessible and efficient approach for individual investors. To evaluate the efficacy of this approach, we analyzed the performance of eight deep learning models: LSTM (4 layers), CNN, BiLSTM (4 layers), CNN Attention, BiGRU CNN BiLSTM Attention, BiLSTM Attention CNN, CNN BiLSTM Attention, and CNN Attention BiLSTM. These models were used to predict stock prices for Samsung Electronics and Celltrion Healthcare. The CNN Attention BiLSTM model displayed superior performance among these models, with the lowest validation mean absolute error value. In addition, an experiment was conducted using WandB Sweep to determine the optimal hyperparameters for four individual hybrid models. These optimal parameters were then implemented in each model to validate their back-testing rate of return. The CNN Attention BiLSTM hybrid model emerged as the highest-performing model, achieving an approximate rate of return of 5 percent. Overall, this study offers valuable insights into the performance of various deep learning and hybrid models in predicting stock prices. These findings can assist individual investors in selecting appropriate models that align with their investment strategies, thereby increasing their likelihood of success in the stock market.

The effectiveness of a recently proposed methodology in the identification of damage in planar, multi-storey, Reinforced Concrete (RC) moment-frames, which develop a plastic-yield mechanism on their beams, is showcased here via the examining of a group of such existing multi-storey frames with three or more unequal spans. According to the methodology, the diagram of the instantaneous Eigen-Frequencies of the frame in the nonlinear regime is drawn as a function of the inelastic seismic roof displacement by performing a sequence of pushover and instantaneous modal analyses with gradually increasing target displacement. Using this key-diagram, the locations of severe seismic damage in an existing moment-frame can be evaluated if the instantaneous fundamental eigen-frequency of the damaged frame, at an analysis step within the nonlinear area, is known in advance by “the monitoring and the identification of frequencies” using a local network of uniaxial accelerometers. This is a hybrid technique because both procedures, the instrumental Monitoring of the structure and the Pushover analysis on the frame (M and P technique), are combined. Moreover, the damage image of the planar multi-storey moment-frame is illustrated, and the lateral stiffness matrix of the damaged frame is calculated with high accuracy.

With the rapid development in Beijing, there is a critical need to explore the circumstance and reveal the mechanisms of precious urban natural resources. In this context, urban blue space has attracted more and more attention by driving microcirculation, cooling heat islands, and relaxing residential. We extracted the UBS at Beijing using remote sensing, explored the spatial and temporal development in the last two decades via USDA methods, uncovered the full spectrum of landscape patterns from an ecological perspective, and simulated the mechanisms of the UBS area and the landscape quantitatively. We found that: (1) The UBS area in Beijing increased with fluctuation from 2000 to 2020. (2) The spatial clustering has distributed stable with some subtle changes. (3)The ecological circumstance has improved in the last 21 years in Beijing, with the increasing habitat diversity and richness, while the inferior landscape fragmentation has indicated some severe challenges. (4) Natural factors impact urban blue space areas more than social ones, while both similarly influence the UBS landscape. (5) Vegetation circumstances and precipitation are the most important natural factors on both area and landscape of UBS, and population and artificial surface are the most important social factors.

Fifty-two kinds of N’-phenylhydrazides were successfully designed and synthesized. Their anti-fungal activity in vitro against five strains of C. alb. was evaluated. All prepared compounds owned varying degrees of activity and their MIC80, TAI, and TSI values were calculated. The in-hibitory activities of 27/52 compounds against fluconazole-resistant fungi C. alb. 4395 and C. alb. 5272 were much higher than those of fluconazole. The MIC80 values of 14/52 compounds against fluconazole-resistant fungus C. alb. 5122 were less than 4 μg/mL, so it was the most sensitive fungus (TSIB = 12.0). A11 showed the highest inhibitory activity against C. alb. SC5314, 4395, and 5272. The antifungal activities of B14 and D5 against four strains of fluconazole-resistant fungi were higher than those of fluconazole. The TAI values of A11 (2.71), B14 (2.13), and D5 (2.25) are the highest. Further exploring the antifungal mechanism revealed that the fungus treated with compound A11 produced free radicals and reactive oxygen species, and their mycelium mor-phology was damaged. In conclusion, the hydrazide scaffold showed potential in the develop-ment of antifungal lead compounds. Among them, A11, B14, and D5 demonstrated particularly promising antifungal activity and hold potential as novel antifungal agents.

Drug-induced pruritus triggers a desire to scratch, thereby diminishing one’s quality of life. Certain instances of this phenomenon follow complex mechanisms of action that diverge from histamine-mediated pathways, known contributors to pruritus. However, investigations into the relationship between drugs and pruritus are limited. In this study, data mining techniques were employed to comprehensively analyze the characteristics of drugs linked to pruritus, using the FDA Adverse Event Reporting System(FAERS) Data maintained in the United States. Reports linked to pruritus demonstrated noteworthy differences in gender, age, and weight when compared with non-pruritus cases. Among the leading candidates for drugs prompting pruritus were ophthalmic drugs, systemic antibacterials, contrast media, dermatological antifungals, and dermatological preparations. Principal component analysis showed that the second principal component served as an indicator for distinguishing between onsets at mucous membranes or the skin surface. Additionally, the third principal component functioned as an indicator for categorizing administration methods as either invasive or noninvasive. Furthermore, a hierarchical cluster analysis conducted on these obtained principal components revealed the potential for classifying drugs based on the site of pruritus onset and the method of drug administration. These findings contribute to the development of targeted prevention and treatment strategies for avoiding pruritus in clinical practice.

Synthetic Aperture Radar (SAR) is an active sensor that uses microwave for sense, it is unrestricted by weather and illumination conditions, and it can observe targets all day and weather. Aircraft targets are important monitoring objects in military and civilian fields, and how to efficiently detect and recognize aircraft targets is an important topic in the field of SAR image interpretation. Based on the features of SAR images, such as complex background, high resolution, and multi-scale, we proposed an improved method based on YOLOv5s. Firstly, this paper proposed the structure of the multi-scale receptive field and channel attention fusion, which is applied to the shallow layer of the backbone of YOLOv5s, it can adjust the weights of the multi-scale receptive field during the training process to enhance the extraction ability of feature information. Secondly, we proposed four de-coupled detection heads to replace the original part in YOLOv5s, which can improve the efficiency and accuracy of SAR image interpretation for small targets. Thirdly, in the case of the limited amount of SAR images, this paper proposed multi methods of data augmentation, which can enhance the diversity and generalization of the network. Fourthly, this paper proposed the K-means++ to re-place the original K-means to improve the network convergence speed and detection accuracy. Finally, Experiments demonstrate that the improved YOLOv5s can enhance the accuracy of SAR image interpretation by 9.3%, and the accuracy of small targets is improved more obviously, reaching 13.1%.

Adipose tissue has acquired considerable importance due to its role in maintaining body homeostasis. Beyond storing energy, adipose tissue is essential in numerous processes of energy metabolism, food intake, endocrine and immunological functions as well as neromodulatory functions by secreting hormones that participate in the regulation of homeostasis and being responsive to soluble proteins secreted by other tissues. An imbalance of these functions will generate structural and functional changes in the adipose tissue, favouring the secretion of deleterious adipocytokines that benefit a pro-inflammatory state, allowing the development of metabolic and cardiovascular diseases. Obesity is a disease that has increased rapidly in recent decades and is caused by environmental, hormonal, and genetic factors, which probably work in combination. Environmental factors that contribute to the increase in obesity include decreased physical activity, associated with increased screen time, increased accessibility and consumption of foods rich in fats and sugars. Weight loss should be recommended for all obese patients and overweight patients with comorbidities. For many patients who need to lose weight for medical reasons, the initial goal is to lose 5-10% of their body weight in the first 6 months. A common theme worldwide has been the development of professional guidelines for the control and treatment of obesity and in Chile, the Chilean Society of Metabolic and Bariatric Surgery led the process of adopting the adult obesity clinical practice guideline, which establishes changes in the approach to managing obesity as a chronic disease, with an emphasis on multifactorial lifestyle interventions that include dietary changes, increased physical activity, and behavioural modifications. Pharmacotherapy, medical devices, and bariatric surgery are other options for patients who need additional interventions, so the objective of this review is to know the pathophysiological mechanisms of obesity from its origin to the current treatments available for treatment.

In the past decade, under the background of the carbon peaking and carbon neutrality era, the rapid development of new energy vehicles has put forward higher requirements for the performance of strike forces such as battery cycle life, energy density and cost. Lithium-ion batteries have gradually become the mainstream of electric vehicle power batteries due to their excellent energy density, rate performance and cycle life. At present, the most widely used cathode materials for power batteries are lithium iron phosphate (LFP) and ternary nickel-cobalt-manganese (NCM). However, these materials exhibit the bottlenecks that limit the improvement and promotion of power battery performance. In this review, the performance characteristics, cycle life attenuation mechanism (including structural damage, gas generation and active lithium loss, etc.) and improvement methods (including surface coating and element-doping modification) of LFP and NCM batteries are reviewed. Finally, the development prospects of this field are proposed.

Retention mechanisms in HILIC have been investigated and reported in literature. However, the current understanding of the retention mechanisms is qualitative and lacks quantitative details. Previously, mechanism elucidation was based on indirect evidence, and unambiguous assignment of retention mechanisms has not been reported based on direct data. This study aims to quantitatively determine the contributions of two major retention mechanisms in HILIC, hydrophilic partitioning and surface adsorption to the overall retention of neutral compounds. Using the methodologies we developed previously, the phase ratio for adsorbed water layer and distribution coefficients were measured and used to calculate the retention factors attributed to hydrophilic partitioning. The methodology allows the determination of the contribution of surface adsorption simultaneously. The evaluation of five test compounds demonstrates that the retention may be controlled by hydrophilic partitioning, surface adsorption or both depending on compound characteristics. Quantitative assessment of retention mechanisms also makes it possible to better understand the effect of acetonitrile on retention in HILIC.

The green peach aphid, Myzus persicae (Sulzer) (Hemiptera: Aphididae), is an economically important pest of crops worldwide. A M. persicae (SEF-R) population from a cabbage field in southeastern China was tested for susceptibilities to 13 insecticides. Compared with the susceptible clone (FFJ-S), extremely high and high-resistance to beta-cypermethrin (324-fold) and imidacloprid (106.9-fold) were detected in SEF-R. More importantly, this is the first report of resistance in field M. persicae population to sulfoxaflor, flupyradifurone, pymetrozine, spirotetramat, flonicamid, and broflanilide in China. The resistance factor decayed to a low level to sulfoxaflor and pymetrozine after 15 generations without any selection pressure. The resistance-related mutations (R81T and kdr) were detected in SEF, conferring target-site resistance to neonicotinoids and pyrethroids, respectively. Biochemistry bioassays confirmed the involvement of monooxygenase enzyme, carboxylesterase, superoxide dismutase and peroxidase in multi-insecticide resistance mechanism. The overexpression of P450s, esterases, and a UDP-glycosyltransferase, might be responsible for the multi-insecticide resistance in SEF-R. The knockdown of CYP6CY3 in SEF-R increased its susceptibility to imidacloprid, thiacloprid, and thiamethoxam, which verified that P450s play vital roles in neonicotinoid metabolism. This study will be helpful for chemical control, as well as for resistance monitoring and management of GPA, in China.

Abstract: Because of outstanding heat resistance and processing qualities, benzoxazine resins are widely employed in a variety of applications. However, traditional benzoxazine resins still confront hurdles in today's engineering applications, such as can’t long-term service in high-temperature settings and inadequate toughness. Four alkyne-functionalized benzoxazines with phthalide side group and cyano group of varying molecular weights were produced in this study. Fourier trans-form infrared spectroscopy (FT-IR) and hydrogen nuclear magnetic resonance spectroscopy ( 1H-NMR ) were used to characterize the resin structure, and differential scanning calorimetry (DSC) was used to investigate the thermal curing kinetics at different warming rates, the apparent activation energy was 116.9 kJ/mol. In-situ FTIR was used to investigate the cure mechanism. Dynamic mechanical analysis (DMA) was used to evaluate the gelationation time of BOZ series resins at various temperatures, and the curing process was designed by combined the results of DSC. The Tg of the composites made using BOZ-1N21 as the matrix was 336°C, which was much higher than the Tg of the BP-a resin made with aniline, phenolphthalein, and formaldehyde (Tg=251°C). As a result, the resin system is expected to be employed in applications requiring high-temperature resistance and toughness.

This study investigates uranium solvent extraction under AMEX process conditions. The use of pure extractants without diluents or phase modifiers allows not only to reduce the use of volatile organic compounds, but also to provide higher extraction yields without third phase formation. Pure extractants are protonated amines or quaternary ammoniums with suitable counter ion, which act at the interface between ion pairs and protic ionic liquids. The mixture of sulfates anion (SO42-) and bis(trifluoromethanesulfonyl)imide anion (NTf2-) revealed unexpected nonlinear extraction behaviors, which appear highly important to rationalize for optimized application. Spectroscopic analysis (NMR, UV-vis, FT-IR, and EXAFS) showed that uranium extraction occurs via protonated amine and three sulphates. Nonlinear extraction could further be interpreted by considering water and acid transfer between the two phases: at lower sulphate ratios, the release of acid from the organic phase into the aqueous phase was shown to influence the number of protonated amines in the organic phase, affecting uranium extraction before its enhancement. Furthermore, the extraction loss at higher sulphate ratios, was assigned to the destabilization of bidentate uranium-sulphate complexes due to a competition between water and sulphates.

Antioxidants are molecules that neutralize free radicals. In general, the reaction mechanisms of antioxidants are well known. The main reaction mechanisms of antioxidants are electron transfer (ET), proton transfer (PT), H atom transfer (HAT) and radical adduction (RAF). The study of these mechanisms is helpful to understand how antioxidants control high free radical levels on the cell. There are many studies focused on determine the main mechanism of an antioxidant to neutralize a wide spectrum of radicals, mainly reactive oxygen species (ROS) type radicals. Most of these antioxidants are polyphenols type compounds. Some esters, amides and metal-antioxidants have shown antioxidant activity. There are few experimental and theoretical studies about the antioxidant reaction mechanism of the aforementioned compounds. In this work, we shown the reaction mechanism proposed of an amide and its metal-antioxidant counterpart. We show how the presence of the metal increase the electron transfer on polar media and the H transfer in non-polar media. Even though, esters and amides are non-polar compound, the scavenger activity is good for the metal-antioxidant compound in no-polar media

The growing interest in Na-ion batteries as a “beyond lithium” technologies for energy storage drives the research for high-performance and environment-friendly materials. Na2Ti3O7 (NTO) as an eco-friendly, low-cost anode material shows a very low working potential of 0.3 V vs Na+/Na but suffers from poor cycling stability, which properties can be significantly influenced by materials synthesis and treatment. Thus, in this work, the influence of the calcination time on the electrochemical performance and the reaction mechanism during cycling were investigated. NTO heat-treated for 48 h at 800 °C (NTO-48h) demonstrated enhanced cycling performance in comparison to NTO heat-treated for only 8 h (NTO-8h). The pristine material was thoroughully characterized by X-ray diffraction, laser granulometry, X-ray photoelectron spectroscopy, and specific surface area measurements. The reaction mechanisms induced by sodiation/desodiation and cycling were investigated by opernado XRD. Electrochemical impedance spectroscopy was used to evidence the evolution of the solid electrolyte interface layer (SEI) and modification of charge transfer resistances as well as the influence of cycling on capacity decay. The evolution of the crystallographic structure of NTO-48h revealed a more ordered structure and lower surface contamination compared to NTO-8h. Moreover, the residual Na4Ti3O7 phase detected after the sodium extraction step in NTO-8h seems correlated to the lower electrochemical performance of NTO-8h compared to NTO-48h.

In recent times, the soft robotics field is gaining numerous research focus owing to its high level of manipulation capabilities unlike traditional rigid robots, which gives room for an increasing use in other areas. However, compared to traditional rigid gripper robots, being capable of controlling/obtaining overall body stiffness when required is yet to be further explored since soft gripper robots have inherently less rigid properties. unlike previous designs with very complex variable stiffness systems, this paper demonstrates a soft gripper design with minimum system complexity while being capable of varying the stiffness of a continuum soft robotic actuator and proves to have potential applications in gripping objects of various shapes, weights & sizes. The soft gripper actuator comprises two separate mechanisms; the pneumatic mechanism for bending control and the mechanical structure for stiffness variation by pulling tendons using stepper motors which compresses the actuator, thereby changing the overall stiffness. The pneumatic mechanism was first fabricated and then embedded into another silicon layer during which it was as well merged with the mechanical structure for stiffness control. By first pneumatically actuating the actuator which causes bending and then pulling the tendons, we found out that the actuator stiffness value can be increased up to 145 % its initial value, and the gripper can grasp & lift a weight of up to 2.075 kg.

This paper proposes a multimodal fusion 3D target detection algorithm based on the attention mechanism to improve the performance of 3D target detection. The algorithm utilizes point cloud data and information from camera. For image feature extraction, the ResNet50+FPN architecture extracts features at four levels. Point cloud feature extraction employs the voxel method and FCN to extract point and voxel features. The fusion of image and point cloud features is achieved through regional point fusion and regional voxel fusion methods. After information fusion, the Coordinate attention mechanism and SimAM attention mechanism extract fusion features at a deep level. The algorithm's performance is evaluated using the DAIR-V2X dataset. The results show that compared to the Part-A2 algorithm, the proposed algorithm improves the mAP value by 7.9% in BEV view and 7.8% in 3D view at IOU=0.5 (cars) and IOU=0.25 (pedestrians and cyclist). At IOU=0.7 (cars) and IOU=0.5 (pedestrians and cyclist), the mAP value of the SECOND algorithm is improved by 5.4% in the BEV view and 4.3% in the 3D view, compared to other comparison algorithms.

In view of the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm all nodes participate in the consensus, the three-stage process has problems such as large communication overhead, random selection of master nodes and lack of reward and punishment mechanism. A Speculative Practical Byzantine Fault Tolerance (SP-PBFT) is proposed. Firstly, the consensus protocol was optimized, and a timeout mechanism was proposed to divide the consensus process into optimistic mode and pessimistic mode, which reduced the communication overhead under the premise of resisting Byzantine node attacks .Secondly, a node evaluation mechanism was proposed to calculate the node reputation value according to the node's historical behavior and give the corresponding reward or punishment, which reduced the possibility of malicious nodes participating in the consensus process. Finally, the Fibonacci grouping mechanism was used to reduce the number of nodes participating in the consensus process, fundamentally improving the consensus efficiency, avoiding the problem of centralization of the consensus process caused by the cumulative reputation value of nodes, and improving the enthusiasm of consensus nodes. Simulation experiments using Docker containers to simulate multiple nodes show that the SP-PBFT consensus algorithm proposed in this paper has better performance than PBFT consensus algorithm and other improved algorithms in terms of consensus delay, throughput, fault tolerance and communication complexity.

Remote sensing imagery is of great significance for policy decisions, especially for disaster assessment and disaster relief. To ensure the privacy and inviolability of personal buildings, the information containing these buildings must be anonymized during the remote sensing mapping process. Traditional processing methods for these targets in remote sensing mapping are mainly based on manual retrieval and image editing tools, which are inefficient. Deep learning provides a new direction for target hiding. Although the image inpainting method based on deep learning is faster than the manual method, the cost of training calculation is a disadvantage. And the element-wise product operation used in the model increases the risk of vanished or exploded gradients. We propose a Residual Attention Target Hiding (RATH) model for remote sensing target hiding based on deep learning. RATH uses residual attention modules to replace gated convolutions, reducing parameters and mitigating gradient issues. The residual attention module preserves gated convolution performance but provides an adjustable kernel size. RATH retains gated convolutions for dynamic feature selection and balances model depth and width. Furthermore, this paper modifies the contextual attention layer by adjusting the fusion process to enlarge the fusion patch size. Finally, we extend the edge-guided function to preserve the original target information and confound viewers. Ablation studies on an open dataset prove RATH’s efficiency for image inpainting and target hiding. RATH achieves state-of-the-art results with lower complexity. And it has the highest similarity for edge-guided target hiding. RATH enables robust, efficient target hiding for privacy protection in remote sensing imagery while balancing performance and complexity. Experiments show RATH's superiority over existing methods in hiding arbitrary-shaped targets.

The ozonation reaction in a bubble column reactor (BCR) has been widely used in the removal of phenol from wastewater, but the phenol removal efficiency in this type of reactor is limited because of low ozone solubility and reactivity in the system. In the present study, the phenol degradation was enhanced using α-Al2O3 as a packing material and a ZnO nanocatalyst in the BCR. The reactor diameter and height were 8 cm and 180 cm, respectively. The gas distributor was designed to include 52 holes of 0.5-mm diameter. Also, the gas holdup, pressure drop, and bubble size were measured as a function of the superficial gas velocity (i.e., 0.5, 1, 1.5, 2, 2.5, and 3 cm/s). The evaluation of the hydrodynamic parameters provided a deep understanding of the ozonation process to select the optimal operating parameters in the reactor. It was found that the best superficial gas velocity was 2.5 cm/s. A complete (100%) phenol removal was achieved for phenol concentrations of 15, 20, and 25 ppm at reaction times of 80, 90, and 100 min, respectively, using α-Al2O3 packing material and a ZnO nanocatalyst in the BCR. Additionally, the results provided a clear reaction mechanism describing the ozonized BCR. Finally, the applied treatment method can be used efficiently to remove phenol from wastewater at a low cost, with little consumed energy, and a simple operation.

Asthma is a prevalent disease that around 300 million worldwide, resulting in substantial morbidity, mortality, and economic burden on a global scale. New clinical and laboratory research has shed light on the immunology causing asthma. Asthma is now recognized as a heterogeneous disease. A personalized medicine is based on the classification of asthma by endotype by linking observable characteristics to immunological mechanisms. Identifying endotype mechanisms is essential for better characterizing patients and personalizing therapeutic approaches with novel biological agents that target specific immune pathways. This article provides a summary of the major immunological mechanisms involved in the pathogenesis and emergence of the disease's phenotypic features, as well as the individualized treatment for severe asthma subtypes

Considering that consumers are more willing to buy products online, companies are increasingly selling remanufactured products online through e-commerce platforms. Notwithstanding the high attention it elicits from researchers and companies, the study about the remanufacturing trading platform is in its infancy. Thus, we investigate 20 remanufacturing trading platforms related nowadays and make a gap analysis among them in terms of: (i) business model, (ii) product display, (iii) delivery products, (iv) quality assurance and after-sales service, (v) product review and star rate and (vi) transaction and payment. On this basis, we analyze features and trends for the development of remanufacturing trading platforms and propose six key applications aimed at filling the identified gaps. The consortium blockchain has the characteristics of security and transparency, high credibility, traceability and unfalsifiability, low cost, and strong scalability, which can provide effective support for the six key applications. Then, we construct the technical framework and the model of a consortium blockchain-supported remanufacturing trading platform. Further, we analyze the coupling mechanism between consortium blockchain and remanufacturing trading platform to explain how the remanufacturing trading platform supported by consortium blockchain achieves development characteristics. This study provides important guidance for the development, construction, and operation management of remanufacturing trading platforms.

Extracellular vesicles (EVs) are emerging mediators of intracellular and inter-organ communications in cardiovascular diseases (CVDs), especially in the pathogenesis of heart failure through the transference of EV-containing bioactive substances. microRNAs (miRNAs) are contained in EV cargo and are involved in the progression of heart failure. Over the past several years, a growing body of evidence has suggested that the biogenesis of miRNAs and EVs are tightly regulated, and the sorting of miRNAs into EVs is highly selective and tightly controlled. Extracellular miRNAs, in particular circulating EV-miRNAs, have shown promising potential as prognostic and diagnostic biomarkers for heart failure and as therapeutic targets. In this review, we summarize the latest progress concerning the role of EV-miRNAs in HF and their application in a therapeutic strategy development for heart failure.

Drugs-drugs interactions(DDI) are entities composed of different chemical substructures (functional groups). In existing methods that predict drug–drug interactions based on the usage of substructures, each node is considered the center of a substructure, and adjacent nodes eventually become centers of similar substructures, resulting in redundancy. Furthermore, the significant differ- ences in structure and properties among compounds can lead to unrelated pairings, making it difficult to integrate information. This heterogeneity negatively affects the prediction results. To address these issues, we propose a drug–drug interaction prediction method based on substructure signature learning (DDI-SSL). This method extracts useful information from local subgraphs surrounding drugs and effectively utilizes substructures to assist in predicting drug side effects. Additionally, a deep clustering algorithm is used to aggregate similar substructures, allowing any individual subgraph to be reconstructed using this set of global signatures. Furthermore, we developed a layer-independent collaborative attention mechanism to model the mutual influence between drugs, generating signal strength scores for each class of drugs to mitigate noise caused by heterogeneity. Finally, we evaluated DDI-SSL on a comprehensive dataset and demonstrated improved performance in DDI prediction compared to state-of-the-art methods.

Dialogue State Tracking is an essential component in multi-domain dialogue systems, aiming to accurately determine the current dialogue state based on the dialogue history. Existing research has addressed the issue of multiple mappings in dialogues by employing slot self-attention as a data-driven approach. However, learning the relationships between slots from a single sample has limitations and introduces noise. In this paper, we propose an External Slot Relation Memory-based Dialogue State Tracking model (ER-DST). By utilizing an external memory storage, we learn the relationships between slots as a dictionary of multi-domain slot relations. Additionally, we employ a small filter to discard slot information irrelevant to the current dialogue state. Experimental results on the MultiWOZ2.0 and MultiWOZ2.1 benchmarks demonstrate significant improvements while reducing the time complexity to O(n).

The safe and efficient mining of coal seams with low porosity, low permeability and high inhomogeneity under complex geological conditions is a major challenge, among which the permeability of coal seams is crucial for coal mine gas extraction. The development of coal seam permeability enhancement technology can help coal mines to produce safely and efficiently, and the extracted coal bed methane can be utilized as a green energy. In order to study the effect of freezing and thawing on the evolution of mechanical and permeability properties of coal, triaxial permeability tests were carried out on low-permeability coal under two kinds of surrounding pressures. At the same time, dry, saturated and freeze-thaw coal were set up for comparison, and the effects of water and freeze-thaw were separated from each other. The triaxial mechanics and percolation laws of dry, saturated and freeze-thaw coal rocks were obtained, and the mechanism of the influence of freeze-thaw on the permeability evolution law of coal was revealed. The research results can provide theoretical guidance for the gas extraction technology of low permeability coal seams with increased permeability.

Target recognition mainly includes three approaches: optical image-based, echo detection-based, and passive signal analysis-based methods. Among them, the passive signal-based method is closely integrated with practical applications due to its strong environmental adaptability. Based on passive radar signal analysis, we design an "end-to-end" model that cascades a noise estimation network with a recognition network to identify working modes in noise environment. The noise estimation network is implemented based on U-Net, which adopts a method of feature extraction and reconstruction to adaptively estimate the noise mapping level of the sample, which can help the recognition network to reduce noise interference. Focusing on the characteristics of radar signal, the recognition network is realized based on Multi-Scale Convolutional Attention Network (MSCANet). Firstly, the deep group convolution is used to isolate the channel interaction in the shallow network. Then, through the multi-scale convolution module, finer-grained features of the signal are extracted without increasing the complexity of the model. Finally, the self-attention mechanism is used to suppress the influence of low-correlation and negative-correlation channels and spaces. This method overcomes the problem that the conventional method is seriously disturbed by noise. We validated the proposed method in 81 kinds of noise environments, achieving an average accuracy of 94.65%. Additionally, we discussed the performance of six machine learning algorithms and four deep learning algorithms. Compared to these methods, proposed MSCANet achieved an accuracy improvement of approximately 17%. Our method demonstrates better generalization and robustness.

The prevalence of antiviral drugs (AVTs) has seen a substantial increase in response to the COVID-19 pandemic, leading to heightened concentrations of these pharmaceuticals in wastewater systems. The hydrophilic nature of AVTs has been identified as a significant factor contributing to the low degradation efficiency observed in wastewater treatment plants. This characteristic often necessitates the implementation of additional treatment steps to achieve complete degradation of AVTs. Semiconductor-based photocatalysis has garnered considerable attention due to its promising potential in achieving efficient degradation rates and subsequent mineralization of pollutants, leveraging the inexhaustible energy of sunlight. However, in recent years, there have been few comprehensive reports that have thoroughly summarized and analyzed the application of photocatalysis for the removal of AVTs. This review commences by summarizing the types and occurrence of AVTs. Furthermore, it places a significant emphasis on delivering a comprehensive summary and analysis of the characteristics pertaining to the photocatalytic elimination of AVTs. Ultimately, the review sheds light on the identified research gaps and key concerns, offering invaluable insights to steer future investigations in this field.

In order to effectively utilize red mud and reduce its occupation of land resources as well as its impact on the environment, experiments were conducted to develop stabilized soil materials using nano-SiO2 synergistically modified red mud and to investigate the curing mechanism of stabilized soil. The unconfined compressive strength, microscopic morphology and curing mechanism of the red mud-based stabilized soil materials with different amounts of modified materials were investigated. The test results show that after 7-days curing, the unconfined compressive strength of red mud-based stabilized soil meets the compressive strength requirement of road base material when nano-SiO2, gypsum and cement are synergistically modified. In such case, the soil structure has the lowest fracture rate and the best structural compactness when the amount of nano-SiO2 is 1%; It is found that the needle-like and columnar calcium alumina in the modified red mud-based stabilized soil increases, and the binding energy of hydration product ions in the modified material is improved; The chemical curing mechanism of modified red mud-based stabilized soil includes hydration reaction, volcanic ash reaction, promotion effect of nano-SiO2, and enhancement effect of gypsum. On this base, a model of early start hydration process of red mud-based stabilized soil promoted by nano-SiO2 is established.

The rise of antibiotic-resistant strains of clinically important pathogens is a major threat to global health security. The World Health Organization (WHO) has recognized the urgent need to develop alternative treatments to address the growing list of priority pathogens. Antimicrobial peptides (AMPs) rank among the various suggested options with proven activity and a high potential to be developed into effective agents. Many AMPs are naturally produced by living organisms and protect the host against pathogens as a part of their innate immunity. Various mechanisms associated with their antimicrobial actions include cell membrane disruption, cell wall weakening, protein synthesis inhibition, and interference in nucleic acid dynamics that can induce apoptosis and necrosis. Acinetobacter baumannii is considered a critical pathogen, and severe clinical problems have appeared with isolates resistant to current antibiotic treatments and conventional control procedures such as UV light, disinfectants, and drying. Here, we review the natural AMPs representing the primary candidates for new anti-A. baumannii drugs in a post-antibiotic era and present the use of computational tools to develop the next generation of AMPs with greater microbicidal activity and reduced toxicity.

Compressed Sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically Generative Adversarial Networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning recon-struction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study we present a novel GAN-based model that delivers superior performance without escalating model complexity. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby fo-cusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies af-firmed the efficacy of the modified modules within the network. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent sta-bility but also outperforming other networks in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

The perturbations of DNA methyltransferase 3 alpha (DNMT3A) may cause uncontrolled gene expression, resulting in cancers and tumors. The DNMT inhibitors Azacytidine (AZA) and Zebu-larine (ZEB) inhibit the DNMT family with no specificities and consequently would bring side effects during the treatment. Therefore, it is vital to understand their inhibitory mechanisms in DNMT3A to inform the new inhibitor design for DNMTs. Herein, we carried out molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) simulations to investigate the inhibitory mechanisms of AZA and ZEB. The results were compared to the methyl transfer of cytosine. We showed AZA might stop the methyl transfer process, whereas the ZEB might be stuck in a me-thyl-transferred intermediate (IM3). Such IM3 state then fails the elimination due to the unique protein dynamics that result in missing the catalytic water chain. Our results brought atomic-level insights into the mechanisms of the two drugs in DNMT3A, which could benefit the new generation of drug design for the DNMTs.

The development of subterranean non-motorized traffic infrastructure, commonly referred to as the underground pedestrian system (UPS), has become increasingly necessary in densely populated megacities worldwide as a means of advancing the Sustainable Development Goal 11, which aims to promote sustainable cities and communities. To improve the overall spatial performance, it is imperative to decipher the fundamental formation mechanism of sustainable underground pedestrian systems (SUPS) that is simultaneously influenced by spatial morphology and pedestrian behaviors. Thereby, two representative case studies, namely Wujiaochang UPS and Loushanguanlu UPS located in Shanghai, were selected for an in-depth investigation. This study employed correlation and regression analysis to examine the impact of spatial configuration variables and spatial attribute factors on pedestrian flow distributions in distinct SUPSs. The findings indicate that the variables of Betweenness, as measured by both Euclidean and Angular metrics, along with the presence of metro station locations and commercial space connected by UPS, are the three most significant factors influencing pedestrian behaviors in both scenarios. The disclosure has been made that Wujiaochang UPS is seamlessly integrated into a comprehensive three-dimensional pedestrian network both above and below ground. On the other hand, it appears that Loushanguanlu UPS exhibits a greater degree of self-sufficiency as an underground system. This study aims to elucidate the mechanism underlying the development of SUPSs, thus offering effective guidance for the implementation of three-dimensional walking systems in cities that prioritize sustainability.

Drafting formations have been long recognized as highly effective for reducing drag and enhancing athletic performance, particularly in race walking events. The precise spacing and positioning of the race walkers are critical to optimizing the effectiveness of drafting. In this study, the drag reduction of 15 drafting formations is investigated using wind-tunnel experiments and CFD numerical simulations. The results show excellent consistency in drag reduction rate between the two methods, with differences being within 10%. This can be attributed to spacing replacing body shape differences as the primary factor influencing drag reduction. Optimal double, triple, and quadruple drafting formations produce the same results in both wind-tunnel experiments and CFD simulation, resulting in drag reductions of 67%, 66%, and 81% (wind-tunnel) and 65%, 72%, and 85% (CFD). The sources of drag differences in the two methods are discussed from various aspects. The flow field obtained through CFD analysis is used to examine the mechanism of drag reduction, revealing that drafting formations have a significant shielding effect on incoming air, which reduces the number and speed of airflow impacting the core race walker. This shielding effect is identified as the primary cause of drag reduction. Using an empirical model for mechanical power output, optimal double, triple, and quadruple drafting formations enhance sport economy (4.4-5.7%), speed (3.61-4.67%), and performance (173.8-223.3s) compared to race walking alone. The findings can serve as a reference for race walkers' positioning strategy and provide insights for considering drafting formations in various running events.

In this paper, the quality change of scallop (Patinopecten yessoensis) in the process of anhydrous storage and transportation after cold-acclimation and induced dormancy was studied, and the regulation mechanism of quality degradation in the storage and transportation in the process of gradient chilling stress and drying exposure was further explored. The results show that while being transferred from anhydrous storage and transportation from hydrous storage and transportation, the breathing pattern of scallops were changed from aerobic to anaerobic. Their gill filaments were altered, and the apparent vitality was constantly declining, which was reflected by the edge shrinkage of pallium and the direct proportion between the edge reduction rate and the stimulus response period. After being in the anhydrous state for 4d, the AEC value dropped to 67.59%. At this time, if they were under hydration again, they would resume a nice growth state. By virtue of proteomics, it was revealed from the analysis that cold acclimation and dry exposure mainly led to changes in biological functions and pathways such as mitochondrial inner membrane and ATP hydrolysis activity. In addition, it can be seen from the functional annotation and enrichment analysis of the metabolite KEGG that cold acclimation promoted the purine metabolism of scallops, while dry exposure inhibited the metabolism of saturated fatty acids. Besides, in this study, the infrared sensing mode was used for the first time to record the heart rate changes of the scallops during circulation, which provides feasibility for the nondestructive vitality monitoring of Lamellibranchia.

Respiratory syncytial virus (RSV) RNA synthesis takes place in cytoplasmic viral factories also called inclusion bodies (IBs), which are membrane-less organelles concentrating the viral RNA polymerase complex. IBs assembly is driven by liquid-liquid phase separation promoted by interactions between the viral nucleoprotein N and the phosphoprotein P. We recently demonstrated that cyclopamine (CPM) inhibits RSV multiplication by disorganizing and hardening IBs. Although a single mutation in the viral transcription factor M2-1 induced resistance to CPM, the mechanism of action of CPM still remains to be characterized. Here, using FRAP experiments on reconstituted pseudo-IBs both in cellula and in vitro, we first demonstrated that CPM activity depends on the presence of M2-1 together with N and P. We show that CPM impairs the competition between P and RNA binding to M2-1. As mutations on both P and M2-1 induced resistance against CPM activity, we suggest that CPM may affect the dynamics of the M2-1−P interaction, thereby affecting the relative mobility of proteins contained in RSV IBs. Overall, our results reveal that stabilizing viral protein-protein interactions is an attractive new antiviral approach. They pave the way for the rational chemical optimization of new specific anti-RSV molecules.

Dye wastewater containing bisphenol A (BPA) and dyes as pollutants have not been adequately studied. Our previous study revealed that thermoplastic polyurethane (TPU) nanofiber membranes (NFMs) modified by the addition of polyethyleneimine (PEI) and polydopamine (PDA) satisfactorily adsorb dyes. Herein, we first optimized the synthesis conditions for such membranes, noting a PEI:PDA monomer ratio of 2:2 and a deposition time of 48 h to be optimal. Experiments using these membranes revealed that binary systems containing BPA and the dyes (Congo red (CR), Eosin yellow (EY), or sunset yellow (SY)) exhibit three adsorption behaviors. CR and BPA compete with each other for adsorption sites, decreasing the maximum adsorption capacity (Qmax) for CR 208.3 mg/g (in a monomeric system) to 182.4 mg/g, whereas for BPA, it decreased from 26.7 to 22.8 mg/g. The adsorption rates for CR and BPA decreased from 0.002 min−1 and 0.331 min−1 in the monomeric systems to 8.37 × 10−4 min−1 and 0.072 min−1, respectively, in the binary CR–BPA system, exhibiting antagonistic effects. When EY and BPA coexist, Qmax for EY increased from 60.0 (monomeric) to 71.9 mg/g, whereas that for BPA increased from 35.6 to 43.2 mg/g, showing a synergistic effect due to the possible bridging effect. The adsorption sites for SY and BPA are independent of each other. Thus, PDA/PEI TPU NFMs exhibit the potential for removal of dye–BPA composites, whereas binary systems containing BPA with different dyes are adsorbed differently.

To realize the energy recovery of wheat straw, the pyrolysis behavior of wheat straw was studied at three heating rates (10, 20, and 30 K/min) based on thermogravimetric analysis (TG–DTG). Kinetics and thermodynamics were analyzed using Flynn–Wall–Ozawa (FWO) and Kissinger–Akahira–Sunose (KAS) model-free methods, and reaction mechanism was determined using Coats–Redfern (CR) model-fitting method. The results show that there are three weightlessness stages in the pyrolysis process, of which the second stage was the main weightlessness stage and two distinct peaks of weightlessness were observed in this stage. With increasing heating rate, the main pyrolytic weightlessness peaks of DTG curve shifts to higher temperature. The pyrolysis activation energy calculated by FWO and KAS methods are 165.17–440.02 kJ/mol and 163.72–452.07 kJ/mol, and the pre-exponential factor vary in the range of 2.58×1012–7.45×1036 s-1 and 1.91×1012–8.66×1037 s-1, respectively. The thermodynamic parameters indicates that wheat straw has the favorable conditions for product formation and containes potential energy to be utilized for bioenergy production, its pyrolysis reaction was non-spontaneous and the energy output is stable. CR method analysis shows that the A1/3 random nucleation model is the most suitable mechanism to characterize the pyrolysis process, and random nucleation may be in charge of the main pyrolysis stage. This study can provide a theoretical basis for the thermochemical conversion and utilization of wheat straw.

The unstable international economic situation is reflected in the supply chain stress, lack or increased cost of some raw materials, fuel or semi-finished products is forcing organizations to perform new optimization initiatives in the utilization of their equipment and assets pointed to obtain the maximum value from them, while maintaining and even improving the quality of their products. The achievement of these objectives involves the reduction or minimization of equipment downtime to maintain the advantage over their competitors and ensure the organization's competitiveness. The intelligent maintenance system (IMS) provides adequate support for decision-making related to equipment maintenance, since poor maintenance results in unplanned stoppages, with the consequent additional cost and increased customer dissatisfaction, and an over-maintenance can result in an additional labor cost, time and the replacement of parts that are in good conditions. The utilization of new tools and technologies introduced by Industry 4.0 offers multiple opportunities for enhancement through communication and computerized data processing, aiming to improve the maintainability of a hydrogen compressor using neural networks based on attention mechanisms combined with linear regression.

Highly efficient, broadband and distinctively structured electromagnetic wave (EMW) absorption materials exhibit huge exceptions with multi-functions in both military and civilian fields. Searching the relationship between structural and EMW performance is beneficial to develop outstanding microwave absorption materials, and has become an attractive research topic. In this study, we briefly reviewed the different EMW absorption mechanisms and key influence factors, then, present the latest developments in low-dimensional (LD) EMW absorption materials. We provide a comprehensive discussion of remarkable examples and summarize the main information related to the structure-function relationship of dimensional design and structural engineering in order to facilitate the rapid evolution of this fascinating field.

Zeolite Y with microporous and hierarchical structure containing Ti–Ni and Ti–Co oxides were obtained as active photocatalysts. Different Ti amounts (5, 10% TiO2) were supported, followed by loading of Ni or Co oxides (5 %). The X-ray diffraction evidenced the presence of TiO2 as anatase. N2 adsorption-desorption results showed type IV isotherms, for hierarchical zeolite Y samples, and a combination of type IV and I isotherms, for zeolite Y samples. UV–Vis diffuse reflectance spectra showed shift of absorption band to visible with increasing of Ti loading and especially after Co and Ni addition. A significant effect of the support was evidenced for Ti and interaction with Co/Ni species. The zeolite Y support stabilizes Ti in the 4+ oxidation state while hierarchical zeolite Y support favored the formation of Ti 3+ species and oxidation of Co and especially of Ni to 3+. Photocatalytic activity, under UV and visible light, was evaluated in degradation of amoxicillin, used as a model test. The photocatalytic mechanism was investigated using ethanol, p-benzoquinone and KI as ·OH, ·O2- radicals and hole (h+) scavengers. The best results were obtained for the immobilized Ni-Ti species on hierarchical zeolite Y support.

In practical applications, the detection of objects with various sizes is a common requirement for most detectors. The feature pyramid network (FPN) is widely adopted as a framework to address this challenge. The field is witnessing an increasing number of transformer-based target detectors due to the widespread adoption of transformer technology. This paper initially examines the design flaws in FPN and transformer-based target detectors, followed by the introduction of a new transformer-based approach called Texturized Instance Guidance (TIG-DETR) to address these issues. Specifically, TIG-DETR comprises a backbone network, a new pyramidal structure known as Texture-Enhanced FPN (TE-FPN), and an enhanced DETR detector.The TE-FPN is composed of three components: a bottom-up pathway for enhancing texture information in the feature map, a lightweight attention module to address confounding effects resulting from cross-scale fusion, and a standard attention module to enhance the final output features.The improved DETR detector utilizes Shifted Window based Self-Attention to replace the multi-headed self-attention module in DETR, thereby accelerating model convergence. Moreover, it incorporates an Instance Based Advanced Guidance Module to enhance instance perception in the image by employing a pre-local self-attentive mechanism for recognizing larger instances. By employing TE-FPN instead of FPN in Faster RCNN with Resnet-50 as the backbone network, we achieve a 1.9% improvement in average accuracy. TIG-DETR achieves an average accuracy of 44.1 with Resnet-50 as the backbone network.

To solve the problem of smooth switching between car-following model and lane-changing model, the Intelligent Driver Model (IDM) for a single lane was used to study the driver's behavior switching mechanism of normally following, generating intentions to change lanes, creating space and speed gains, and performing lane change. In the case of sufficient lane-changing space and speed gains, the ego vehicle's intention to change lane was considered to solve the switching boundary between car-following behavior and lane-changing behavior, which is also the IDM failure point. In the event that there is no lane-changing gains, the IDM was optimized by incorporating the constraint components of the target lane vehicles in conjunction with the actual motion state of the ego vehicle, and the Stepless Switching Intelligent Driver Model (SSIDM) was constructed. Drivers' natural driving information was collected and scenario mining was performed on structured roads. On the basis of the collected data, an elliptic equation was used to fit the behavior switching boundary, and the two component balance coefficients of the front and rear vehicles on the target lane were identified. According to the test set verification results, the Mean Square Error (MSE) of the SSIDM is 2.172, which is 57.98% less than that of the conventional single-lane IDM. The SSIDM can accomplish stepless switching comparable to driver's behavior between the car-following behavior and the lane-changing behavior, with greater precision than IDM.

With the evolution of toxic chemicals continuing to progress, developing methods for the destruction of chemical warfare agents (CWAs) has become an increasingly important research topic. Metal-organic frameworks (MOFs) are a class of porous crystalline solids that have sparked interest in this area. Due to their exceptional porosities and large surface areas, MOFs possess superior adsorption, reactivity, and catalytic abilities, making them structurally ideal candidates for the capture and decomposition of target species. Additionally, the tunable networks of MOFs allow their chemical functionalities to be customized for various applications and operating conditions, making them practicable in personal protective equipment and adjustable to dynamic environments. This paper reviews experimental and computational studies on CWA removal by MOFs, with a special emphasis on nerve agent (GB, GD, and VX) removal via hydrolysis and sulfur mustard (HD) removal via selective photooxidation. With extraordinary structural stability and reusability, zirconium-based MOFs are the most promising materials for hydrolytic and photooxidative degradation of CWAs and are thus the primary focus of this work. First-principles approximations of the intrinsic catalytic reaction mechanisms towards different agents in Zr-MOFs are summarized, and developments in the structure-property relationships governing Zr-MOF design rules for efficient degradation in the aqueous and solid phases are discussed. We also examine recent progress in tuning and functionalizing MOFs to promote practical CWA removal under realistic battlefield conditions.

The development of high-performance sensing materials for detecting or removing SF₆ decomposition gases is crucial for gas-insulated switchgear (GIS). In this work, the adsorption characteristics of the pristine porphyrin (Pr) and Fe-doped porphyrin (FePr) monolayers towards SF₆ decomposition gases were investigated using the first-principles calculations. Additionally, the electronic properties and sensing-mechanism were also examined. The findings indicate that both Pr and FePr monolayers exhibit thermodynamic stability. The intrinsic Pr monolayer only exhibits a moderate affinity towards the SO₂ molecule, however, its poor sensitivity of the Pr monolayer renders it unsuitable as the sensing-material. In contrast, the FePr monolayer exhibits favorable adsorption strength towards all five toxic gases (H₂S, SO₂, SOF₂, SO₂F₂, HF). The FePr monolayer exhibits chemisorption towards SO₂/SOF₂ gases, Furthermore, the microscopic mechanism of gases-substrate interaction is elucidated through the analysis of charge density difference, charge transfer, and density of states. Moreover, the FePr has excellent sensitivity for H₂S, SO₂, SO₂F₂, SOF₂ and HF molecules owing to the significant variations of the band gap, electrical conductivity and work function. In addition, the τ of H₂S, SO₂, and SOF₂ is 12.6 μs, 18.5 s and 15.4 s at 298 K, respectively. Our calculated results can offer theoretical guidance for the practical application of FePr-based sensing materials to detect SF6 decompositions gases.

With an increase in energy consumption globally, Fischer-Tropsch (FT) synthesis is a good alternative for producing fuels and chemicals from coal, natural gas or biomass. Among them, coal to liquids has been put into production in countries that have large coal reserves. In this process, Fe-based catalysts are commonly used due to their earth abundance, comparatively wide operation range and ready availability to handle low H2/CO ratio from coal. Despite their extensive applications, the kinetic and mechanistic understandings of Fe carburization and FT reaction on Fe-carbides are relatively limited due to the complexity of phase composition of the applied catalysts. This review summarizes the current state of knowledge of FT synthesis on Fe-carbide with an emphasis on underlying mechanism. Specifically, the employment of model catalyst, such as Raney-Fe, could provide a convenient way to furnish kinetic information regarding Fe carburization and subsequent FT reaction. A major challenge for further understanding catalytic reactions occurring at the Fe-carbide surface is correlating FT activity and selectivity to specific active site. To address this issue, the advancements of both DFT calculations and surface science techniques are highly demanded.

In this work, nitrite intercalated CaAl-LDH was firstly prepared in this work. Scanning electronic microscopy (SEM) coupled with the energy dispersive spectrometer (EDS) was used to characterize the morphology and element composition of the synthesized powder. Fourier transform infrared spectroscopy (FTIR) was used to characterize the chemical composition and X-ray diffraction (XRD) was used to analyze the structure. The nitrite release curve and the chloride concentration change curve were obtained to study the anion-exchange reaction. The corrosion protection effect of the CaAl-LDH loaded with nitrites towards the carbon steel was evaluated in 0.02 M NaCl solution by electrochemical impedance spectroscopy (EIS). Then the powder was added in the epoxy coating with a percentage of 2% (vs. epoxy resin). The coating morphology and roughness was evaluated by SEM and laser microscopy, and the corrosion protection effect was investigated by EIS in an immersion period of 21 d. Local electrochemical impedance spectra (LEIS) was used to characterize the corrosion development process in micro-corrosion sites. The corrosion product of the scratched area after salt spray exposure was analyzed by EDS and Raman spectroscopy. The corrosion protection mechanism of the CaAl-LDH loaded with nitrites was proposed based on the above experimental results.

This article presents a study conducted within a wind tunnel to enhance understanding of the excitation mechanism of stay cable vibration under arid conditions. Numerous wind tunnel tests were meticulously analyzed. Initially, the vibration of the stay cable was measured under steady flow conditions at a flow angle of 45 degrees and an inclination of 25 degrees, while varying wind velocities were applied. Additionally, an investigation into the flow field surrounding the stay cable was conducted in both vertical and horizontal directions. By utilizing two hot wire anemometers in the cable wake, an extensive database of flow field measurements was obtained. The experimental results revealed that the vibration characteristics of the stay cable under the arid conditions considered in this study aligned with findings reported in existing literature. Notably, a deeper comprehension of the excitation mechanism of a stay cable in a dry state was attained. This mechanism is closely associated with the inhibition of Karman vortices and the development of low-frequency vortices. At low wind speeds, Karman vortices predominated, resulting in small-amplitude vibrations. However, as the wind speed increased, the influence of Karman vortices diminished progressively, while the low-frequency vortices grew stronger. These low-frequency vortices exhibited high energy and a significant correlation with shedding along the stay cable, thereby inducing cable vibration in a dry environment.

The composite coatings prepared by mixing the 5 wt.% of polyaniline with commercial alkyd-based paint are applied on carbon steel. The polyaniline emeraldine chloride salt is prepared by procedure recommended by IUPAC, deprotonated by ammonia hydroxide, and reprotonated with the sulfamic, succinic, citric, and acetic acids with different doping degrees or oxidation states. The steel samples with base and composite coatings are immersed in 3% NaCl and the corrosion current density is determined after 96 h in-site using ASTM 1,10-phenanthroline method. The samples are also inspected by the optical microscope. It is shown that composite coatings reduce the possibility of blister formations and delamination. The corrosion current density and the appearance of the corrosion products, which area is determined by ImageJ softer, closely follow the initial oxidation state of the polyaniline. The role of the initial state of the polyaniline is discussed. It is suggested that such behavior could be connected with the oxygen reduction reaction mechanism that proceeds mainly via two electron paths on the polyaniline particles, releasing a much smaller amount of hydroxyl ions, responsible for the delamination and blister formations of the commercial coatings.

In recent years, intelligent identification and prediction of ore deposits based on deep learning algorithm and image processing technology has gradually become one of the research frontiers in the field of geological and metallogenic prediction. However, this method also has many problems that need to be solved. For example, (1) very few trainable image samples containing mineral point labels; (2) The geological image features are small and irregular, and the image similarity is high; (3) It is difficult to calculate the influence of different geological prospecting factors on ore mineralization. Based on this, this paper constructs a deep learning network model Multiscale Feature Attention Framework (MFAF) based on geo-image data. The results show that MFCA-Net module in MFAF model can solve the problem of scarce mine label images to a certain extent. In addition, the channel attention mechanism SE-Net module can quantify the difference of influence of different source factors on mineralization. The prediction map is obtained by applying the MFAF model in the study of deposit identification and prediction in the research area of the southern section of Qin-hang metallogenic Belt. In the forecast map, many regions have high metallogenic potential. Through observation, it is found that the above predicted area covers 100% of the known ore deposits at present, which has a good prediction effect. Later, we can find out the specific reasons through field verification. The multi-scale feature fusion and attention mechanism MFA Framework in this paper can provide a new way of thinking for geologists in mineral exploration. The research of this paper also provides resource guarantee and technical support for the sustainable exploitation of mineral resources and the sustainable growth of society and economy.

Fine-grained image classification remains an ongoing challenge in the computer vision field, which is particularly intended to identify objects within sub-categories. It is a difficult task since there is a minimal and substantial intra-class variance. The current methods address the issue by first locating selective regions with Region Proposal Networks (RPN), object localization, or part localization, followed by implementing a CNN Network or SVM classifier to those selective regions. This approach, however, makes the process simple by implementing a single-stage end-to-end feature encoding with a localization method, which leads to improved feature representations of individual tokens/regions by integrating the transformer encoder blocks into the Yolov5 backbone structure. These Transformer Encoder Blocks, with their self-attention mechanism, effectively captured the global dependencies and enabled the model to learn relationships between distant regions. This improved the model ability to understand context and captured long-range spatial relationships in the image. We also replaced the Yolov5 detection heads with three transformer heads at the output for object recognition using the discriminative and informative features maps from transformer encoder blocks. We established the potential of the single stage detector for the fine-grained image recognition task, by achieving state of the art 93.4% accuracy, as well as outperforming the existing Yolov5 model. The effectiveness of our approach is assessed using the Stanford car dataset, which includes 16,185 images of 196 different classes of vehicles with significantly identical visual appearances.

The coronavirus disease 2019 (COVID-19) is a novel global pandemic infectious disease with higher potential for outbreaks than the other epidemic disease such as severe acute respiratory syndrome (SARS), influenza A (H1N1), and the Middle East respiratory syndrome (MERS), which identified in China on December 31, 2019. This disease is caused by a new generation of betacoronavirus termed as the 2019 novel coronavirus (2019-nCoV) or SARS-CoV-2. Although, the first report of this disease was in recent months, now, the COVID-19 is known as a global pandemics. Hence, the aim of this article is the quick review of the recent studies on the novel coronavirus disease 2019 including researches on the epidemiological parameters, mechanism of action, diagnosis, and treatment of the novel coronavirus disease, as well as clinical features of patients infected with COVID-19. Moreover, the novel COVID-19 has comprised of SARS, H1N1, and MERS.

Euphorbia ingens is traditionally used to treat and manage cancer in Ambeere community of Embu County in Kenya. Whilst research has demonstrated the bioactivities of E. ingens including antimicrobial, antitubercular and antifungal activities, scientific validation of its anticancer properties is limited. This study evaluated the antiproliferative potentials of E. ingens on human prostate cancer cell line (DU-145). The 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide assay was used to assess the antiproliferative activity, chemical constituents were analysed by qualitative colour method and Gas Chromatography-Mass Spectrometry analysis. At the same time, the investigation of putative molecular targets and mechanisms of action of E. ingens was done through network pharmacological analysis. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was carried out to validate the network predictions of putative targets. Our result showed E. ingens ethyl acetate inhibited DU-145 growth (IC50 of 9.71 ± 0.4 µg/ml) with a high selectivity index of 8.26. There was the presence of phenols, terpenoids, flavonoids, tannins, sterols, and saponins; additional 18 compounds were identified by the GC-MS approach. ESR1, IL6, MMP9, CDK2, MAP2K1, AR, PRKCD, CDK1, CDC25B, and JAK2 were indicated as key targets of E. ingens against prostate cancer with the PI3K-AKT, MAPK, and p53 signalling pathways identified as the most probable mechanisms of action. There was significant downregulation of AR and BCL2, and upregulation of p53 and caspase-3 in E. ingens-treated DU-145 cells compared to 0.2 % DMSO negative control. Our results suggest that E. ingens has phytochemical compounds efficacious at inhibiting the proliferation of DU-145 cells; therefore, the plant can be considered a potential source of compounds that may be used to manage and treat prostate cancer; however, further in vivo evaluations are needed.

Forest fires are one of the world's deadliest natural disasters. Early detection of forest fires can help minimize the damage to ecosystems and forest life. In this paper, we propose an improved fire detection method YOLOv5-IFFDM for YOLOv5. Firstly, the fire and smoke detection accuracy and the network perception accuracy of small targets are improved by adding attention mechanism in the backbone network. Secondly, the loss function is improved and the SoftPool pyramid pooling structure is used to improve the regression accuracy and detection effect of the model and the robustness of the model. In addition, random Mosaic augmentation technique is used to enhance the data to increase the generalization ability of the model, and re-clustering of flame and smoke detection a priori frames are used to improve the accuracy and speed. Finally, the parameters of the convolutional and normalization layers of the trained model are homogeneously merged to further reduce the model responsibility to improve the detection speed. Experimental results on homemade forest fire and smoke datasets show that this algorithm has high detection accuracy and fast detection speed, with average accuracy of fire up to 90.5% and smoke up to 84.3%, and detection speed up to 75 FPS (frames per second transmission), which can meet the requirements of real-time and efficient fire detection.

Gamma-aminobutyric acid (GABA), a non-protein-producing amino acid, is extensively found in microorganisms, plants and vertebrates, and is abundantly expressed in the spinal cord and brain. To date, GABA is considered to be the major inhibitory neurotransmitter in the central nervous system. Its physiological effects are related to the regulation of synaptic transmission, the promotion of neuronal development and relaxation, and the prevention of insomnia and depres-sion. Mediated through its specific receptors, it plays a pivotal role in the control of neuronal ex-citability, which can serve as anovel target for developing analgesics for pain management. This review provides an update on the accumulating evidence of specific GABA receptors and their subtypes in the involvement of pain analgesia.

The growing increase in infections caused by C. tropicalis, associated with its drug resistance and consequent high mortality, especially in immunosuppressed people, today generates a serious global public health problem. In the search for new potential drug candidates that can be used as treatments or adjuvants in the control of infections by these pathogenic yeasts, the objective of this research was to evaluate the action of isoespintanol (ISO) against the formation of biofilms fungal, the mitochondrial membrane potential (∆Ψm) and its effect on the integrity of the cell wall. We report the ability of ISO to inhibit the formation of biofilms by up to 89.35%, in all cases higher than the values expressed by amphotericin B (AFB). Flow cytometric experiments using rhodamine 123 (Rh123) showed the ability of ISO to cause mitochondrial dysfunction in these cells. Likewise, experiments using calcofluor white (CFW) and analyzed by flow cytometry, showed the ability of ISO to affect the integrity of the cell wall by stimulating chitin synthesis; these changes in the integrity of the wall were also observed through transmission electron microscopy (TEM). These mechanisms are involved in the antifungal action of this monoterpene.

In this paper, through high-temperature tensile tests of 55NiCrMoV7 steel, the high-temperature fracture behavior, microstructure evolution and carbide distribution characteristics of both thermal-mechanical coupling zone (fracture zone) and thermal stress zone (clamping zone) at different temperatures were studied. Intrinsic relationship between high temperature fracture and carbides types, distribution and size were revealed and evolution mechanism of microstructure near cracks in 55NiCrMoV7 hot-working die steel during high temperature deformation was clarified. Samples were stretched at different temperatures from 25 °C to 700 °C, and microscopic examinations were carried out by using SEM and TEM. The results showed that: With the increase of temperature, tensile strength and yield strength decreased, elongation and reduction of area increased, and fracture mode changed from brittle fracture to ductile fracture by transition temperature at about 400℃. During high temperature deformation, the grain dislocation density decreased, the tempered martensite decomposed, recovered, recrystallized, and then grain grew. M7C3 and M23C6 carbides precipitated and grew along the grain boundary, and a small amount of fine granular MC carbides were dispersed in the grain. The work done by the external force on the deformation zone would cause the temperature of it to be higher than tensile temperature, which provides thermodynamic conditions for the re-dissolution of small carbides near the fracture zone and the grain growth of large carbides, resulting in the decrease of small carbides and increase of large carbides in thermal-mechanical coupling zone.

Species diversity has been shown to be influenced by environmental factors, but the mechanism underlying their relationship remains unclear across spatial scales. Based on field investigation data collected from 3,077 sample plots in temperate forest ecosystems, we compared tree species richness, evenness and dominance at 10 km × 10 km, 30 km × 30 km and 90 km × 90 km spatial scales. Then, we detected the scale dependence of changes in tree species composition on climate, topography and forest structure using variation partitioning and quantified their contribution to tree diversity with gradient boosted models (GBMs) and fitted their relationships. The magnitude of tree richness, evenness and dominance significantly increased with spatial scale. Ecological factors jointly accounted for 24.3%, 26.5% and 38.5% of the variation in tree species composition at the three spatial scales, respectively. Annual mean temperature had a strong impact on tree richness, evenness and dominance and peaked at an intermediate scale. Tree evenness and dominance increased with the variation of temperature but had upper and lower limits. Tree richness obviously increased with annual precipitation on multiple scales and decreased with annual sunshine duration at large spatial scale. Tree richness, evenness and dominance obviously increased with the variation elevation and diameter at breast height at large scales and small scales, respectively. Tree dominance decreased with tree height in a hump at small scale. The dependence of tree diversity on ecological factors increased with spatial scales. Furthermore, different factors exert various controls on tree diversity at different spatial scales, representing a comprehensive mechanism regulating tree diversity.

Low-temperature plays a vital role in the growth and development of woody plants. In this research, based on the way of artificial low temperature to break dormancy, the Utah model was used to determine the chilling requirements of four early-flowering Prunus mume cultivars, which were widely planted in Henan area of China. At the same time, changes in the carbohydrates, antioxidant enzyme activities, and endogenous hormone contents of the flower buds (FBs) of the above P.mume cultivars were measured during the low-temperature storage process, and the physiological changes of the four cultivars during the low-temperature induction period were explored. The main research conclusions are as follows: (1) The chilling requirements of ‘Gulihong’, ‘Nanjing gongfen’, ‘Zaoyudie’ and ‘Zaohualve’ were 408CU, 396CU, 372CU and 348CU respectively. All the P.mume cultivars belonged to low chilling demand cultivars. The P.mume also bloomed 4 months earlier (2) During the process of releasing dormancy at low temperature, the contents of soluble sugar (SS) in the osmoregulation system of the four Prunus mume cultivars showed an upward trend, while the contents of starch (ST) basically showed an opposite trend with the increase of chilling. The superoxide dismutase (SOD) activity in the FBs of each cultivar gradually decreased with the accumulation of cold and maintained at a low level, the peroxidase (POD) activity showed the opposite trend, and the dynamic changes of the catalase (CAT) activity generally showed a trend of first increasing and then decreasing. The content of abscisic acid (ABA) showed a trend of increasing first and then decreasing as a whole. The changing trend of gibberellin (GA3) content was similar to that of ABA. In addition, it was found that before dormancy was released, the SS and Pro contents of cultivars with lower chilling requirements and early FB germination were significantly higher than those of other cultivars with higher chilling requirements, and the contents of ST and SP were lower. The cultivars with higher chilling requirement and late flower bud germination had higher ABA content, lower GA3 contents, and their enzyme activities were significantly higher than those of the cultivars with lower chilling requirement. Therefore, the changes in the content of various substances in FBs are related to the amount of cooling required by the cultivar and the sooner or later the FBs germinate, and the changes in their contents can be used as one of the indicators for judging the dormancy process of FBs.

Reinforcement learning is used for air combat problems in recent years, and the idea of curriculum learning is often used for reinforcement learning, but traditional curriculum learning suffers from the problem of plasticity loss in neural networks. Plasticity loss is the difficulty of learning new knowledge after the network has converged. To this end, we propose a motivational curriculum learning distributed proximal policy optimization (MCLDPPO) algorithm, through which agents trained can significantly outperform the predictive game tree and mainstream reinforcement learning methods. The motivational curriculum learning is to help the agent gradually improve their combat ability by observing the agent's unsatisfactory performance and providing corresponding rewards as a guide. Additionally, a complete tactical maneuver is encapsulated based on existing air combat knowledge, and through the flexible use of these maneuvers, some tactics beyond human knowledge can be realized. In addition, we designed an interruption mechanism for the agent to increase the frequency of decision-making when the agent faces an emergency. When the number of threats the agent receives changes, the current action will be interrupted to reacquire observations and make decisions again. Using the interruption mechanism can significantly improve the ability of the agent. To simulate actual air combat better, we built an air combat environment based on digital twin technology to create an air combat engine core such as missiles and radars. Based on this environment, we develop a distributed intelligent training system that employs Redis as the cache and obtains the data generated in the distributed environment through a multi-threaded TCP/IP protocol, which significantly increases the training data throughput and helps the agent accelerates convergence. The experimental results demonstrate that the agent can fully exploit the situational information to make reasonable decisions and affords air combat tactical adaption, verifying the effectiveness of the algorithmic framework proposed in this paper.

Plant flavonoids have increasingly paid a close attention to for new antimicrobial agents or adjuvants. In our previous work, it was confirmed that the cell membrane is the major site of plant flavonoids acting on the gram-positive bacteria, and which likely involves the inhibition of the respiratory chain. Inspired by the similar structural and antioxidant characters of plant flavonoids to MKH₂, we deduced that the quinone pool is probably a key target of plant flavonoids inhibiting gram-positive bacteria. To verify this, twelve plant flavonoids with six structural subtypes were preliminary selected, and their MICs against gram-positive bacteria were predicted from the antimicrobial quantitative relationship of plant flavonoids to gram-positive bacteria. The results showed they have different antimicrobial activities. After their MICs against S. aureus were determined using broth microdilution method, nine compounds with the MICs ranged from 2 to 4,096 μg/mL or more than 1,024 μg/mL were eventually selected, and then their MICs against S. aureus were determined interfered with different concentrations of MK-4 and the MKs extracted from S. aureus. The results showed that the greater the antibacterial activities of plant flavonoids were, the more greatly their antibacterial activities decreased along with the increase of the interfering concentrations of MK-4 (from 2 to 256 μg/mL) and MK extract (from 4 to 512 μg/mL), and while those, with the MICs equal to or more than 512 μg/mL, decreased a little or remained unchanged. Especially, under the interference of MK-4 (256 μg/mL) and MK extract (512 μg/mL), the MICs of α-mangostin, a compound with greatest inhibitory activity to S. aureus in these twelve plant flavonoids, increased by 16 times and 8 to16 times, respectively. Based on these above, it was proposed that the quinone pool is a key target of plant flavonoids inhibiting gram-positive bacteria, and which likely involves multiple mechanisms including some enzyme and non-enzyme inhibitions.

Under the increasingly severe global heat threat, continuous high temperature has far-reaching effects on plant growth and development and become a major constraint to crop production. The development of heat-resistant varieties has become research hotspot in many fields, and it is also necessary to establish effective identification methods. In this study, twenty Brassica rapa varieties were selected to to investigate the physiological and biochemical characteristics under heat stress, explore the relationship between physiological response and the heat resistance mechanism, select some typical heat-resistant and heat-sensitive varieties. The effects of photosynthetic electron transfer and antioxidant pathway on heat resistance of Brassica rapa were identified. These findings will providing an important reference for the physiological regulation and identification method of high-temperature stress in plants.

Citation: To be added by editorial staff during production.

Microbial communities inhabiting the human body play a crucial role in protecting the host against pathogens and inflammation. Disruptions to the microbial composition can lead to various health issues. Microbial transfer therapy (MTT) has emerged as a potential treatment option to address such issues. Fecal microbiota transplantation (FMT) is the most widely used form of MTT and has been successful in treating several diseases. Another form of MTT is vaginal microbiota transplantation (VMT), which involves transferring vaginal microbiota from a healthy female donor to a diseased patient's vaginal cavity with the goal of restoring normal vaginal microbial composition. However, VMT has not been extensively studied due to safety concerns and a lack of research. This paper explores the therapeutic mechanisms of VMT and discusses future perspectives. Further research is necessary to advance the clinical applications and techniques of VMT.

Smart mechatronics system in agriculture can be traced back to the mid-1980s, when research into automated fruit harvesting systems began in Japan, Europe, and the United States. Since then, impressive advances have been made smart mechatronics systems. Furthermore, smart mechatronics systems are promising areas, as results, we were intrigued to learn more about them. Consequently, the purpose of this study was to examine the smart mechatronic systems that have been applied to agricultural areas so far, with inspiration from smart mechatronic system in other sectors. To get an overview of the current state of the art, benefits and drawbacks of the smart mechatronics systems, various approaches were investigated. Moreover, smart mechatronic modules, and various networks applied in agriculture processing were examined. Finally, we were explored how the data retrieved using the one-way analysis of variance related to each other. The result showed that there were strong related keywords for different journals. The virtually limited use of sophisticated mechatronics in the agricultural industry, and at the same time, the low production rate, the demand for food security has fallen dramatically. Therefore, the application of smart mechatronics system in agricultural sectors would be taken into consideration in order to overcome these issues.

Although the APOBEC3 family of single-stranded DNA cytosine deaminases are well-known as antiviral factors, these enzymes are rapidly gaining attention as prominent sources of mutation in cancer. APOBEC3 signature single base substitutions, C-to-T and C-to-G in TCA and TCT motifs, are evident in over 70% of human malignancies and dominate the mutational landscape of numerous individual tumors. Recent murine studies have established cause-and-effect relationships, with both human APOBEC3A and APOBEC3B proving capable of promoting tumor formation in vivo. Here, we investigate the molecular mechanism of APOBEC3A-driven tumor development using the murine Fah liver complementation and regeneration system. First, we show that APOBEC3A alone is capable of driving tumor development (without Tp53 knockdown as in prior studies). Second, we show that the catalytic glutamic acid residue of APOBEC3A (E72) is required for tumor formation. Third, we show that an APOBEC3A separation-of-function mutant with compromised DNA deamination activity and wildtype RNA editing activity is defective in promoting tumor formation. Collectively, these results indicate that APOBEC3A is a “master driver” that fuels tumor formation through a DNA deamination-dependent mechanism.

The successive steps of the oxidation mechanism of crocin, major compound of saffron, by the free OH• radical are investigated by the pulse radiolysis, steady-state (gamma) radiolysis methods and molecular simulations. The optical absorption properties of the transient species and their reaction rate constants are determined. The absorption spectrum of the oxidized radical of crocin resulting from the H-abstraction presents a maximum at 678 nm and a band at 441 nm, almost as intense as that of crocin. The spectrum of the covalent dimer of this radical contains an intense band at 441 nm and a weaker one at 330 nm. The final oxidized crocin, issued from radical disproportionation, absorbs weaker with a maximum at 330 nm. The molecular simulation results suggest that the OH• radical is electrostatically attracted by the terminal sugar and is scavenged predominantly by the neighbor methyl site of the polyene chain as in a sugar-driven mechanism. Based on detailed experimental and theoretical investigations, the antioxidant properties of crocin are highlighted.