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Dual-port direct drive wave energy power generation systems (DP-DDWEPGS) have received widespread attention due to their smooth and zero-free output power, compared to single-port direct drive wave energy power generation systems (SP-DDWEPGS) which have the disadvantage of large out-put power fluctuations. To further enhance the performance of the DP-DDWEPGS, optimal power capture control is proposed to achieve maximum power point tracking. Meanwhile, a multiport converter is applied to the DP-DDWEPGS to solve the problem caused by an excessive number of switching devices in the overall system converter. The multiport converter fulfills all the functional requirements of the DP-DDWEPGS while reducing the number of switching devices. However, switch multiplexing of the multiport converter also introduces coupling relationships between each port and the wave force exhibits time-varying characteristics, necessitating advanced control methods with superior fast-tracking capability. Therefore, in this paper, a decoupling duty cycle optimization model predictive control for DP-DDWEPGS is proposed. Based on the characteristics of switching multiplexing, NSC finite control set model predictive control (FCS-MPC) decouples the current prediction and the cost function, reduces the number of candidate voltage vectors in each operation, and shortens the operation time by 70%. To address the issues of high ripple value and increased error due to decoupling in FCS-MPC, duty cycle optimization control is added, greatly reducing the fluctuations in electromagnetic force and power of the permanent magnet linear generator (PMLG). Based on the established simulation model, the feasibility and superiority of the multiport converter and decoupling duty cycle optimization model predictive current control method are verified.

Deep sea water (DSW) is a globally utilized source of renewable energy and other resources. To understand the characteristics of DSW resources in the South China Sea, in July 2022, the Guangzhou Marine Geological Survey (GMGS) investigated temperature, salinity, pH, dissolved oxygen (DO), inorganic salts (DIN, PO43−-P, and SiO3-Si), heavy metals (Hg, Pb, As, and Cd), trace elements (Cu, Zn, Fe, Mn, Ni, Se, and Mo), and other related indicators. The results of this investigation elucidate the horizontal and vertical changes in the physical and chemical properties of deep sea water in the Xisha Sea. The surface seawater quality in Xisha was found to be excellent and to meet first-class seawater survey standards. However, the concentrations of various nutrient salts in the surface layer were relatively low. As the seawater depth increased, different trace elements and heavy metals exhibited variations, and the concentrations of various nutrients also gradually increased.

The proficient application of multistage fracturing methods enhances the status of the Duvernay shale formation as a highly esteemed shale reservoir on a global scale. Nevertheless, the challenge is in accurately characterizing unconventional fracture behavior and predicting shale productivity due to the complex distributions of natural fractures, pre-existing faults, and reservoir heterogeneity. The present study puts forth a Geo-Engineering approach to comprehensively investigate the Duvernay shale reservoir in the vicinity of Crooked Lake. To begin with, on the basis of the experimental results and well-logging interpretations, a high-quality petrophysical and geomechanical model is constructed. Subsequently, the establishment of an unconventional fracture model (UFM) takes into account the heterogeneity of the reservoir and the interactions between hydraulic fractures and pre-existing natural fractures/faults and is further validated by 18,040 microseismic events. Finally, the analysis of well productivity is conducted by numerical simulations, revealing that the agreement between the simulated and observed production magnitudes exceeds 89%. This paper will guide the efficient development of increasingly important unconventional shale resources.

In order to achieve the goals of carbon neutrality and reduced carbon emissions, China is increasingly focusing on the development and utilization of renewable energy sources. Among these, ocean thermal energy conversion (OTEC) has the advantages of small periodic fluctuations and large potential reserves, making it an important research field. With the development of the “Maritime Silk Road”, the Xisha Islands in the South China Sea will see a growing demand for electricity, providing the potential for OTEC development in this region. Optimal site selection of OTEC power plants is a prerequisite for developing thermal energy provision, affecting both the construction costs and future benefits of the power plants. This study establishes a scientific evaluation model based on the decision-making frameworks of geographic information systems (GISs) and multi-criteria decision-making (MCDM) methods, specifically the analytic hierarchy process (AHP) for assigning weights, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to reclassify the factors, and weighted linear combination (WLC) to compute the suitability index. In addition to commonly considered factors such as temperature difference and marine usage status, this study innovatively incorporates geological conditions and maximum offshore distances of cold seawater based on cost control. The final evaluation identifies three suitable areas for OTEC development near the Xuande Atoll and the Yongle Atoll in the Xisha Sea Area, providing valuable insights for energy developers and policymakers.

In this study, three different substrate holder shapes—trapezoidal, circular frustum, and adjustable cyclic—were designed and optimized to enhance the quality of polycrystalline diamond films grown using microwave plasma chemical vapor deposition (MPCVD). Simulation results indicate that altering the shape of the substrate holder leads to a uniform distribution of the electric field on the surface, significantly suppressing the formation of secondary plasma. This design ensures a more even distribution of the temperature field and plasma environment on the substrate holder, resulting in a heart-shaped distribution. Polycrystalline diamond films were synthesized under these three different substrate holder conditions, and their morphology and crystal quality were characterized using optical microscopy, Raman spectroscopy, and high-resolution X-ray diffraction. Under conditions of 5 kW power and 90 Torr pressure, the adjustable cyclic substrate holder produced high-quality 3-inch diamond films with low stress and narrow Raman full width at half maximum (FWHM). The results confirm the reliability of the simulations and the effectiveness of the adjustable cyclic substrate holder. This approach provides a viable method for scaling up the size and improving the quality of polycrystalline diamond films for future applications.

Liver cancer, recognized as a significant global health issue, is increasingly correlated with Hepatitis B virus (HBV) infection, as evidenced by numerous scientific studies. This study aims to examine the correlation between HBV infection and the development of liver cancer, focusing on using RNA sequencing (RNA-seq) to detect HBV sequences and applying deep learning techniques to estimate the likelihood of oncogenic transformation in individuals with HBV. Our study utilized RNA-seq data and employed Pathseq software and sophisticated deep learning models, including a convolutional neural network (CNN), to analyze the prevalence of HBV sequences in the samples of patients with liver cancer. Our research successfully identified the prevalence of HBV sequences and demonstrated that the CNN model achieved an exceptional Area Under the Curve (AUC) of 0.998 in predicting cancerous transformations. We observed no viral synergism that enhanced the pathogenicity of HBV. A detailed analysis of sequences misclassified by the CNN model revealed that longer sequences were more conducive to accurate recognition. The findings from this study provide critical insights into the management and prognosis of patients infected with HBV, highlighting the potential of advanced analytical techniques in understanding the complex interactions between viral infections and cancer development.

Wear-resistant steel/carbon steel composite plates not only have the double performance advantages of high strength and wear resistance but can also reduce energy consumption and production costs. Based on a 50% reduction rate, the wear resistance of the BTW1/Q345 composite was studied at different annealing temperatures, and the dry friction and wear tests of the BTW1/Q345 composite at different annealing temperatures were carried out using RETC MFT-5000. By using the white-light interference three-dimensional surface profiler, scanning electron microscope (SEM), and backscattered electron diffraction (EBSD) technology, we carried out a detailed analysis of the macroscopic and microscopic morphology and wear mechanism of wear traces at different annealing temperatures. The effects of the annealing process on the thickness and composition of the wear layer were studied, and the causes of wear failure were analyzed based on the results of scanning electron microscopy. It was found that as the annealing temperature gradually increased, the particle size near the scratch of BTW1 in the wear-resistant layer of the composite plate became smaller. On this basis, the effects of different annealing temperatures on the friction and wear characteristics of the composite plate were further studied. At the annealing temperature of 860 ° C, the wear resistance of the material was the best.

In order to better understand the shear creep behavior of weak interlayers (carbonaceous shale) under the coupling effect of the rainfall dry–wet cycle and blasting vibration, as well as quantitatively characterize the coupled damage of the rainfall dry–wet cycle and blasting vibration, a series of shear creep tests were carried out. The results show that the combined damage of the rainfall dry–wet cycle and blasting vibration greatly intensifies the creep effect of carbonaceous shale, leading to an increase in deceleration creep time, an increase in steady-state creep rate, and a decrease in long-term strength. The coupling damage of the rainfall dry–wet cycle and blasting vibration in carbonaceous shale was quantitatively characterized. Based on the fractional-order theory, a fractional-order creep-damage constitutive model (DNFVP) was established by introducing the Abel dashpot to describe the coupled damage of the rainfall wet–dry cycle and blasting vibration and the nonlinear creep acceleration characteristics. The three-dimensional creep equation of the model was derived. The effectiveness of the DNFVP model was verified through the inversion of model parameters and fitting of experimental data, providing a basis for in-depth research on the long-term stability of high slopes in mines with weak interlayers.

In order to improve the energy capture efficiency of direct-drive wave power generation (DDWEG) systems and enhance the robustness of the reference power tracking control, a Fourier coefficient-based energy capture (FCBEC) and a position sensorless disturbance suppression (PSDS) control strategy are proposed. For energy capture, FCBEC is proposed to construct the objective function by maximizing the average power over a period of time and expanding the variables in the Fourier basis when the maximum power is captured, which is used as the basis for obtaining the reference trajectory. To address the limitations of the mechanical encoder, the position sensorless technique, based on a sliding mode observer (SMO), is used in the power tracking control, and the position information is obtained through an inverse tangent function. The perturbation caused by the inverse electromotive force error in the system is theoretically analyzed. A full-order terminal sliding mode approach is employed to design a current controller that suppresses the perturbation and ensures accurate tracking of the reference current. Simulation results show that the ocean-wave energy capture strategy proposed in this paper can make the energy captured by the PTO reach the optimal value under the impedance matching condition, and that the response speed and robustness of the full-order terminal sliding mode are better than the traditional PI control.

Developing novel catalysts with high activity and high stability for the methanol oxidation reaction (MOR) is of great importance for the ever-broader applications of methanol fuel cells. Herein, we present a facile technique for synthesizing Au10Pt1@MnO2 catalysts using a wet chemical method and investigate their catalytic performance for the MOR. Notably, the Au10Pt1@MnO2-M composite demonstrated a significantly high peak mass activity of 15.52 A mg(Pt)−1, which is 35.3, 57.5, and 21.9 times greater than those of the Pt/C (0.44 A mg(Pt)−1), Pd/C (0.27 A mg(Pt)−1), and Au10Pt1 (0.71 A mg(Pt)−1) catalysts, respectively. Comparative analysis with commercial Pt/C and Pd/C catalysts, as well as Au10Pt1 HSNRs, revealed that the Au10Pt1@MnO2-M composite exhibited the lowest initial potential, the highest peak current density, and superior CO anti-poisoning capability. The results demonstrate that the introduction of MnO2 nanosheets, with excellent oxidation capability, not only significantly increases the reactive sites, but also promotes the reaction kinetics of the catalyst. Furthermore, the high surface area of the MnO2 nanosheets facilitates charge transfer and induces modifications in the electronic structure of the composite. This research provides a straightforward and effective strategy for the design of efficient electrocatalytic nanostructures for MOR applications.

Two-dimensional (2D) materials have drawn extensive attention due to their exceptional characteristics and potential uses in electronics and energy storage. This investigation employs simulations using molecular dynamics to examine the mechanical and thermal transport attributes of the 2D silicene–germanene (Si-Ge) lateral heterostructure. The pre-existing cracks of the Si-Ge lateral heterostructure are addressed with external strain. Then, the effect of vacancy defects and temperature on the mechanical attributes is also investigated. By manipulating temperature and incorporating vacancy defects and pre-fabricated cracks, the mechanical behaviors of the Si-Ge heterostructure can be significantly modulated. In order to investigate the heat transport performance of the Si-Ge lateral heterostructure, a non-equilibrium molecular dynamics approach is employed. The efficient phonon average free path is obtained as 136.09 nm and 194.34 nm, respectively, in the Si-Ge heterostructure with a zigzag and armchair interface. Our results present the design and application of thermal management devices based on the Si-Ge lateral heterostructure.

A novel method for synthesizing dumbbell-shaped (Gd1−xTbx)2O(CO3)2·H2O (GOC:xTb3+) phosphors using sodium carbonate was investigated. An amount of 1 mmol of stable fluorescent powder can be widely prepared using 3–11 mmol of Na2CO3 at a pH value of 8.5–10.5 in the reaction solution. The optimal reaction conditions for the phosphors were determined to be 7 mmol for the amount of sodium carbonate and a pH of 9.5 in the solution. Mapping analysis of the elements confirmed uniform distribution of Gd3+ and Tb3+ elements in GOC:xTb3+. The analysis of fluorescence intensity shows that an optimal excitation wavelength of 273 nm is observed when the concentration of Tb3+ is between 0.005 and 0.3. The highest emission intensity was observed for GOC:0.05Tb3+ with a 57.5% maximum quantum efficiency. The chromaticity coordinates show that the color of GOC:Tb3+ is stable and suitable for fluorescence recognition. Latent fingerprint visualization reveals distinctive features like whorls, hooks, and bifurcations. Therefore, the sodium carbonate method offers an effective alternative to traditional urea chemical reaction conditions for preparing GOC:Tb3+.

The Neurospora crassa genome has a gene cluster for the synthesis of galactosaminogalactan (GAG). The gene cluster includes the following: (1) UDP-glucose-4-epimerase to convert UDP-glucose and UDP-N-acetylglucosamine to UDP-galactose and UDP-N-acetylgalactosamine (NCU05133), (2) GAG synthase for the synthesis of an acetylated GAG (NCU05132), (3) GAG deacetylase (/NCW-1/NCU05137), (4) GH135-1, a GAG hydrolase with specificity for N-acetylgalactosamine-containing GAG (NCU05135), and (5) GH114-1, a galactosaminidase with specificity for galactosamine-containing GAG (NCU05136). The deacetylase was previously shown to be a major cell wall glycoprotein and given the name of NCW-1 (non-GPI anchored cell wall protein-1). Characterization of the polysaccharides found in the growth medium from the wild type and the GAG synthase mutant demonstrates that there is a major reduction in the levels of polysaccharides containing galactosamine and N-acetylgalactosamine in the mutant growth medium, providing evidence that the synthase is responsible for the production of a GAG. The analysis also indicates that there are other galactose-containing polysaccharides produced by the fungus. Phenotypic characterization of wild-type and mutant isolates showed that deacetylated GAG from the wild type can function as an adhesin to a glass surface and provides the fungal mat with tensile strength, demonstrating that the deacetylated GAG functions as an intercellular adhesive. The acetylated GAG produced by the deacetylase mutant was found to function as an adhesive for chitin, alumina, celite (diatomaceous earth), activated charcoal, and wheat leaf particulates.

Ethanol sensors have found extensive applications across various industries, including the chemical, environmental, transportation, and healthcare sectors. With increasing demands for enhanced performance and reduced energy consumption, there is a growing need for developing new ethanol sensors. Micro-electromechanical system (MEMS) devices offer promising prospects in gas sensor applications due to their compact size, low power requirements, and seamless integration capabilities. In this study, SnO2-TiO2 nanocomposites with varying molar ratios of SnO2 and TiO2 were synthesized via ball milling and then printed on MEMS chips for ethanol sensing using electrohydrodynamic (EHD) printing. The study indicates that the two metal oxides dispersed evenly, resulting in a well-formed gas-sensitive film. The SnO2-TiO2 composite exhibits the best performance at a molar ratio of 1:1, with a response value of 25.6 to 50 ppm ethanol at 288 °C. This value is 7.2 times and 1.8 times higher than that of single SnO2 and TiO2 gas sensors, respectively. The enhanced gas sensitivity can be attributed to the increased surface reactive oxygen species and optimized material resistance resulting from the chemical and electronic effects of the composite.

Rosemary essential oil (REO) is widely recognized as a food flavoring and traditional herb and possesses potential antioxidant activity. However, its low yield rate and unclarified antioxidant mechanism warrant further investigation. In this study, an enzyme pretreatment-assisted extraction method with Box–Behnken design (BBD) and response surface methodology (RSM) models was employed to optimize the main factors of REO, and its antioxidant molecular mechanism under oxidative stress was elucidated in hydrogen peroxide-induced human lung carcinoma (A549) cells. The optimized yield (4.10%) of REO was recorded with the following optimum conditions: enzyme amount 1.60%, enzyme digestion pH 5.0, enzyme digestion temperature 46.50 °C, and enzyme digestion time 1.7 h. Meanwhile, 1.8-cineole (53.48%) and β-pinene (20.23%) exhibited radical scavenging activity higher than that of BHA and BHT. At the cellular level, REO (12.5–50 µg/mL) increased the levels of cell viability, CAT, SOD, and GSH significantly while reducing the contents of ROS, MDA, and GSSG, when compared to H2O2 exposure. Mechanically, REO relieved oxidative stress via activating the Nrf2 signaling pathway and enhancing the protein expression of Nrf2, NQO-1, and HO-1, which was further verified by molecular docking between the main component 1.8-cineole and the Kelch domain of KEAP1. Therefore, REO could be considered as a potent natural antioxidant with a potential strategy in the food and pharmaceutical industries.

The digital economy plays an important role in promoting the high quality and sustainable development of the marine fishery economy. Based on the panel data of the digital economy and marine fishery development from 2011 to 2022, we firstly adopted the entropy method to comprehensively evaluate the economy quality level of the digital economy and marine fishing. Secondly, we constructed a two-way fixed effect model to empirically analyze the impact of digital economy development on the marine fishery economy quality and the mediating role of marine green science and technology innovation, and further explored the regional heterogeneity of the digital economy on the marine fishery economy quality. Several findings emerge. The digital economy and the marine fishery economy quality level was relatively low and showed a fluctuating increase. The development of the digital economy can significantly improve the economy quality of marine fishing, and the conclusion was still valid after considering a series of robustness tests. The digital economy can drive the development quality of the marine fishery economy through marine green technology innovation. This paper proposes fostering the advancement of digital technology in the marine fishery sector, to effectively harness the innovation-driven potential of the digital economy, so as to facilitate the harmonious development of both the marine and digital economies.

Skeletal muscle satellite cells, the resident stem cells in pig skeletal muscle, undergo proliferation and differentiation to enable muscle tissue repair. The proliferative and differentiative abilities of these cells gradually decrease during in vitro cultivation as the cell passage number increases. Despite extensive research, the precise molecular mechanisms that regulate this process are not fully understood. To bridge this knowledge gap, we conducted transcriptomic analysis of skeletal muscle satellite cells during in vitro cultivation to quantify passage number-dependent changes in the expression of genes associated with proliferation. Additionally, we explored the relationships between gene transcriptional activity and chromatin accessibility using transposase-accessible chromatin sequencing. This revealed the closure of numerous open chromatin regions, which were primarily located in intergenic regions, as the cell passage number increased. Integrated analysis of the transcriptomic and epigenomic data demonstrated a weak correlation between gene transcriptional activity and chromatin openness in expressed genic regions; although some genes (e.g., GNB4 and FGD5) showed consistent relationships between gene expression and chromatin openness, a substantial number of differentially expressed genes had no clear association with chromatin openness in expressed genic regions. The p53-p21-RB signaling pathway may play a critical regulatory role in cell proliferation processes. The combined transcriptomic and epigenomic approach taken here provided key insights into changes in gene expression and chromatin openness during in vitro cultivation of skeletal muscle satellite cells. These findings enhance our understanding of the intricate mechanisms underlying the decline in cellular proliferation capacity in cultured cells.

Efforts have been made to improve the therapeutic efficiency of tumor treatments, and metal-organic frameworks (MOFs) have shown excellent potential in tumor therapy. Monotherapy for the treatment of tumors has limited effects due to the limitation of response conditions and inevitable multidrug resistance, which seriously affect the clinical therapeutic effect. In this study, we chose to construct a multiple cascade synergistic tumor drug delivery system MIL−101(Fe)−DOX−TCPP−MnO2@PDA−Ag (MDTM@P−Ag) using MOFs as drug carriers. Under near-infrared (NIR) laser irradiation, 5,10,15,20-tetrakis(4-carboxyphenyl)porphyrin (TCPP) and Ag NPs loaded on MDTM@P−Ag can be activated to generate cytotoxic reactive oxygen species (ROS) and achieve photothermal conversion, thus effectively inducing the apoptosis of tumor cells and achieving a combined photodynamic/photothermal therapy. Once released at the tumor site, manganese dioxide (MnO2) can catalyze the decomposition of hydrogen peroxide (H2O2) in the acidic microenvironment of the tumor to generate oxygen (O2) and alleviate the hypoxic environment of the tumor. Fe3+/Mn2+ will mediate a Fenton/Fenton-like reaction to generate cytotoxic hydroxyl radicals (·OH), while depleting the high concentration of glutathione (GSH) in the tumor, thus enhancing the chemodynamic therapeutic effect. The successful preparation of the tumor drug delivery system and its good synergistic chemodynamic/photodynamic/photothermal therapeutic effect in tumor treatment can be demonstrated by the experimental results of material characterization, performance testing and in vitro experiments.

A stringent DNA probe to profile microRNA (miRNA) expression within a specific cell remains a key challenge in biology. To address this issue, an intracellular ATP-activated Y-DNA probe for accurate imaging of miRNA in living cells was designed. Y-DNA was based on the fabrication of tripartite function modules, which consisted of a folate (FA)-modified targeting module, an ATP aptamer-sealed driver, and a miRNA sensing module. The Y-DNA probe could be specifically activated by ATP after it efficiently internalized into FA-receptor-overexpressed cells based on caveolar-mediated endocytosis, leading to the activation of the miRNA sensing module. The activated Y-DNA probe allowed for the imaging of miRNA in living cells with high sensitivity. The design of the ATP-activated Y-DNA sensor opens the door for bioorthogonal miRNA imaging and promotes the development of various responsive DNA molecular probes with enhanced anti-interference ability for clinical diagnosis.

To investigate the potential of modified epoxy resin for repairing and strengthening historical wooden structures, this study utilized polyurethane and silicone-modified epoxy resin as the base, alongside a polyamine curing agent. The resin mixture was cured at ambient temperature, resulting in the creation of ten unique epoxy resin systems. Investigation into the chemical structure and alterations to the glass transition temperature were conducted. The study conducted tests and characterization of viscosity, curing rate, mechanical properties, stress failure mode, hygrothermal aging resistance, and bonding properties. The results reveal that the curing degree of the two modified epoxy resins is high after being cured at room temperature, and the chemical structure and curing rate show insignificant changes. The range of the glass transition temperature for the modified epoxy resin is between 61.31 °C and 70.51 °C. The incorporation of polyurethane and silicone molecular chains into the epoxy resin cross-linking curing system enhances the toughness of the epoxy resin. The modified resin achieves a maximum elongation at break that is 5.18 times greater than that of the unmodified resin, along with a maximum tensile strength and a compressive strength that are 7.94 and 1.74 times, respectively, higher than those in the Chinese technical specifications for the maintenance and reinforcement of ancient wooden structures. The increase in toughness changes the failure mode of the cured epoxy resin. The modified epoxy resin exhibits great bonding ability to aged wood, with a shear strength of up to 9.6 MPa along the grain. As a result, the modified epoxy resin meets the requirements for the reinforcement and repair of the timber members of ancient buildings.

Construction waste is an inevitable byproduct of urban renewal, causing severe pressure on the environment, health, and ecology. Accurately estimating the production of construction waste is crucial for assessing the consumption of urban renewal. However, traditional manual estimation methods rely heavily on statistical data and historical experience, which lack flexibility in practical applications and are time-consuming and labor-intensive. In addition, their accuracy and timeliness need to be improved urgently. Fortunately, with the advantages of high-resolution remote sensing images (HRSIs) such as strong timeliness, large amounts of information, and macroscopic observations, they are suitable for the large-scale dynamic change detection of construction waste. However, the existing deep learning models have a relatively poor ability to extract and fuse features for small and multi-scale targets, and it is difficult to deal with irregularly shaped and fragmented detection areas. Therefore, this study proposes a Multi-scale Target Attention-Enhanced Network (MT-AENet), which is used to dynamically track and detect changes in buildings and construction waste disposal sites through HRSIs and accurately estimate the annual production of urban construction waste. The MT-AENet introduces a novel encoder–decoder architecture. In the encoder, ResNet-101 is utilized to extract high-level semantic features. A depthwise separable-atrous spatial pyramid pooling (DS-ASPP) module with different dilation rates is constructed to address insufficient receptive fields, resolving the issue of discontinuous holes when extracting large targets. A dual-attention mechanism module (DAMM) is employed to better preserve positional and channel details. In the decoder, multi-scale feature fusion (MS-FF) is utilized to capture contextual information, integrating shallow and intermediate features of the backbone network, thereby enhancing extraction capabilities in complex scenes. The MT-AENet is used to extract buildings and construction waste at different periods in the study area, and the actual production and landfill volume of construction waste are calculated based on area changes, indirectly measuring the rate of urban construction waste resource conversion. The experimental results in Changping District, Beijing demonstrate that the MT-AENet outperforms existing baseline networks in extracting buildings and construction waste. The results of this study are validated according to government statistical standards, providing a promising direction for efficiently analyzing the consumption of urban renewal.

Water resources are relatively scarce in Northwest China. Therefore, this study aimed to identify the hydrochemical characteristics and sources of different water bodies in the Northwest Coal–Electricity Agglomeration area, and the utilization of water resources in the region. Hydrochemical diagrams and correlation analysis were applied to data obtained through the collection of 40, 14, and 42 surface water, shallow groundwater, and deep groundwater samples, respectively. The Positive Definite Matrix Factor Decomposition (PMF) model was used to explore the origins of ions in different water bodies. The results show the following: (1) The rank of anions in surface water, shallow groundwater, and deep groundwater in water bodies of the Bulianta mining area during the wet period according to concentration was as follows: SO42− > Cl− > HCO3− > NO3−; that of cations was as follows: Na+ > Ca2+ > Mg2+ > K+; (2) The chemical composition of surface water is mainly regulated by the dissolution of evaporites; that of shallow groundwater was regulated by silicates; that of deep groundwater was mainly regulated by the hydrolysis of silicates and the dissolution of evaporites; (3) Four main sources of ions in different water bodies were identified, namely agricultural activities, rock weathering, primary geology, and unknown sources. Two natural factors, namely rock weathering and primary geology, and human activities contributed to 35.2% and 38.8% of ions in shallow groundwater, respectively. Rock weathering and human activities contributed to 20.6% and 63.9% of ions of deeper groundwater, respectively. This study can provide a basis for the conservation and rational planning and utilization of water resources in the Northwest Coal–Electricity Agglomeration area.

A large core size and bending resistance are very important properties of mid-infrared energy transfer fibers, but large core sizes usually lead to the deterioration of bending properties. A negative-curvature nested node-free anti-resonant hollow-core fiber (AR-HCF) based on quartz is proposed. It was made by adding a nested layer to a previous AR-HCF design to provide an additional anti-resonance region while keeping the gap between adjacent tubes strictly correlated with the core diameter to produce a node-free structure. These features improve the fiber’s bending resistance while achieving a larger core diameter. The simulation results show that the radial air–glass anti-resonant layer is increased by the introduction of the nested anti-resonant tube, and the weak interference overlap between the fiber core and the cladding mode is reduced, so the fiber core’s limiting loss and sensitivity to bending are effectively reduced. When the capillary wall thickness t of the fiber is 0.71 μm, the core diameter D is 70 μm, the ratio of the inner diameter of the cladding capillary to the core diameter d/D is 0.62, the diameter of the nested tube is d0 = 29 μm, the fiber has a lower limiting loss at the wavelength of 2.79 μm, and the limiting loss is 3.28 × 10−4 dB/m. At the same time, the optimized structure also has good bending resistance. When the bending radius is 30 mm, the bending loss is only 4.72 × 10−2 dB/m. An anti-bending low-loss micro-structure hollow fiber with a bending radius of less than 30 mm was successfully achieved in the 2.79 μm band. An anti-bending low-loss anti-resonant hollow-core fiber with this structure constitutes a reliable choice for the light guiding system of a 2.79 μm band Er, Cr: YSGG laser therapy instrument.

As the petroleum industry increasingly exploits unconventional reservoirs with low permeability and porosity, accurate predictions of post-fracture production are becoming critical for investment decisions, energy policy development, and environmental impact assessments. However, despite extensive research, accurately forecasting post-fracture production using well-log data continues to be a complex challenge. This study introduces a new method of data volume expansion, which is to subdivide the gas production of each well on the first day according to the depth of logging data, and to rely on the correlation model between petrophysical parameters and gas production to accurately combine the gas production data while matching the accuracy of the well-log data. Twelve pipelines were constructed utilizing a range of techniques to fit the regression relationship between logging parameters and post-fracture gas production These included data preprocessing methods (StandardScaler and RobustScaler), feature extraction approaches (PCA and PolynomialFeatures), and advanced machine learning models (XGBoost, Random Forest, and neural networks). Hyperparameter optimization was executed via GridSearchCV. To assess the efficacy of diverse models, metrics including the coefficient of determination (R2), standard deviation (SD), Pearson correlation coefficient (PCC), mean absolute error (MAE), mean squared error (MSE), and root-mean-square error (RMSE) were invoked. Among the several pipelines explored, the PFS-NN exhibited excellent predictive capability in specific reservoir contexts. In essence, integrating machine learning with logging parameters can be used to effectively assess reservoir productivity at multi-meter formation scales. This strategy not only mitigates uncertainties endemic to reservoir exploration but also equips petroleum engineers with the ability to monitor reservoir dynamics, thereby facilitating reservoir development. Additionally, this approach provides reservoir engineers with an efficient means of reservoir performance oversight.

The measurement and analysis of the spatial attributes of the street canyon hold significant importance in the advancement of photovoltaic integrated shading devices (PVSDs). This study offers the space aspect ratio index AR(h) as a more efficient method for determining the optimal location for installing PVSDs on building facades in various street canyons. The AR(h) index addresses the limitations of the current quantitative index. This study examined the evolving regulations of indoor thermal conditions, natural lighting, and the performance of PVSDs in various street canyons. It assessed the viability of implementing PVSDs in different canyons and suggested development plans based on the variation law. The findings demonstrated that AR(h) is capable of effectively assessing and directing the implementation of PVSDs. When AR(h) is below 0.6, the shade of surrounding buildings has the least impact on the photovoltaic power output and building energy consumption in various street canyons. In this scenario, the building has the largest yearly energy-saving rate, making it highly ideal for implementing PVSDs on the building façade. In summary, the suitability of the AR(h) index in various street sceneries was assessed, offering valuable insights for the widespread implementation of PVSDs and street planning, thereby optimizing the utilization of solar energy. The findings of this study will be advantageous in diminishing the utilization of non-renewable energy sources in urban areas and mitigating carbon emissions to safeguard the environment.

Microelectromechanical systems (MEMS) gas sensors have numerous advantages such as compact size, low power consumption, ease of integration, etc., while encountering challenges in sensitivity and high resistance because of their low sintering temperature. This work utilizes the in situ growth of Zeolitic Imidazolate Framework-8 (ZIF-8) followed by its conversion to N-doped ZnO. The results obtained from scanning electron microscopy (SEM) and transmission electron microscopy (TEM) indicate that the in situ derivation of ZIF-8 facilitates the adhesion of ZnO particles, forming an island-like structure and significantly reducing the interfaces between these particles. Furthermore, powder X-ray diffraction (XRD) analysis, elemental mapping, and X-ray photoelectron spectroscopy (XPS) analysis confirm the conversion of ZIF-8 to ZnO, the successful incorporation of N atoms into the ZnO lattice, and the creation of more oxygen vacancies. The ZIF-8-derived N-doped ZnO/MEMS sensor (ZIF (3)-ZnO/MEMS) exhibits remarkable gas sensitivity for ethanol detection. At an operating temperature of 290 °C, it delivers a substantial response value of 80 towards 25 ppm ethanol, a 13-fold enhancement compared with pristine ZnO/MEMS sensors. The sensor also exhibits an ultra-low theoretical detection limit of 11.5 ppb to ethanol, showcasing its excellent selectivity. The enhanced performance is attributed to the incorporation of N-doped ZnO, which generates abundant oxygen vacancies on the sensor’s surface, leading to enhanced interaction with ethanol molecules. Additionally, a substantial two-order-of-magnitude decrease in the resistance of the gas-sensitive film is observed. Overall, this study provides valuable insights into the design and fabrication strategies applicable to high-performance MEMS gas sensors in a broader range of gas sensing.

Although the long-term survival rate for leukemia has made significant progress over the years with the development of chemotherapeutics, patients still suffer from relapse, leading to an unsatisfactory outcome. To discover the new effective anti-leukemia compounds, we synthesized a series of dianilinopyrimidines and evaluated the anti-leukemia activities of those compounds by using leukemia cell lines (HEL, Jurkat, and K562). The results showed that the dianilinopyrimidine analog H-120 predominantly displayed the highest cytotoxic potential in HEL cells. It remarkably induced apoptosis of HEL cells by activating the apoptosis-related proteins (cleaved caspase-3, cleaved caspase-9 and cleaved poly ADP-ribose polymerase (PARP)), increasing apoptosis protein Bad expression, and decreasing the expression of anti-apoptotic proteins (Bcl-2 and Bcl-xL). Furthermore, it induced cell cycle arrest in G2/M; concomitantly, we observed the activation of p53 and a reduction in phosphorylated cell division cycle 25C (p-CDC25C) / Cyclin B1 levels in treated cells. Additionally, the mechanism study revealed that H-120 decreased these phosphorylated signal transducers and activators of transcription 3, rat sarcoma, phosphorylated cellular RAF proto-oncogene serine / threonine kinase, phosphorylated mitogen-activated protein kinase kinase, phosphorylated extracellular signal-regulated kinase, and cellular myelocytomatosis oncogene (p-STAT3, Ras, p-C-Raf, p-MEK, p-MRK, and c-Myc) protein levels in HEL cells. Using the cytoplasmic and nuclear proteins isolation assay, we found for the first time that H-120 can inhibit the activation of STAT3 and c-Myc and block STAT3 phosphorylation and dimerization. Moreover, H-120 treatment effectively inhibited the disease progression of erythroleukemia mice by promoting erythroid differentiation into the maturation of erythrocytes and activating the immune cells. Significantly, H-120 also improved liver function in erythroleukemia mice. Therefore, H-120 may be a potential chemotherapeutic drug for leukemia patients.

In recent years, fluoride pollution in water is a problem that has attracted much attention from researchers. The removal of fluoride-containing wastewater by adsorption with metal oxide as an adsorbent is the most common treatment method. Based on this, the effect of the doping ratio of La2O3, Fe2O3, and Al2O3 on the fluoride-removal performance was discussed by constructing a phase diagram. In this study, the adsorption mechanism of nanocrystalline lanthanum oxide terpolymer was investigated by density functional theory calculation and experiment. The optimal pH condition selected in the experiment was three, and the adsorption kinetics of fluoride ions were more consistent with the quasi-second-order kinetic model. The adsorption thermodynamics was more consistent with the Langmuir model. When the La-Fe-Al ternary composite oxides achieved the optimal adsorption efficiency for fluoride ions, the mass synthesis ratio was Al2O3:(Fe2O3:La2O3 = 1:2) = 1:100, resulting in a fluoride ion removal rate of up to 99.78%. Density functional calculations revealed that the La-Fe-Al ternary composite oxides had three important adsorption sites for La, Fe, and Al. Among them, the adsorption capacity for HF was Fe2O3 > La2O3 > Al2O3, and for F− was La2O3 > Al2O3 > Fe2O3. This provided good guidance for designing adsorbents to remove fluoride.

(1) To address issues such as a low compression ratio and severe leakage in uniform wall thickness vortex compressors, this paper adopts a new scroll compressor model with a variable diameter double arc combined profile, which has a larger pressure ratio and smaller leakage than the scroll compressor with equal wall thickness that can bring new ideas and methods to the research in the field of scroll compressors. (2) This paper determines the theoretical equation of the variable diameter dual circular arc vortex profile and the combined profile, and infers mathematical models for the leakage line length, working chamber volume, and gas leakage per unit rotation angle of the vortex compressor, followed by simulation. (3) The results show that compared to the uniform wall thickness involute profile, the variable diameter dual circular arc combined profile model reduces the leakage line length by 25%, increases the average total pressure at the working chamber outlet by 2.38%, and decreases the leakage amount by 3.2%, consistent with theoretical analysis. (4) The tangential leakage caused by the radial gap in the variable diameter dual circular arc combined profile model has a significant impact on the uneven distribution of temperature and velocity fields in the working chamber of the vortex compressor, but a smaller impact on the pressure field.

Dy3+, Ce3+ co-doped KY(CO3)2 phosphors with a monoclinic structure were synthesized using the hydrothermal method to create a fixed yellow-to-blue ratio emission. The [YO8] polyhedron, consisting of a Y atom and eight oxygen atoms, forms a relatively independent microstructure within the KY(CO3)2 host. Y3+ ions are partially replaced by Ce3+ or Dy3+ ions to construct the [CeO8] or [DyO8] polyhedral fluorescence emission unit. The spectral measurements indicate that Ce3+ and Dy3+ can maintain relatively independent fluorescence emission characteristics in the KY(CO3)2 host. The yellow-to-blue intensity ratio of Dy3+ remains close to 1 and does not change with the variation in the doping concentration of KY(CO3)2:Dy3+ and KY(CO3)2:Dy3+,Ce3+ phosphors. When Ce3+ and Dy3+ are co-doped with KY(CO3)2, the emission intensities of Dy3+ under 339 nm and 365 nm excitation increase by 8.43 and 2.32 times, respectively, through resonance energy transfer and cross-relaxation. All Ce3+-doped KY(CO3)2:Dy3+ phosphors can emit white light. Among them, the emitted light of KY(CO3)2:3%Dy3+,5%Ce3+ is closest to standard daylight. Therefore, a stable [YO8] polyhedral structure can be used to achieve more color tuning of light.

Moso bamboo forests, recognized as a distinctive and significant forest resource in subtropical China, contribute substantially to efficient carbon sequestration. The accurate assessment of the aboveground biomass (AGB) in Moso bamboo forests is crucial for evaluating their impact on the carbon balance within forest ecosystems at a regional scale. In this study, we focused on the Moso bamboo forest located in Shanchuan Township, Zhejiang Province, China. The primary objective was to utilize various data sources, namely UAV-LiDAR (UL), Sentinel-2 (ST), and a combination of UAV-LiDAR with Sentinel-2 (UL + ST). Employing the Boruta algorithm, we carefully selected characterization variables for analysis. Our investigation delved into establishing correlations between UAV-LiDAR characterization parameters, Sentinel-2 feature parameters, and the aboveground biomass (AGB) of the Moso bamboo forest. Ground survey data on Moso bamboo forest biomass served as the basis for our analysis. To enhance the accuracy of AGB estimation in the Moso bamboo forest, we employed three distinct modeling techniques: multivariate linear regression (MLR), support vector regression (SVR), and random forest (RF). Through this approach, we aimed to compare the impact of different data sources and modeling methods on the precision of AGB estimation in the studied bamboo forest. This study revealed that (1) the point cloud intensity of UL, the variables of canopy cover (CC), gap fraction (GF), and leaf area index (LAI) reflect the structure of Moso bamboo forests, and the variables indicating the height of the forest stand (AIH1, AIHiq, and Hiq) had a significant effect on the AGB of Moso bamboo forests, significantly impact Moso bamboo forest AGB. Vegetation indices such as DVI and SAVI in ST also exert a considerable effect on Moso bamboo forest AGB. (2) AGB estimation models constructed based on UL consistently demonstrated higher accuracy compared with ST, achieving R2 values exceeding 0.7. Regardless of the model used, UL consistently delivered superior accuracy in Moso bamboo forest AGB estimation, with RF achieving the highest precision at R2 = 0.88. (3) Integration of ST with UL substantially improved the accuracy of AGB estimation for Moso bamboo forests across all three models. Specifically, using RF, the accuracy of AGB estimation increased by 97.7%, with R2 reaching 0.89 and RMSE reduced by 124.4%. As a result, the incorporation of LiDAR data, which reflects the stand structure, has proven to enhance the accuracy of aboveground biomass (AGB) estimation in Moso bamboo forests when combined with multispectral remote sensing data. This integration serves as an effective solution to address the limitations of single optical remote sensing methods, which often suffer from signal saturation, leading to lower accuracy in estimating Moso bamboo forest biomass. This approach offers a novel perspective and opens up new possibilities for improving the precision of Moso bamboo forest biomass estimation through the utilization of multiple remote sensing sources.

The recognition of coal–rock interface is very crucial for research in the intelligent production of coal mines. To this end, the study investigated the application of terahertz time-domain spectroscopy in the recognition of coal–rock interface, including the identification of coal–rock and coal–rock mixtures, as well as the accurate characterization of coal seam thickness. Terahertz detection was used to obtain the optical parameter information of pressed pellets prepared by mixing two different kinds of coal and two kinds of rock. Based on the experiment’s results, a database was established for the identification of coal–rock interfaces for coal mining machines. The terahertz detection was performed on 10 different kinds of sheet anthracite with different thicknesses, and the terahertz spectra of coal seams with different thicknesses were simulated and calculated using simulation software. By comparing the two effective mining thicknesses, parameters can be provided for coal seam mining. The experiment and simulation show that the terahertz time-domain spectroscopy technology has a promising application prospect in the identification of coal–rock interface.

Caragana, a xerophytic shrub genus widely distributed in northern China, exhibits distinctive geographical substitution patterns and ecological adaptation diversity. This study employed transcriptome sequencing technology to investigate 12 Caragana species, aiming to explore genic-SSR variations in the Caragana transcriptome and identify their role as a driving force for environmental adaptation within the genus. A total of 3666 polymorphic genic-SSRs were identified across different species. The impact of these variations on the expression of related genes was analyzed, revealing a significant linear correlation (p < 0.05) between the length variation of 264 polymorphic genic-SSRs and the expression of associated genes. Additionally, 2424 polymorphic genic-SSRs were located in differentially expressed genes among Caragana species. Through weighted gene co-expression network analysis, the expressions of these genes were correlated with 19 climatic factors and 16 plant functional traits in various habitats. This approach facilitated the identification of biological processes associated with habitat adaptations in the studied Caragana species. Fifty-five core genes related to functional traits and climatic factors were identified, including various transcription factors such as MYB, TCP, ARF, and structural proteins like HSP90, elongation factor TS, and HECT. The roles of these genes in the ecological adaptation diversity of Caragana were discussed. Our study identified specific genomic components and genes in Caragana plants responsive to heterogeneous habitats. The results contribute to advancements in the molecular understanding of their ecological adaptation, lay a foundation for the conservation and development of Caragana germplasm resources, and provide a scientific basis for plant adaptation to global climate change.

The Loess Plateau is an important grain-producing area and energy base in China and is an area featuring dramatic changes in both surface and underground processes. However, the associations between surface deformation and groundwater storage changes in different landscape types in the region are still unclear. Based on Sentinel-1 and GRACE (Gravity Recovery and Climate Experiment) data, this study monitored and verified the surface deformation and groundwater storage changes in different landscape types, such as those of the Kubuqi Desert, Hetao Irrigation District, Jinbei Mining Area, and Shendong Mining Area, in the Loess Plateau of China from 2020 to 2021. Through time series and cumulative analysis using the same spatial and temporal resolution, the associations between these two changes in different regions are discussed. The results show that: (1) the surface deformation rates in different landscape types differ significantly. The minimum surface deformation rate in the Kubuqi Desert is −5~5 mm/yr, while the surface deformation rates in the Hetao Irrigation District, the open-pit mine recovery area in the Jinbei Mining Area, and the Shendong Mining Area are −60~25 mm/yr, −25~25 mm/yr, and −95.33~26 mm/yr, respectively. (2) The regional groundwater reserves all showed a decreasing trend, with the Kubuqi Desert, Hetao Irrigation District, Jinbei Mining Area, and Shendong Mining Area declining by 359.42 mm, 103.30 mm, 45.60 mm, and 691.72 mm, respectively. (3) The surface elasticity deformation had the same trend as the temporal fluctuation in groundwater storage, and the diversion activity was the main reason why the temporal surface deformation in the Hetao Irrigation District lagged behind the change in groundwater storage by 1~2 months. The measure of “underground water reservoirs in coal mines” slows down the rate of collapse of coal mine roof formations, resulting in the strongest time-series correlation between mild deformation of the surface of the Shendong mine and changes in the amount of groundwater reserves (R = 0.73). This study analyzes the associations between surface deformation and groundwater storage changes in different landscape areas of the Loess Plateau of China and provides new approaches to analyzing the dynamic associations between the two and the causes of changes in both variables.

Metabolism underlies the pathogenesis of acute myeloid leukemia (AML) and can be influenced by gut microbiota. However, the specific metabolic changes in different tissues and the role of gut microbiota in AML remain unclear. In this study, we analyzed the metabolome differences in blood samples from patients with AML and healthy controls using UPLC-Q-Exactive. Additionally, we examined the serum, liver, and fecal metabolome of AML model mice and control mice using UPLC-Q-Exactive. The gut microbiota of the mice were analyzed using 16S rRNA sequencing. Our UPLC-MS analysis revealed significant differences in metabolites between the AML and control groups in multiple tissue samples. Through cross-species validation in humans and animals, as well as reverse validation of Celastrol, we discovered that the Carnosine–Histidine metabolic pathway may play a potential role in the occurrence and progression of AML. Furthermore, our analysis of gut microbiota showed no significant diversity changes, but we observed a significant negative correlation between the key metabolite Carnosine and Peptococcaceae and Campylobacteraceae. In conclusion, the Carnosine–Histidine metabolic pathway influences the occurrence and progression of AML, while the gut microbiota might play a role in this process.

Herpes zoster (HZ) is a disease caused by the reactivation of latent varicella-zoster virus (VZV). The subunit vaccine, Shingrix®, and live attenuated vaccine, Zostavax®, could be used as an HZ vaccine that prevents HZ from being developed due to the reactivation of latent VZV in the sensory ganglia due to aging, stress or immunosuppression. In this study, the recombinant adenoviruses rChAd63/gE expressing glycoprotein E (gE) of VZV based on chimpanzee adenovirus serotype 63 (ChAd63) were constructed and investigated for the immunogenicity of different immune pathways in C57BL/6 mice. The results showed similar CD4+ T and CD8+ T cell responses to Shingrix® were induced in mice vaccinated using rChAd63/gE via different immune pathways. This study elucidates that recombinant adenoviruses expressing VZV gE could be appropriate for further development as a new HZ vaccine candidate via different immune pathways.

Trionyx sinensis hemorrhagic syndrome virus (TSHSV) is an aquatic arterivirus causing a high mortality rate for T. sinensis (Chinese soft-shelled turtle), but the detailed infection properties of TSHSV are unclear, and no effective treatment is available. In this study, cell culture and histopathology were performed to elucidate the infection properties of TSHSV. Furthermore, the anti-TSHSV and immune-enhancing effects of curcumin were evaluated using survival statistics, qPCR, and tissue immunofluorescence. The results demonstrated that TSHSV could proliferate in the spleen cell line of T. sinensis, leading to cytopathic effects. TSHSV damaged the livers, kidneys, and lungs, characterized by cell disintegration and hyperemia. Curcumin at 250 mg/kg improved the survival of T. sinensis, and significantly reduced the viral load in the spleens, kidneys, and lungs. Moreover, curcumin inhibited the mRNA expression of immune-related genes, RSAD2, IFN-γ, and TNF-α (p < 0.05). In conclusion, these results imply that TSHSV is pathogenic to the spleen cell line, liver, spleen, kidney, and lung of T. sinensis. Curcumin effectively inhibits TSHSV and modulates the immune function of T. sinensis, so it holds promise as a means to prevent TSHSV.

Rock Quality Designation (RQD) is among the widely used measures of the quality of rock masses and can be derived through Monte Carlo stochastic process-based fracture network simulations. However, repeated simulations can yield variable RQD results. Here, we introduce a four-step approach that incorporates class ratio analysis to estimate the representative RQD, which includes (1) extracting the mean and confidence interval of the RQD sample, in terms of the Confidence Neutrosophic Number Cubic Value (CNNCV), (2) employing class ratio analysis to determine the thresholds of the number of virtual boreholes and that of the number of models for a given size D, beyond which the CNNCV remains substantially unchanged, (3) accepting the CNNCV at the thresholds of the number of models as the representative RQD for the model of size D (RQD(D)) and (4) determining the representative RQD (rRQD), defined as the specific value which, once D exceeds, the RQD(D) does not change significantly. The introduced approach is illustrated with a case study of an open-pit slope in China, and it was tested for its performance. The RQD calculation results of the proposed method and the traditional single-model approach exhibit differences, which diminish with increasing model sizes. At the 95% confidence level, the stable size of the RQD determined by the proposed method is 13 m, compared to 25 m for the single-model approach. This method enhances the accuracy of representative elementary volume predictions by accounting for the diversity in the simulation results of RQDs for the same size. Overall, the introduced approach offers a reliable method for obtaining RQD estimates.

Vessel detection and tracking is of utmost importance to river traffic. Efficient detection and tracking technology offer an effective solution to address challenges related to river traffic safety and congestion. Traditional image-based object detection and tracking algorithms encounter issues such as target ID switching, difficulties in feature extraction, reduced robustness due to occlusion, target overlap, and changes in brightness and contrast. To detect and track vessels more accurately, a vessel detection and tracking algorithm based on the LiDAR point cloud was proposed. For vessel detection, statistical filtering algorithms were integrated into the Euclidean clustering algorithm to mitigate the effect of ripples on vessel detection. Our detection accuracy of vessels improved by 3.3% to 8.3% compared to three conventional algorithms. For vessel tracking, L-shape fitting of detected vessels can improve the efficiency of tracking, and a simple and efficient tracking algorithm is presented. By comparing three traditional tracking algorithms, an improvement in multiple object tracking accuracy (MOTA) and a reduction in ID switch times and number of missed detections were achieved. The results demonstrate that LiDAR point cloud-based vessel detection can significantly enhance the accuracy of vessel detection and tracking.

One common problem in omics data analysis is missing values, which can arise due to various reasons, such as poor tissue quality and insufficient sample volumes. Instead of discarding missing values and related data, imputation approaches offer an alternative means of handling missing data. However, the imputation of missing omics data is a non-trivial task. Difficulties mainly come from high dimensionality, non-linear or non-monotonic relationships within features, technical variations introduced by sampling methods, sample heterogeneity, and the non-random missingness mechanism. Several advanced imputation methods, including deep learning-based methods, have been proposed to address these challenges. Due to its capability of modeling complex patterns and relationships in large and high-dimensional datasets, many researchers have adopted deep learning models to impute missing omics data. This review provides a comprehensive overview of the currently available deep learning-based methods for omics imputation from the perspective of deep generative model architectures such as autoencoder, variational autoencoder, generative adversarial networks, and Transformer, with an emphasis on multi-omics data imputation. In addition, this review also discusses the opportunities that deep learning brings and the challenges that it might face in this field.

The dynamic evolution and regional heterogeneity of fishery efficiency development must be explored from an ecological perspective to reveal the spatial pattern of fishery ecological efficiency. Thus, taking 30 provinces/cities in China between 2006 and 2020 as research objects, we measured the level of the fishery ecological efficiency and regional fishery ecological efficiency convergence indexes using the superefficiency and convergence indexes, respectively. We found the following: (1) The ecological efficiency of China’s fishery shows a wavelike upward trend with significant fluctuations, but the overall efficiency level is not high. (2) The development of regional fishery ecological efficiency is not well coordinated; however, the variation curve of fishery ecological efficiency in the eastern and central regions is gradually decreasing. (3) A trend of “catching up and surpassing” is observed in the development of fishery ecological efficiency in various regions of China, and regional fishery ecological efficiency gradually converges to the regions’ respective steady-state levels over time. The results indicate a significant gap in regional fishery ecological efficiency, as well as polarization; areas with lower fishery efficiency are catching up with high-efficiency areas at different speeds, and regional fishery ecological efficiency is stabilizing.

With the global population aging, balancing urban development with the social lives of the elderly and disabled has become an important issue. Network analysis was employed as the primary research method, utilizing tools such as Network Analysis on the ArcGIS platform and Python. To enhance the applicability of the analytical model in population demand and travel characteristic studies, a dataset was established based on the behavioral characteristics of elderly and disabled individuals. Resistance values and evaluation indicators were selected for this purpose. We summarized the travel preferences of the elderly and disabled and proposed corresponding accessibility optimization strategies. Through the analysis of the experimental results, we believe that setting the optimization goal as a 10-min walk for the elderly and a 500-m walk for the disabled to access parks and green spaces is reasonable. Additionally, the service area ratio and service population ratio should exceed 90% to ensure equitable access to the ecological services of urban parks for the elderly and disabled population. We also found that connecting and extending linear green spaces are of significant importance in improving green space accessibility, because linear green spaces have more entrances and exits than dot green spaces and area green spaces.

With the accelerated development of nanotechnology in recent years, nanomaterials have become increasingly prevalent in the medical field. The poly (lactic acid–glycolic acid) copolymer (PLGA) is one of the most commonly used biodegradable polymers. It is biocompatible and can be fabricated into various nanostructures, depending on requirements. Ischemic stroke is a common, disabling, and fatal illness that burdens society. There is a need for further improvement in the diagnosis and treatment of this disease. PLGA-based nanostructures can facilitate therapeutic compounds’ passage through the physicochemical barrier. They further provide both sustained and controlled release of therapeutic compounds when loaded with drugs for the treatment of ischemic stroke. The clinical significance and potential of PLGA-based nanostructures can also be seen in their applications in cell transplantation and imaging diagnostics of ischemic stroke. This paper summarizes the synthesis and properties of PLGA and reviews in detail the recent applications of PLGA-based nanostructures for drug delivery, disease therapy, cell transplantation, and the imaging diagnosis of ischemic stroke.

Antarctic mapping with satellite images is an important basic task for polar environmental monitoring. Since the first Chinese marine satellite was launched in 2002, China has formed three series of more than 10 marine satellites in orbit. As global operational monitoring satellites of ocean color series, HY-1C and HY-1D have good coverage characteristics and imaging performance in polar regions, and they provide an effective tool for Antarctic monitoring and mapping. In this paper, Antarctic images acquired by the HY-1 satellite Coastal Zone Imager (CZI) sensor were used to study color matching in the mosaic process. According to the CZI characteristics for Antarctic imaging, experiments were carried out on the illuminance nonuniformity of a single image and color registration of multiple images. A gray-level segmentation color-matching method is proposed to solve the problem of image overstretching in the Antarctic image color-matching process. The results and statistical analysis show that the proposed method can effectively eliminate the color deviation between HY-1 Antarctic images, and the mosaic results have a good effect.

Accurate individual tree detection by unmanned aerial vehicles (UAVs) is a critical technique for smart forest management and serves as the foundation for evaluating ecological functions. Existing object detection and segmentation methods, on the other hand, have reduced accuracy when detecting and segmenting individual trees in complicated urban forest landscapes, as well as poor mask segmentation quality. This study proposes a novel Mask-CSP-attention-coupled network (MCAN) based on the Mask R-CNN algorithm. MCAN uses the Cross Stage Partial Net (CSPNet) framework with the Sigmoid Linear Unit (SiLU) activation function in the backbone network to form a new Cross Stage Partial Residual Net (CSPResNet) and employs a convolutional block attention module (CBAM) mechanism to the feature pyramid network (FPN) for feature fusion and multiscale segmentation to further improve the feature extraction ability of the model, enhance its detail information detection ability, and improve its individual tree detection accuracy. In this study, aerial photography of the study area was conducted by UAVs, and the acquired images were used to produce a dataset for training and validation. The method was compared with the Mask Region-based Convolutional Neural Network (Mask R-CNN), Faster Region-based Convolutional Neural Network (Faster R-CNN), and You Only Look Once v5 (YOLOv5) on the test set. In addition, four scenes—namely, a dense forest distribution, building forest intersection, street trees, and active plaza vegetation—were set up, and the improved segmentation network was used to perform individual tree segmentation on these scenes to test the large-scale segmentation ability of the model. MCAN’s average precision (AP) value for individual tree identification is 92.40%, which is 3.7%, 3.84%, and 12.53% better than that of Mask R-CNN, Faster R-CNN, and YOLOv5, respectively. In comparison to Mask R-CNN, the segmentation AP value is 97.70%, an increase of 8.9%. The segmentation network’s precision for the four scenes in multi-scene segmentation ranges from 95.55% to 92.33%, showing that the proposed network performs high-precision segmentation in many contexts.

Love surface acoustic wave (L-SAW) sensors are miniaturized, easy to integrate, and suitable for detection in liquid environments. In this paper, an L-SAW sensor with a thin Si3N4-SiO2 double-covered layer was proposed for samples with small mass loads. The output response, phase velocity of the acoustic wave, and the mass sensitivity were analyzed using the finite element method (FEM). The simulation results show that the Si3N4 layer with high wave velocity greatly weakens the limitation of SiO2 on the phase velocity. The phase velocity can reach about 4300 m/s, which can increase the frequency shift when the same mass load is applied. Within a certain range, the mass sensitivity of the sensor is enhanced with the increase in the total thickness of the waveguiding layer and the thickness ratio of Si3N4 in the double-covered layer. When the thickness ratio is 1:2, the peak value of the mass sensitivity of the sensor is approximately 50% higher than that achieved with only the SiO2 waveguiding layer. The surface average stress of the delay line region follows the same trend as the mass sensitivity. The increase in mass sensitivity is the result of the heightened stress on the sensor surface. This L-SAW sensor, featuring a double-covered waveguiding layer, demonstrates high sensitivity and a simple structure. The simulation results lay a foundation for the design and manufacture of SAW sensors.

Ex vivo lung perfusion (EVLP) has increased donor lung utilization through assessment of “marginal” lungs prior to transplantation. To develop it as a donor lung reconditioning platform, prolonged EVLP is necessary, and new perfusates are required to provide sufficient nutritional support. Human pulmonary microvascular endothelial cells and epithelial cells were used to test different formulas for basic cellular function. A selected formula was further tested on an EVLP cell culture model, and cell confluence, apoptosis, and GSH and HSP70 levels were measured. When a cell culture medium (DMEM) was mixed with a current EVLP perfusate—Steen solution, DMEM enhanced cell confluence and migration and reduced apoptosis in a dose-dependent manner. A new EVLP perfusate was designed and tested based on DMEM. The final formula contains 5 g/L Dextran-40 and 7% albumin and is named as D05D7A solution. It inhibited cold static storage and warm reperfusion-induced cell apoptosis, improved cell confluence, and enhanced GSH and HSP70 levels in human lung cells compared to Steen solution. DMEM-based nutrient-rich EVLP perfusate could be a promising formula to prolong EVLP and support donor lung repair, reconditioning and further improve donor lung quality and quantity for transplantation with better clinical outcome.

The damage monitoring and identification of arch bridges provide an important means to ensure the safe operation of arch bridges. At present, many methods have been developed, and the applicability and effectiveness of these methods depend on the damage type, structural configuration and available data. To guide the practical application of these methods, a systematic review is implemented in this paper. Specifically, the damage monitoring and identification methods of arch bridges are divided into the damage monitoring of local diseases and damage identification of overall performance. Firstly, the research on the damage monitoring of the local diseases of arch bridges is reviewed. According to the disease type, it is divided into four categories, including suspender inspection, void monitoring, stress detection and corrosion detection. For each disease, this paper analyzes the principles, advantages and shortcomings of various methods. Then, the damage identification methods of the overall performance of arch bridges are reviewed, including masonry arch bridges, steel arch bridges, reinforced concrete arch bridges and concrete-filled steel tubular arch bridges. And the commonly used damage indexes of damage identification methods are summarized. This review aims to help researchers and practitioners in implementing existing damage detection methods effectively and developing more reliable and practical methods for arch bridges in the future.

Giant Stropharia (S. rugoso-annulata) is an edible mushroom recommended for consumption by the Food and Agriculture Organization of the United Nations. It possesses significant culinary and medicinal functionalities. The characteristics of this mushroom include high protein content, abundant bioactive compounds, delicious and sweet taste, and pleasant aroma. In recent years, the S. rugoso-annulata industry has seen strong growth, especially in China. This article presents the first comprehensive and systematic review of the nutritional, bioactive, and flavor components of S. rugoso-annulata, as well as their influencing factors. This article provides scientific evidence for the production of high-quality S. rugoso-annulata mushrooms, the extraction of bioactive components, post-harvest storage, and culinary processing, aiming to promote the consumption of S. rugoso-annulata and the health of consumers.

With the continuous development of public transportation, the impact of unexpected events on the operation of bus networks has become increasingly severe due to the growing demand for public transportation and passenger volume. To accurately assess the impact of unexpected events on the operation of bus networks and scientifically evaluate their resilience, this paper proposes a framework for analyzing the resilience of bus networks. With the aim of providing scientific evidence to enhance the reliability of public transportation networks, this framework can be used to determine the resilience of bus networks to unexpected events. The main contributions of this framework include three aspects: 1. Construction of the CRITIC–entropy weighting model for screening and calculating key indicators of the resilience of the bus network; 2. Use of resilience cycle theory to construct a model for analyzing the resilience of bus routes, and design a set of resilience quantification factors to calculate the resilience of bus routes; 3. Use of complex network theory to construct a model for analyzing the resilience of the bus network, by taking the bus route resilience obtained in the second step as the edge weight to calculate the resilience of the bus network. This paper takes the Beijing public transit system as an example and uses real data to verify the accuracy, scientificity, and feasibility of the proposed framework for analyzing the resilience of public transit networks to sudden events. The resilience analysis framework constructed in this paper has improved the existing research on transportation network resilience in theoretical aspects. Furthermore, the results outputted by this framework can provide a decision-making basis for network adjustment and disaster recovery for the management departments of public transportation networks in practical applications.