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Abstract Background Out of the 21 neglected tropical diseases (NTDs) listed by the World Health Organization, 15 affect the People’s Republic of China. Despite significant achievements in controlling NTDs, comprehensive assessments of the disease burden based on actual case data and detailed information on spatial and temporal dynamics are still lacking. This study aims to assess the disease burden and spatial–temporal distribution of NTDs in China from 2005 to 2020, to provide a reference for the formulation of national health agendas in line with the global health agenda, and guide resource allocation. Methods The number of cases and deaths of major NTDs in China from 2005 to 2020 were downloaded from the China Public Health Science Data Center ( https://www.phsciencedata.cn/Share/index.jsp ) of the Chinese Center for Disease Control and Prevention and relevant literatures. Simplified formulas for disability-adjusted life years (DALYs) helped estimate the years of life lost (YLLs), years lived with disability (YLDs), and total DALYs. Spatial autocorrelation analysis of the average NTDs burden data for the years 2005 to 2020 was evaluated using Moran's I statistic. Results China's overall NTDs burden decreased significantly, from 245,444.53 DALYs in 2005 to 18,984.34 DALYs in 2020, marking a reduction of 92.27%. In 2005, the DALYs caused by schistosomiasis and rabies represent a substantial proportion of the total disease burden, accounting for 65.37% and 34.43% respectively. In 2015, Hunan and Sichuan provinces had the highest diversity of NTDs, with 9 and 8 number of different NTDs reported respectively. And the highest disease burden was observed in Sichuan (242,683.46 DALYs), Xizang Zizhiqu (178,318.99 DALYs) and Guangdong (154,228.31 DALYs). The “high-high” clustering areas of NTDs were mainly in China's central and southern regions, as identified by spatial autocorrelation analysis. Conclusions China has made unremitting efforts in the prevention and control of NTDs, and the disease burden of major NTDs in China has decreased significantly. Using the One Health concept to guide disease prevention and control in the field to effectively save medical resources and achieve precise intervention. Graphical abstract

Abstract Background Biogeochemical processing of metals including the fabrication of novel nanomaterials from metal contaminated waste streams by microbial cells is an area of intense interest in the environmental sciences. Results Here we focus on the fate of Ce during the microbial reduction of a suite of Ce-bearing ferrihydrites with between 0.2 and 4.2 mol% Ce. Cerium K-edge X-ray absorption near edge structure (XANES) analyses showed that trivalent and tetravalent cerium co-existed, with a higher proportion of tetravalent cerium observed with increasing Ce-bearing of the ferrihydrite. The subsurface metal-reducing bacterium Geobacter sulfurreducens was used to bioreduce Ce-bearing ferrihydrite, and with 0.2 mol% and 0.5 mol% Ce, an Fe(II)-bearing mineral, magnetite (Fe(II)(III)2O4), formed alongside a small amount of goethite (FeOOH). At higher Ce-doping (1.4 mol% and 4.2 mol%) Fe(III) bioreduction was inhibited and goethite dominated the final products. During microbial Fe(III) reduction Ce was not released to solution, suggesting Ce remained associated with the Fe minerals during redox cycling, even at high Ce loadings. In addition, Fe L 2,3 X-ray magnetic circular dichroism (XMCD) analyses suggested that Ce partially incorporated into the Fe(III) crystallographic sites in the magnetite. The use of Ce-bearing biomagnetite prepared in this study was tested for hydrogen fuel cell catalyst applications. Platinum/carbon black electrodes were fabricated, containing 10% biomagnetite with 0.2 mol% Ce in the catalyst. The addition of bioreduced Ce-magnetite improved the electrode durability when compared to a normal Pt/CB catalyst. Conclusion Different concentrations of Ce can inhibit the bioreduction of Fe(III) minerals, resulting in the formation of different bioreduction products. Bioprocessing of Fe-minerals to form Ce-containing magnetite (potentially from waste sources) offers a sustainable route to the production of fuel cell catalysts with improved performance. Graphical Abstract

Enhanced oil recovery (EOR) remains paramount for tapping into residual oil post primary and secondary recovery stages. While conventional methods hold their ground, the rise of chemical EOR, especially utilizing nanofluids, stands out due to its economic viability, enhanced recovery efficiency, and adaptability. The allure of nanofluid-enhanced oil recovery (N-EOR) has grown amongst researchers, albeit with underlying mechanisms that still harbor inconsistencies and ambiguities. This review meticulously examines the micro-mechanics of nanofluid interactions with heavy oil molecules, particles, and surfaces, the methodologies underpinning nanofluid-assisted EOR, multiphase displacement within pores and cores, and the fluid–solid coupling during such flows. Key findings show that nanofluids alter mineral wettability, adjust oil/water interfacial tension, shift structural disjoining pressure, and curtail viscosity. The prowess of N-EOR largely hinges on nanoparticle adsorption. Their affinity for mineral surfaces induces a shift towards water-wet states, while their interplay at oil/water boundaries can tweak interfacial tensions, fostering emulsification. One standout revelation is the adsorption of asphaltenes on nanoparticles, which mitigates asphaltene concentrations in heavy oil, thereby diminishing viscosity and amplifying oil extraction. Contrary to previous studies that merely spotlighted outcomes, our review delves deep into the complexities of nanoparticle adsorption, spotlighting the harmonious interplay between nanoparticle adsorption features and N-EOR operations. We unravel the intricacies of nanofluid behaviors during multiphase core displacement and provide a detailed overview of pertinent simulations. To encapsulate, this study demystifies potential N-EOR techniques and charts fresh research trajectories. Our revelations aim to enrich the comprehension of N-EOR phenomena, accentuating the pivotal role of nanofluids in multiphase core transitions and computational simulations. Furthermore, we highlight lingering challenges, directing the scientific community towards continued nanoparticle innovations and exploration. The novelty of this study is as follows: Nanofluids are mainly used in the third oil recovery process, and are not used in the first and second oil recovery processes. In addition, during the third oil recovery, some nanofluids may remain in the mine due to the sedimentation characteristics of nanomaterials. Currently, we are also committed to developing new processes to reduce the deposition of nanomaterials in the mine. At present, it is not clear how nanofluids enhance the oil recovery mechanism, and the oil recovery mechanism is relatively chaotic. In this article, we analyzed these mechanisms.

With the development of the national economy, a large number of small and medium-sized enterprises have rapidly expanded in scale, and their internal structures have become increasingly complex. Traditional single project management is no longer suitable for enterprise management, demanding the need for improved project scheduling management to optimize resource utilization and enhance operational efficiency. Traditional single project management approaches are found to be inadequate due to the increased complexity and scale of small and medium-sized enterprises. These traditional methods struggle to efficiently manage resources and schedule tasks effectively within the context of multiple projects, leading to increased consumption and inefficiency. This study constructs a human resource scheduling model based on the second-generation non-dominated sorting genetic algorithm, aiming to determine the task sequence and time relationship during project implementation more effectively. By promoting the creation of the initial population and cross inheritance, the testing efficiency of the algorithm is improved. The proposed method significantly outperforms simple genetic algorithms in terms of accuracy, convergence speed, entropy measurement value, and distance value, thereby optimizing resource utilization and reducing consumption by 40%. The proposed model’s effectiveness is shown by using a project from a certain company as the test subject. This demonstrates how the second-generation non-dominated sorting genetic algorithm can make scheduling management of multiple projects better. It also helps to save resources and make overall project management more efficient.

In mine operations, the safe operation of transportation equipment is crucial to ensure the safety of miners and the efficiency of mine production. However, it is notable that there is little research on perception technology for unstructured environments such as underground mining tunnels. The underground mining environment is characterized by its intricate nature, with narrow passageways, dim lighting, and complex spatial topological structures. Large-scale mining trucks operating in such environments have a restricted field of view and pose a serious safety hazard. In this paper, we propose an underground mining obstacle detection method based on 3D light detection and ranging (LiDAR) technology to augment the environmental perception capabilities of mining vehicles. This method uses point cloud data collected by LiDAR as input, employing an improved random sample consensus (RANSAC) to segment rugged ground points. Additionally, an innovative point cloud processing module for tunnel walls and the application of Euclidean clustering and obstacle recognition strategies ensure accurate obstacle detection. Experimental results demonstrate that the proposed method achieves a detection accuracy of over 95% within a 50-meter region of interest, and the running time is kept within 0.14 seconds on an ordinary computer. The effectiveness of the proposed method is discussed across varying distances, numbers, and tunnel types, revealing satisfactory outcomes and robust applicability. The proposed efficient method meets the requirements of underground mining truck obstacle detection, making a substantial contribution to underground unmanned production.

Abstract Background Gene therapy for lung cancer has emerged as a novel tumor-combating strategy for its superior tumor specificity, low systematical toxicity and huge clinical translation potential. Especially, the applications of microRNA shed led on effective tumor ablation by directly interfering with the crucial gene expression, making it one of the most promising gene therapy agents. However, for lung cancer therapy, the microRNA treatment confronted three bottlenecks, the poor tumor tissue penetration effect, the insufficient lung drug accumulation and unsatisfied gene transfection efficiency. To address these issues, an inhalable RGD-TAT dual peptides-modified cationic liposomes loaded with microRNA miR-34a and gap junction (GJ) regulation agent all-trans retinoic acid (ATRA) was proposed, which was further engineered into dry powder inhalers (DPIs). Results Equipped with a rough particle surface and appropriate aerodynamic size, the proposed RGD-TAT-CLPs/ARTA@miR-34a DPIs were expected to deposit into the deep lung and reach lung tumor lesions guided by targeting peptide RGD. Assisted by cellular transmembrane peptides TAT, the RGD-TAT-CLPs/ARTA@miR-34a was proven to be effectively internalized by cancer cells, enhancing gene transfection efficiency. Then, the GJ between tumor cells was upregulated by ARTA, facilitating the intercellular transport of miR-34a and boosting the gene expression in the deep tumor. Conclusion Overall, the proposed RGD-TAT-CLPs/ARTA@miR-34a DPIs could enhance tumor tissue penetration, elevate lung drug accumulation and boost gene transfection efficiency, breaking the three bottlenecks to enhancing tumor elimination in vitro and in vivo. We believe that the proposed RGD-TAT-CLPs/ARTA@miR-34a DPIs could serve as a promising pulmonary gene delivery platform for multiple lung local disease treatments.

MicroRNAs (miRNA) exert regulatory actions via base pairing with their binding sites on target mRNAs. Cooperative binding, i.e., synergism, among binding sites on an mRNA is biochemically well characterized. We studied whether this synergism is reflected in the global relationship between miRNA-mediated regulatory activity and miRNA binding site count on the target mRNAs, i.e., leading to a non-linear relationship between the two. Recently, using our own and public datasets, we have enquired into miRNA regulatory actions: first, we analyzed the power-law distribution pattern of miRNA binding sites; second, we found that, strikingly, mRNAs for core miRNA regulatory apparatus proteins have extraordinarily high binding site counts, forming self-feedback-control loops; third, we revealed that tumor suppressor mRNAs generally have more sites than oncogene mRNAs; and fourth, we characterized enrichment of miRNA-targeted mRNAs in translationally less active polysomes relative to more active polysomes. In these four studies, we qualitatively observed obvious positive correlation between the extent to which an mRNA is miRNA-regulated and its binding site count. This paper summarizes the datasets used. We also quantitatively analyzed the correlation by comparative linear and non-linear regression analyses. Non-linear relationships, i.e., accelerating rise of regulatory activity as binding site count increases, fit the data much better, conceivably a transcriptome-level reflection of cooperative binding among miRNA binding sites on a target mRNA. This observation is potentially a guide for integrative quantitative modeling of the miRNA regulatory system.

Eddy current sensors are increasingly being used to measure the dynamic blade tip clearance in turbines due to their robust anti-interference capabilities and non-contact measurement advantages. However, the current research primarily focuses on enhancing the performance of eddy current sensors themselves, with few studies investigating the influence of turbine rotor parameters on the measurements taken by these sensors for dynamic blade tip clearance. Hence, this paper addresses this gap by using COMSOL Multiphysics 6.2 software to establish a finite model with circuit interfaces. Additionally, the model’s validity was verified through experiments. This model is used to simulate the voltage output of the sensor and the measurement of dynamic blade tip clearance under various rotor parameters. The results indicate that the length and number of blades, as well as the hub radius, significantly affect the sensor voltage output in comparison to rotation speed. Furthermore, we show that traditional static calibration methods are inadequate for measuring dynamic blade tip clearance using eddy current sensors. Instead, it is demonstrated that incorporating rotor parameters into the calibration of eddy current sensors can enhance the accuracy of dynamic blade tip clearance measurements.

Given the increasing application of eddy current sensors for measuring turbine tip clearance in aero engines, enhancing the performance of these sensors is essential for improving measurement accuracy. This study investigates the influence of coil shape on the measurement performance of planar eddy current sensors and identifies an optimal coil shape to enhance sensing capabilities. To achieve this, various coil shapes—specifically circular, square, rectangular wave, and triangular wave—were designed and fabricated, featuring different numbers of turns for the experiment at room temperature. By employing a method for calculating coil inductance, the performance of each sensor was evaluated based on key metrics: measurement range, sensitivity, and linearity. Experimental results reveal that the square coil configuration outperforms other shapes in overall measurement performance. Notably, the square coil demonstrated a measurement range of 0 mm to 8 mm, a sensitivity of 0.115685 μH/mm, and an impressive linearity of 98.41% within the range of 0 mm to 2 mm. These findings indicate that the square coil configuration enhances measurement capabilities. The conclusions drawn from this study provide valuable insights for selecting coil shapes and optimizing the performance of planar eddy current sensors, thereby contributing to the advancement of turbine tip clearance measurement techniques in aero engines.

Introduction: This paper introduces a deep learning approach based on Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and attention mechanism for stock market prediction and investment decision making in financial management. These methods leverage the advantages of deep learning to capture complex patterns and dependencies in financial time series data. Stock market prediction and investment decision-making have always been important issues in financial management.Methods: Traditional statistical models often struggle to handle nonlinear relationships and complex temporal dependencies, thus necessitating the use of deep learning methods to improve prediction accuracy and decision effectiveness. This paper adopts a hybrid deep learning model incorporating CNN, BiLSTM, and attention mechanism. CNN can extract meaningful features from historical price or trading volume data, while BiLSTM can capture dependencies between past and future sequences. The attention mechanism allows the model to focus on the most relevant parts of the data. These methods are integrated to create a comprehensive stock market prediction model. We validate the effectiveness of the proposed methods through experiments on real stock market data. Compared to traditional models, the deep learning model utilizing CNN, BiLSTM, and attention mechanism demonstrates superior performance in stock market prediction and investment decision-making.Results and Discussion: Through ablation experiments on the dataset, our deep learning model achieves the best performance across all metrics. For example, the Mean Absolute Error (MAE) is 15.20, the Mean Absolute Percentage Error (MAPE) is 4.12%, the Root Mean Square Error (RMSE) is 2.13, and the Mean Squared Error (MSE) is 4.56. This indicates that these methods can predict stock market trends and price fluctuations more accurately, providing financial managers with more reliable decision guidance. This research holds significant implications for the field of financial management. It offers investors and financial institutions an innovative approach to better understand and predict stock market behavior, enabling them to make wiser investment decisions.

The goal of ''carbon peak, carbon neutral'' and the increasing expansion of new energy have helped to advance the development of energy storage. However, since the operating cost of energy storage is high, carbon emission trading and power market trading have emerged, effectively improving the efficiency. In this paper, a trading strategy and bidding framework of energy storage participation in the day-ahead joint market are studied. A market bidding model has been established in a framework based on the Stackelberg game. Finally, the ''Day-Ahead and Intra-Day and Carbon Emission Trading (CET)'' market clearing model has been constructed. It has been simplified to solve the equivalent mixed-integer linear programming (MILP) problem with equilibrium constraints through the use of the Karush–Kuhn–Tucker (KKT) optimality criterion and duality principle. The proposed model is validated through improved examples to obtain thermal unit output cuts of up to 32.2% during load trough periods, and up to 16.75% increase in clearing prices during peak load periods. The storage life is extended and the storage output variation is minimized.

Abstract While the mechanism of general anesthesia has been extensively studied, the underlying neural circuitry has yet to be fully understood. The parabrachial nucleus (PBN) plays a crucial role in modulating wakefulness and promoting arousal from general anesthesia. However, the specific role of PBN projections in the process of general anesthesia remains unclear. In this study, we bilaterally injected AAV-associated viruses encoding excitatory or inhibitory optogenetic probes into the PBN and implanted optical fibers in the LH or BF area. After four weeks, we optogenetically activated or inhibited the PBN-LH and PBN-BF pathways under 1.5 vol% isoflurane. We calculated the time it took for anesthesia induction and emergence, simultaneously monitoring changes in the burst-suppression ratio using electroencephalogram recording. Our findings indicate that optogenetic activation of the PBN-LH and PBN-BF projections plays a significant role in promoting both cortical and behavioral emergence from isoflurane inhalation, without significantly affecting the induction time. Conversely, photoinhibition of these pathways prolonged the recovery time, with no notable difference observed during the induction phase. In summary, our results demonstrate that the PBN-LH and PBN-BF pathways are crucial for promoting arousal from isoflurane general anesthesia, but do not have a pronounced impact on the induction phase.

As an industrial waste residue, Electrolytic Manganese Residue (EMR) can greatly promote sludge dewatering and further particle-size optimization can significantly strengthen sludge dewaterability. In this study, the effects of ammonium sulfate, calcium sulphate dihydrate, and manganese carbonate in EMR on sludge dewatering performance were investigated using the response surface optimization method. It was found that the optimized ratio of three components in EMR was 1.0:1.6:2.2 based on capillary suction time (CST), specific resistance of filtration (SRF), and zeta potential of dewatered sludge. The composition ratio of particle-size optimized EMR was modified based on the above optimization, resulting in a significant increase in sludge dewatering performance (CST and SRF reduced by 8.7% and 11.2%, respectively). Compared with those in original sludge, the content of bound extracellular polymeric substances in the conditioned sludge with optimized ratio was drastically reduced while that of soluble extracellular polymeric substances was slightly increased, which was in accordance with the decline of fluorescence intensity. These findings indicated the disintegration of extracellular polymeric substances, the enhancement of hydrophobicity, and dewatering properties of the sludge. In summary, optimized EMR can effectively intensify the dewaterability of sludge, providing a competitive solution for dewatering and further disposal of sludge.

With the frequent occurrence of drone-related criminal cases, drone forensics has become a hot spot of concern. During drone-related criminal investigations, the implicated drones are often forcibly brought down, which poses significant challenges in conducting forensic analysis. In order to restore the truth of criminal cases, it is necessary to extract data not only from the external TF card but also from internal chip memory in drone forensics. To address this issue, a drone data parser (DRDP) is proposed to extract internal and external data from criminal-implicated drones. In this paper, we present comprehensive forensics on the DJI Mavic 2 Pro, analyzing the main file structure and encryption model. According to its file structures, three case studies are conducted on various file types (DAT files, TXT files, and default files) to verify the effectiveness and applicability of the designed procedure. The results show that the encrypted data of the implicated drone, such as GPS information, flight time, flight altitude, flight distance, three velocity components (x, y, z) and other information can be extracted and decrypted correctly, which provides evidence for the identification of the case facts.

The application prospects of composite sponges with antibacterial and drug-carrying functions in the field of medical tissue engineering are extensive. A solution of cassava silk fibroin (CSF) was prepared with Ca(NO3)2 as a solvent, which was then combined with chitosan (CS) to create a sponge-porous material by freeze-drying. The CSF-CS composite sponge with a mesh structure was successfully fabricated through hydrogen bonding. Scanning electron microscopy (SEM), Fourier transform infrared absorption (FTIR) and X-ray diffraction (XRD) were employed to investigate the appearance and structure of the cassava silk’s fibroin materials, specifically examining the impact of different mass percentages of CS on the sponge’s structure. The swelling rate and mechanical properties of the CSF-CS sponge were analyzed, along with its antibacterial properties. Furthermore, by incorporating ibuprofen as a model drug into these loaded sponges, their potential efficacy as efficient drug delivery systems was demonstrated. The results indicate that the CSF-CS sponge possesses a three-dimensional porous structure with over 70% porosity and an expansion rate exceeding 400% while also exhibiting good resistance against pressure. Moreover, it exhibits excellent drug-carrying ability and exerts significant bacteriostatic effects on Escherichia coli. Overall, these findings support considering the CSF-CS composite sponge as a viable candidate for use in drug delivery systems or wound dressings.

Abstract Robust oxygenic photosynthesis requires a suite of accessory factors to ensure efficient assembly and repair of the oxygen-evolving photosystem two (PSII) complex. The highly conserved Ycf48 assembly factor binds to the newly synthesized D1 reaction center polypeptide and promotes the initial steps of PSII assembly, but its binding site is unclear. Here we use cryo-electron microscopy to determine the structure of a cyanobacterial PSII D1/D2 reaction center assembly complex with Ycf48 attached. Ycf48, a 7-bladed beta propeller, binds to the amino-acid residues of D1 that ultimately ligate the water-oxidising Mn4CaO5 cluster, thereby preventing the premature binding of Mn2+ and Ca2+ ions and protecting the site from damage. Interactions with D2 help explain how Ycf48 promotes assembly of the D1/D2 complex. Overall, our work provides valuable insights into the early stages of PSII assembly and the structural changes that create the binding site for the Mn4CaO5 cluster.

Background The Index of Consciousness (IoC) is a new monitoring index of anesthesia depth reflecting the state of consciousness of the brain independently developed by China. The research on monitoring the depth of anesthesia mainly focuses on propofol, and bispectral index (BIS) is a sensitive and accurate objective index to evaluate the state of consciousness at home and abroad. This study mainly analyzed the effect of IoC on monitoring the depth of sevoflurane anesthesia and the consistency and accuracy with BIS when monitoring sevoflurane maintenance anesthesia. Objective To investigate the monitoring value of the Index of Consciousness (IoC) for the depth of sevoflurane anesthesia in laparoscopic surgery. Methods The study population consisted of 108 patients who experienced elective whole-body anesthesia procedures within the timeframe of April 2020 to June 2023 at our hospital. Throughout the anesthesia process, which encompassed induction and maintenance using inhaled sevoflurane, all patients were diligently monitored for both the Bispectral Index (BIS) and the Index of Consciousness (IoC). We conducted an analysis to assess the correlation between IoC and BIS throughout the anesthesia induction process and from the maintenance phase to the regaining of consciousness. To evaluate the predictive accuracy of IoC and BIS for the onset of unconsciousness during induction and the return of consciousness during emergence, we employed receiver operating characteristic (ROC) curve analysis. Results The mean difference between BIS and IoC, spanning from the pre-anesthesia induction phase to the completion of propofol induction, was 1.3 (95% Limits of Agreement [−53.4 to 56.0]). Similarly, during the interval from the initiation of sevoflurane inhalation to the point of consciousness restoration, the average difference between BIS and IoC was 0.3 (95% LOA [−10.8 to 11.4]). No statistically significant disparities were observed in the data acquired from the two measurement methodologies during both the anesthesia induction process and the journey from maintenance to the regaining of consciousness (P > 0.05). The outcomes of the ROC curve analysis disclosed that the areas under the curve (AUC) for prognosticating the occurrence of loss of consciousness were 0.967 (95% CI [0.935–0.999]) for BIS and 0.959 (95% CI [0.924–0.993]) for IoC, with optimal threshold values set at 81 (sensitivity: 88.10%, specificity: 92.16%) and 77 (sensitivity: 79.55%, specificity: 95.45%) correspondingly. For the prediction of recovery of consciousness, the AUCs were 0.995 (95% CI [0.987–1.000]) for BIS and 0.963 (95% CI [0.916–1.000]) for IoC, each associated with optimal cutoff values of 76 (sensitivity: 92.86%, specificity: 100.00%) and 72 (sensitivity: 86.36%, specificity: 100.00%) respectively. Conclusion The monitoring of sevoflurane anesthesia maintenance using IoC demonstrates a level of comparability to BIS, and its alignment with BIS during the maintenance phase of sevoflurane anesthesia is robust. IoC displays promising potential for effectively monitoring the depth of anesthesia.

Developing efficient and cost‐effective electrocatalysts as substitutes for noble metals remains a big challenge, which demands significant advancements in both material designing and mechanistic understanding. Herein, Co3O4/CeO2 heterojunction nanonetworks are successfully synthesized through metal organic framework precursor. Notably, Co3O4/CeO2 heterojunctions can effectively regulate electronic structure of Co3O4, thus inducing oxygen atom from Co3O4 lattice to participating in oxygen evolution reaction (OER) via lattice oxygen‐mediated mechanism, which reduces reaction overpotential. Additionally, the porous network structure can facilitate electrolyte transfer and provide more active sites for electrocatalytic reactions. Consequently, Co3O4/CeO2 heterojunction nanonetworks exhibit great electrocatalytic performance and high durability, requiring only an OER overpotential of 259 mV at current density of 100 mA cm−2 in 1 M KOH, markedly outperforming Co3O4 nanocatalysts (309 mV) and showing promising potential as substitutable non‐noble OER catalysts.

Abstract Helix-fiber composites (HFCs), which involve wrapping a material around a flexible core, are of interest for intelligent stretchable functional materials or devices. Here, we reveal that mechanical coupling between the core and wrapping plays a critical role in controlling the structure and mechanical properties. Our results show that the mechanical properties of the HFC can be programmed by precisely tuning the mechanical coupling strength among them. Interestingly, the HFC also shows unique pseudo-elastic behavior under large strain due to the mechanical coupling effects. The HFC is demonstrated as an elastic metal conductor, programmable sensor, and finally as an elastic mold for structure transplantation by mechanically decoupling the elastomer core from the helix fiber. The results not only deepen the fundamental understanding of HFCs, but also may promote future structure-properties and fabrication studies on helix-structured functional composite and devices.

In this study, we investigated the feasibility of using electroencephalogram (EEG) signals to differentiate between four distinct subject-driven cognitive states: resting state, narrative memory, music, and subtraction tasks. EEG data were collected from seven healthy male participants while performing these cognitive tasks, and the raw EEG signals were transformed into time–frequency maps using continuous wavelet transform. Based on these time–frequency maps, we developed a convolutional neural network model (TF-CNN-CFA) with a channel and frequency attention mechanism to automatically distinguish between these cognitive states. The experimental results demonstrated that the model achieved an average classification accuracy of 76.14% in identifying these four cognitive states, significantly outperforming traditional EEG signal processing methods and other classical image classification algorithms. Furthermore, we investigated the impact of varying lengths of EEG signals on classification performance and found that TF-CNN-CFA demonstrates consistent performance across different window lengths, indicating its strong generalization capability. This study validates the ability of EEG to differentiate higher cognitive states, which could potentially offer a novel BCI paradigm.

Background: Functional constipation is a common gastrointestinal disorder especially severely affecting the life quality of the aged. Jichuanjian (JCJ) has been widely used for aged functional constipation (AFC) in clinic. Yet, the mechanisms of JCJ merely scratch the surface with being studied at a single level, rather than from a systematic perspective of the whole. Aim: The purpose of this study was to explore the underlying mechanisms of JCJ in treating AFC from the perspectives of fecal metabolites and related pathways, gut microbiota, key gene targets and functional pathways, as well as “behaviors-microbiota-metabolites” relationships. Methods: 16S rRNA analysis and fecal metabolomics combined with network pharmacology were applied to investigate the abnormal performances of AFC rats, as well as the regulatory effects of JCJ. Results: JCJ significantly regulated the abnormalities of rats' behaviors, the microbial richness, and the metabolite profiles that were interrupted by AFC. 19 metabolites were found to be significantly associated with AFC involving in 15 metabolic pathways. Delightfully, JCJ significantly regulated 9 metabolites and 6 metabolic pathways. AFC significantly interrupted the levels of 4 differential bacteria while JCJ significantly regulated the level of SMB53. HSP90AA1 and TP53 were the key genes, and pathways in cancer was the most relevant signaling pathways involving in the mechanisms of JCJ. Conclusion: The current findings not only reveal that the occurrence of AFC is closely related to gut microbiota mediating amino acid and energy metabolism, but also demonstrate the effects and the underlying mechanisms of JCJ on AFC.

SLAM (Simultaneous Localization and Mapping) based on 3D LiDAR (Laser Detection and Ranging) is an expanding field of research with numerous applications in the areas of autonomous driving, mobile robotics, and UAVs (Unmanned Aerial Vehicles). However, in most real-world scenarios, dynamic objects can negatively impact the accuracy and robustness of SLAM. In recent years, the challenge of achieving optimal SLAM performance in dynamic environments has led to the emergence of various research efforts, but there has been relatively little relevant review. This work delves into the development process and current state of SLAM based on 3D LiDAR in dynamic environments. After analyzing the necessity and importance of filtering dynamic objects in SLAM, this paper is developed from two dimensions. At the solution-oriented level, mainstream methods of filtering dynamic targets in 3D point cloud are introduced in detail, such as the ray-tracing-based approach, the visibility-based approach, the segmentation-based approach, and others. Then, at the problem-oriented level, this paper classifies dynamic objects and summarizes the corresponding processing strategies for different categories in the SLAM framework, such as online real-time filtering, post-processing after the mapping, and Long-term SLAM. Finally, the development trends and research directions of dynamic object filtering in SLAM based on 3D LiDAR are discussed and predicted.

Dry powder inhalers (DPIs) had been widely used in lung diseases on account of direct pulmonary delivery, good drug stability and satisfactory patient compliance. However, an indistinct understanding of pulmonary delivery processes (PDPs) hindered the development of DPIs. Most current evaluation methods explored the PDPs with over-simplified models, leading to uncompleted investigations of the whole or partial PDPs. In the present research, an innovative modular process analysis platform (MPAP) was applied to investigate the detailed mechanisms of each PDP of DPIs with different carrier particle sizes (CPS). The MPAP was composed of a laser particle size analyzer, an inhaler device, an artificial throat and a pre-separator, to investigate the fluidization and dispersion, transportation, detachment and deposition process of DPIs. The release profiles of drug, drug aggregation and carrier were monitored in real-time. The influence of CPS on PDPs and corresponding mechanisms were explored. The powder properties of the carriers were investigated by the optical profiler and Freeman Technology four powder rheometer. The next generation impactor was employed to explore the aerosolization performance of DPIs. The novel MPAP was successfully applied in exploring the comprehensive mechanism of PDPs, which had enormous potential to be used to investigate and develop DPIs.

Protein tyrosine phosphatase 1B (PTP1B) is a crucial therapeutic target for multiple human diseases comprising type 2 diabetes (T2DM) and obesity because it is a seminal part of a negative regulator in both insulin and leptin signaling pathways. PTP1B inhibitors increase insulin receptor sensitivity and have the ability to cure insulin resistance-related diseases. However, the few PTP1B inhibitors that entered the clinic (Ertiprotafib, ISIS-113715, Trodusquemine, and JTT-551) were discontinued due to side effects or low selectivity. Molecules with broad chemical diversity extracted from natural products have been reported to be potent PTP1B inhibitors with few side effects. This article summarizes the recent PTP1B inhibitors extracted from natural products, clarifying the current research progress, and providing new options for designing new and effective PTP1B inhibitors.

Abstract Particleboard surface defect detection technology is of great significance to the automation of particleboard detection, but the current detection technology has disadvantages such as low accuracy and poor real-time performance. Therefore, this paper proposes an improved lightweight detection method of You Only Live Once v5 (YOLOv5), namely PB-YOLOv5 (Particle Board-YOLOv5). Firstly, the gamma-ray transform method and the image difference method are combined to deal with the uneven illumination of the acquired images, so that the uneven illumination is well corrected. Secondly, Ghost Bottleneck lightweight deep convolution module is added to Backbone module and Neck module of YOLOv5 detection algorithm to reduce model volume. Thirdly, the SELayer module of attention mechanism is added into Backbone module. Finally, replace Conv in Neck module with depthwise convolution (DWConv) to compress network parameters. The experimental results show that the PB-YOLOv5 model proposed in this paper can accurately identify five types of defects on the particleboard surface: Bigshavings, SandLeakage, GlueSpot, Soft and OliPollution, and meet the real-time requirements. Specifically, recall, F1 score, mAP@.5, mAP@.5:.95 values of pB-Yolov5s model were 91.22%, 94.5%, 92.1%, 92.8% and 67.8%, respectively. The results of Soft defects were 92.8%, 97.9%, 95.3%, 99.0% and 81.7%, respectively. The detection of single image time of the model is only 0.031 s, and the weight size of the model is only 5.4 MB. Compared with the original YOLOv5s, YOLOv4, YOLOv3 and Faster RCNN, the PB-Yolov5s model has the fastest Detection of single image time. The Detection of single image time was accelerated by 34.0%, 55.1%, 64.4% and 87.9%, and the weight size of the model is compressed by 62.5%, 97.7%, 97.8% and 98.9%, respectively. The mAP value increased by 2.3%, 4.69%, 7.98% and 13.05%, respectively. The results show that the PB-YOLOV5 model proposed in this paper can realize the rapid and accurate detection of particleboard surface defects, and fully meet the requirements of lightweight embedded model.

Real-time and accurate bucket pose estimation plays a vital role in improving the intelligence level of mining excavators, as the bucket is a crucial component of the excavator. Existing methods for bucket pose estimation are realized by installing multiple non-visual sensors. However, these sensors suffer from cumulative errors caused by loose connections and short service lives caused by strong vibrations. In this paper, we propose a method for bucket pose estimation based on deep neural network and registration to solve the large registration error problem caused by occlusion. Specifically, we optimize the Point Transformer network for bucket point cloud semantic segmentation, significantly improving the segmentation accuracy. We employ point cloud preprocessing and continuous frame registration to reduce the registration distance and accelerate the Fast Iterative Closest Point algorithm, enabling real-time pose estimation. By achieving precise semantic segmentation and faster registration, we effectively address the problem of intermittent pose estimation caused by occlusion. We collected our own dataset for training and testing, and the experimental results are compared with other relevant studies, validating the accuracy and effectiveness of the proposed method.

Abstract An AlInN/GaN superjunction fin‐gate high electron mobility transistor (SJFin‐HEMT) is proposed in this work. A superjunction region with GaN/AlInN/GaN/AlInN/GaN structure is defined between the gate and drain, and two‐dimensional‐hole‐gas/two‐dimensional‐electron‐gas/two‐dimensional‐hole‐gas/two‐dimensional‐electron‐gas (2DHG/2DEG/2DHG/2DEG) are respectively induced due to the polarization charges at the heterojunction interfaces. The 2DHGs and 2DEGs compensate each other, resulting a charge balanced superjunction in the gate‐drain spacing, thus inducing a uniform electric field distribution under off‐state maximizing the breakdown voltage. Additionally, because the current flows through both the 2DEGs, higher output current and lower on‐state resistance are observed in contrast to the conventional Fin‐HEMT. Simulation results show breakdown voltage of 2957 V (VGS = –6 V) and on‐state resistance of 0.31 mΩ·cm2 (VDS = 0.1 V, VGS = 0 V) for the SJFin‐HEMT, comparing with that of 241 V and 0.36 mΩ·cm2 for the conventional Fin‐HEMT.

A straightforward and fast transient temperature rise calculation model is necessary for electrical engineers to manage power cable load. A self-heating lumped parameter model (LPM) without the requirement of monitoring cable surface temperature is developed to evaluate the real-time transient temperature rise of the power cable in ducts. A mutual-heating lumped parameter model to describe the mutual heat interaction between any two cables is proposed in this paper as well. The transient temperature rise of any power cable in duct is the sum of the self-heating temperature rise and the mutual-heating temperature rise from all the other power cables in ducts. All parameters of these two models are obtained by transient thermal circuit analysis, CYMCAP calculation results and generic algorithm. A case study has been conducted to verify the feasibility of the proposed LPMs and the self and mutual temperature rise for each cable in ducts have been obtained. These results agree with the accurate data obtained from CYMCAP well, indicating that LPM can be used as a fast and straightforward tool to calculate the transient temperature rise of power cables in duct.

Clinical scientists have been investigating the relationships between sex hormones, personality, and eating disorders for decades. However, there is a lack of direct research that addresses whether personality mediates or moderates the relationships between sex hormones and eating pathology. Moreover, the neural mechanisms that underlie the interactive associations between these variables remain unclear. This review aims to summarize the associations between these constructs, describe a neural mechanism mediating these relationships, and offer clinical strategies for the early identification and intervention of eating disorders. The gathered evidence shows that aggressiveness, impulsivity, and obsessive-compulsiveness may mediate or moderate the relationships between sex hormones and eating pathology, but only among females. Furthermore, sex hormone receptor density in the mesocorticolimbic dopamine pathway may explain the neural mechanism of these associations. Future research should use more comprehensive personality measurements and assess the mediation and moderation effects of temperament while taking the hormone levels of women across menstrual cycles into account. Additionally, electroencephalography and functional magnetic resonance imaging should be implemented to directly assess brain activity and corroborate these findings.

The China Fusion Engineering Test Reactor (CFETR), a superconducting magnetic confinement tokamak fusion reactor, will develop a high-performance torus cryopump to pump torus plasma exhaust gas. The inlet valve is one of the key components of the cryopump, and it is used to isolate the cryopump from the plasma for regeneration, to control the pumping speed of the cryopump, and to operate as a pressure relief valve in case of a failure, such as the cryopipe breaking inside the cryopump chamber. This paper presents a novel inlet valve. Ensuring that the design of the inlet valve meets the above requirements will be a challenge. In order to verify the reliability of the inlet valve, its critical components are analyzed and optimized by the Finite Element Method. The effect of the stroke of the inlet valve on pumping performance is then estimated by the Monte Carlo Method, and the pressure profile in the whole flow field is studied to predict the cryopump’s behavior. Finally, the seismic capacity of the optimized inlet valve is analyzed, and the mechanical performance of the inlet valve is shown to meet CFETR design criteria. These design and analysis results will provide technical support and references for the development of the CFETR torus cryopump.

ABSTRACT: Duck circovirus disease (DuCVD), as an immunosuppressive disease, is a threat to the poultry industry. In order to diagnose this disease quickly and accurately, a real-time fluorescence-based recombinase-aided amplification (RF-RAA) method was established to detect duck circovirus (DuCV). The results showed that the quantity of amplification products was positively correlated with the value of fluorescence signal. Obvious detection results can be observed at 41°C after 15 min reaction. This method has good specificity and has no cross reaction with Muscovy duck parvovirus (MDPV), duck enteritis virus (DEV), fowl adenovirus (FAdV), porcine circovirus (PCV), and duck hepatitis A virus (DHAV). The sensitivity test showed that the minimum concentration of template detected by RF-RAA for DuCV was 10° copies/μL, and its sensitivity was 10 times higher than that of real-time fluorescence-based quantitative PCR (RFQ-PCR) and 10,000 times higher than that of polymerase chain reaction (PCR). Fifty-two clinical samples were detected by RF-RAA and RFQ-PCR, and the coincidence rate of the two methods was 98.08%. This method has the advantages of simple operation, good specificity and high sensitivity, and can be used for laboratory detection and clinical diagnosis of DuCV.

Abstract Aim To explore an effective personalized training model for nurses working in emergency isolation wards of COVID‐19 in a short period. Design This study is a longitudinal study from 24 January 2020 to 28 February 2020. Methods There are 71 nursing staff working in the emergency isolation wards of Sichuan Provincial People's Hospital that participated in this study. The questionnaires were conducted with Likert scale. The operation assessment teachers have received standardized training. The self‐rating anxiety scale (SAS) and self‐rating depression Scale (SDS) were applied to assess the mental state of nurses. Results After short‐term training, these nurses can handle the emergency tasks in a timely manner. The pass rate of nurse theory and operation assessment is 100%. The 111 suspected patients admitted to the emergency isolation ward have been scientifically diagnosed and treated, the three confirmed patients have received appropriate treatment. No nurses have been infected. Conclusions In this study, the personalized emergency training mode was feasible in the emergency isolation ward during the COVID‐19 epidemic, which rapidly improved the rescue ability of nurses and effectively avoid the occurrence of cross‐infection. This mode can provide a valuable reference for the emergency training of nurses in the future.

An explicit illustration of pulmonary delivery processes (PDPs) was a prerequisite for the formulation design and optimization of carrier-based DPIs. However, the current evaluation approaches for DPIs could not provide precise investigation of each PDP separately, or the approaches merely used a simplified and idealized model. In the present study, a novel modular modified Sympatec HELOS (MMSH) was developed to fully investigate the mechanism of each PDP separately in real-time. An inhaler device, artificial throat and pre-separator were separately integrated with a Sympatec HELOS. The dispersion and fluidization, transportation, detachment and deposition processes of pulmonary delivery for model DPIs were explored under different flow rates. Moreover, time-sliced measurements were used to monitor the PDPs in real-time. The Next Generation Impactor (NGI) was applied to determine the aerosolization performance of the model DPIs. The release profiles of the drug particles, drug aggregations and carriers were obtained by MMSH in real-time. Each PDP of the DPIs was analyzed in detail. Moreover, a positive correlation was established between the total release amount of drug particles and the fine particle fraction (FPF) values (R2 = 0.9898). The innovative MMSH was successfully developed and was capable of illustrating the PDPs and the mechanism of carrier-based DPIs, providing a theoretical basis for the design and optimization of carrier-based DPIs.

Current state-of-the-art trajectory methods do not perform well in the terminal airspace that surrounds an airport due to its complex airspace structure and the frequently changing flight postures of aircraft. Since an aircraft that takes off or lands in an airport must follow a specified procedure, this paper will learn a data-driven trajectory prediction model from many historical trajectories to improve the accuracy and robustness of trajectory prediction in the terminal airspace. A regularization method is utilized to reconstruct each aircraft trajectory to obtain a high-quality trajectory with equal time intervals and no noise. Furthermore, we formulate the 4D trajectory prediction problem as a sequence-to-sequence learning problem, and we propose a sequence-to-sequence deep long short-term memory network (SS-DLSTM) for trajectory prediction, which can effectively capture the long and short temporal dependencies and the repetitive nature among trajectories. The proposed model is composed of an encoding module and a decoding module, where the encoding mode realizes the feature representation of historical trajectories, while the decoding module accepts the output of the encoding module as its initial input and recursively outputs the predicted trajectory sequence. The proposed method is applied to a dataset for the terminal airspace in Guangzhou, China. The experimental results demonstrate that our approach has relatively high robustness and outperforms mainstream data-driven trajectory prediction methods in terms of accuracy.

The widespread use of bisphenol S (BPS) as an alternative to bisphenol A has captured attention due to its potential toxicity to aquatic organisms. In the present study, the zebrafish was used as a model to evaluate the toxicity of BPS and determine the underlying mechanisms. The environmental concentration-dependent (0, 0.1, 1, 10, 100, and 1000 μg/L BPS) transcriptome approach was employed in combination with toxicity assays to address the problem. Based on a weighted correlation network analysis, we speculated that excess reactive oxygen species (ROS) may initiate cellular events in BPS-exposed zebrafish, leading to multiple toxic effects. Furthermore, we used pathway enrichment analysis to identify key pathways (MAPK signalling pathway and metabolic pathways) that link the molecular mechanisms with different toxic effects. In addition, we performed protein-protein network and shortest path analyses to identify six hub genes (erbb2, rrm2, rps27a, his2h3c, cdk1, and mcm5) and their interactions. Moreover, we suggest that BPS may interact with erbb2 by molecular docking. Thus, the BPS-erbb2 interaction may activate the MAPK signalling and metabolic pathways, resulting in ROS production and then caused multiple toxic effects in zebrafish. This study provides information for characterising the mechanisms of BPS exposure in aquatic environments.

Forest fires may have devastating consequences for the environment and for human lives. The prediction of forest fires is vital for preventing their occurrence. Currently, there are fewer studies on the prediction of forest fires over longer time scales in China. This is due to the difficulty of forecasting forest fires. There are many factors that have an impact on the occurrence of forest fires. The specific contribution of each factor to the occurrence of forest fires is not clear when using conventional analyses. In this study, we leveraged the excellent performance of artificial intelligence algorithms in fusing data from multiple sources (e.g., fire hotspots, meteorological conditions, terrain, vegetation, and socioeconomic data collected from 2003 to 2016). We have tested several algorithms and, finally, four algorithms were selected for formal data processing. There were an artificial neural network, a radial basis function network, a support-vector machine, and a random forest to identify thirteen major drivers of forest fires in China. The models were evaluated using the five performance indicators of accuracy, precision, recall, f1 value, and area under the curve. We obtained the probability of forest fire occurrence in each province of China using the optimal model. Moreover, the spatial distribution of high-to-low forest fire-prone areas was mapped. The results showed that the prediction accuracies of the four forest fire prediction models were between 75.8% and 89.2%, and the area under the curve (AUC) values were between 0.840 and 0.960. The random forest model had the highest accuracy (89.2%) and AUC value (0.96). It was determined as the best performance model in this study. The prediction results indicate that the areas with high incidences of forest fires are mainly concentrated in north-eastern China (Heilongjiang Province and northern Inner Mongolia Autonomous Region) and south-eastern China (including Fujian Province and Jiangxi Province). In areas at high risk of forest fire, management departments should improve forest fire prevention and control by establishing watch towers and using other monitoring equipment. This study helped in understanding the main drivers of forest fires in China over the period between 2003 and 2016, and determined the best performance model. The spatial distribution of high-to-low forest fire-prone areas maps were produced in order to depict the comprehensive views of China’s forest fire risks in each province. They were expected to form a scientific basis for helping the decision-making of China’s forest fire prevention authorities.

Particleboard surface defects have a significant impact on product quality. A surface defect detection method is essential to enhancing the quality of particleboard because the conventional defect detection method has low accuracy and efficiency. This paper proposes a YOLO v5-Seg-Lab-4 (You Only Look Once v5 Segmentation-Lab-4) model based on deep learning. The model integrates object detection and semantic segmentation, which ensures real-time performance and improves the detection accuracy of the model. Firstly, YOLO v5s is used as the object detection network, and it is added into the SELayer module to improve the adaptability of the model to receptive field. Then, the Seg-Lab v3+ model is designed on the basis of DeepLab v3+. In this model, the object detection network is utilized as the backbone network of feature extraction, and the expansion rate of atrus convolution is reduced to the computational complexity of the model. The channel attention mechanism is added onto the feature fusion module, for the purpose of enhancing the feature characterization capabilities of the network algorithm as well as realizing the rapid and accurate detection of lightweight networks and small objects. Experimental results indicate that the proposed YOLO v5-Seg-Lab-4 model has mAP (Mean Average Precision) and mIoU (Mean Intersection over Union) of 93.20% and 76.63%, with a recognition efficiency of 56.02 fps. Finally, a case study of the Huizhou particleboard factory inspection is carried out to demonstrate the tiny detection accuracy and real-time performance of this proposed method, and the missed detection rate of surface defects of particleboard is less than 1.8%.

The complex environment background, lighting conditions, and other action-irrelevant visual information in the video frame bring a lot of redundancy and noise to the action spatial features, which seriously affects the accuracy of action recognition. Aiming at this point, we propose a recurrent region attention cell to capture the action-relevant regional visual information in the spatial feature, and according to the temporal sequential natures of the video, on the basis of the recurrent region attention cell, a Recurrent Region Attention model (RRA) is proposed. The recurrent region attention cell in the RRA iterates according to the temporal sequence of the video, so that the attention performance of the RRA is gradually improved. Secondly, we propose a Video Frame Attention model (VFA) that can highlight the more important frames in the whole action video sequence, so as to reduce the interference caused by the similarity between the heterogeneous action video sequences. Finally, we propose an end-to-end trainable network: Two-level Attention Model based video action recognition network (TAMNet). We experimented on two video action recognition benchmark datasets: UCF101 and HMDB51. Experiments show that our end-to-end TAMNet network can reliably focus on the more important video frames in the video sequence, and effectively capture the action-relevant regional visual information in the spatial features of each frame of the video sequence. Inspired by the two-stream structure, we construct a two-modalities TAMNet network. In the same training conditions, the two-modalities TAMNet network achieved optimal performance on both datasets.

L. monocytogenes, consisting of 13 serotypes, is an opportunistic food-borne pathogen that causes different host reactions depending on its serotypes. In this study, highly toxic L. monocytogenes 10403s resulted in more severe infections and lower survival rates. Additionally, to investigate the remodeling of the host proteome by strains exhibiting differential toxicity, the cellular protein responses of intestinal organoids were analyzed using tandem mass tag (TMT) labeling and high-performance liquid chromatography–mass spectrometry. The virulent strain 10403s caused 102 up-regulated and 52 down-regulated proteins, while the low virulent strain M7 caused 188 up-regulated and 25 down-regulated proteins. Based on the analysis of gene ontology (GO) and KEGG databases, the expressions of differential proteins in organoids infected by L. monocytogenes 10403s (virulent strain) or M7 (low virulent strain) were involved in regulating essential processes such as the biological metabolism, the energy metabolism, and immune system processes. The results showed that the immune system process, as the primary host defense response to L. monocytogenes, comprised five pathways, including ECM–receptor interaction, the complement and coagulation cascades, HIF-1, ferroptosis, and NOD-like receptor signaling pathways. As for the L. monocytogenes 10403s vs. M7 group, the expression of differential proteins was involved in two pathways: systemic lupus erythematosus and transcriptional mis-regulation in cancer. All in all, these results revealed that L. monocytogenes strains with different toxicity induced similar biological functions and immune responses while having different regulations on differential proteins in the pathway.

Carboplatin resistance in ovarian cancer (OV) is a major medical problem. Thus, there is an urgent need to find novel therapeutic targets to improve the prognosis of patients with carboplatin-resistant OV. Accumulating evidence indicates that the gene COL1A1 (collagen type I alpha 1 chain) has an important role in chemoresistance and could be a therapeutic target. However, there have been no reports about the role of COL1A1 in carboplatin-resistant OV. This study aimed to establish the detailed molecular mechanism of COL1A1 and predict potential drugs for its treatment. We found that COL1A1 had a pivotal role in carboplatin resistance in OV by weighted gene correlation network analysis and survival analysis. Moreover, we constructed a competing endogenous RNA network (LINC00052/SMCR5-miR-98-COL1A1) based on multi-omics data and experiments to explore the upstream regulatory mechanisms of COL1A1. Two key pathways involving COL1A1 in carboplatin resistance were identified by co-expression analysis and pathway enrichment: the “ECM-receptor interaction” and “focal adhesion” Kyoto Encyclopedia of Genes and Genomes pathways. Furthermore, combining these results with those of cell viability assays, we proposed that ZINC000085537017 and quercetin were potential drugs for COL1A1 based on virtual screening and the TCMSP database, respectively. These results might help to improve the outcome of OV in the future.

The autonomous walking of an underground load-haul-dump (LHD) machine is a current research hotspot. The route of an underground LHD machine is generally definite, and most research is based on the logic of positioning-decision control. Based on a reinforcement learning algorithm, a new autonomous walking training algorithm, Traditional Control Based DQN (TCB-DQN), combining the methods of traditional reflective navigation and reinforcement learning deep q-networks (DQN), is proposed. Compared with the logic of location-decision control, TCB-DQN does not require accurate positioning, but only determines how to reach the endpoint by sensing the distance from the endpoint. Through experimental verification, after using the TCB-DQN algorithm for training in a simple tunnel, the LHD machine could achieve a walking effect similar to that of a human driver’s manual operation, while after training in a more complex tunnel, the TCB-DQN algorithm could reach the endpoint smoothly.

Traffic signs can be seen everywhere in daily life. Traffic signs are symmetrical, and traffic sign detection is easily affected by distortion, distance, light intensity and other factors, which also increases the potential safety hazards of assisted driving in practical application. In order to solve this problem, a symmetrical traffic sign detection algorithm M-YOLO for complex scenes is proposed. The algorithm optimizes the delay problem by reducing the computational overhead of the network, and speeds up the speed of feature extraction. While improving the detection efficiency, it ensures a certain degree of generalization and robustness, and enhances the detection performance of traffic signs in complex environments, such as scale and illumination changes. Experimental results on CCTSDB dataset containing traffic signs in complex scenes and HRRSD small target dataset show that M-YOLO algorithm has good detection performance. Compared with other algorithms, it has higher detection accuracy and detection speed. The test results in real complex scenes show that the detection effect of this algorithm is better than that of YOLOv5l algorithm, and M-YOLO algorithm can accurately detect the traffic signs that cannot be detected by YOLOv5l algorithm. Therefore, the algorithm proposed in this article can effectively improve the detection accuracy of traffic signs, is suitable for complex scenes, and has a good detection effect on small targets.

We have investigated the influences of high-frequency vibration (HFV) superimposed onto the monotonic uniaxial tension in single-crystal aluminum (Al) specimens by molecular dynamics simulations. It was found that HFV induces softening, i.e., reduction in peak stress. Similar to previous experimental results, the softening increases with the increasing HFV amplitude. Dependences on lattice orientation, tensile strain rate, and a preset notch are considered. Lattice orientation plays an important role in peak stress and plasticity. The evolution of the atomic structure reveals that dislocations have enough time to annihilate under a lower tensile strain rate, resulting in strong ups and downs in the strain–stress curves. Under a higher strain rate, newly appearing dislocations interact with previous ones before the latter annihilate, densifying the dislocation network. As a result, further dislocation motions and annihilations are considerably impeded, leading to a relatively smooth flow stage. Furthermore, by modifying the propagation direction of shear bands, a preset notch can strengthen the peak tensile stress under low-level amplitude HFVs.

Cracks are the most significant pre-disaster of a road, and are also important indicators for evaluating the damage level of a road. At present, road crack detection mainly depends on manual detection and road detection vehicles, with which the safety of detection workers is not guaranteed and the detection efficiency is low. A road detection vehicle can speed up the efficiency to a certain extent, but the automation level is low and it is easy to block the traffic. Unmanned Aerial Vehicles (UAV) have the characteristics of low energy consumption and easy control. If UAV technology can be applied to road crack detection, it will greatly improve the detection efficiency and produce huge economic benefits. In order to find a way to apply UAV to road crack detection, we developed a new technique for road crack detection based on UAV pictures, called DenxiDeepCrack, which is a trainable deep convolutional neural network for automatic crack detection which utilises learning high-level features for crack representation. In addition, we create a new dataset based on drone images called UCrack 11 to enrich the crack database of drone images for future crack detection research.

The study of forest fire prediction is of great environmental and scientific significance. China’s Guangxi Autonomous Region has a high incidence rate of forest fires. At present, there is little research on forest fires in this area. The application of the artificial neural network and support vector machines (SVM) in forest fire prediction in this area can provide data for forest fire prevention and control in Guangxi. In this paper, based on Guangxi’s 2010–2018 satellite monitoring hotspot data, meteorology, terrain, vegetation, infrastructure, and socioeconomic data, the researchers determined the main forest fire driving factors in Guangxi. They used feature selection and backpropagation neural networks and radial basis SVM to build forest fire prediction models. Finally, the researchers use the accuracy, precision, and area under the characteristic curve (ROC-AUC) and other indicators to evaluate the predictive performance of the two models. The results showed that the prediction accuracy of the BP neural network and SVM is 92.16% and 89.89%, respectively. As both results are over 85%, the requirements of prediction accuracy is met. These results can be used for forest fire prediction in the Guangxi Autonomous Region. Specifically, the accuracy of the BP neural network was 0.93, which was higher than that of the SVM model (0.89); the recall of the SVM model was 0.84, which was lower than the BANN model (0.92), and the AUC value of the SVM model was 0.95, which was lower than the BP neural network model. The obtained results confirm that the BP neural network model can provide more prediction accuracy than support vector machines and is therefore more suitable for forest fire prediction in Guangxi, China. This research provides the necessary theoretical basis and data support for application in the field of forestry of the Guangxi Autonomous Region, China.

One of the most important tasks in remote sensing image analysis is remote sensing image Change Detection (CD), and CD is the key to helping people obtain more accurate information about changes on the Earth’s surface. A Multi-Attention Guided Feature Fusion Network (MAFF-Net) for CD tasks has been designed. The network enhances feature extraction and feature fusion by building different blocks. First, a Feature Enhancement Module (FEM) is proposed. The FEM introduces Coordinate Attention (CA). The CA block embeds the position information into the channel attention to obtain the accurate position information and channel relationships of the remote sensing images. An updated feature map is obtained by using an element-wise summation of the input of the FEM and the output of the CA. The FEM enhances the feature representation in the network. Then, an attention-based Feature Fusion Module (FFM) is designed. It changes the previous idea of layer-by-layer fusion and chooses cross-layer aggregation. The FFM is to compensate for some semantic information missing as the number of layers increases. FFM plays an important role in the communication of feature maps at different scales. To further refine the feature representation, a Refinement Residual Block (RRB) is proposed. The RRB changes the number of channels of the aggregated features and uses convolutional blocks to further refine the feature representation. Compared with all compared methods, MAFF-Net improves the F1-Score scores by 4.9%, 3.2%, and 1.7% on three publicly available benchmark datasets, the CDD, LEVIR-CD, and WHU-CD datasets, respectively. The experimental results show that MAFF-Net achieves state-of-the-art (SOTA) CD performance on these three challenging datasets.

Background/Aims: Fetal macrosomia and its associated complications are the most frequent and serious morbidities for infants associated with gestational diabetes mellitus (GDM). The aim of this study was to evaluate the lncRNAs involvement in GDM, especially for the prediction of risk for fetal overgrowth. Methods: The peripheral blood obtained from four group including healthy control (NC), healthy volunteers with pregnancy (NC-P), GDM patients with and without macrosomia were screened by lncRNA microarray and validated by quantitative real-time PCR (RT-qPCR) arranged in the training and a two-stage validation sets. The positive and negative prediction ability for candidate lncRNAs were analyzed by risk score analysis. Results: A multiple venny analysis was performed revealed five candidate lncRNA including XLOC_014172, RP11-230G5.2, PCBP1-AS1, LOC149086 and RP11-160H22.5 which was consistence with the following parameter: i, increased in GDM patients with macrosomia (GDM-M) comparing with patients without macrosomia; ii, increased in GDM-M comparing with NC-P group; iii, increased in GDM-M comparing with NC. Further validation found XLOC_014172 and RP11-230G5.2 was final consistence with these parameter in 150 samples each group. Further receiver operating characteristic curve (ROC) analysis, with the combined two stably expressed lncRNAs indicated a high diagnostic ability an area under ROC curve value (AUC) of 0.955 and 0.962 in training set and validation set respectively. Conclusions: Circulating XLOC_014172 and RP11-230G5.2 may act as novel biomarkers in GDM patients as fingerprint for the risk of macrosomia outcome.

During the operation of open-pit mining, the loading position of a haulage truck often changes, bringing a new challenge concerning how to plan an optimal truck transportation path considering the terrain factors. This paper proposes a path planning method based on a high-precision digital map. It contains two parts: (1) constructing a high-precision digital map of the cutting zone and (2) planning the optimal path based on the modified Hybrid A* algorithm. Firstly, we process the high-precision map based on different terrain feature factors to generate the obstacle cost map and surface roughness cost map of the cutting zone. Then, we fuse the two cost maps to generate the final cost map for path planning. Finally, we incorporate the contact cost between tire and ground to improve the node extension and path smoothing part of the Hybrid A* algorithm and further enhance the algorithm’s capability of avoiding the roughness. We use real elevation data with different terrain resolutions to perform random tests and the results show that, compared with the path without considering the terrain factors, the total transportation cost of the optimal path is reduced by 10%–20%. Moreover, the methods demonstrate robustness.

In recent decades, size effects caused by grain size, strain gradient, typical defects etc., have been widely investigated. Nevertheless, the dependence of tensile strength on the specimen length, addressed by Da Vinci around 500 hundred years ago, has received rather limited attention, even though it is one unavoidable question to answer if people attempt to bring materials’ amazing nano-scale strengths up to macro-level. Therefore, we make efforts to study tensile behaviors of copper nanowires with a common cross-section and various lengths by employing the molecular dynamics simulations. Surprisingly, a strong size effect of Da Vinci type indeed arises. We have shown the influences of lattice orientation, temperature and prescribed notch on such a Da Vinci size effect. Two different theoretical explanations are briefly proposed for a qualitative understanding. Finally, a simple scaling rule is summarized to cover the tendencies observed.